Search Results (6,263)

Practical applications of piezoelectric vibration energy harvesting systems are required to produce a stable DC output through the nonlinear process of AC-DC rectification. In most simulation studies of such systems, the diodes have been idealised as switches, an assumption that is valid only if the vibration-induced voltage is high enough, which is frequently not the case in practice. This paper presents an experimentally validated simulation of a base excited vibration energy harvester connected to a full wave rectified load, combining the analytical modal transformation of the Euler–Bernoulli model of a piezoelectric beam with the nonlinear current-voltage characteristic of a real (non-ideal) diode. Three types of diodes with significantly different model parameters sourced from industry-standard datasets are considered. Discrepancies between simulated and measured resonant voltage levels are found to be less than 10% on average, and the discrepancy in resonant frequency is less than 1%, demonstrating the reliability of the Shockley diode model despite its omission of the dynamic behaviour of the diode. Full article

The increasing prevalence of common cold viruses and bacteria in daily life has heightened interest in sterilization lamp technologies. Compared with traditional mercury-based ultraviolet (UV) lamps, modern UV lamps offer advantages including extended operational lifespan, high energy efficiency, compact form factor, and the absence of hazardous materials, rendering them both safer and environmentally sustainable. In particular, UV-C LED lamps, which emit at short wavelengths, are capable of disrupting the molecular structure of DNA or RNA in microbial cells, thereby inhibiting cellular replication and achieving effective disinfection and sterilization. Conventional UV-C LED sterilization lamp driver circuits frequently employ a two-stage architecture, which requires a large number of components, occupies substantial physical space, and exhibits reduced efficiency due to multiple stages of power conversion. To address these limitations, this paper proposes a UV-C LED sterilization lamp driver circuit for an AC voltage supply, employing boundary conduction mode (BCM) control with integrated power factor correction (PFC). The proposed single-stage, single-switch topology combines a buck PFC converter and a flyback converter while recovering transformer leakage energy to further improve efficiency. Compared with conventional two-stage designs, the proposed circuit reduces the number of power switches and components, thereby lowering manufacturing cost and enhancing overall energy conversion efficiency. The operating principles of the proposed driver circuit are analyzed, and a prototype is developed for a 110 V AC input with an output specification of 10.8 W (90 V/0.12 A). Experimental results demonstrate that the prototype achieves an efficiency exceeding 92%, a power factor of 0.91, an output voltage ripple of 1.298%, and an output current ripple of 4.44%. Full article

Greater Casablanca, one of Africa’s largest metropolitan regions, is undergoing significant spatial and demographic transformation. Yet, the underlying patterns of these dynamics remain poorly understood. This study investigates population dynamics and spatial inequalities in Greater Casablanca between 2014 and 2024. The analysis combines geospatial data, regression modeling, and clustering techniques to explore the interplay between demographic change, housing affordability, public-transport accessibility, and economic activity, providing a data-driven perspective on how these factors shape spatial inequalities and the region’s urban development trajectory. The results reveal a clear core–periphery divide. The central prefecture has lost population despite continued land consumption, while peripheral communes have experienced rapid demographic and economic expansion. This growth is strongly associated with affordable housing and high rates of new-firm formation, but it occurs where transport access remains weakest. Cluster analysis identifies four socio-spatial types, ranging from a shrinking but well-served core to fast-growing, poorly connected peripheries. The study underscores the need for integrated policy interventions to improve transport connectivity, implement inclusive housing strategies, and manage economic decentralization in ways that foster balanced and sustainable metropolitan development. By situating Greater Casablanca’s trajectory within global urbanization debates, this research extends core–periphery and shrinking-city frameworks to a North African context and provides evidence-based insights to support progress towards Sustainable Development Goal 11. Full article

This paper presents a modified Orthogonal Frequency Division Multiplexing (OFDM) system that combines Discrete Wavelet Transform (DWT) with Discrete Sine Transform (DST) to enhance data rate capacity over traditional Discrete Fourier Transform (DFT)-based OFDM systems. By applying Inverse Discrete Wavelet Transform (IDWT) to the modulated Binary Phase Shift Keying (BPSK) bits, the constellation diagram reveals that half of the time-domain samples after single-level Haar IDWT are zeros, while the other half are real. The proposed system utilizes these $0.5N$ zero values, modulating them with the DST (IDST) and assigning them as the imaginary part of the signal. Performance comparisons demonstrate that the Bit-Error-Rate (BER) of this hybrid DWT-DST configuration lies between that of BPSK and Quadrature Phase Shift Keying (QPSK) in a DWT-based system, while also achieving data rate improvement of $0.5N$. Additionally, simulation results indicate that the proposed approach demonstrates stable performance even in the presence of estimation errors, with less than 3.4% BER degradation for moderate errors, and consistently better robustness than QPSK-based systems while offering improved data rate efficiency over BPSK. This novel configuration highlights the potential for more efficient and reliable data transmission in OFDM systems, making it a promising alternative to conventional DWT or DFT-based methods. Full article

The aviation industry is responsible for approximately 2–3% of worldwide CO₂ emissions and is increasingly subjected to demands for the attainment of net-zero emissions targets by the year 2050. Traditional fossil jet fuels, which exhibit lifecycle emissions of approximately 89 kg CO₂-eq/GJ, play a substantial role in exacerbating climate change, contributing to local air pollution, and fostering energy insecurity. In contrast, Sustainable Aviation Fuels (SAFs) derived from renewable feedstocks, including biomass, municipal solid waste, algae, or through CO₂- and H₂-based power-to-liquid (PtL) represent a pivotal solution for the immediate future. SAFs generally accomplish lifecycle greenhouse gas (GHG) reductions of 50–80% (≈20–30 kg CO₂-eq/GJ), possess reduced sulfur and aromatic content, and markedly diminish particulate emissions, thus alleviating both climatic and health-related repercussions. In addition to their environmental advantages, SAFs promote energy diversification, lessen reliance on unstable fossil fuel markets, and invigorate regional economies, with projections indicating the creation of up to one million green jobs by 2030. This comprehensive review synthesizes current knowledge on SAF sustainability advantages compared to conventional aviation fuels, identifying critical barriers to large-scale deployment and proposing integrated solutions that combine technological innovation, supportive policy frameworks, and international collaboration to accelerate the aviation industry’s sustainable transformation. Full article

Accurate short-term photovoltaic (PV) power generation forecasting is critical for the stable integration of renewable energy into the grid. This study proposes a Transformer model enhanced with an adaptive sparse self-attention (ASSA) mechanism for PV power forecasting. The ASSA framework employs a dual-branch attention structure that combines sparse and dense attention paths with adaptive weighting to effectively filter noise while preserving essential spatiotemporal features. This design addresses the critical issues of computational redundancy and noise amplification in standard self-attention by adaptively filtering irrelevant interactions while maintaining global dependencies in Transformer-based PV forecasting. In addition, a deep feedforward network and a feature refinement feedforward network (FRFN) adapted from the ASSA–Transformer are incorporated to further improve feature extraction. The proposed algorithms are evaluated using time-series data from the Desert Knowledge Australia Solar Centre (DKASC), with input features including temperature, relative humidity, and other environmental variables. Comprehensive experiments demonstrate that the ASSA models’ accuracy in short-term PV power forecasting increases with longer forecast horizons. For 1 h ahead forecasts, it achieves an R${}^2$ of 0.9115, outperforming all other models. Under challenging rainfall conditions, the model maintains a high prediction accuracy, with an R${}^2$ of 0.7463, a mean absolute error of 0.4416, and a root mean square error of 0.6767, surpassing all compared models. The ASSA attention mechanism enhances the accuracy and stability in short-term PV power forecasting with minimal computational overhead, increasing the training time by only 1.2% compared to that for the standard Transformer. Full article

Accurate forecasting of electricity sales holds significant practical importance. On the one hand, it helps to implement and achieve the annual goals of power companies, and on the other hand, it helps to control the balance of enterprise profits. This study was conducted in China using data from the State Grid Corporation (Henan, Fujian, and national data) from the Wind database. Based on collected data such as electricity sales, this study addresses the limitations of the existing literature, which mostly employs a single feature decomposition method for forecasting. We simultaneously apply three decomposition techniques—seasonal adjustment decomposition (X13), empirical mode decomposition (EMD), and discrete wavelet transform (DWT)—to decompose electricity sales into multiple components. Subsequently, we model each component using the ADL, SARIMAX, and LSTM models, synthesize the component-level forecasts, and realize the comparison of electricity sales forecasting models based on different feature decomposition methods. The findings reveal (1) forecasting performance based on feature decomposition generally outperforms direct forecasting without decomposition; (2) different regions may benefit from different decomposition methods—EMD is more suitable for regions with high sales volatility, while DWT is preferable for more stable regions; and (3) among the forecasting models, ADL performs better than SARIMAX, while LSTM yields the least accurate results when combined with decomposition methods. Full article

In conventional logistics optimization problems, an objective function describes the relationship between parameters. However, in many industrial practices, such a relationship is unknown, and only observational data is available. The objective of the research is to use machine learning-based regression models to uncover patterns in the warehousing dataset and use them to generate an accurate objective function. The models are not only suitable for prediction, but also for interpreting the effect of input variables. This data-driven approach is consistent with the automated, intelligent systems of Industry 4.0, while Industry 5.0 provides opportunities for sustainable, flexible, and collaborative development. In this research, machine learning (ML) models were tested on a fictional dataset using Automated Machine Learning (AutoML), through which Light Gradient Boosting Machine (LightGBM) was selected as the best method (R² = 0.994). Feature Importance and Partial Dependence Plots revealed the key factors influencing storage performance and their functional relationships. Defining performance as a cost indicator allowed us to interpret optimization as cost minimization, demonstrating that ML-based methods can uncover hidden patterns and support efficiency improvements in warehousing. The proposed approach not only achieves outstanding predictive accuracy, but also transforms model outputs into actionable, interpretable insights for warehouse optimization. By combining automation, interpretability, and optimization, this research advances the practical realization of intelligent warehouse systems in the era of Industry 4.0. Full article

Background and objectives: Large language models (LLMs) show promise in automating open-ended evaluation tasks, yet their reliability in rubric-based assessment remains uncertain. Variability in scoring, feedback, and rubric adherence raises concerns about transparency and pedagogical validity in educational contexts. This study introduces CourseEvalAI, a framework designed to enhance consistency and fidelity in rubric-guided evaluation by fine-tuning a general-purpose LLM with authentic university-level instructional content. Methods: The framework employs supervised fine-tuning with Low-Rank Adaptation (LoRA) on rubric-annotated answers and explanations drawn from undergraduate computer science exams. Responses generated by both the base and fine-tuned models were independently evaluated by two human raters and two LLM judges, applying dual-layer rubrics for answers (technical or argumentative) and explanations. Inter-rater reliability was reported as intraclass correlation coefficient (ICC(2,1)), Krippendorff’s α, and quadratic-weighted Cohen’s κ (QWK), and statistical analyses included Welch’s t tests with Holm–Bonferroni correction, Hedges’ g with bootstrap confidence intervals, and Levene’s tests. All responses, scores, feedback, and metadata were stored in a Neo4j graph database for structured exploration. Results: The fine-tuned model consistently outperformed the base version across all rubric dimensions, achieving higher scores for both answers and explanations. After multiple-testing correction, only the Generative Pre-trained Transformer (GPT-4)—judged Technical Answer contrast remains statistically significant; other contrasts show positive trends without passing the adjusted threshold, and no additional significance is claimed for explanation-level results. Variance in scoring decreased, inter-model agreement increased, and evaluator feedback for fine-tuned outputs contained fewer vague or critical remarks, indicating stronger rubric alignment and greater pedagogical coherence. Inter-rater reliability analyses indicated moderate human–human agreement and weaker alignment of LLM judges to the human mean. Originality: CourseEvalAI integrates rubric-guided fine-tuning, dual-layer evaluation, and graph-based storage into a unified framework. This combination provides a replicable and interpretable methodology that enhances the consistency, transparency, and pedagogical value of LLM-based evaluators in higher education and beyond. Full article

One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields. Full article

Optimal airfoil design remains a critical challenge in aerodynamic engineering, with traditional methods requiring extensive computational resources and iterative processes. This paper presents GEO-DSGA, a novel framework integrating hybrid geometric neural networks with deep symbiotic genetic algorithms for enhanced airfoil optimization. The methodology employs graph-based representations of airfoil geometries through a hybrid architecture combining graph convolutional networks with traditional deep learning, enabling precise capture of spatial geometric relationships. The parametric modeling stage utilizes CST, Bézier curves, and PARSEC methods to generate mathematically robust airfoil representations, subsequently transformed into graph structures preserving local and global shape characteristics. The optimization framework incorporates a deep symbiotic genetic algorithm enhanced with dominant feature phenotyping, applying biological symbiotic principles where design parameters achieve superior performance through mutual enhancement rather than independent optimization. This systematic exploration maintains geometric feasibility and aerodynamic validity throughout the design space. Experimental results demonstrate an 88.6% reduction in computational time while maintaining prediction accuracy within 1.5% error margin for aerodynamic coefficients across diverse operating conditions. The methodology successfully identifies airfoil geometries outperforming baseline NACA profiles by up to 12% in lift-to-drag ratio while satisfying manufacturing and structural constraints, establishing GEO-DSGA as a significant advancement in computational aerodynamic design optimization. Full article

Waste management is essential for advancing sustainable development and applying circular economy principles. The growing generation of waste—particularly organic municipal waste—combined with limited processing technologies, financial constraints, and overconsumption, intensifies its negative environmental and social impacts. This study examines the conditions necessary for implementing the circular economy concept in the context of organic municipal waste management. The research is based on literature review and an experiment involving the composting of biodegradable waste classified under code 20 02 01, analyzing its transformation into a soil improver commonly known as compost. Two composting approaches—single-stage and two-stage—were compared to evaluate their effectiveness in producing a high-quality end product that complies with national and EU legal standards, as well as the requirements for obtaining decisions (certificates) from the Ministry of Agriculture and Rural Development (MARD). The study is particularly relevant in light of the increasing volume of this waste stream, which exceeds 1.8 million tons annually in Poland, and the ambitious recycling targets set by the European Union, requiring 55% to be achieved by 2025. Results demonstrate that both composting methods contribute to circular resource use but differ in process efficiency and final product quality. These findings provide practical guidance for selecting composting technologies and support progress towards more sustainable, circular waste management. Moreover, they help define the output parameters of the products, which enables proper categorization and facilitates the issuance of relevant decisions from the MARD. Full article

Against the backdrop of global agricultural transformation, rural China faces the critical challenge of reconciling economic development with environmental conservation and social well-being. This study, grounded in the rural revitalization strategy, investigates the internal mechanisms, level measurement, and sustainable development paths of rural industrial integration based on the “Triple Integration of Production, Livelihood and Ecology” (PLE) philosophy. Firstly, we discussed the suitability and the mechanisms of this philosophy on China’s rural industrial integration. Secondly, based on a textual corpus extracted from academic journals and policy documents, we employed an LDA topic model to cluster the themes and construct an evaluation indicator system comprising 29 indicators. Then, utilizing data from the China Statistical Yearbook and the China Rural Statistical Yearbook (2013–2022), we measured the level of China’s rural industrial integration using the entropy method. The composite integration index displays a continuous upward trend over 2013–2022, accelerating markedly after the 2015 stimulus policy, yet a temporary erosion of “production–livelihood–ecology” synergy occurred in 2020 owing to an exogenous shock. Lastly, combining the system dynamics model, we simulated over the period 2023–2030 the three sustainable development scenarios: green ecological development priority, livelihood standard development priority and production level development priority. Research has shown that (1) the “Triple Synergy of Production, Livelihood and Ecology” philosophy and China’s rural industrial integration are endogenously unified, and they form a two-way mutual mechanism with the common goal of sustainable development. (2) China’s rural industrial integration under this philosophy is characterized by production-dominated development and driven mainly by processing innovation and service investment, but can be constrained by ecological fragility and external shocks. (3) System dynamics simulations reveal that the production-development priority scenario (Scenario 3) is the most effective pathway, suggesting that the production system is a vital engine driving the sustainable development of China’s rural industrial integration, with digitalization and technological innovation significantly improving integration efficiency. In the future, efforts should focus on transitioning towards a people-centered model by restructuring cooperative equity for farmer ownership, building community-based digital commons to bridge capability gaps, and creating market mechanisms to monetize and reward conservation practices. Full article

Accurately predicting the reception area of the Automatic Identification System (AIS) is critical for ship tracking and anomaly detection, as errors in signal interpretation may lead to incorrect vessel localization and behavior analysis. However, traditional propagation models, whether they are deterministic, empirical, or semi-empirical, face limitations when applied to dynamic environments due to their reliance on detailed atmospheric and terrain inputs. Therefore, to address these challenges, we propose a data-driven approach based on graph neural networks (GNNs) to model AIS reception as a function of environmental and geographic variables. Specifically, inspired by attention mechanisms that power transformers in large language models, our framework employs the SAmple and aggreGatE (GraphSAGE) framework convolutions to aggregate neighborhood features, then combines layer outputs through Jumping Knowledge (JK) with Bidirectional Long Short-Term Memory (BiLSTM)-derived attention coefficients and integrates an attentional pooling module at the graph-level readout. Moreover, trained on real-world AIS data enriched with terrain and meteorological features, the model captures both local and long-range reception patterns. As a result, it outperforms classical baselines—including ITU-R P.2001 and XGBoost in F1-score and accuracy. Ultimately, this work illustrates the value of deep learning and AIS sensor networks for the detection of positioning anomalies in ship tracking and highlights the potential of data-driven approaches in modeling sensor reception. Full article

As a key domain within smart buildings, Smart Sports Venues represent a strategic direction for the future development of the construction industry and hold immense potential to drive the transformation and upgrading of the sports industry. To explore the underlying mechanisms influencing consumer willingness to use Smart Sports Venues, this study constructs a theoretical model based on cognitive evaluation theory and collects data from 632 spectators in core cities of Western China (a region undergoing rapid urbanization where the sports industry is accelerating its development). As an emerging consumption scenario, Smart Sports Venues demonstrate significant development potential and representativeness in these cities. Empirical testing using structural equation modeling (SEM) combined with mediation and moderation analysis revealed the following results: (1) Perceptions of technology and convenience positively influence consumption intention; (2) Risk perceptions negatively influence consumption intention; (3) Critical experiences mediate the effects of technology perceptions, convenience perceptions, and risk perceptions on consumption intention; (4) Subjective Experience exerts a moderating effect. This study offered a novel theoretical explanation for how smart sports venues enhanced sports consumption willingness by revealing the “cognition-experience-behavior” transmission pathway—the complete journey consumers traversed from forming perceptions and experiencing on-site activities to ultimately making purchase decisions. Compared to existing research, this model innovatively integrated psychological cognition with behavioral response mechanisms, breaking away from traditional studies’ isolated analysis of technical parameters or consumption motivations. From an interdisciplinary perspective of sports consumption psychology and behavioral science, this study not only highlighted the value of smart sports venues as a pivotal link in technological innovation and industrial upgrading but also filled a gap in existing literature regarding how smart technologies influenced consumer behavior through psychological mechanisms. The findings provided theoretical foundations for optimizing smart sports architecture through user behavior data analysis and offered practical insights for the widespread adoption and development of smart building technologies. Full article