Search Results (126)

Abnormal gas emissions are one of the main risk factors evoking coal mine gas accidents. How to accurately and efficiently predict gas emissions and identify the risk of gas anomalies has become a key issue in coal mine safety management. This study takes the Yuqia No.1 Mine of Qinghai Energy Group as the research object, collecting environmental variable data such as the gas emission quantity of the mining face, coal seam depth, coal seam thickness, coal seam gas content, temperature, wind speed, and respirable dust concentration. Multi-parameter data fusion, gray correlation degree analysis, least square support vector machine (LS-SVM), random forest (RF), back propagation neural network (BPNN), and other methods were adopted in this paper to explore the prediction accuracy and risk factors of mine gas emissions. The results show the following: (1) The correlation coefficients between coal seam depth, coal seam thickness, coal seam gas content, daily progress, daily output, and wind speed and gas emission quantity are 0.955, 0.975, 0.963, −0.912, 0.983, and 0.681, respectively, showing a significance level of 0.01, and are used as external input characteristic quantities for gas emission quantity prediction. (2) For the LS-SVM model, the root mean square error (RMSE) and mean absolute error (MAE) values on the training set were 0.015 and 0.012, respectively, while the corresponding test errors were 0.216 and 0.094, which represent the lowest among all models. The R² values for the training and test sets were 0.993 and 0.951, respectively, indicating higher predictive accuracy compared to the other three benchmark models. (3) According to the comprehensive correlation degree, the top five factors that had a greater impact on the amount of gas emissions were, successively, as follows: coal seam thickness (0.8896), coal seam gas content (0.8849), daily output (0.6456), coal seam depth (0.6258), and wind speed (0.5578). The research results can provide a reference for high-precision prediction of gas emissions. Full article

Chromium is a critical material for stainless steel production. With economic growth and the optimization and upgrading of industrial structure, China’s demand for chromium has been increasing year by year. Conducting research on chromium demand forecasting holds significant practical implications for the sustainable development of China’s chromium industrial chain. China’s chromium consumption accounts for one-third of the global, over 95% of which has long-term depended on imports, and 90% of which is used in stainless steel production. In this paper, a linear correlation model between chromium consumption and stainless steel production is constructed by using the department demand forecasting method. The importance of influencing factors on chromium demand is analyzed using the gray correlation degree, and a PSO-BP neural network algorithm is constructed to predict China’s chromium demand from 2025 to 2040. The results indicate that the predictions of the two methods are relatively consistent, with demand for chromium expected to peak in 2035 and then decline gradually thereafter. This provides an important reference basis for the security and sustainable development of China’s chromium supply chain. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

To enhance the safety risk management and control capabilities for TBM (Tunnel Boring Machine) construction in hydraulic tunnels, this study conducts a correlation analysis and dynamic evolution study of safety risks. Data were collected through multiple channels, including a literature review, on-site records, and expert interviews. Grounded theory was employed for three-level coding to initially identify risk factors, and gray relational analysis was used for indicator optimization, ultimately establishing a safety risk system comprising 5 categories and 21 indicators. A multi-level hierarchical structure of risk correlation was established using fuzzy DEMATEL and ISM, which was then mapped into a Bayesian network (BN). The degree of correlation was quantified based on probabilistic information, leading to the construction of a risk correlation analysis model based on fuzzy DEMATEL–ISM–BN. Furthermore, considering the risk correlations, a safety risk evolution model for TBM construction in hydraulic tunnels was developed based on system dynamics. The validity of the model was verified using the AY project as a case study. The results indicate that the safety risk correlation structure for TBM construction in hydraulic tunnels consists of 7 levels, with the closest correlation found between “inadequate management systems” and “failure to implement safety training and technical disclosure”. As the number of interacting risk factors increases, the trend of risk level evolution also rises, with the interrelations within the management subsystem being the key targets for prevention and control. The most sensitive factors within each subsystem were further identified as adverse geological conditions, improper construction parameter settings, inappropriate equipment selection and configuration, weak safety awareness, and inadequate management systems. The control measures proposed based on these findings can provide a basis for project risk prevention and control. The main limitations of this study are that some probability parameters rely on expert experience, which could be optimized in the future by incorporating more actual monitoring data. Additionally, the applicability of the established model under extreme geological conditions requires further verification. Full article

Background: The voxel-based specific regional analysis system for Alzheimer’s disease (VSRAD) allows quantitative evaluation of the degree of an individual’s brain atrophy through statistical comparison of brain magnetic resonance imaging (MRI) of their brain to a normative database of healthy Japanese individuals. Currently, the VSRAD is used in routine clinical practice in the diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB). Recent studies using VSRAD have explored its utility in the assessment of brain atrophy associated with various conditions, including diabetes, oral health status, and olfactory dysfunction. This review summarizes the principles of the VSRAD and its foundational method, voxel-based morphometry (VBM), and their clinical and research applications. Methods: This narrative review was conducted by performing a literature search of PubMed to identify articles regarding VBM and the VSRAD that were published between 2005 and 2025. Results: VSRAD yields four indices for quantifying the severity and extent of gray matter atrophy, especially in the medial temporal lobe. Studies have demonstrated its high diagnostic accuracy in distinguishing among AD, mild cognitive impairment (MCI), and DLB. Furthermore, it is correlated with neuropsychological test scores and has been applied to evaluate brain changes associated with diabetes, olfactory dysfunction, and physical inactivity. Motion-corrected MR images, which utilize AI techniques, have also been validated using VSRAD-derived metrics. Conclusions: Quantifying brain atrophy using the VSRAD allows objective evaluation and facilitates the investigation of its association with various diseases. Specifically, VSRAD can be considered a useful adjunctive tool for diagnosing AD and DLB. Full article

Background/Objective: Computed tomography (CT) can be used to non-invasively assess the health of the liver; however, radiologist evaluation and simple thresholding alone are insufficient for diagnosis of hepatic steatosis, necessitating biopsies. This study explored CT radiomics and machine learning to enable non-invasive, objective, and quantitative prediction of steatosis severity across the macaque liver. Methods: In this retrospective study, CT images of 42 crab-eating macaques (age [yr] = 6.1 ± 1.7; sex [male/female] = 26/16) with varying degrees of hepatic steatosis were analyzed, and the results were compared to histology-based steatosis scores of livers from the same animals. After extracting radiomic features, a thorough array of statistical analyses, feature selection techniques, and machine learning models were applied to identify a distinct radiomic signature of histologically defined hepatic steatosis. Results: We identified 12 radiomic features that correlated with steatosis scores, and hierarchical clustering based on radiomic attributes alone revealed clusters roughly aligning with steatosis severity groups. The k-nearest neighbors model architecture best predicted histopathologic steatosis scores in both classification and regression tasks (area under the receiver operating characteristic curve [AUC ROC] = 0.89 ± 0.09; root-mean-square error [RMSE] = 0.60 ± 0.10). Feature analyses identified seven key radiomic features (six first-order features and one gray-level co-occurrence matrix feature) that were most important when predicting steatosis. Conclusions: We identified a CT radiomic signature of steatosis and demonstrated that histology-based steatosis scores can be predicted non-invasively and objectively using machine learning and CT radiomics as a potential alternative to invasive core biopsies. Given the strong similarities in liver structure, liver function, and hepatic steatosis pathophysiology between macaques and humans, these findings have the potential to translate to humans. Full article

Understanding the spatiotemporal variations and driving mechanisms of coastlines is crucial for their adequate protection, utilization, and sustainable development. In this study, the changes in various coastline types in Zhuhai from 1987 to 2022 were analyzed by using long-term Landsat and GaoFen satellite imagery. The Index of Coastline Type Diversity (ICTD), Index of Coastline Utilization Degree (ICUD) and the Digital Shoreline Analysis System (DSAS) analysis indicators were employed to investigate coastline change. Both quantitative and qualitative analyses were integrated to comprehensively elucidate the impacts of various driving factors. The results indicate that the total length of Zhuhai coastline increased from 761.50 km in 1987 to 798.91 km in 2022, with natural coastlines decreasing by 89.82 km and artificial coastlines increasing by 153.40 km. The rapid expansion of artificial coastlines since 2007 led to a marked decline in the ICTD indicator, while the ICUD indicator increased from 146.42 in 1987 to 216.37 in 2022, reflecting the intensified and continuous influence of anthropogenic activities. Additionally, the end point rate (EPR) and Weighted Linear Regression Rate (WLR) changed by 8.09 m/yr and 6.62 m/yr, respectively. The Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) exhibited average changes of 331.42 m and 224.32 m, respectively. Gray correlation and regression analyses further revealed that climate factors exhibited the strongest association with natural coastline changes, while economic development indicators showed the strongest correlation with artificial coastline dynamics. The relationship of Number of Berths in Main Ports (Nb) with coastline changes strongly suggests that human activities are the primary driver of these changes. These findings provide a robust scientific basis for coastal zone management in Zhuhai. Full article

Utilizing the considerable cold energy in liquefied natural gas (LNG) through the organic Rankine cycle is a highly important initiative. A multi-stage Rankine-based power generation system using LNG cold energy for waste heat utilization was proposed in this study. Moreover, a comprehensive assessment method was used to select the working fluid for this proposed system. Not only were thermodynamic and economic indicators considered, but also the environmental impact of candidate working fluids was taken into account in the evaluation process. The optimal operating points of the system were determined using non-dominated sorting genetic algorithm II and TOPSIS methods, while employing Gray Relational Analysis was applied to compute the gray relational coefficients of candidate working fluids at the optimal operating points. In addition, four weighting methods were used to calculate the final gray correlation degree of the candidate working fluids by considering the weighting influence. The stability of the calculated gray correlation degree was observed by performing a standard deviation analysis. The results indicate that R245ca was chosen as the optimal working fluid due to its superior performance based on the entropy weighting method, the independent weighting coefficient method, and the mean weighting method. Simultaneously, R245ca exhibits the best specific net power output and levelized cost of energy values of 0.283 USD/kWh and 106.9 kWh/t, respectively, among all candidate working fluids. The gray correlation degree of R1233zd(E) is 0.948, exceeding that of R245ca under the coefficient of variation method. The gray correlation degree under the mean value method is the most stable, with a standard deviation of only 0.162, while the gray correlation degree under the coefficient of variation method exhibits the greatest fluctuation, with a standard deviation of 0.17, in the stability assessment. Full article

Amid global rural tourism growth and rural revitalization policies, traditional villages’ resource protection and tourism development have drawn international academic attention. To guide villages’ resource planning and management, this study constructed an evaluation index system of cultural, ecological, industrial, talent, and organizational amenities in traditional villages from the perspective of tourists’ perceptions using grounded theory and measured the spatial agglomeration differences, synergistic effects and their influencing factors of traditional village amenities by using location entropy, spatial autocorrelation, and gray correlation degree analysis. The results show that (1) the spatial distributions of cultural, ecological, industrial, and organizational amenities are more balanced, while talent amenities exhibit a more concentrated distribution. (2) The spatial concentration of amenities in traditional villages has a strong positive spatial correlation, the agglomeration level of the high-high type of concentration is distributed in clusters, the low–low type tends to be contiguous, and the low–high type is distributed sporadically around the high–high type; significant synergy between ecological and industrial amenities, and organizations play a supportive role in the spatial agglomeration of cultural, ecological, ecological and talent amenities. (3) Gross regional product, slope, and distance to 3A and above scenic spots significantly influence the spatial agglomeration of amenities. This study provides reference for the sustainable development of traditional villages from the perspectives of exerting agglomeration and radiation effects, synergistically promoting villages’ development, constructing the memory symbol system, and integrating the resource structural system based on the spatial agglomeration difference characteristics of traditional village amenities. Full article

The study block is located on the eastern edge of the Ordos Basin and is one of the typical medium coalbed methane blocks in China that have previously been subjected to exploration and development work. The rich CBM resource base and good exploration and development situation in this block mean there is an urgent need to accelerate development efforts, but compared with the current situation for tight sandstone gas where development is in full swing in the area, the production capacity construction of CBM wells in the area shows a phenomenon of lagging to a certain degree. In this study, taking the 4 + 5 coal seam of the YJP block in the Ordos Basin as the research object, we carried out technical research on an integrated program concerning CBM geology and engineering and put forward a comprehensive seismic geology analysis method for the prediction of the CBM content. The study quantitatively assessed the tectonic conditions, depositional environment, and coal seam thickness as potential controlling factors using gray relationship analysis, trend surface analysis, and seismic geological data integration. The results show that tectonic conditions, especially the burial depth, residual deformation, and fault development, are the main controlling factors affecting the coalbed methane content, showing a strong correlation (gray relational value greater than 0.75). The effects of the depositional environment (sand–shale ratio) and coal bed thickness were negligible. A weighted fusion model incorporating seismic attributes and geological parameters was developed to predict the gas content distribution, achieving relative prediction errors of below 15% in validation wells, significantly outperforming traditional interpolation methods. The integrated approach demonstrated enhanced spatial resolution and accuracy in delineating the lateral CBM distribution, particularly in structurally complex zones. However, limitations persist due to the seismic data resolution and logging data reliability. This method provides a robust framework for CBM exploration in heterogeneous coal reservoirs, emphasizing the critical role of tectonic characterization in gas content prediction. Full article

Background/Objectives: Ultrasound (US) imaging and echotexture analysis are emerging techniques for assessing muscle tissue quality in the post-stroke population. Clinical studies suggest that echovariation (EV) and echointensity (EI) serve as objective indicators of muscle impairment, although methodological limitations hinder their clinical translation. This secondary analysis aimed to refine the assessment of echotexture by using robust statistical techniques. Methods: A total of 130 regions of interest (ROIs) extracted from the gastrocnemius medialis of 22 post-stroke individuals were analyzed. First, inter-examiner reliability between two physiotherapists was assessed by using Cohen’s kappa for muscle impairment classification (low/high) for each echotexture feature. For each examiner, the correlation between the classification of the degree of impairment and the modified Heckmatt scale for each feature was analyzed. The dataset was then reduced to 44 ROIs (one image per leg per patient) and assessed by three physiotherapists to analyze inter-examiner reliability by using Light´s kappa and correlation between both assessment methods globally. Statistical differences in 21 echotexture features were evaluated according to the degree of muscle impairment. A binary logistic regression model was developed by using features with a Cohen’s kappa value greater than 0.9 as predictors. Results: A strong and significant degree of agreement was observed among the three examiners regarding the degree of muscle impairment (Kappa_light = 0.85, p < 0.001), with nine of the 21 features showing excellent inter-examiner reliability. The correlation between muscle impairment classification with the modified Heckmatt scale was very high and significant both globally and for each echotexture feature. Significant differences (<0.05) were found for EV, EI, dissimilarity, energy, contrast, maximum likelihood, skewness, and the modified Heckmatt scale. Logistic regression highlighted dissimilarity, entropy, EV, Gray-Level Uniformity (GLU), and EI as the main predictors of muscle tissue impairment. The EV and EI models showed high explanatory power (Nagelkerke’s pseudo-R² = 0.74 and 0.76) and robust classification performance (AUC = 94.20% and 95.45%). Conclusions: This secondary analysis confirms echotexture analysis as a reliable tool for post-stroke muscle assessment, validating EV and EI as key indicators while identifying dissimilarity, entropy, and GLU as additional relevant features. Full article

Urban–rural integration (URI) is important for achieving rural revitalization and sustainable development. Currently, there is a lack of research on URI in prefecture-level cities in the Yellow River Basin Urban Agglomerations (YRBUAs), and the spatial relationship between URI and economic development is not clear. This paper evaluates the URI index of the YRBUA from 2010 to 2022, and applies research methods such as an exploratory data analysis, spatial variation function, coupling coordination degree, and gray correlation analysis to explore the spatial relationship between URI and economic development. The study found the following: (1) The integration level is highest in the Shandong Peninsula Urban Agglomeration and lowest in the Ningxia Along the Yellow River Urban Agglomeration. The rank structure of the URI index within the city cluster is dominated by a single core. (2) The URI index roughly shows the spatial distribution characteristics of high in the east and low in the west. (3) The level of URI and economic development are spatially positively correlated. The high-value agglomeration areas of both are mainly distributed in Shandong Peninsula. (4) The high-value areas of coupling coordination and gray correlation degree are distributed in Shandong Peninsula Urban Agglomeration. Full article

The planting of oat varieties is influenced by factors such as their inherent traits, ecological regional climate, altitude conditions, and resistance differences, resulting in a decrease in both forage yield and quality. It is crucial to carefully select appropriate oat varieties for different ecological regions in order to enhance forage yield and quality, thereby facilitating the advancement of the grass industry. The correlation between the indices and the relationship between the indices and varieties were investigated through rigorous correlation analysis and principal component analysis. By employing gray correlation analysis, the 21 indices were transformed into 15 independent comprehensive indices. Subsequently, based on a comprehensive analysis, oat varieties suitable for cultivation in different ecological regions were identified. In this study, fifteen domestic and foreign oat varieties were cultivated in the semi-arid region of Weiqi Town and the alpine region of Damaying Town in Shandan County throughout 2023. Among the yield traits, Everleaf 126 exhibited a significantly lower plant height while possessing the largest leaf area, the highest number of effective tillers, and achieving the highest hay and seed yields (p < 0.05), which were 13,199 kg/ha and 5136 kg/ha, respectively. The plant height of Longyan No.3 in Damaying Town was significantly higher than that of other varieties. This variety also demonstrated the highest number of effective tillers, along with the greatest hay yield (7783 kg/ha) and seed yield (5033 kg/ha). Among the evaluated quality traits, Everleaf 126 in Weiqi Town exhibited the highest leaf–stem ratio, crude protein content, and crude fat content (p < 0.05). In contrast, Mengshi in Damaying Town had the highest leaf–stem ratio, while Longyan No.3 demonstrated the highest levels of crude protein and crude fat content. Furthermore, Molasses displayed the highest soluble sugar content in both locations (p < 0.05). The resistance of 15 oat varieties to pests and diseases was found to be lower in Weiqi Town compared to Damaying Town. Notably, Everleaf 126 exhibited the highest resistance to powdery mildew, red leaf disease, leaf spot disease, and aphids among the varieties tested in Weiqi Town. In contrast, Longyan No.3 demonstrated superior resistance in Damaying Town. In conclusion, based on a comprehensive analysis of the gray correlation degree, in the semi-arid region, Everleaf 126 exhibited the most superior performance, followed by Molasses and Longyan No.3. In the alpine region, Longyan No.3 demonstrated the highest overall performance, followed closely by Molasses and Mengshi. These varieties exhibit significant potential for promotion as high-yield, high-quality forage oats in semi-arid and alpine environments. Full article

The synergistic relation among land resources, water resources, and food production plays a crucial role in sustainable agricultural development. This research constructs a coupling coordination assessment system of the land–water–food (LWF) nexus from 2005 to 2020 for 31 provinces (municipal cities, autonomous regions) in China, and explores the current development status of land, water, and food systems at multiple scales as well as the coupling coordination characteristics of the LWF nexus. The exploring spatial data analysis and spatial Tobit model are used to explain the spatial correlations and influencing factors of coupling coordination development on the LWF nexus. On that basis, the gray GM (1,1) model is used to forecast the future development of the LWF nexus in China. The results show that the comprehensive development indexes of the land system, water system, food system, and LWF nexus are on the rise, but the land system lags behind the water system and food system. The coupling coordination degree of the LWF nexus in different regions ranges from 0.538 to 0.754, and the coupling coordination development of the LWF nexus in China has reached the preliminary coupled coordination type, with an evolutionary process similar to that of its comprehensive development level. Further empirical research shows that there is a significant positive spatial correlation between coupling coordination development levels for the LWF nexus in China. The level of urbanization and agricultural industry agglomeration have negative effects, while economic development, ecological environment, and scientific and technological progress have positive effects. The prediction results indicate that the coupling coordination degree of the LWF nexus in China will show a stable upward trend from 2024 to 2025, and most provinces will reach the intermediate coupled coordination type in 2025. This study can inform decision-making for policy-makers and practitioners and enrich the knowledge hierarchy of the LWF nexus’ sustainable development on the national and regional scales. Full article

The development of park cities is an important exploration for better satisfying people’s aspirations for a better life, promoting sustainable social development, and advancing the transformation of green ecological values. As a basic industry for sustainable development, the combination of agriculture and urban development is an important way to build an ecological civilization. Clarifying the relationship between a park city and green development of agriculture is of great significance to the construction of a green base and ecological system of the city, sustainable development of agriculture, and integrated development of urban and rural areas. Chengdu is a mega-city in western China, and the Chengdu-led park city development program is unique in Chinese urban development. Chengdu’s park city development is a pioneering example of urban ecological civilization construction. Taking Chengdu as an example and combining the data of other prefecture-level cities in Sichuan, this study explored the correlation and interaction between agricultural green development (AGD) and park city development (PCD) in Chengdu and other prefecture-level cities in Sichuan from 2011 to 2022 based on the coupling coordination degree, gray correlation degree, and spatial autocorrelation analysis. The results showed the following: (1) Based on the entropy method, the level of AGD in Chengdu rises from 0.353 in 2011 to 0.537 in 2022, and the level of PCD rises from 0.368 to 0.826. The level of AGD and the level of PCD as a whole show an upward trend. (2) The degree of coupling and coordination between the PCD and AGD levels rises from 0.600 to 0.816, realizing the leap from coordination to good coordination, and the degree of coupling has been at a high level. (3) Based on the grey correlation degree, in the process of the influence of AGD on the PCD, the correlation degree of the influencing factors of each indicator is basically above 0.5, and each influencing factor has a strong contribution to the level of the PCD. (4) Spatial self-analysis shows that the coupling coordination degree of AGD and PCD in a region is affected by the neighboring region. Therefore, we believe that AGD plays a more obvious role in driving and radiating PCD and that it can effectively promote the economic, social, and ecological upgrading in the process of PCD. Full article