Search Results (2,147)

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

Soundscape monitoring has become an increasingly important tool for studying ecological processes and supporting habitat conservation. While many recent advances focus on identifying species through supervised learning, there is growing interest in understanding the soundscape as a whole while considering patterns that extend beyond individual vocalizations. This broader view requires unsupervised approaches capable of capturing meaningful structures related to temporal dynamics, frequency content, spatial distribution, and ecological variability. In this study, we present a fully unsupervised framework for analyzing large-scale soundscape data using deep learning. We applied a convolutional autoencoder (Soundscape-Net) to extract acoustic representations from over 60,000 recordings collected across a grid-based sampling design in the Rey Zamuro Reserve in Colombia. These features were initially compared with other audio characterization methods, showing superior performance in multiclass classification, with accuracies of 0.85 for habitat cover identification and 0.89 for time-of-day classification across 13 days. For the unsupervised study, optimized dimensionality reduction methods (Uniform Manifold Approximation and Projection and Pairwise Controlled Manifold Approximation and Projection) were applied to project the learned features, achieving trustworthiness scores above 0.96. Subsequently, clustering was performed using KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), with evaluations based on metrics such as the silhouette, where scores above 0.45 were obtained, thus supporting the robustness of the discovered latent acoustic structures. To interpret and validate the resulting clusters, we combined multiple strategies: spatial mapping through interpolation, analysis of acoustic index variance to understand the cluster structure, and graph-based connectivity analysis to identify ecological relationships between the recording sites. Our results demonstrate that this approach can uncover both local and broad-scale patterns in the soundscape, providing a flexible and interpretable pathway for unsupervised ecological monitoring. Full article

Conventional wildfire assessment products emphasize regional-scale ignition likelihood and potential spread derived from fuels and weather. While useful for broad planning, they do not directly support boundary-aware, scenario-specific decision-making for localized threats to communities in the Wildland–Urban Interface (WUI). This limitation constrains the ability of fire managers to effectively prioritize mitigation efforts and response strategies for ignition events that may lead to severe local impacts. This paper introduces WUI-BTI—a scenario-based, simulation-driven boundary-threat index for the Wildland–Urban Interface that quantifies consequences conditional on an ignition under standardized meteorology, rather than estimating risk. WUI-BTI evaluates ignition locations—referred to as Fire Amplification Sites (FAS)—based on their potential to compromise the defined boundary of a community. For each ignition location, a high-resolution fire spread simulation is conducted. The resulting fire perimeter dynamics are analyzed to extract three key metrics: (1) the minimum distance of fire approach to the community boundary ($D_{min}$) for non-breaching fires; and for breaching fires, (2) the time required for the fire to reach the boundary ($T_p$), and (3) the total length of the community boundary affected by the fire ($L_c$). These raw outputs are mapped through monotone, sigmoid-based transformations to yield a single, interpretable score: breaching fires are scored by the product of an inverse-time urgency term and an extent term, whereas non-breaching fires are scored by proximity alone. The result is a continuous boundary-threat surface that ranks ignition sites by their potential to rapidly and substantially compromise a community boundary. By converting complex simulation outputs into scenario-specific, boundary-aware intelligence, WUI-BTI provides a transparent, quantitative basis for prioritizing fuel treatments, pre-positioning suppression resources, and guiding protective strategies in the WUI for fire managers, land use planners, and emergency response agencies. The framework complements regional hazard layers (e.g., severity classifications) by resolving fine-scale, consequence-focused priorities for specific communities. Full article

Eryngium foetidum L. belongs to the Apiaceae family and is a perennial herb. The entire plant is rich in essential oils, which have a distinctive aroma similar to cilantro. This plant exhibits significant biological activity and possesses characteristics such as disease resistance and antimicrobial properties, showing great potential in medical and food applications. Additionally, its essential oil has substantial commercial value. Mitochondria play a crucial role as organelles within plant cells; however, the mitochondrial genome of E. foetidum remains underexplored. To fill this research gap, we conducted sequencing and assembly of the mitochondrial genome of E. foetidum, aiming to uncover its genetic mechanisms and evolutionary trajectories. Our investigation reveals that the mitochondrial genome of E. foetidum is a circular structure, similar to that of other species, with a length of 241,660 bp and a GC content of 45.35%, which is within the range observed in other organisms. This genome encodes 59 genes, comprising 37 protein-coding sequences, 18 tRNA genes, and 4 rRNA genes. Comparative analysis highlighted 16 homologous regions between the mitochondrial and chloroplast genomes, with the longest segment spanning 992 bp. By analyzing 37 protein-coding genes (PCGs), we identified 479 potential RNA editing sites, which induce the formation of stop codons in the nad3 and atp6 genes, as well as start codons in the ccmFC, atp8, nad4L, cox2, cox1, and nad7 genes. Meanwhile, the genome shows a preference for A/T bases and A/T-ending codons, with 32 codons having a relative synonymous codon usage (RSCU) value greater than 1. The codon usage bias is relatively weak and mainly influenced by natural selection. Most PCGs are under purifying selection (Ka/Ks < 1), while only a few genes, such as rps7 and matR, may be under positive selection. Phylogenetic analysis of mitochondrial PCGs from 21 species showed E. foetidum at the basal node of Apiaceae, consistent with the latest APG angiosperm classification and chloroplast genome-based phylogenetic relationships. In summary, our comprehensive characterization of the E. foetidum mitochondrial genome not only provides novel insights into its evolutionary history and genetic regulation but also establishes a critical genomic resource for future molecular breeding efforts targeting mitochondrial-associated traits in this economically important species. 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

Efficient transportation management is essential for the sustainability and safety of modern urban infrastructure. Traditional road inspection and transport management methods are often labor-intensive, time-consuming, and prone to inaccuracies, limiting their effectiveness. This study presents a UAV-based transport management system that leverages machine learning techniques to enhance road anomaly detection and severity assessment. The proposed approach employs a structured three-tier model architecture: A unified obstacle detection model identifies six critical road hazards—road cracks, potholes, animals, illegal dumping, construction sites, and accidents. In the second stage, six dedicated severity classification models assess the impact of each detected hazard by categorizing its severity as low, medium, or high. Finally, an aggregation model integrates the results to provide comprehensive insights for transportation authorities. The systematic approach seamlessly integrates real-time data into an interactive dashboard, facilitating data-driven decision-making for proactive maintenance, improved road safety, and optimized resource allocation. By combining accuracy, scalability, and computational efficiency, this approach offers a robust and scalable solution for smart city infrastructure management and transportation planning. Full article

Accurate classification of defects in the semiconductor manufacturing process is critical for improving yield and ensuring quality. While previous works have mainly focused on improving classification accuracy, we propose a model that can simultaneously assess accuracy, prediction confidence, and interpretability in wafer defect classification. To solve the class imbalance problem, we used a weighted cross-entropy loss function and convolutional neural network–based model to achieve a high accuracy of 97.8% on the test dataset and applied a temperature-scaling technique to enhance confidence. Furthermore, by simultaneously employing local interpretable model-agnostic explanations and gradient-weighted class activation mapping, the rationale for the predictions of the model was visualized, allowing users to understand the decision-making process of the model from various perspectives. This research can provide a direction for the next generation of intelligent quality management systems by enhancing the applicability of the proposed model in actual semiconductor production sites through explainable predictions. Full article

The landfill mining and reclamation (LFMR) project is increasingly recognized as crucial for achieving sustainable waste management and supporting global environmental goals, such as the United Nations Sustainable Development Goals related to clean energy, responsible consumption, and sustainable cities. This study evaluated the potential of combustible polymer composites (CPCs) derived from landfill mining waste for energy recovery and chemical recycling as well as resource recovery potential of soil-like fractions (SLFs). Through physico-chemical analysis and pyrolysis reaction with catalytic upgrading process, the study evaluates the suitability of CPCs for energy recovery as a solid recovered fuel (SRF) and chemical recycling feedstock. For assessing the SLFs for potential use as recycled aggregates and cover materials, total organic carbon, heavy metal concentration, and biodegradability were investigated. CPCs exhibited varied SRF and chemical feedstock qualities depending on site-specific polymer composition, while SLFs met environmental criteria for both inert waste and stabilization soil classification. The findings not only highlight technical feasibility, but also provide a transferable evaluation framework supporting ‘circular economy’ policies. Therefore, LFMR projects can contribute to sustainable waste management and energy production and provide solutions for effective material recycling, aligning with global environmental and resource conservation goals. Full article

Research into oral mucosa-targeted drug delivery systems (DDS) is rapidly evolving, with growing emphasis on enhancing bioavailability and precision targeting while overcoming the unique anatomical and physiological barriers of the oral environment. Despite considerable progress, challenges such as enzymatic degradation, limited mucosal penetration, and solubility issues continue to hinder therapeutic success. Recent advancements have focused on innovative formulation strategies—including nanoparticulate and biomimetic systems—to improve delivery efficiency and systemic absorption. Simultaneously, smart and stimuli-responsive materials are emerging, offering dynamic, environment-sensitive drug release profiles. One particularly promising area involves the application of glycosaminoglycans, a class of naturally derived polysaccharides with excellent biocompatibility, mucoadhesive properties, and hydrogel-forming capacity. These materials not only enhance drug residence time at the mucosal site but also enable controlled release kinetics, thereby improving therapeutic outcomes. However, critical research gaps remain: standardized, clinically meaningful mucoadhesion/permeation assays and robust in vitro–in vivo correlations are still lacking; long-term stability, batch consistency of GAGs, and clear regulatory classification (drug, device, or combination) continue to impede scale-up and translation. Patient-centric performance—palatability, mouthfeel, discreet wearability—and head-to-head trials versus standard care also require systematic evaluation to guide adoption. Overall, converging advances in GAG-based films, hydrogels, and nanoengineered carriers position oral mucosal delivery as a realistic near-term option for precision local and selected systemic therapies—provided the field resolves standardization, stability, regulatory, and usability hurdles. Full article

Mapping forest tree species is vital for the habitat assessment, ecosystem services estimation, and implementation of European environmental policies such as the Habitats Directive. This study explores how repeated satellite observations over time, known as multitemporal data, can improve the mapping of tree species in riparian forests. Although many studies have shown that the use of multitemporal data improves tree species classification accuracies, there is a lack of research on how different multitemporal models perform compared to each other. We compared three multitemporal remote sensing approaches using Sentinel-2 imagery to map tree species within the Austrian riparian Natura 2000 site, Salzachauen. Seven tree species (five native and two non-native riparian species) were mapped using random forest models trained on a dataset of 444 validated tree samples. The three multitemporal approaches tested were: (i) multi-date image stacking, (ii) seasonal mean composites, and (iii) spectral–temporal metrics (STMs). The three approaches were compared to twenty single-date image classifications. The multitemporal models achieved 62 to 65% overall accuracy, while the median accuracy of single-date classification was 50% (SD = 6%). The seasonal model obtained the highest overall accuracy (65%), with F1 scores exceeding 73% for four individual species. However, differences among the three multitemporal approaches were not statistically significant. The mapping of native versus non-native riparian species achieved 92% accuracy. We evaluated misclassification patterns of individual species according to the two riparian forest habitats, 91E0* and 91F0, as defined in Annex I of the Habitats Directive. Most omission and commission errors occurred between species within the same habitat type. These findings underline the potential of translating tree species mapping to habitat-type classifications and the need to further explore the capabilities of satellite remote sensing to fill data gaps in Natura 2000 areas. Full article

Background and Objectives: HER-2 expression plays a critical role in the biological behavior and treatment of gastric cancer. With the emergence of HER-2-targeted therapies, classification into negative, low, and positive groups has gained clinical importance. The present study focused on assessing the link between HER-2 status and clinical–pathological variables, metastatic involvement, and overall survival (OS) among advanced gastric cancer patients. Materials and Methods: A total of 300 patients with advanced gastric adenocarcinoma were retrospectively analyzed. The mean age of the 300 patients included in the study was 61.8 years, and 70% of them were male. Based on immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), patients were classified as HER-2-negative (IHC 0), HER-2-low (IHC 1+ or 2+/FISH-negative), or HER-2-positive (IHC 3+ or 2+/FISH-positive). Clinicopathological variables, metastatic sites, and OS were compared among groups using Pearson’s Chi-square, Fisher’s exact test, ANOVA, and Kaplan–Meier survival analysis. Results: Significant differences were observed among HER-2 subgroups in pathological subtype (p = 0.006), liver metastasis (p = 0.009), lung metastasis (p = 0.006), and other metastatic sites (p = 0.001). HER-2-positive patients demonstrated higher rates of adenocarcinoma histology and increased liver and lung metastases. In female patients, HER-2 status was significantly associated with lung (p = 0.001) and other metastases (p < 0.001). Median OS for the entire cohort was 9.83 months (95% CI: 8.29–11.36). HER-2-positive patients had a significantly longer OS (15.06 months) compared with HER-2-negative patients (8.73 months; p = 0.039). Conclusions: HER-2 status is an important predictor of metastatic behavior and survival in advanced gastric cancer. HER-2-positive patients display distinct metastatic patterns and improved outcomes, supporting the value of HER-2-targeted therapies. The HER-2-low group may represent a biologically and clinically relevant intermediate subtype requiring further investigation. Full article

The twin earthquakes that struck Kahramanmaraş in 2023 (Mw 7.7 and Mw 7.6) caused widespread structural destruction across southeastern Türkiye, underscoring the need for more refined approaches to seismic damage assessment. In this study, a large-scale machine learning (ML) analysis is conducted to identify and classify damage patterns among 304,299 buildings across 11 cities. Ten ML algorithms are implemented, and their performance in the multiclass classification of damage severity is comparatively evaluated (collapsed, urgent demolition, moderately damaged, and severely damaged). Unlike conventional methods that rely on single-station data, the proposed approach integrates ground motion parameters from the six seismic stations closest to each building. These parameters include peak ground acceleration, several distance measures (Joyner–Boore, rupture, and epicentral distances), and site condition indicators such as mean shear wave velocity in the upper 30 m and soil classification, yielding 60 engineered features per building. The analysis reveals that ensemble learning models, particularly the random forest and a voting ensemble, achieve the highest classification accuracies (79.65% and 79.62%, respectively). Moreover, classification performance varies across damage categories: severely damaged structures exhibit the highest F1-score (0.891), whereas collapsed buildings exhibit lower accuracy (F1-score: 0.408). These findings offer practical value for post-earthquake emergency operations. Furthermore, the methodology establishes a precedent for future seismic risk assessments and supports data-driven decision-making. Full article

Powdered foods are matrices transformed into fine, loose solid particles through dehydration and/or milling, which enhances stability, storage, and transport. Due to their high commercial value and susceptibility to fraudulent practices, detecting adulterants in powdered foods is essential for ensuring food safety and protecting consumer health and the economy. Food fraud in powdered products, such as spices, cereals, dairy-based powders, and dietary supplements, poses an increasing risk to public health and consumer trust. These products were selected as representative matrices due to their high nutritional and economic relevance, which also makes them more susceptible to adulteration and hidden potential health risks from hidden contaminants. Recent studies highlight the potential of spectroscopic techniques combined with chemometrics as rapid, non-destructive, and cost-effective tools for authentication. This narrative review synthesizes recent literature (2020–2025) on the application of near-infrared (NIR) spectroscopy combined with chemometric techniques for adulterant detection in powdered foods. Advances in spectral preprocessing, variable selection, classification, and regression models are discussed alongside the most common adulterants and their nutritional and toxicological implications. Furthermore, the applicability of portable versus benchtop NIR devices is compared. The main contribution of this review lies in critically analyzing methodological frameworks, mapping current gaps, and identifying emerging trends, such as digital integration, self-adaptive chemometric models, and real-time on-site authentication, positioning NIR spectroscopy as a promising tool for food authentication and quality control. Full article

This study proposes a novel method to address the instability issues in multi-target bearing-only tracking for passive sonar systems. Utilizing a particle filter-guided on-site training mechanism, the complex multi-classification task is simplified into binary classification (target vs. non-target) by assigning an independent tracker to each target. This enables simultaneous on-site training and deployment of the neural network for tracking. A hybrid CNN-BiLSTM network is constructed: the Convolutional Neural Network (CNN) enhances target feature extraction and non-target discrimination, while the Bidirectional Long Short-Term Memory (BiLSTM) models spatiotemporal dependencies. Their synergy improves trajectory continuity and smoothness. Under simulated conditions, the proposed method reduces the minimum required SNR for stable tracking to −31.78 dB, a significant improvement over the −29.69 dB required by pure particle filtering methods. The average tracking error is also reduced from 0.61° to 0.34°. Both simulations and sea trial data demonstrate that the method maintains stable tracking even during target trajectory crossings, significantly enhancing multi-target tracking accuracy in complex underwater acoustic environments. Full article

Restoration practitioners specify goals that describe how the focal ecosystem will look or function upon reaching recovery goals. Goals may be influenced by the level of degradation, surrounding landscape conditions, societal choice, and a changing climate regime. The Society for Ecological Restoration’s International Principles and Standards for the Practice of Ecological Restoration recommend that goals should be informed by reference models of site conditions, which include the biotic composition, the environmental setting, and dynamic processes—had anthropogenic degradation not occurred—while accounting for anticipated changes. The SER principles address many aspects of ecological restoration, and practical steps include conceptualizing the structure and function of the natural system, measuring ecological integrity, and assessing potential climate change effects and adaptations. Models optimally reflect a variety of information sources and are based, where possible, on multiple reference sites of similar native ecological conditions. Using a project site from the Colorado National Monument in the USA, we illustrate a stepwise process to address these principles and standards by compiling and synthesizing map, text, and tabular information from reference materials and sites. By addressing these principles and systematically utilizing existing frameworks and locally available data, practitioners can streamline the establishment of reference models for ecological restoration. Full article