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Search Results (2,145)

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Keywords = trade-off mechanism

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16 pages, 1362 KiB  
Article
A Robust Fuzzy Adaptive Control Scheme for PMSM with Sliding Mode Dynamics
by Guangyu Cao, Zhihan Chen, Daoyuan Wang, Xiujing Zhao and Fanwei Meng
Processes 2025, 13(8), 2635; https://doi.org/10.3390/pr13082635 - 20 Aug 2025
Abstract
A key trade-off persists in the control of permanent magnet synchronous motors (PMSMs): achieving fast finite-time convergence often exacerbates control chattering, while conventional chattering-suppression methods can compromise the system’s dynamic response. The existing literature often addresses these challenges in isolation. The core original [...] Read more.
A key trade-off persists in the control of permanent magnet synchronous motors (PMSMs): achieving fast finite-time convergence often exacerbates control chattering, while conventional chattering-suppression methods can compromise the system’s dynamic response. The existing literature often addresses these challenges in isolation. The core original contribution of this research lies in proposing a novel robust fuzzy adaptive control scheme that effectively resolves this trade-off through a synergistic design. The contributions are as follows: (1) A novel reaching law is formulated to significantly accelerate error convergence, achieving finite-time stability and improving upon conventional reaching law designs. (2) A super-twisting sliding mode observer is integrated into the control loop, providing accurate real-time estimation of load torque disturbances, which is used for feedforward compensation to drastically improve the system’s disturbance rejection capability. (3) A fuzzy adaptive mechanism is developed to dynamically tune key gains in the sliding mode law. This approach effectively suppresses chattering without sacrificing response speed, enhancing system robustness. (4) The stability and convergence of the proposed controller are rigorously analyzed. Simulations, comparing the proposed method with conventional adaptive sliding mode control (ASMC), demonstrate its marked superiority in control accuracy, transient behavior, and disturbance rejection. This work provides an integrated solution that balances rapidity and smoothness for high-performance motor control, offering significant theoretical and engineering value. Full article
(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)
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26 pages, 484 KiB  
Article
Exploring Governance Failures in Australia: ESG Pillar-Level Analysis of Default Risk Mediated by Trade Credit Financing
by Thuong Thi Le, Tanvir Bhuiyan, Thi Le and Ariful Hoque
J. Risk Financial Manag. 2025, 18(8), 464; https://doi.org/10.3390/jrfm18080464 - 20 Aug 2025
Abstract
This study examines the impact of overall Environmental, Social, and Governance (ESG) performance and its pillars on the default probability of Australian-listed firms. Using a panel dataset spanning 2014 to 2022 and applying the Generalized Method of Moments (GMM) regression, we find that [...] Read more.
This study examines the impact of overall Environmental, Social, and Governance (ESG) performance and its pillars on the default probability of Australian-listed firms. Using a panel dataset spanning 2014 to 2022 and applying the Generalized Method of Moments (GMM) regression, we find that firms with higher ESG scores exhibit a significantly lower likelihood of default. Disaggregating the ESG components reveals that the Environmental and Social pillars have a negative association with default risk, suggesting a risk-mitigating effect. In contrast, the Governance pillar demonstrates a positive relationship with default probability, which may reflect potential greenwashing behavior or an excessive focus on formal governance mechanisms at the expense of operational and financial performance. Furthermore, the analysis identifies trade credit financing (TCF) as a partial mediator in the ESG–default risk nexus, indicating that firms with stronger ESG profiles rely less on external short-term financing, thereby reducing their default risk. These findings provide valuable insights for corporate management, investors, regulators, and policymakers seeking to enhance financial resilience through sustainable practices. Full article
(This article belongs to the Special Issue Emerging Trends and Innovations in Corporate Finance and Governance)
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24 pages, 2893 KiB  
Article
Assessment of the Food–Energy–Water Nexus Considering the Carbon Footprint and Trade-Offs in Crop Production Systems in China
by Beibei Guo, Xian Zou, Tingting Cheng, Yan Li, Jie Huang, Tingting Sun and Yi Tong
Land 2025, 14(8), 1674; https://doi.org/10.3390/land14081674 - 19 Aug 2025
Abstract
To elucidate the food–energy–water (FEW) nexus, in this paper, a food–energy–water–carbon (FEWC) measurement method is established, and the evolutionary mechanisms within the nexus are determined to optimize crop production systems (CPSs). A quantitative assessment of the trade-offs and synergies among the constituent sub-nexuses [...] Read more.
To elucidate the food–energy–water (FEW) nexus, in this paper, a food–energy–water–carbon (FEWC) measurement method is established, and the evolutionary mechanisms within the nexus are determined to optimize crop production systems (CPSs). A quantitative assessment of the trade-offs and synergies among the constituent sub-nexuses is presented. This assessment is achieved through carbon footprint analysis of CPSs. In addition to examining FEW resource interactions, we employ the logarithmic mean divisia index methodology—a tool well-suited for practical energy decomposition—to explore the nexus interrelationships. This research further accounts for anthropogenic inputs in CPSs, specifically using blue water and energy consumption as key indicators for characterizing water and energy dynamics, respectively. Five crops are selected for CPS carbon emissions analysis to inform cropping structure optimization. The results show that during 2000–2022, greenhouse gas (GHG) emissions from China’s CPSs exhibited significant fluctuations characterized by a concentrated–dispersed–concentrated distribution pattern: the food system’s carbon footprint decreased notably, the food–energy (FE) system’s impact increased substantially, and the food–water (FW) system’s footprint fluctuated before decreasing. The spatial diversity in the FE system’s provincial carbon footprint increased over time, while the FW nexus exhibited fluctuating yet significant efficiency gains, indicating movement toward more balanced spatial distribution along the Hu Huanyong Line and Botai Line. The net effect of the FEW nexus interactions on GHG emissions exhibited a slight mitigating influence. Full article
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31 pages, 7032 KiB  
Review
Rheological, Structural, and Biological Trade-Offs in Bioink Design for 3D Bioprinting
by Jeevithan Elango and Camilo Zamora-Ledezma
Gels 2025, 11(8), 659; https://doi.org/10.3390/gels11080659 - 19 Aug 2025
Abstract
Bioinks represent the core of 3D bioprinting, as they are the carrier responsible for enabling the fabrication of anatomically precise, cell-laden constructs that replicate native tissue architecture. Indeed, their role goes beyond structural support, as they must also sustain cellular viability, proliferation, and [...] Read more.
Bioinks represent the core of 3D bioprinting, as they are the carrier responsible for enabling the fabrication of anatomically precise, cell-laden constructs that replicate native tissue architecture. Indeed, their role goes beyond structural support, as they must also sustain cellular viability, proliferation, and differentiation functions, which are critical for applications in the field of regenerative medicine and personalized therapies. However, at present, a persistent challenge lies in reconciling the conflicting demands of rheological properties, which are essential for printability and biological functionality. This trade-off limits the clinical translation of bioprinted tissues, particularly for vascularized or mechanically dynamic organs. Despite huge progress during the last decade, challenges persist in standardizing bioink characterization, scaling production, and ensuring long-term biomimetic performance. Based on these challenges, this review explores the inherent trade-off faced by bioink research optimizing rheology to ensure printability, shape fidelity, and structural integrity, while simultaneously maintaining high cell viability, proliferation, and tissue maturation offering insights into designing next-generation bioinks for functional tissue engineering. Full article
(This article belongs to the Special Issue Polysaccharide Gels for Biomedical and Environmental Applications)
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26 pages, 6608 KiB  
Article
Sim-Geometry Modal Decomposition (SGMD)-Based Optimization Strategy for Hybrid Battery and Supercapacitor Energy Storage Frequency Regulation
by Yongling He, Zhengquan Zuo, Kang Shen, Jun Gao, Qiuyu Chen, Jianqun Liu and Haoyu Liu
Symmetry 2025, 17(8), 1356; https://doi.org/10.3390/sym17081356 - 19 Aug 2025
Abstract
This study examines the issue of wind power smoothing in renewable-energy-grid integration scenarios. Under the “dual-carbon” policy initiative, large-scale renewable energy integration (particularly wind power) has become a global focus. However, the intermittency and uncertainty of wind power output exacerbate grid power fluctuations, [...] Read more.
This study examines the issue of wind power smoothing in renewable-energy-grid integration scenarios. Under the “dual-carbon” policy initiative, large-scale renewable energy integration (particularly wind power) has become a global focus. However, the intermittency and uncertainty of wind power output exacerbate grid power fluctuations, posing challenges to power system stability. Consequently, smoothing strategies for wind power energy storage systems are desperately needed to improve operational economics and grid stability. According to current research, single energy storage technologies are unable to satisfy both the system-level economic operating requirements and high-frequency power fluctuation compensation at the same time, resulting in a trade-off between economic efficiency and precision of frequency regulation. Therefore, hybrid energy storage technologies have emerged as a key research focus in wind power energy storage. This study employs the SE-SGMD method, utilizing the distinct characteristics of lithium batteries and supercapacitors to decompose frequency regulation commands into low- and high-frequency components via frequency separation strategies, thereby controlling the output of supercapacitors and lithium batteries, respectively. Additionally, the GA-GWO algorithm is applied to optimize energy-storage-system configuration, with experimental validation conducted. The theoretical contributions of this study include the following: (1) introducing the SE-SGMD frequency separation strategy into hybrid energy storage systems, overcoming the performance limitations of single energy storage devices, and (2) developing a power allocation mechanism on the basis of the inherent properties of energy storage devices. In terms of methodological innovation, the designed hybrid GA-GWO algorithm achieves a balance between optimization accuracy and efficiency. Compared to PSO-DE and GWO-PSO, the GA-GWO energy storage system demonstrates improvements of 21.10% and 17.47% in revenue, along with reductions of 6.26% and 12.57% in costs, respectively. Full article
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14 pages, 2110 KiB  
Article
Environmental Drivers of Regeneration in Scyphiphora hydrophyllacea: Thresholds for Seed Germination and Seedling Establishment in Hainan’s Intertidal Zones
by Haijie Yang, Bingjie Zheng, Jiayi Li, Xu Chen, Xiaobo Lv, Cairong Zhong and He Bai
Forests 2025, 16(8), 1346; https://doi.org/10.3390/f16081346 - 19 Aug 2025
Abstract
The endangered mangrove Scyphiphora hydrophyllacea is found in China only in Hainan’s intertidal zones. Its populations are declining severely due to anthropogenic disturbances and regeneration failure. To clarify its environmental adaptation mechanisms, we investigated the effects of temperature, light intensity, photoperiod, salinity, soil, [...] Read more.
The endangered mangrove Scyphiphora hydrophyllacea is found in China only in Hainan’s intertidal zones. Its populations are declining severely due to anthropogenic disturbances and regeneration failure. To clarify its environmental adaptation mechanisms, we investigated the effects of temperature, light intensity, photoperiod, salinity, soil, and flooding cycle on seed germination, seedling growth, and physiological traits, revealing that (1) the optimal germination conditions for seeds were 30–35 °C, 24 h continuous illumination at 25,000 lux, and 0‰ salinity, with soil type showing no significant effect (p > 0.05); (2) seedlings at 1–2 months post-germination achieve maximal growth at 30 °C in non-saline conditions, with salinity suppressing growth and light intensity affecting only crown expansion; and (3) flooding responses are age-dependent: seedlings at 1–2 months post-germination show optimal growth at 8 h per day (100% survival), while 12 h (h) per day reduces survival by 13.3%. One-year-old seedlings exhibit distinct strategies: 4 h per day flooding induces escape responses (peak growth, chlorophyll, sugars), 8 h per day shows photosynthetic compensation despite metabolic trade-offs, and 12 h per day triggers tolerance mechanisms (biomass maximization via structural reinforcement). These findings demonstrate S. hydrophyllacea’s multifactorial adaptation to intertidal conditions, providing critical physiological benchmarks for conservation strategies targeting this threatened ecosystem engineer. Full article
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29 pages, 3075 KiB  
Article
A Synchronized Optimization Method of Frequency Setting, Timetabling, and Train Circulation Planning for URT Networks with Overlapping Lines: A Case Study of the Addis Ababa Light Rail Transit Service
by Wenliang Zhou, Addishiwot Alemu and Mehdi Oldache
Mathematics 2025, 13(16), 2654; https://doi.org/10.3390/math13162654 - 18 Aug 2025
Abstract
Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study [...] Read more.
Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems. Full article
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25 pages, 2249 KiB  
Article
Collaborative Operation Strategy of Virtual Power Plant Clusters and Distribution Networks Based on Cooperative Game Theory in the Electric–Carbon Coupling Market
by Chao Zheng, Wei Huang, Suwei Zhai, Guobiao Lin, Xuehao He, Guanzheng Fang, Shi Su, Di Wang and Qian Ai
Energies 2025, 18(16), 4395; https://doi.org/10.3390/en18164395 - 18 Aug 2025
Abstract
Against the backdrop of global low-carbon transition, the integrated development of electricity and carbon markets demands higher efficiency in the optimal operation of virtual power plants (VPPs) and distribution networks, yet conventional trading mechanisms face limitations such as inadequate recognition of differentiated contributions [...] Read more.
Against the backdrop of global low-carbon transition, the integrated development of electricity and carbon markets demands higher efficiency in the optimal operation of virtual power plants (VPPs) and distribution networks, yet conventional trading mechanisms face limitations such as inadequate recognition of differentiated contributions and inequitable benefit allocation. To address these challenges, this paper proposes a collaborative optimal trading mechanism for VPP clusters and distribution networks in an electricity–carbon coupled market environment by first establishing a joint operation framework to systematically coordinate multi-agent interactions, then developing a bi-level optimization model where the upper level formulates peer-to-peer (P2P) trading plans for electrical energy and carbon allowances through cooperative gaming among VPPs while the lower level optimizes distribution network power flow and feeds back the electro-carbon comprehensive price (EACP). By introducing an asymmetric Nash bargaining model for fair benefit distribution and employing the Alternating Direction Method of Multipliers (ADMM) for efficient computation, case studies demonstrate that the proposed method overcomes traditional models’ shortcomings in contribution evaluation and profit allocation, achieving 2794.8 units in cost savings for VPP clusters while enhancing cooperation stability and ensuring secure, economical distribution network operation, thereby providing a universal technical pathway for the synergistic advancement of global electricity and carbon markets. Full article
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22 pages, 5085 KiB  
Article
Energy-Efficient Scheduling in Heat Treatment Workshops Based on Task Clustering and Job Batching
by Dapeng Su, Tianyi Zhang, Siyang Ji and Jihong Yan
Machines 2025, 13(8), 732; https://doi.org/10.3390/machines13080732 - 18 Aug 2025
Abstract
The development of green and efficient manufacturing has brought on complex trade-offs between energy consumption control and resource utilization efficiency in heat treatment tasks. Traditional single-piece scheduling methods are challenged in addressing the complexity of multiple tasks and energy optimization. In this paper, [...] Read more.
The development of green and efficient manufacturing has brought on complex trade-offs between energy consumption control and resource utilization efficiency in heat treatment tasks. Traditional single-piece scheduling methods are challenged in addressing the complexity of multiple tasks and energy optimization. In this paper, an optimized scheduling method for heat treatment workshops is proposed by integrating task grouping and batch combination strategies. Specifically, a genetic algorithm enhanced with local search and adaptive mutation operators is proposed under constraints such as delivery deadlines and equipment capacity. During the strategy generation process, equipment changeover and idle time are considered. By performing multi-dimensional matching of workpiece processing processes, heat treatment requirements, and quality characteristics, an innovative clustering mechanism for dynamic production batches based on task similarity is constructed. To validate the effectiveness, actual production data from a heat treatment workshop were selected for analysis and evaluation. The results show that the proposed method reduces the total production time by 31.6% with on-time delivery of orders, and the equipment operation frequency is reduced by 28.4%, which verifies the practicality and advancement of the proposed method. Full article
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24 pages, 566 KiB  
Article
Liquidity Drivers in Illiquid Markets: Evidence from Simulation Environments with Heterogeneous Agents
by Lars Fluri, Ahmet Ege Yilmaz, Denis Bieri, Thomas Ankenbrand and Aurelio Perucca
Int. J. Financial Stud. 2025, 13(3), 145; https://doi.org/10.3390/ijfs13030145 - 18 Aug 2025
Viewed by 5
Abstract
This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital [...] Read more.
This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article
(This article belongs to the Special Issue Market Microstructure and Liquidity)
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17 pages, 16198 KiB  
Article
Identifying Agronomic Strategy for a Low-Carbon Economy Under the Effects of Climate Change by Using a Simulation-Optimization Hybrid Model
by Haomiao Cheng, Siyu Sun, Wei Jiang, Qilin Yu, Wei Ma, Shaoyuan Feng, Fusheng Wang and Zuping Xu
Agronomy 2025, 15(8), 1980; https://doi.org/10.3390/agronomy15081980 - 18 Aug 2025
Viewed by 49
Abstract
Agronomic practices and future climate change lead to divergent responses in crop growth and greenhouse gas (GHG) emissions, which challenge a sustainable low-carbon agricultural economy. Therefore, this study developed a simulation-optimization hybrid model to identify long-term best management practices (BMPs) for economic and [...] Read more.
Agronomic practices and future climate change lead to divergent responses in crop growth and greenhouse gas (GHG) emissions, which challenge a sustainable low-carbon agricultural economy. Therefore, this study developed a simulation-optimization hybrid model to identify long-term best management practices (BMPs) for economic and social benefits under the effects of future climate change. This model, i.e., RZWQM2 coupled with an orthogonal optimization algorithm (RZWQM2-OOA), integrates four core components, including an orthogonal sampling module, climate prediction module, RZWQM2 simulation module, and optimization analysis module. The model enabled a high-fidelity simulation of crop growth and carbon emissions across complex management practice-climate combinations, while efficiently identifying BMPs and circumventing dimensionality challenges through orthogonality and balanced dispersion mechanisms. To validate the applicability of the developed model, it was applied to a real-world, irrigated, continuous corn (Zea mays L.) production system in the USA. Results indicated that the maximum increases in direct and indirect economic benefits (F1 and F2) and potential social benefits (F3) were 35.7%, 42.6%, and 155.5%, respectively, compared to the actual practice. Fertilization amount was the key regulating factor for direct economic and potential social benefits, which exhibited the largest contribution rates (44.3% for direct economic benefit and 53.9% for potential social benefit). Irrigation exerted the most significant influence on indirect economic benefits (Contribution rate = 53.9%). This study provides a replicable and scalable methodology for policy-makers to balance the trade-offs between the economy and carbon emissions in agricultural sustainability. Full article
(This article belongs to the Special Issue Modeling Soil-Water-Salt Interactions for Agricultural Sustainability)
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20 pages, 4666 KiB  
Article
Strain and Electric Field Engineering for Enhanced Thermoelectric Performance in Monolayer MoS2: A First-Principles Investigation
by Li Sun, Ensi Cao, Wentao Hao, Bing Sun, Lingling Yang and Dongwei Ao
Quantum Beam Sci. 2025, 9(3), 26; https://doi.org/10.3390/qubs9030026 - 18 Aug 2025
Viewed by 156
Abstract
Optimizing thermoelectric (TE) performance in two-dimensional materials has emerged as a pivotal strategy for sustainable energy conversion. This study systematically investigates the regulatory mechanisms of uniaxial strain (−2% to +2%), temperature (300–800 K), and out-of-plane electric fields (0–1.20 eV/Å) on the thermoelectric properties [...] Read more.
Optimizing thermoelectric (TE) performance in two-dimensional materials has emerged as a pivotal strategy for sustainable energy conversion. This study systematically investigates the regulatory mechanisms of uniaxial strain (−2% to +2%), temperature (300–800 K), and out-of-plane electric fields (0–1.20 eV/Å) on the thermoelectric properties of monolayer MoS2 via first-principles calculations combined with Boltzmann transport theory. Key findings reveal that uniaxial strain modulates the bandgap (1.56–1.86 eV) and carrier transport, balancing the trade-off between the Seebeck coefficient and electrical conductivity. Temperature elevation enhances carrier thermal excitation, boosting the power factor to 28 × 1010 W·m−1·K−2·s−1 for p-type behavior and 27 × 1010 W·m−1·K−2·s−1 for n-type behavior at 800 K. The breakthrough lies in the exceptional suppression of lattice thermal conductivity (κ1) by out-of-plane electric fields—at 1.13 eV/Å, κ1 is reduced to single-digit values (W·m−1·K−1), driving ZT to ~4 for n-type MoS2 at 300 K. This work demonstrates that synergistic engineering of strain, temperature, and electric fields effectively decouples the traditional trade-off among the Seebeck coefficient, conductivity, and thermal conductivity, providing a core optimization pathway for 2D thermoelectric materials via electric field-mediated κ1 regulation. Full article
(This article belongs to the Special Issue Quantum Beam Science: Feature Papers 2025)
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33 pages, 3040 KiB  
Article
A Physical-Enhanced Spatio-Temporal Graph Convolutional Network for River Flow Prediction
by Ruixi Huang, Yin Long and Tehseen Zia
Appl. Sci. 2025, 15(16), 9054; https://doi.org/10.3390/app15169054 - 17 Aug 2025
Viewed by 201
Abstract
River flow forecasting remains a critical yet challenging task in hydrological science, owing to the inherent trade-offs between physics-based models and data-driven methods. While physics-based models offer interpretability and process-based insights, they often struggle with real-world complexity and adaptability. Conversely, purely data-driven models, [...] Read more.
River flow forecasting remains a critical yet challenging task in hydrological science, owing to the inherent trade-offs between physics-based models and data-driven methods. While physics-based models offer interpretability and process-based insights, they often struggle with real-world complexity and adaptability. Conversely, purely data-driven models, though powerful in capturing data patterns, lack physical grounding and often underperform in extreme scenarios. To address this gap, we propose PESTGCN, a Physical-Enhanced Spatio-Temporal Graph Convolutional Network that integrates hydrological domain knowledge with the flexibility of graph-based learning. PESTGCN models the watershed system as a Heterogeneous Information Network (HIN), capturing various physical entities (e.g., gauge stations, rainfall stations, reservoirs) and their diverse interactions (e.g., spatial proximity, rainfall influence, and regulation effects) within a unified graph structure. To better capture the latent semantics, meta-path-based encoding is employed to model higher-order relationships. Furthermore, a hybrid attention mechanism incorporating both local temporal features and global spatial dependencies enables comprehensive sequence learning. Importantly, key variables from the HEC-HMS hydrological model are embedded into the framework to improve physical interpretability and generalization. Experimental results on four real-world benchmark watersheds demonstrate that PESTGCN achieves statistically significant improvements over existing state-of-the-art models, with relative reductions in MAE ranging from 5.3% to 13.6% across different forecast horizons. These results validate the effectiveness of combining physical priors with graph-based temporal modeling. Full article
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33 pages, 22477 KiB  
Article
Spatial Synergy Between Carbon Storage and Emissions in Coastal China: Insights from PLUS-InVEST and OPGD Models
by Chunlin Li, Jinhong Huang, Yibo Luo and Junjie Wang
Remote Sens. 2025, 17(16), 2859; https://doi.org/10.3390/rs17162859 - 16 Aug 2025
Viewed by 199
Abstract
Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict [...] Read more.
Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km2 loss of farmland and a 43,711 km2 increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article
(This article belongs to the Special Issue Carbon Sink Pattern and Land Spatial Optimization in Coastal Areas)
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18 pages, 4894 KiB  
Article
Machine Learning-Based Fracture Failure Analysis and Structural Optimization of Adhesive Joints
by Yalong Liu, Zewen Gu, Mingze Sun, Claire Guo and Xiaoxuan Ding
Appl. Sci. 2025, 15(16), 9041; https://doi.org/10.3390/app15169041 - 15 Aug 2025
Viewed by 195
Abstract
The growing use of composites in automotive and aerospace fields highlights the need for effective joining of dissimilar materials. Adhesive bonding offers significant advantages over traditional methods. Therefore, comprehensively exploring the relationship between multiple design variables and joint strength, and subsequently achieving accurate [...] Read more.
The growing use of composites in automotive and aerospace fields highlights the need for effective joining of dissimilar materials. Adhesive bonding offers significant advantages over traditional methods. Therefore, comprehensively exploring the relationship between multiple design variables and joint strength, and subsequently achieving accurate prediction of joint strength based on this understanding, is essential for enhancing the effectiveness and efficiency of adhesive joint structural optimization. However, the joint—the critical yet weakest part—has strength governed by complex structural variables that are not fully understood, limiting optimization potential. Based on the effectiveness of finite element simulation in tensile fracture mechanics, this study developed a deep neural network (DNN). Combining the DNN model with a genetic algorithm (GA), both single-objective and multi-objective optimization were conducted. The single-objective optimization focused solely on maximizing joint strength, while the multi-objective GA further quantified the Pareto optimal trade-offs between joint strength and bond area, identifying compromise solutions. The effectiveness of the optimized parameters was validated, demonstrating higher efficiency and accuracy compared to traditional optimization methods such as response surface methodology (RSM). This integrated approach provides a robust framework for predicting joint strength and achieving effective optimization of bonded structures. Full article
(This article belongs to the Special Issue New Sciences and Technologies in Composite Materials)
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