Search Results (13,465)

Carbon emissions have a profound impact on the transformation goals and development paths of cities. In the context of carbon neutrality, it is of great significance to explore the coupling coordination level of the social–ecological systems in resource-based cities for realizing regional low-carbon and sustainable development. In this study, the entropy weighting method, coupling coordination degree model and geographical detector were used to measure the comprehensive development level and coupling coordination level of the social–ecological system in 116 resource-based cities in China from 2010 to 2020 and their spatial–temporal characteristics and influencing mechanism were analyzed. The results show the following: (1) The comprehensive development level of the social system in China’s resource-based cities has a significant upward trend, while the comprehensive development level of the ecological system has a gentle upward trend, and the coupling and coordination level of the social–ecological system has a fluctuating upward trend. (2) There is obvious spatial differentiation between the comprehensive development level and the coupling coordination level of the social–ecological systems in resource-based cities in China, and the relative difference is gradually increasing. (3) The digital economy index, urbanization level, science and education investment, and population density are important factors affecting the coupling coordination level, and the interaction between digital economy index, urbanization level, and population density has a strong explanatory power in the differentiation of the coupling coordination level. Based on the above conclusions, effective policy recommendations are put forward: formulate more refined and differentiated development paths, co-ordinate the spatial layout to give full play to the role of urban agglomeration, vigorously develop the digital economy, increase investment in science and education, rely on scientific and technological innovation to create development advantages, reasonably guide the population layout and take a new urbanization development route. Full article

Simulation is a cornerstone of planning and facilitating the transition towards electric mobility in sub-Saharan Africa’s informal public transport. The primary objective of this study is to validate and refine the electro-kinetic model used to simulate electric versions of the sector’s minibuses. A systematic simulation methodology is also developed to correct the simulation parameters and improve the high-frequency GPS data used with the model. A retrofitted electric minibus was used to capture high-frequency GPS mobility data and power draw from the battery. The method incorporates key refinements such as corrections for gross vehicle mass, elevation and speed smoothing, radial drag, hill-climb forces, and the calibration of propulsion and regenerative braking parameters. The refined simulation demonstrates improved alignment with measured power draw and trip energy usage, reducing error margins and enhancing model reliability. Factors such as trip characteristics and environmental conditions, including wind resistance, are identified as potential contributors to observed discrepancies. These findings highlight the importance of precise data handling and model calibration for accurate energy simulation and decision making in the transition to electric public transport. This work provides a robust framework for future studies and practical implementations, offering insights into the technical and operational challenges of electrifying informal public transport systems in resource-constrained regions. Full article

With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult. To address these issues, we use YOLOv8s as the basic framework and introduce a multi-level feature fusion algorithm. Additionally, we design an attention mechanism that links distant pixels to improve small object feature extraction. To address missed detections and inaccurate localization, we replace the detection head with a dynamic head, allowing the model to route objects to the appropriate head for final output. We also introduce Slideloss to improve the model’s learning of difficult samples and ShapeIoU to better account for the shape and scale of bounding boxes. Experiments on datasets like VisDrone2019 show that our method improves accuracy by nearly 10% and recall by about 11% compared to the baseline. Additionally, on the AI-TODv1.5 dataset, our method improves the mAP50 from 38.8 to 45.2. Full article

Marine nuclear power plants (MNPPs) represent items of forward-looking high-end engineering equipment combining nuclear power and ocean engineering, with unique advantages and broad application prospects. When a nuclear accident occurs, it causes considerable economic losses and casualties. The traditional accident analysis of nuclear power plants only considers the failure of a single system or component, without considering the coupling between the system and the operator, the environment, and other factors. In this study, the cause mechanism of nuclear accidents in MNPPs is analyzed from the perspective of a social technology system. The causal analysis model is constructed by using the internal core causal analysis (e.g., technical control) and external stimulation causal analysis (e.g., social intervention) of accidents, after which the mechanism of the coupled evolution of each influencing factor is analyzed. A Bayesian network inference model is used to quantify the coupling relationship between the factors that affect the deterioration of nuclear accidents. The results show that the main influencing factors are pump failure, valve failure, insufficient response time, poor psychological state, unfavorable sea conditions, unfavorable offshore operating environments, communication failure, inappropriate organizational procedures, inadequate research and design institutions, inadequate regulatory agencies, and inadequate policies. These 12 factors have a high degree of causality and are the main factors influencing the deterioration of the small break loss of coolant accident (SBLOCA). In addition, the causal chain that is most likely to influence the development of SBLOCA into a severe accident is obtained. This provides a theoretical basis for preventing the occurrence of marine nuclear power accidents. Full article

Adopting and widely implementing solar photovoltaic (PV) systems are regarded as a promising solution to address energy crises by providing a sustainable and independent electricity supply while significantly reducing greenhouse gas emissions to combat climate change. This encourages households, organizations, and enterprises to install solar PV systems. However, there are many solar PV systems that have been connected to the power distribution grid without following the required procedures. Power distribution grid operators cannot detect the locations of these solar PV systems. Thus, it is necessary to assess the impact of solar PV systems on the power distribution grid in detail, even though there are multiple economic and environmental advantages associated with installing solar PV systems. This study analyzes the changes in an overloaded power distribution grid’s power losses and voltage deviations with solar PV systems. There are two main factors considered for assessing the impact of the solar PV system on the power distribution grid: the total installed capacity of the solar PV systems and the location of the connection. Based on a comparison between the measurement results of three feeders with higher loads in the Ulaanbaatar area, the Dambadarjaa feeder, which has the highest load, was selected. The impact of the solar PV systems on the selected feeder was analyzed by connecting eight solar PV systems at four different locations. Their total installed capacities vary between 25 and 80 percent of the highest daily load of the selected feeder. The results show that the power loss of the feeder can be greatly reduced when the total installed capacity of the solar PV systems is selected optimally, and the location of the connection is at the end of the power distribution grid. Full article

The rapid proliferation of EVs has ushered in a transformative era for the power industry, characterized by increased demand volatility and grid frequency instability. In response to these challenges, this paper introduces a novel approach that combines fuzzy logic with adaptive inertia control to improve the frequency stability of grids amidst large-scale electric vehicle (EV) integration. The proposed methodology not only adapts to varying charging scenarios but also strikes a balance between steady-state and dynamic performance considerations. This research establishes a solid theoretical foundation for the inertia-adaptive virtual synchronous generator (VSG) concept and introduces a pioneering fuzzy inertia-based VSG methodology. Additionally, it incorporates adaptive output scaling factors to enhance the robustness and adaptability of the control strategy. These contributions offer valuable insights into the evolving landscape of adaptive VSG strategies and provide a pragmatic solution to the pressing challenges arising from the integration of large-scale EVs, ultimately fostering the resilience and sustainability of contemporary power systems. Finally, simulation results illustrate that the new proposed fuzzy adaptive inertia-based VSG method is effective and has superior advantages over the traditional VSG and droop control strategies. Specifically, the proposed method reduces the maximum frequency change by 25% during load transitions, with a peak variation of 0.15 Hz compared to 0.2 Hz for the traditional VSG. Full article

With the recent proliferation of electric vehicles, there is increasing attention on drive motors that are powerful and efficient, with a higher power density. To meet such high power density requirements, the cooling technology used for drive motors is particularly important. To further optimize the cooling effects, the use of direct oil-cooling technology for drive motors is gaining more attention, especially regarding the requirements for electric vehicle electric oil pumps (EOPs) in motor cooling. In such high-temperature environments, it is also necessary for the EOP to maintain its performance under high temperatures. This research explores the feasibility of using high-temperature-resistant ferrite magnets in the rotors of EOPs. For a 150 W EOP motor with the same stator size, three different rotor configurations are proposed: a surface permanent magnet (SPM) rotor, an interior permanent magnet (IPM) rotor, and a spoke-type IPM rotor. While the rotor sizes are the same, to maximize the power density while meeting the rotor’s mechanical strength requirements, the different rotor configurations make the most use of ferrite magnets (weighing 58 g, 51.8 g, and 46.3 g, respectively). Finite element analysis (FEA) was used to compare the performance of these models with that of the basic rotor design, considering factors such as the no-load back electromotive force, no-load voltage harmonics (<10%), cogging torque (<0.1 Nm), load torque, motor loss, and efficiency (>80%). Additionally, a comprehensive analysis of the system efficiency and energy loss was conducted based on hypothetical electric vehicle traction motor parameters. Finally, by manufacturing a prototype motor and conducting experiments, the effectiveness and superiority of the finite element method (FEM) design results were confirmed. Full article

Marine life exploration is constrained by factors such as limited scuba diving time, depth restrictions for divers, costly expeditions, safety risks to divers’ health, and minimizing harm to marine ecosystems, where traditional diving often risks disturbing marine life. This paper introduces Nu (named after an ancient Egyptian deity), a 3D-printed Remotely Operated Underwater Vehicle (ROUV) designed in an attempt to address these challenges. Nu employs Long Range (LoRa), a low-power and long-range communication technology, enabling wireless operation via a manual controller. The vehicle features an onboard live-feed camera with a separate communication system that transmits video to an external real-time machine learning (ML) pipeline for fish species classification, reducing human error by taxonomists. It uses Brushless Direct Current (BLDC) motors for long-distance movement and water pump motors for precise navigation, minimizing disturbance, and reducing damage to surrounding species. Nu’s functionality was evaluated in a controlled 2.5-m-deep body of water, focusing on connectivity, maneuverability, and fish identification accuracy. The fish detection algorithm achieved an average precision of 60% in identifying fish presence, while the classification model achieved 97% precision in assigning species labels, with unknown species flagged correctly. The testing of Nu in a controlled environment has met the system design expectations. Full article

The radiation mode of the interaction between electromagnetic waves and materials has always been a research hotspot in nanophotonics, and bound states in the continuum (BICs) belong to one of the nonradiative modes. Owing to their high-quality factor characteristics, BICs are extensively employed in nonlinear harmonic generators and sensors. Here, the influence of structural parameters on radiation modes has been systematically analyzed using band theory; the mechanisms of quasi-BIC mode and BIC mode were also analyzed through multipole decomposition of scattered power and near-field distribution. Notably, this study presents the discovery that the toroidal dipole-BIC (TD-BIC) arises from the interference and cancellation of electric and toroidal dipoles. The research results indicate that the structure, which supports symmetry-protected BICs, is sensitive to variations in the concentration of NaCl solution in its surroundings, making it applicable for liquid detection in miniaturized metal sensors. The proposed scheme broadens the applicability of BIC-based sensors and provides a prospective platform for biological and chemical sensing. Full article

A peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analysis (QLFA) accurately assesses the maximum loading conditions in distribution networks by considering factors such as load profiles, system topology, and network constraints. Achieving maximum peak shaving requires optimizing battery charging and discharging cycles based on real-time energy generation and consumption patterns. Seamless integration of battery storage with solar photovoltaic (PV) systems and industrial processes is essential for effective peak shaving strategies. This paper proposes a model predictive control (MPC) scheme that can effectively perform peak shaving of the total industrial load. Adopting an MPC-based algorithm design framework enables the development of an effective control strategy for complex systems. The proposed MPC methodology was implemented and tested on the Indian Utility 29 Node Distribution Network (IU29NDN) using the DIgSILENT Power Factory environment. Additionally, the analysis encompasses technical and economic results derived from a simulated storage operation and, taking Puducherry State Electricity Department tariff details, provides significant insights into the application of this method. Full article

In the present study, for the first time, aluminum-doped zinc oxide (AZO) thin films with nanoinclusions of amorphous carbon have been synthesized via spin coating, and the thermoelectric performances were investigated varying the aging period of the solution, the procedure of carbon nanoparticles’ addition, and the annealing atmosphere. The addition of nanoparticles has been pursued to introduce phonon scattering centers to reduce thermal conductivity. All the samples showed a strong orientation along the [002] crystallographic direction, even though the substrate is amorphous silica, with an intensity of the diffraction peaks reaching its maximum in samples annealed in the presence of hydrogen, and generally decreasing by the addition of carbon nanoparticles. Absolute values of the Seebeck coefficient improve when nanoparticles are added. At the same time, electric conductivity is higher for the sample with 1 wt.% of carbon and annealed in Ar with 1% of H₂, both increasing in absolute value with the temperature rise. Among all the samples, the lowest thermal conductivity value of 1.25 W/(m∙K) was found at room temperature, and the highest power factor was 111 μW/(m∙K²) at 325 °C. Thus, the introduction of carbon effectively reduced thermal conductivity, while also increasing the power factor, giving promising results for the further development of AZO-based materials for thermoelectric applications. Full article

In a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase significantly. To improve users’ charging experience and ensure stable operation of the MMG, we propose a new joint scheduling strategy that considers both time cost of users and spatial load balancing among MMGs. The time cost encompasses many factors, such as traveling time, queue waiting time, and charging time. Meanwhile, spatial load balancing seeks to mitigate the impact of large-scale EV charging on MMG loads, promoting a more equitable distribution of power resources across the MMG system. Compared to the Shortest Distance Matching Strategy (SDMS) and the Time Minimum Matching Strategy (TMMS) methods, our approach improves the average peak-to-valley ratio by 9.5% and 10.2%, respectively. Similarly, compared to the Load Balancing Matching Strategy (LBMS) and the Improved Load Balancing Matching Strategy (ILBMS) methods, our approach reduces the average time cost by 31.8% and 25% while maintaining satisfactory spatial load balancing. These results demonstrate that the proposed method achieves good results in handling electric vehicle scheduling problems. Full article

This paper describes a mathematical model of the AC/DC converter. The analytic expressions define fundamental physical variables of the converter and their relations: phase current and voltage, shift angle between these quantities, power factor, and supply voltage U_D. The mains voltage is defined as a digitalized sine wave while the current’s wave takes the form of a line segment defined in an appropriate time interval. The model permits the description of two modes of operation: inverter and rectifier. The assumed control method of the converter depends on the successive switching of selected vectors. They are qualified according to the principle of the lowest error between the reference and measured phase current value. The control method is realized by using hysteresis algorithms. Five different algorithm solutions and comparative results are implemented. Several examples of current, voltage, and vectors taken during the simulation and experimental works are executed. Full article

Incremental Delta-Sigma (I-DS) analog-to-digital converters (ADCs) are one of the best candidates for integrated sensor interface systems when it comes to high resolution and power efficiency. Advanced architectures such as Multistage noise shaping (MASH) or extended counting (EC) I-DS ADCs can be used to achieve a high resolution and fast conversion times and avoid stability issues. Different architectures have been proposed in the state of the art (SoA), but there exists no extensive quantitative or qualitative comparison between them. This manuscript fills this gap by providing a detailed system-level comparison between MASH, EC, and other architectural options in I-DS ADCs, where different performances between these architectures are realized depending on the employed oversampling ratio (OSR) and the chosen number of quantizer bits. Also, for specific MASH designs, the appropriate choice of the digital filter improves the SQNR. The advantages, disadvantages, and limitations of the different architectures are presented including non-idealities such as coefficient mismatch showing that 2-1 MASH-LI is less sensitive to mismatch and provides a high maximum stable amplitude (MSA) relative to the simulated architectures. Furthermore, the 2-1 EC achieves good results and comes with the advantage of a lower noise penalty factor compared to the MASH architectures. This work is intended to assist designers in selecting the most appropriate enhanced I-DS MASH architecture for their specific requirements and applications. Full article

Methanol is widely recognized as a promising alternative fuel for achieving carbon neutrality in internal combustion engines. Its use in direct injection spark-ignition (DISI) engines, either as pure methanol or blended fuels, has demonstrated improvements in thermal efficiency and reductions in certain gaseous pollutants. However, due to the complex influencing factors and the great harm to human health, its particulate emissions need to be further explored and controlled, which is also an inevitable requirement for the development of energy conservation and carbon reduction in internal combustion engines. This study explores the effects of two-stage injection strategies combined with fuel blending on the combustion characteristics, stability, and particulate emissions of DISI engines. By testing four methanol blending ratios and four injection ratios, the presented study identifies that M20 fuel with an 8:2 injection ratio achieves optimal combustion performance, stability, and increased indicated mean effective pressure. Furthermore, under low methanol blending ratios, the 8:2 injection ratio can reduce particulate number concentrations by approximately 20%. These findings suggest that a well-designed two-stage injection strategy combined with methanol–gasoline blends can effectively control particulate emissions while maintaining the power, efficiency, and combustion stability of DISI engines. Full article