Search Results (30,461)

This paper analyzes how distortion in the primary current waveform affects the magnetic flux density in the magnetic core of an inductive current transformer and its transformation accuracy. Keeping the primary current’s RMS value constant, it studies the impact of changes in the RMS values and phase angles of low-order harmonics on the core’s flux density and the values of current error and phase displacement of their transformation. The distorted current waveforms, resulting flux density, and hysteresis loops are examined to identify the operating conditions of the inductive current transformer. This study also highlights the strong influence of low-order harmonics and the diminishing effect of higher-frequency harmonics on the magnetic flux density in its magnetic core, e.g., third, fifth, and seventh higher harmonics may cause an increase in magnetic flux density in the magnetic core of the inductive current transformer in relation to that obtained for a sinusoidal current with a frequency of 50 Hz by about 8.5%, while with additional second, fourth, and sixth harmonics, the increase may reach about 23%. Therefore, the testing procedure should consider not only the load impedance and the RMS values of the primary current but also its harmonic content, including the RMS values of individual harmonics and their phase angles. Full article

China has identified the high-quality development of its green manufacturing transition as the top priority for upgrading their industrial structure system which will lead to the sustainable development of an innovation ecosystem. To assess their progress in this area, this study selects the panel data of 31 provinces in China from 2011 to 2021 and constructs an evaluation index system for the green transformation of the manufacturing industry from four dimensions: environment, resources, economy, and industrial structure. This not only comprehensively and systematically reflects the dynamic changes in the green transformation of the manufacturing industry but also addresses the limitations of currently used indices. The entropy value method is used to calculate the comprehensive score of the green transformation of the manufacturing industry, while the key factors influencing the convergence of the green transformation of the manufacturing industry are further explored. The results show that first, the overall level of the green transformation of the manufacturing industry has significantly improved as evidenced by an approximate 32% increase. Second, regional differences are significant with the eastern region experiencing significantly higher levels of transformation compared to the central and western regions, along with a decreasing trend from the east to the central and western regions. From a policy perspective, the findings suggest that tailored production methods for each region should be adopted with a greater emphasis on knowledge exchanges to promote green transition in less developed regions. In addition, further regulations are required which, in part, focus on increasing the degree of openness to the outside world to promote the level of green manufacturing transition. Full article

In river-connected lake regions, both land use and hydrological regime changes may affect the ecosystem services; however, few studies have attempted to elucidate their complex influences. In this study, the spatiotemporal dynamics of eight ecosystem services (crop production, aquatic production, water yield, soil retention, flood regulation, water purification, net primary productivity, and habitat quality) were investigated through remote-sensing images and the InVEST model in the Dongting Lake Region during 2000–2020. Results revealed that crop and aquatic production increased significantly from 2000 to 2020, particularly in the northwestern and central regions, while soil retention and net primary productivity also improved. However, flood regulation, water purification, and habitat quality decreased, with the fastest decline in habitat quality occurring at the periphery of the Dongting Lake. Land-use types accounted for 63.3%, 53.8%, and 40.3% of spatial heterogeneity in habitat quality, flood regulation, and water purification, respectively. Land-use changes, particularly the expansion of construction land and the conversion of water bodies to cropland, led to a sharp decline in soil retention, flood regulation, water purification, net primary productivity, and habitat quality. In addition, crop production and aquatic production were higher in cultivated land and residential land, while the accompanying degradation of flood regulation, water purification, and habitat quality formed a “production-pollution-degradation” spatial coupling pattern. Furthermore, hydrological fluctuations further complicated these dynamics; wet years amplified agricultural outputs but intensified ecological degradation through spatial spillover effects. These findings underscore the need for integrated land-use and hydrological management strategies that balance human livelihoods with ecosystem resilience. Full article

To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits. Full article

Employee turnover remains a critical challenge for organizations, prompting an examination of how leadership approaches influence employees’ intentions to leave. This study investigates the impact of transformational leadership on turnover intention, focusing on emotional exhaustion and creativity as potential mediators. The study employs a quantitative design grounded in leadership and organizational psychology theory and surveys 182 professionals working in the information technology sector across Bosnia and Herzegovina, Croatia, Serbia, and Montenegro. Structural equation modeling reveals that transformational leadership reduces turnover intention by alleviating emotional exhaustion, highlighting the importance of psychological well-being in employee retention. While transformational leadership enhances employee creativity, creativity did not significantly mediate turnover intention in this context. These findings suggest that strategies that foster engagement and reduce burnout in knowledge-intensive industries can strengthen organizational commitment and improve retention. This study contributes to the understanding of behavioral mechanisms linking leadership to employee outcomes and offers actionable insights for modern organizations aiming to address turnover through supportive, empowering leadership practices. Additional mediators and contextual variables should be explored in further research. Full article

Platelet-rich plasma (PRP) is widely used in regenerative medicine, yet clinical outcomes remain inconsistent. While traditional strategies have focused on platelet concentration and activation methods, emerging evidence suggests that the biological age of platelets, especially platelet senescence, may be a critical but overlooked factor influencing therapeutic efficacy. Senescent platelets display reduced granule content, impaired responsiveness, and heightened pro-inflammatory behavior, all of which can compromise tissue repair and regeneration. This review explores the mechanisms underlying platelet aging, including oxidative stress, mitochondrial dysfunction, and systemic inflammation, and examines how these factors influence PRP performance across diverse clinical contexts. We discuss the functional consequences of platelet senescence, the impact of comorbidities and aging on PRP quality, and current tools to assess platelet functionality, such as HLA-I–based flow cytometry. In addition, we present strategies for pre-procedural optimization, advanced processing techniques, and adjunctive therapies aimed at enhancing platelet quality. Finally, we challenge the prevailing emphasis on high-volume blood collection, highlighting the limitations of quantity-focused protocols and advocating for a shift toward biologically precise, function-driven regenerative interventions. Recognizing and addressing platelet senescence is a key step toward unlocking the full therapeutic potential of PRP-based interventions. Full article

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

In recent years, growing concerns over environmental degradation and deepening awareness of the necessity of sustainable development have propelled green and low-carbon energy transition into a focal issue for both academia and policymakers. By decomposing energy transition into the transformation of energy structure and the upgrading of energy efficiency, this study investigates the impact and mechanisms of green finance on energy transition across 30 provinces (municipalities and autonomous regions) in China, with the exception of Tibet. In addition, the impact of climate change is incorporated into the analytical framework. Empirical results demonstrate that green finance development significantly accelerates energy transition, a conclusion robust to rigorous validation. Analysis of the mechanism shows that green finance promotes energy transition through the facilitation of technological innovation and the upgrade of industrial structures. Moreover, empirical evidence reveals that climate change undermines the promotional influence of sustainable finance on energy system transformation. The magnitude of this suppression varies nonlinearly across provincial jurisdictions with differing energy transition progress. Regional heterogeneity analyses further uncover marked discrepancies in climate–finance interactions, demonstrating amplified effects in coastal economic hubs, underdeveloped western provinces, and regions with mature eco-financial markets. According to these findings, actionable policy suggestions are put forward to strengthen green finance and accelerate energy transition. Full article

This study explores the migration intentions of university students—representing the potential creative class—in Erzurum, a medium-sized city in eastern Turkey experiencing shrinkage. Within the theoretical framework of shrinking cities, it investigates how economic, social, physical, and personal factors influence students’ post-graduation stay or leave decisions. Survey data from 742 Architecture and Fine Arts students at Atatürk University were analyzed using factor analysis, logistic regression, and correlation to identify key migration drivers. Findings reveal that, in addition to economic concerns such as limited job opportunities and low income, personal development opportunities and social engagement also play a decisive role. In particular, the perception of limited chances for skill enhancement and the belief that Erzurum is not a good place to meet people emerged as the strongest predictors of migration intentions. These results suggest that members of the creative class are influenced not only by economic incentives but also by broader urban experiences related to self-growth and social connectivity. This study highlights spatial inequalities in access to cultural, educational, and social infrastructure, raising important questions about spatial justice in shrinking urban contexts. This paper contributes to the literature on shrinking cities by highlighting creative youth in mid-sized Global South cities. It suggests smart shrinkage strategies focused on creative sector development, improved quality of life, and inclusive planning to retain young talent and support sustainable urban revitalization. Full article

Additive manufacturing (AM) techniques, especially fused deposition modeling (FDM), offer significant advantages in terms of cost, material efficiency, and design flexibility. In this study, the mechanical performance of biodegradable PLA/PHA composite samples produced via FDM was optimized by evaluating the influence of key printing parameters—layer height, printing orientation, and printing speed—on both the tensile and compressive strength. A full factorial design (3 × 3 × 3) was employed, and all of the samples were triplicated to ensure the consistency of the results. Grey relational analysis (GRA) was used as a multi-objective optimization method to determine the optimal parameter combinations. An analysis of variance (ANOVA) was also conducted to assess the statistical significance of each parameter. The ANOVA results revealed that printing orientation is the most significant parameter for both tensile and compression strength. The optimal parameter combination for maximizing mechanical properties was a layer height of 0.1 mm, an X printing orientation, and a printing speed of 50 mm/s. This study demonstrates the effectiveness of GRA in optimizing the mechanical properties of biodegradable composites and provides practical guidelines to produce environmentally sustainable polymer parts. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

The accurate and precise measurement of lubricating oil consumption is critical for developing environmentally friendly internal combustion engines, particularly hydrogen-fueled internal combustion engines. The deuterium tracer method is based on the addition of poly-deuterated base oil tracers to fully formulated oils for precise, accurate, and fast lubricating oil consumption measurements. Previously performed measurements have shown that the use of poly-deuterated poly-alpha olefins has minimal impact on lubricating oil properties, except for a slight drop in oil viscosity. To further reduce the impact on lubricating oil characteristics, a new base oil for the synthesis of a poly-deuterated tracer is introduced, and its influence on the lubricating oil’s chemical, tribological, and rheological properties is analyzed. Furthermore, the influence of the tracer addition on the preignition tendencies of the fully formulated oil is also examined. Based on the analyses, no relevant changes in the lubricating oil properties, such as viscosity, density, and thermal degradation behavior, can be observed. Additionally, the deuterium tracer does not negatively influence combustion anomalies, thus reducing preignition tendencies. These results establish the method’s compatibility with new-generation engines, especially hydrogen-fueled internal combustion engines. Full article

This study investigates the dynamics of dust acoustic periodic waves in a three-component, unmagnetized dusty plasma system using generalized $(r^{\star },q^{\star })$ distributions. First, boundary conditions are applied to reduce the model to a second-order nonlinear ordinary differential equation. The Galilean transformation is subsequently applied to reformulate the second-order ordinary differential equation into an unperturbed dynamical system. Next, phase portraits of the system are examined under all possible conditions of the discriminant of the associated cubic polynomial, identifying regions of stability and instability. The Runge–Kutta method is employed to construct the phase portraits of the system. The Hamiltonian function of the unperturbed system is subsequently derived and used to analyze energy levels and verify the phase portraits. Under the influence of an external periodic perturbation, the quasi-periodic and chaotic dynamics of dust ion acoustic waves are explored. Chaos detection tools confirm the presence of quasi-periodic and chaotic patterns using Basin of attraction, Lyapunov exponents, Fractal Dimension, Bifurcation diagram, Poincaré map, Time analysis, Multi-stability analysis, Chaotic attractor, Return map, Power spectrum, and 3D and 2D phase portraits. In addition, the model’s response to different initial conditions was examined through sensitivity analysis. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

The growing demand for sustainable materials has led to increased interest in biodegradable polymer fibers and nonwoven mats due to their eco-friendly characteristics and potential to reduce plastic pollution. This review highlights how mechanical properties influence the performance and suitability of biodegradable polymer fibers across diverse applications. This covers synthetic polymers such as polylactic acid (PLA), polyhydroxyalkanoates (PHAs), polycaprolactone (PCL), polyglycolic acid (PGA), and polyvinyl alcohol (PVA), as well as natural polymers including chitosan, collagen, cellulose, alginate, silk fibroin, and starch-based polymers. A range of fiber production methods is discussed, including electrospinning, centrifugal spinning, spunbonding, melt blowing, melt spinning, and wet spinning, with attention to how each technique influences tensile strength, elongation, and modulus. The review also addresses advances in composite fibers, nanoparticle incorporation, crosslinking methods, and post-processing strategies that improve mechanical behavior. In addition, mechanical testing techniques such as tensile test machine, atomic force microscopy, and dynamic mechanical analysis are examined to show how fabrication parameters influence fiber performance. This review examines the mechanical performance of biodegradable polymer fibers and fibrous mats, emphasizing their potential as sustainable alternatives to conventional materials in applications such as tissue engineering, drug delivery, medical implants, wound dressings, packaging, and filtration. Full article