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Search Results (556)

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Keywords = heuristic exploration

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25 pages, 2100 KiB  
Article
Flexible Demand Side Management in Smart Cities: Integrating Diverse User Profiles and Multiple Objectives
by Nuno Souza e Silva and Paulo Ferrão
Energies 2025, 18(15), 4107; https://doi.org/10.3390/en18154107 (registering DOI) - 2 Aug 2025
Viewed by 119
Abstract
Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, [...] Read more.
Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO2 emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO2 emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article
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27 pages, 3602 KiB  
Article
Optimal Dispatch of a Virtual Power Plant Considering Distributed Energy Resources Under Uncertainty
by Obed N. Onsomu, Erman Terciyanlı and Bülent Yeşilata
Energies 2025, 18(15), 4012; https://doi.org/10.3390/en18154012 - 28 Jul 2025
Viewed by 294
Abstract
The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, [...] Read more.
The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article
(This article belongs to the Special Issue Review Papers in Energy Storage and Related Applications)
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23 pages, 1604 KiB  
Article
Fine-Tuning Large Language Models for Kazakh Text Simplification
by Alymzhan Toleu, Gulmira Tolegen and Irina Ualiyeva
Appl. Sci. 2025, 15(15), 8344; https://doi.org/10.3390/app15158344 - 26 Jul 2025
Viewed by 342
Abstract
This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 [...] Read more.
This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 examples and comparing it against a purely LLM-based selection method; we then use this pipeline to assemble a parallel corpus of 8709 complex–simple pairs via LLM augmentation. For the simplification task, we benchmark KazSim against standard Seq2Seq systems, domain-adapted Kazakh LLMs, and zero-shot instruction-following models. On an automatically constructed test set, KazSim (Llama-3.3-70B) achieves BLEU 33.50, SARI 56.38, and F1 87.56 with a length ratio of 0.98, outperforming all baselines. We also explore prompt language (English vs. Kazakh) and conduct human evaluation with three native speakers: KazSim scores 4.08 for fluency, 4.09 for meaning preservation, and 4.42 for simplicity—significantly above GPT-4o-mini. Error analysis shows that remaining failures cluster into tone change, tense change, and semantic drift, reflecting Kazakh’s agglutinative morphology and flexible syntax. Full article
(This article belongs to the Special Issue Natural Language Processing and Text Mining)
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18 pages, 12552 KiB  
Article
Identification of AI-Generated Rock Thin-Section Images by Feature Analysis Under Data Scarcity
by Magdalena Habrat and Maciej Dwornik
Appl. Sci. 2025, 15(15), 8314; https://doi.org/10.3390/app15158314 - 25 Jul 2025
Viewed by 210
Abstract
An important aspect of geoscience and energy research is the analysis of microscopic images, where the assessment of rock properties combines imaging methods with numerical analysis. Given the significant advancements in generative artificial intelligence technologies in recent years, which have enabled the creation [...] Read more.
An important aspect of geoscience and energy research is the analysis of microscopic images, where the assessment of rock properties combines imaging methods with numerical analysis. Given the significant advancements in generative artificial intelligence technologies in recent years, which have enabled the creation of realistic images, a need arises to assess the authenticity of synthetic visual data compared to authentic geological data images. This article evaluates the potential for identifying artificially generated microscopic rock images. Synthetic images were generated using a widely accessible diffusion model, based on real training data. Expert evaluation noted high realism, though some structural and rock-type differences remained detectable. In the study, image descriptors were analyzed to assess their usefulness in distinguishing synthetic data from real data. Discriminative feature selection was conducted, and the effectiveness of various classification models based on the selected parameter sets was compared. The study also proposes a heuristic coefficient demonstrating discriminative potential for the analyzed images. The results confirm the feasibility of building classifiers for synthetic images that could aid in detecting generated visual data in geological and petrographic research. They also serve as a foundation for further exploration of the importance of individual features in such applications. Full article
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22 pages, 8682 KiB  
Article
Predicting EGFRL858R/T790M/C797S Inhibitory Effect of Osimertinib Derivatives by Mixed Kernel SVM Enhanced with CLPSO
by Shaokang Li, Wenzhe Dong and Aili Qu
Pharmaceuticals 2025, 18(8), 1092; https://doi.org/10.3390/ph18081092 - 23 Jul 2025
Viewed by 214
Abstract
Background/Objectives: The resistance mutations EGFRL858R/T790M/C797S in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims [...] Read more.
Background/Objectives: The resistance mutations EGFRL858R/T790M/C797S in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFRL858R/T790M/C797S mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation (QLOO2), 5-fold cross validation (Q5fold2) and concordance correlation coefficient (CCC), QF12, and QF22. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: R2 and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; QLOO2, Q5fold2, CCC, QF12, and QF22 are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC50 values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFRL858R/T790M/C797S inhibitors. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Drug Design and Discovery)
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23 pages, 346 KiB  
Article
Thirst for Change in Water Governance: Overcoming Challenges for Drought Resilience in Southern Europe
by Eleonora Santos
Water 2025, 17(15), 2170; https://doi.org/10.3390/w17152170 - 22 Jul 2025
Viewed by 281
Abstract
This article investigates the institutional and informational foundations of water governance in Southern Europe amid escalating climate stress. Focusing on Portugal, Spain, Italy, and Greece, it develops a multi-level analytical framework to explore how information asymmetries and governance fragmentation undermine coordinated responses to [...] Read more.
This article investigates the institutional and informational foundations of water governance in Southern Europe amid escalating climate stress. Focusing on Portugal, Spain, Italy, and Greece, it develops a multi-level analytical framework to explore how information asymmetries and governance fragmentation undermine coordinated responses to water scarcity. Integrating theories of information economics, polycentric governance, and critical institutionalism, this study applies a stylized economic model and comparative institutional analysis to assess how agents—such as farmers, utilities, regulators, and civil society—respond to varying incentives, data access, and coordination structures. Using secondary data, normalized indicators, and scenario-based simulations, the model identifies three key structural parameters—institutional friction (θi), information cost (βi), and incentive strength (αi)—as levers for governance reform. The simulations are stylized and not empirically calibrated, serving as heuristic tools rather than predictive forecasts. The results show that isolated interventions yield limited improvements, while combined reforms significantly enhance both equity and effectiveness. Climate stress simulations further reveal stark differences in institutional resilience, with Greece and Italy showing systemic fragility and Portugal emerging as comparatively robust. This study contributes a flexible, policy-relevant tool for diagnosing governance capacity and informing reform strategies while also underscoring the need for integrated, equity-oriented approaches to adaptive water governance. Full article
19 pages, 7154 KiB  
Article
A Heuristic Exploration of Zonal Flow-like Structures in the Presence of Toroidal Rotation in a Non-Inertial Frame
by Xinliang Xu, Yihang Chen, Yulin Zhou, Zhanhui Wang, Xueke Wu, Bo Li, Jiang Sun, Junzhao Zhang and Da Li
Plasma 2025, 8(3), 29; https://doi.org/10.3390/plasma8030029 - 22 Jul 2025
Viewed by 129
Abstract
The mechanisms by which rotation influences zonal flows (ZFs) in plasma are incompletely understood, presenting a significant challenge in the study of plasma dynamics. This research addresses this gap by investigating the role of non-inertial effects—specifically centrifugal and Coriolis forces—on Geodesic Acoustic Modes [...] Read more.
The mechanisms by which rotation influences zonal flows (ZFs) in plasma are incompletely understood, presenting a significant challenge in the study of plasma dynamics. This research addresses this gap by investigating the role of non-inertial effects—specifically centrifugal and Coriolis forces—on Geodesic Acoustic Modes (GAMs) and ZFs in rotating tokamak plasmas. While previous studies have linked centrifugal convection to plasma toroidal rotation, they often overlook the Coriolis effects or inconsistently incorporate non-inertial terms into magneto-hydrodynamic (MHD) equations. In this work, we derive self-consistent drift-ordered two-fluid equations from the collisional Vlasov equation in a non-inertial frame, and we modify the Hermes cold ion code to simulate the impact of rotation on GAMs and ZFs. Our simulations reveal that toroidal rotation enhances ZF amplitude and GAM frequency, with Coriolis convection playing a critical role in GAM propagation and the global structure of ZFs. Analysis of simulation outcomes indicates that centrifugal drift drives parallel velocity growth, while Coriolis drift facilitates radial propagation of GAMs. This work may provide valuable insights into momentum transport and flow shear dynamics in tokamaks, with implications for turbulence suppression and confinement optimization. Full article
(This article belongs to the Special Issue New Insights into Plasma Theory, Modeling and Predictive Simulations)
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18 pages, 1713 KiB  
Article
Exploring Pedestrian Satisfaction and Environmental Consciousness in a Railway-Regenerated Linear Park
by Lankyung Kim and Chul Jeong
Land 2025, 14(7), 1475; https://doi.org/10.3390/land14071475 - 16 Jul 2025
Viewed by 322
Abstract
This study employs Hannah Arendt’s (1958) the human condition as a philosophical framework to examine walking not merely as a physical activity but as a meaningful form of environmental consciousness. Homo faber, which denotes tool making, corresponds to the nature-based railway regeneration [...] Read more.
This study employs Hannah Arendt’s (1958) the human condition as a philosophical framework to examine walking not merely as a physical activity but as a meaningful form of environmental consciousness. Homo faber, which denotes tool making, corresponds to the nature-based railway regeneration exemplified by the Gyeongui Line Forest Park in Seoul City, South Korea. By applying walking as a method, bifurcated themes are explored: a pedestrian-provision focus on walkability and an environmentally oriented focus consisting of nature and culture, supporting the notion that environmental elements are co-experienced through the embodied activity of walking. Thematic findings are supported by generalized additive models, grounded in a between-method triangulation attempt. The results confirm the interdependencies among the park’s environment, pedestrian satisfaction, and environmental consciousness. Specifically, the environment surrounding the park, which traverses natural and cultural elements, is strongly associated with both pedestrian satisfaction and environmental sensitivity. The research reifies walking as a fundamental human condition, encompassing labor, work, and action, while arguing for heuristic reciprocity between homo faber and nature, as well as framing walking as a sustainably meaningful urban intervention. This study contributes to maturing the theoretical understanding of walking as a vital human condition and suggests practical insights for pedestrian-centered spatial transformation. Full article
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25 pages, 4903 KiB  
Article
Intelligent Joint Space Path Planning: Enhancing Motion Feasibility with Goal-Driven and Potential Field Strategies
by Yuzhou Li, Yefeng Yang, Kang Liu and Chih-Yung Wen
Sensors 2025, 25(14), 4370; https://doi.org/10.3390/s25144370 - 12 Jul 2025
Viewed by 291
Abstract
Traditional path-planning algorithms for robotic manipulators typically focus on end-effector planning, often neglecting complete collision avoidance for the entire manipulator. Additionally, many existing approaches suffer from high time complexity and are easily trapped in local extremes. To address these challenges, this paper proposes [...] Read more.
Traditional path-planning algorithms for robotic manipulators typically focus on end-effector planning, often neglecting complete collision avoidance for the entire manipulator. Additionally, many existing approaches suffer from high time complexity and are easily trapped in local extremes. To address these challenges, this paper proposes a goal-biased bidirectional artificial potential field-based rapidly-exploring random tree* (GBAPF-RRT*) algorithm, which enhances both target guidance and obstacle avoidance capabilities of the manipulator. Firstly, we utilize a Gaussian distribution to add heuristic guidance into the exploration of the robotic manipulator, thereby accelerating the search speed of the RRT*. Then, we combine the modified repulsion function to prevent the random tree from trapping in a local extreme. Finally, sufficient numerical simulations and physical experiments are conducted in the joint space to verify the effectiveness and superiority of the proposed algorithm. Comparative results indicate that our proposed method achieves a faster search speed and a shorter path in complex planning scenarios. Full article
(This article belongs to the Section Sensors and Robotics)
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18 pages, 3227 KiB  
Article
Optimized Adversarial Tactics for Disrupting Cooperative Multi-Agent Reinforcement Learning
by Guangze Yang, Xinyuan Miao, Yabin Peng, Wei Huang and Fan Zhang
Electronics 2025, 14(14), 2777; https://doi.org/10.3390/electronics14142777 - 10 Jul 2025
Viewed by 327
Abstract
Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on [...] Read more.
Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article
(This article belongs to the Special Issue AI Applications of Multi-Agent Systems)
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28 pages, 9666 KiB  
Article
An Efficient Path Planning Algorithm Based on Delaunay Triangular NavMesh for Off-Road Vehicle Navigation
by Ting Tian, Huijing Wu, Haitao Wei, Fang Wu and Jiandong Shang
World Electr. Veh. J. 2025, 16(7), 382; https://doi.org/10.3390/wevj16070382 - 7 Jul 2025
Viewed by 324
Abstract
Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments [...] Read more.
Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function g(n), and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function h(n). Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article
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26 pages, 10064 KiB  
Article
TCDE: Differential Evolution for Topographical Contour-Based Prediction to Solve Multimodal Optimization Problems
by Zhen Cheng, Yun Zhang, Caifu Fan, Xingwei Gao, Haohan Jia and Lei Jiang
Appl. Sci. 2025, 15(13), 7557; https://doi.org/10.3390/app15137557 - 5 Jul 2025
Viewed by 294
Abstract
Multimodal optimization problems represent a category of complex optimization challenges characterized by the presence of multiple global optimal solutions. Addressing these problems requires an algorithm that can not only efficiently locate and identify as many peaks as possible but also pinpoint the precise [...] Read more.
Multimodal optimization problems represent a category of complex optimization challenges characterized by the presence of multiple global optimal solutions. Addressing these problems requires an algorithm that can not only efficiently locate and identify as many peaks as possible but also pinpoint the precise coordinates of these peaks with a high degree of accuracy. Evolutionary algorithms are frequently employed to tackle multimodal optimization problems, and their heuristic approach often leads to the high-probability discarding of suboptimal individuals generated during the evolutionary process. If all individuals are utilized to depict the contours of the problem space, the contours will be depicted increasingly accurately as the algorithm iterates. By optimizing in this way, the results will become increasingly accurate. Therefore, in this paper, a topographical-contour-based differential evolution (TCDE) method is introduced to address multimodal optimization problems. The method initially applies DE for terrain exploration, followed by the construction of terrain contours to obtain more accurate landform representation, and it finally employs a niching search to investigate the landforms for optimal peaks. Experiments were conducted on a set of eight widely recognized benchmark functions with 15 excellent optimization algorithms, including both DE and non-DE multimodal optimization approaches. The outcomes of these experiments conclusively demonstrate the superior performance of the TCDE algorithm over its counterparts. Full article
(This article belongs to the Special Issue Machine Learning and Soft Computing: Current Trends and Applications)
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30 pages, 2294 KiB  
Article
Exploring the Influencing Factors of Learning Burnout: A Network Comparison in Online and Offline Environments
by Jiayao Lu, Sihang Zhu, Ranran Wang and Tour Liu
Behav. Sci. 2025, 15(7), 903; https://doi.org/10.3390/bs15070903 - 3 Jul 2025
Viewed by 281
Abstract
This study aims to explore the interrelationships among key factors influencing learning burnout, such as motivation and negative emotions (depression, anxiety, and stress) along with other factors influencing including problematic mobile phone use, nomophobia, and interactive learning, as well as whether their pathways [...] Read more.
This study aims to explore the interrelationships among key factors influencing learning burnout, such as motivation and negative emotions (depression, anxiety, and stress) along with other factors influencing including problematic mobile phone use, nomophobia, and interactive learning, as well as whether their pathways of influence on learning burnout differ between online and offline learning contexts. Using the convenience sampling method, data from 293 college students were collected. Measurements were carried out using the Nomophobia Scale, the Problematic Mobile Phone Use Scale, the Depression Anxiety Stress Scale (DASS), the Interactive Learning Scale, the Learning Burnout Scale, and the Scale of Motivation for Activity Participation. By applying network analysis and network comparison methods, and based on the Social Comparison Theory and the Affective Socialization Heuristics Model, it was found that under the online learning condition the motivation to pursue value directly affects learning burnout. In contrast, under the offline learning condition learning motivation indirectly affects learning burnout through negative emotions. This study posits that this difference is caused by peer comparison. In a collective learning atmosphere, students’ comparison with their peers triggers negative emotions such as anxiety and stress. These negative emotions weaken the learning motivation to pursue value, ultimately resulting in an elevated level of learning burnout. Full article
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42 pages, 4946 KiB  
Article
Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection
by Kaijie Zhang, Yuchen Ye, Kaihao Chen, Zao Li and Kangshun Li
J. Mar. Sci. Eng. 2025, 13(7), 1294; https://doi.org/10.3390/jmse13071294 - 30 Jun 2025
Viewed by 304
Abstract
Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with [...] Read more.
Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with Deep Q-Networks (DQN) to achieve robust AUV autonomy. The MEO-BIT* component delivers efficient global path planning through (1) a multithreaded batch sampling mechanism for rapid state-space exploration, (2) heuristic-driven search accelerated by KD-tree spatial indexing for optimized path discovery, and (3) an energy-aware cost function balancing path length and steering effort for enhanced endurance. Critically, the DQN component facilitates dynamic obstacle detection and adaptive local navigation, enabling the AUV to adjust its trajectory intelligently in real time. This integrated approach leverages the strengths of both algorithms. The global path intelligence of MEO-BIT* is dynamically informed and refined by the DQN’s learned perception. This allows the DQN to make effective decisions to avoid moving obstacles. Experimental validation in a simulated Achao waterway (Chile) demonstrates the MEO-BIT* + DQN system’s superiority, achieving a 46% reduction in collision rates (directly reflecting improved detection and avoidance fusion), a 15.7% improvement in path smoothness, and a 78.9% faster execution time compared to conventional RRT* and BIT* methods. This work presents a robust solution that effectively fuses two key components: the computational efficiency of MEO-BIT* and the adaptive capabilities of DQN. This fusion significantly advances the integration of navigation with dynamic obstacle detection. Ultimately, it enhances AUV operational performance and autonomy in complex maritime scenarios. Full article
(This article belongs to the Special Issue Navigation and Detection Fusion for Autonomous Underwater Vehicles)
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21 pages, 677 KiB  
Article
Exploring Tabu Tenure Policies with Machine Learning
by Anna Konovalenko and Lars Magnus Hvattum
Electronics 2025, 14(13), 2642; https://doi.org/10.3390/electronics14132642 - 30 Jun 2025
Viewed by 255
Abstract
Tabu search is a well-known local search-based metaheuristic, widely used for tackling complex combinatorial optimization problems. As with other metaheuristics, its performance is sensitive to parameter configurations, requiring careful tuning. Among the critical parameters of tabu search is the tabu tenure. This study [...] Read more.
Tabu search is a well-known local search-based metaheuristic, widely used for tackling complex combinatorial optimization problems. As with other metaheuristics, its performance is sensitive to parameter configurations, requiring careful tuning. Among the critical parameters of tabu search is the tabu tenure. This study aims to identify key search attributes and instance characteristics that can help establish comprehensive guidelines for a robust tabu tenure policy. First, a review different tabu tenure policies is provided. Next, critical baselines to understand the fundamental relationship between tabu tenure settings and solution quality are established. We verified that generalizable parameter selection rules provide value when implementing metaheuristic frameworks, specifically showing that a more robust tabu tenure policy can be achieved by considering whether a move is improving or non-improving. Finally, we explore the integration of machine learning techniques that exploits both dynamic search attributes and static instance characteristics to obtain effective and robust tabu tenure policies. A statistical analysis confirms that the integration of machine learning yields statistically significant performance gains, achieving a mean improvement of 12.23 (standard deviation 137.25, n= 10,000 observations) when compared to a standard randomized tabu tenure selection (p-value < 0.001). While the integration of machine learning introduces additional computational overhead, it may be justified in scenarios where heuristics are repeatedly applied to structurally similar problem instances, and even small improvements in solution quality can accumulate to large overall gains. Nonetheless, our methods have limitations. The influence of the tabu tenure parameter is difficult to detect in real time during the search process, complicating the reliable identification of when and how tenure adjustments impact search performance. Additionally, the proposed policies exhibit similar performance on the chosen instances, further complicating the evaluation and differentiation of policy effectiveness. Full article
(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)
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