Search Results (610)

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers. Full article

This study investigated the hydrogeochemistry of a shallow Quaternary sedimentary aquifer in an esker deposition in western Finland, where distinct spatial and temporal variability in groundwater hydrogeochemistry has been observed. Field investigation and hydrogeochemical data were obtained from autumn 2010 to autumn 2013. The data were analyzed using the multivariate statistical methods principal component analysis (PCA) and hierarchical cluster analysis (HCA), in conjunction with groundwater classification based on the main ionic composition. The stable isotope ratios of δ¹⁸O and δD were used to determine the origin of the groundwater and its connection to surface water bodies. The groundwater geochemistry is characterized by distinct redox zones caused by the influence of organic matter, pyrite oxidation, and preferential flow pathways due to different hydrogeological conditions. The groundwater is of the Ca-HCO₃ type and locally of the Ca-HCO₃-SO₄ type, with low TDS, alkalinity, and pH, but elevated Fe and Mn concentrations, KMnO₄ consumption, and, occasionally, Ni concentrations. The decomposition of organic matter adds CO₂ to the groundwater, and in this study, the dissolution of CO₂ was found to increase the pH and enhance the buffering capacity of the groundwater. The mobility of redox-sensitive elements and trace metals is controlled by pH and redox conditions, which are affected by the pumping rate, precipitation, and temperature. With the expected future increases in precipitation and temperature, the buffering capacity of the aquifer system will enhance the balance between alkalinity from bioactivity and acidity from recharge and pyrite oxidation. Full article

Currently, more studies are focusing on traffic classification in software-defined networks (SDNs). Accurate classification and selecting the appropriate controller have benefited from the application of machine learning (ML) in practice. In this research, we study different classification models to see which one best classifies the generated dataset and goes on to be implemented for real-time classification. In our case, the classification and regression tree (CART) classifier produces the best classification results for the generated dataset, and logistic regression is also considerable. Based on the evaluation of various algorithmic outputs for the training and validation datasets, and also when execution time is taken into account, the CART is found to be the best algorithm. While testing the impact of load balancing in a multi-controller SDN environment, in different load case scenarios, we observe network performance parameters like bit rate, packet rate, and jitter. Here, the use of traffic classification-based load balancing improves the bit rate as well as the packet rate of traffic flow on a network and thus considerably enhances throughput. Finally, the reduction in jitter while increasing the controllers confirms the improvement in QoS in a balanced multi-controller SDN environment. Full article

The increasing adoption of plug-in electric vehicles (PEVs) leads to negative impacts on distribution network efficiency due to the extra load added to the system. To overcome this problem, this manuscript aims to optimally integrate distributed generators (DGs) in radial distribution networks (RDNs), while including uncoordinated charging of PEVs added to the basic daily load curve with different load models. The main objectives are minimizing the network’s daily energy losses, improving the daily voltage profile, and enhancing voltage stability considering various constraints like power balance, buses’ voltages, and line flow. These objectives are combined using weighting factors to formulate a weighted sum multi-objective function (MOF). A very recent metaheuristic approach, namely the Walrus optimization algorithm (WO), is addressed to identify the DGs’ best locations and sizes that achieve the lowest value of MOF, without violating different constraints. The proposed optimization model along with a repetitive backward/forward load flow (BFLF) method are simulated using MATLAB 2016a software. The WO-based optimization model is applied to IEEE 33-bus, 69-bus, and a real system in El-Shourok City-district number 8 (ShC-D8), Egypt. The simulation results show that the proposed optimization method significantly enhanced the performance of RDNs incorporated with PEVs in all aspects. Moreover, the proposed WO approach proved its superiority and efficiency in getting high-quality solutions for DGs’ locations and ratings, compared to other programmed algorithms. Full article

Mixed-flow pumps, optimized for marine engineering, provide a balance of high efficiency and adaptability, accommodating varied flow and head demands across challenging oceanic settings and are essential for reliable operations in tidal energy and subsea applications. The primary purpose of this paper is to perform a numerical analysis of the cavitation flow characteristics of the mixed-flow pump under differing operational circumstances. The cavitation simulation was implemented to explore the cavitation bubbles evolution and the pressure pulsation characteristics in the impeller region under diverse flow rates, utilizing the Shear Stress Transport (SST) turbulence model and the Zwart-Gerber-Belamri cavitation model as a foundation. The findings indicate that cavitation bubbles initially distribute at the leading edge of blade suction surfaces at the cavitation growth stage. The bubbles spread gradually with the decline of the available net positive suction head (NPSH_a). At the same time, many bubbles appear in the area below the blade and extend to the rim of the suction side of blades. As the flow rate decreases, the critical net positive suction head (NPSH_c) gradually declines. The dominant pressure pulsation frequency at the impeller inlet is the blade passing frequency, and the vibration at the impeller shroud inlet is more intense than that at the hub. The dominant frequency at the impeller outlet is mainly the blade passing frequency. With the development of cavitation, it changes to impeller rotation frequency at low flow rates, while the dominant frequency remains unchanged at high flow rates. Full article

With the gradually accelerating pace of global decarbonization, highly efficient and clean proton exchange membrane fuel cells (PEMFCs) are considered to be an energy solution for the future. During the operation of a fuel cell, it is necessary to keep the internal proton exchange membrane in a good state of hydration, so an appropriate method of detecting the hydration state is essential. At present, fuel cell systems are rapidly developing towards high power, but methods for detecting the hydration state of high-power fuel cell systems are still relatively lacking. Therefore, this paper studies the hydration state of high-power fuel cell systems and builds a condensation tail-gas water collection device for calculating the water flow out of a fuel cell system, deriving the hydration status inside the high-power fuel cell system. To verify the proposed water balance model, a series of experiments were conducted based on controlled variables such as working temperature, air metering ratio, and load current. Experiments were conducted on a 100 KW fuel cell system to collect water flow from the fuel cell system. Finally, based on the experimental data, the change rate of the internal water content of the fuel cell system under different conditions was calculated. The results show that, under the same load current, as the working temperature and air metering ratio increase, the change rate of the internal water content of the fuel cell system gradually decreases. Therefore, at low power, it is necessary to maintain an appropriate working temperature, while at high power, maintaining an appropriate air metering ratio is more important. Full article

This research explores the effects of significant global economic shocks, such as the 2008 Global Financial Crisis and the 2020 COVID-19 pandemic, on GDP growth in the Gulf Cooperation Council (GCC) nations. Employing a dynamic generalized method of moments (GMM) model, the analysis highlights the strong momentum effect of lagged GDP growth, where past performance plays a critical role in shaping current economic outcomes. The findings also reveal that natural resources continue to positively influence short-term growth, but with diminishing returns over time, supporting the resource curse hypothesis and underscoring the need for broader structural reforms to ensure long-term sustainability. In addition, the results show that external investments flowing into the country, trade balance, and inflation emerge as key drivers of economic growth. While moderate inflation is positively associated with economic expansion, unemployment exerts a significant negative effect on GDP growth, particularly in models that account for country-specific characteristics. This emphasizes the need for labor market reforms to improve employment rates and support sustainable development. The role of gross capital formation, particularly in both the dynamic GMM and random effects models, further underscores the importance of strategic domestic investment, especially during periods of global disruption. These findings emphasize the critical need for economic diversification in the GCC. Policymakers should focus on attracting foreign investment, managing inflation, enhancing human capital, and boosting domestic investment to mitigate the adverse effects of the resource curse and secure sustainability. While market capitalization and oil rents may stimulate short-term growth, their long-term sustainability remains uncertain without greater diversification. Both external and domestic investments emerge as critical drivers of long-term growth, while persistent challenges such as inflation and unemployment continue to pose risks to economic stability. The study highlights the need to reduce reliance on oil and leverage human capital to build more resilient economies capable of adapting to future challenges. By offering dynamic, empirical insights into the balance between resource reliance and sustainable growth, this research adds valuable insights to the policy discussion on economic diversification in the GCC. Policymakers are urged to prioritize FDI, inflation management, domestic capital formation, and human capital development to mitigate vulnerabilities and ensure sustainable economic growth in the face of ongoing global uncertainties. Full article

Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment. Full article

Based on the systematization of relevant problems in the agricultural sector of Kazakhstan and other countries, the purpose of the research is to aid in the development and implementation of a methodology for the econometric analysis of sustainability, the classification of economic growth, and an alternative strategy for gross value added depending on time phases with time lags of 0, 1, and 2 years, and on the gross fixed capital formation in the agricultural sector of Kazakhstan. The research has used a variety of quantitative techniques, including the logistic growth difference equation, applied statistics, econometric models, operations research, nonlinear mathematical programming models, economic modeling simulations, and sustainability analysis. In the work on three criteria: equilibrium, balanced and optimal growth, we have defined the main trends of growth of Gross added value of agriculture, hunting and forestry. The first, depending on the time phases, the second, depending on the Gross fixed capital formation transactions for equilibrium growth, for the growth of an alternative strategy, for the endogenous growth rate and the growth of exogenous flows. And we also received a classification of the trend of Productive, Moderate and Critical growth for the agricultural industry depending on the correlated linkaged industry of the national economy of Kazakhstan. The results of this work can be used in data analytics and artificial intelligence, digital transformation and technology in agriculture, as well as in the areas of sustainability and environmental impact. Full article

The kidney, an essential excretory organ of the body, performs a series of crucial physiological functions such as waste removal, maintenance of electrolyte and acid–base balance, and endocrine regulation. Due to its rich blood flow and high metabolic activity, the kidney is susceptible to damage. Currently, kidney injury is classified into acute kidney injury (AKI) and chronic kidney disease (CKD), both of which are associated with high rates of morbidity and mortality on a global scale. The current clinical diagnosis of renal injury relies on the assessment of renal filtration function using creatinine and urea nitrogen as “gold-standard” markers. However, the delayed response time, limited specificity, and reduced accuracy of creatinine and urea nitrogen in evaluating kidney injury have significantly hindered advancements in diagnostic methods for kidney injury. Urinary protein is widely utilized as a biomarker for the early diagnosis of kidney injury due to the selectivity of the glomerular filtration system determining whether proteins can pass through the filtration barrier based on their size and charge. Therefore, as a complex biological sample with varying charges and particle sizes, urinary protein is considered an ideal indicator for monitoring the progression of kidney disease. Exploring the relationship between urinary protein and the advancement of kidney injury based on differences in particle size and charge offers a new perspective for assessing and treating such injuries. Hence, we conducted a comprehensive review of 74 relevant studies to gain a thorough understanding of the physiological mechanism and significance of proteinuria production. The aim was to explore the challenges and opportunities in clinical urine protein detection, as well as to discuss strategies targeting glomerular filtration barriers in order to effectively reduce urine protein levels and treat kidney injury, which could provide a new perspective for identifying the progression of kidney injury. Full article

Due to its simple structure and stable operation, the Organic Rankine Cycle (ORC) has gained significant attention as a primary solution for low-grade thermal power generation. However, the economic challenges associated with development difficulties in hot dry rock (HDR) geothermal power systems have necessitated a better balance between performance and cost effectiveness within ORC systems. This paper establishes a game pattern of the Organic Rankine Cycle with performance as the master layer and economy as the slave layer, based on the Stackelberg game theory. The optimal working fluid for the ORC is identified as R600. At the R600 mass flow rate of 50 kg/s, the net system cycle work is 4186 kW, the generation efficiency is 14.52%, and the levelized cost of energy is 0.0176 USD/kWh. The research establishes an optimization method for the Organic Rankine Cycle based on the Stackelberg game framework, where the network of the system is the primary optimization objective, and the heat transfer areas of the evaporator and condenser serve as the secondary optimization objective. An iterative solving method is utilized to achieve equilibrium between the performance and economy of the ORC system. The proposed method is validated through a case study utilizing hot dry rock data from Qinghai Gonghe, allowing for a thorough analysis of the working fluid and system parameters. The findings indicate that the proposed approach effectively balances ORC performance with economic considerations, thereby enhancing the overall revenue of the HDR power system. Full article

The injection molding process is one of the most widely used methods for polymer processing in mass production. Three critical factors in this process include the type of polymer, injection molding machines, and processing molds. Polypropylene (PP) is a widely used semi-crystalline polymer due to its favorable flow characteristics, including a high melt flow index and the absence of a need for a mold temperature controller. Additionally, PP exhibits good elongation and toughness, making it suitable for applications such as box hinges. However, its tensile strength is a limitation; thus, glass fiber is added to enhance this property. It is important to note that the incorporation of glass fiber increases the viscosity of PP. Multi-cavity molds are commonly employed to achieve cost-effective and efficient mass production. The filling challenges associated with geometrically balanced layouts are well documented in the literature. These issues arise due to the varying shear rates of the melt in the runner. High shear rate melts lead to high melt temperatures, which decrease melt viscosity and facilitate easier flow. Consequently, this results in an imbalanced filling phenomenon. This study examines the impact of runner size, gate size, polymer viscosity, and molding process on the filling imbalanced problem in multi-cavity injection molds. Tensile bar injection molding was performed using conventional injection molding (CIM) and microcellular injection molding (MIM) techniques. The tensile properties of the imbalanced multi-cavity molds were analyzed. Flow length within the cavity served as an indicator of the filling imbalance. Additionally, computer simulations were conducted to assess the shear rate’s effect on the runner’s melt temperature. The results indicated that small runner and gate sizes exacerbate the filling imbalance. Conversely, glass fiber-filled polymer composites also contribute to increased filling imbalance. However, foamed polymers can mitigate the filling imbalance phenomenon. Full article

Ischemic stroke (IS) is a severe complication and leading cause of mortality in patients under extracorporeal membrane oxygenation (ECMO). The aim of our narrative review is to summarize the existing evidence and provide a deep examination of the diagnosis and treatment of acute ischemic stroke patients undergoing ECMO support. The incidence rate of ISs is estimated to be between 1 and 8%, while the mortality rate ranges from 44 to 76%, depending on several factors, including ECMO type, duration of support and patient characteristics. Several mechanisms leading to ISs during ECMO have been identified, with thromboembolic events and cerebral hypoperfusion being the most common causes. However, considering that most of the ECMO patients are severely ill or under sedation, stroke symptoms are often underdiagnosed. Multimodal monitoring and daily clinical assessment could be useful preventive techniques. Early recognition of neurological deficits is of paramount importance for prompt therapeutic interventions. All ECMO patients with suspected strokes should immediately receive brain computed tomography (CT) and CT angiography (CTA) for the identification of large vessel occlusion (LVO) and assessment of collateral blood flow. CT perfusion (CTP) can further assist in the detection of viable tissue (penumbra), especially in cases of strokes of unknown onset. Catheter angiography is required to confirm LVO detected on CTA. Intravenous thrombolytic therapy is usually contraindicated in ECMO as most patients are on active anticoagulation treatment. Therefore, mechanical thrombectomy is the preferred treatment option in cases where there is evidence of LVO. The choice of the arterial vascular access used to perform mechanical thrombectomy should be discussed between interventional radiologists and an ECMO team. Anticoagulation management during the acute phase of IS should be individualized after the thromboembolic risk has been carefully balanced against hemorrhagic risk. A multidisciplinary approach is essential for the optimal management of ISs in patients treated with ECMO. Full article

A submersible sewage pump is designed for conveying solid–liquid two-phase media containing sewage, waste, and fiber components, through its small and compact design and its excellent anti-winding and anti-clogging capabilities. In this paper, the computational fluid dynamics–discrete element method (CFD-DEM) coupling model is used to study the influence of different conveying conditions and particle parameters on the wear of the flow components in a submersible sewage pump. At the same time, the energy balance equation is used to explore the influence mechanism of different tip clearance sizes on the internal flow pattern, wear, and energy conversion mechanism of the pump. This study demonstrates that increasing the particle volume fraction decreases the inlet particle velocity and intensifies wear in critical areas. When enlarging the tip clearance thickness from 0.4 mm to 1.0 mm, the leakage vortex formation at the inlet is enhanced, leading to increased wear rates in terms of the blade and volute. Consequently, the total energy loss and turbulent kinetic energy generation increased by 3.57% and 2.25%, respectively, while the local loss coefficient in regard to the impeller channel cross-section increased significantly. The findings in this study offer essential knowledge for enhancing the performance and ensuring the stable operation of pumps under solid–liquid two-phase flow conditions. Full article

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability. Full article