Search Results (3,242)

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in different situations, studies on traffic control models for WCLs are relatively lacking. Thus, this paper aims to design a coordinated optimization strategy for managing electric vehicle (EV) traffic on an expressway network, which integrates a corridor traffic flow model with a wireless power transmission model. Two components are considered in the control objective: the total energy increased for the EVs and the total number of EVs served by the expressway, over the problem horizon. By setting the trade-off coefficients for these two objectives, our model can be used to achieve mixed optimization of WCL traffic management. The decisions include metering of different on-ramps as well as routing plans for different groups of EVs defined by origin/destination pairs and initial SOC levels. The control problem is formulated as a novel linear programming model, rendering an efficient solution. Numerical examples are used to verify the effectiveness of the proposed traffic control model. The results show that with the properly designed traffic management strategy, a notable increase in charging performance can be achieved by compromising slightly the traffic performance while maintaining overall smooth operation throughout the expressway system. Full article

To address the triple bottlenecks of data scarcity, oversized models, and slow inference that hinder Cantonese automatic speech recognition (ASR) in low-resource and edge-deployment settings, this study proposes a cost-effective Cantonese ASR system based on LoRA fine-tuning and INT8 quantization. First, Whisper-tiny is parameter-efficiently fine-tuned on the Common Voice zh-HK training set using LoRA with rank = 8. Only 1.6% of the original weights are updated, reducing the character error rate (CER) from 49.5% to 11.1%, a performance close to full fine-tuning (10.3%), while cutting the training memory footprint and computational cost by approximately one order of magnitude. Next, the fine-tuned model is compressed into a 60 MB INT8 checkpoint via dynamic quantization in ONNX Runtime. On a MacBook Pro M1 Max CPU, the quantized model achieves an RTF = 0.20 (offline inference 5 × real-time) and 43% lower latency than the FP16 baseline; on an NVIDIA A10 GPU, it reaches RTF = 0.06, meeting the requirements of high-concurrency cloud services. Ablation studies confirm that the LoRA-INT8 configuration offers the best trade-off among accuracy, speed, and model size. Limitations include the absence of spontaneous-speech noise data, extreme-hardware validation, and adaptive LoRA structure optimization. Future work will incorporate large-scale self-supervised pre-training, tone-aware loss functions, AdaLoRA architecture search, and INT4/NPU quantization, and will establish an mJ/char energy–accuracy curve. The ultimate goal is to achieve CER ≤ 8%, RTF < 0.1, and mJ/char < 1 for low-power real-time Cantonese ASR in practical IoT scenarios. Full article

The transition of South Africa from coal-dependent energy systems to renewable energy alternatives presents economic and environmental trade-off complexities that require empirical investigation. This study employed threshold-switching dynamic models, NARDL analysis, and threshold Granger causality tests to investigate nonlinear relationships between renewable energy generation, economic growth, and carbon dioxide emissions in South Africa from 1980 to 2023. The threshold-switching dynamic models revealed critical structural breakpoints: a 56.4% renewable energy threshold for carbon dioxide emissions reduction, a 397.9% trade openness threshold for economic growth optimisation, and a 385.32% trade openness threshold for coal consumption transitions. The NARDL bounds test confirmed asymmetric effects in the carbon dioxide emissions and renewable energy relationship. The threshold Granger causality test established significant unidirectional causality from renewable energy to carbon dioxide emissions, economic growth to carbon dioxide emissions, and bidirectional causality between coal consumption and trade openness. However, renewable energy demonstrated no significant causal relationship with economic growth, contradicting traditional growth-led energy hypotheses. This study concluded that South Africa’s energy transition demonstrates distinct regime-dependent characteristics, with renewable energy deployment requiring critical mass thresholds to generate meaningful environmental benefits. The study recommended that optimal trade integration and renewable energy thresholds could fundamentally transform the economy’s carbon intensity while maintaining sustainable growth patterns. Full article

Traffic-induced noise pollution is a significant environmental issue, driving the development of advanced noise-reducing pavement materials. Semi-dense graded asphalt mixtures (SDAMs) present a promising compromise, offering enhanced acoustic properties compared to conventional dense-graded asphalt mixtures while maintaining superior durability to porous asphalt mixtures. However, the mechanism underlying the relationship between the energy dissipation characteristics and noise reduction effects of such mixtures remains unclear, which limits further optimization of their noise reduction performance. This study designed and prepared semi-dense graded noise-reducing asphalt mixtures SMA-6 TM, SMA-10 TM, and SMA-13 TM (SMA TM represents noise-reducing SMA mixture) based on traditional dense-graded asphalt mixtures SMA-6, SMA-10, and SMA-13, and conducted tests for water stability, high-temperature performance (60 °C), and low-temperature performance (−10 °C). Subsequently, energy loss parameters such as loss factor and damping ratio were calculated through dynamic modulus tests to characterize their energy dissipation properties. The mechanism linking the energy dissipation characteristics of semi-dense graded asphalt mixtures to noise reduction was investigated. Finally, the noise reduction effect was further verified through a tire free fall test and a close-proximity (CPX) method. The indoor test results indicate that the semi-dense mixtures exhibited a trade-off in performance: their dynamic stability was 11.1–11.3% lower and low-temperature performance decreased by 4.2% (SMA-13 TM) to 14.1% (SMA-6 TM), with moisture stability remaining comparable. Conversely, they demonstrated superior damping, with consistently higher loss factors and damping ratios. All mixtures reached peak damping at 20 °C, and the loss factor showed a strong positive correlation (R² > 0.91) with energy dissipation. Field results from a test section showed that the optimized SMA-10 TM mixture yielded a significant tire–road noise reduction of 3–5 dB(A) relative to the SMA-13, while concurrently meeting key performance criteria for anti-water ability and durability. This study establishes a link between the energy dissipation in SDAM and their noise reduction efficacy. The findings provide a theoretical framework for optimizing mixture designs and support the wider application of SDAM as a practical noise mitigation solution. Full article

To further improve the economic benefits of operators and the low-carbon performance within the system, this paper proposes a hierarchical distributed low-carbon economic dispatch strategy for regional integrated energy systems (RIESs) based on the Alternating Direction Method of Multipliers (ADMM). First, the energy coupling relationships among conversion devices in RIESs are analyzed, and a structural model of RIES incorporating an energy generation operator (EGO) and multiple load aggregators (LAs) is established. Second, considering the stepwise carbon trading mechanism (SCTM) and the average thermal comfort of residents, economic optimization models for operators are developed. To ensure optimal energy trading strategies between conflicting stakeholders, the EGO and LAs are embedded into a master–slave game trading framework, and the existence of the game equilibrium solution is rigorously proven. Furthermore, considering the processing speed of the optimization problem by the operators and the operators’ data privacy requirement, the optimization problem is solved in a hierarchical distributed manner using ADMM. To ensure the convergence of the algorithm, the non-convex feasible domain of the subproblem bilinear term is transformed into a convex polyhedron defined by its convex envelope so that the problem can be solved by a convex optimization algorithm. Finally, an example analysis shows that the scheduling strategy proposed in this paper improves the economic efficiency of energy trading participants by 3% and 3.26%, respectively, and reduces the system carbon emissions by 10.5%. Full article

Globally, winery wastewaters (WWWs) are estimated to account for about 62.5 billion L annually (2021), with COD levels up to 300,000 mg O₂/L primarily attributed to residual ethanol, posing serious environmental concerns. Conventional treatments are effective in COD removal, but they often miss opportunities for energy recovery and resource valorization. This study investigates the aqueous phase reforming (APR) of ethanol-rich wastewater as an alternative treatment for both COD reduction and energy generation. Two scenarios were assessed: electricity and heat cogeneration (S1) and hydrogen production (S2). Process simulations in Aspen Plus^® V14, based on lab-scale APR data, provided upscaled material and energy flows for techno-economic analysis, life cycle assessment, and energy sustainability analysis of a 2.5 m³/h plant. At 75% ethanol conversion, the minimum selling price (MSP) was USD0.80/kWh with a carbon footprint of 0.08 kg CO₂-eq/kWh for S1 and USD7.00/kg with 2.57 kg CO₂-eq/kg H₂ for S2. Interestingly, S1 revealed a non-linear trade-off between APR performance and energy integration, with higher ethanol conversion leading to a higher electricity selling price because of the increased heat reactor duty. In both cases, the main contributors to global warming potential (GWP) were platinum extraction/recovery and residual COD treatment. Both scenarios achieved a positive energy balance, with an energy return on investment (EROI) of 1.57 for S1 and 2.71 for S2. This study demonstrates the potential of APR as a strategy for self-sufficient energy valorization and additional revenue generation in wine-producing regions. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

Inspired by natural gradient structures observed in biological systems such as lobster exoskeletons and bamboo, this study proposes a biomimetic strategy for developing advanced automotive materials that achieve an optimal balance between strength and ductility. Against this backdrop, the present work systematically reviews the design principles underlying natural gradient structures and examines the advantages and limitations of current additive manufacturing—specifically selective laser melting (AM-SLM)—as well as conventional forming and machining processes, in fabricating nature-inspired architectures. The research systematically explores hierarchical gradient designs which endow materials with superior mechanical properties, including enhanced strength, stiffness, and energy absorption capabilities. Two primary strengthening mechanisms—hetero-deformation-induced (HDI) hardening and precipitation hardening—were employed to overcome the conventional strength–ductility trade-off. Gradient-structured materials were fabricated using selective laser melting, and microstructural analyses demonstrated that controlled interface zones and tailored precipitation distribution critically influence property improvements. Based on these findings, an integrated material design strategy combining nature-inspired gradient architectures with post-processing treatments is presented, providing a versatile methodology to meet the specific performance requirements of automotive components. Overall, this work offers new insights for developing next-generation lightweight structural materials with exceptional ductility and damage tolerance and establishes a framework for translating bioinspired concepts into practical engineering solutions. Full article

This study evaluates the trade-offs and environmental impacts of transitioning from coal to natural gas (NG) for electricity generation in San Juan County, with a focus on greenhouse gas emissions and water consumption. It addresses key questions, including how water use and emissions change as the county shifts from coal to natural gas. The research analyzes water usage and emissions of CO₂, NO_x, and SO₂ during both the extraction and combustion phases of coal and natural gas. Specifically, it compares water consumption and direct emissions from coal-fired and natural gas-fired power plants. The analysis utilizes ten years of combustion-phase data from the Four Corners (coal-fired) and Afton (natural gas-fired) power plants in New Mexico. Linear regression was applied to the historical data, and four transition scenarios were modeled: (1) 100% coal-generated electricity, (2) a 20% reduction in coal with a corresponding increase in NG, (3) a 50% reduction in coal with a corresponding increase in NG, and (4) a complete transition to NG. Regression analysis and scenario calculations indicate that switching to NG results in significant water savings and reduced emissions. Water savings in the combustion phase decrease by up to 2750 gallons per MWh, valued at USD 0.743 per MWh when electricity is generated 100% from NG. CO₂ emissions are substantially reduced, with the largest decrease being 0.6127 metric tons per MWh, valued at USD 61.26 per MWh. NO_x emissions in the combustion phase decline by 0.0018 metric tons per MWh, with an economic valuation of USD 14.61 per MWh, while SO₂ emissions decrease by 0.0006 metric tons per MWh, valued at USD 11.91 per MWh when electricity generation is 100% NG-based. The results highlight the environmental and economic advantages of transitioning from coal to NG. The findings underscore the environmental and economic advantages of transitioning from coal to natural gas. Water conservation is particularly vital in San Juan County’s semi-arid climate. Additionally, lower emissions support climate change mitigation, enhance air quality, and improve public health. The economic valuation of emissions reductions further highlights the financial benefits of this transition, positioning natural gas as a more sustainable and economically viable energy source for the region. Ultimately, this study emphasizes the need to adopt cleaner energy sources such as renewable energy to achieve long-term environmental sustainability and economic efficiency. Full article

The increasing demand for renewable energy has intensified research on lignocellulosic biomass pyrolysis as a versatile route for sustainable energy and resource recovery. This study provides a comparative overview of main pyrolysis regimes (slow, intermediate, fast, and flash), emphasizing operational parameters, typical product yields, and technological readiness levels (TRLs). Reactor configurations, including fixed-bed, fluidized-bed, rotary kiln, auger, and microwave-assisted systems, are analyzed in terms of design, advantages, limitations, and TRL status. Key process parameters, such as temperature, heating rate, vapor residence time, reaction atmosphere, and catalyst type, critically influence the yields and properties of biochar, bio-oil, and syngas. Increased temperatures and fast heating rates favor liquid and gas production, whereas lower temperatures and longer residence times enhance biochar yield and carbon content. CO₂ and H₂O atmospheres modify product distribution, with CO₂ increasing gas formation and biochar surface area and steam enhancing bio-oil yield at the expense of solid carbon. Catalytic pyrolysis improves selectivity toward target products, though trade-offs exist between char and oil yields depending on feedstock and catalyst choice. These insights underscore the interdependent effects of process parameters and reactor design, highlighting opportunities for optimizing pyrolysis pathways for energy recovery, material valorization, and sustainable bioeconomy applications. Full article

Wireless body area networks (WBANs) are a pivotal solution for continuous health monitoring, but their energy constraints pose a significant challenge for long-term operation. This paper provides a comprehensive review of state-of-the-art energy-efficient mechanisms, critically evaluating solutions across various network layers. We focus on three key approaches: energy-aware MAC protocols that reduce idle listening and optimize duty cycling; energy-efficient routing protocols that enhance data transmission and network longevity; and emerging energy harvesting techniques that offer a path toward energy-autonomous WBANs. Furthermore, the paper provides a detailed analysis of the inherent trade-offs between energy efficiency and other critical performance metrics, such as latency, reliability, and security. It also explores the transformative potential of emerging technologies, such as AI and blockchain, for dynamic energy management and secure data handling. By synthesizing these findings, this work contributes to the development of sustainable WBAN solutions and outlines clear directions for future research. Full article

Asymmetric nanomotors are a class of self-propelled nanoparticles that exhibit asymmetries in shape, composition, or surface properties. Their unique asymmetry, combined with nanoscale dimensions, endows them with significant potential in environmental and biomedical fields. For instance, glutathione (GSH) induced chemotactic nanomotors can respond to the overexpressed glutathione gradient in the tumor microenvironment to achieve autonomous chemotactic movement, thereby enhancing deep tumor penetration and drug delivery for efficient induction of ferroptosis in cancer cells. Moreover, self-assembled spearhead-like silica nanomotors reduce fluidic resistance owing to their streamlined architecture, enabling ultra-efficient catalytic degradation of lipid substrates via high loading of lipase. This review focuses on three core areas of asymmetric nanomotors: scalable fabrication (covering synthetic methods such as template-assisted synthesis, physical vapor deposition, and Pickering emulsion self-assembly), propulsion mechanisms (chemical/photo/biocatalytic, ultrasound propelled, and multimodal driving), and functional applications (environmental remediation, targeted biomedicine, and microelectronic repair). Representative nanomotors were reviewed through the framework of structure–activity relationship. By systematically analyzing the intrinsic correlations between structural asymmetry, energy conversion efficiency, and ultimate functional efficacy, this framework provides critical guidance for understanding and designing high-performance asymmetric nanomotors. Despite notable progress, the prevailing challenges primarily reside in the biocompatibility limitations of metallic catalysts, insufficient navigation stability within dynamic physiological environments, and the inherent trade-off between propulsion efficiency and biocompatibility. Future efforts will address these issues through interdisciplinary synthesis strategies. Full article

This study presents a machine learning-based framework for predicting the electrical output of a vibration energy harvesting system (VEHS) integrated with a Jeffcott rotor model. Vibration induced by rotor imbalance is converted into electrical energy via piezoelectric elements, and the system’s dynamic response is simulated using the fourth-order Runge–Kutta method across varying mass ratios, rotational speeds, and eccentricities. The resulting dataset is validated experimentally with a root-mean-square error below 5%. Three predictive models—Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost)—are trained and evaluated. While DNN and LSTM yield a high predictive accuracy (R² > 0.9999), XGBoost achieves comparable accuracy (R² = 0.9994) with significantly lower computational overhead. The results demonstrate that among the tested models, XGBoost provides the best trade-off between speed and accuracy, achieving R² > 0.999 while requiring the least training time. These results demonstrate that XGBoost might be particularly suitable for real-time evaluation and edge deployment in rotor-based VEHS, offering a practical balance between speed and precision. Full article

Aiming at the demands for low-carbon transformation in multi-energy-coupled industrial parks, a model is proposed that incorporates a carbon trading system incorporating incentives and penalties. This model includes joint combined heat and power (CHP) units, carbon capture technologies, and power-to-gas (P2G) conversion equipment. Firstly, we develop a modeling framework for the joint operation of cogeneration units to establish a comprehensive energy system within the industrial park that integrates electricity, heat, gas, and cold energy sources. Subsequently, we introduce a reward and punishment carbon trading mechanism into an industrial park to regulate carbon emissions effectively. With an optimization objective focused on minimizing the overall operating costs of the system while considering relevant constraints, we formulate an optimization model. The Gurobi solver is employed through the Yalmip toolkit to address this optimization problem. Finally, four operational scenarios are established to compare and validate the feasibility of our proposed optimization strategy. The results from our computational example demonstrate that integrating combined heat and power along with carbon capture and P2G technologies—coupled with a tiered reward and punishment carbon trading mechanism—can significantly enhance the energy consumption structure of the system. Under this model, the overall expenses are decreased by 12.36%, CO₂ emissions decrease by 33.37%, and renewable energy utilization increases by 36.7%. This approach has effectively improved both wind power consumption capacity and low-carbon economic benefits within the system while ensuring sustainable economic development in alignment with “dual carbon” goals. Full article