Search Results (179)

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. Full article

Background and Objectives: Estimating the need for palliative care for children is a crucial step in addressing the needs of children facing life-threatening conditions, while providing a powerful argument to combat unacceptably wide disparities in access to care. The need for palliative care for children in Romania remains insufficiently quantified. More accurate estimates are indispensable to assess the true extent of need and to support adequate policy responses. Materials and Methods: This retrospective cross-sectional study aimed to estimate the need for pediatric palliative care in Romania for 2022–2023. We analyzed secondary data obtained from the General Directorates of Social Assistance and Child Protection (DGASPC) in 41 counties and the six sectors of Bucharest. The analysis focused on life-limiting conditions as defined by the WHO Annex 3, Order no. 253/2018. Results: The study identified 14,499 pediatric cases with palliative care needs, showing a highly uneven national distribution, especially across diagnostic groups and age categories. We observed a higher number of cases in rural areas (7553) compared with urban areas (6946). Our own data do not include resource estimates; however, prior reports indicate only 50 palliative care beds for children in Romania. Conclusions: This study reveals a substantial and unevenly distributed need for pediatric palliative care in Romania, with notable disparities across age groups, diagnostic categories, and urban-rural areas. The identification of 14,499 eligible cases underscores the urgency of developing targeted policies and allocating adequate resources to ensure equitable access to specialized palliative services for all children in need. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in the new generation of communication systems. They serve as access points, base stations, relays, and gateways to extend network coverage, enhance connectivity, or offer communications services in places lacking telecommunication infrastructure. However, optimizing UAV placement in three-dimensional (3D) environments with diverse user distributions and uneven terrain conditions is a crucial challenge. Therefore, this paper proposes a novel framework to minimize UAV transmission power while ensuring a guaranteed data rate in realistic and complex scenarios. To this end, using the particle swarm optimization evolution (PSO-E) algorithm, this paper analyzes the impact of user-truncated distribution models for suburban, urban and dense urban environments. Extensive simulations demonstrate that dense urban environments demand higher power than suburban and urban environments, with uniform user distributions requiring the most power in all scenarios. Conversely, Gaussian and exponential distributions exhibit lower power requirements, particularly in scenarios with concentrated user hotspots. The proposed model provides insight into achieving efficient network deployment and power optimization, offering practical solutions for future communication networks in complex 3D scenarios. Full article

Achieving energy-efficient, human-like gait remains a major challenge in bipedal humanoid robotics, as traditional serial actuation architectures often lead to high instantaneous power peaks and uneven load distribution. This study addresses the lack of research on how mechanical symmetry, achieved through parallel actuation, can improve power management in lower-limb joints. We developed a 14-degree-of-freedom (DOF) hip-sized bipedal robot model and conducted simulations comparing a conventional serial configuration—using single-DOF rotary actuators—with a novel parallel configuration that employs paired linear actuators at the hip pitch, hip roll, ankle pitch, and ankle roll joints. Simulation results over a standardized walking cycle show that the parallel configuration reduces peak hip-pitch power by 80.4% and peak ankle-pitch power by 53.5%. These findings demonstrate that incorporating actuator symmetry through parallel joint design significantly reduces actuator stress, improves load sharing, and enhances overall energy efficiency in bipedal locomotion. Full article

The battery pack is a critical component of electric vehicles, with lithium-ion cells being a frequently preferred choice. Lithium-ion cells are known for long life, high power and energy density, and are reliable for a broad range of temperatures. However, these batteries have a drawback of over-voltage, under-voltage, thermal runaway, and especially, state of charge or voltage imbalance. Among these, the cell imbalance is particularly important because it causes an uneven power dissipation in each cell, resulting in non-uniform temperature distribution. This uneven temperature distribution negatively affects the lifetime and efficiency of a battery pack. Cell imbalance is mitigated by cell balancing techniques, of which several methods have been presented over the last few years. These methods consider different power electronics circuits and control approaches to optimise cell balancing characteristics. This paper reviews basic to advanced cell balancing techniques and compares their circuit designs, costs, switching stresses, complexity, sizes, and control techniques to highlight the recent trends and future directions. This paper also compares the recent trend of machine learning integration with basic cell balancing topologies and provides a critical analysis of the outcomes. Full article

Accurate real-time icing grid fields are critical for preventing ice-related disasters during winter and protecting property. These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. However, developing precise real-time icing grids is challenging due to the uneven distribution of monitoring stations, data confidentiality restrictions, and the limitations of existing interpolation methods. In this study, we propose a new approach for constructing real-time icing grid fields using 1339 online terminal monitoring datasets provided by the China Southern Power Grid Research Institute Co., Ltd. (CSPGRI) during the winter of 2023. Our method integrates static geographic information, dynamic meteorological factors, and ice_kriging values derived from parameter-optimized Empirical Bayesian Kriging Interpolation (EBKI) to create a spatiotemporally matched, multi-source fused icing thickness grid dataset. We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. The stacking model performed best, achieving an R-value of 0.634 (0.893), RMSE of 3.424 mm (2.834 mm), CSI of 0.514 (0.774), MAR of 0.309 (0.091), FAR of 0.332 (0.161), and fbias of 1.034 (1.084), respectively, when comparing predicted icing values with actual measurements on pylons. Additionally, we employed the SHAP model to provide a physical interpretation of the stacking model, confirming the independence of selected features. Meteorological factors such as relative humidity (RH), 10 m wind speed (WS₁₀), 2 m temperature (T₂), and precipitation (PRE) demonstrated a range of positive and negative contributions consistent with the observed growth of icing. Thus, our multi-source remote-sensing data-fusion approach, combined with the stacking model, offers a highly accurate and interpretable solution for generating real-time icing grid fields. Full article

The acceleration of digitalization has caused an increase in demand for autonomous devices. In this paper, the technologies of artificial intelligence (AI), and especially machine learning (ML), integrated into applications that use self-powered Internet of Things (IoT) sensors are analyzed. The study addresses the issue of the lack of a standardized classification of IoT domains and the uneven distribution of AI integration in these domains. The systematic bibliometric analysis of the scientific literature between 1 January 2020 and 30 April 2025, using the Web of Science database, outlines the seven main areas of IoT sensor usage: smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. The thematic searches highlight the consistent number of articles in the health sector and the underrepresentation of other areas, such as agriculture. The study identifies that the most commonly used sensors are the accelerometer, electrocardiogram, humidity sensor, motion sensor, and temperature sensor, and analyzes the performance of AI models in self-powered systems, identifying accuracies that can reach up to 99.92% in medical and industrial applications. The conclusions drawn from these results underscore the need for an interdisciplinary approach and detailed exploration of ML algorithms to be adapted to the hardware infrastructures of autonomous sensors. The paper proposes future research directions to expand AI’s applicability in developing systems that integrate self-powered IoT sensors. The paper lays the groundwork for future projects in this field, serving as a reference for researchers who wish to explore these areas. Full article

Urban heat stress is becoming increasingly urgent, yet it remains understudied within the broader intersection of climate change and spatial justice. While urban climate scholarship has largely focused on climatic impacts such as flooding, rising sea levels, and prolonged droughts, the socio-spatial lens of urban heat in marginalised and low-income urban communities has received limited attention. This article, grounded in a systematic review of the global literature, foregrounds the mechanisms through which heat functions as a site of socio-environmental injustice. We argue that fragmented urban morphologies, entrenched spatial inequalities, and uneven adaptation strategies collectively produce and sustain heat vulnerability. The article identifies three interrelated conceptual framings that elucidate the production and persistence of heat inequity: transitional injustice, green climate gentrification, and intersectional adaptation praxis. These lenses reveal how heat risk is differentially distributed, governed, and experienced with broader discourses of urban marginalisation, environmental dispossession, and epistemic exclusion. We contend that advancing climate justice in the context of urban heat requires moving beyond technocratic and elite-oriented adaptation, toward multi-scalar planning paradigms that recognise embodied vulnerability, structural inequality, and the socio-political ecologies of thermal exposure. By theorising urban heat through the lens of climate justice, this article contributes to a more expansive and critical understanding of urban climate risk, one that situates heat inequity within the broader structures of power, governance, and spatial exclusion shaping contemporary urban environments. Full article

Given the problems of uneven distribution, strong time variability of ground service demands, and low utilization rate of on-board resources in Low-Earth-Orbit (LEO) satellite communication systems, how to efficiently utilize limited beam resources to flexibly and dynamically serve ground users has become a research hotspot. This paper studies the dynamic resource allocation and interference suppression strategies for beam hopping satellite communication systems. Specifically, in the full-frequency-reuse scenario, we adopt spatial isolation techniques to avoid co-channel interference between beams and construct a multi-objective optimization problem by introducing weight coefficients, aiming to maximize user satisfaction and minimize transmission delay simultaneously. We model this optimization problem as a Markov decision process and apply a value decomposition network (VDN) algorithm based on cooperative multi-agent reinforcement learning (MARL-VDN) to reduce computational complexity. In this algorithm framework, each beam acts as an agent, making independent decisions on hopping patterns and power allocation strategies, while achieving multi-agent cooperative optimization through sharing global states and joint reward mechanisms. Simulation results show that the applied algorithm can effectively enhance user satisfaction, reduce delay, and maintain high resource utilization in dynamic service demand scenarios. Additionally, the offline-trained MARL-VDN model can be deployed on LEO satellites in a distributed mode to achieve real-time on-board resource allocation on demand. Full article

Heating, ventilation, and air conditioning (HVAC) systems and microgrids have garnered significant attention in recent research, with temperature control and renewable energy integration emerging as key focus areas in urban distribution power systems. This paper proposes a robust predictive temperature control (RPTC) method and a microgrid control strategy incorporating asymmetrical challenges, including uneven power load distribution and uncertainties in renewable outputs. The proposed method leverages a thermodynamics-based R-C model to achieve precise indoor temperature regulation under external disturbances, while a multisource disturbance compensation mechanism enhances system robustness. Additionally, an HVAC load control model is developed to enable real-time dynamic regulation of airflow, facilitating second-level load response and improved renewable energy accommodation. A symmetrical power tracking and voltage support secondary controller is also designed to accurately capture and manage the fluctuating power demands of HVAC systems for supporting operations of distribution power systems. The effectiveness of the proposed method is validated through power electronics simulations in the Matlab/Simulink/SimPowerSystems environment, demonstrating its practical applicability and superior performance. Full article

The safety of power transmission lines is crucial to public well-being, with insulators being prone to failures such as self-detonation. However, images captured by unmanned aerial vehicles (UAVs) carrying optical sensors often face challenges, including uneven object scales, complex backgrounds, and difficulties in feature extraction due to distance, angles, and terrain. Additionally, conventional models are too large for UAV deployment. To address these issues, this paper proposes RML-YOLO, an improved insulator defect detection method based on YOLOv8. The approach introduces a tiered scale fusion feature (TSFF) module to enhance multi-scale detection accuracy by fusing features across network layers. Additionally, the multi-scale feature extraction network (MSFENet) is designed to prioritize large-scale features while adding an extra detection layer for small objects, improving multi-scale object detection precision. A lightweight multi-scale shared detection head (LMSHead) reduces model size and parameters by sharing features across layers, addressing scale distribution imbalances. Lastly, the receptive field attention channel attention convolution (RFCAConv) module aggregates features from various receptive fields to overcome the limitations of standard convolution. Experiments on the UID, SFID, and VISDrone 2019 datasets show that RML-YOLO outperforms YOLOv8n, reducing model size by 0.8 MB and parameters by 500,000, while improving AP by 7.8%, 2.74%, and 3.9%, respectively. These results demonstrate the method’s lightweight design, high detection performance, and strong generalization capability, making it suitable for deployment on UAVs with limited resources. Full article

A traditional SAW (surface acoustic wave) atomizer directly supplies liquid to the surface of the atomized chip through a paper strip located in the path of the acoustic beam, resulting in irregular distribution of the liquid film, which generates an aerosol with an uneven particle size distribution and poor directional controllability, and a high heating phenomenon that can easily break the chip in the atomization process. This paper presents a novel atomization method: a paper strip located at the edge of the atomizer (PSLEA), which forms a micron-sized narrow liquid film at the junction of the atomization chip edge and the paper strip under the effect of acoustic wetting. By using this method, physical separation of the atomized aerosol and jetting droplets can be achieved at the initial stage of atomizer startup, and an ideal aerosol plume with no jetting of large droplets, a uniform particle size distribution, a vertical and stable atomization direction, and good convergence of the aerosol beam can be quickly formed. Furthermore, the effects of the input power, and different paper strips and liquid supply methods on the atomization performance, as well as the heating generation capacity of the liquid in the atomization zone during the atomization process were explored through a large number of experiments, which highlighted the advantages of PSLEA atomization. The experiments demonstrated that the maximum atomization rate under the PSLEA atomization mode reached 2.6 mL/min initially, and the maximum thermal stress was 45% lower compared with that in the traditional mode. Additionally, a portable handheld atomizer with stable atomization performance and a median aerosol particle size of 3.95 μm was designed based on the proposed PSLEA atomization method, showing the great potential of SAW atomizers in treating respiratory diseases. Full article

With the continuous expansion of power grids, traditional manual inspection methods face numerous challenges, including low efficiency, high costs, and significant safety risks. As critical infrastructure in power transmission systems, power grid towers require intelligent recognition and monitoring to ensure the reliable and stable operation of power grids. However, existing methods struggle with accuracy and efficiency when processing large-scale point cloud data in complex environments. To address these challenges, this paper presents a comprehensive approach combining multi-sensor fusion and deep learning for power grid tower recognition. A data acquisition scheme that integrates LiDAR and a binocular depth camera, implementing the FAST-LIO algorithm, is proposed to achieve the spatiotemporal synchronization and fusion of sensor data. This integration enables the construction of a colored point cloud dataset with rich visual and geometric features. Based on the RandLA-Net framework, an efficient processing method for large-scale point cloud segmentation is developed and optimized explicitly for power grid tower scenarios. Experimental validation demonstrates that the proposed method achieves 90.8% precision in tower body recognition and maintains robust performance under various environmental conditions. The proposed approach successfully processes point cloud data containing over ten million points while effectively handling challenges such as uneven point distribution and environmental interference. These results validate the reliability of the proposed method in providing technical support for intelligent inspection and the management of power grid infrastructure. Full article