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Volume 10
Issue 6
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Systems, Volume 10, Issue 6 (December 2022) – 70 articles

Cover Story (view full-size image): Function-oriented model-based systems engineering (MBSE) is an auspicious approach to the virtual development of cyberphysical systems. The behavior of the system’s elements is thus represented by specialized simulation models integrated into a system model. Although many simulation models have been developed for the common system elements, their integration remains a major challenge: the parameter interfaces of the simulation models must be coupled correctly with each other and with the system elements, which is typically carried out by model experts in a time-consuming and error-prone manner. Therefore, in this paper, we propose a model signature for simulation models that supports the valid and automated coupling for the virtual development of system elements in model-based systems engineering. View this paper
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21 pages, 8769 KiB  
Article
Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)
by Niamat Ullah Ibne Hossain, Mostafa Lutfi, Ifaz Ahmed, Aditya Akundi and Daniel Cobb
Systems 2022, 10(6), 264; https://doi.org/10.3390/systems10060264 - 19 Dec 2022
Cited by 3 | Viewed by 7882
Abstract
The use of unmanned aerial vehicles (UAVs) has seen a significant increase over time in several industries such as defense, healthcare, and agriculture to name a few. Their affordability has made it possible for industries to venture and invest in UAVs for both [...] Read more.
The use of unmanned aerial vehicles (UAVs) has seen a significant increase over time in several industries such as defense, healthcare, and agriculture to name a few. Their affordability has made it possible for industries to venture and invest in UAVs for both research and commercial purposes. In spite of their recent popularity; there remain a number of difficulties in the design representation of UAVs, including low image analysis, high cost, and time consumption. In addition, it is challenging to represent systems of systems that require multiple UAVs to work in cooperation, sharing resources, and complementing other assets on the ground or in the air. As a means of compensating for these difficulties; in this study; we use a model-based systems engineering (MBSE) approach, in which standardized diagrams are used to model and design different systems and subsystems of UAVs. SysML is widely used to support the design and analysis of many different kinds of systems and ensures consistency between the design of the system and its documentation through the use of an object-oriented model. In addition, SysML supports the modeling of both hardware and software, which will ease the representation of both the system’s architecture and flow of information. The following paper will follow the Magic Grid methodology to model a UAV system across the SysML four pillars and integration of SysML model with external script-based simulation tools, namely, MATLAB and OpenMDAO. These pillars are expressed within standard diagram views to describe the structural, behavior, requirements, and parametric aspect of the UAV. Finally, the paper will demonstrate how to utilize the simulation capability of the SysML model to verify a functional requirement. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)
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<p>Representation of a requirement diagram.</p> Full article ">Figure 2
<p>Representation of a requirement table showing the functional requirements.</p> Full article ">Figure 3
<p>Package diagram of model organization.</p> Full article ">Figure 4
<p>Block definition diagram (BDD) of unmanned aerial vehicle (UAV).</p> Full article ">Figure 5
<p>BDD of Air vehicle system (AVS).</p> Full article ">Figure 6
<p>BDD of the payload subsystem.</p> Full article ">Figure 7
<p>Internal block definition diagram (IBD) of UAV.</p> Full article ">Figure 8
<p>Use case diagram of UAV for surveillance purpose.</p> Full article ">Figure 9
<p>Use case diagram of UAV for military application.</p> Full article ">Figure 10
<p>State machine diagram of the concept of operation of UAV in military warfare.</p> Full article ">Figure 11
<p>State machine diagram of the operational states of UAVs.</p> Full article ">Figure 12
<p>Activity diagram of drag force calculation of UAVs.</p> Full article ">Figure 13
<p>Before simulation run.</p> Full article ">Figure 14
<p>Execution of actions shown by color.</p> Full article ">Figure 15
<p>Printing drag force in the simulation console.</p> Full article ">Figure 16
<p>OpenMDAO script snippets.</p> Full article ">Figure 17
<p>Defined input parameters in the Cameo Simulation Toolkit environment.</p> Full article ">Figure 18
<p>Activity diagram enabling the integration with the OpenMDAO script.</p> Full article ">Figure 19
<p>Execution results shown in the CST console.</p> Full article ">Figure 20
<p>OpenMDAO solutions in the Python environment.</p> Full article ">Figure 21
<p>Parametric diagram showing the binding parameters and script function as constraint.</p> Full article ">Figure 22
<p>Flight time value property populated by the execution of the MATLAB script.</p> Full article ">Figure 23
<p>CST simulation console showing the environment as Matlab.</p> Full article ">
22 pages, 3899 KiB  
Article
Haze Risk Assessment Based on Improved PCA-MEE and ISPO-LightGBM Model
by Hongbin Dai, Guangqiu Huang, Huibin Zeng and Rongchuan Yu
Systems 2022, 10(6), 263; https://doi.org/10.3390/systems10060263 - 19 Dec 2022
Cited by 38 | Viewed by 2928
Abstract
With the economic development in China, haze risks are frequent. It is important to study the urban haze risk assessment to manage the haze disaster. The haze risk assessment indexes of 11 cities in Fenwei Plain were selected from three aspects: the sensitivity [...] Read more.
With the economic development in China, haze risks are frequent. It is important to study the urban haze risk assessment to manage the haze disaster. The haze risk assessment indexes of 11 cities in Fenwei Plain were selected from three aspects: the sensitivity of disaster-inducing environments, haze component hazards and the vulnerability of disaster-bearing bodies, combined with regional disaster system theory. The haze hazard risk levels of 11 cities in Fenwei Plain were evaluated using the matter-element extension (MEE) model, and the indicator weights were determined by improving the principal component analysis (PCA) method using the entropy weight method, and finally, five haze hazard risk assessment models were established by improving the particle swarm optimization (IPSO) light gradient boosting machine (LightGBM) algorithm. It is used to assess the risk of affected populations, transportation damage risk, crop damage area risk, direct economic loss risk and comprehensive disaster risk before a disaster event occurs. The experimental comparison shows that the haze risk index of Xi’an city is the highest, and the full index can improve the evaluation accuracy by 4–16% compared with only the causative factor index, which indicates that the proposed PCA-MEE-ISPO-LightGBM model evaluation results are more realistic and reliable. Full article
(This article belongs to the Topic Climate Change and Environmental Sustainability, 2nd Edition)
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<p>Location of study area.</p> Full article ">Figure 2
<p>IPSO-LightGBM model construction process.</p> Full article ">Figure 3
<p>Haze disaster risk assessment model building process.</p> Full article ">Figure 4
<p>Five disaster risks in Fenwei Plain.</p> Full article ">Figure 5
<p>Importance of indicators for different risk assessment types.</p> Full article ">
16 pages, 2297 KiB  
Article
System “Person-State-Society” in Period of Social Turbulence and Big Challenges (Case Study: Tomsk City, the Russian Federation)
by Anatoly Sidorov, Elena Pokrovskaya and Margarita Raitina
Systems 2022, 10(6), 262; https://doi.org/10.3390/systems10060262 - 18 Dec 2022
Cited by 1 | Viewed by 2183
Abstract
The article reflects the role of society in an era of uncertainty and people’s behavior in response to big challenges. The aim is to consider the responsibility for resolving crisis situations by state power. Comprehending is possible on the theory formed by the [...] Read more.
The article reflects the role of society in an era of uncertainty and people’s behavior in response to big challenges. The aim is to consider the responsibility for resolving crisis situations by state power. Comprehending is possible on the theory formed by the concepts of social turbulence and aggravated regimes, which are based on such characteristics of processes as nonlinearity, spontaneity, uncertainty, and high speeds. This study offers the hypothesis about the shift in the value orientations of the population from the rational to the irrational area in the face of growing uncertainty and turbulence in the environment, which should become the subject of managerial influence when forming a corrective or anti-crisis policy, and about the formation public demand for “strong” state intervention, protecting the population from the negative consequences of regimes with escalations. The article concludes the practical significance and applicability of the research, but also as a theoretical basis for the development of methods and technologies for diagnostics of public demand within the framework of information and analytical support of public administration. Full article
(This article belongs to the Special Issue Research Trends on the Development of Integrated and Robust Social Development Management Systems)
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<p>Respondents’ answers distribution on the question, “What, in your opinion, were the main changes in people’s behavior at the beginning of the COVID-19 pandemic?”, %.</p> Full article ">Figure 2
<p>Respondents’ answers distribution on the question, “How destabilized was your usual rhythm of life and behavior due to the development of the COVID-19 pandemic? Rate on a scale from 1 to 10, where 1—the rhythm of life remained unchanged, and 10—the rhythm of life had to be completely rebuilt”, %.</p> Full article ">Figure 3
<p>Respondents’ ratings distribution on approval/disapproval of restrictions related to the mandatory wearing of masks in public places, %.</p> Full article ">Figure 4
<p>Respondents’ ratings distribution on approval/non-approval of bans on holding events involving mass gatherings of people, %.</p> Full article ">Figure 5
<p>Respondents’ ratings distribution on approval/non-approval of bans on the work of organizations in the leisure sector, %.</p> Full article ">Figure 6
<p>Respondents’ ratings distribution on approval/disapproval of the transition to a remote work format, %.</p> Full article ">Figure 7
<p>Respondents’ ratings distribution on approval/disapproval of the establishment of special regimes for social groups, %.</p> Full article ">Figure 8
<p>Respondents’ ratings distribution on approval/disapproval of the establishment of requirements focused on the need to maintain social distance, %.</p> Full article ">Figure 9
<p>Respondents’ ratings distribution on approval/non-approval of quarantine measures for persons arriving from abroad, %.</p> Full article ">Figure 10
<p>Respondents’ ratings distribution on approval/disapproval of the transition to education in an online format, %.</p> Full article ">Figure 11
<p>Respondents’ ratings distribution on approval/non-approval of the ban on leisure activities for children in crowded places, %.</p> Full article ">Figure 12
<p>Respondents’ ratings distribution on approval/non-approval of the vaccination campaign, %.</p> Full article ">Figure 13
<p>Respondents’ ratings distribution on approval/disapproval of the establishment of QR-permissions for goods and activities, %.</p> Full article ">Figure 14
<p>Respondents’ ratings distribution on approval/non-approval of the restriction of planned medical care, %.</p> Full article ">
23 pages, 5182 KiB  
Article
Optimal Decision Making for Customer-Intensive Services Based on Queuing System Considering the Heterogeneity of Customer Advertising Perception
by Gang Fu, Linxiao Dong, Wentao Zhan and Minghui Jiang
Systems 2022, 10(6), 261; https://doi.org/10.3390/systems10060261 - 18 Dec 2022
Viewed by 1481
Abstract
In customer-intensive services, advertising can increase customers’ patience and bring more utility to customers. However, customers’ different perceptions of advertising can affect their utility and indirectly affect the decision making of the service provider. Thus, this paper uses the M/M/1 queueing model to [...] Read more.
In customer-intensive services, advertising can increase customers’ patience and bring more utility to customers. However, customers’ different perceptions of advertising can affect their utility and indirectly affect the decision making of the service provider. Thus, this paper uses the M/M/1 queueing model to study the optimal decision making of customer-intensive service providers in different markets according to the customers’ heterogeneity. We first classify customers into two categories: high sensitivity and low sensitivity, and then we analyze the consumption behavior of these two types of customers in the service system as the potential customer arrival rate increases. Finally, the optimal decisions of the service provider with different demands are determined. We find that the service provider can benefit from making optimal decisions based on market demand as the potential customer arrival rate increases. If the potential arrival rate exceeds a certain threshold, the service provider has more dominance in the market, and relevant decision making is no longer affected by the potential customer arrival rate. Furthermore, it is not always beneficial for the service provider to serve all customers regardless of whether there are low-sensitivity customers in the service system, and advertising can tap more highly sensitive customers and help to further increase the revenue of service providers. The results also show that ignoring the heterogeneity of customers’ sensitivity to advertising very likely leads to losses in revenue. Full article
(This article belongs to the Section Systems Practice in Social Science)
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<p>The left panel illustrates the optimal service rate with respect to <math display="inline"><semantics> <mi mathvariant="sans-serif">Λ</mi> </semantics></math>. The right panel illustrates the optimal price with respect to <math display="inline"><semantics> <mi mathvariant="sans-serif">Λ</mi> </semantics></math>.</p> Full article ">Figure 2
<p>The left panel illustrates the optimal effective customer arrival rate with respect to <math display="inline"><semantics> <mi mathvariant="sans-serif">Λ</mi> </semantics></math>. The right panel illustrates the optimal revenue with respect to <math display="inline"><semantics> <mi mathvariant="sans-serif">Λ</mi> </semantics></math>.</p> Full article ">Figure 3
<p>The revenue between the homogeneous L decision and the heterogeneous decision (parameters: <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <msub> <mi>V</mi> <mi>b</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mo> </mo> <msub> <mi>μ</mi> <mi>b</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>c</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>k</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>k</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>q</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.)</p> Full article ">Figure 4
<p>The left panel illustrates the revenue between the homogeneous M decision and the heterogeneous decision at <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi mathvariant="normal">M</mi> </msub> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>. The right panel illustrates the revenue between the homogeneous M decision and the heterogeneous decision at <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi mathvariant="normal">M</mi> </msub> <mo>=</mo> <mn>0.65</mn> </mrow> </semantics></math>. (Parameters: <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <msub> <mi>V</mi> <mi>b</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mo> </mo> <msub> <mi>μ</mi> <mi>b</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>c</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>k</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> <msub> <mi>k</mi> <mi mathvariant="normal">M</mi> </msub> <mo>=</mo> <mn>0.6</mn> <mo> </mo> <mi>o</mi> <mi>r</mi> <mo> </mo> <mn>0.65</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>k</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>q</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.)</p> Full article ">Figure 5
<p>The revenue between the homogeneous H decision and the heterogeneous decision. (Parameters: <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <msub> <mi>V</mi> <mi>b</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mo> </mo> <msub> <mi>μ</mi> <mi>b</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>c</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>k</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>k</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>q</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.)</p> Full article ">Figure 6
<p>The left panel illustrates the revenue in the homogeneous H decision and that in <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>L</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>. The middle panel illustrates the revenue in the homogeneous H decision and that in <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>H</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>0.55</mn> </mrow> </semantics></math>. The right panel illustrates the revenue in the homogeneous H decision and that in <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>H</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>.</p> Full article ">
15 pages, 563 KiB  
Communication
Human–Artificial Intelligence Systems: How Human Survival First Principles Influence Machine Learning World Models
by Stephen Fox
Systems 2022, 10(6), 260; https://doi.org/10.3390/systems10060260 - 17 Dec 2022
Cited by 4 | Viewed by 2384
Abstract
World models is a construct that is used to represent internal models of the world. It is an important construct for human-artificial intelligence systems, because both natural and artificial agents can have world models. The term, natural agents, encompasses individual people and human [...] Read more.
World models is a construct that is used to represent internal models of the world. It is an important construct for human-artificial intelligence systems, because both natural and artificial agents can have world models. The term, natural agents, encompasses individual people and human organizations. Many human organizations apply artificial agents that include machine learning. In this paper, it is explained how human survival first principles of interactions between energy and entropy influence organization’s world models, and hence their implementations of machine learning. First, the world models construct is related to human organizations. This is done in terms of the construct’s origins in psychology theory-building during the 1930s through its applications in systems science during the 1970s to its recent applications in computational neuroscience. Second, it is explained how human survival first principles of interactions between energy and entropy influence organizational world models. Third, a practical example is provided of how survival first principles lead to opposing organizational world models. Fourth, it is explained how opposing organizational world models can constrain applications of machine learning. Overall, the paper highlights the influence of interactions between energy and entropy on organizations’ applications of machine learning. In doing so, profound challenges are revealed for human-artificial intelligence systems. Full article
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<p>Triple-loop learning in heterarchical preference competitions. Based on innate preferences for energy-positive options and ingroup-positive options, prosumption preferences are inferred from targeted and general preference options across triple-loop learning.</p> Full article ">
18 pages, 2724 KiB  
Article
Parallel Learning of Dynamics in Complex Systems
by Xueqin Huang, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang and Ailin Liang
Systems 2022, 10(6), 259; https://doi.org/10.3390/systems10060259 - 15 Dec 2022
Cited by 2 | Viewed by 1877
Abstract
Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in [...] Read more.
Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this paper, the Dynamics-METIS (D-METIS) and the Partitioned Graph Neural Dynamics Learner (PGNDL). The general framework combines D-METIS and PGNDL to perform tasks for large graphs. D-METIS is a new algorithm that can partition a large graph into multiple subgraphs. D-METIS innovatively considers the dynamic changes in the graph. PGNDL is a new parallel model that consists of ordinary differential equation systems and graph neural networks (GNNs). It can quickly learn the dynamics of subgraphs in parallel. In this framework, D-METIS provides PGNDL with partitioned subgraphs, and PGNDL can solve the tasks of interpolation and extrapolation prediction. We exhibit the universality and superiority of our framework on four kinds of graphs with three kinds of dynamics through an experiment. Full article
(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)
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<p>Illustration of an NDCN instance.</p> Full article ">Figure 2
<p>The three steps of multilevel k-way graph partitioning. <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is the input, which is also the finest graph, <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo> </mo> </mrow> </semantics></math> is the second most coarse graph of <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>i</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mn>4</mn> </msub> </mrow> </semantics></math> is the coarsest graph.</p> Full article ">Figure 3
<p>Dynamic process compression on a graph. (<b>a</b>) Original graph with dynamics process; (<b>b</b>) Compression result of a graph with dynamics.</p> Full article ">Figure 4
<p>General framework.</p> Full article ">Figure 5
<p>Balance analysis: dynamic cumulative change of subgraphs.</p> Full article ">Figure 6
<p>Balance analysis: vertex distribution of subgraphs.</p> Full article ">
20 pages, 548 KiB  
Article
A Gaussian-Shaped Fuzzy Inference System for Multi-Source Fuzzy Data
by Yun Zhang and Chaoxia Qin
Systems 2022, 10(6), 258; https://doi.org/10.3390/systems10060258 - 15 Dec 2022
Cited by 2 | Viewed by 2255
Abstract
Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop [...] Read more.
Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop a novel Gaussian-shaped Fuzzy Inference System (GFIS) driven by multi-source fuzzy data. To this end, we first propose an interval-value normalization method to address the heterogeneity of multi-source fuzzy data. The contribution of our interval-value normalization method involves mapping heterogeneous fuzzy data to a unified distribution space by adjusting the mean and variance of data from each information source. As a result of combining the normalized descriptions from various sources for an object, we can obtain a fused representation of that object. We then derive an adaptive Gaussian-shaped membership function based on the addition law of the Gaussian distribution. GFIS uses it to dynamically granulate fusion inputs and to design inference rules. This proposed membership function has the advantage of being able to adapt to changing information sources. Finally, we integrate the normalization method and adaptive membership function to the Takagi–Sugeno (T–S) model and present a modified fuzzy inference framework. Applying our methodology to four datasets, we confirm that the data do lend support to the theory implying the improved performance and effectiveness. Full article
(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)
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<p>The architecture of a fuzzy inference system (FIS). The architecture of FIS describes the mapping process from a given input to an output. The process consists of five parts: defining input and output, formulating a fuzzification strategy, building a knowledge base, designing fuzzy inference mechanism, and defuzzification of output. See the main text for details.</p> Full article ">Figure 2
<p>Sample diagram of a T–S model. A T–S model is a nonlinear system characterized by a set of “IF–THEN” fuzzy rules. Each rule indicates a subsystem, and the entire T–S model is a linear combination of all these subsystems. See the text for details.</p> Full article ">Figure 3
<p>Clustering precision of GFIS inference on different datasets. The normalization of data will reduce the clustering precision of the inference results (0.18–18.23%) because normalization scales the distance between the original data and adds some noise to the results. Data fusion increases the clustering precision by 3.84–19.11% due to its ability to eliminate part of the errors from diverse information sources.</p> Full article ">Figure 4
<p>Clustering precision of GFIS inference with different numbers of information sources (IS). When it comes to the number of IS, the clustering precision of Fused GFIS is better than that of Non-fused GFIS under all test conditions. In either model, the clustering precision does not appear to be related to the number of information sources.</p> Full article ">Figure 5
<p>Time cost of data normalization for heterogeneous fuzzy data with different numbers of data objects. For all datasets, the normalization time cost has a positive linear relationship with the number of data objects, which is consistent with the time complexity <math display="inline"><semantics> <mrow> <mi>O</mi> <mo>(</mo> <mn>3</mn> <mo>×</mo> <mi>n</mi> <mo>×</mo> <mi>k</mi> <mo>)</mo> </mrow> </semantics></math> in the normalization algorithm (see Algorithm 1 for details).</p> Full article ">Figure 6
<p>Time cost of normalized data fusion with different numbers of IS. For all datasets, the fusion time cost and the number of IS are positively correlated. Using the core formula of the fusion method, if we assume that the time cost of fusing one information source is <math display="inline"><semantics> <mrow> <mi>O</mi> <mo>(</mo> <mi>T</mi> <mo>)</mo> </mrow> </semantics></math>, and <span class="html-italic">m</span> is the number of sources, we obtain <math display="inline"><semantics> <mrow> <mi>O</mi> <mo>(</mo> <mi>T</mi> <mo>)</mo> <mo>⋉</mo> <mi>m</mi> </mrow> </semantics></math> as the total time cost.</p> Full article ">Figure 7
<p>Time cost of GFIS inference with different numbers of IS. It has been found that the inference time cost for both two GFIS models increases linearly with the number of IS for all datasets. Fused GFIS, however, has a much lower time cost than non-fused GFIS, demonstrating the effectiveness and adaptability of the proposed membership function.</p> Full article ">Figure 8
<p>Influence of noise on the accuracy of different GFIS models. (<b>a</b>) non-fused GFIS; (<b>b</b>) fused GFIS.</p> Full article ">
10 pages, 850 KiB  
Hypothesis
Identifying Policy Gaps in a COVID-19 Online Tool Using the Five-Factor Framework
by Janet Michel, David Evans, Marcel Tanner and Thomas C. Sauter
Systems 2022, 10(6), 257; https://doi.org/10.3390/systems10060257 - 15 Dec 2022
Viewed by 1944
Abstract
Introduction: Worldwide health systems are being faced with unprecedented COVID-19-related challenges, ranging from the problems of a novel condition and a shortage of personal protective equipment to frequently changing medical guidelines. Many institutions were forced to innovate and many hospitals, as well as [...] Read more.
Introduction: Worldwide health systems are being faced with unprecedented COVID-19-related challenges, ranging from the problems of a novel condition and a shortage of personal protective equipment to frequently changing medical guidelines. Many institutions were forced to innovate and many hospitals, as well as telehealth providers, set up online forward triage tools (OFTTs). Using an OFTT before visiting the emergency department or a doctor’s practice became common practice. A policy can be defined as what an institution or government chooses to do or not to do. An OFTT, in this case, has become both a policy and a practice. Methods: The study was part of a broader multiphase sequential explanatory design. First, an online survey was carried out using a questionnaire to n = 176 patients who consented during OFTT usage. Descriptive analysis was carried out to identify who used the tool, for what purpose, and if the participant followed the recommendations. The quantitative results shaped the interview guide’s development. Second, in-depth interviews were held with a purposeful sample of n = 19, selected from the OFTT users who had consented to a further qualitative study. The qualitative findings were meant to explain the quantitative results. Third, in-depth interviews were held with healthcare providers and authorities (n = 5) that were privy to the tool. Framework analysis was adopted using the five-factor framework as a lens with which to analyze the qualitative data only. Results: The five-factor framework proved useful in identifying gaps that affected the utility of the COVID-19 OFTT. The identified gaps could fit and be represented by five factors: primary, secondary, tertiary, and extraneous factors, along with a lack of systems thinking. Conclusion: A theory or framework provides a road map to systematically identify those factors affecting policy implementation. Knowing how and why policy practice gaps come about in a COVID-19 OFFT context facilitates better future OFTTs. The framework in this study, although developed in a universal health coverage (UHC) context in South Africa, proved useful in a telehealth context in Switzerland, in Europe. The importance of systems thinking in developing digital tools cannot be overemphasized. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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<p>The five-factor framework [<a href="#B11-systems-10-00257" class="html-bibr">11</a>].</p> Full article ">
19 pages, 5039 KiB  
Article
Matching Supply and Demand with Lead-Time Dependent Price and with Safety Stocks in a Make-to-Order Production System
by Sonu Kumar Das and Thyagaraj S. Kuthambalayan
Systems 2022, 10(6), 256; https://doi.org/10.3390/systems10060256 - 14 Dec 2022
Cited by 3 | Viewed by 3308
Abstract
We studied the ability to reduce the supply–demand mismatch of a periodic Make-to-Order (MTO) production system using safety stocks with marketing managing demand using lead-time guarantee and price as levers. The aim is to understand the interdependencies between lead-time guarantee, price, and safety [...] Read more.
We studied the ability to reduce the supply–demand mismatch of a periodic Make-to-Order (MTO) production system using safety stocks with marketing managing demand using lead-time guarantee and price as levers. The aim is to understand the interdependencies between lead-time guarantee, price, and safety stocks. We modeled the problem as an unconstrained stochastic non-linear programming problem, maximizing the expected profit per-unit time and obtaining a closed-form solution. The price is a function of the lead-time guarantee. Based on the sensitivity analysis of problem parameters, we found that lead-time competitiveness is adversely affected by a low safety stock level, MTO production rate (i.e., low supply capability), and product price (i.e., high demand volume). A shorter lead-time requires higher safety stock through reduced product and inventory holding costs. A higher price for a shorter lead-time in a lead-time-sensitive market reduces the safety stock. In a price-sensitive market, lead-time is decreased instead of the price. Demand variation results in longer lead-time and higher safety stock (provided the holding cost is low). For a higher price premium, price increases and lead-time decrease (safety stock increases). The integrated operation-marketing model captures the complex trade-offs not seen in a hierarchical model to produce better solutions. Full article
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<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math>(<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">b<sub>1</sub></span> for demand as <span class="html-italic">f(T, p)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mi>e</mi> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>95</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">b</span><sub>1</sub> for demand as <span class="html-italic">f(T)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>96</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">b<sub>2</sub></span> for demand as <span class="html-italic">f(T, p)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>e</mi> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>95</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>12</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math>(<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">e</span> for demand as <span class="html-italic">f(T, p)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2.5</mn> <mo>,</mo> <msub> <mi>b</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>95</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">t<sub>1</sub></span> for demand as <span class="html-italic">f(T, p)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <msub> <mi>b</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>90</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>6</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">t</span><sub>1</sub> for demand as <span class="html-italic">f(T)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>96</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">t<sub>2</sub></span> for demand as <span class="html-italic">f(T, p)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <msub> <mi>b</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>101</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>6</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <span class="html-italic">t</span><sub>2</sub> for demand as <span class="html-italic">f(T)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in holding cost for demand as <span class="html-italic">f(T, p)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <msub> <mi>b</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>95</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in holding cost for demand as <span class="html-italic">f(T)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>95</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mi>p</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> for demand as <span class="html-italic">f(T, p)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>4</mn> <mo>,</mo> <msub> <mi>b</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mi>e</mi> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>96</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Change in (<b>a</b>) profit, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <msup> <mrow/> <mo>*</mo> </msup> <mo>,</mo> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> with change in <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> for demand as <span class="html-italic">f(T)</span> at <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msub> <mi>b</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>105</mn> <mo>,</mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>96</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>r</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.04</mn> <mo>,</mo> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p> Full article ">
18 pages, 1353 KiB  
Article
Green Growth or Gray Growth: Measuring Green Growth Efficiency of the Manufacturing Industry in China
by Xiaofei Lv and Xiaoli Lu
Systems 2022, 10(6), 255; https://doi.org/10.3390/systems10060255 - 14 Dec 2022
Cited by 3 | Viewed by 1836
Abstract
The manufacturing industry has created a rapid evolution of the economy, but it has also negatively impacted the ecosystem. A better understanding of the manufacturing industry in green growth is crucial to achieving the sustainability goals in China’s high-quality development stage and is [...] Read more.
The manufacturing industry has created a rapid evolution of the economy, but it has also negatively impacted the ecosystem. A better understanding of the manufacturing industry in green growth is crucial to achieving the sustainability goals in China’s high-quality development stage and is better for identifying the impact of scale effect or technological effect in EKC. In this research, a super-efficiency slacks-based measure model is proposed to evaluate the green growth efficiency of 27 manufacturing industries, and a Luenberger index method is adopted to interpret the driving forces of efficiency. The results demonstrate that green growth efficiency in the manufacturing industry shows a fluctuating upward trend, and more than 60% of the industries are in a gray growth state. The growth of green growth efficiency mainly depends on the pulling effect of technological dividends brought by technological progress, rather than the improvement of technical efficiency. As the industry heterogeneity is analyzed, technology-intensive industries still dominate in the process of manufacturing industry and have shown a significant upward trend. Finally, some suggestions are proposed from the perspective of the government and enterprises. Full article
(This article belongs to the Special Issue Research Trends on the Development of Integrated and Robust Social Development Management Systems)
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Figure 1

Figure 1
<p>Frontier production function principle.</p> Full article ">Figure 2
<p>This is the change principle of TE and TP: (<b>a</b>) the change in TE; (<b>b</b>) the change in TP.</p> Full article ">Figure 3
<p>Average change in green growth efficiency on manufacturing industry in 2005–2017.</p> Full article ">Figure 4
<p>Industry heterogeneity results of green growth efficiency.</p> Full article ">Figure 5
<p>The results of industry heterogeneity.</p> Full article ">
66 pages, 1943 KiB  
Article
Consciousness, Sapience and Sentience—A Metacybernetic View
by Maurice Yolles
Systems 2022, 10(6), 254; https://doi.org/10.3390/systems10060254 - 13 Dec 2022
Cited by 4 | Viewed by 13842
Abstract
Living systems are complex dynamic information processing energy consuming entities with properties of consciousness, intelligence, sapience, and sentience. Sapience and sentience are autonomous attributes of consciousness. While sapience has been well studied over the years, that of sentience is relatively rare. The nature [...] Read more.
Living systems are complex dynamic information processing energy consuming entities with properties of consciousness, intelligence, sapience, and sentience. Sapience and sentience are autonomous attributes of consciousness. While sapience has been well studied over the years, that of sentience is relatively rare. The nature of sapience and sentience will be considered, and a metacybernetic framework using structural information will be adopted to explore the metaphysics of consciousness. Metacybernetics delivers a cyberintrinsic model that is cybernetic in nature, but also uses the theory of structural information arising from Frieden’s work with Fisher information. This will be used to model sapience and sentience and their relationship. Since living systems are energy-consuming entities, it is also natural for thermodynamic metaphysical models to arise, and most of the theoretical studies of sentience have been set within a thermodynamic framework. Hence, a thermodynamic approach will also be introduced and connected to cyberintrinsic theory. In metaphysical contexts, thermodynamics uses free-energy, which plays the same role in cyberintrinsic modelling as intrinsic structural information. Since living systems exist at the dynamical interface of information and thermodynamics, the overall purpose of this paper is to explore sentience from the alternative cyberintrinsic perspective of metacybernetics. Full article
(This article belongs to the Section Complex Systems and Cybernetics)
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<p>Venn Diagram indicating Epistemic Values of Lane’s [<a href="#B41-systems-10-00254" class="html-bibr">41</a>] Ontological Classes.</p> Full article ">Figure 2
<p>Ontology Map for the Concept of Consciousness, Defined through the Ontological Entities of Sapience and Sentience with their Epistemic Content.</p> Full article ">Figure 3
<p>(<b>a</b>): Metacybernetic Model of the Cognitive/Sapience Agency, (<b>b</b>): Metacybernetic model of the Affect/sentience Agency.</p> Full article ">Figure 4
<p>Cross-fire Model (adapted from Yolles and Fink [<a href="#B1-systems-10-00254" class="html-bibr">1</a>]).</p> Full article ">Figure 5
<p>Agency Model Showing the Anterior and Posterior Intelligences respectively related to Internalisation and Externalisation.</p> Full article ">
17 pages, 1170 KiB  
Article
New Approach for Quality Function Deployment Using an Extended CoCoSo Method with Spherical Fuzzy Sets
by Xue-Guo Xu, Ling Zhang, Ling-Xiang Mao and Ke Li
Systems 2022, 10(6), 253; https://doi.org/10.3390/systems10060253 - 13 Dec 2022
Cited by 3 | Viewed by 2318
Abstract
Quality function deployment (QFD) is a customer-driven quality management tool that can improve system quality, promote innovation, and enhance the core competitiveness of enterprises. Nonetheless, the traditional QFD method has defects in handling the experts’ assessments, measuring customer requirement importance, and prioritizing engineering [...] Read more.
Quality function deployment (QFD) is a customer-driven quality management tool that can improve system quality, promote innovation, and enhance the core competitiveness of enterprises. Nonetheless, the traditional QFD method has defects in handling the experts’ assessments, measuring customer requirement importance, and prioritizing engineering characteristics, which affect its efficiency and limit its application in the real world. In this study, a new QFD approach based on spherical fuzzy sets (SFSs) and a combined compromise solution (CoCoSo) method is proposed to overcome the shortcomings associated with the traditional QFD. To be specific, the linguistic relationship assessments between the customer requirements and engineering characteristics provided by the experts were described by the SFSs, the relative weights of the customer requirements were obtained via the decision-making trial and evaluation laboratory (DEMATEL) method, and the importance ranking orders of the engineering characteristics were determined with an improved CoCoSo method. The feasibility and effectiveness of the proposed QFD approach are illustrated by an empirical case of accommodation service design. The results show that the new QFD approach provides a useful and practical way to represent the relationship assessment information of experts and determine the priority of engineering characteristics in product development. Full article
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<p>Flowchart of the proposed QFD approach.</p> Full article ">Figure 2
<p>The EC ranking results of the comparative analysis.</p> Full article ">
26 pages, 1482 KiB  
Article
Prediction and Visualisation of SICONV Project Profiles Using Machine Learning
by Adriano de Oliveira Andrade, Leonardo Garcia Marques, Osvaldo Resende, Geraldo Andrade de Oliveira, Leandro Rodrigues da Silva Souza and Adriano Alves Pereira
Systems 2022, 10(6), 252; https://doi.org/10.3390/systems10060252 - 10 Dec 2022
Cited by 1 | Viewed by 1879
Abstract
Background: Inefficient use of public funds can have a negative impact on the lives of citizens. The development of machine learning-based technologies for data visualisation and prediction has opened the possibility of evaluating the accountability of publicly funded projects. Methods: This study describes [...] Read more.
Background: Inefficient use of public funds can have a negative impact on the lives of citizens. The development of machine learning-based technologies for data visualisation and prediction has opened the possibility of evaluating the accountability of publicly funded projects. Methods: This study describes the conception and evaluation of the architecture of a system that can be utilised for project profile definition and prediction. The system was used to analyse data from 20,942 System of Management of Agreements and Transfer Contracts (SICONV) projects in Brazil, which are government-funded projects. SICONV is a Brazilian Government initiative that records the entire life cycle of agreements, transfer contracts, and partnership terms, from proposal formalisation to final accountability. The projects were represented by seven variables, all of which were related to the timeline and budget of the project. Data statistics and clustering in a lower-dimensional space calculated using t-SNE were used to generate project profiles. Performance measures were used to test and compare several project-profile prediction models based on classifiers. Results: Data clustering was achieved, and ten project profiles were defined as a result. Among 25 prediction models, k-Nearest-Neighbor (kknn) was the one that yielded the highest accuracy (0.991±0.002). Conclusions: The system predicted SICONV project profiles accurately. This system can help auditors and citizens evaluate new and ongoing project profiles, identifying inappropriate public funding. Full article
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<p>System architecture depicting the processes, task, user, and data. The diagram follows the BMPN standard.</p> Full article ">Figure 2
<p>Probability of values for each variable.</p> Full article ">Figure 3
<p>Boxplot of the transformed variables.</p> Full article ">Figure 4
<p>Empirical cumulative distribution functions of the logarithmic transformed variables.</p> Full article ">Figure 5
<p>Scree plot.</p> Full article ">Figure 6
<p>Loadings plots showing the correlation and directions of distinct variables according to PC dimensions.</p> Full article ">Figure 7
<p>Results of the projection of the high-dimensional data into the lower bi-dimensional space given by t-SNE. Each observation represents a project (20,948 in total). The data points were clustered by k-means (<math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>) and the data points which belong to the same group are in the same ellipsoidal coloured region.</p> Full article ">Figure 8
<p>Loading plots for each cluster.</p> Full article ">Figure 9
<p>Definition of project profile according to the data presented in <a href="#systems-10-00252-t004" class="html-table">Table 4</a> and <a href="#systems-10-00252-t007" class="html-table">Table 7</a>.</p> Full article ">
23 pages, 1877 KiB  
Article
Behavioral Framework of Asset Price Bubbles: Theoretical and Empirical Analyses
by Cong Chen, Changsheng Hu and Hongxing Yao
Systems 2022, 10(6), 251; https://doi.org/10.3390/systems10060251 - 9 Dec 2022
Viewed by 2613
Abstract
Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated [...] Read more.
Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated investors, and we assessed the time series characteristics of asset prices under different types of information shocks. According to the research results, good news about the fundamentals can lead to positive asset price bubbles, and correspondingly, bad news can lead to negative asset price bubbles; however, the decrease in asset prices in the case of negative bubbles is not as substantial as the increase in prices in the case of positive bubbles, and the time for prices to reverse is also long, which can be explained by the short-selling constraints. According to the comparative static analysis, the scales of the positive and negative foams depend on the proportion of investors in the market and the extrapolation coefficient. We verified the conclusion of the theoretical model from two aspects: (1) we analyzed the relationship between investor sentiment and the prevalence of informed trading, and according to the results, the increase (decrease) in investor sentiment can reduce the information content of asset prices and increase price volatility; however, the impact of low sentiment is not substantial, which preliminarily tests the conclusion of the theoretical model; (2) we examined the relationship between the cumulative change in investor sentiment and future portfolio returns, and we found that the cumulative increase in investor sentiment can have a positive impact on future portfolio returns at the initial stage, and depress future portfolio returns in the long term, which forms positive asset price bubbles. The cumulative depression of investor sentiment can depress the future portfolio returns at the initial stage, and positively influence the future portfolio returns in the long term, which forms negative asset price bubbles. Moreover, these two nonlinear relationships exhibit cross-sectional differences in different types of asset portfolios, which further validates the key proposition of the theoretical model. Full article
(This article belongs to the Section Complex Systems and Cybernetics)
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<p>Asset price bubbles under the impact of good news.</p> Full article ">Figure 2
<p>Comparative static analysis (the impact of good news).</p> Full article ">Figure 3
<p>Asset price bubbles under the impact of bad news.</p> Full article ">Figure 4
<p>Comparative static analysis (the impact of bad news).</p> Full article ">Figure 5
<p>Cumulative growth in sentiment and portfolio returns (fitting).</p> Full article ">Figure 6
<p>Cumulative decline in sentiment and portfolio returns (fitting).</p> Full article ">
17 pages, 2870 KiB  
Article
Automated Identification of Valid Model Networks Using Model-Based Systems Engineering
by Julius Moritz Berges, Kathrin Spütz, Georg Jacobs, Julia Kowalski, Thilo Zerwas, Jörg Berroth and Christian Konrad
Systems 2022, 10(6), 250; https://doi.org/10.3390/systems10060250 - 9 Dec 2022
Cited by 6 | Viewed by 2432
Abstract
To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery [...] Read more.
To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery lifetime calculation), there are typically several models at several fidelity levels. Since the model signatures (i.e., necessary inputs for the models and their outputs) differ depending on the fidelity level, not all models can be used in any development phase. In addition, due to the different model signatures, not all models can be combined arbitrarily to model networks. Currently, valid model networks in system models must be determined in a time-consuming, manual process. Therefore, this paper presents an approach that automates this task via the implementation of an algorithm that analyzes a system model and the model signatures and automatically returns all valid model networks. When input parameters, models or their signatures change, the algorithm updates automatically, and the user receives the valid model network without any manual effort. The approach is demonstrated with the running example of battery system development. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering: From Design to Practical Systems Engineering)
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<p>Seamless linkage of elements at a system level with the motego method illustrated for a battery system.</p> Full article ">Figure 2
<p>Selection of purposes and classified models for the development of battery systems [<a href="#B39-systems-10-00250" class="html-bibr">39</a>,<a href="#B40-systems-10-00250" class="html-bibr">40</a>,<a href="#B41-systems-10-00250" class="html-bibr">41</a>,<a href="#B42-systems-10-00250" class="html-bibr">42</a>] are illustrated for the scope “battery cell”.</p> Full article ">Figure 3
<p>Model signatures with input, output and internal parameters for two exemplary models of battery system development (according to [<a href="#B26-systems-10-00250" class="html-bibr">26</a>]).</p> Full article ">Figure 4
<p>Structure of motego system models and integration of domain models into the <span class="html-italic">SystemSolution</span> of a battery system based on model classification and model signatures according to [<a href="#B25-systems-10-00250" class="html-bibr">25</a>].</p> Full article ">Figure 5
<p>Flowchart of a model network algorithm for the automated analysis of valid model networks using system models built with the motego method.</p> Full article ">Figure 6
<p>Reading elements and relationships between SysML system models using JavaScript to determine valid model networks.</p> Full article ">Figure 7
<p>Implementation of the model network algorithm with profile mechanisms in the SysML modeling environment Cameo Systems Modeler.</p> Full article ">Figure 8
<p>Resulting valid model networks for the specification of two versions of the <span class="html-italic">SystemSolution</span> battery pack with the applied <span class="html-italic">ModelNetworkProfile</span> without cell positions (<b>a</b>) and with cell positions (<b>b</b>).</p> Full article ">
20 pages, 490 KiB  
Article
An Investigation into the Trend Stationarity of Local Characteristics in Media and Social Networks
by Sergei Sidorov, Sergei Mironov, Alexey Grigoriev and Sophia Tikhonova
Systems 2022, 10(6), 249; https://doi.org/10.3390/systems10060249 - 9 Dec 2022
Cited by 3 | Viewed by 1373
Abstract
We studied the evolution of complex social networks over time. The elements of the networks are users, and the connections correspond to the interactions between them. At a particular moment in time, each node of a complex network has such characteristics as its [...] Read more.
We studied the evolution of complex social networks over time. The elements of the networks are users, and the connections correspond to the interactions between them. At a particular moment in time, each node of a complex network has such characteristics as its degree, as well as the total degree of its neighbors. Obviously, in the process of network growth, these characteristics are constantly changing due to the fact that new edges are attached to this node or its neighbors. In this paper, we study the dynamics of these characteristics over time for networks generated on the basis of a nonlinear preferential attachment mechanism, and we find both the asymptotics of their expected values and the characteristics of their spread around the mean. In addition, we analyze the behavior of these local characteristics for three real social networks. The applicability of the findings to actual problems in the study of social media in the digital humanities is discussed. Full article
(This article belongs to the Section Complex Systems and Cybernetics)
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<p>Trajectories of the averaged degrees (<b>top left</b>), variances (<b>top right</b>), and coefficients of variation (<b>bottom</b>) for three groups of nodes from iterations (21, 121), (401, 501), and (901, 1001) over the growth of the network based on the data of users adding posts to their favourites on StackOverflow.</p> Full article ">Figure 2
<p>Trajectories of the averaged sum of degrees (<b>top left</b>), variances (<b>top right</b>), and coefficients of variation (<b>bottom</b>) for three groups of nodes from iterations (21, 121), (401, 501), and (901, 1001) over the growth of the network based on the data of users adding posts to their favourites on StackOverflow.</p> Full article ">Figure 3
<p>Trajectories of the averaged degrees (<b>top left</b>), variances (<b>top right</b>), and coefficients of variation (<b>bottom</b>) for three groups of nodes from iterations (21, 121), (401, 501), and (901, 1001) over the growth of the network based on the data of user interactions inside the question and answer service of AskUbuntu.</p> Full article ">Figure 4
<p>Trajectories of the averaged sum of degrees (<b>top left</b>), variances (<b>top right</b>), and coefficients of variation (<b>bottom</b>) for three groups of nodes from iterations (21, 121), (401, 501), and (901, 1001) over the growth of the network based on the data of user interactions inside the question and answer service of AskUbuntu.</p> Full article ">Figure 5
<p>Trajectories of the averaged degrees (<b>top left</b>), variances (<b>top right</b>), and coefficients of variation (<b>bottom</b>) for three groups of nodes from iterations (21, 121), (401, 501), and (901, 1001) over the growth of the network based on the data of user interactions inside the question and answer service of SuperUser.</p> Full article ">Figure 6
<p>Trajectories of the averaged sum of degrees (<b>top left</b>), variances (<b>top right</b>), and coefficients of variation (<b>bottom</b>) for three groups of nodes from iterations (21, 121), (401, 501), and (901, 1001) over the growth of the network based on the data of user interactions inside the question and answer service of SuperUser.</p> Full article ">Figure 7
<p>The dynamics of the ratio <math display="inline"><semantics> <mrow> <mfrac> <mn>1</mn> <mi>t</mi> </mfrac> <msub> <mo>∑</mo> <mrow> <msub> <mi>v</mi> <mi>j</mi> </msub> <mo>∈</mo> <msub> <mi>V</mi> <mi>t</mi> </msub> </mrow> </msub> <msubsup> <mi>d</mi> <mi>j</mi> <mi>γ</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> in BA networks with <span class="html-italic">t</span> up to 160,000 iterations. Networks are generated with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Mean value of the node degree in networks generated by the BA model with nonlinear PA for the node <math display="inline"><semantics> <msub> <mi>v</mi> <mi>i</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>50</mn> <mo>,</mo> <mn>100</mn> </mrow> </semantics></math>, with <span class="html-italic">t</span> going up to 160,000 iterations. Networks were generated for <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and for the values of <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math> (<b>c</b>).</p> Full article ">Figure 9
<p>The dynamics of the standard deviation of node degrees in networks generated by the BA model with nonlinear PA for the node <math display="inline"><semantics> <msub> <mi>v</mi> <mi>i</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>50</mn> <mo>,</mo> <mn>100</mn> </mrow> </semantics></math>, with <span class="html-italic">t</span> going up to 160,000 iterations. The networks were generated with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and the values of <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math> (<b>c</b>).</p> Full article ">Figure 10
<p>The dynamics of the empirical values of the variation coefficient of the node degrees in networks generated by the BA model with nonlinear PA for the node <math display="inline"><semantics> <msub> <mi>v</mi> <mi>i</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>50</mn> <mo>,</mo> <mn>100</mn> </mrow> </semantics></math>, with <span class="html-italic">t</span> going up to 160,000 iterations. The networks were generated with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and the values of <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math> (<b>c</b>).</p> Full article ">Figure 11
<p>Dynamics of the sum of the neighbors’ degrees in BA networks with the NPA generation mechanism for selected nodes <math display="inline"><semantics> <msub> <mi>v</mi> <mi>i</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>50</mn> <mo>,</mo> <mn>100</mn> </mrow> </semantics></math>, as <span class="html-italic">t</span> grows to 25,000. The network in (<b>a</b>) was modeled with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, (<b>b</b>) was simulated with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, and (<b>c</b>) was simulated with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Trajectories of empirical values for <math display="inline"><semantics> <msqrt> <mrow> <mi>mean</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>−</mo> <mi>mean</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </semantics></math> in BA networks with the NPA mechanism for selected nodes <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>50</mn> </mrow> </semantics></math> as <span class="html-italic">t</span> was iterated up to 160,000. The network in (<b>a</b>) was modeled with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, that in (<b>b</b>) was modeled with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, and that in (<b>c</b>) was modeled with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>Trajectories of empirical values for the variation coefficient of <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> in BA networks with the NPA mechanism for selected nodes <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>50</mn> </mrow> </semantics></math> as <span class="html-italic">t</span> iterates up to 160,000. The network in (<b>a</b>) was modeled with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, that in (<b>b</b>) was modeled with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, and that in (<b>c</b>) was modeled with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>.</p> Full article ">
21 pages, 2462 KiB  
Article
Novel Hybrid MPSI–MARA Decision-Making Model for Support System Selection in an Underground Mine
by Miloš Gligorić, Zoran Gligorić, Suzana Lutovac, Milanka Negovanović and Zlatko Langović
Systems 2022, 10(6), 248; https://doi.org/10.3390/systems10060248 - 9 Dec 2022
Cited by 16 | Viewed by 2669
Abstract
An underground mine is a very complex production system within the mining industry. Building up the underground mine development system is closely related to the installation of support needed to provide the stability of mine openings. The selection of the type of support [...] Read more.
An underground mine is a very complex production system within the mining industry. Building up the underground mine development system is closely related to the installation of support needed to provide the stability of mine openings. The selection of the type of support system is recognized as a very hard problem and multi-criteria decision making can be a very useful tool to solve it. In this paper we developed a methodology that helps mining engineers to select the appropriate support system with respect to geological conditions and technological requirements. Accordingly, we present a novel hybrid model that integrates the two following decision-making components. First, this study suggests a new approach for calculating the weights of criteria in an objective way named the Modified Preference Selection Index (MPSI) method. Second, the Magnitude of the Area for the Ranking of Alternatives (MARA) method is proposed as a novel multi-criteria decision-making technique for establishing the final rank of alternatives. The model is tested on a hypothetical example. Comparative analysis confirms that the new proposed MPSI–MARA model is a very useful and effective tool for solving different MCDM problems. Full article
(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)
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<p>Function of the optimal and <span class="html-italic">i</span>th alternative.</p> Full article ">Figure 2
<p>Hybrid MPSI–MARA model framework.</p> Full article ">Figure 3
<p>Illustration of A1.</p> Full article ">Figure 4
<p>Illustration of A2.</p> Full article ">Figure 5
<p>Illustration of A3.</p> Full article ">Figure 6
<p>Illustration of A4.</p> Full article ">Figure 7
<p>The weights of criteria obtained by MPSI method.</p> Full article ">Figure 8
<p>Final rank of the MARA method.</p> Full article ">Figure 9
<p>The weights of criteria obtained by applied methods.</p> Full article ">Figure 10
<p>Chart of the final rank of alternatives.</p> Full article ">
21 pages, 1903 KiB  
Article
Research on Enterprise Financial Risk Conduction Mechanism Based on System Dynamics
by Zhi Zhang
Systems 2022, 10(6), 247; https://doi.org/10.3390/systems10060247 - 8 Dec 2022
Cited by 1 | Viewed by 2502
Abstract
Affected by the COVID-19 pandemic and the economic situation, many enterprises have fallen into financial crisis. In order to explore the causes of enterprise financial risk and the conduction path of risk sources, this paper starts from the theory, characteristics, and path of [...] Read more.
Affected by the COVID-19 pandemic and the economic situation, many enterprises have fallen into financial crisis. In order to explore the causes of enterprise financial risk and the conduction path of risk sources, this paper starts from the theory, characteristics, and path of financial risk conduction, combines Hall three-dimensional structure and system dynamics models, establishes the path of enterprise financial risk conduction (causality graph), and combines the value-at-risk VaR model to measure the risk. Based on this methodology, a three-dimensional multiple risk interaction and dynamic–static combination of an enterprise financial risk conduction model is established, aiming at identifying the sources of financial risk in different periods and providing timely risk control countermeasures to avoid financial crises. This paper does not refine some of the indicators and takes into account the probability of different scenarios and/or the number of trigger strategies to avoid or reduce risk. In the future, refining the indicators to include considerations such as production technology will enable a more robust model of corporate financial risk conduction. Full article
(This article belongs to the Section Systems Practice in Social Science)
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<p>Diagram of the elements of enterprise financial risk conduction.</p> Full article ">Figure 2
<p>Hall three-dimensional structure diagram.</p> Full article ">Figure 3
<p>Enterprise financial risk conduction path diagram.</p> Full article ">Figure 4
<p>Financial risk conduction model diagram.</p> Full article ">Figure 5
<p>Net cash flow from investing, financing, and operating activities of China Dive Company Limited from 2017 to 2021.</p> Full article ">Figure 6
<p>Corporate financial risk values for 2017–2021 for China Dive Company Limited.</p> Full article ">Figure 7
<p>China Dive Company Limited’s product sales 2017−2021.</p> Full article ">Figure 8
<p>Product sales, yield, and inventory of China Dive Company Limited, 2017–2021.</p> Full article ">
19 pages, 556 KiB  
Article
Demand-Side Economies of Scope in Big Tech Business Modelling and Strategy
by Anders Henten and Iwona Windekilde
Systems 2022, 10(6), 246; https://doi.org/10.3390/systems10060246 - 8 Dec 2022
Cited by 2 | Viewed by 4230
Abstract
The purpose of the paper is to discuss the issue of economies of scope in platform research and to attract attention to the importance of scope economies for the strength and growth of Big Tech corporations. Hitherto, most attention has been on network [...] Read more.
The purpose of the paper is to discuss the issue of economies of scope in platform research and to attract attention to the importance of scope economies for the strength and growth of Big Tech corporations. Hitherto, most attention has been on network effects and demand-side economies of scale, on the role of platforms in lowering transaction costs, and on the importance of big data. More specifically, the research question addressed in this paper is how economies of scope, driven by the demand side, contribute to the strength of successful Big Tech corporations. The answer is related to two aspects: one is concerned with bundling of services and products, and the other with the acquisition and processing of data on users and their activities using digital services and applications. Full article
(This article belongs to the Special Issue Business Model–the Perspective of Systems Thinking and Innovation)
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<p>Demand-side economies of scale and demand-side economies of scope.</p> Full article ">
24 pages, 1034 KiB  
Article
Experimental Validation of Systems Engineering Resilience Models for Islanded Microgrids
by Justin J. He, Douglas L. Van Bossuyt and Anthony Pollman
Systems 2022, 10(6), 245; https://doi.org/10.3390/systems10060245 - 6 Dec 2022
Cited by 3 | Viewed by 2826
Abstract
Microgrids are used in many applications to power critical loads that have significant consequences if they lose power. Losing power to medical centers, water treatment plants, data centers, national defense installations, airports, and other critical infrastructure can cause loss of money and loss [...] Read more.
Microgrids are used in many applications to power critical loads that have significant consequences if they lose power. Losing power to medical centers, water treatment plants, data centers, national defense installations, airports, and other critical infrastructure can cause loss of money and loss of life. Although such microgrids are generally reliable at providing stable power, their resilience to disruption can be poor. Common interruptions include natural disasters like earthquakes, and man-made causes such as cyber or physical attacks. Previous research into microgrid resilience evaluation efforts centered on theoretical modeling of total electrical microgrid loading, critical electrical load prioritization, assumed capacity of renewable energy sources and their associated energy storage systems, and assumed availability of emergency generators. This research assesses the validity of two microgrid resilience models developed for analyzing islanded microgrids by using experimental data from a scaled microgrid system. A national defense context is provided to motivate the work and align with the intended purpose two microgrid resilience models. The results of this research validate that the simulation models are valid to use in some situations, and highlight some areas for further model improvement. Full article
(This article belongs to the Section Systems Engineering)
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<p>Components of a basic microgrid architecture.</p> Full article ">Figure 2
<p>Typical resilience curve showing the phases of disruption, and the key measures of invulnerability and recovery time.</p> Full article ">Figure 3
<p>Peterson’s Simulation Model architecture. Components not used in this analysis are indicated in gray. Adapted with permission from [<a href="#B11-systems-10-00245" class="html-bibr">11</a>]. Copyright 2021, copyright Peterson, Van Bossuyt, Giachetti, and Oriti.</p> Full article ">Figure 4
<p>Peterson’s Simulation Model adjusted architecture results for battery state of charge and power flow.</p> Full article ">Figure 5
<p>Anderson’s Simulation Model resilience curve function for a power disruption with annotations for invulnerability and recovery measures.</p> Full article ">Figure 6
<p>Anderson’s Simulation Model resilience curve showing simulation of diesel generator and solar power disruption.</p> Full article ">Figure 7
<p>Scaled Experimental Microgrid Diagram showing connected power generation and demand components.</p> Full article ">Figure 8
<p>Experimental microgrid baseline result for a clear day with no power disruption is shown in this figure. It shows power delivered from energy resources, and power delivered to load demands.</p> Full article ">Figure 9
<p>Experimental microgrid simulated loss of utility grid disruption results.</p> Full article ">Figure 10
<p>Experimental microgrid simulated loss of utility grid disruption resilience curve results.</p> Full article ">Figure 11
<p>Peterson’s Simulation Model battery charge and component power over length of simulation.</p> Full article ">Figure 12
<p>Anderson’s Simulation Model power rating, power demands and power delivered over length of simulation overlaid with experimental data.</p> Full article ">Figure 13
<p>Peterson’s Simulation Model battery charge results show a positive trend because the model batteries were not charged by excess diesel generator power.</p> Full article ">Figure 14
<p>Peterson’s Simulation Model solar power residuals trend flat and slightly below 0 due to difference in assumptions for solar efficiency and weather conditions.</p> Full article ">Figure 15
<p>Anderson’s Simulation Model power rating residuals trend flat and close to 0.</p> Full article ">
19 pages, 1727 KiB  
Article
Socio-Technical and Political Complexities: Findings from Two Case Studies of Large IT Project-Based Organizations
by Navid Ahmadi Eftekhari, Saba Mani, Javad Bakhshi, Larissa Statsenko and Leila Moslemi Naeni
Systems 2022, 10(6), 244; https://doi.org/10.3390/systems10060244 - 3 Dec 2022
Cited by 3 | Viewed by 2607
Abstract
Information technology (IT) projects are becoming more complex due to technological advancements, increased sociopolitical demand, and competition. In recent years, the project complexity field has attracted increasing attention with diverse strategies and methods proposed to identify, evaluate, and respond to various complexities. This [...] Read more.
Information technology (IT) projects are becoming more complex due to technological advancements, increased sociopolitical demand, and competition. In recent years, the project complexity field has attracted increasing attention with diverse strategies and methods proposed to identify, evaluate, and respond to various complexities. This study aims to identify and prioritize factors contributing to complexity in IT projects by reporting two case studies conducted on large IT organizations. The literature on project complexity informed and guided this exploratory research. The data were collected through 21 semi-structured interviews and analyzed by applying open and axial coding content analysis. Underpinned by complexity theories, 19 factors contributing to the complexity of IT projects were identified, and their importance was highlighted using the Friedman test. The top five factors contributing to IT project complexity were identified as follows: the diversity of stakeholders; technological newness of the project; conflicting goals of stakeholders; variety of product sub-systems and components; and uncertainty of project objectives. This study’s findings contribute to the project management literature and inform practitioners about how to achieve more effective management of complex IT projects. Full article
(This article belongs to the Special Issue Simplifying the Complex: Modeling and Analysis for System Design and Management)
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<p>Typology of projects adapted from [<a href="#B18-systems-10-00244" class="html-bibr">18</a>].</p> Full article ">Figure 2
<p>Research process.</p> Full article ">Figure 3
<p>Classification of codes: (<b>a</b>) coding process for complexity factors; (<b>b</b>) connections between complexity factors and interviewees; (<b>c</b>) categorization of complexity factors. Acronyms are placed in <a href="#app2-systems-10-00244" class="html-app">Appendix B</a>.</p> Full article ">Figure 4
<p>ITCF model—Complexity factors in IT (ITCF) projects.</p> Full article ">
31 pages, 1897 KiB  
Article
A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits
by Ching-Hsue Cheng, Ming-Chi Tsai and Chin Chang
Systems 2022, 10(6), 243; https://doi.org/10.3390/systems10060243 - 3 Dec 2022
Cited by 11 | Viewed by 3892
Abstract
A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but [...] Read more.
A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but due to several shortcomings, these models cannot apply all available information to make a forecast. The relationship between stock prices and related factors is nonlinear and involves nonstationary fluctuations, and accurately forecasting stock prices is not an easy task. Therefore, this study used support vector machines (linear and radial basis functions), gene expression programming, multilayer perceptron regression, and generalized regression neural networks to calculate the importance of indicators. We then integrated the five indicator selection methods to find the key indicators. Next, we used long short-term memory (LSTM) and gated recurrent units (GRU) to build time series models for forecasting stock prices and compare them with the listing models. To evaluate the effectiveness of the proposed model, we collected six different stock market data from 2011 to 2019 to evaluate their forecast performance based on RMSE and MAPE metrics. It is worth mentioning that this study proposes two trading policies to evaluate trading profits and compare them with the listing methods, and their profits are pretty good to investors. After the experiments, the proposed time series model (GRU/LSTM combined with the selected key indicators) exhibits better forecast ability in fluctuating and non-fluctuating environments than the listing models, thus presenting an effective reference for stakeholders. Full article
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<p>Proposed computational procedure [Source: Authors’ own processing].</p> Full article ">Figure 2
<p>Four forecast models vs. actual 2017 data from the DJI [Source: Authors’ own processing].</p> Full article ">Figure 3
<p>Four forecast models vs. actual 2017 HSI data [Source: Authors’ own processing].</p> Full article ">Figure 4
<p>Four forecast models vs. actual 2017 data of the Nikkei 225 [Source: Authors’ own processing].</p> Full article ">Figure 5
<p>Comparative total profits of trading policies 1 and 2 for the six stock markets [Source: Authors’ own processing].</p> Full article ">
19 pages, 3853 KiB  
Article
Research on the Effectiveness of Deep Learning−Based Agency Cost Suppression Strategy: A Case Study of State−Owned Enterprises in Mainland China
by Dongxue Zhai, Xuefeng Zhao, Yanfei Bai and Delin Wu
Systems 2022, 10(6), 242; https://doi.org/10.3390/systems10060242 - 2 Dec 2022
Cited by 1 | Viewed by 1770
Abstract
The mixed ownership reform aims to improve the property rights structure of the state−owned enterprises (SOEs) and reduce agency costs, and the current mixed reform strategies mainly include equity blending by introducing external non−state capital, executive assignments, and employee stock ownership. In this [...] Read more.
The mixed ownership reform aims to improve the property rights structure of the state−owned enterprises (SOEs) and reduce agency costs, and the current mixed reform strategies mainly include equity blending by introducing external non−state capital, executive assignments, and employee stock ownership. In this paper, 953 valid data of A−shares listed in Shanghai and Shenzhen from 2008 to 2020 are used as samples to construct the indicators of mixed reform strategy by the literature statistics method. After obtaining multiple impact indicators, the regression impact model of corporate agency cost suppression strategy is constructed by MATLAB software using a machine learning algorithm. On this basis, the performance of multiple machine learning algorithms is compared, and it is found that the integrated optimization−based bag−boosting model is used to study the effect of hybrid reform strategy to reduce the agency costs of SOEs, and the proportional setting of indicators when the effect is optimal is also explored. Finally, the laws of different influencing factors on the agency costs of enterprises are explored separately by the eigenvalue method. The results of the study show that the proportion of shareholding of the first largest non−state shareholder is sin−functional with the agency costs of SOEs when non−state majority shareholders are introduced into SOEs’ equity mix, and the agency costs tend to decrease after SOEs become privately held enterprises. The greater the number and proportion of supervisors appointed by non−state shareholders, the greater the supervisory restraint effect on SOE managers and the better the effect of suppressing agency costs. The participation of non−state−owned shareholders in the company’s business decisions by appointed executives and the special resource advantages of SOEs intensify the occurrence of the self−interest of appointed executives and the increase of agency costs of SOEs. The implementation of an employee stock ownership plan plays the role of employee supervision and restraint on SOE managers, which reduces the agency costs of SOEs. Based on this, it can provide support for the government to improve the hybrid reform policy and promote the process layer by layer, and also provide theoretical reference for SOEs to deepen the equity mix, incentivize employee shareholding, and empower non−state shareholders to govern and thus reduce agency costs. Full article
(This article belongs to the Section Systems Practice in Social Science)
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<p>Research framework diagram.</p> Full article ">Figure 2
<p>Bag−boosting model algorithm flow chart.</p> Full article ">Figure 3
<p>Selection of indicators and construction process.</p> Full article ">Figure 4
<p>Training period about MSE.</p> Full article ">Figure 5
<p>Prediction effect of different models.</p> Full article ">Figure 6
<p>Effectiveness chart of the hybrid reform strategy: (<b>a</b>) shareholding ratio of the first largest non-state shareholder; (<b>b</b>) dummy variable for the implementation of employee share ownership plan; (<b>c</b>) number of supervisors appointed by non-state shareholders, (<b>d</b>) proportion of supervisors appointed by non-state shareholders.</p> Full article ">Figure 7
<p>Shareholding ratio of the first largest non−state shareholder and agency costs.</p> Full article ">Figure 8
<p>Number and proportion of executive assignments and agency costs.</p> Full article ">
15 pages, 1320 KiB  
Article
Reducing Children’s Obesity in the Age of Telehealth and AI/IoT Technologies in Gulf Countries
by Mohammed Faisal, Hebah ElGibreen, Nora Alafif and Chibli Joumaa
Systems 2022, 10(6), 241; https://doi.org/10.3390/systems10060241 - 2 Dec 2022
Cited by 3 | Viewed by 3015
Abstract
Childhood obesity has become one of the major health issues in the global population. The increasing prevalence of childhood obesity is associated with serious health issues and comorbidities related to obesity. Several studies mentioned that childhood obesity became even worse recently due to [...] Read more.
Childhood obesity has become one of the major health issues in the global population. The increasing prevalence of childhood obesity is associated with serious health issues and comorbidities related to obesity. Several studies mentioned that childhood obesity became even worse recently due to the effect of COVID-19 and the consequent policies and regulations. For that reason, Internet of Things (IoT) technologies should be utilized to overcome the challenges related to obesity management and provide care from a distance to improve the health care services for obesity. However, IoT by itself is a limited resource and it is important to consider other artificial intelligent (AI) components. Thus, this paper contributes into the literature of child obesity management by introducing a comprehensive survey for obesity management covering clinical work measuring the association between sleep disturbances and childhood obesity alongside physical activity and diet and comparatively analyzing the emerging technologies used to prevent childhood obesity. It further contributes to the literature by proposing an interactive smart framework that combines clinical and emerging AI/telehealth technologies to manage child obesity. The proposed framework can be used to reduce children obesity and improve their quality of life using Machine Learning (ML). It utilizes IoT devices to integrate information from different sources and complement it with a mobile application and web-based platform to connect parents and physicians with their child. Full article
(This article belongs to the Special Issue Artificial Intelligence and Intelligent Control for Autonomous Systems)
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<p>Phases of the proposed framework.</p> Full article ">Figure 2
<p>AI agent architecture.</p> Full article ">Figure 3
<p>The proposed framework.</p> Full article ">
18 pages, 2858 KiB  
Article
How China Achieves the Delicate Balance in Ecological Poverty Alleviation: A Systems Thinking Perspective
by Linlin Wang, Meng Wu, Jenson Goh, John Richardson and Haiyan Yan
Systems 2022, 10(6), 240; https://doi.org/10.3390/systems10060240 - 2 Dec 2022
Cited by 1 | Viewed by 2336
Abstract
Ecological poverty alleviation (EPA) is a syngenetic approach to tackling challenges in alleviating extreme poverty and ecological protection. Such an approach is crucial to help countries facing these two challenges to accelerate their progression towards meeting the United Nations Sustainable Development Goals in [...] Read more.
Ecological poverty alleviation (EPA) is a syngenetic approach to tackling challenges in alleviating extreme poverty and ecological protection. Such an approach is crucial to help countries facing these two challenges to accelerate their progression towards meeting the United Nations Sustainable Development Goals in 2030. Prior research on EPA was focusing on understanding EPA from a national perspective and limited consideration was given to regional pertinence. This study uses systems thinking to construct causal loop diagrams (CLDs) and analyzes the mechanisms of EPA in Lanping County, Yunnan Province based on qualitative material. It reveals that the dynamics mechanism of EPA in Lanping County consists of seven reinforcing feedback loops and ten balancing feedback loops. Results indicate that external support, funding resources, employment, and endogenous-driven industrial development are the key drivers to successful EPA. Policies should be taken to avoid the risk of returning to poverty caused by the withdrawal of external support. This study proposes an effective tool with system foresight for exploring the mechanism of EPA and provides reference suggestions for poverty alleviation and development worldwide. Full article
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<p>Systems thinking process of EPA.</p> Full article ">Figure 2
<p>Location of Lanping County and its land use.</p> Full article ">Figure 3
<p>Reference modes of EPA in Lanping. (<b>a</b>) Cumulative new forest and grassland area in Lanping from 2016 to 2020; (<b>b</b>) Poverty incidence of Lanping from 2016 to 2020.</p> Full article ">Figure 4
<p>Causal loop diagram of ecological restoration and resettlement projects and ecological compensation.</p> Full article ">Figure 5
<p>Causal loop diagram of ecological industries.</p> Full article ">Figure 6
<p>Causal loop diagram of clean energy projects.</p> Full article ">Figure 7
<p>Merged causal loop diagram of EPA in Lanping.</p> Full article ">Figure 8
<p>The mechanism of EPA in Lanping County.</p> Full article ">
12 pages, 1758 KiB  
Article
Applying the SMED Methodology to Tire Calibration Procedures
by Vitor Santos, Vitor F. C. Sousa, Francisco J. G. Silva, João C. O. Matias, Rúben D. Costa, Arnaldo G. Pinto and Raul D. S. G. Campilho
Systems 2022, 10(6), 239; https://doi.org/10.3390/systems10060239 - 2 Dec 2022
Cited by 1 | Viewed by 3196
Abstract
Due to the automotive industry’s strict demands, customers submit constant production pressure, leading to the adoption of new methodologies, techniques, and management ideas. The goal is always to minimise losses and waste. These demands also affect the maintenance department, which has to keep [...] Read more.
Due to the automotive industry’s strict demands, customers submit constant production pressure, leading to the adoption of new methodologies, techniques, and management ideas. The goal is always to minimise losses and waste. These demands also affect the maintenance department, which has to keep the balance between machines’ availability for production and ensuring that the machines’ proper running conditions translate into excellent-quality products. Thus, continuous improvement and correct management of maintenance activities are crucial for a company to maintain effective production, without defects, breakdowns, and accidents. Nevertheless, some maintenance activities should also prevent the degradation of equipment conditions in order to produce high-quality products. This paper presents an improvement of maintenance activities conducted on equipment that produces large tires. The main problems and technical difficulties of Machine Tolerance Check (MTC) activities are explored by analysing existing documents, internal knowledge, and changes to working methods. We discuss the implementation of the SMED (Single-Minute Exchange of Die) methodology in calibration procedures, as this method is commonly applied to machines’ setups to reduce downtime. At the end of the study, a 31% decrease in the duration of machine tolerance check activities was achieved, which led to a significant increase in the equipment’s availability. Full article
(This article belongs to the Topic Toward the New Era of Sustainable Design, Manufacturing and Management)
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<p>Ishikawa diagram analysis applied to Machine Tolerance Check activities.</p> Full article ">Figure 2
<p>Laser spotlight check (old procedure) (<b>a</b>). Laser spotlight check (new procedure) (<b>b</b>).</p> Full article ">Figure 3
<p>Parallelism check, old procedure (<b>a</b>). New procedure for concentricity and parallelism check (<b>b</b>).</p> Full article ">Figure 4
<p>Time improvement with SMED implementation through all steps.</p> Full article ">
20 pages, 1237 KiB  
Article
Health Spending Patterns and COVID-19 Crisis in European Union: A Cross-Country Analysis
by Silvia Marginean and Ramona Orastean
Systems 2022, 10(6), 238; https://doi.org/10.3390/systems10060238 - 1 Dec 2022
Cited by 3 | Viewed by 3324
Abstract
The COVID-19 virus outbreak generated new questions about the health policy all over the world. Last several years’ evolutions proved that short-term financing solutions could help health systems to deal with shocks, but the research regarding the relationship between the ability to react [...] Read more.
The COVID-19 virus outbreak generated new questions about the health policy all over the world. Last several years’ evolutions proved that short-term financing solutions could help health systems to deal with shocks, but the research regarding the relationship between the ability to react to unexpected events such as pandemics and steady long-term health policies is limited. The purpose of this paper is to study if EU countries that were consistent in financing national health systems were more prepared to deal with the pandemic shock. Using Current Health Expenditures for 2000–2019, a K-means cluster analysis was conducted, and the 27 EU countries were classified into three groups: high, medium, and low health spenders, with 10, 7, and 10 countries per group, respectively. one-way ANOVA (analysis of variance with one dependent variable) was carried out to identify if there are significant differences between the three groups during the COVID-19 pandemic regarding the general level of preparedness (measured by the Global Health Security Index), impact (measured by excess mortality), and digitalisation as a key factor in implementing successful health and economic policies (measured by the Digital Economy and Society Index). The conclusion was that health systems of the countries from the high health spenders cluster performed better for all three dimensions, followed by medium and low health spenders, showing that better financing could increase the performance and the resilience to future shocks of the health systems. Full article
(This article belongs to the Section Systems Practice in Social Science)
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<p>Outliers—GHS Index.</p> Full article ">Figure 2
<p>Outliers—excess mortality.</p> Full article ">Figure 3
<p>Outliers—DESI.</p> Full article ">
19 pages, 3618 KiB  
Article
Co-Opetition Strategy for Remanufacturing the Closed-Loop Supply Chain Considering the Design for Remanufacturing
by Jiafu Su, Fengting Zhang, Hongyuan Hu, Jie Jian and Dan Wang
Systems 2022, 10(6), 237; https://doi.org/10.3390/systems10060237 - 30 Nov 2022
Cited by 6 | Viewed by 1828
Abstract
The co-opetition strategy between manufacturers and remanufacturers is a key problem of the closed-loop supply chain, especially for the manufacturers often facing decision-making dilemmas when undertaking the environmental responsibilities of the design for remanufacturing (DfRem). Since DfRem is thought to be advantageous for [...] Read more.
The co-opetition strategy between manufacturers and remanufacturers is a key problem of the closed-loop supply chain, especially for the manufacturers often facing decision-making dilemmas when undertaking the environmental responsibilities of the design for remanufacturing (DfRem). Since DfRem is thought to be advantageous for recycling and remanufacturing, it will lower the production costs for remanufacturers but raise them for manufacturers. On the other hand, manufacturers cannot abandon the DfRem because of environmental responsibilities. This work thus formulates three two-period game models of the competition model with patent protection, the competition model without patent protection, and the cooperation model, which consists of a manufacturer and a remanufacturer, to investigate the decision of the manufacturer and remanufacturer co-opetition strategies. The price, the level of DfRem, the recovery rate, the profit, and other factors are compared across the three models using reverse induction and numerical simulation. In addition, we analyzed the influence of different equilibrium solutions on customer willingness to pay for remanufactured products. We find that cooperation not only contributes to the improvement of DfRem but is also the best choice to ensure the stable development of the supply chain system. Manufacturers, in particular, prefer to work together wherever feasible and actively pursue collaboration rather than advocating for patent protection to fend against remanufacturers, even when they have patent rights. Full article
(This article belongs to the Section Supply Chain Management)
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<p>Schematic diagram of three remanufacturing game models.</p> Full article ">Figure 2
<p>Price of new products in the first period.</p> Full article ">Figure 3
<p>The level of DfRem.</p> Full article ">Figure 4
<p>Price of remanufactured products.</p> Full article ">Figure 5
<p>Recycling rate of end-of-life products.</p> Full article ">Figure 6
<p>Unit patent license fee.</p> Full article ">Figure 7
<p>Unit resale price of remanufactured products.</p> Full article ">Figure 8
<p>Manufacturer’s total profit.</p> Full article ">Figure 9
<p>Remanufacturer’s total profit.</p> Full article ">Figure 10
<p>The total profit of CLSC.</p> Full article ">
23 pages, 2063 KiB  
Article
EEG-Based Emotion Recognition by Retargeted Semi-Supervised Regression with Robust Weights
by Ziyuan Chen, Shuzhe Duan and Yong Peng
Systems 2022, 10(6), 236; https://doi.org/10.3390/systems10060236 - 29 Nov 2022
Cited by 4 | Viewed by 2179
Abstract
The electroencephalogram (EEG) can objectively reflect the emotional state of human beings, and has attracted much attention in the academic circles in recent years. However, due to its weak, non-stationary, and low signal-to-noise properties, it is inclined to cause noise in the collected [...] Read more.
The electroencephalogram (EEG) can objectively reflect the emotional state of human beings, and has attracted much attention in the academic circles in recent years. However, due to its weak, non-stationary, and low signal-to-noise properties, it is inclined to cause noise in the collected EEG data. In addition, EEG features extracted from different frequency bands and channels usually exhibit different levels of emotional expression abilities in emotion recognition tasks. In this paper, we fully consider the characteristics of EEG and propose a new model RSRRW (retargeted semi-supervised regression with robust weights). The advantages of the new model can be listed as follows. (1) The probability weight is added to each sample so that it could help effectively search noisy samples in the dataset, and lower the effect of them at the same time. (2) The distance between samples from different categories is much wider than before by extending the ϵ-dragging method to a semi-supervised paradigm. (3) Automatically discover the EEG emotional activation mode by adaptively measuring the contribution of sample features through feature weights. In the three cross-session emotion recognition tasks, the average accuracy of the RSRRW model is 81.51%, which can be seen in the experimental results on the SEED-IV dataset. In addition, with the support of the Friedman test and Nemenyi test, the classification of RSRRW model is much more accurate than that of other models. Full article
(This article belongs to the Topic Human–Machine Interaction)
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<p>Emotion models VA (<b>a</b>) and VAD (<b>b</b>). (<b>a</b>) The VA model consists of the dimensions named valence and arousal, (<b>b</b>) The VAD model consists of the dimensions named valence, arousal and dominance.</p> Full article ">Figure 2
<p>The general framework of RSRRW model.</p> Full article ">Figure 3
<p>Experimental protocol for SEED-IV [<a href="#B42-systems-10-00236" class="html-bibr">42</a>].</p> Full article ">Figure 4
<p>Nemenyi test result.</p> Full article ">Figure 5
<p>The average importance of EEG channels (<b>a</b>) and frequency bands (<b>b</b>) obtained by RSRRW.</p> Full article ">Figure 6
<p>Top 10 EEG channels.</p> Full article ">Figure 7
<p>Top 10 EEG channels.</p> Full article ">Figure 8
<p>Examples to show the effectiveness of the <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math>-dragging.</p> Full article ">Figure 9
<p>Visualization of sample weights <math display="inline"><semantics> <mi mathvariant="bold">s</mi> </semantics></math>.</p> Full article ">Figure 9 Cont.
<p>Visualization of sample weights <math display="inline"><semantics> <mi mathvariant="bold">s</mi> </semantics></math>.</p> Full article ">
22 pages, 4431 KiB  
Article
Comprehensive Evaluation of Low-Carbon City Competitiveness under the “Dual-Carbon” Target: A Cross-Sectional Comparison between Huzhou City and Neighboring Cities in China
by Shouzhen Zeng, Yi Chu, Yiling Yang, Pengkun Li and Huihong Liu
Systems 2022, 10(6), 235; https://doi.org/10.3390/systems10060235 - 27 Nov 2022
Cited by 3 | Viewed by 1790
Abstract
Under the background of “dual-carbon” target construction, the low-carbon environmental protection and ecological construction of Huzhou city in China have received high attention. To scientifically measure the low-carbon construction effect of the city, this study constructs a reasonable comprehensive evaluation system of low-carbon [...] Read more.
Under the background of “dual-carbon” target construction, the low-carbon environmental protection and ecological construction of Huzhou city in China have received high attention. To scientifically measure the low-carbon construction effect of the city, this study constructs a reasonable comprehensive evaluation system of low-carbon city competitiveness from four aspects, including low-carbon economic foundation, low-carbon lifestyle, low-carbon environmental construction, and low-carbon technology development. An integrated weight model of attributes consisting of the analytic hierarchy process (AHP) and entropy weight method is then established, and on this basis, an integrated TOPSIS model is constructed to assess the development of low-carbon competitiveness in Huzhou City. A horizontal comparative analysis of five cities around Huzhou is also conducted, and the current level of low-carbon competitiveness of cities in the central region of the Yangtze River Delta is further explored. Finally, several relevant reference suggestions for Huzhou city are provided to build an ecological model city and a green low-carbon national model and help the government to accelerate the pace of building a low-carbon city in the whole region. Full article
(This article belongs to the Special Issue Decision-Making Process and Its Application to Business Analytic)
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<p>Technical roadmap.</p> Full article ">Figure 2
<p>An illustrative diagram for the proposed evaluation method.</p> Full article ">Figure 3
<p>The weights of each index under different methods.</p> Full article ">Figure 4
<p>Scores of low-carbon city level of Huzhou from 2015 to 2020.</p> Full article ">Figure 5
<p>The geographic location of five cities.</p> Full article ">Figure 6
<p>Changes in the competitiveness of low-carbon cities from 2015 to 2020.</p> Full article ">Figure 7
<p>Scores of low-carbon cities under AHP–TOPSIS method from 2015 to 2020.</p> Full article ">Figure 8
<p>Scores of low-carbon cities under the entropy–TOPSIS method from 2015 to 2020.</p> Full article ">Figure 9
<p>Scores of low-carbon cities with different values in 2015.</p> Full article ">Figure 10
<p>Scores of low-carbon cities with different values in 2016.</p> Full article ">Figure 11
<p>Scores of low-carbon cities with different values in 2017.</p> Full article ">Figure 12
<p>Scores of low-carbon cities with different values in 2018.</p> Full article ">Figure 13
<p>Scores of low-carbon cities with different values in 2019.</p> Full article ">Figure 14
<p>Scores of low-carbon cities with different values in 2020.</p> Full article ">
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