Search Results (5,098)

Urban underground space construction frequently encounters issues of inadequate prevention and ineffective resistance to various disturbances, resulting in safety accidents that are difficult to recover from. Resilience pertains to a system’s capacity to absorb, resist, recover, and adapt when faced with disruptions. Enhancing the construction safety resilience of underground spaces can effectively tackle the issue of frequent accidents and the challenge of pre-controlling risks at construction sites. Utilizing systems engineering theory, this paper investigates the factors that affect the construction safety resilience of underground spaces and establishes a general framework for evaluating the safety performance of the construction process. Utilizing a large-scale underground construction project as a case study, the Bayesian network inference technique is applied to ascertain the project’s safety resilience value. Through reverse reasoning, the method identifies the most likely sequence of causes leading to construction safety incidents, and subsequently, the resilience assessment framework’s efficacy is tested. The research findings suggest that the core of construction safety management is the prevention of unsafe human behaviors and that the key to enhancing resilience lies in the optimization of response capabilities. The proposed “PFR-EFR-LFR” whole-process resilience analysis method can be applied to safety assessments for various types of underground space construction projects. Full article

Hydraulic fracturing is a key technology to build productivity in shale reservoirs; however, the evolution mechanism of fractures is extremely complex, especially in reservoirs with natural weak-planes development. There is an urgent need to conduct systematic research on the influence of natural weak planes on the vertical propagation of hydraulic fractures. This article takes the deep shale gas block of Luzhou in Southern Sichuan as the research basis and conducts different conditions of true triaxial large-scale hydraulic fracturing physical simulation experiments as well as the characteristics of natural weak-plane reservoir development and reservoir geological characteristics. This study clarifies the interaction mechanism between hydraulic fractures and natural weak planes and identifies the influence of parameters such as vertical stress difference, natural fracture strength, and approach angle on the propagation path of hydraulic fractures in reservoirs with developed natural weak planes, which help us gain a deeper insight into the interaction mechanism between fracture and weak plane. This study indicates that the widely developed natural weak planes in shale reservoirs significantly affect the initiation, propagation, and final distribution of hydraulic fractures. Based on pressure response characteristics, the fracture initiation types can be categorized into two scenarios: initiation along the direction of the maximum principal stress and initiation along natural weak planes. The propagation modes of fractures can be divided into three types: propagation perpendicular to natural weak planes, propagation parallel to natural weak planes, and multi-fracture propagation. The post-pressure fracture distribution patterns can be classified into four types: through-going fractures, T-shaped fractures, compound fractures, and complex fracture networks. The absence of developed natural weak planes, high vertical stress differences, high natural weak-plane cementation strength, and large intersection angles are favorable conditions for the vertical propagation of hydraulic fractures. The research findings enrich the fundamental theory of vertical propagation of hydraulic fractures in shale reservoirs with developed natural weak planes and provide a scientific basis for the formulation and optimization of stimulation schemes for deep shale reservoirs, contributing to better stimulation effects in the Southern Sichuan shale gas block. Full article

Since the research of $\mu$-synchronization helps to explore how complex networks (CNs) work together to produce complex behaviors, the $\mu$-synchronization task for uncertain time-delayed CNs is studied in our work. Especially, bounded external perturbations and variable delays of unknown bounds containing coupling delays, internal delays, and pulse delays are all taken into consideration, making the model more general. Through the $\mu$-stable theory together with the hybrid impulsive control technique, the problems caused by uncertain inner couplings, time-varying delays, and perturbations can be solved, and novel synchronization criteria are gained for the $\mu$-synchronization of the considered CNs. Different from traditional models, it is not necessary for the coupling matrices to meet the zero-row-sum condition, and the control protocol relaxes the constraint of time delays on impulse intervals. Moreover, numerical experiments and image encryption algorithms are carried out to verify our theoretical results’ effectiveness. Full article

Temporal lobe epilepsy (TLE) is considered a network disorder rather than a localized lesion, making it essential to study the network mechanisms underlying TLE. In this study, we constructed directed brain networks based on clinical MEG data using the Granger Causality Analysis (GCA) method, aiming to provide new insights into the network mechanisms of TLE. MEG data from 13 lTLE and 21 rTLE patients and 14 healthy controls (HCs) were analyzed. The preprocessed MEG data were used to construct directed brain networks using the GCA method and undirected brain networks using the Pearson Correlation Coefficient (PCC) method. Graph theoretical analysis extracted global and local topologies from the binary matrix, and SVM classified topologies with significant differences (p < 0.05). Comparative studies were performed on connectivity strengths, graph theory metrics, and SVM classifications between GCA and PCC, with an additional analysis of GCA-weighted network connectivity. The results show that TLE patients showed significantly increased functional connectivity based on GCA compared to the control group; similarities of the hub brain regions between lTLE and rTLE patients and the cortical–limbic–thalamic–cortical loop were identified; TLE patients exhibited a significant increase in GCA-based Global Clustering Coefficient (GCC) and Global Local Efficiency (GLE); most brain regions with abnormal local topological properties in TLE patients overlapped with their hub regions. The directionality of brain connectivity has played a significantly more pivotal role in research on TLE. GCA may be a potential tool in MEG analysis to distinguish TLE patients and HC effectively. Full article

Internet services are increasingly being deployed using cloud computing. However, the workload of an Internet service is not constant; therefore, the required cloud computing resources need to be allocated elastically to minimize the associated costs. Thus, this study proposes a proactive cloud resource scheduling framework. First, we propose a new workload prediction method—named the adaptive two-stage multi-neural network based on long short-term memory (LSTM)—which can adaptively route prediction tasks to the corresponding LSTM sub-model according to the workload change trend (i.e., uphill and downhill categories), in order to improve the predictive accuracy. To avoid the cost associated with manual labeling of the training data, the first-order gradient feature is used with the k-means algorithm to cluster and label the original training data set automatically into uphill and downhill training data sets. Then, based on stochastic queueing theory and the proposed prediction method, a maximum cloud service profit resource search algorithm based on the network workload prediction algorithm is proposed to identify a suitable number of virtual machines (VMs) in order to avoid delays in resource adjustment and increase the service profit. The experimental results demonstrate that the proposed proactive adaptive elastic resource scheduling framework can improve the workload prediction accuracy (MAPE: 0.0276, RMSE: 3.7085, $R^2$: 0.9522) and effectively allocate cloud resources. Full article

The accelerating digital transformation necessitates a paradigm shift in manufacturing, requiring a structured transition from traditional to smart manufacturing. To address the challenges of fragmented integration, this study proposes an evolutionary model known as the integration of informatization and industrialization (TIOII) that systematically analyzes the dynamic interactions among product, technique, and business integration using a back-propagation neural network approach. A significant research gap exists in understanding how the chaotic and nonlinear interactions between these dimensions influence enterprise stability and adaptability. Prior studies have primarily focused on static models, failing to capture the evolutionary and dynamic nature of TIOII. To address this gap, this study employs stability theory and chaos theory to uncover the mechanisms through which TIOII disrupts pre-existing equilibrium states, leading to chaotic fluctuations before stabilizing into new structural configurations. This research also incorporates robust control theory to formulate strategies for enterprises to effectively manage instability and uncertainty throughout this transformation process. The findings reveal that TIOII is not a linear progression but an iterative process marked by instability and self-organized restructuring. The proposed model successfully explains the intricate, nonlinear interactions and evolutionary trajectories of TIOII dimensions, demonstrating that enterprise transformation follows a chaotic yet structured pattern. Moreover, the robust control methodology proves effective in mitigating uncontrolled instability, offering enterprises practical guidelines for refining investment strategies and adapting business operations amidst disruptive changes. This study enhances the theoretical understanding of industrial transformation by revealing the pivotal role of chaos in transitioning from stability to new stability, contributing to research on complex adaptive systems in enterprise management. The findings highlight the necessity of proactive strategic reconfiguration in technology, management, and product development, enabling enterprises to restructure investment strategies, refine business models, and achieve resilient, innovation-driven growth. Full article

Based on three theories—the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Responsible Environmental Behavior (REB)—the present study proposes a model of AI product factors and pro-environmental behavior. This study aims to investigate AI product factors that promote pro-environmental behavior by examining behavioral intentions to use AI technology. Unlike previous research, which predominantly focused on external variables such as social norms, cost, and inconvenience, or individual variables like demographic and psychological factors, this study emphasizes the underexplored role of technological factors. It integrates the Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL), Structural Equation Modeling (SEM), and Artificial Neural Network (ANN) approaches to assess the relationships among constructs. For the F-DEMATEL, opinions were collected from 20 experts in the environmental field, while SEM and ANN data were gathered from 1726 participants in Taiwan. F-DEMATEL results demonstrated causal relationships between external factors (perceived trust, self-efficacy, and perceived awareness) and the main variables of the TAM. Likewise, SEM results revealed that perceived trust (PT), self-efficacy (SE), and perceived awareness (PA) influence the main variables of TAM. However, the direct relationships between PT and behavioral intention (BI) and PA and BI were not significant. PT and PA indirectly influence BI through perceived usefulness (PU) and perceived ease of use (PEOU). The results also established that BI positively influences pro-environmental behavior. The author has also outlined how stakeholders aiming to encourage sustainable environmental behaviors can utilize the study’s findings to protect the environment. Full article

With the rapid development of quantum computers and quantum computing, Internet of Things (IoT) networks equipped with traditional cryptographic algorithms have become very weak against quantum attacks. This paper focuses on the privacy-preserving problem in IoT networks and proposes a certificateless ring signature (CLRS) scheme. This CLRS is constructed with lattice theories, which show promising advantages in resisting quantum attacks. Meanwhile, the certificateless mechanism reduces the key control ability of the key generation center (KGC) by adding personal secret keys to the private key generated by the system. Meanwhile, the ring signature mechanism protects users’ privacy information through a non-central control mechanism. Next, the security proof in a random oracle model is given, which shows that this CLRS scheme can obtain unforgeability and ensure the signer’s anonymity. Its security properties include non-repudiation, traceability, and post-quantum security. Then, the efficiency comparison and performance results show that this CLRS scheme is more efficient and practical than similar schemes. Moreover, this work presents an exploration of the post-quantum cryptographic algorithm and its application in IoT networks. Full article

“Cause analysis” constitutes an indispensable component in quality management systems, serving to systematically identify the causes of quality defects, thereby enabling the development of targeted improvement strategies that concurrently address surface-level manifestations and fundamental drivers. However, relying solely on personal experience makes it challenging to conduct a comprehensive and in-depth analysis of quality problems. The reason is that, when analyzing the causes of quality problems, it is essential not only to consider the specific context in which the problems occur. This enables “specific problems” to be “specifically analyzed” for the formulation of temporary containment measures. Additionally, the context of the problem needs to be stripped. This allows for a general and in-depth analysis of the “class problem” or the causal linkages underlying the problem, thereby determining the root cause of the problem and formulating a corresponding long-term program. The analysis of the causes of quality problems exhibits “duality” characteristics. Based on this, this study proposes and constructs a two-layer causal knowledge network by leveraging the causal knowledge generated and applied in the process of quality problem solving to address the “duality” characteristic of the cause analysis of quality problems. The proposed network can assist front-line employees in analyzing the quality problems of products from diverse perspectives and overcome the challenge of relying solely on personal experience to comprehensively and profoundly analyze the causal relationships of quality problems. Our method not only contributes to enhancing the efficiency of quality problem solving but also makes a valuable contribution to the advancement of theories and methods related to quality management and knowledge management. Full article

The symbiotic relationship between cover crops and soil microorganisms is closely linked to nutrient cycling and crop growth within agroecosystems. However, how cover crops with different root functional traits influence soil microbial communities, soil properties, and crop yields has remained understudied. This study assessed the root traits of hairy vetch (HV) and rapeseed (RP), along with soil properties, sweet potato yield, and microbial enzyme activity under red soil dryland conditions. High-throughput sequencing was also employed to characterize the diversity, composition, and network structure of soil bacterial and fungal communities. According to the plant economic spectrum theory and our research results on plant root traits, HV can be identified as a resource-acquisitive cover crop, and RP treatment can be identified as a resource-conservative cover crop. Although RP treatment did not significantly increase the sweet potato yield, the increase rate reached 8.49%. Resource-conservative cover crops were associated with increased pH, SOC, and TP, which enhanced bacterial species diversity and boosted the populations of Chloroflexi and Alphaproteobacteria. In contrast, resource-acquisitive cover crops promoted the proliferation of Gammaproteobacteria. Network analysis indicated that resource-conservative cover crops facilitated network complexity through intensified intra-community competition. Resource-acquisitive cover crops enhanced the stability of microbial communities. Collectively, these findings underscore the distinct advantages of cover crops with varying root functional traits in shaping soil microbial communities. Appropriate cover crop rotations can effectively regulate microbial communities and hold the potential to enhance crop yield. Full article

Molecular fractals are geometric patterns that appear self-similar across all length scales and are constructed by repeating a single unit on a regular basis. Entropy, as a core thermodynamic function, is an extension based on information theory (such as Shannon entropy) and is used to describe the topological structural complexity or degree of disorder in networks. A topological index is a numeric quantity associated with a network or a graph that characterizes its whole structural properties. In this study, we focus on fractal structures formed by systematically repeating a fixed unit of coronene, a polycyclic aromatic hydrocarbon composed of six benzene rings fused in a hexagonal pattern. In this paper, three types of coronal fractal structures, namely zigzag (ZHCF), armchair (AHCF), and rectangular (RCF), are studied, and their five degree-based topological indices and corresponding entropies are calculated. Full article

This study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of work enables advanced multilingual data processing, advanced statistics in population trends, evaluation of policy outcomes, and the development of evidence-based interventions. A key focus is on the theoretical integration of social order mechanisms, including communication modes as institutional structures, token optimization as an efficiency mechanism, and institutional memory adaptation. A mixed methods approach was used that included sophisticated visualization techniques and use cases in the hospitality sector, in global food security, and in educational development. The framework demonstrates its capacity to inform government and industry policies by leveraging statistics, visualization, and AI-driven decision support. We introduce the concept of “institutional intelligence”—the synergistic integration of human expertise, AI capabilities, and institutional theory—to create adaptive yet stable policy-making systems. This research highlights the transformative potential of data-driven approaches combined with large language models in supporting sustainable and inclusive policy-making processes. Full article

The shift towards decentralized energy systems demands innovative strategies to manage renewable energy integration, optimize resource allocation, and ensure grid stability. This review investigates the application of game theory and robust predictive control as essential tools for decentralized and peer-to-peer energy management. Game theory facilitates strategic decision-making and cooperation among prosumers, distributors, and consumers, enabling efficient energy trading and dynamic resource distribution. Robust predictive control complements this by addressing uncertainties in renewable energy generation and demand, ensuring system stability through adaptive and real-time optimization. By examining recent advancements, this study highlights key methodologies, challenges, and emerging technologies such as blockchain, artificial intelligence, and digital twins, which enhance these approaches. The review also explores their alignment with global sustainability objectives, emphasizing their role in promoting affordable clean energy, reducing emissions, and fostering resilient urban energy infrastructures. A systematic review methodology was employed, analyzing 153 selected articles published in the last five years, filtered from an initial dataset of over 200 results retrieved from ScienceDirect and IEEE Xplore. Practical insights and future directions are provided to guide the implementation of these innovative methodologies in decentralized energy networks. Full article

The electrocatalyst layers (ECLs) in polymer electrolyte membrane (PEM) electrolyzers are fundamentally comprised of IrOx catalysts, support material, and an ionomer. Their stability is critically dependent on structure and composition, necessitating a thorough understanding of ionization potential and work function. We employ Density Functional Theory (DFT) to determine the ionization states of ECLs and to optimize their electronic properties. Furthermore, advanced deep learning simulations (DLSs) significantly enhance the kinetic and transport behaviors of these layers. This work integrates DFT and DLS to elucidate the characteristics of ECLs within PEM electrolyzer cells. We strategically utilize DFT to refine catalyst molecules and assess their electronic properties, while DLS is employed to predict the potential energy of support molecules in the catalyst layers. We establish a clear relationship between the energy and geometry of IrOx molecules. The DFT-DLS framework robustly calculates potential energy and reaction coordinates, effectively bridging theoretical computations with the dynamic behavior of molecules in catalyst layers. We validate our model by comparing it with the experimental polarization curve of the IrOx-based anode catalyst layer in a functioning electrolyzer. The observed Tafel slope and exchange current density unequivocally confirm that the oxygen evolution reaction (OER) occurs through a well-defined electrochemical pathway, with oxygen generation proceeding according to the charge transfer mechanism predicted by the DFT-DLS framework. Full article

Recently, the rate of offshore oil production has increased, which creates a need to develop technical solutions for the implementation of more efficient processes on offshore platforms. A relevant solution is the development and application of digital twins. Offshore production platforms are specially protected objects due to the high risk of environmental pollution. Therefore, such objects are especially distinguished for the implementation of advanced technological solutions. In this study, the authors conduct a study of the input, output, and resulting parameters that affect the production process. Using the theory of systems analysis, they determine the list of critical factors and build a conceptual and then a mathematical model of the field. It differs from existing analogues by the introduction of additional parameters that provide higher modeling accuracy. Based on the resulting mathematical model, a neural network is trained to identify the optimal operating mode. An assessment of the economic feasibility of the provided development is carried out. Full article