Search Results (2,871)

Lethocerus patruelis (Stål, 1854), the sole European belostomatid, is an apex invertebrate predator in Mediterranean freshwater systems and a species known for its strong flight capacity and growing range expansion record. While its continental distribution in Greece is increasingly well documented, its presence across the Aegean islands has remained poorly characterized, with historical records scattered and often unpublished or fragmentary. Here, we present new, photographically verified records that substantially refine the species’ insular distribution and provide the first coordinated synthesis of its emerging archipelagic footprint. These include the first confirmed live individual from Samothraki and a newly documented specimen from Naxos, recovered beside a nearly desiccated summer stream indicating a very recent arrival. When integrated with additional verified observations from Sifnos, Ikaria, Chios, Euboea, Tilos, and Crete, as well as earlier published records, a coherent spatial pattern emerges. Rather than isolated vagrants, the records align along three longitudinal dispersal axes spanning the northern, central, and southern Aegean. These axes reflect plausible biological and anthropogenic pathways influenced by regional winds, maritime transport, and the distribution of natural and artificial freshwater habitats. Collectively, the evidence indicates that L. patruelis is undergoing a sustained, multi-vector archipelagic expansion, underscoring the importance of integrating citizen-science observations with targeted field documentation to monitor freshwater biodiversity across Mediterranean islands. Full article

The evolution towards 6G is set to transform Vehicle-to-Everything (V2X) networks by introducing advanced technologies such as Six-Dimensional Movable Antenna (6DMA). This technology endows Roadside Units (RSUs) with dynamic beam-steering capabilities, enabling adaptive coverage. However, traditional RSU deployment strategies, optimized for static coverage, are fundamentally mismatched with these new dynamic capabilities, leading to a critical deployment–optimization mismatch. This paper addresses this challenge by proposing DyDO, a novel Dynamic Deployment and Optimization framework for the utilization of 6DMA-RSUs. Our framework strategically decouples the problem into two modules operating on distinct timescales. On a slow timescale, an offline deployment module analyzes long-term historical traffic data to identify optimal RSU locations. This is guided by a newly proposed metric, the Dynamic Potential Score (DPS), which quantifies a location’s intrinsic value for dynamic adaptation by integrating spatial concentration, temporal volatility, and traffic magnitude. On a fast timescale, an online control module employs an efficient Sequential Angular Search (SAS) algorithm to perform real-time, adaptive beam steering based on immediate traffic patterns. Extensive experiments on a large-scale, real-world trajectory dataset demonstrate that DyDO outperforms conventional static deployment methodologies. This work highlights the necessity of dynamic-aware deployment to fully unlock the potential of 6DMA in future V2X systems. Full article

Prediction of stock closing price plays a critical role in financial planning, risk management, and informed investment decision-making. In this study, we propose a novel model that synergistically amalgamates Bidirectional GRU (BiGRU) with three complementary attention techniques—Top-k Sparse, Global, and Bahdanau Attention—to tackle the complex, intricate, and non-linear temporal dependencies in financial time series. The proposed Fused Attention Model is validated on two highly volatile, non-linear, and complex- patterned stock indices: NIFTY 50 and S&P 500, with 80% of the historical price data used for model learning and the remaining 20% for testing. A comprehensive analysis of the results, benchmarked against various baseline and hybrid deep learning architectures across multiple regression performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R² Score, demonstrates the superiority and noteworthiness of our proposed Fused Attention Model. Most significantly, the proposed model yields the highest prediction accuracy and generalization capability, with R² scores of 0.9955 on NIFTY 50 and 0.9961 on S&P 500. Additionally, to mitigate the issues of interpretability and transparency of the deep learning model for financial forecasting, we utilized three different Explainable Artificial Intelligence (XAI) techniques, namely Integrated Gradients, SHapley Additive exPlanations (SHAP), and Attention Weight Analysis. The results of these three XAI techniques validated the utilization of three attention techniques along with the BiGRU model. The explainability of the proposed model named as BiGRU based Fused Attention (BiG-FA), in addition to its superior performance, thus offers a robust and interpretable deep learning model for time-series prediction, making it applicable beyond the financial domain. Full article

The elasmobranch fauna was studied in the Khor Faridah region of Abu Dhabi, which is a mangrove-dominated inshore habitat historically reported to host a diversity of elasmobranch species. A stereo-baited remote underwater video system (Stereo-BRUVS) survey was conducted from September 2021 to August 2022 to assess the species diversity and relative abundance of elasmobranch fishes. A total of 12 elasmobranch taxa were encountered during the study, consisting of five rays (Myliobatiformes), four sharks (Selachii), two wedgefish and one guitarfish (Rhinopristiformes). The area was dominated by honeycomb-patterned rays in the genus Himantura and the Critically Endangered Arabic whipray Maculabatis arabica. Since Himantura uarnak and H. leoparda could not be reliably distinguished from footage, all sex- and size-based results are reported for a combined Himantura species complex and should be interpreted cautiously. Furthermore, the broad size range of individuals found in the area highlights its importance to all life stages of these taxa. This underlines the need for a conservation strategy to avoid detrimental changes to the elasmobranch fauna due to ongoing coastal development. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

Rice blast, caused by Magnaporthe oryzae, is a devastating disease that jeopardizes global rice production and food security. Precision agriculture demands timely and accurate monitoring tools to enable targeted intervention. This study introduces a novel deep learning framework that fuses time-series hyperspectral imaging with an advanced Autoformer model (AutoMSD) to dynamically track rice blast progression. The proposed AutoMSD model integrates multi-scale convolution and adaptive sequence decomposition, effectively decoding complex spatio-temporal patterns associated with disease development. When deployed on a 7-day hyperspectral dataset, AutoMSD achieved 86.67% prediction accuracy using only 3 days of historical data, surpassing conventional approaches. This accuracy at an early infection stage underscores the model’s strong potential for practical field deployment. Our work provides a scalable and robust decision-support tool that paves the way for site-specific disease management, reduced pesticide usage, and enhanced sustainability in rice cultivation systems. Full article

Traffic flow anomalies represent significant deviations from normal traffic behavior and disrupt the smooth operation of transportation systems. These may appear as unusually high or low traffic volumes compared to historical trends. Unexpectedly high volume can lead to congestion exceeding usual capacity, while unusually low volume might indicate incidents like road closures, or malfunctioning traffic signals. Identifying and understanding both types of anomalies is crucial for effective traffic management. This paper presents a clustering based approach for enhanced characterization of anamolies in traffic flows. Anomalies in traffic patterns are determined using three anomaly detection techniques: Elliptic Envelope, Isolation Forest, and Local Outlier Factor. These anomalies were newly detected in this work on the Montréal dataset after preprocessing, rather than directly reused from earlier studies. These methods were applied to a dataset that had been pre-processed using windowing techniques with different configuration settings to enhance the detection process. Then, to leverage the detected anomalies, we utilized clustering algorithms, specifically k-means and hierarchical clustering, to segment these anomalies. Each clustering algorithm was used to determine the optimal number of clusters. Subsequently, we characterized these clusters through detailed visualization and mapped them according to their unique characteristics. This approach not only identifies traffic anomalies effectively but also provides a comprehensive understanding of their spatial and temporal distributions, which is crucial for traffic management and urban planning. Full article

Against the background of rapid global urbanization, the renewal and renovation of historic districts have become an increasingly important concern. As a city with a long and rich history, Beijing contains numerous historic districts that are in urgent need of systematic renewal and renovation. This study proposes a functional enhancement and renovation design methodology for urban historic districts based on space syntax theory and analytical methods, applying it to the Qianmen Historic District in Beijing. Through traffic flow and business format analysis, the research examines traffic patterns and business format distribution characteristics in the Qianmen area and ultimately guides the design based on these findings. Research indicates that restrooms and attractions in Beijing’s Qianmen historic district exhibit dispersed space distribution, broad service coverage, high metric step depth (447 m and 436 m, respectively), and low topological connectivity. In contrast, hotels and restaurants feature smaller service areas, lower metric step depth (395 m and 297 m, respectively), and higher topological connectivity. Based on these findings, this study proposes targeted design recommendations for Qianmen’s street renovations based on traffic flow analysis results. Considering the need for vehicle parking and pedestrian rest demands in urban functional renewal, rest seats and shared charging piles are set up on the streets with big pedestrian flow to meet the needs of pedestrians. Moreover, cycling routes are designed to connect big-traffic-flow streets with small-traffic-flow ones. These renewal measures aim to enhance the overall vitality of the Qianmen district. The renovation approach and methodology proposed in this study can serve as a reference for future updates and renovations of historic districts. Full article

This article explores how female entrepreneurs construct and negotiate entrepreneurial identities across socialist, transition, and post-socialist periods in Slovenia. Drawing on feminist, post-structuralist, and identity-theory perspectives, we ask what determines women’s entrepreneurial identities and how multiple roles and changing institutions shape them. Using a qualitative multiple-case design, we analyze 15 information-rich cases selected through purposive sampling and based on in-depth semi-structured interviews and supporting documents. Qualitative content analysis and cross-case comparison identified patterns within and across the three periods. Results show that women’s motives combine economic, autonomy, and mission-driven goals; that entrepreneurial identity is closely intertwined with motherhood, partnership, and community roles; and that evolving ecosystems offer increasing but still fragmented support. Identity work intensifies at transitions between employment and entrepreneurship and when growth ambitions confront care responsibilities. We conclude that female entrepreneurial identities in Slovenia are historically and institutionally embedded and that gender-integrative, context-sensitive ecosystem measures are needed to support diverse entrepreneurial pathways and long-term, socially responsible growth. Full article

Cyprus launched its Competitive Electricity Market on 1 October 2025, marking a historic transition from monopolistic to liberalized electricity trading. This paper presents a comprehensive analysis of the market’s first month of operation, evaluating technical performance, price dynamics, market structure, and identifying critical barriers to achieving competitive benefits. Analysis reveals technically successful operation of clearing mechanisms and settlement processes, but economically constrained performance driven by persistent structural limitations. The market exhibits extreme price volatility characteristic of isolated systems, ranging from zero to 500 EUR/MWh, with pronounced diurnal patterns reflecting solar generation dynamics. The monthly wholesale price averaged at 167.78 EUR/MWh. The market remains highly concentrated with only 17 participants, shallow liquidity, and heavy reliance on conventional generation (86%) despite installed renewable capacity exceeding 1000 MW. Critical infrastructure deficits including absent natural gas infrastructure, lack of utility-scale storage, electrical isolation, and incomplete smart metering deployment represent fundamental barriers to achieving EU Target Model objectives. Based on infrastructure deployment scenarios and international benchmarking, we suggest potential reductions in the wholesale price of 12.5% (base scenario) to 15% (optimistic scenario) by the end of 2027, dependent on timely natural gas commissioning, storage deployment, and regulatory reform. Policy recommendations address immediate regulatory actions, medium-term market development priorities, and critical infrastructure investments essential for transitioning from technically operational to economically beneficial market operation. This analysis contributes to understanding the challenges that small, isolated electricity markets face when implementing EU liberalization frameworks while highlighting policy interventions required for successful market maturation. Full article

This paper presents the development and implementation of a consumer Digital Twin (DT) for energy demand prediction under the SENDER (Sustainable Consumer Engagement and Demand Response) project, funded by HORIZON 2020. This project aims to engage consumers in the energy sector with innovative energy service applications to achieve proactive Demand Response (DR) and optimized usage of Renewable Energy Sources (RES). The proposed DT model is designed to digitally represent occupant behaviors and energy consumption patterns using Artificial Neural Networks (ANN), which enable continuous learning by processing real-time and historical data in different pilot sites and seasons. The DT development incorporates the International Energy Agency (IEA)—Energy in Buildings and Communities (EBC) Annex 66 and Drivers-Needs-Actions-Systems (DNAS) framework to standardize occupant behavior modeling. The research methodology consists of the following steps: (i) a mock-up simulation environment for three pilot sites was created, (ii) the DT was trained and calibrated using the artificial data from the previous step, and (iii) the DT model was validated with real data from the Alginet pilot site in Spain. Results showed a strong correlation between DT predictions and mock-up data, with a maximum deviation of ±2%. Finally, a set of selected Key Performance Indicators (KPIs) was defined and categorized in order to evaluate the system’s technical effectiveness. Full article

Historical landslide inventory serves as a critical tool for analyzing landslide activity patterns and evaluating the long-term geological impacts of triggering events, including earthquakes, extreme weather events, and large-scale infrastructure projects. Although various methods—including visual interpretation, heuristic approaches, machine learning, and deep learning models—have been employed for landslide detection, efficient techniques for historical landslide mapping remain understudied. As a result, comprehensive historical landslide inventories continue to be scarce worldwide. In this study, we developed an advanced landslide detection model using a Swin Transformer architecture integrated with a Pyramid Segmentation Attention mechanism. Subsequently, we applied a network fine-tuning method to achieve cross-domain adaptation, enabling the reconstruction of a decadal-scale landslide inventory across the Wenchuan earthquake-affected region efficiently. Experimental results from the Wenchuan earthquake area demonstrate the proposed approach’s superior temporal transfer mapping performance compared to state-of-the-art models. The proposed historical map also exhibits high accuracy and completeness, offering significant value for analyzing landslide spatiotemporal activity and long-term regional stability. Findings reveal that landslides stabilized overall between 2008 and 2021, with key influences including altitude, slope, and aspect. The results lay the groundwork for regional stability analysis and eco-environment recovery, enabling informed decisions in urban planning and infrastructure investments. Full article

Early-onset colorectal cancer (EOCRC) continues to rise, with the steepest increases observed among Hispanic/Latino (H/L) populations, underscoring the urgency of identifying ancestry- and treatment-specific biomarkers. The JAK-STAT signaling axis plays a central role in colorectal tumor biology, yet its relevance under FOLFOX-based chemotherapy in EOCRC remains poorly defined. In this study, we evaluated 2515 colorectal cancer (CRC) cases (266 H/L; 2249 non-Hispanic White [NHW]), stratifying analyses by ancestry, age of onset, and FOLFOX exposure. Statistical comparisons were performed using Fisher’s exact and chi-square tests, and survival patterns were assessed via Kaplan–Meier analysis. To extend conventional analytics, we deployed AI-HOPE and AI-HOPE-JAK-STAT, conversational artificial intelligence platforms capable of harmonizing genomic, clinical, demographic, and treatment variables through natural language queries, to accelerate multi-parameter biomarker exploration. JAK-STAT pathway alterations showed marked variation by ancestry and treatment context. Among H/L EOCRC cases, alterations were significantly enriched in patients who did not receive FOLFOX compared with those who did (21.2% vs. 4.1%; p = 0.003). A similar pattern emerged in late-onset CRC (LOCRC) NHW patients, where alterations were more frequent without FOLFOX exposure (13.3% vs. 7.5%; p = 0.0002). Notably, JAK-STAT alterations were significantly more common in untreated H/L EOCRC compared with untreated NHW EOCRC (21.2% vs. 9.9%; p = 0.002). Survival analyses revealed that JAK-STAT pathway alterations conferred improved overall survival across several NHW strata, including EOCRC treated with FOLFOX (p = 0.0008), EOCRC not treated with FOLFOX (p = 0.07), and LOCRC not treated with FOLFOX (p = 0.01). These findings suggest that JAK-STAT alterations may function as ancestry- and treatment-dependent prognostic markers in EOCRC, particularly among disproportionately affected H/L patients. However, prognostic interpretation in H/L subgroups is limited by small mutation-positive sample sizes, reflecting historical underrepresentation and highlighting the need for larger ancestry-balanced studies. The integration of AI-enabled platforms streamlined analyses and reveals the potential of artificial intelligence to accelerate discovery and advance precision medicine for populations historically underrepresented in cancer genomics research. Full article

(1) Background: Rubus geoides Sm., a native species of southern Patagonia, faces increasing threats due to climate change and anthropogenic land-use changes. Historically widespread, its distribution has become restricted by overgrazing, urban expansion, extractive industries, and direct harvesting from natural populations driven by interest in its nutraceutical potential since the first European settlements. (2) Methods: To assess its resilience and conservation prospects, we analyzed the morphological variability, genetic diversity, and population structure, complemented by species distribution modeling under past and future climate scenarios. (3) Results: Our findings reveal moderate genetic differentiation and private alleles in specific populations, alongside significant variation in flowering phenology. Paternity analysis indicates a tendency toward self-pollination, although this conclusion is constrained by the limited number of microsatellite markers employed. These results suggest post-glacial dispersal patterns and highlight the species’ potential for expansion under certain climate scenarios. (4) Conclusions: This study provides critical insights for biodiversity conservation and sustainable land management, directly aligned with the UN Sustainable Development Goals SDG 15 (Life on Land). Indirectly, this study contributes to SDG 2 (Zero Hunger) by highlighting the importance of threatened species that hold value for human consumption and food security. Land-use changes, particularly mining and green hydrogen industry settlements, may represent stronger limitations to species expansion than climate change itself. Full article

Open access (OA) has expanded scholarly publishing, yet concerns remain about the sustainability of journals indexed in selective databases. This study analyzes editorial predictors of discontinuation among 8730 journals simultaneously registered in the Directory of Open Access Journals (DOAJ) and indexed in Scopus, including 58 (0.66%) discontinued titles as of June 2025 (latest available update at the time of data extraction). The analyses revealed that a journal’s history of prior discontinuation was the strongest and most consistent predictor of future instability, confirming that discontinuation follows a path-dependent pattern rather than isolated events. Financial structure also played a decisive role: journals applying other editorial fees beyond standard article processing charges (APCs) were nearly four times more likely to experience discontinuation (IRR = 3.877, p = 0.048), while those following standardized APC models showed a protective but non-significant tendency (IRR = 0.378, p = 0.084). Journal age exhibited a modest yet significant positive effect (IRR = 1.032, p = 0.031), suggesting that older titles face a gradual accumulation of risk over time. By contrast, editorial practices such as plagiarism detection, waiver policies, and turnaround time showed no significant association. Overall, the findings indicate that discontinuation in Scopus-indexed OA journals is statistically associated with historical trajectories, financial transparency, and governance capacity, rather than by routine editorial procedures. Full article