Search Results (2,233)

As a cultural corridor connecting the Central Plains and Northeast China, the Liaoxi Corridor has a special position in the transmission of traditional Chinese culture. Traditional villages in the region have preserved rich intangible cultural heritage and traditional architectural features, which highlight the historical heritage of multicultural intermingling. This study fills the gap in the spatial distribution of traditional villages in the Liaoxi Corridor and reveals their spatial distribution pattern, which is of great theoretical significance. Using Geographic Information System (GIS) spatial analysis and quantitative geography, this study analyzes the spatial pattern of traditional villages and the influencing factors. The results show that traditional villages in the Liaoxi Corridor are clustered, forming high-density settlement areas in Chaoyang County and Beizhen City. Most villages are located in hilly and mountainous areas and river valleys and are affected by the natural geographic environment (topography and water sources) and historical and human factors (immigration and settlement, border defense, ethnic integration, etc.). In conclusion, this study provides a scientific basis and practical reference for rural revitalization, cultural heritage protection, and regional coordinated development, aiming at revealing the geographical and cultural mechanisms behind the spatial distribution of traditional villages. Full article

Fibrocapsa japonica (Raphidophyceae) is a cosmopolitan species frequently associated with harmful algal blooms (HABs) and fish mortality events, representing a potential threat to aquaculture and coastal ecosystems. This study provides the first comprehensive morphological, phylogenetic, pigmentary, and toxicological characterization of F. japonica strains isolated from Argentina. Light and transmission electron microscopy confirmed key diagnostic features of the species, including anterior flagella and the conspicuous group of mucocyst in the posterior region. Phylogenetic analysis based on the LSU rDNA D1–D2 region revealed monophyletic relationships with strains from geographically distant regions. Pigment analysis by HPLC identified chlorophyll-a (62.3 pg cell⁻¹) and fucoxanthin (38.4 pg cell⁻¹) as the main dominant pigments. Cytotoxicity assays using RTgill-W1 cells exposed for 2 h to culture supernatants and intracellular extracts showed strain-specific effects. The most toxic strain (LPCc049) reduced gill cell viability down to 53% in the supernatant exposure, while LC₅₀ values ranged from 1.6 × 10⁴ to 4.7 × 10⁵ cells mL⁻¹, depending directly on the strain and treatment type. No brevetoxins (PbTx-1, -2, -3, -6, -7, -8, -9, -10, BTX-B1 and BTX-B2) were detected by LC–MS/MS, suggesting that the cytotoxicity may be linked to the production of reactive oxygen species (ROS), polyunsaturated fatty acids (PUFAs), or hemolytic compounds, as previously hypothesized in the literature. These findings offer novel insights into the toxic potential of F. japonica in South America and underscore the need for further research to elucidate the mechanisms underlying its ichthyotoxic effect. Full article

Geographic object-based image analysis (GEOBIA) has demonstrated substantial utility in remote sensing tasks. However, its integration with deep learning remains largely confined to image-level classification. This is primarily due to the irregular shapes and fragmented boundaries of segmented objects, which limit its applicability in semantic segmentation. While convolutional neural networks (CNNs) excel at local feature extraction, they inherently struggle to capture long-range dependencies. In contrast, Transformer-based models are well suited for global context modeling but often lack fine-grained local detail. To overcome these limitations, we propose GOFENet (Geo-Object Feature Enhanced Network)—a hybrid semantic segmentation architecture that effectively fuses object-level priors into deep feature representations. GOFENet employs a dual-encoder design combining CNN and Swin Transformer architectures, enabling multi-scale feature fusion through skip connections to preserve both local and global semantics. An auxiliary branch incorporating cascaded atrous convolutions is introduced to inject information of segmented objects into the learning process. Furthermore, we develop a cross-channel selection module (CSM) for refined channel-wise attention, a feature enhancement module (FEM) to merge global and local representations, and a shallow–deep feature fusion module (SDFM) to integrate pixel- and object-level cues across scales. Experimental results on the GID and LoveDA datasets demonstrate that GOFENet achieves superior segmentation performance, with 66.02% mIoU and 51.92% mIoU, respectively. The model exhibits strong capability in delineating large-scale land cover features, producing sharper object boundaries and reducing classification noise, while preserving the integrity and discriminability of land cover categories. Full article

This paper presents a comprehensive review of research on Historic Building Information Modeling (HBIM), focusing on its role as a tool for managing knowledge and supporting conservation practices of Architectural Heritage. While previous review articles and most research works have predominantly addressed geometric modeling—given its significant challenges in the context of historic buildings—this study places greater emphasis on the integration of non-geometric data within the BIM environment. A systematic search was conducted in the Scopus database to extract the 451 relevant publications analyzed in this review, covering the period from 2008 to mid-2024. A bibliometric analysis was first performed to identify trends in publication types, geographic distribution, research focuses, and software usage. The main body of the review then explores three core themes in the development of the information system: the definition of model entities, both semantic and geometric; the data enrichment phase, incorporating historical, diagnostic, monitoring and conservation-related information; and finally, data use and sharing, including on-site applications and interoperability. For each topic, the review highlights and discusses the principal approaches documented in the literature, critically evaluating the advantages and limitations of different information management methods with respect to the distinctive features of the building under analysis and the specific objectives of the information model. Full article

Infrared ship detection plays a vital role in maritime surveillance systems. As a critical remote sensing application, it enables maritime surveillance across diverse geographic scales and operational conditions while offering robust all-weather operation and resilience to environmental interference. However, infrared imagery in complex maritime environments presents significant challenges, including low contrast, background clutter, and difficulties in detecting small-scale or distant targets. To address these issues, we propose an Infrared Ship Detection Network (IRSD-Net), a lightweight and efficient detection network built upon the YOLOv11n framework and specially designed for infrared maritime imagery. IRSD-Net incorporates a Hierarchical Multi-Kernel Convolution Network (HMKCNet), which employs parallel multi-kernel convolutions and channel division to enhance multi-scale feature extraction while reducing redundancy and memory usage. To further improve cross-scale fusion, we design the Dynamic Cross-Scale Feature Pyramid Network (DCSFPN), a bidirectional architecture that combines up- and downsampling to integrate low-level detail with high-level semantics. Additionally, we introduce Wise-PIoU, a novel loss function that improves bounding box regression by enforcing geometric alignment and adaptively weighting gradients based on alignment quality. Experimental results demonstrate that IRSD-Net achieves 92.5% ${\mathrm{mAP}}_{50}$ on the ISDD dataset, outperforming YOLOv6n and YOLOv11n by 3.2% and 1.7%, respectively. With a throughput of 714.3 FPS, IRSD-Net delivers high-accuracy, real-time performance suitable for practical maritime monitoring systems. Full article

In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning techniques. Utilizing the SCORPAN model framework, we systematically combined diverse remote sensing datasets and innovatively established nine distinct strategies for soil salinity prediction. We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. The results reveal that among the models evaluated across the nine strategies, the SVR model demonstrated the highest accuracy, followed by RF. Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation. Full article

The ongoing genetic erosion of natural Prunus armeniaca populations in their native habitats underscores the urgent need for targeted conservation and restoration strategies. This study provides the first comprehensive characterization of P. armeniaca populations in the Almaty region of Kazakhstan, integrating morphological descriptors (46 parameters), molecular markers, geobotanical, and remote sensing analyses. Geobotanical and remote sensing analyses enhanced understanding of accession distribution, geological features, and ecosystem health across sites, while also revealing their vulnerability to various biotic and abiotic threats. Of 111 morphologically classified accessions, 54 were analyzed with 13 simple sequence repeat (SSR) markers and four DNA barcoding regions. Our findings demonstrate the necessity of integrated morphological and molecular analyses to differentiate closely related accessions. Genetic analysis identified 11 distinct populations with high heterozygosity and substantial genetic variability. Eight populations exhibited 100% polymorphism, indicating their potential as sources of adaptive genetic diversity. Cluster analysis grouped populations into three geographic clusters, suggesting limited gene flow across Gorges (features of a mountainous landscape) and greater connectivity within them. These findings underscore the need for site-specific conservation strategies, especially for genetically distinct, isolated populations with unique allelic profiles. This study provides a valuable foundation for prioritizing conservation targets, confirming genetic redundancies, and preserving genetic uniqueness to enhance the efficiency and effectiveness of the future conservation and use of P. armeniaca genetic resources in the region. Full article

This study investigates the genetic diversity and population structure of Castanopsis tribuloides, a vital tree species in Asian forest ecosystems. Understanding the genetic patterns of keystone forest species provides critical insights into forest resilience and ecosystem function and informs conservation strategies. We analyzed population samples collected from three distinct locations within Doi Suthep Mountain in northern Thailand using Short Tandem Repeat (STR) markers to assess both intra- and inter-population genetic relationships. DNA was extracted from leaf samples and analyzed using a panel of polymorphic microsatellite loci specifically optimized for Castanopsis species. Statistical analyses included the assessment of forensic parameters (number of alleles, observed and expected heterozygosity, gene diversity, polymorphic information content), population differentiation metrics (G_ST), inbreeding coefficients (F_IS), and gene flow estimates (N_m). We further examined population history through bottleneck analysis using three models (IAM, SMM, and TPM) and visualized genetic relationships through principal coordinate analysis and cluster analysis. Our results revealed significant patterns of genetic structuring across the sampled populations, with genetic distance metrics showing statistically significant differentiation between certain population pairs. The PCA and cluster analyses confirmed distinct population groupings that correspond to geographic distribution patterns. These findings provide the first comprehensive assessment of C. tribuloides population genetics in this region, establishing baseline data for monitoring genetic diversity and informing conservation strategies. This research contributes to our understanding of how landscape features and ecological factors shape genetic diversity patterns in essential forest tree species, with implications for managing forest genetic resources in the face of environmental change. Full article

The article provides an analysis of the geographic origins of Karaites in four areas where Karaite congregations were commonly found after the Middle Ages, namely, Arabic Middle East (territories of modern Iraq, Syria, Israel, and Egypt), Constantinople/Istanbul and its area, the Crimean Peninsula, and Eastern European territories belonging today to Lithuania and Ukraine. It combines available historical, onomastic, and linguistic data revealing the migrations of Karaites to and inside these regions. For the first two regions, no ambiguity exists about the roots of local Karaites. Their ancestors were Jews who adopted the Karaite version of Judaism. For the Crimean communities, various factors favor the hypothesis about the territories of the Byzantine Empire (which later became Ottoman), and more specifically, Constantinople and its area are the only major source for their development. The Karaite communities in such historical Eastern European provinces as Lithuania (properly speaking), Volhynia, and Red Ruthenia were created after migrations from Crimea to these territories. The article also discusses medieval, cultural, and potentially genetic links between Karaites and Rabbanite Jews in the areas in question. It also addresses one phonological feature, the sibilant confusion, shared by the Galician–Volhynian dialect of the Karaim language and the Lithuanian dialect of Yiddish. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

Lung adenocarcinoma exhibits a heterogeneous molecular landscape shaped by key oncogenic drivers and tumor suppressor gene alterations. Mutation frequencies vary geographically, influenced by genetic ancestry and environmental factors. However, the molecular profile of lung adenocarcinoma in Bulgarian patients remains largely uncharacterized. We conducted a prospective study of 147 Bulgarian patients with metastatic lung adenocarcinoma, analyzing clinicopathologic features and somatic mutation frequencies using next-generation sequencing. Key mutations and their prevalence were assessed and compared with published data from other populations. The cohort included predominantly male patients (68.0%) with a median age of 67 years. TP53 mutations were most frequent (41.5%), followed by EGFR alterations (19.0%) and KRAS c.34G>T (p.Gly12Cys) (17.0%). Over half of the patients (51.0%) harbored two or more gene mutations. Mutation frequencies aligned closely with European cohorts, exhibiting a lower prevalence of EGFR mutations compared to East Asian populations. This study characterizes the molecular landscape of lung adenocarcinoma in Bulgaria, highlighting the predominance of TP53 and KRAS mutations. The findings emphasize the need for comprehensive molecular profiling to inform targeted therapies and support precision oncology approaches tailored to the Bulgarian population. Further research is needed to validate these results and improve clinical outcomes. Full article

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article

One of the goals of Agenda 2030 is to increase the share of renewable energy in the global energy mix. In this context, photovoltaic systems play a key role in the transition to clean energy. According to the International Energy Agency, in 2023, solar photovoltaic alone accounted for three-quarters of renewable capacity additions worldwide. Designing a performing photovoltaic system requires careful planning that takes into account various factors, both internal and external, in order to maximize energy production and optimize costs. In addition to the technical characteristics of the system (internal factors), the positions and the shapes of external buildings and surrounding obstacles (external factors) have a significant impact on the output of photovoltaic systems. However, given the complexity of these environmental factors, they cannot be treated accurately in manual design practice. For this reason, this paper proposes a Building Information Modeling-based workflow for the design of a photovoltaic system that can guide the professional step-by-step throughout the design process, starting from the embryonic phase to the definitive, and therefore more detailed, one. The developed methodology allows for an in-depth analysis of the shading, the photovoltaic potential of the building, the performance of the photovoltaic system, and the costs for its construction in order to evaluate the appropriateness of the investment. The main aim of the paper is to create a standardized procedure applicable on a large scale for photovoltaic integration within Building Information Modeling workflows. The methodology is tested on two case studies, characterized by different architectural features and geographical positions. Full article

In geographic information data processing, the matching of road data at different scales is crucial. Due to scale differences, road features can change, posing a challenge to multi-scale matching. Spatial relationship is the key to matching because it remains stable at different scales. In this paper, we propose an improved summation product of direction and distance (ISOD) descriptor, which combines features such as included angle chain and camber variance with similarity features such as length, direction, and Hausdorff distance to construct an integrated similarity metric model for multi-scale road matching. The experiments proved that the model achieved 94.75% and 93.34% precision and recall in 1:50,000 and 1:10,000 scale road data matching and 86.39% and 94.06% in 1:250,000 and 1:50,000 scale road data matching, respectively. This proves the effectiveness and practicality of the method. The ISOD descriptor and integrated similarity metric model in this paper provide an effective method for multi-scale road data matching, which helps the integration and fusion of geographic information data, and has an important application value in the fields of intelligent transport and urban planning. Full article

The accurate prediction of soil organic matter (SOM) content is essential for promoting sustainable soil management and addressing global climate change. Due to multiple factors such as topography and climate, especially in mountainous areas, SOM spatial prediction faces significant challenges. The main novelty of this study lies in proposing a geographic positional encoding mechanism that embeds geographic location information into the feature representation of a Transformer model. The encoder structure is further modified to enhance spatial awareness, resulting in the development of the Geo-Positional Transformer (GPTransformer). Furthermore, this model is integrated with a 1D-CNN to form a dual-branch neural network called the Geo-Positional Transformer-CNN (GPTransCNN). This study collected 1490 topsoil samples (0–20 cm) from cultivated land in Longshan County to develop a predictive model for mapping the spatial distribution of SOM across the entire cultivated area. Different models were comprehensively evaluated through ten-fold cross-validation, ablation experiments, and uncertainty analysis. The results show that GPTransCNN has the best performance, with an R² improvement of approximately 43% over the Transformer, 19% over the GPTransformer, and 15% over the 1D-CNN. This study demonstrates that by incorporating geographic positional information, GPTransCNN effectively combines the global modeling capabilities of the GPTransformer with the local feature extraction strengths of the 1D-CNN, which can improve the accuracy of SOM mapping in mountainous areas. This approach provides data support for sustainable soil management and decision-making in response to global climate change. Full article