Search Results (2,472)

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article

Recently, there has been a growing interest in issues related to mining equipment maintenance, with particular focus on pumping systems’ continuous operation. However, despite wide applications of pump system maintenance in a wide range of industries, such as water and wastewater, aviation, petrochemical, building (HVAC system), and nuclear power plant industries, the literature on maintenance of pump systems operating in the mining industry still needs development. This study aims to review the existing literature to present an up-to-date analysis of maintenance strategies for mining pumps, with a particular focus on proactive maintenance approaches. Key aspects considered include predictive diagnostics and prognosis, health status monitoring, maintenance management, and the integration of intelligent mining systems to enhance operational reliability and efficiency in harsh mining environments. The proposed methodology includes a systematic literature review with the use of the Primo multi-search tool, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The selection criteria focused on English studies published between 2005 and 2024, resulting in 88 highly relevant papers. These papers were categorized into six groups: (a) condition/health status monitoring, (b) dewatering system operation and maintenance, (c) health diagnosis and prognosis, (d) intelligent mining (modern technologies), (e) maintenance management, and (f) operational efficiency and reliability optimization. A notable strength of this study is its use of diverse scientific databases facilitated by the multi-search tool. Additionally, a bibliometric analysis was performed, showcasing the evolution of research on pump maintenance in the mining sector over the past decade and identifying key areas such as predictive diagnostics, dewatering system optimization, and intelligent maintenance management. This study highlights the varied levels of research and practical implementation across industries, emphasizing the mining sector’s unique challenges and opportunities. Significant research gaps were identified, including the need for tailored diagnostic tools, real-time monitoring systems, and cost-effective maintenance strategies specific to harsh mining environments. Future research directions are proposed, focusing on advancing predictive maintenance technologies, integrating intelligent systems, and enhancing operational efficiency and reliability. The study concludes with a detailed discussion of the findings and their implications, offering a roadmap for innovations in pump maintenance within the mining industry. Full article

This study examines news avoidance behaviors in Egypt’s media system, addressing a significant gap in understanding how audiences disengage from news content in non-Western contexts. Using a mixed-methods approach combining focus groups (n = 16), surveys (n = 512), and expert interviews (n = 4), we investigate the relationships between news overload, trust in formal media, and selective attention in shaping news avoidance behaviors. Our structural equation model demonstrates strong explanatory power (R² = 0.505), with news overload emerging as the strongest predictor of avoidance behaviors (β = 0.481). Trust in formal media (β = −0.265) and selective attention (β = −0.184) show significant negative relationships with news avoidance. Qualitative findings reveal how Egypt’s media system creates unique conditions for news avoidance, with audiences developing sophisticated strategies for managing information flow within an environment of state control. The study advances the theoretical understanding of news avoidance by demonstrating how Media Saturation Theory operates within authoritarian contexts while providing practical insights for news organizations operating under state control. Our findings suggest that news avoidance in authoritarian systems represents not simply audience disengagement but rather a complex adaptation to specific institutional and social conditions. Full article

The air traffic growth expected for future years will likely cause an imbalance between traffic demand and available capacity. This could lead to increased airspace congestion, heightened complexity, and a higher workload for controllers attempting to manage the situation. Nowadays, available tools can identify 4D Area of Relatively High Air Traffic Control Complexity (4DARHAC) events up to 20 min before they occur. Nonetheless, state-of-the-art Artificial Intelligence applications can significantly increase this prediction horizon. Powered by a combination of different Machine Learning models, the ASTRA solution aims to both detect and provide resolution strategies for 4DARHACs up to 1 h before onset. To validate ASTRA’s operational concept, a series of workshops and interviews with Flow Management Position operators were conducted, focusing on assessing the initial concept and identifying end user needs. The feedback collected was validated by a board of Subject Matter Experts (SMEs) and transformed into a concrete set of functional and non-functional requirements. Overall, ASTRA’s operational concept was endorsed as a promising solution for reducing airspace complexity while alleviating operator workload during the tactical phase of operations. Experts further highlighted the importance of integrating ASTRA with existing Flow Management Position software tools to maximize its operational impact and facilitate adoption. Full article

The meteorology-driven multiscale behavior of snow depth over the Tibetan Plateau was investigated via analyzing the spatio-temporal variability of snow depth over 28 intraseasonal continuous snow cover regions. By employing power spectra and the Kullback–Leibler (K-L) distance, the spectral similarities between snow depth and meteorological factors were examined at scales of 5 km, 10 km, 20 km, and 50 km across seasons from 2008 to 2014. Results reveal distinct seasonal and scale-dependent dynamics: in spring and winter, snow depth exhibits lower spectral variance with scale breaks around 50 km, emphasizing the critical roles of precipitation, atmospheric moisture, and temperature, with lower K-L distances at smaller scales. Summer shows the highest spatial variance, with snow depth primarily influenced by wind and radiation, as indicated by lower K-L distances at 15–45 km. Autumn demonstrates the lowest spatial heterogeneity, with windspeed driving snow redistribution at finer scales. The alignment between spatial variance maps and power spectra implies that snow depth data can be effectively downscaled or upscaled without significant loss of spatial information. These findings are essential for improving snow cover modeling and forecasting, particularly in the context of climate change, as well as for effective water resource management and climate adaptation strategies in this strategically vital plateau. Full article

Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal charging framework designed to facilitate fast charging and mitigate grid stress by smoothing the “duck curve”. Data from Caltech’s Adaptive Charging Network (ACN) at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected and categorized into day and night patterns to predict charging duration based on key features, including start charging time and energy requested. The AI-driven charging strategy developed optimizes energy management, reduces peak loads, and alleviates grid strain. Additionally, the study evaluates the impact of integrating 1.5 million, 3 million, and 5 million EVs under various AI-based charging strategies, demonstrating the framework’s effectiveness in managing large-scale EV adoption. The peak power consumption reaches around 22,000 MW without EVs, 25,000 MW for 1.5 million EVs, 28,000 MW for 3 million EVs, and 35,000 MW for 5 million EVs without any charging strategy. By implementing an AI-driven optimal charging optimization strategy that considers both early charging and duck curve smoothing, the peak demand is reduced by approximately 16% for 1.5 million EVs, 21.43% for 3 million EVs, and 34.29% for 5 million EVs. Full article

A ship’s diesel–electric hybrid power system is complex, with hardware configuration and energy management parameters being crucial to its economic performance. However, existing optimization methods typically involve designing and optimizing the hardware configuration on the basis of typical operating conditions, followed by the design and optimization of the energy management parameters, which makes it difficult to achieve optimal system performance. Moreover, when co-optimizing hardware configurations and energy management parameters, the parameter relationships and complex constraints often lead conventional optimization algorithms to converge slowly and become trapped in local optima. To address this issue, a hybrid Ivy-SA algorithm is developed for the co-optimization of both the hardware configuration and energy management parameters. First, the main engine and hybrid ship models are established on the basis of the hardware configuration, and the accuracy of the models is validated. An energy management strategy based on the equivalent fuel consumption minimization strategy (ECMS) is then formulated, and energy management parameters are designed. A sensitivity analysis is conducted on the basis of both the hardware configuration and energy management parameters to evaluate their impacts under various conditions, enabling the selection of key optimization parameters, such as diesel engine parameters, battery configuration, and charge/discharge factors. The Ivy-SA algorithm, which integrates the advantages of both the Ivy algorithm (IVYA) and the simulated annealing algorithm (SA), is developed for the co-optimization. The algorithm is tested with the CEC2017 benchmark functions and outperforms 11 other algorithms. Furthermore, when the top five performing algorithms are applied for the co-optimization, the results show that the Ivy-SA algorithm outperforms the other four algorithms with a 14.49% increase in economic efficiency and successfully escapes local optima. Full article

In this study, the systemic challenges of water governance in Guanajuato, Mexico, are examined through a political ecology framework, identifying how governance failures, power asymmetries, and socio-environmental inequalities contribute to water scarcity and mismanagement. Guanajuato, a key agricultural and industrial hub in Mexico’s semi-arid Bajío region, faces severe aquifer depletion, pollution, and institutional fragmentation, disproportionately affecting rural and marginalized communities. Using a qualitative research design, 25 semi-structured expert interviews and a case study analysis were conducted, applying thematic coding and content analysis to examine governance structures, regulatory gaps, and socio-environmental conflicts. The findings revealed that institutional fragmentation, preferential water allocation to industry, and weak enforcement mechanisms perpetuate governance failures, with community resistance and alternative governance strategies emerging as key responses. The results of this study emphasize the need for adaptive governance reforms, including measures such as integrating local knowledge, strengthening participatory decision-making, and fostering cross-sector collaboration to ensure equitable resource distribution and environmental sustainability. Guanajuato’s case offers critical insights for improving water governance in arid regions globally, demonstrating the relevance of political ecology in analyzing and addressing governance asymmetries in water management. Full article

In the face of the rapid development of smart grid technologies, it is increasingly difficult for traditional power system management methods to support the increasingly complex operation of modern power grids. This study systematically reviews new challenges and research trends in the field of smart grid optimization, focusing on key issues such as power flow optimization, load scheduling, and reactive power compensation. By analyzing the application of reinforcement learning in the smart grid, the impact of distributed new energy’s high penetration on the stability of the system is thoroughly discussed, and the advantages and disadvantages of the existing control strategies are systematically reviewed. This study compares the applicability, advantages, and limitations of different reinforcement learning algorithms in practical scenarios, and reveals core challenges such as state space complexity, learning stability, and computational efficiency. On this basis, a multi-agent cooperation optimization direction based on the two-layer reinforcement learning framework is proposed to improve the dynamic coordination ability of the power grid. This study provides a theoretical reference for smart grid optimization through multi-dimensional analysis and research, advancing the application of deep reinforcement learning technology in this field. Full article

To address the critical challenge of high energy consumption in single-source electric vehicles, this study proposes a hybrid energy storage system (HESS)-integrated energy management strategy (EMS). Firstly, the car-following and HESS models are constructed. Secondly, a multi-objective optimization framework balancing adaptive cruise control (ACC) optimal tracking quality and energy economy is developed, where the fast, non-dominated sorting genetic algorithm (NSGA-II) resolves dynamic power demands. Thirdly, the third-order Haar wavelet enables online rolling decomposition of power profiles. The high-frequency transient power is matched by a supercapacitor, while the low-frequency steady-state power is utilized as an input variable to the optimization controller. Then, a fuzzy logic controller dynamically optimizes HESS’s energy distribution based on state-of-charge (SOC) and load conditions. Finally, the cruise simulation model has been constructed utilizing the MATLAB/Simulink platform. Comparative analysis under the Urban Dynamometer Driving Schedule (UDDS) demonstrates a 3.71% reduction in the total power demand of the ego vehicle compared to the front vehicle. Compared to single-source configurations, the HESS ensures smoother SOC dynamics in lithium-ion batteries. After employing the third-order Haar wavelet for online rolling decomposition of the demand power, the high-frequency transient power matched by the lithium-ion battery is substantially reduced. Comparative analysis of three control strategies demonstrates that the wavelet-fuzzy logic approach exhibits superior comprehensive performance. Consequently, the proposed strategy effectively mitigates high-frequency transient peak charge/discharge currents in the lithium-ion battery and the energy consumption of the entire vehicle. This study provides a novel solution for energy storage systems in hybrid energy storage electric vehicles (HESEV) under ACC scenarios. Full article

Efficient water management is crucial for sustainable agriculture, and the integration of Internet of Things (IoT) technologies in irrigation systems offers innovative solutions to optimize resource use. In this systematic review, the current landscape of Internet of Things (IoT) applications in irrigation management was investigated. The study aimed to identify key research trends and technological developments in the field. Using VOSviewer (CWTS, Leiden, The Netherlands) for bibliometric mapping, the influential research clusters were identified. The analysis revealed a significant rise in scholarly interest, with peak activity between 2020 and 2022, and a shift towards interdisciplinary and applied research. Additionally, the content analysis revealed prevalent agricultural applications, frequently employed microcontroller units (MCUs), widely used sensors, and trends in communication technologies such as the increasing adoption of low-power, scalable communication protocols for real-time data acquisition. This study not only offers a comprehensive overview of the current status of IoT integration in smart irrigation but also highlights the technological advancements. Future research directions include integrating IoT with emerging technologies such as artificial intelligence, edge computing, and blockchain to enhance decision-support systems and predictive irrigation strategies. By examining the transformative potential of IoT, this study provides valuable insights for researchers and practitioners seeking to enhance agricultural productivity, optimize resource use, and improve sustainability. Full article

Hybrid ships offer significant advantages in energy efficiency and environmental sustainability. However, their complex structures present challenges in developing effective energy management strategies to ensure optimal power distribution and stable, efficient operation of the power system. This study establishes a mathematical model of a hybrid system for a specific ship and proposes an energy management strategy based on the deep deterministic policy gradient (DDPG) algorithm, a reinforcement learning technique. The proposed strategy’s feasibility and effectiveness are validated through comparisons with alternative energy management strategies and real-world ship data. Simulation results demonstrate that the DDPG-based strategy optimizes the diesel engine’s operating conditions and reduces total fuel consumption by 3.6% compared to a strategy based on the deep Q-network (DQN) algorithm. Full article

Lettuce, a vital economic crop, benefits significantly from intelligent advancements in its production, which are crucial for sustainable agriculture. Deep learning, a core technology in smart agriculture, has revolutionized the lettuce industry through powerful computer vision techniques like convolutional neural networks (CNNs) and YOLO-based models. This review systematically examines deep learning applications in lettuce production, including pest and disease diagnosis, precision spraying, pesticide residue detection, crop condition monitoring, growth stage classification, yield prediction, weed management, and irrigation and fertilization management. Notwithstanding its significant contributions, several critical challenges persist, including constrained model generalizability in dynamic settings, exorbitant computational requirements, and the paucity of meticulously annotated datasets. Addressing these challenges is essential for improving the efficiency, adaptability, and sustainability of deep learning-driven solutions in lettuce production. By enhancing resource efficiency, reducing chemical inputs, and optimizing cultivation practices, deep learning contributes to the broader goal of sustainable agriculture. This review explores research progress, optimization strategies, and future directions to strengthen deep learning’s role in fostering intelligent and sustainable lettuce farming. Full article

Due to the volatility and uncertainty of wind power generation, energy storage can help mitigate the fluctuations in wind power grid integration. During its use, the health of the energy storage system, defined as the ratio of the current available capacity to the initial capacity, deteriorates, leading to a reduction in the available margin for power fluctuation smoothing. Therefore, it is necessary to predict the state of health (SOH) and adjust its charge/discharge control strategy based on the predicted SOH results. This study first adopts a Genetic Algorithm-Optimized Support Vector Regression (GA-SVR) model to predict the SOH of the energy storage system. Secondly, based on the health prediction results, a control strategy based on the model predictive control (MPC) algorithm is proposed to manage the energy storage system’s charge/discharge process, ensuring that the power meets grid integration requirements while minimizing energy storage lifespan loss. Further, since the lifespan loss caused by smoothing the same fluctuation differs at different health levels, a fuzzy adaptive control strategy is used to adjust the parameters of the MPC algorithm’s objective function under varying health conditions, thereby optimizing energy storage power and achieving the smooth control of the wind farm grid integration power at different energy storage health levels. Finally, a simulation is conducted in MATLAB for a 50 MW wind farm grid integration system, with experimental parameters adjusted accordingly. The experimental results show that the GA-SVR algorithm can accurately predict the health of the energy storage system, and the MPC-based control strategy derived from health predictions can improve grid power stability while adaptively adjusting energy storage output according to different health levels. Full article

Urban mass tourism and rural depopulation increasingly threaten heritage sites worldwide, leading to socio-economic and environmental challenges. This study adopts a holistic approach to sustainable tourism planning by examining 84 cultural and natural heritage sites in and around Safranbolu and Amasra, two cities in Türkiye that are listed on the UNESCO World Heritage List and the Tentative List. Inspired by historical travelers’ itineraries, it proposes an eco-cultural tourism route to create a resilient heritage network. A participatory methodology integrates charettes within Erasmus+ workshops, crowdsourcing, various analysis methods while engaging stakeholders, and AI-powered clustering for route determination. The study follows a four-stage framework: (1) data collection via collaborative GIS, (2) eco-cultural route development, (3) stakeholder participation for inclusivity and viability, and (4) assessments and recommendations. Results highlight the strong potential of heritage assets for sustainable tourism while identifying key conservation risks. Interviews and site analysis underscore critical challenges, including the absence of integrated site management strategies, insufficient capacity-building initiatives, and ineffective participatory mechanisms. Moreover, integrating GIS-based crowdsourcing, machine learning clustering, and multi-criteria decision-making can be an effective planning support system. In conclusion, this study enhances the sustainability of heritage and tourism by strengthening participatory eco-cultural development and mitigating mass tourism and abandonment’s negative impacts on the heritage sites. Full article