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Search Results (6,771)

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Keywords = the production of space

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23 pages, 5064 KiB  
Article
Study on Reasonable Well Spacing for Geothermal Development of Sandstone Geothermal Reservoir—A Case Study of Dezhou, Shandong Province, China
by Shuai Liu, Yan Yan, Lanxin Zhang, Weihua Song, Ying Feng, Guanhong Feng and Jingpeng Chen
Energies 2025, 18(15), 4149; https://doi.org/10.3390/en18154149 - 5 Aug 2025
Abstract
Shandong Province is rich in geothermal resources, mainly stored in sandstone reservoirs. The setting of reasonable well spacing in the early stage of large-scale recharge has not attracted enough attention. The problem of small well spacing in geothermal engineering is particularly prominent in [...] Read more.
Shandong Province is rich in geothermal resources, mainly stored in sandstone reservoirs. The setting of reasonable well spacing in the early stage of large-scale recharge has not attracted enough attention. The problem of small well spacing in geothermal engineering is particularly prominent in the sandstone thermal reservoir production area represented by Dezhou. Based on the measured data of temperature, flow, and water level, this paper constructs a typical engineering numerical model by using TOUGH2 software. It is found that when the distance between production and recharge wells is 180 m, the amount of production and recharge is 60 m3/h, and the temperature of reinjection is 30 °C, the temperature of the production well will decrease rapidly after 10 years of production and recharge. In order to solve the problem of thermal breakthrough, three optimization schemes are assumed: reducing the reinjection temperature to reduce the amount of re-injection when the amount of heat is the same, reducing the amount of production and injection when the temperature of production and injection is constant, and stopping production after the temperature of the production well decreases. However, the results show that the three schemes cannot solve the problem of thermal breakthrough or meet production demand. Therefore, it is necessary to set reasonable well spacing. Therefore, based on the strata near the Hydrological Homeland in Decheng District, the reasonable spacing of production and recharge wells is achieved by numerical simulation. Under a volumetric flux scenario ranging from 60 to 80 m3/h, the well spacing should exceed 400 m. For a volumetric flux between 80 and 140 m3/h, it is recommended that the well spacing be greater than 600 m. Full article
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23 pages, 23638 KiB  
Article
Enhanced YOLO and Scanning Portal System for Vehicle Component Detection
by Feng Ye, Mingzhe Yuan, Chen Luo, Shuo Li, Duotao Pan, Wenhong Wang, Feidao Cao and Diwen Chen
Sensors 2025, 25(15), 4809; https://doi.org/10.3390/s25154809 - 5 Aug 2025
Abstract
In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of [...] Read more.
In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article
(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)
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23 pages, 5217 KiB  
Article
High-Performance Pd-Pt/α-MnO2 Catalysts for the Oxidation of Toluene
by Ning Dong, Wenjin Wang, Xuelong Zheng, Huan Liu, Jingjing Zhang, Qing Ye and Hongxing Dai
Catalysts 2025, 15(8), 746; https://doi.org/10.3390/catal15080746 - 5 Aug 2025
Abstract
Herein, α-MnO2-supported Pt-Pd bimetal (xPd-yPt/α-MnO2; x and y are the weight loadings (wt%) of Pd and Pt, respectively; x = 0, 0.23, 0.47, 0.93, and 0.92 wt%; and y = 0.91, 0.21, [...] Read more.
Herein, α-MnO2-supported Pt-Pd bimetal (xPd-yPt/α-MnO2; x and y are the weight loadings (wt%) of Pd and Pt, respectively; x = 0, 0.23, 0.47, 0.93, and 0.92 wt%; and y = 0.91, 0.21, 0.46, 0.89, and 0 wt%) catalysts were prepared using the polyvinyl alcohol-protected NaBH4 reduction method. The physicochemical properties of the catalysts were determined by means of various techniques and their catalytic activities for toluene oxidation were evaluated. It was found that among the xPd-yPt/α-MnO2 samples, 0.93Pd-0.89Pt/α-MnO2 showed the best catalytic performance, with the toluene oxidation rate at 156 °C (rcat) and space velocity = 60,000 mL/(g h) being 6.34 × 10−4 mol/(g s), much higher than that of 0.91Pt/α-MnO2 (1.31 × 10−4 mol/(g s)) and that of 0.92Pd/α-MnO2 (6.13 × 10−5 mol/(g s)) at the same temperature. The supported Pd-Pt bimetallic catalysts possessed higher Mn3+/Mn4+ and Oads/Olatt molar ratios, which favored the enhancement in catalytic activity of the supported Pd-Pt bimetallic catalysts. Furthermore, the 0.47Pd-0.46Pt/α-MnO2 sample showed better resistance to sulfur dioxide poisoning. The partial deactivation of 0.47Pd-0.46Pt/α-MnO2 was attributed to the formation of sulfate species on the sample surface, which covered the active site of the sample, thus decreasing its toluene oxidation activity. In addition, the in situ DRIFTS results demonstrated that benzaldehyde and benzoate were the intermediate products of toluene oxidation. Full article
(This article belongs to the Section Environmental Catalysis)
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10 pages, 174 KiB  
Article
Between Place and Identity: Spatial Production and the Poetics of Liminality in Jeffrey Eugenides’ Fiction
by Maria Miruna Ciocoi-Pop
Literature 2025, 5(3), 19; https://doi.org/10.3390/literature5030019 - 4 Aug 2025
Abstract
This article investigates the role of space in the fiction of Jeffrey Eugenides, focusing on The Virgin Suicides (1993) and Middlesex (2002) through the lens of spatial theory. Drawing on key thinkers such as Henri Lefebvre, Michel Foucault, Edward Soja, Yi-Fu Tuan, and [...] Read more.
This article investigates the role of space in the fiction of Jeffrey Eugenides, focusing on The Virgin Suicides (1993) and Middlesex (2002) through the lens of spatial theory. Drawing on key thinkers such as Henri Lefebvre, Michel Foucault, Edward Soja, Yi-Fu Tuan, and Doreen Massey, the study explores how Eugenides constructs spatial environments that not only frame but actively shape the identities, desires, and traumas of his characters. In The Virgin Suicides, suburban domestic spaces are shown to function as heterotopias—sites of surveillance, repression, and mythologized femininity—while Middlesex engages with transnational and urban spaces to narrate diasporic and intersex identity as dynamic, embodied, and liminal. The analysis reveals that Eugenides uses space as both a narrative device and a thematic concern to interrogate gender, memory, and power. Rather than passive backdrops, the novelistic spaces become charged arenas of conflict and transformation, reflecting and resisting dominant socio-cultural discourses. This study argues that space in Eugenides’ fiction operates as a critical register for understanding the politics of belonging and the production of subjectivity. By situating Eugenides within the broader field of literary spatiality, this article contributes to contemporary debates in literary geography, gender studies, and American fiction. Full article
25 pages, 5978 KiB  
Review
Global Research Trends on the Role of Soil Erosion in Carbon Cycling Under Climate Change: A Bibliometric Analysis (1994–2024)
by Yongfu Li, Xiao Zhang, Yang Zhao, Xiaolin Yin, Xiong Wu and Liping Su
Atmosphere 2025, 16(8), 934; https://doi.org/10.3390/atmos16080934 (registering DOI) - 4 Aug 2025
Abstract
Against the backdrop of multifaceted strategies to combat climate change, understanding soil erosion’s role in carbon cycling is critical due to terrestrial carbon pool vulnerability. This study integrates bibliometric methods with visualization tools (CiteSpace, VOSviewer) to analyze 3880 Web of Science core publications [...] Read more.
Against the backdrop of multifaceted strategies to combat climate change, understanding soil erosion’s role in carbon cycling is critical due to terrestrial carbon pool vulnerability. This study integrates bibliometric methods with visualization tools (CiteSpace, VOSviewer) to analyze 3880 Web of Science core publications (1994–2024, inclusive), constructing knowledge graphs and forecasting trends. The results show exponential publication growth, shifting from slow development (1994–2011) to rapid expansion (2012–2024), aligning with international climate policy milestones. The Chinese Academy of Sciences led productivity (519 articles), while the US demonstrated major influence (H-index 117; 52,297 citations), creating a China–US bipolar research pattern. It was also found that Dutch journals dominate this research field. A keyword analysis revealed a shift from erosion-driven carbon transport to ecosystem service assessments. Emerging hotspots include microbial community regulation, climate–erosion feedback, and model–policy integration, though developing country collaboration remains limited. Future research should prioritize isotope tracing, multiscale modeling, and studies in ecologically vulnerable regions to enhance global soil carbon management. This study provides a novel analytical framework and forward-looking perspective for the soil erosion research on soil carbon cycling, serving as an extension of climate change mitigation strategies. Full article
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20 pages, 2800 KiB  
Article
An Enhanced NSGA-II Driven by Deep Reinforcement Learning to Mixed Flow Assembly Workshop Scheduling System with Constraints of Continuous Processing and Mold Changing
by Bihao Yang, Jie Chen, Xiongxin Xiao, Sidi Li and Teng Ren
Systems 2025, 13(8), 659; https://doi.org/10.3390/systems13080659 - 4 Aug 2025
Abstract
Mixed-flow assembly lines are widely employed in industrial manufacturing to handle diverse production tasks. For mixed flow assembly lines that involve mold changes and greater processing difficulties, there are currently two approaches: batch production and production according to order sequence. The first approach [...] Read more.
Mixed-flow assembly lines are widely employed in industrial manufacturing to handle diverse production tasks. For mixed flow assembly lines that involve mold changes and greater processing difficulties, there are currently two approaches: batch production and production according to order sequence. The first approach struggles to meet the processing constraints of workpieces with higher production difficulty, while the second approach requires the development of suitable scheduling schemes to balance mold changes and continuous processing. Therefore, under the second approach, developing an excellent scheduling scheme is a challenging problem. This study addresses the mixed-flow assembly shop scheduling problem, considering continuous processing and mold-changing constraints, by developing a multi-objective optimization model to minimize additional production time and customer waiting time. As this NP-hard problem poses significant challenges in solution space exploration, the conventional NSGA-II algorithm suffers from limited local search capability. To address this, we propose an enhanced NSGA-II algorithm (RLVNS-NSGA-II) integrating deep reinforcement learning. Our approach combines multiple neighborhood search operators with deep reinforcement learning, which dynamically utilizes population diversity and objective function data to guide and strengthen local search. Simulation experiments confirm that the proposed algorithm surpasses existing methods in local search performance. Compared to VNS-NSGA-II and SVNS-NSGA-II, the RLVNS-NSGA-II algorithm achieved hypervolume improvements ranging from 19.72% to 42.88% and 12.63% to 31.19%, respectively. Full article
(This article belongs to the Section Systems Engineering)
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16 pages, 1618 KiB  
Article
Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation
by Bingchen Liu, Guangyuan Dong, Zihao Li, Yuanyuan Fang, Jingchen Li, Wenqi Sun, Bohan Zhang, Changzhi Li and Xin Li
Mathematics 2025, 13(15), 2496; https://doi.org/10.3390/math13152496 - 3 Aug 2025
Viewed by 148
Abstract
Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constructed from users’ historical interaction [...] Read more.
Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constructed from users’ historical interaction data now incorporates multiattribute information, including timestamps, images, and textual content. The information of multiple modalities is difficult to effectively utilize due to their different representation structures and spaces. The existing methods attempt to utilize the above information through simple embedding representation and aggregation, but ignore targeted representation learning for information with different attributes and learning effective weights for aggregation. In addition, existing methods are not sufficient for effectively modeling temporal information. In this article, we propose MTR, a knowledge graph recommendation framework based on mixture of experts network. To achieve this goal, we use a mixture-of-experts network to learn targeted representations and weights of different product attributes for effective modeling and utilization. In addition, we effectively model the temporal information during the user shopping process. A thorough experimental study on popular benchmarks validates that MTR can achieve competitive results. Full article
(This article belongs to the Special Issue Data-Driven Decentralized Learning for Future Communication Networks)
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28 pages, 2448 KiB  
Article
ATENEA4SME: Industrial SME Self-Evaluation of Energy Efficiency
by Antonio Ferraro, Giacomo Bruni, Marcello Salvio, Milena Marroccoli, Antonio Telesca, Chiara Martini, Federico Alberto Tocchetti and Antonio D’Angola
Energies 2025, 18(15), 4094; https://doi.org/10.3390/en18154094 - 1 Aug 2025
Viewed by 117
Abstract
Promoting energy efficiency in the Italian production sector is significantly hampered by the lack of knowledge, the scarcity and the limited distribution of tools for supporting energy audits in small and medium-sized enterprises (SMEs) in a wide range of Italian economic sectors (industry, [...] Read more.
Promoting energy efficiency in the Italian production sector is significantly hampered by the lack of knowledge, the scarcity and the limited distribution of tools for supporting energy audits in small and medium-sized enterprises (SMEs) in a wide range of Italian economic sectors (industry, tertiary sector, transport). The Advanced Tool for ENErgy Audit for SMEs, ATENEA4SME, is intended to help SMEs promote energy-efficiency projects, supports energy audits and self-evaluation of energy consumption. The tool uses an original mathematical model that takes into account the results of questionnaires and a multi-criteria analysis to generate recommendations for energy efficiency investments. This article will give a thorough explanation of the tool, emphasizing and outlining the sections as well as the procedures to get the ultimate summary of the energy usage of the enterprises under investigation and the potential for energy saving. From a technological and financial perspective, the tool helps to remove obstacles to the development of energy-efficiency measures. In this article, the IT and methodological structure of the tool will therefore be extensively described, and its operation for the context of SMEs will be illustrated, with application cases. Ample space will be allocated to the dissemination campaign and the replicability of the tool for all economic sectors of the industrial and tertiary sectors. Full article
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36 pages, 2272 KiB  
Article
Failure Cause Analysis Under Progressive Type-II Censoring Using Generalized Linear Exponential Competing Risks Model with Medical and Industrial Applications
by Shafya Alhidairah, Farouq Mohammad A. Alam and Mazen Nassar
Axioms 2025, 14(8), 595; https://doi.org/10.3390/axioms14080595 - 1 Aug 2025
Viewed by 182
Abstract
This study focuses on analyzing progressive Type-II right censoring competing risks datasets. The latent causes of failures are assumed to follow independent generalized linear exponential distributions. The maximum likelihood and maximum product of spacing methods are employed to estimate the unknown parameters and [...] Read more.
This study focuses on analyzing progressive Type-II right censoring competing risks datasets. The latent causes of failures are assumed to follow independent generalized linear exponential distributions. The maximum likelihood and maximum product of spacing methods are employed to estimate the unknown parameters and survival indices. Furthermore, approximate confidence intervals are derived using the asymptotic normality of the maximum likelihood and the maximum product of spacing estimators. Additionally, bootstrap methods are employed to construct confidence intervals. A comprehensive simulation study is carried out to evaluate the effectiveness of these estimation approaches. Finally, real-world datasets are analyzed to illustrate the practical applicability of the proposed model. Full article
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21 pages, 4761 KiB  
Article
Enhanced Dynamic Game Method for Offshore Wind Turbine Airfoil Optimization Design
by Rui Meng, Jintao Song, Xueqing Ren and Xuhui Chen
J. Mar. Sci. Eng. 2025, 13(8), 1481; https://doi.org/10.3390/jmse13081481 - 31 Jul 2025
Viewed by 156
Abstract
The novel enhanced dynamic game method (EDGM) is proposed to advance game-based design approaches, with a focus on enhancing solution distribution, precision, and the ability to reveal the dynamic influence sensitivity of design variables on objective functions. An integrated mathematical model is developed [...] Read more.
The novel enhanced dynamic game method (EDGM) is proposed to advance game-based design approaches, with a focus on enhancing solution distribution, precision, and the ability to reveal the dynamic influence sensitivity of design variables on objective functions. An integrated mathematical model is developed by combining EDGM with PARSEC and CST parameterization methods, forming a systematic framework for offshore wind turbine airfoil optimization. Targeting airfoils with approximately 30% and 35% thickness, the study aims to improve annual energy production (AEP) and optimize the polar moment of inertia. Redesigned airfoils using the EDGM-integrated model exhibit significant enhancements in aerodynamic performance and anti-flutter capability compared to baseline airfoils DU97W300 and DU99W350. The methodology’s superiority is validated through analyses of pressure distributions, lift-to-drag ratios, and streamline patterns, as well as comparative evaluations using HV and Spacing metrics, demonstrating EDGM’s potential for broader engineering applications in complex multi-objective optimization scenarios. Full article
(This article belongs to the Section Coastal Engineering)
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11 pages, 1219 KiB  
Article
The Church and Academia Model: New Paradigm for Spirituality and Mental Health Research
by Marta Illueca, Samantha M. Meints, Megan M. Miller, Dikachi Osaji and Benjamin R. Doolittle
Religions 2025, 16(8), 998; https://doi.org/10.3390/rel16080998 (registering DOI) - 31 Jul 2025
Viewed by 185
Abstract
Ongoing interest in the intersection of spirituality and health has prompted a need for integrated research. This report proposes a distinct approach in a model that allows for successful and harmonious cross-fertilization within these latter two areas of interest. Our work is especially [...] Read more.
Ongoing interest in the intersection of spirituality and health has prompted a need for integrated research. This report proposes a distinct approach in a model that allows for successful and harmonious cross-fertilization within these latter two areas of interest. Our work is especially pertinent to inquiries around the role of spirituality in mental health, with special attention to chronic pain conditions. The latter have become an open channel for novel avenues to explore the field of spirituality-based interventions within the arena of psychological inquiry. To address this, the authors developed and implemented the Church and Academia Model, a prototype for an innovative collaborative research project, with the aim of exploring the role of devotional practices, and their potential to be used as therapeutic co-adjuvants or tools to enhance the coping skills of patients with chronic pain. Keeping in mind that the church presents a rich landscape for clinical inquiry with broad relevance for clinicians and society at large, we created a unique hybrid research model. This is a new paradigm that focuses on distinct and well-defined studies where the funding, protocol writing, study design, and implementation are shared by experts from both the pastoral and clinical spaces. A team of theologians, researchers, and healthcare providers, including clinical pain psychologists, built a coalition leveraging their respective skill sets. Each expert is housed in their own environs, creating a functional network that has proven academically productive and pastorally effective. Key outputs include the creation and validation of a new psychometric measure, the Pain-related PRAYER Scale (PPRAYERS), an associated bedside prayer tool and a full-scale dissemination strategy through journal publications and specialty society conferences. This collaborative prototype is also an ideal fit for integrated knowledge translation platforms, and it is a promising paradigm for future collaborative projects focused on spirituality and mental health. Full article
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25 pages, 3785 KiB  
Article
Evolutionary Algorithms for the Optimal Design of Robotic Cells: A Dual Approximation for Space and Time
by Raúl-Alberto Sánchez-Sosa and Ernesto Chavero-Navarrete
Appl. Sci. 2025, 15(15), 8455; https://doi.org/10.3390/app15158455 - 30 Jul 2025
Viewed by 197
Abstract
The optimization of robotic cells is a key challenge in the manufacturing industry due to the need to maximize efficiency in limited spaces and minimize operation times. Traditional cell design methods often face challenges due to the high complexity and dynamic nature of [...] Read more.
The optimization of robotic cells is a key challenge in the manufacturing industry due to the need to maximize efficiency in limited spaces and minimize operation times. Traditional cell design methods often face challenges due to the high complexity and dynamic nature of real-world applications. In response, this study presents a dual approach to optimize both spatial design and traversal time in robotic cells, using bioinspired evolutionary algorithms. Initially, a genetic algorithm is employed to optimize the layout of the cell elements, reducing space usage and avoiding interferences between workstations. Subsequently, an ant colony optimization algorithm is used to optimize the robots’ trajectories, minimizing cycle time. Through simulations and a digital model of the cell, key metrics such as total space reduction, operational time improvement, and productivity increase are evaluated. The results demonstrate that the combination of both approaches achieves significant improvements, enabling an average reduction of 21.19% in the occupied area and up to 20.15% in operational cycle time, consistently outperforming traditional methods. This approach has the potential to be applied in various industrial configurations, representing a relevant contribution in the integration of artificial intelligence techniques for the enhancement of robotic systems. Full article
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15 pages, 2006 KiB  
Article
Hydrological Responses to Territorial Spatial Change in the Xitiaoxi River Basin: A Simulation Study Using the SWAT Model Driven by China Meteorological Assimilation Driving Datasets
by Dongyan Kong, Huiguang Chen and Kongsen Wu
Water 2025, 17(15), 2267; https://doi.org/10.3390/w17152267 - 30 Jul 2025
Viewed by 244
Abstract
The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined [...] Read more.
The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article
(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Edition)
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27 pages, 9975 KiB  
Article
Study on the Hydrogeological Characteristics of Roof Limestone Aquifers After Mining Damage in Karst Mining Areas
by Xianzhi Shi, Guosheng Xu, Ziwei Qian and Weiqiang Zhang
Water 2025, 17(15), 2264; https://doi.org/10.3390/w17152264 - 30 Jul 2025
Viewed by 232
Abstract
To study hydrogeological characteristics after the occurrence of abnormal water bursts from the weak water-rich (permeable) aquifer of the Changxing Formation limestone overlying deep working faces during production in Guizhou karst landform mining areas, hydrogeological data covering the exploration and production periods of [...] Read more.
To study hydrogeological characteristics after the occurrence of abnormal water bursts from the weak water-rich (permeable) aquifer of the Changxing Formation limestone overlying deep working faces during production in Guizhou karst landform mining areas, hydrogeological data covering the exploration and production periods of the Xinhua mining region in Jinsha County, Guizhou Province, were collected. On the basis of surface and underground drilling, geophysical exploration techniques, empirical equations, and indoor material simulation methods, the hydrogeological evolution characteristics of the Changxing Formation limestone in the mining region after mining damage to coalbed 9 were studied. The research results indicated that the ratio of the height of the roof failure fracture zone (as obtained via numerical simulation and ground borehole detection) to the mining height exceeded 25.78, which is far greater than the empirical model calculation values (from 13.0 to 15.8). After mining the underlying coalbed 9, an abnormal water-rich area developed in the Changxing Formation limestone, and mining damage fractures led to the connection of the original dissolution fissures and karst caves within the limestone, resulting in the weak water-rich (permeable) aquifer of the Changxing Formation limestone becoming a strong water-rich (permeable) aquifer, which served as the water source for mine water bursts. Over time, after mining damage occurrence, the voids in the Changxing Formation limestone were gradually filled with various substances, yielding water storage space and connectivity decreases. The specific yield decreased with an increasing water burst time and interval after the cessation of mining in the supply area, and the correlation coefficient R was 0.964, indicating a high degree of correlation between the two parameters. Full article
(This article belongs to the Section Hydrogeology)
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21 pages, 764 KiB  
Article
Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
by Yung-Tsan Jou, Hsueh-Lin Chang and Riana Magdalena Silitonga
Appl. Sci. 2025, 15(15), 8417; https://doi.org/10.3390/app15158417 - 29 Jul 2025
Viewed by 218
Abstract
This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt [...] Read more.
This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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