Search Results (1,522)

Against the backdrop of accelerating global energy transition, developing high-performance energy-storage systems is crucial for achieving carbon neutrality. Traditional electrode materials are limited by a single densification storage mechanism and low conductivity, struggling to meet demands for high energy/power density and a long cycle life. Carbon/high-entropy alloy nanocomposites provide an innovative solution through multi-component synergistic effects and cross-scale structural design: the “cocktail effect” of high-entropy alloys confers excellent redox activity and structural stability, while the three-dimensional conductive network of the carbon skeleton enhances charge transfer efficiency. Together, they achieve synergistic enhancement via interfacial electron coupling, stress buffering, and dual storage mechanisms. This review systematically analyzes the charge storage/attenuation mechanisms and performance advantages of this composite material in diverse energy-storage devices (lithium-ion batteries, lithium-sulfur batteries, etc.), evaluates the characteristics and limitations of preparation techniques such as mechanical alloying and chemical vapor deposition, identifies five major challenges (including complex and costly synthesis, ambiguous interfacial interaction mechanisms, lagging theoretical research, performance-cost trade-offs, and slow industrialization processes), and prospectively proposes eight research directions (including multi-scale structural regulation and sustainable preparation technologies, etc.). Through interdisciplinary perspectives, this review aims to provide a theoretical foundation for deepening the understanding of carbon/high-entropy alloy composite energy-storage mechanisms and guiding industrial applications, thereby advancing breakthroughs in electrochemical energy-storage technology under the energy transition. Full article

Traditional urbanization prioritizes economic growth but often degrades the environment, challenging SDGs 9 and 13. China’s New-Type Urbanization Policy (NTUP) balances economic expansion, energy conservation, and environmental protection. By applying the difference-in-differences (DID) method, this study examines the causal effect of NTUP on urban air quality, taking the full implementation of NTUP in 2014 and the designated pilot cities as the policy shock and treatment group, respectively. Furthermore, we explore the mediating roles of land use efficiency and innovation efficiency in this relationship. The results show the following: (1) NTUP significantly lowers urban PM2.5, robust to confounders and selection bias; (2) land use and innovation efficiency mediate this effect, verified by Sobel and Bootstrap tests; and (3) policy effectiveness varies by city level, industrial base, and economic structure. These findings highlight NTUP’s environmental benefits and inform sustainable urbanization strategies globally. Full article

Moderate electric field (MEF) technology is an electro-heating technology that involves the application of electric fields less than 1000 V cm⁻¹, with or without the effect of heat, to induce heating and enhance mass transfer in food processing operations. The rapid heating capabilities and higher energy efficiency make MEF a viable alternative to traditional processing methods in the food industry. Recent advancements in MEF processing of foods have focused on optimizing equipment design and process parameters and integrating digital tools to broaden their application across a wide range of food processes. This review provides a comprehensive overview of recent developments related to the design of MEF systems for various operations, including single and multicomponent food systems. The thermal efficiency and energy saving of MEF treatment in various food processing operations largely depend on the type and arrangement of the electrodes, and operating frequency and composition of the food matrix. A thorough understanding of the electrical properties of single and multicomponent food systems is crucial for analyzing their behavior and interactions with applied electric fields, and for designing an efficient MEF system. In addition, integrating digital tools and physics-based models could play a significant role in real-time monitoring, predictive process control, and process optimization to enhance productivity, reduce energy consumption, and ensure improved product quality and safety. This makes the MEF technology economically viable and sustainable, which also improves the scalability and integration into existing processing lines. Full article

The electric power equipment industry is rapidly advancing toward “informationization,” with the swift progression of intelligent sensing technology serving as a key driving force behind this transformation, thereby triggering significant changes in global electric power equipment. In this process, intelligent sensing has created an urgent demand for high-performance integrated power systems that feature compact size, lightweight design, long operational life, high reliability, high energy density, and low cost. However, the performance metrics of traditional power supplies have increasingly failed to meet the requirements of modern intelligent sensing, thereby significantly hindering the advancement of intelligent power equipment. Energy harvesting technology, characterized by its long operational lifespan, compact size, environmental sustainability, and self-sufficient operation, is capable of capturing renewable energy from ambient power sources and converting it into electrical energy to supply power to sensors. Due to these advantages, it has garnered significant attention in the field of power sensing. This paper presents a comprehensive review of the current state of development of energy harvesting technologies within the power environment. It outlines recent advancements in magnetic field energy harvesting, electric field energy harvesting, vibration energy harvesting, wind energy harvesting, and solar energy harvesting. Furthermore, it explores the integration of multiple physical mechanisms and hybrid energy sources aimed at enhancing self-powered applications in this domain. A comparative analysis of the advantages and limitations associated with each technology is also provided. Additionally, the paper discusses potential future directions for the development of energy harvesting technologies in the power environment. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation. Full article

The objective of this study was to analyze price variability and the factors influencing the formation of monthly prices of by-products of the wood industry in Poland between October 2017 and January 2025. The analysis considered the impact of economic variables, including energy commodity prices (natural gas and coal) and industrial wood prices, on the pricing of wood industry by-products. The adopted approach enabled the identification of key determinants shaping the prices of these by-products. The effectiveness of two tree-based regression models—Random Forest (RF) and CatBoost (CB)—was compared in the analysis. Although RF offers greater interpretability and lower computational requirements, CB proved more effective in modeling dynamic, time-dependent phenomena. The results indicate that industrial wood prices exerted a weaker influence on by-product prices than natural gas prices, suggesting that the energy sector plays a leading role in shaping biomass prices. Coal prices had only a marginal impact on the biomass market, implying that changes in coal availability and pricing did not directly translate into changes in the prices of wood industry by-products. The growing role of renewable energy sources derived from natural gas and wood biomass is contributing to the emergence of a distinct market, increasingly independent of the traditional coal market. In Poland, due to limited access to alternative energy sources, biomass plays a critical role in the decarbonization of the energy sector. Full article

This review is focused on glass fibers and natural fibers, exploring their applications in vehicles and buildings and emphasizing their significance in promoting sustainability and enhancing performance across various industries. Glass fibers, or fiberglass, are lightweight, have high-strength (3000–4500 MPa) and a Young’s modulus range of 70–85 GPa, and are widely used in automotive, aerospace, construction, and marine applications due to their excellent mechanical properties, thermal conductivity of ~0.045 W/m·K, and resistance to fire and corrosion. On the other hand, natural fibers, derived from plants and animals, are increasingly recognized for their environmental benefits and potential in sustainable construction, offering advantages such as biodegradability, lower carbon footprints, and reduced energy consumption, with a sound absorption coefficient (SAC) range of 0.7–0.8 at frequencies above 2000 Hz and thermal conductivity range of 0.07–0.09 W/m·K. Notably, the integration of these materials in construction and automotive sectors reflects a growing trend towards sustainable practices, driven by the need to mitigate carbon emissions associated with traditional building materials and enhance fuel efficiency, as seen in hybrid composites achieving 44.9 dB acoustic insulation at 10,000 Hz and a thermal conductivity range of 0.05–0.06 W/m·K in applications such as the BMW i3 door panels. Natural fibers contribute to reducing reliance on fossil fuels, supporting a circular economy through the recycling of agricultural waste, while glass fibers are instrumental in creating lightweight composites for improved vehicle performance and structural integrity. However, both materials face distinct challenges. Glass fibers, while offering superior strength, are vulnerable to chemical degradation and can pose recycling difficulties due to the complex processes involved. On the other hand, natural fibers may experience moisture absorption, affecting their durability and mechanical properties, necessitating innovations to enhance their application in demanding environments. The ongoing research into optimizing the performance of both materials highlights their relevance in future sustainable engineering practices. In summary, this review underscores the growing importance of glass and natural fibers in addressing modern environmental challenges while also improving product performance. As industries increasingly prioritize sustainability, these materials are poised to play crucial roles in shaping the future of construction and transportation, driving innovations that align with ecological goals and consumer expectations. Full article

Stevioside, a natural high-intensity sweetener, is widely employed across the food, pharmaceutical, and daily chemical industries due to its intense sweetness and health benefits. However, traditional extraction and purification processes for steviol glycosides from Stevia rebaudiana are plagued by low efficiency, high energy consumption, substantial environmental impact, and inconsistent product quality. This study systematically optimized the extraction, decolorization, decontamination, and desalting processes to overcome these challenges. The extraction method was refined using 20% ethanol as the solvent, an optimal temperature of 50 °C, and a 1:10 material-to-liquid ratio, increasing the steviol glycoside yield from 32.0% to 49.1%. Decolorization employing a combination of resins D940 and T5 achieved decolorization rates of 89–92% with minimized steviol glycoside loss, surpassing the non-selective adsorption limitations of activated carbon. For decontamination, calcium hydroxide (Ca(OH)₂) outperformed diatomaceous earth, attaining a 98% protein removal rate while maintaining steviol glycoside loss below 20%. The desalting resin LXP-016 demonstrated superior performance at 40 °C, enhancing the ability of ionic impurity removal. These optimizations collectively improve the efficiency, sustainability, and quality of steviol glycoside production, offering a promising framework for industrial-scale applications. Full article

In the construction sector, cement and concrete are among the most widely utilized manufactured materials, yet their environmental impact remains a significant concern. The concrete industry is a major contributor to carbon dioxide emissions, accounting for over 8% of global greenhouse gas emissions annually. Several reports have estimated that between 1930 and 2013, a total of 4.5 gigatons of carbon was sequestered through the carbonation of cement-based materials. This process offset approximately 43% of the carbon dioxide (CO₂) emissions resulting from cement production during the same period, excluding emissions related to fossil fuel consumption in the manufacturing process. It is well established that producing one ton of cement results in approximately 0.60–0.98 tons of CO₂ emissions, coupled with substantial energy consumption. To mitigate these environmental effects, developing low-carbon or cement-free binders has become crucial. Alkali-activated binders (AABs), derived from industrial by-products or agricultural waste materials and activated with a low-molarity or one-part activator, are increasingly recommended as sustainable alternatives to reduce greenhouse gas emissions in the cement industry and minimize the consumption of natural resources. The production of alkali-activated concrete (AAC) involves several critical factors that significantly influence its mix design, fresh properties, and compressive strength (CS) performance. This study aims to provide a comprehensive review of the key factors affecting AAC’s mix design, workability, and CS characteristics. Firstly, the study discusses various methods employed for AAC mix design and the factors influencing these designs. Secondly, it examines the impact of binder type, source, chemical, mineralogical, and physical properties, as well as alkaline activator solutions, water content, and fillers on AAC’s workability, setting times, and strength development. Additionally, the study explores the correlation matrix and predictive performance models for fresh and strength properties. Lastly, the relationship between workability and CS is extensively analyzed. The review concludes by highlighting the existing challenges and prospects of AACs as sustainable construction materials to replace traditional cement and reduce carbon emissions. Full article

This research evaluates the carbon dioxide emission-reduction efficiency of new energy vehicles (NEVs) in China from 2018 to 2023 by applying a three-stage super-SBM data envelopment analysis (DEA) model that incorporates undesirable outputs. This model offers significant advantages over traditional DEA models, as it effectively disentangles the influences of external environmental factors and stochastic noise, thereby providing a more accurate and robust assessment of true efficiency. Its super-efficiency characteristic also allows for effective ranking of all decision-making units (DMUs) on the efficiency frontier. The empirical findings reveal several key insights. (1) The NEV industry’s carbon-reduction efficiency in China between 2018 and 2023 displayed an upward trend accompanied by pronounced fluctuations. Its mean super-efficiency score was 0.353, indicating substantial scope for improvements in scale efficiency. (2) Significant interprovincial disparities in efficiency appear. Unbalanced coordination between production and consumption in provinces such as Shaanxi, Beijing, and Liaoning has produced correspondingly high or low efficiency values. (3) Although accelerated urbanization has reduced the capital and labor inputs required by the NEV industry and has raised energy consumption, the net effect enhances carbon-reduction efficiency. Household consumption levels and technological advancement exerts divergent effects on efficiency. The former negatively relates to efficiency, whereas the latter is positively associated. Full article

The integration of 5G technology with cloud and edge computing is redefining the digital landscape by enabling ultra-fast connectivity, low-latency communication, and scalable solutions across diverse application domains. This paper investigates the synergistic impact of 5G on cloud-to-edge architectures, emphasizing its transformative role in revolutionizing sectors such as healthcare, smart cities, industrial automation, and autonomous systems. Key advancements in 5G—including Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are examined for their role in enabling real-time data processing, edge intelligence, and IoT scalability. In addition to conceptual analysis, the paper presents simulation-based evaluations comparing 5G cloud-to-edge systems with traditional 4G cloud models. Quantitative results demonstrate significant improvements in latency, energy efficiency, reliability, and AI prediction accuracy. The study also explores challenges in infrastructure deployment, cybersecurity, and latency management while highlighting the growing opportunities for innovation in AI-driven automation and immersive consumer technologies. Future research directions are outlined, focusing on energy-efficient designs, advanced security mechanisms, and equitable access to 5G infrastructure. Overall, this study offers comprehensive insights and performance benchmarks that will serve as a valuable resource for researchers and practitioners working to advance next-generation digital ecosystems. Full article

Traditional crosslinked-polyethylene (XLPE) insulation suffers from high recycling costs and low efficiency due to its thermosetting properties. In contrast, thermoplastic polypropylene (PP), with advantages of melt recyclability, low energy consumption, and excellent comprehensive performance, has emerged as an ideal alternative to XLPE. This study conducts a comparative analysis of low-voltage cables insulated with PP, silane-crosslinked XLPE (XLPE-S), and UV-crosslinked XLPE (XLPE-U), focusing on production processes, mechanical properties, thermal stability, and electrical performance. Tensile test results show that PP exhibits the highest elongation at break (>600%) before aging, and its tensile strength (>20 MPa) after aging outperforms that of XLPE, indicating superior flexibility and anti-aging capability. PP exhibits a lower thermal elongation (<50%) at 140 °C compared to XLPE, and its high-crystallinity molecular structure endows better heat-resistant deformation performance. The volume resistivity of PP reaches 9.2 × 10¹⁵ Ω·m, comparable to that of XLPE-U (3.9 × 10¹⁵ Ω·m) and significantly higher than XLPE-S (3.0 × 10¹⁴ Ω·m). All three materials pass the 4-h voltage withstand test, confirming their satisfied insulation reliability. PP-insulated low-voltage cables demonstrate balanced performance in production efficiency, energy consumption cost, mechanical toughness, and electrical insulation. Notably, their recyclability significantly surpasses traditional XLPE, showing potential to promote green upgrading of the cable industry and providing a sustainable insulation solution for low-voltage power distribution systems. Full article

In the digital economy era, artificial intelligence, big data, and 5G are widely applied across various industries. The deep integration of digitalization and traditional sectors has been facilitated by this trend, which has injected new momentum into industrial development. In this context, this paper employs panel data from 29 Chinese provinces that span the years 2017 to 2023. This paper transcends the constraints of current research by integrating the digital economy with the export of new energy vehicles. Furthermore, this paper provides a regional analysis of this impact, thereby contributing to the existing literature. The following are the conclusions: (1) The export of new energy vehicles is substantially stimulated by the development of the digital economy. (2) Exports are indirectly facilitated by the digital economy, which promotes technological innovation and financial services. (3) The digital economy shows a significantly greater impact on the export of new energy vehicles in the eastern and inland areas than in other regions. Based on these discoveries, the paper suggests four critical policy recommendations: expanded openness, technological innovation, intelligent digital marketing, and government support. The objective is to foster the sustainable growth of China’s new energy vehicle export trade. This paper offers theoretical support for the sustainability of Chinese enterprises’ competitiveness in the international market. It also provides policymakers and industry stakeholders with practical advice. Full article

In cognitive radio networks (CRNs), primary users (PUs) have the highest priority in channel resource allocation. Secondary users (SUs) can generally only utilize temporarily unused channels of PUs, share channels with PUs, or cooperate with PUs to gain priority through the interweave, underlay, and overlay modes. Traditional optimization schemes for channel resource allocation often lead to structural wastage of channel resources, whereas approaches such as reinforcement learning—though effective—require high computational power and thus exhibit poor adaptability in industrial deployments. Moreover, existing works typically optimize a single performance metric with limited scenario scalability. To address these limitations, this paper proposes a CR network algorithm based on the hybrid users (HU) concept, which links the Interweave and Underlay modes through an adaptive threshold for mode switching. The algorithm employs the Hungarian method for SU channel allocation and applies a multi-level power adjustment strategy when PUs and SUs share the same channel to maximize channel resource utilization. Simulation results under various parameter settings show that the proposed algorithm improves the average signal to interference plus noise ratio (SINR) of SUs while ensuring PU service quality, significantly enhances network energy efficiency, and markedly improves Jain’s fairness among SUs in low-power scenarios. Full article