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18 pages, 899 KiB  
Article
Biomarkers of Metabolism and Inflammation in Individuals with Obesity and Normal Weight: A Comparative Analysis Exploring Sex Differences
by Eveline Gart, Jessica Snabel, Jelle C. B. C. de Jong, Lars Verschuren, Anita M. van den Hoek, Martine C. Morrison and Robert Kleemann
Int. J. Mol. Sci. 2025, 26(15), 7576; https://doi.org/10.3390/ijms26157576 (registering DOI) - 5 Aug 2025
Abstract
Blood-based biomarkers allow monitoring of an individual’s health status and provide insights into metabolic and inflammatory processes in conditions like obesity, cardiovascular, and liver diseases. However, selecting suitable biomarkers and optimizing analytical assays presents challenges, is time-consuming and laborious. Moreover, knowledge of potential [...] Read more.
Blood-based biomarkers allow monitoring of an individual’s health status and provide insights into metabolic and inflammatory processes in conditions like obesity, cardiovascular, and liver diseases. However, selecting suitable biomarkers and optimizing analytical assays presents challenges, is time-consuming and laborious. Moreover, knowledge of potential sex differences remains incomplete as research is often carried out in men. This study aims at enabling researchers to make informed choices on the type of biomarkers, analytical assays, and dilutions being used. More specifically, we analyzed plasma concentrations of >90 biomarkers using commonly available ELISA or electrochemiluminescence-based multiplex methods, comparing normal weight (BMI < 25; n = 40) with obese (BMI > 30; n = 40) adult blood donors of comparable age. To help choose optimal biomarker sets, we grouped frequently employed biomarkers into biological categories (e.g., adipokines, acute-phase proteins, complement factors, cytokines, myokines, iron metabolism, vascular inflammation), first comparing normal-weight with obese persons, and thereafter exploratively comparing women and men within each BMI group. Many biomarkers linked to chronic inflammation and dysmetabolism were elevated in persons with obesity, including several adipokines, interleukins, chemokines, acute-phase proteins, complement factors, and oxidized LDL. Further exploration suggests sex disparities in biomarker levels within both normal-weight and obese groups. This comprehensive dataset of biomarkers across diverse biological domains constitutes a reference resource that may provide valuable guidance for researchers in selecting appropriate biomarkers and analytical assays for own studies. Moreover, the dataset highlights the importance of taking possible sex differences into account. Full article
28 pages, 11519 KiB  
Article
Identifying Sustainable Offshore Wind Farm Sites in Greece Under Climate Change
by Vasiliki I. Chalastani, Elissavet Feloni, Carlos M. Duarte and Vasiliki K. Tsoukala
J. Mar. Sci. Eng. 2025, 13(8), 1508; https://doi.org/10.3390/jmse13081508 - 5 Aug 2025
Abstract
Wind power has gained attention as a vital renewable energy source capable of reducing emissions and serving as an effective alternative to fossil fuels. Floating wind farms could significantly enhance the energy capacities of Mediterranean countries. However, location selection for offshore wind farms [...] Read more.
Wind power has gained attention as a vital renewable energy source capable of reducing emissions and serving as an effective alternative to fossil fuels. Floating wind farms could significantly enhance the energy capacities of Mediterranean countries. However, location selection for offshore wind farms (OWFs) is a challenge for renewable energy policy and marine spatial planning (MSP). To address these issues, this study considers the marine space of Greece to propose a GIS-based multi-criteria decision-making (MCDM) framework employing the Analytic Hierarchy Process (AHP) to identify suitable sites for OWFs. The approach assesses 19 exclusion criteria encompassing legislative, environmental, safety, and technical constraints to determine the eligible areas. Subsequently, 10 evaluation criteria are weighted to determine the selected areas’ level of suitability. The study considers baseline conditions (1981–2010) and future climate scenarios based on RCP 4.5 and RCP 8.5 for two horizons (2011–2040 and 2041–2070), integrating projected wind velocities and sea level rise to evaluate potential shifts in suitable areas. Results indicate the central and southeastern Aegean Sea as the most suitable areas for OWF deployment. Climate projections indicate a modest increase in suitable areas. The findings serve as input for climate-resilient MSP seeking to promote sustainable energy development. Full article
(This article belongs to the Section Marine Energy)
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15 pages, 750 KiB  
Review
Using Biocontrol Fungi to Control Helminthosis in Wild Animals: An Innovative Proposal for the Health and Conservation of Species
by Júlia dos Santos Fonseca, Beatriz Bacelar Barbosa, Adolfo Paz Silva, María Sol Arias Vázquez, Cristiana Filipa Cazapal Monteiro, Huarrisson Azevedo Santos and Jackson Victor de Araújo
Pathogens 2025, 14(8), 775; https://doi.org/10.3390/pathogens14080775 (registering DOI) - 5 Aug 2025
Abstract
Helminth parasites of wild animals represent a major threat to the health of these animals, leading to significant losses in performance, health, and zoonotic implications. In some zoos, anthelmintics have traditionally been used to control these parasites, many of which are also zoonotic. [...] Read more.
Helminth parasites of wild animals represent a major threat to the health of these animals, leading to significant losses in performance, health, and zoonotic implications. In some zoos, anthelmintics have traditionally been used to control these parasites, many of which are also zoonotic. Other actions, such as the removal of organic waste, have also been adopted. Few or no control measures are applied to free-ranging wild animals. Helminthophagous fungi are a promising biological alternative. When animals ingest fungal spores, they are excreted in their feces, where they trap and destroy helminth larvae and eggs, preventing and reducing the parasite load in the environment. Another alternative is to administer fungi by spraying them directly into the environment. This review aims to examine the use of helminthophagous fungi in the control of helminthiases in wild animals, highlighting their potential to minimize dependence on chemical treatments and promote sustainable animal breeding and production. There are many challenges to making this viable, such as environmental variability, stability of formulations, and acceptance of this new technology. These fungi have been shown to reduce parasite burdens in wild animals by up to 75% and can be administered through the animals’ feeding troughs. To date, evidence shows that helminthophagous fungi can reliably curb environmental parasite loads for extended periods, offering a sustainable alternative to repeated anthelmintic dosing. Their use has been linked to tangible gains in body condition, weight, and overall welfare in various captive and free-ranging wildlife species. Full article
(This article belongs to the Section Parasitic Pathogens)
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23 pages, 1714 KiB  
Article
Physicochemical and Biological Properties of Quercetin-Loaded Low-Molecular-Weight Chitosan Nanoparticles Derived from Hermetia illucens Larvae and Crustacean Sources: A Comparative Study
by Anna Guarnieri, Rosanna Mallamaci, Giuseppe Trapani, Dolores Ianniciello, Carmen Scieuzo, Francesco Iannielli, Luigi Capasso, Maria Chiara Sportelli, Alessandra Barbanente, Michela Marsico, Angela De Bonis, Stefano Castellani, Patrizia Falabella and Adriana Trapani
Pharmaceutics 2025, 17(8), 1016; https://doi.org/10.3390/pharmaceutics17081016 - 5 Aug 2025
Abstract
Introduction. Larvae of the insect Hermetia illucens can represent an alternative source for low-molecular-weight chitosan (CS) production compared with CS from crustaceans (CScrustac), making it appealing in terms of pharmaceutical applications. Hence, the performances of CSlarvae and CScrustac [...] Read more.
Introduction. Larvae of the insect Hermetia illucens can represent an alternative source for low-molecular-weight chitosan (CS) production compared with CS from crustaceans (CScrustac), making it appealing in terms of pharmaceutical applications. Hence, the performances of CSlarvae and CScrustac were compared herein by investigating the in vitro features of nanoparticles (NPs) made from each polysaccharide and administered with the antioxidant quercetin (QUE). Methods. X-ray diffraction and FT-IR spectroscopy enabled the identification of each type of CS. Following the ionic gelation technique and using sulfobutylether-β-cyclodextrin as a cross-linking agent, NPs were easily obtained. Results. Physicochemical data, release studies in PBS, and the evaluation of antioxidant effects via the 1,1-diphenyl-2-picrylhydrazyl (DPPH) test were studied for both CSlarvae and CScrustac. QUE-loaded NP sizes ranged from 180 to 547 nm, and zeta potential values were between +7.5 and +39.3 mV. In vitro QUE release in PBS was faster from QUE-CSlarvae NPs than from CScrustac, and high antioxidant activity—according to the DPPH test—was observed for all tested NP formulations. Discussion. The agar diffusion assay, referring to Escherichia coli and Micrococcus flavus, as well as the microdilution assay, showed the best performance as antimicrobial formulations in the case of QUE-CSlarvae NPs. QUE-CSlarvae NPs can represent a promising vehicle for QUE, releasing it in a sustained manner, and, relevantly, the synergism noticed between QUE and CSlarvae resulted in a final antimicrobial product. Conclusions. New perspectives for low-molecular-weight CS are disclosed by adopting renewable sources from insects instead of the commercial CScrustac. Full article
(This article belongs to the Section Biopharmaceutics)
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34 pages, 4124 KiB  
Article
Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification
by Ruimin Han, Shuli Cheng, Shuoshuo Li and Tingjie Liu
Remote Sens. 2025, 17(15), 2705; https://doi.org/10.3390/rs17152705 - 4 Aug 2025
Abstract
Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in [...] Read more.
Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. In contrast, Vision Transformers (ViTs) are widely used in HSI due to their superior feature extraction capabilities. However, existing Transformer models have challenges in achieving spectral–spatial feature fusion and maintaining local structural consistency, making it difficult to strike a balance between global modeling capabilities and local representation. To this end, we propose a Prompt-Gated Transformer with a Spatial–Spectral Enhancement (PGTSEFormer) network, which includes a Channel Hybrid Positional Attention Module (CHPA) and Prompt Cross-Former (PCFormer). The CHPA module adopts a dual-branch architecture to concurrently capture spectral and spatial positional attention, thereby enhancing the model’s discriminative capacity for complex feature categories through adaptive weight fusion. PCFormer introduces a Prompt-Gated mechanism and grouping strategy to effectively model cross-regional contextual information, while maintaining local consistency, which significantly enhances the ability for long-distance dependent modeling. Experiments were conducted on five HSI datasets and the results showed that overall accuracies of 97.91%, 98.74%, 99.48%, 99.18%, and 92.57% were obtained on the Indian pines, Salians, Botswana, WHU-Hi-LongKou, and WHU-Hi-HongHu datasets. The experimental results show the effectiveness of our proposed approach. Full article
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12 pages, 688 KiB  
Article
Matrix Modeling of the Selection of Electric Generators for Home Use Based on the Analytical Hierarchical Process (AHP) Algorithm in War Conditions in Ukraine
by Barbara Dybek, Igor Ilge, Serhiy Zaporozhtsev, Adam Koniuszy and Grzegorz Wałowski
Energies 2025, 18(15), 4130; https://doi.org/10.3390/en18154130 - 4 Aug 2025
Abstract
The problem of choosing an electric generator in order to increase the reliability and continuity of energy supply to households in Ukraine was considered. It was shown that this choice is made under conditions of uncertainty. The methods of choosing alternatives to technical [...] Read more.
The problem of choosing an electric generator in order to increase the reliability and continuity of energy supply to households in Ukraine was considered. It was shown that this choice is made under conditions of uncertainty. The methods of choosing alternatives to technical systems under conditions of uncertainty, based on axiomatic, heuristic and verbal decision-making methods described in the sources, were analyzed, and the Analytical Hierarchical Process (AHP) was selected to develop a model for choosing an electric generator. The technical, economic, operational and ergonomic criteria for choosing an electric generator were justified. The novelty of the article lies in the use of the developed structural hierarchical model for choosing an electric generator for a household, and the selection of the appropriate generator option for a household was carried out using the AHP. The selected F3001 generator model is characterized by the highest value of the generalized weighting factor due to the impact of estimates based on economic and operational criteria. The use of the cogeneration unit in an agricultural biogas plant was also indicated—as an alternative to household energy supply. Full article
(This article belongs to the Section A: Sustainable Energy)
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18 pages, 797 KiB  
Article
On Becoming a Senior Staff Nurse in Taiwan: A Narrative Study
by Yu-Jen Hsieh and Yu-Tzu Dai
Healthcare 2025, 13(15), 1896; https://doi.org/10.3390/healthcare13151896 - 4 Aug 2025
Abstract
Background/Objectives: Senior nurses in Taiwan shoulder layered responsibilities shaped by professional roles, gendered expectations, and family duty. Although Taiwan faces a persistent shortage of experienced clinical nurses, limited research has explored how long-serving nurses sustain identity and commitment across decades of caregiving. [...] Read more.
Background/Objectives: Senior nurses in Taiwan shoulder layered responsibilities shaped by professional roles, gendered expectations, and family duty. Although Taiwan faces a persistent shortage of experienced clinical nurses, limited research has explored how long-serving nurses sustain identity and commitment across decades of caregiving. This study examines how senior staff nurses understand their journeys of becoming—and remaining—nurses within a culturally and emotionally complex landscape. Methods: Interviews were conducted between May 2019 and September 2023 in locations chosen by participants, with most sessions face-to-face and others undertaken via video conferencing during COVID-19. This narrative inquiry involved in-depth, multi-session interviews with five female senior staff nurses born in the 1970s to early 1980s. Each participant reflected on her life and career, supported by co-constructed “nursing life lines.” Thematic narrative analysis was conducted using McCormack’s five-lens framework and Riessman’s model, with ethical rigor ensured through reflexive journaling and participant validation. Results: Three overarching themes emerged: (1) inner strength and endurance, highlighting silent resilience and the ethical weight of caregiving; (2) support and responsibility in relationships, revealing the influence of family, faith, and relational duty; and (3) role navigation and professional identity, showing how nurses revisit meaning, self-understanding, and tensions across time. Participants described emotionally powerful moments, identity re-connection, and cultural values that shaped their paths. Conclusions: These narratives offer a relational and culturally embedded understanding of what it means to sustain a career in nursing. Narrative inquiry created space for reflection, meaning-making, and voice in a system where such voices are often unheard. Identity was not static—it was lived, reshaped, and held in story. Full article
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26 pages, 1085 KiB  
Article
Evaluating Sustainable Battery Recycling Technologies Using a Fuzzy Multi-Criteria Decision-Making Approach
by Chia-Nan Wang, Nhat-Luong Nhieu and Yen-Hui Wang
Batteries 2025, 11(8), 294; https://doi.org/10.3390/batteries11080294 - 4 Aug 2025
Abstract
The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling [...] Read more.
The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling technologies under uncertainty. Ten key evaluation criteria—encompassing environmental, economic, and technological dimensions—were identified through expert consultation and literature synthesis. The T-Spherical Fuzzy DEMATEL method was first applied to analyze the causal interdependencies among criteria and determine their relative weights, revealing that environmental drivers such as energy consumption, greenhouse gas emissions, and waste generation exert the most systemic influence. Subsequently, six recycling alternatives were assessed and ranked using the CoCoSo method enhanced by Einstein-based aggregation, which captured the complex interactions present in the experts’ evaluations and assessments. Results indicate that Direct Recycling is the most favorable option, followed by the Hydrometallurgical and Bioleaching methods, while Pyrometallurgical Recycling ranked lowest due to its high energy demands and environmental burden. The proposed hybrid model effectively handles linguistic uncertainty, expert variability, and interdependent evaluation structures, offering a robust decision-support tool for sustainable technology selection in the circular battery economy. The framework is adaptable to other domains requiring structured expert-based evaluations under fuzzy environments. Full article
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15 pages, 412 KiB  
Article
Analysis of Risk Factors in the Renovation of Old Underground Commercial Spaces in Resource-Exhausted Cities: A Case Study of Fushun City
by Kang Wang, Meixuan Li and Sihui Dong
Sustainability 2025, 17(15), 7041; https://doi.org/10.3390/su17157041 - 3 Aug 2025
Viewed by 96
Abstract
Resource-exhausted cities have long played a key role in national energy development. Urban renewal projects, such as the renovation of old underground commercial spaces, can improve urban vitality and promote sustainable development. However, in resource-based cities, traditional industries dominate, while new industries such [...] Read more.
Resource-exhausted cities have long played a key role in national energy development. Urban renewal projects, such as the renovation of old underground commercial spaces, can improve urban vitality and promote sustainable development. However, in resource-based cities, traditional industries dominate, while new industries such as modern commerce develop slowly. This results in low economic dynamism and weak motivation for urban development. To address this issue, we propose a systematic method for analyzing construction risks during the decision-making stage of renovation projects. The method includes three steps: risk value assessment, risk factor identification, and risk weight calculation. First, unlike previous studies that only used SWOT for risk factor analysis, we also applied it for project value assessment. Then, using the Work Breakdown Structure–Risk Breakdown Structure framework method (WBS-RBS), we identified specific risk sources by analyzing key construction technologies throughout the entire lifecycle of the renovation project. Finally, to enhance expert consensus, we proposed an improved Delphi–Analytic Hierarchy Process method (Delphi–AHP) to calculate risk indicator weights for different construction phases. The risk analysis covered all lifecycle stages of the renovation and upgrading project. The results show that in the Fushun city renovation case study, the established framework—consisting of five first-level indicators and twenty s-level indicators—enables analysis of renovation projects. Among these, management factors and human factors were identified as the most critical, with weights of 0.3608 and 0.2017, respectively. The proposed method provides a structured approach to evaluating renovation risks, taking into account the specific characteristics of construction work. This can serve as a useful reference for ensuring safe and efficient implementation of underground commercial space renovation projects in resource-exhausted cities. Full article
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25 pages, 28131 KiB  
Article
Landslide Susceptibility Assessment in Ya’an Based on Coupling of GWR and TabNet
by Jiatian Li, Ruirui Wang, Wei Shi, Le Yang, Jiahao Wei, Fei Liu and Kaiwei Xiong
Remote Sens. 2025, 17(15), 2678; https://doi.org/10.3390/rs17152678 - 2 Aug 2025
Viewed by 328
Abstract
Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes [...] Read more.
Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article
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19 pages, 1160 KiB  
Article
Multi-User Satisfaction-Driven Bi-Level Optimization of Electric Vehicle Charging Strategies
by Boyin Chen, Jiangjiao Xu and Dongdong Li
Energies 2025, 18(15), 4097; https://doi.org/10.3390/en18154097 - 1 Aug 2025
Viewed by 180
Abstract
The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic [...] Read more.
The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic classification of user types. A multidimensional decision-making environment is established for three representative user categories—residential, commercial, and industrial—by synthesizing time-variant electricity pricing models with dynamic carbon emission pricing mechanisms. A bi-level optimization architecture is subsequently formulated, leveraging deep reinforcement learning (DRL) to capture user-specific demand characteristics through customized reward functions and adaptive constraint structures. Validation is conducted within a high-fidelity simulation environment featuring 90 autonomous EV charging agents operating in a metropolitan parking facility. Empirical results indicate that the proposed typology-driven approach yields a 32.6% average cost reduction across user groups relative to baseline charging protocols, with statistically significant improvements in expenditure optimization (p < 0.01). Further interpretability analysis employing gradient-weighted class activation mapping (Grad-CAM) demonstrates that the model’s attention mechanisms are well aligned with theoretically anticipated demand prioritization patterns across the distinct user types, thereby confirming the decision-theoretic soundness of the framework. Full article
(This article belongs to the Section E: Electric Vehicles)
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19 pages, 12406 KiB  
Article
Optimizing Advertising Billboard Coverage in Urban Networks: A Population-Weighted Greedy Algorithm with Spatial Efficiency Enhancements
by Jiaying Fu and Kun Qin
ISPRS Int. J. Geo-Inf. 2025, 14(8), 300; https://doi.org/10.3390/ijgi14080300 - 1 Aug 2025
Viewed by 94
Abstract
The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and [...] Read more.
The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article
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33 pages, 3561 KiB  
Article
A Robust Analytical Network Process for Biocomposites Supply Chain Design: Integrating Sustainability Dimensions into Feedstock Pre-Processing Decisions
by Niloofar Akbarian-Saravi, Taraneh Sowlati and Abbas S. Milani
Sustainability 2025, 17(15), 7004; https://doi.org/10.3390/su17157004 - 1 Aug 2025
Viewed by 208
Abstract
Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria [...] Read more.
Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials. Full article
(This article belongs to the Special Issue Sustainable Enterprise Operation and Supply Chain Management)
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23 pages, 3153 KiB  
Article
Research on Path Planning Method for Mobile Platforms Based on Hybrid Swarm Intelligence Algorithms in Multi-Dimensional Environments
by Shuai Wang, Yifan Zhu, Yuhong Du and Ming Yang
Biomimetics 2025, 10(8), 503; https://doi.org/10.3390/biomimetics10080503 - 1 Aug 2025
Viewed by 177
Abstract
Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence [...] Read more.
Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article
(This article belongs to the Section Biological Optimisation and Management)
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18 pages, 2724 KiB  
Article
Uncertainty-Aware Earthquake Forecasting Using a Bayesian Neural Network with Elastic Weight Consolidation
by Changchun Liu, Yuting Li, Huijuan Gao, Lin Feng and Xinqian Wu
Buildings 2025, 15(15), 2718; https://doi.org/10.3390/buildings15152718 - 1 Aug 2025
Viewed by 80
Abstract
Effective earthquake early warning (EEW) is essential for disaster prevention in the built environment, enabling a rapid structural response, system shutdown, and occupant evacuation to mitigate damage and casualties. However, most current EEW systems lack rigorous reliability analyses of their predictive outcomes, limiting [...] Read more.
Effective earthquake early warning (EEW) is essential for disaster prevention in the built environment, enabling a rapid structural response, system shutdown, and occupant evacuation to mitigate damage and casualties. However, most current EEW systems lack rigorous reliability analyses of their predictive outcomes, limiting their effectiveness in real-world scenarios—especially for on-site warnings, where data are limited and time is critical. To address these challenges, we propose a Bayesian neural network (BNN) framework based on Stein variational gradient descent (SVGD). By performing Bayesian inference, we estimate the posterior distribution of the parameters, thus outputting a reliability analysis of the prediction results. In addition, we incorporate a continual learning mechanism based on elastic weight consolidation, allowing the system to adapt quickly without full retraining. Our experiments demonstrate that our SVGD-BNN model significantly outperforms traditional peak displacement (Pd)-based approaches. In a 3 s time window, the Pearson correlation coefficient R increases by 9.2% and the residual standard deviation SD decreases by 24.4% compared to a variational inference (VI)-based BNN. Furthermore, the prediction variance generated by the model can effectively reflect the uncertainty of the prediction results. The continual learning strategy reduces the training time by 133–194 s, enhancing the system’s responsiveness. These features make the proposed framework a promising tool for real-time, reliable, and adaptive EEW—supporting disaster-resilient building design and operation. Full article
(This article belongs to the Section Building Structures)
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