Search Results (6,543)

Finding reliable, sustainable, and economical methods for addressing the relentless increase in plastic production and the corresponding rise in plastic waste within terrestrial and marine environments has garnered significant attention from environmental organizations and policymakers worldwide. This study presents a comprehensive analysis of the low-heating-rate thermal degradation of high-density polyethylene (HDPE) plastic in conjunction with date seed powder (DSP), utilizing thermogravimetric analysis coupled with Fourier transform infrared spectroscopy (TGA/FTIR), machine learning convolutional deep neural networks (CDNNs), multiple linear regression model (MLRM) and thermokinetics. The TGA/FTIR experimental measurements indicated a synergistic interaction between the selected materials, facilitated by the presence of hemicellulose and cellulose in the DSP biomass. In contrast, the presence of lignin was found to hinder degradation at elevated temperatures. The application of machine learning CDNNs facilitated the formulation and training of learning algorithms, resulting in an optimized architectural composition comprising three hidden neurons and employing 27,456 epochs. This modeling approach generated predicted responses that are closely aligned with experimental results (R²~0.939) when comparing the responses from a formulated MLRM model (R²~0.818). The CDNN models were utilized to estimate interpolated thermograms, representing the limits of experimental variability and conditions, thereby highlighting temperature as the most sensitive parameter governing the degradation process. The Borchardt and Daniels (BD) model-fitting and Kissinger–Akahira–Sunose (KAS) model-free kinetic methods were employed to estimate the kinetic and thermodynamic parameters of the degradation process. This yielded activation energy estimates ranging from 40.419 to 91.010 kJ·mol⁻¹ and from 96.316 to 226.286 kJ·mol⁻¹ for the selected kinetic models, respectively, while the D2 and D3 diffusion models were identified as the preferred solid-state reaction models for the process. It is anticipated that this study will aid plastic manufacturers, environmental organizations, and policymakers in identifying energy-reducing pathways for the end-of-life thermal degradation of plastics. Full article

Staphylococcus aureus-induced mastitis is a significant cause of economic losses in the dairy industry, yet its molecular mechanisms remain poorly defined. Although ferroptosis, a regulated cell death process, is associated with inflammatory diseases, its role in bovine mastitis is unknown. In this study, 11 S. aureus strains were isolated from milk samples obtained from cows with clinical or subclinical mastitis. Transcriptome analysis of Mac-T cells challenged with isolated S. aureus identified differentially expressed genes (DEGs). Enrichment analysis revealed significant associations between DEG clusters and traits related to bovine mastitis. KEGG pathway enrichment revealed ferroptosis, Toll-like receptor, and TNF signaling as significantly enriched pathways. Weighted gene co-expression network analysis (WGCNA) further prioritized ferroptosis-related genes (HMOX1, SLC11A2, STEAP3, SAT1, and VDAC2) involved in iron metabolism. Notably, the expression levels of HMOX1 and SAT1 were significantly increased in S. aureus-challenged Mac-T cells, and this upregulation was consistent with trends observed in transcriptome data from mother–daughter pairs of cows with subclinical mastitis caused by S. aureus infection. Furthermore, Ferrostatin-1 treatment significantly reduced the expression of HMOX1 and SAT1 in S. aureus-challenged cells, confirming the involvement of ferroptosis in this process. This study reveals that ferroptosis plays a key role in S. aureus-induced mastitis and highlights its potential as a target for molecular breeding strategies aimed at improving bovine mastitis resistance. Full article

Nowadays, induction motors are an essential part of industrial development. Faults due to short-circuit turns within induction motors are “incipient faults”. This type of failure affects engine operation through undesirable vibrations. Such vibrations negatively affect the operation of the system or the products with which said motor is in contact. Early fault detection prevents sudden downtime in the industry that can result in heavy economic losses. The incipient failures these motors can present have been a vast research topic worldwide. Existing methodologies for detecting incipient faults in alternating current motors have the problem that they are implemented at the simulation level, or are invasive, or do not allow in situ measurements, or their digital implementation is complex. This article presents the design and development of a purpose-specific system capable of detecting short-circuit faults in the turns of the induction motor winding without interrupting the motor’s working conditions, allowing online measurements. This system is standalone, portable and allows non-invasive and in situ measurements to obtain phase currents. These data form classified descriptors using a multilayer perceptron neural network. This type of neural network enables agile and efficient digital processing. The developed neural network could classify current faults with an accuracy rate of 93.18%. The neural network was successfully implemented on a low-cost and low-range purpose-specific Field Programmable Gate Array board for online processing, taking advantage of its computing power and real time processing features. The measurement of phase current and the class of fault detected is displayed on a liquid-crystal display screen, allowing the user to take necessary actions before major faults occur. Full article

This article examines the relationship between environmental income dependence and household vulnerability in rural settings. Using household-level livelihood data from the Poverty Environment Network (PEN) dataset of Nepal, we construct a household vulnerability index and analyze its relationship with environmental dependence, measured as the share of environmental income in total income, while controlling for other variables. The findings reveal that higher environmental dependence significantly increases household vulnerability. In contrast, household debt helps mitigate vulnerability by providing financial support and enabling productive investments. However, high dependency ratios and exposure to shocks exacerbate vulnerability by limiting income generation and destabilizing livelihoods. Policy measures such as promoting economic diversification and social safety net programs could reduce environmental dependence and mitigate household vulnerability in rural Nepal. Furthermore, providing timely access to credit during hardships and addressing unforeseen shocks could enhance household resilience. Full article

Acid rain has many negative effects on the ecological environment and poses serious abiotic stress onto plants, resulting in substantial ecological and economic impairments annually. Ilex chinensis, a well-known medicinal plant, is sensitive to acid rain, but its response mechanisms are unclear. In this study, we simulated sulfuric acid rain (SAR), mixed acid rain (MIX), and nitric acid rain (NAR) at different pH values to investigate their effects on growth condition, photosynthesis, antioxidants, and nitrogen metabolites. We also explored the metabolic pathways and key genes involved in the response of I. chinensis to acid rain through transcriptome analysis. Physiological analysis showed that I. chinensis suffered the most significant inhibition at pH 3.0, which is manifested in the decrease in height growth rate, specific leaf weight, photosynthetic pigments content, net photosynthetic rate, stomatal conductance, and transpiration rate; the increase in MDA content and SOD activity; and the reduction in glutamine synthetase activity, nitrogen content, and proline content. Transcriptome analysis isolated 314 and 21 shared differentially expressed genes (DEGs) from I. chinensis treated with acid rain at pH 3.0 for 5 d and 15 d, respectively. KEGG enrichment analysis found that different types of acid rain caused changes in multiple metabolic pathways of I. chinensis, and the shared DEGs in 5 d treatment were mainly enriched in ribosomes, oxidative phosphorylation, and glycolysis/glycolysis, etc. The shared DEGs in 115 d treatment were mainly enriched in sulfur metabolism, RNA polymerase, cysteine and methionine metabolism, etc. Further research on gene regulatory networks at the two time points showed that the key pathways of I. chinensis, in response to acid rain stress, include plant–pathogen interaction, MAPK signaling pathway-plant, protein processing in the endoplasmic reticulum, ubiquitin mediated proteolysis, etc., in which 6 hub genes were identified, including TRINITY_DN13584_c0_g1, TRINITY_DN164_c0_g4, TRINITY_DN654_c0_g1, TRINITY_DN13611_c1_g2, TRINITY_DN21290_c0_g2, TRINITY_DN44216_c0_g1. Our findings provide a basis for exploring the regulatory mechanisms of I. chinensis in response to acid rain at the physiological and molecular levels, and for identifying candidate genes with acid tolerance potential. Full article

This review paper provides a comprehensive examination of current strategies and technical considerations for reducing return temperatures in district heating (DH) systems, aiming to enhance the utilization of available thermal energy. Return temperature, a parameter indirectly influenced by various system-level factors, cannot be adjusted directly but requires careful management throughout the design, commissioning, operation, and control phases. This paper explores several key factors affecting return temperature, including DH network, heat storage, and control strategies as well as the return temperature effect on the heat source. This paper also considers the influence of non-technical aspects, such as pricing strategies and maintenance practices, on system performance. The discussion extends to the complex interplay between low return temperatures and temperature differences, and between operational temperature schemes and economic considerations. Concluding remarks emphasize the importance of adopting a holistic approach that integrates technical, operational, and economic factors to improve DH system efficiency. This review highlights the need for comprehensive system-level optimization, effective management of system components, and consideration of unique heat production characteristics. By addressing these aspects, this study provides a framework for advancing DH system performance through optimized return temperature management. Full article

Coastal areas are highly dynamic environments, vulnerable to natural processes and human interventions. This study presents the first application of the Integrated Coastal Vulnerability Index (ICVI) in Cyprus, focusing on two major tourism-dependent beaches, Fig Tree Bay and Vrysi Beach, located along the Protaras coastline. Despite their economic significance, these coastal areas face increasing vulnerability due to intensive tourism-driven modifications and natural coastal dynamics, necessitating a structured assessment framework. This research addresses this gap by integrating the ICVI with geographical information system (GIS) and analytic hierarchy process (AHP) methodologies to evaluate the coastal risks in this tourism-dependent environment, providing a replicable approach for similar Mediterranean coastal settings. Ten key parameters were analysed, including coastal slope, rate of coastline erosion, geomorphology, elevation, tidal range, wave height, relative sea level rise, land cover, population density, and road network. The results revealed spatial variations in vulnerability, with 16% of the coastline classified as having very high vulnerability and another 16% as having high vulnerability. Fig Tree Bay, which is part of this coastline, emerged as a critical hotspot due to its geomorphological instability, low elevation, and intensive human interventions, including seasonal beach modifications and infrastructure development. This study underscores the need for sustainable coastal management practices, including dune preservation, controlled development, and the integration of the ICVI into planning frameworks to balance economic growth and environmental conservation. Full article

With the intensification of global climate change, the frequency and intensity of urban flood disasters have been increasing significantly, highlighting the necessity for a scientific assessment of urban flood risks. However, most existing studies focus primarily on the spatial distribution of urban flood data and their socio-economic impacts, with limited attention on the urban flood control engineering system (UFCES) itself and the analysis of urban flood risks from the perspective of the degree of system failure. To address this gap, we proposed a rapid prediction method for key information of the UFCES based on a machine learning model. With the aim of improving the accuracy and timeliness of information prediction, we employed a coupled modeling approach that integrates physical mechanisms with data-driven methods to simulate and predict the information. Taking the Wusha River Basin in Nanchang City as a case study, we generated the training, validation, and testing datasets for machine learning using the urban flood mechanism model. Subsequently, we compared the prediction performance of four machine learning models: random forest (RF), XGBoost (XGB), support vector regression (SVR), and the backpropagation neural network (BP). The results indicate that the XGB model provides more stable and accurate simulation outcomes for key information, with Nash coefficient (R²) values above 0.87 and relative error (RE) values below 0.06. Additionally, the XGB model exhibited significant advantages in terms of simulation speed and model generalization performance. Furthermore, we explored methods for selecting key information indicators and generating samples required for the coupled model. These findings are crucial for the rapid prediction of key information in the UFCES. These achievements improve the technical level of urban flood simulation and provide richer information for urban flood risk management. Full article

The Qinghai–Xizang Plateau (QXP) is the highest plateau on Earth, with a significant quantity of iron resources that significantly contribute to regional economic development in Western China. However, the exploitation of these iron deposits on the QXP is confronted with dual challenges. The complex geography and weak infrastructure lead to inadequate transport accessibility, while the strict ecological regulations and stringent environmental protection policies further complicate resource development. This study focuses on the transport accessibility issues related to iron deposits on the QXP, aiming to assess the suitability for regional iron resource development. This study conducts a comprehensive, multidimensional analysis encompassing the spatial distribution of iron deposits, the characteristics of the transport network, and economic dynamics. Based on these analyses, an integrated suitability evaluation model is developed to assess the accessibility of iron deposits on the QXP. The results indicate that the transport accessibility of iron deposits on the QXP displays obvious spatial disparities. The deposits on the western QXP exhibit lower accessibility due to the remoteness from major economic centers and underdeveloped transport infrastructure. In contrast, the deposits on the eastern QXP, which are closer to transportation and economic centers, show greater development potential. Additionally, this study innovatively incorporates economic dynamics and ecological protection factors into the transport accessibility evaluation framework, revealing the coupling relationship between the transport conditions, economic patterns, and mineral resource development potential. It provides scientific evidence for the balancing of resource development and environmental protection in ecologically sensitive areas. The findings could contribute to optimizing the iron resource development strategies on the QXP and provide theoretical support for future regional infrastructure planning. Full article

The study of ecological security patterns is of great significance to the balance between regional economic development and environmental protection. By optimizing the regional ecological security pattern through reasonable land-use planning and resource management strategies, the purpose of maintaining ecosystem stability and improving ecosystem service capacity can be achieved, and ultimately regional ecological security can be achieved. As a typical ecological civilization city in the middle reaches of the Yangtze River, Yichang City is also facing the dual challenges of urban expansion and environmental pressure. The construction and optimization of its ecological security pattern is the key to achieving the harmonious coexistence of economic development and environmental protection and ensuring regional sustainable development. Based on the ecological environment characteristics and land-use data of Yichang City, this paper uses morphological spatial pattern analysis and landscape connectivity analysis to identify core ecological sources, constructs a comprehensive ecological resistance surface based on the sensitivity–pressure–resilience (SPR) model, and combines circuit theory and Linkage Mapper tools to extract ecological corridors, ecological pinch points, and ecological barrier points and construct the ecological security pattern of Yichang City with ecological elements of points, lines, and surfaces. Finally, the community mining method was introduced and combined with habitat quality to analyze the spatial topological structure of the ecological network in Yichang City and conduct ecological security zoning management. The following conclusions were drawn: Yichang City has a good ecological background value. A total of 64 core ecological sources were screened out with a total area of 3239.5 km². In total, 157 ecological corridors in Yichang City were identified. These corridors were divided into 104 general corridors, 42 important corridors, and 11 key corridors according to the flow centrality score. In addition, 49 key ecological pinch points and 36 ecological barrier points were identified. The combination of these points, lines, and surfaces formed the ecological security pattern of Yichang City. Based on the community mining algorithm in complex networks and the principle of Thiessen polygons, Yichang City was divided into five ecological functional zones. Among them, Community No. 2 has the highest ecological security level, high vegetation coverage, close distribution of ecological sources, a large number of corridors, and high connectivity. Community No. 5 has the largest area, but it contains most of the human activity space and construction and development zones, with low habitat quality and severely squeezed ecological space. In this regard, large-scale ecological restoration projects should be implemented, such as artificial wetland construction and ecological island establishment, to supplement ecological activity space and mobility and enhance ecosystem service functions. This study aims to construct a multi-scale ecological security pattern in Yichang City, propose a dynamic zoning management strategy based on complex network analysis, and provide a scientific basis for ecological protection and restoration in rapidly urbanizing areas. Full article

The increasing penetration of photovoltaic (PV) systems and the need for reliable backup power solutions have led to the development of hybrid uninterruptible power supply (UPS) systems. These systems integrate PV energy storage with battery backup and grid power to optimize real-time energy management. This paper proposes an advanced energy management strategy and an artificial neural network (ANN)-based control method for PV-integrated hybrid UPS systems. The proposed strategy dynamically determines the optimal power-sharing ratio between battery storage and the grid based on real-time economic parameters, load demand, and battery state of charge (SoC). A centralized ANN-based controller ensures precise control of the LLC converter and rectifier, achieving stable and efficient power distribution. Additionally, a genetic algorithm is implemented to optimize the power sharing ratio, minimizing the LCOE under varying load and electricity pricing conditions. The proposed approach is validated through simulations, demonstrating significant improvements in cost-effectiveness, system stability, and dynamic adaptability compared to conventional control methods. These findings suggest that integrating ANN-based control with optimized energy management can enhance the efficiency and sustainability of hybrid UPS systems, particularly in fluctuating energy markets. Full article

Traditional classifications of global development, such as the developed/developing dichotomy or Global North/South, often oversimplify the intricate landscape of human development. This paper leverages computational tools, advanced visualization techniques, and mathematical modeling to challenge these conventional categories and reveal a continuous development spectrum among nations. By applying hierarchical clustering, multidimensional scaling, and interactive visualizations to Human Development Index (HDI) data, we identify “development neighborhoods”—clusters of countries that exhibit similar development patterns, sometimes across geographical boundaries. Our methodology combines network theory, statistical physics, and digital humanities approaches to model development as a continuous field, introducing novel metrics for development potential and regional inequality. Through analysis of HDI data from 193 countries (1990–2022), we demonstrate significant regional variations in development trajectories, with Africa showing the highest mean change rate (28.36%) despite maintaining the lowest mean HDI (0.557). The implementation of circle packing and radial dendrogram visualizations reveals both population dynamics and development continuums, while our mathematical framework provides rigorous quantification of development distances and cluster stability. This approach not only uncovers sophisticated developmental progressions but also emphasizes the importance of continuous frameworks over categorical divisions. The findings highlight how digital humanities tools can enhance our understanding of global development, providing policymakers with insights that traditional methods might overlook. Our methodology demonstrates the potential of computational social science to offer more granular analyses of development, supporting policies that recognize the diversity within regional and developmental clusters, while our mathematical framework provides a foundation for future quantitative studies in development economics. Full article

Smart grids have enormous potential in terms of reliability and sustainability, but with the large-scale integration of distributed energy like solar energy, the network security risks of smart grids have also increased. In response to the physical and information network threats faced in the network security risk assessment of solar powered smart grids, this study develops a smart grid theft detection model based on TimesNet and a smart grid intrusion detection model based on bidirectional long short-term memory networks. The results indicated that when the proportion of electricity theft data was 25%, the false detection rate of the proposed model was 3.52. The area under the curve of the proposed model was 0.98, and the detection rate, false negative rate, F1 value, and accuracy were 97.04%, 1.21%, 92.69%, and 97.15%, respectively. The loss value of the proposed intrusion detection model was stable at around 0.012 in the NSL-KDD dataset and around 0.02 in the CICIDS2017 dataset, with a detection accuracy of 97.54% and a false positive rate of 1.21%. The experiment demonstrated the electricity theft behavior and network intrusion detection performance of the proposed model, which can effectively detect security threats faced by solar smart grids and provide practical basis for network security risk assessment. The research results can help reduce the economic losses of power companies, maintain a good order of electricity consumption, and ensure the safe and stable operation of solar smart grids. Full article

Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed a novel deep learning-based framework for the classification of seven macrofungi species from the genera Mycena and Marasmius, leveraging their unique ecological and morphological characteristics. The proposed approach integrates a custom convolutional neural network (CNN) with a self-organizing map (SOM) adapted for supervised learning and a Kolmogorov–Arnold Network (KAN) layer to enhance classification performance. The experimental results demonstrate significant improvements in classification metrics when using the CNN-SOM and CNN-KAN architectures. Additionally, advanced pretrained models such as MaxViT-S and ResNetV2-50 achieved high accuracy rates, with MaxViT-S achieving 98.9% accuracy. Statistical analyses using the chi-square test confirmed the reliability of the results, emphasizing the importance of validating evaluation metrics statistically. This research represents the first application of SOM in fungal classification and highlights the potential of deep learning in advancing fungal taxonomy. Future work will focus on optimizing the KAN architecture and expanding the dataset to include more fungal classes, further enhancing classification accuracy and ecological understanding. Full article

Intercity railways are key transportation infrastructures in the interconnection of urban agglomerations. Their stations are usually distributed based on densely populated and economically active areas, and they also play roles as regional network nodes, intra-city nodes, and functional areas. However, the academic research on the spatial development of station areas is still very limited. In particular, there is no sufficient in-depth discussion about the coordinated development mechanism of the “regional node-place” and “urban node-place” of intercity railways. Based on the case study of Guangdong–Hong Kong–Macao Greater Bay Area in China (GBA), this paper provides an in-depth analysis of the regional node development level, urban node development level, station area development level, comprehensive station area development level, and coordinated development of “regional node-place” and “urban node-place” in the GBA in 2012, 2016, 2020, and 2023 by constructing a node-place model, development index of regional nodes, development level index, and coupling coordination degree model. Findings: (1) From 2012 to 2023, the development of regional nodes, urban nodes, and places of the GBA intercity railway saw a significant improvement, with the proportion of high-value stations increasing by 13.3%, 7%, and 8.8%, respectively. Despite some improvement on the whole, the three still exhibited an unbalanced spatial distribution of “high in the middle-low in the periphery”; (2) The relative gap in development levels between “regional node-place” and “urban node-place” of intercity railways decreased by 0.159 and 0.168, respectively, showing an overall upward trend, but still showing an unbalanced spatial distribution of “high in the middle-low in the periphery”; (3) The development level of regional nodes and urban nodes is lower than that of areas and is dominated by the unbalance place and dependence types, while the unbalance node and balance types account for less; (4) The coordination of the “regional node-place” and “urban node-place” of intercity railways is gradually improved, and the stations with high coordination and high coordination levels accounts for an increased proportion from 4% to 7% and 8%, respectively. However, the coordination remains at a low level on the whole, with most sites still in the low-level coupling and lower-level coupling stages. Some stations in Guangzhou, Shenzhen, Foshan, and Dongguan have witnessed a level leap and are showing a transition towards a medium to high level of coordinated development, with the surrounding areas moving away from low-level coupling and coordination. Full article