Search Results (9,955)

Driven by the global energy transition and the pursuit of dual carbon goals (carbon peaking and carbon neutrality), the innovation network of the new energy vehicle (NEV) industry, composed of enterprises, universities, and research institutes, has become a key driver of sustainable industrial development. The evolution of this network is jointly shaped by both supply chain networks (SCNs) and executive networks (ENs), representing formal and informal relational structures, respectively. To systematically explore these dynamics, this study analyzes panel data from Chinese A-share-listed NEV firms covering the period 2003–2024. Employing social network analysis (SNA) and Quadratic Assignment Procedure (QAP) regression, we investigate how SCNs and ENs influence the formation and structural evolution of innovation networks. The results reveal that although all three networks exhibit sparse connectivity, they differ substantially in their structural characteristics. Moreover, both SCNs and ENs have statistically significant positive effects on innovation network development. Building on these findings, we propose an integrative policy framework to strategically enhance the innovation ecosystem of China’s NEV industry. This study not only provides practical guidance for fostering collaborative innovation but also offers theoretical insights by integrating formal and informal network perspectives, thereby advancing the understanding of multi-network interactions in complex industrial systems. Full article

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

Given the climate change observed in the past few decades, sustainable development and the use of renewable energy sources are urgent. In this scenario, hydrogen production through electrolyzers is a promising renewable source and energy vector because of its ultralow greenhouse emissions and high energy content. Hydrogen can be used in a variety of applications, from transportation to electricity generation, contributing to the diversification of the energy matrix. In this context, this paper presents an autonomous isolated DC microgrid system for generating and storing electrical energy to be exclusively used for feeding an electrolyzer hydrogen production plant, which has been retrofitted for green hydrogen production. Experimental verification was performed at Itaipu Parquetec, which consists of an alkaline electrolysis unit directly integrated with a battery energy storage system and renewable sources (e.g., photovoltaic and wind) by using an isolated DC microgrid concept based on DC/DC and AC/DC converters. Experimental results revealed that the new electrolyzer DC microgrid increases the system’s overall efficiency in comparison to the legacy thyristor-based power supply system by 26%, and it autonomously controls the energy supply to the electrolyzer under optimized conditions with an extremely low output current ripple. Another advantage of the proposed DC microgrid is its ability to properly manage the startup and shutdown process of the electrolyzer plant under power generation outages. This paper is the result of activities carried out under the R&D project of ANEEL program No. PD-10381-0221/2021, entitled “Multiport DC-DC Converter and IoT System for Intelligent Energy Management”, which was conducted in partnership with CTG-Brazil. Full article

Machine learning (ML) methods have been successfully employed to support decision-making for Software as a Service (SaaS) providers. While most of the published research primarily emphasizes prediction accuracy, other important aspects, such as cloud deployment efficiency and environmental impact, have received comparatively less attention. It is also critical to effectively use factors such as training time, prediction time and carbon footprint in production. SaaS decision support systems use the output of ML models to provide actionable recommendations, such as running reactivation campaigns for users who are likely to churn. To this end, in this paper, we present a benchmarking comparison of 17 different ML models for churn prediction in SaaS, which include cloud deployment efficiency metrics (e.g., latency, prediction time, etc.) and sustainability metrics (e.g., CO₂ emissions, consumed energy, etc.) along with predictive performance metrics (e.g., AUC, Log Loss, etc.). Two public datasets are employed, experiments are repeated on four different machines, locally and on the cloud, while a new weighted Green Efficiency Weighted Score (GEWS) is introduced, as steps towards choosing the simpler, greener and more efficient ML model. Experimental results indicated XGBoost and LightGBM as the models capable of offering a good balance on predictive performance, fast training, inference times, and limited emissions, while the importance of region selection towards minimizing the carbon footprint of the ML models was confirmed. Full article

To gain a comprehensive understanding of the current research landscape in the field of new energy vehicle (NEV) fires and to explore its knowledge base and emerging trends, bibliometric methods—such as co-occurrence, clustering, and co-citation analyses—were employed to examine the relevant literature. A research knowledge framework was established, encompassing four primary themes: thermal management and performance optimization of power batteries, battery materials and their safety characteristics, thermal runaway (TR) and fire risk assessment, and fire prevention and control strategies. The key research frontiers in this domain could be classified into five categories: mechanisms and propagation of TR, development of high-safety battery materials and flame-retardant technologies, thermal management and thermal safety control, intelligent early warning and fault diagnosis, and fire suppression and firefighting techniques. The focus of research has gradually shifted from passive identification of causes and failure mechanisms to proactive approaches involving thermal control, predictive alerts, and integrated system-level fire safety solutions. As the field advances, increasing complexity and interdisciplinary integration have emerged as defining trends. Future research is expected to benefit from broader cross-disciplinary collaboration. These findings provide a valuable reference for researchers seeking a rapid overview of the evolving landscape of NEV fire-related studies. Full article

In the context of climate change and energy transition, the growing frequency of extreme weather events threatens the safety and stability of power systems. Given the limitations of existing research on load characteristic analysis and load forecasting during extreme weather events, this paper proposes a load-integrated forecasting model that accounts for extreme weather. First, an improved power load clustering method is proposed, combining Kernel PCA for nonlinear dimensionality reduction and an enhanced k-means algorithm, enabling both qualitative analysis and quantitative representation of load characteristics under extreme weather. Second, an optimal combination forecasting model is developed, integrating improved SVM and enhanced LSTM networks. Building upon the improved power load clustering algorithm, a load-integrated forecasting model considering extreme weather is established. Finally, based on the proposed load-integrated forecasting model, a time-series production simulation model considering extreme weather is constructed to quantitatively analyze the power and electricity balance risks of the system. Case studies demonstrate that the proposed integrated forecasting model can effectively analyze load characteristics under extreme weather and achieve more accurate load forecasting, which can provide guidance for the planning and operation of new power systems under extreme weather conditions. Full article

A new energy storage unit, which is fed by a piezoelectric wind energy harvester, is explored. The outputs of a three-phase piezoelectric wind energy device have been initially recorded from the laboratory experiments. Following the records of voltage outputs, the power ranges of the device were measured at several hundred microwatts. The main issue of piezoelectric voltage generation is that voltage waveforms of piezoelectric materials have high total harmonic distortion (THD) with incredibly high subharmonics and superharmonics. Therefore, such a material reply causes a certain power loss at the output of the wind energy generator. In order to fix this problem, we propose a combination of a rectifier and a storage system, where they can operate compatibly under high THD rates (i.e., 125%). Due to high THD values, current–voltage characteristics are not linear-dependent; indeed, because of capacitive effect of the piezoelectric (i.e., lead zirconium titanite) material, harvested power from the material is reduced by nearly a factor of 20% in the output. That also negatively affects the storage on the Li-based battery. In order to compensate, the output waveform of the device, the waveforms, which are received from the energy-harvester device, are first rectified by a full-wave rectifier that has a maximum power point tracking (MPPT) unit. The SOC values prove that almost 40% of the charge is stored in 1.2 s under moderate wind speeds, such as 6.1 m/s. To conclude, a better harvesting performance has been obtained by storing the energy into the Li-ion battery under a current–voltage-controlled boost converter technique. Full article

The increasing integration of high-frequency power electronic converters in renewable energy-grid systems has escalated reliability concerns, necessitating FPGA-accelerated large-scale real-time electromagnetic transient (EMT) computation to prevent failures. However, most existing studies prioritize computational performance and struggle to achieve large-scale EMT computation. To enhance the computational scale, we propose a scalable hardware architecture comprising domain-specific components and data-centric processing element (PE) arrays. This architecture is further enhanced by a graph-based matrix mapping methodology and matrix-aware fixed-point quantization for hardware-efficient computation. We demonstrate our principles with FPGA implementations of large-scale multi-converter systems. The experimental results show that we set a new record of supporting 1200 switches with a computation latency of 373 ns and an accuracy of 99.83% on FPGA implementations. Compared to the state-of-the-art large-scale EMT computation on FPGAs, our design on U55C FPGA achieves an up-to 200.00× increase in the switch scale, without I/O resource limitations, and demonstrates up-to 71.70% reduction in computation error and 51.43% reduction in DSP consumption, respectively. Full article

In response to the climate crisis, Colombia has committed to reducing greenhouse gas (GHG) emissions by 2030 through an energy transition strategy that promotes Non-Conventional Renewable Energy Sources (NCRES) and, increasingly, natural gas. Although natural gas is regarded as a transitional fuel with lower carbon intensity than other fossil fuels, existing reserves could be depleted by 2030 if no new discoveries are made. To assess this risk, a System Dynamics model was developed to project supply and demand under alternative transition pathways. The model integrates: (1) GDP, urban population growth, and adoption of clean energy, (2) the behavior of six major consumption sectors, and (3) the role of gas-fired thermal generation relative to NCRES output and hydroelectric availability, influenced by the El Niño river-flow variability. The novelty and contribution of this study lie in the integration of supply and demand within a unified System Dynamics framework, allowing for a holistic understanding of the Colombian natural gas market. The model explicitly incorporates feedback mechanisms such as urbanization, vehicle replacement, and hydropower variability, which are often overlooked in traditional analyses. Through the evaluation of twelve policy scenarios that combine hydrogen, wind, solar, and new gas reserves, the study provides a comprehensive view of potential energy transition pathways. A comparative analysis with official UPME projections highlights both consistencies and divergences in long-term forecasts. Furthermore, the quantification of demand coverage from 2026 to 2033 reveals that while current reserves can satisfy demand until 2026, the expansion of hydrogen, wind, and solar sources could extend full coverage until 2033; however, ensuring long-term sustainability ultimately depends on the discovery and development of new reserves, such as the Sirius-2 well. Full article

Promoting economic prosperity, social justice, and ecological sustainability requires the rapid decarbonization of our global energy system in favor of renewable sources of energy. Recent news analysis estimates that 15% of counties across the US have banned wind turbines, solar fields, and other green energy developments. We answer two overarching research questions: (1) How do regulations of wind facilities vary at the county level? And (2) what factors appear to explain the variation in local wind regulations? We created a GIS database of energy regulations for all 105 counties in Kansas, a top state for wind potential and a recent hotbed of local actions. We coupled descriptive statistics, mapping, and regression modeling to describe the variation in local policy approaches and identify factors driving the variation. We find counties using at least five different policy approaches to enable or block wind regulations. Factors driving variation include a combination of infrastructure capacity, demographic characteristics that shape local planning capacity, and the apparent reliance on large farming operations for local economic output but not partisan voting patterns or underlying wind capacity. Our findings provide vital insights for policymakers at the federal, state, and local levels, as well as providing a foundation for future scholarship on planning for a just energy future. Full article

In the previous research that aimed to enhance the toughness and tribological properties of silicon nitride ceramics, a lignin precursor was added to the ceramic matrix, which achieved conversion through pyrolysis and sintering, resulting in a silicon nitride-based composite ceramic containing nitrogen-doped graphene quantum dots (N-GQDs). This composite material demonstrated excellent comprehensive mechanical properties and friction-wear performance. Based on the existing experimental results, the first-principles plane wave mode conservation pseudopotential method of density functional theory was adopted in this study to build a microscopic heterostructure model of Si₃N₄-based composite ceramics containing N-GQDs. Meanwhile, the surface energy of Si₃N₄ and the system energy of the N-GQDs@Si₃N₄ heterostructure were calculated. The calculation results showed that when the distance between N-GQDs and Si₃N₄ in the heterostructure was 2.3 Å, the structural energy was the smallest and the structure was the steadiest. This is consistent with the previous experimental results and further validates the coating mechanism of N-GQDs covering the Si₃N₄ column-shaped crystals. Simultaneously, based on the results of the previous experiments, the stress of the heterostructure composed of Si₃N₄ particles coated with different numbers of layers of nitrogen quantum dots was calculated to predict the optimal lignin doping amount. It was found that when the doping amount was between 1% and 2%, the best microstructure and mechanical properties were obtained. This paper provides a new method for studying the graphene quantum dot coating structure. Full article

Plant-based residues generated within the agri-food system represent an abundant resource with significant potential for sustainable valorization. However, they are still underutilized and place a substantial burden on the environment and climate. This review discusses research trends over the past decade, combining bibliometric analysis with an overview of emerging technologies applied to the processing of residues generated from conventional crops and medicinal and aromatic plants. The bibliometric analysis reveals main valorization pathways, ranging from energy production to recovery of high-value bioactive compounds. Recent advances in this field are discussed in detail, with emphasis on low-energy and non-thermal processing (ultrasound, microwave, cold plasma), green solvents (natural deep eutectic solvents, bio-based solvents), biological pretreatments (with ligninolytic microorganisms and enzymes), thermochemical technologies (hydrothermal carbonization, pyrolysis), and emerging cascade strategies applied for multi-product recovery. Published research proves that these approaches have a great potential for sustainable valorization, while process optimization and economic feasibility remain a challenge at industrial scales for wider adoption. By providing an integrated perspective on diverse types of plant-based residues, this review highlights the importance of developing cascade and circular processing strategies, which align with global sustainability goals and encourage innovation in bio-based industries. New knowledge and advances in this field are highly required and will further help the transition of the current agri-food system towards greater circularity and sustainability. Full article

High-performance electrical contact materials are crucial for electric power systems, new energy vehicles, and rail transportation, as their properties directly impact the reliability and safety of electronic devices. Enhancing these materials not only improves energy efficiency but also offers notable environmental and economic advantages. However, traditional composite contact materials often suffer from poor dispersion of the reinforcing phase, which restricts further performance improvement. Graphene (G), with its unique two-dimensional structure and exceptional electrical, mechanical, and tribological properties, is considered an ideal reinforcement for metal matrix composites. Yet, its tendency to agglomerate poses a significant challenge to achieving uniform dispersion. To overcome this, the study introduces a dual approach: modulation of the zeta potential (ζ) in the silver-plated liquid to enhance G’s dispersion stability, and concurrent optimization of the composite electrodeposition process. Experimental results demonstrate that this synergistic strategy enables the uniform distribution of G within the silver matrix. The resulting silver–graphene (Ag-G) composite coatings exhibit outstanding overall performance at both micro and macro levels. This work offers a novel and effective pathway for the design of advanced electrical contact materials with promising application potential. Full article

Particle accelerators are powerful tools in fundamental research, medicine, and industry that provide high-energy beams that can be used to study matter and to enable advanced applications. The state-of-the-art particle accelerators are fundamentally constructed from superconducting radio-frequency (SRF) cavities, which act as resonant structures for the acceleration of charged particles. The performance of such cavities is governed by inherent superconducting material properties such as the transition temperature, critical fields, penetration depth, and other related parameters and material quality. For the last few decades, bulk niobium has been the preferred material for SRF cavities, enabling accelerating gradients on the order of ~50 MV/m; however, its intrinsic limitations, high cost, and complicated manufacturing have motivated the search for alternative strategies. Among these, sputter-deposited superconducting thin films offer a promising route to address these challenges by reducing costs, improving thermal stability, and providing access to numerous high-T_c superconductors. This review focuses on progress in sputtered superconducting materials for SRF applications, in particular Nb, NbN, NbTiN, Nb₃Sn, Nb₃Al, V₃Si, Mo–Re, and MgB₂. We review how deposition process parameters such as deposition pressure, substrate temperature, substrate bias, duty cycle, and reactive gas flow influence film microstructure, stoichiometry, and superconducting properties, and link these to RF performance. High-energy deposition techniques, such as HiPIMS, have enabled the deposition of dense Nb and nitride films with high transition temperatures and low surface resistance. In contrast, sputtering of Nb₃Sn offers tunable stoichiometry when compared to vapour diffusion. Relatively new material systems, such as Nb₃Al, V₃Si, Mo-Re, and MgB₂, are just a few of the possibilities offered, but challenges with impurity control, interface engineering, and cavity-scale uniformity will remain. We believe that future progress will depend upon energetic sputtering, multilayer architectures, and systematic demonstrations at the cavity scale. Full article

Stroke is one of the leading causes of disability worldwide. Its sequelae require early, intensive, and repetitive rehabilitation, but is often ineffective due to a lack of patient motivation. Gamification has been incorporated in recent years as a response to this issue. The aim of incorporating games is to motivate patients to perform therapeutic exercises. This study presents PACTUS, a new version of a gamified device for stroke neurorehabilitation. Using a series of colored cards, a touchscreen station, and a sensorized handle with an RGB sensor, patients can interact with three games specifically programmed to work on different areas of neurorehabilitation. In addition to presenting the technical design (including energy consumption and sensor signal processing), the results of an observational study conducted with neurological patients, healthy older adults, and caregivers (who also completed the System Usability Scale) are also presented. This usability, safety, and satisfaction study provided an assessment of the device for future iterations. The inclusion of the experiences of the three groups (patients, caregivers, and older adults) provided a more comprehensive and integrated view of the device, enriching our understanding of its strengths and limitations. Although the results were preliminarily positive, areas for improvement were identified. Full article