Search Results (2,189)

Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal the physical mechanism of rainfall in the basin and integrate monthly scale meteorological data to achieve monthly rainfall prediction. In this paper, we propose a rainfall prediction model coupled with a physical mechanism and a temporal convolutional network (TCN) to achieve the prediction of monthly rainfall in the basin, aiming to reveal the physical mechanism between rainfall factors in the basin based on the transfer entropy and the multidimensional Copula function and based on the physical mechanism which is embedded into the TCN to construct a dual-driven prediction model with both physical knowledge and data, while the SHAP is used to analyze the interpretability of the prediction model. The results are as follows: (1) Temperature, relative humidity, and evaporation are key characteristic factors driving rainfall. (2) The physical mechanism features between temperature, relative humidity, and evaporation can be described by the three-dimensional Gumbel–Hougaard Copula function, with a more concentrated data distribution of their joint distribution probability. (3) The PHY-TCN model can accurately fit the extremes of the rainfall series, improving the model accuracy in the training set by 3.82%, 1.39%, and 9.82% compared to TCN, CNN, and LSTM, respectively, and in the test set by 6.04%, 2.55%, and 8.91%, respectively. (4) Embedding physical mechanisms enhances the contribution of individual feature variables in the PHY-TCN model and increases the persuasiveness of the model. This study provides a new research framework for rainfall prediction in the YRB and analyzes the physical relationship between the input data and output results of the deep learning model. It has important practical significance and strategic value for guiding the optimal scheduling of water resources, improving the risk management level of the basin, and promoting the ecological protection and high-quality development of the YRB. Full article

Employee satisfaction, as a critical form of organisational social capital, represents a significant interdisciplinary topic in management and finance. A key question is whether it can be transformed into sustainable innovation momentum for corporates amid extreme crisis shocks. This study examines Chinese A-share listed corporates, utilising large-scale anonymous employee evaluation data from the Chinese employer review platform ‘KanZhun.com’, to construct corporate-level employee satisfaction indicators. Through econometric modelling, it investigates the impact of employee satisfaction on corporate innovation output during major crises and its underlying mechanisms. Findings reveal that during crises, employee satisfaction significantly enhances overall corporate innovation levels, with a particularly pronounced effect on green innovation. Mechanism analysis indicates that high employee satisfaction primarily drives innovation, especially green innovation, through two channels. These channels include reducing internal governance costs and alleviating external financing constraints. Heterogeneity tests further reveal that this effect is particularly pronounced in high-tech industries, technology-intensive sectors, non-state-owned corporates, and corporates under strong external institutional constraints or with relatively weak innovation capabilities. This study expands the theoretical boundaries of employee satisfaction’s economic value from an innovation perspective. It further provides Chinese empirical evidence for corporates seeking to enhance innovation resilience in complex environments via employee feedback and quality labour relations. Full article

Licorice (Glycyrrhiza glabra) is a perennial herb traditionally valued for its aromatic and therapeutic properties. In recent years, however, growing attention has shifted toward the technical and environmental potential of the plant’s industrial by-products, particularly the fibrous material left after extraction. This review integrates botanical knowledge with engineering and industrial perspectives, highlighting the role of licorice fiber in advancing sustainable innovation. The natural fiber obtained from licorice roots exhibits notable physical and mechanical qualities, including lightness, biodegradability, and compatibility with bio-based polymer matrices. These attributes make it a promising candidate for biocomposites used in green building and other sectors of the circular economy. Developing efficient recovery processes requires collaboration across disciplines, combining expertise in plant science, materials engineering, and industrial technology. The article also examines the economic and regulatory context driving the transition toward more circular and traceable production models. Increasing interest from companies, research institutions, and public bodies in valorizing licorice fiber and its derivatives is opening new market opportunities. Potential applications extend to agroindustry, eco-friendly cosmetics, bioeconomy, and sustainable construction. By linking botanical insights with innovative waste management strategies, licorice emerges as a resource capable of supporting integrated, competitive, and environmentally responsible industrial practices. Full article

Artificial intelligence technology holds significant importance for building intelligent question-answering systems in the field of coal mine safety and enhancing safety management levels. Currently, there is a lack of specialized large language models and high-quality question-answering datasets in this field. To address this, this study proposes a two-stage fine-tuning method based on Low-Rank Adaptation (LoRA) and Group Sequence Policy Optimization (GSPO) for training a question-answering model tailored to the coal mine safety domain. The research begins by constructing a dedicated question-answering dataset based on domain-specific regulatory documents. Subsequently, using Qwen2.5-7B Instruct as the base model, the study fine-tunes the model through supervised learning with LoRA technology, followed by further optimization of the model’s performance using the GSPO reinforcement learning algorithm. Experiments show that the model trained with this method exhibits significant improvements in coal mine safety-related tasks, achieving superior results on multiple automated evaluation metrics compared to contrast models of similar scale. This study validates the effectiveness of the two-stage fine-tuning method in adapting large language models (LLMs) to specific domains, providing a new technical approach for the intelligentization of coal mine safety. It should be noted that due to the lack of external data, this study relies on a self-constructed dataset and has not yet undergone external independent validation, which constitutes the main limitation of the current work. Full article

The rapidly evolving business landscape, driven by stringent energy conservation policies, compels construction firms to adopt energy-efficient project-centric structures, particularly in modern construction projects. These firms face a complex, multi-mode, resource-constrained, multi-project scheduling problem characterized by dynamic project arrivals and multiple resource constraints, including global, local, and non-renewable capacities. This environment pressures managers to simultaneously optimize the conflicting objectives of minimizing total project duration and total energy consumption. To address this challenge, we propose a novel multi-objective Smart Raccoon Family Optimization (SRFO) algorithm. The SRFO, a hybrid evolutionary approach, is designed to enhance global exploration and local exploitation. Its performance is boosted by integrating a non-dominated sorting mechanism, a dedicated energy-efficient search strategy, and enhanced genetic operators. The SRFO simultaneously optimizes two conflicting objectives: minimizing the total project duration and total energy consumption. This approach effectively integrates the unique constraint of off-site component production and on-site assembly within an intelligent scheduling framework. Empirical validation across benchmark problems and a real-world case study is conducted, comparing the SRFO with existing multi-objective approaches, such as NSGA-III, MOABC, and MOSMO. Performance is assessed using convergence and distribution metrics, augmented by TOPSIS-based multi-criteria decision-making. Results conclusively demonstrate that the proposed SRFO significantly outperforms existing approaches and offers a robust, high-quality solution for project management in energy-constrained environments. Full article

The Santa Elena Peninsula has experienced local subduction earthquakes in 1901 (7.7 Mw) and 1933 (6.9 Mw), during which local ground conditions, including deposits of longshore-current sediments, paleo-lagoon or marsh, sandspit, and ancient tidal channel sediments, exhibited various coseismic deformation behaviors in Quaternary soils of inferior geotechnical quality. This study shows that geophysical profiles from seismic refraction and shear-wave velocities are correlated with stratigraphic data from sedimentary sequences obtained from slope cutting and geotechnical drilling. This database is used to create a comprehensive map to describe the lithological units of Salinas’ urban geology. The thickness of the Tertiary–Quaternary sedimentary sequences and the depth to the bedrock of the Piñon and Cayo geological formations determine the periods of sites in these stratigraphic sequences, which range from 0.3 to 1.5 s. This study provides the first geotechnical zoning map for the city of Salinas at a scale of 1:25,000, which is a technical requirement of the Ecuadorian construction standard. This geotechnical zoning information is essential for appropriate land management in Salinas and its neighboring cities, La Libertad and Santa Elena, as well as for outlining municipal restrictions on future construction. Full article

The impacts of land use on stormwater runoff quality and Best Management Practices to mitigate these impacts have been investigated since the 1970s, yet challenges remain in providing a modeling approach that concomitantly considers contributions from different land use types. In densely developed urban areas, a buildup/washoff approach is often applied, while in rural areas, some type of erosion modeling is employed, as the processes of detachment, entrainment, and transport are fundamentally different. This study presents a coupled modeling approach within PCSWMM, integrating exponential buildup/washoff for impervious surfaces with the Modified Universal Soil Loss Equation (MUSLE) for pervious areas, including construction sites, to characterize water quality in the large mixed urban–rural Sparrovale catchment in Geelong, Australia. The watershed includes an innovative cascading system of 12 online NbS wetlands along one of the main tributaries, Armstrong Creek, to manage runoff quantity and quality, as well as 16 offline NbS wetlands that are tributary to the online system. A total of 78 samples for Total Suspended Solids (TSS), Total Phosphorus (TP), and Total Nitrogen (TN) were collected from six monitoring sites along Armstrong Creek during wet- and dry-weather events between May and July 2024 for model validation. The data were supplemented with six other catchment stormwater quality datasets collected during earlier studies, which provided an understanding of water quality status for the broader Geelong region. Results showed that average nutrient concentrations across all the sites ranged from 0.44 to 2.66 mg/L for TP and 0.69 to 5.7 mg/L for TN, spanning from within to above the ecological threshold ranges for eutrophication risk (TP: 0.042 to 1 mg/L, TN: 0.3 to 1.5 mg/L). In the study catchment, upstream wetlands reduced pollutant levels; however, downstream wetlands that received runoff from agriculture, residential areas, and, importantly, construction sites, showed a substantial increase in sediment and nutrient concentration. Water quality modeling revealed washoff parameters primarily influenced concentrations from established urban neighborhoods, whereas erosion parameters substantially impacted total pollutant loads for the larger system, demonstrating the importance of integrated modeling for capturing pollutant dynamics in heterogeneous, urbanizing catchments. The study results emphasize the need for spatially targeted management strategies to improve stormwater runoff quality and also show the potential for cascading wetlands to be an important element of the Nature-based Solution (NbS) runoff management system. Full article

In the field of precision agriculture, accurately detecting rice panicles is crucial for monitoring rice growth and managing rice production. To address the challenges posed by complex field backgrounds, including variety differences, variations across growth stages, background interference, and occlusion due to dense distribution, this study develops an improved YOLO11-based rice panicle detection model, termed DRPU-YOLO11. The model incorporates a task-oriented CSP-PGMA module in the backbone to enhance multi-scale feature extraction and provide richer representations for downstream detection. In the neck network, DySample and CGDown are adopted to strengthen global contextual feature aggregation and suppress background interference for small targets. Furthermore, fine-grained P2 level information is integrated with higher-level features through a cross-scale fusion module (CSP-ONMK) to improve detection robustness in dense and occluded scenes. In addition, the PowerTAL strategy adapts quality-aware label assignment to emphasize high-quality predictions during training. The experimental results based on a self-constructed dataset demonstrate that DRPU-YOLO11 significantly outperforms baseline models in rice panicle detection under complex field environments, achieving an accuracy of 82.5%. Compared with the baseline model YOLO11 and RT-DETR, the mAP₅₀ increases by 2.4% and 5.0%, respectively. These results indicate that the proposed task-driven design provides a practical and high-precision solution for rice panicle detection, with potential applications in rice growth monitoring and yield estimation. Full article

Tibetan sheep, a unique breed indigenous to the Qinghai–Tibet Plateau, exhibit remarkable adaptations to high-altitude hypoxia, and their muscle quality is a key economic determinant. However, the molecular mechanisms by which exercise regulates meat quality in this breed remain poorly understood. This study aimed to systematically investigate the effects of different exercise volumes on the biceps femoris muscle of Tibetan sheep, integrating histological analysis with high-throughput transcriptome sequencing. We compared a low-exercise group with a high-exercise group and found that long-term endurance exercise resulted in phenotypic changes suggestive of a shift toward oxidative muscle fiber characteristics. This adaptation was characterized by significantly reduced muscle fiber diameter and cross-sectional area, alongside a crucial increase in intramuscular fat content, collectively enhancing meat tenderness, flavor, and juiciness. Transcriptomic analysis revealed extensive gene expression reprogramming, identifying 208 mRNAs and 490 lncRNAs that were differentially expressed and primarily associated with muscle fiber transition and energy metabolism. Furthermore, we constructed a putative lncRNA-miRNA-mRNA competing endogenous RNA network based on expression correlations and bioinformatic predictions, highlighting potential key regulatory axes such as LOC105603384/miR-16-z/MYLK3, LOC121820630/miR-381-y/NOX4, and LOC132659150/oar-miR-329a-3p/NF1. These findings provide a new perspective on the molecular basis of exercise-induced muscle adaptation in high-altitude animals and offer a solid theoretical framework for improving meat quality through scientific livestock management. Full article

Surface water is essential for sustaining ecosystems and supporting human socio-economic development, yet pollution from urbanization increasingly threatens its ecological sustainability. The accurate prediction of dissolved oxygen (DO), as an important indicator of water quality, is crucial for water resource protection. To address the methodological gaps in current research, we propose a hybrid deep learning model (GCG) that integrates spatiotemporal correlations to enhance DO prediction accuracy through the systematic exploitation of latent data dependencies. This study proposes a three-stage modeling framework: (1) A novel adjacency matrix construction methodology based on Pearson correlation coefficients is developed to quantify spatial correlations between monitoring stations, enabling spatial feature aggregation via graph convolutional networks (GCNs); (2) the spatially enhanced features are subsequently processed through 1D convolutional neural networks (CNNs) to capture temporal local patterns; (3) model performance is comprehensively evaluated using four metrics: R², RMSE, MAE, and MAPE. The proposed model was implemented for DO prediction in Lake Taihu, China. Experimental results demonstrate that compared to conventional adjacency matrix construction methods, the Pearson correlation-based adjacency matrix confers advantages, achieving at least a 5% reduction in RMSE and over 10% improvement in MAE and MAPE. Furthermore, the GCG model outperformed the comparison model, with an R² enhancement of 8%, while reducing RMSE and MAE by over 70% and 60%, respectively. These results validate the model’s effectiveness in mining spatiotemporal correlations for regional water quality forecasting, offering a reliable tool toward sustainable water monitoring and ecosystem-based management. Full article

A strong safety culture is essential for managing human factors in complex systems and constitutes a strategic resource for supporting the sustainable operation of organizations. However, conventional approaches remain limited by unclear conceptual boundaries and a lack of mechanisms linking safety culture with other organizational safety elements. To address these gaps, this study develops a sustainable safety culture construction method grounded in accident causation theory. Using the 24Model, we establish a concise “culture–system–ability–acts” framework that operationalizes the pathways through which safety culture shapes organizational safety performance. The method integrates four components: conceptual clarification of safety culture, quantitative assessment, factor identification based on the 24Model, and Bayesian network analysis to quantify interdependencies among culture, systems, ability, and acts. Empirical evidence from coal mining enterprises shows that safety culture influences safety performance indirectly by shaping system implementation quality, workers’ safety ability, and safety-related actions. Enhancing “demand of safety training” substantially mitigated system deficiencies related to ineffective implementation of procedures, failure in enforcing procedures, lack of qualifications, and insufficient supervision. Improved training also strengthened workers’ knowledge of accident cases, consequences of violations, and technical standards, thereby reducing competence-related gaps and promoting more consistent safety supervision behaviors. Sensitivity analysis highlights the importance of reinforcing “safety responsibilities of line departments” and improving the dissemination of safety knowledge, particularly accident case knowledge. Overall, the findings empirically validate the dynamic “culture–system–ability–acts” transmission mechanism of the 24Model and provide a structured, quantitative pathway for advancing sustainable safety culture development. Full article

Cotton is an important economic crop, and its weed management directly affects yield and quality. In actual cotton fields, detection accuracy still faces challenges due to the complex types of weeds, variable morphologies, and environmental factors. Most existing models rely on the attention mechanism to improve performance, but channel attention tends to ignore spatial information, while full spatial attention brings high computational costs. Therefore, this paper proposes a grouped enhanced fusion attention mechanism (GEFA), which combines grouped convolution and local spatial attention to reduce complexity and parameter quantity while effectively enhancing feature expression ability. The GEFAY detection model constructed based on GEFA achieves good balance in efficiency, accuracy, and complexity on the CottonWeedDet12, VOC, and COCO datasets. Compared with classic attention methods, this model has the smallest increase in parameters and computational costs while significantly improving accuracy. It is more suitable for deployment on edge devices. The further designed end-to-end intelligent weed detection system and edge device deployment can achieve image detection on local maps and real-time cameras, with good practicality and scalability, providing effective technical support for intelligent visual applications in precision agriculture. Full article

In response to the difficulties in multi-disciplinary coordination, the complexity of schedule management, and the weakness of risk control in the construction of high-voltage substations, and based on the current construction status and historical experience of high-voltage projects in Jilin Province, this paper, from the perspectives of schedule and risk management, proposes a multi-disciplinary coordination and risk control strategy that integrates the work breakdown structure (WBS), design structure matrix (DSM), critical chain project management (CCPM), and the fuzzy analytic hierarchy process (FAHP). First, the task flow is decomposed using WBS, and DSM-based coupling analysis is employed to identify interdependencies among disciplines, thereby optimizing task sequencing and parallel arrangements. Second, an optimized project schedule model is established using CCPM, with aggregated buffers that enhance the reliability and flexibility of schedule management. Finally, a risk register is developed based on field investigations, and a three-dimensional quality–schedule–safety risk assessment model is constructed using FAHP; targeted risk prevention and control measures are then proposed according to the quantitative evaluation results. A 500 kV substation project in Jilin Province is adopted as a case study for application and verification. Compared with traditional serial scheduling, the proposed schedule optimization strategy shortens the overall project duration by 29.1%. Furthermore, targeted management recommendations were proposed based on the risk assessment results of the project. The proposed optimization strategy can provide theoretical support and practical guidance for the construction of high-voltage substations and their associated projects, forming an effective technical solution that is scalable and replicable, and it is of great significance for improving the level of project construction management. Full article

Background: Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder, with a prevalence of approximately 16% in Saudi Arabia. PCOS is associated with various health complications. Assessing the quality of life (QoL) of women with PCOS is crucial for effective management. Objectives: This study aims to translate and validate the Polycystic Ovary Syndrome Quality of Life scale (PCOSQOL) into Arabic for use among Arabic-speaking women. The study was designed to evaluate the psychometric properties of the Arabic PCOSQOL, including its reliability, validity, and responsiveness. Methods: A cross-sectional study was conducted among 207 Saudi women diagnosed with PCOS. Participants were recruited from family medicine and obstetrics and gynecology clinics at King Saud University Medical City, Riyadh, through an online survey. The PCOSQOL was translated into Arabic following the World Health Organization’s (WHO) forward–backward translation protocol. Psychometric evaluation included internal consistency, test–retest reliability (ICC), and construct validity. Results: The Arabic PCOSQOL demonstrated excellent psychometric performance, with high internal consistency (Cronbach’s α = 0.951) and good-to-excellent test–retest reliability (ICC = 0.760–0.885). Construct validity was supported by a four-factor structure explaining 62.5% of the total variance (KMO = 0.92; Bartlett’s p < 0.001). The subscales showed strong factor loadings (0.49–0.97). Older women (>25 years), married participants, and residents of the western and central regions reported significantly better quality of life (p < 0.05). Conclusions: The Arabic version of the PCOSQOL demonstrated excellent reliability, validity, and stability, confirming its suitability for assessing quality of life among Arabic-speaking women with PCOS. This validated tool can support both clinical practice and future research across Arabic populations. Full article

A well-designed layout for urban parks plays a crucial role in constructing livable cities and enhancing residents’ well-being. The provision of age-friendly park access is fundamental to building an elderly-friendly city. However, previous studies have lacked comprehensive analyses that integrate the distribution of the elderly population, park accessibility, park quality, environmental characteristics, and social equity within a unified framework. Specifically, the supply–demand imbalance mechanism underlying the spatial variations in accessibility has not been adequately addressed. This study employs an improved two-step floating catchment area (2SFCA) method, combined with Lorenz curves and urban park-adapted Gini coefficients, to examine the supply–demand relationship and allocation differences between the elderly population and parks at the neighborhood and community levels. The analysis highlights issues related to equity and accessibility and explores their spatial disparity and influencing factors. The key findings are as follows: (1) The classic 2SFCA model exhibits significant biases in evaluating park supply–demand relationships, accessibility, and equity at a fine-grained scale, indicating the necessity of high-precision modeling. (2) Park accessibility in the Old City of Nanjing follows a dual-ring pattern of high accessibility, contrasted with clustered areas of low accessibility, while accessibility equity shows a central–peripheral gradient. Overall equity is relatively low, with good walking accessibility within only about one-third of communities. (3) Park supply levels, neighborhood construction year, and plot ratios are the primary factors influencing park accessibility for elderly residents. The comprehensive aging index is positively correlated with the equity in park layout, whereas housing prices and neighborhood size do not exhibit a simple linear relationship with park accessibility or equity for elderly residents. These findings provide a comprehensive and realistic perspective for understanding elderly park accessibility and equity, offering decision-making references for enhancing urban livability, managing an aging society, and formulating spatial equity policies in the future. Full article