Search Results (1,347)

After the original support system in the auxiliary transportation roadway of the northern wing of the Zhaoxian Mine failed, the extent of damage and deformation varied significantly across different sections of the drift. A single support method could not meet the engineering requirements. Therefore, this paper conducted research on the classification of roadway damage and zoning repair. The overall damage characteristics of the roadway are described by three indicators: roadway deformation, development of rock mass fractures, and water seepage conditions. These are further refined into nine secondary indicators. In summary, a rock mass damage combination weighting evaluation model based on the FAHP–entropy weight TOPSIS method is proposed. According to this model, the degree of damage to the roadway is divided into five grades. After analyzing the damage conditions and support requirements at each grade, corresponding zoning repair plans are formulated by adjusting the parameters of bolts, cables, channel steel beams, and grouting materials. At the same time, the reliability of partition repair is verified using FLAC3D 6.0 numerical simulation software. Field monitoring results demonstrated that this approach not only met the support requirements for the roadway but also improved the utilization rate of support materials. This provides valuable guidance for the design of support systems for roadways with similar heterogeneous damage. Full article

Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network. Full article

Addressing the challenge of efficient gas control in high-gas coal mines with ultra-long panels, this study focuses on the No. 8 coal seam in the Baode Mine. A multi-parameter integrated methodology was developed to establish a hierarchical classification system of Gas Geological Units (GGUs), aiming to identify regions suitable for large-scale gas extraction. The results indicate that the overall structure of the No. 8 coal seam is a simple monocline. Both gas content (ranging from 2.0 to 7.0 m³/t) and gas pressure (ranging from 0.2 to 0.65 MPa) generally increase with burial depth. However, local anomalies in these parameters, caused by geological structures and hydrogeological conditions, significantly limit the effectiveness of large-scale drainage using ultra-long boreholes. Based on key criteria, the seam was classified into three Grade I and ten Grade II GGUs, distinguishing anomalous zones from homogeneous units. Among the Grade II units, eight (II-i to II-viii) were identified as anomalous zones with distinct geological constraints, while two (II-ix and II-x) exhibited homogeneous gas geological parameters. Practical implementation of large-scale gas extraction strategies—including underground ultra-long boreholes and a U-shaped surface well—within the homogeneous Unit II-x demonstrated significantly improved gas drainage performance, characterized by higher methane concentration, greater flow rate, enhanced temporal stability, and more favorable decay characteristics compared to conventional boreholes. These findings confirm the critical role of GGU delineation in guiding efficient regional gas control and ensuring safe production in similar high-gas coal mines. Full article

Linear cultural heritage poses significant challenges in tourism development, primarily due to the complexities involved in implementing scientific zoning and differentiated management strategies. Systematic optimization of its tourism spatial structure has thus become crucial for achieving sustainable utilization. This study adopts a case study approach based on deductive reasoning to examine the morphological characteristics and evolutionary patterns of the tourism space along linear cultural heritage. Taking the Beijing–Hangzhou Grand Canal as an example, it proposes a targeted optimization pathway from a spatial positioning perspective. The findings indicate that the tourism value of linear cultural heritage exhibits a “vine-shaped structure” spatially, and the development process of the tourism space structure follows the “growth pole” evolution law. Moreover, spatial optimization can be achieved through the dual dimensions of spatial form and utilization intensity. Based on this pathway, a three-level tourism zone system is constructed for the Beijing–Hangzhou Grand Canal: the primary tourism zone, located in southern sections, such as Yangzhou and Hangzhou, serves as leading regions that play a pivotal and driving role; the secondary tourism zone, encompassing Beijing, Tianjin, Langfang, and Cangzhou, requires focused enhancement and functional upgrading; and the tertiary tourism zone, mainly including Shandong Province and Xuzhou, Suqian, in Jiangsu Province, necessitate comprehensive and integrated development to achieve overall improvement. This classification not only facilitates coordinated tourism development along the entire canal from a holistic perspective but also provides a basis for formulating targeted strategies for segments with varying tourism values and utilization intensities. Full article

Against the backdrop of climate change and the accelerated process of urbanization, the risks of extreme weather and natural disasters that cities are facing are increasing day by day. Based on the framework of the local climate zone (LCZ), this paper studies the spatio-temporal evolution of the urban surface morphology and the heat island effect of Tongren City. Using the comprehensive mapping technology of remote sensing and GIS, combined with the inversion of surface temperature, the distribution of LCZs and the changes in heat island intensity were analyzed. The results show that: (1) The net increase in forest coverage area leads to a decrease in shrub and grassland area, resulting in an ecological deficit. (2) The built-up area expands along transportation routes, and industrial areas encroach upon natural space. (3) The urban heat island pattern has evolved from a single core to multiple cores and eventually becomes fragmented. (4) Among the seasonal dominant driving factors of urban heat islands, the impervious water surface is in summer, the terrain roughness and building height are in winter, and the building density is in spring and autumn. These findings provide feasible insights into mitigating the heat island effect through climate-sensitive urban planning. Full article

Diabrotica virgifera virgifera Le Conte, 1868 (Coleoptera: Chrysomelidae), known as the western corn rootworm, is one of the most important alien insect pests affecting maize crops globally. It causes significant economic losses by feeding on the roots, which affects plant stability and nutrient absorption, as well as by attacking essential aerial organs (leaves, silk, pollen). Since its accidental introduction into Europe, the species has expanded its range across maize-growing regions, raising concerns about future distribution under climate change. This study aimed to estimate the risk of pest establishment across Europe over three future time frames (2034, 2054, 2074) based on geographic coordinates, climate data, and maize distribution. Spatial simulations were performed in QGIS using national centroid datasets, risk classification criteria, and temperature anomaly maps derived from Copernicus and ECA&D databases for 1992–2024. The results indicate consistently high risk in southern and southeastern regions, with projected expansion toward central and western areas by 2074. Risk zones showed clear spatial aggregation and directional spread correlated with warming trends and maize availability. The pest’s high reproductive potential, thermal tolerance, and capacity for human-assisted dispersal further support these predictions. The model emphasizes the need for expanded surveillance in at-risk zones and targeted policies in areas where D. v. virgifera has not yet established. Future work should refine spatial predictions using field validation, genetic monitoring, and dispersal modeling. The results contribute to anticipatory pest management planning and can support sustainable maize production across changing agroclimatic zones in Europe. Full article

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

Urban growth and environmental pressures in rapidly transforming cities require innovative governance tools that integrate advanced technologies with institutional assessment. This study develops and applies a strategic integration framework that combines spatial analysis, Convolutional Neural Networks (CNNs)-based land-use classification, SHAP-based feature attribution, and stakeholder interviews to evaluate Yerevan, Armenia, as a case of a mid-income city facing accelerated urbanization. The case selection is justified by Yerevan’s rapid built-up expansion, fragmented green areas, and institutional challenges in aligning urban development with sustainability goals. The CNN model achieved 92.4% accuracy in land-use classification, and projections under a business-as-usual scenario indicate a 12.8% increase in built-up areas and a 6.5% decline in green zones by 2030. SHAP analysis identified land surface temperature and NDVI as the most influential predictors, while governance interviews highlighted gaps in regulatory support and technical capacity. The proposed framework advances the literature by integrating AI-driven geospatial analysis with qualitative governance assessment, providing actionable insights for urban policymakers. Findings underscore the potential of combining machine learning, geospatial technologies, and institutional diagnostics to guide smart city planning in transition economies. Full article

Classification of island coastal landscapes is a challenge to incorporate both the terrestrial and the aquatic environment characteristics, and place biological diversity in a regional and insular context. The Coastal and Marine Ecological Classification Standard (CMECS) was developed for use in the United States and incorporates geomorphic data, substrate data, biological information, as well as water column characteristics. The CMECS framework was applied to the island of Great Exuma, The Bahamas. The classification used data from existing studies to include oceanographic data, seawater temperature, salinity, benthic invertebrate surveys, sediment analysis, marine plant surveys, and coastal geomorphology. The information generated is a multi-dimensional description of benthic and shoreline biotopes characterized by dominant species. Biotopes were both mapped and described in hierarchical classification schemes that captured unique components of diversity in the mosaic of coastal natural communities. Natural community classification into biotopes is a useful tool to quantify ecological landscapes as a basis to develop monitoring over time for biotic community response to climate change and human alteration of the coastal zone. Full article

Using Landsat series imagery and the deep learning model CITNet, this study conducted high-accuracy classification and spatiotemporal change analysis of wetlands on Chongming Island from 2000–2020 and explored the driving mechanisms by integrating climatic and anthropogenic factors. The results demonstrate that the total wetland area decreased by approximately 125.5 km² over the two decades. Among natural wetlands, tidal mudflats and shallow seawater zones continuously shrank, while herbaceous marshes exhibited a “decline recovery” trajectory. Artificial wetlands expanded before 2005 but contracted significantly thereafter, mainly due to aquaculture pond reduction. Wetland transformation was dominated by wetland-to-non-wetland conversions, peaking during 2005–2010. Driving factor analysis revealed a “human pressure dominated, climate modulated” pattern: nighttime light index (NTL) and GDP demonstrated strong negative correlations with wetland extent, while minimum temperature and the Palmer Drought Severity Index (PDSI) promoted herbaceous marsh expansion and accelerated artificial wetland contraction, respectively. The findings indicate that wetland changes on Chongming Island result from the combined effects of policy, economic growth, and ecological processes. Sustainable management should focus on restricting urban expansion in ecologically sensitive zones, optimizing water resource allocation under drought conditions, and incorporating climate adaptation and invasive species control into restoration programs to maintain both the extent and ecological quality of wetlands. Full article

The farming-pastoral ecotone (FPE) of northern China serves as a critical ecological transition zone, in which poplar plantations significantly contribute to afforestation for large-scale ecological restoration projects. Due to concerns about sustainability, precise monitoring of the spatiotemporal dynamics of poplar plantations is needed, but systematic research is lacking. This study investigated the spatiotemporal dynamics of poplar plantation area and growth status from 1989 to 2022, taking the Anguli Nao watershed, a typical region in the FPE of northern China, as an example. Firstly, by utilizing satellite images and the random forest classification algorithm, the poplar plantation areas were well extracted, with a high accuracy over 93% and extremely strong consistency as demonstrated by a Kappa coefficient larger than 0.88. Significant changes in poplar plantation areas existed from 1989 to 2022, with an overall increasing trend (1989: 130.3 km², 2002: 275.9 km², 2013: 256.0 km², and 2022: 289.2 km²). Furthermore, the accuracy of our extraction method significantly outperformed six widely used global land cover products, all of which failed to capture the distribution of poplar plantations (producer’s accuracy < 0.21; Kappa coefficient < 0.18). In addition, the analysis of vegetation growth status revealed large-scale degradation from 2002 to 2013, with a degradation ratio of 24.4% that further increased to 31.1% by 2022, satisfying the significance test via Theisl–Sen trend analysis and the Mann–Kendall test. This study points out the uncertainty of existing land cover products and risk of poplar plantations in the FPE of northern China and provides instructive reference for similar research. Full article

Coastal ecosystems, located at the interface of terrestrial and marine environments, provide significant ecological functions and resource value. Coastal salt pans, as critical coastal resources with significant implications for coastal ecosystem health and resource management, have attracted extensive research attention. However, current studies on the extraction of spatiotemporal patterns of coastal salt pans remain relatively limited and superficial. This study takes coastal salt pans in Laizhou Bay as a case study, proposing a hierarchical classification method—Salt Pan Feature-Enhanced Fusion Image Random Forest (SPFEFI-RF)—based on multi-index synergy guidance and deep-shallow feature fusion, achieving high-precision extraction of coastal salt pans. First, a Modified Water Index (MWI) and Salt Pan Crystallization Index (SCI) were constructed from image spectral features, specifically targeting the extraction of evaporation ponds. Concurrently, a salt pan sample dataset was developed for the DeepLabv3+ (DL) method to extract deep semantic features and perform multi-scale feature fusion. Subsequently, a three-channel fusion strategy—R(MWI)-G(SCI)-B(DL)—was employed to produce the Salt Pan Feature-Enhanced Fusion Image (SPFEFI), enhancing distinctions between salt pans and background land cover. Finally, the Random Forest (RF) classifier using shallow spectral features was applied to extract salt pan information, further optimized by spatial domain denoising techniques. Results indicate that the SPFEFI-RF approach effectively extracts coastal salt pan features, achieving an overall accuracy of 92.29% and a spatial consistency of 85.14% with ground-truth data. The SPFEFI-RF method provides advanced technical support for high-precision extraction of global coastal salt pan spatiotemporal characteristics, optimizing coastal zone management decisions and promoting the sustainable development of coastal ecosystems and resources. Full article

Ground settlement caused by urban subway construction can significantly impact surrounding buildings and underground infrastructure, posing risks to structural safety and long-term performance. Accurate prediction of settlement trends is therefore essential for ensuring building integrity and supporting informed decision-making during construction. This study proposes a hierarchical prediction framework that incorporates waveform-based curve classification and machine learning to forecast ground settlement patterns. Monitoring data from the Urumqi Metro construction project are analyzed, and settlement curve types are identified using Fréchet distance, categorized into five distinct forms: inverse cotangent, exponential, multi-step, one-shaped, and oscillating. Each type is then matched with the most suitable predictive model, including the Autoregressive Integrated Moving Average (ARIMA), Attention Mechanism-enhanced Long Short-Term Memory (AM-LSTM), Genetic Algorithm-optimized Support Vector Regression (GA-SVR), and Particle Swarm Optimization-based Backpropagation neural network (PSO-BP). Results show that AM-LSTM achieves the best performance for inverse cotangent and large-sample exponential curves; ARIMA excels for small-sample exponential curves; PSO-BP is most effective for multi-step curves; and GA-SVR offers superior accuracy for one-shaped and oscillating curves. Validation on a newly excavated section of Urumqi Metro Line 2 confirms the model’s potential in enhancing the safety management of buildings and infrastructure in subway construction zones. Full article

Cancer is one of the most lethal diseases in the modern world. Early diagnosis significantly contributes to prolonging the life expectancy of patients. The application of intelligent systems and AI methods is crucial for diagnosing oncological diseases. Primarily, expert systems or decision support systems are utilized in such cases. This research explores early lung cancer diagnosis through protocol-based questioning, considering the impact of nuclear testing factors. Nuclear tests conducted historically continue to affect citizens’ health. A classification of regions into five groups was proposed based on their proximity to nuclear test sites. The weighting coefficient was assigned accordingly, in proportion to the distance from the test zones. In this study, existing expert systems were analyzed and classified. Approaches used to build diagnostic expert systems for oncological diseases were grouped by how well they apply to different tumor localizations. An online questionnaire based on the lung cancer diagnostic protocol was created to gather input data for the neural network. To support this diagnostic method, a functional block diagram of the intelligent system “Oncology” was developed. The following methods were used to create the mathematical model: gradient boosting, multilayer perceptron, and Hamming network. Finally, a web application architecture for early lung cancer detection was proposed. Full article