Search Results (5,862)

In Search and Rescue (SAR) missions, UAVs must be capable of detecting small objects from complex and noise-prone maritime images. Existing small object detection methods typically rely on super-resolution techniques or complex structural designs, which often demand significant computational resources and fail to meet the real-time requirements for small mobile devices in SAR tasks. To address this challenge, we propose SAQ-YOLO, an efficient small object detection model based on the YOLO framework. We design a Small Object Auxiliary Query branch, which uses deep semantic information to guide the fusion of shallow features, thereby improving small object capture efficiency. Additionally, SAQ-YOLO incorporates a series of lightweight channel, spatial, and group (large kernel) gated attention mechanisms to suppress background clutter in complex maritime environments, enhancing feature extraction at a low computational cost. Experiments on the SeaDronesSee dataset demonstrate that, compared to YOLOv11s, SAQ-YOLO reduces the number of parameters by approximately 70% while increasing mAP@50 by 2.1 percentage points. Compared to YOLOv11n, SAQ-YOLO improves mAP@50 by 8.7 percentage points. When deployed on embedded platforms, SAQ-YOLO achieves an inference latency of only 35 milliseconds per frame, meeting the real-time requirements of maritime SAR applications. These results suggest that SAQ-YOLO provides an efficient and deployable solution for UAV SAR operations in vast and highly dynamic marine environments. Future work will focus on enhancing the robustness of the detection model. Full article

Salt stress represents a significant abiotic factor that constrains maize growth. Epigenetic modifications play a crucial role in enabling plants to respond effectively to such stresses. Among these alterations, m⁶A methylation, which is the most common post-transcriptional modification of eukaryotic mRNA, shows dynamic variations that are closely linked to stress responses. In this study, we conducted a transcriptome-wide m⁶A methylation analysis on maize roots from the inbred line PH4CV, following treatment with 180 mM NaCl. The results identified 1309 differentially m⁶A methylated peaks (DMPs) and 2761 differentially expressed genes (DEGs) under salt stress conditions. Association analysis revealed that 179 DEGs contain DMPs. Key pathways involved in stress responses, including Ca²⁺ signaling transduction and ABA signaling, as well as ion homeostasis regulation (involving AKT, HKT, and other families) and the reactive oxygen species scavenging system (including POD, SOD, and CAT), play crucial roles in coping with salt stress. Furthermore, we identified a total of 26 m⁶A-related genes, comprising 7 eraser genes, 10 reader genes, and 9 writer genes. Notably, several key salt-responsive genes, such as RBOHB, AKT1, HKT1, and POD12, are correlated with m⁶A modification. This study provides a comprehensive map of m⁶A methylation dynamics in maize roots under salt stress, laying a foundational resource for future investigations into the epigenetic regulation of salt tolerance in maize. Full article

To overcome the issues of excessive computation and resource usage in distracted driving detection systems, this study introduces a compact detection framework named YOLOv8s-FPNE, built upon the YOLOv8 architecture. The proposed model incorporates FasterNet, Partial Convolution (PConv) layers, a Normalized Attention Mechanism (NAM), and the Focal-EIoU loss to achieve an optimal trade-off between accuracy and efficiency. FasterNet together with PConv enhances feature extraction while reducing redundancy, NAM strengthens the model’s sensitivity to key spatial and channel information, and Focal-EIoU refines bounding-box regression, particularly for hard-to-detect samples. Experimental evaluations on a public distracted driving dataset show that YOLOv8s-FPNE reduces the number of parameters by 21.7% and computational cost (FLOPS) by 23.6% relative to the original YOLOv8s, attaining an mAP@0.5 of 81.6%, which surpasses existing lightweight detection methods. Ablation analyses verify the contribution of each component, and comparative studies further confirm the advantages of NAM and Focal-EIoU. The results demonstrate that the proposed method provides a practical and efficient solution for real-time distracted driving detection on embedded and resource-limited platforms. Full article

Climate change in the Pannonian region is accelerating a shift toward autumn sowing of cool-season grain legumes (pea, faba bean, lentil, chickpea, lupine) to achieve higher yields, greater biomass production, enhanced nitrogen fixation, improved soil cover, and superior resource use efficiency compared with spring sowing. However, successful overwintering depends on the availability of robust winter-hardy cultivars. This review synthesizes recent breeding advances, integrating traditional approaches—such as germplasm screening, hybridization, and field-based selection—with genomics-assisted strategies, including genome-wide association studies (GWAS), quantitative trait locus (QTL) mapping, marker-assisted selection (MAS), and CRISPR/Cas-mediated editing of CBF transcription factors. Key physiological mechanisms—LT₅₀ determination, cold acclimation, osmoprotectant accumulation (sugars, proline), and membrane stability—are assessed using field survival rates, electrolyte leakage assays, and chlorophyll fluorescence measurements. Despite challenges posed by genotype × environment interactions, variable winter severity, and polygenic trait control, the release of cultivars worldwide (e.g., ‘NS-Mraz’, ‘Lavinia F’, ‘Ghab series’, ‘Pinklevi’, and ‘Rézi’) and ongoing breeding programs demonstrate substantial progress. Future breeding efforts will increasingly rely on genomic selection (GS), high-throughput phenomics, pangenomics, and G×E modeling to accelerate the development of climate-resilient legume cultivars, ensuring stable and sustainable production under increasingly unpredictable winter conditions. Full article

People with visual impairments (VIPs) can participate in orientation and mobility (O&M) training to learn how to navigate to their desired goal locations. During O&M training, personal wayfinding preferences with regard to cue use and wayfinding strategy choice are taken into account. However, there is still a lack of clarity about which factors shape VIPs’ wayfinding experiences and how. Background/Objectives: In this study, we mapped individual differences in preferred sensory modality (both orientation- and mobility-related), and classified which personal and environmental factors are relevant for these preferences. Methods: To this end, interviews were conducted with eleven Dutch VIPs whose impairment varied in onset, ontology, and severity. Results: We concluded from our thematic analysis that hearing is the most important sensory modality to VIPs for orientation purposes, although it varies per person how and how often other resources are relied upon (i.e., other sensory modalities, existing knowledge of an environment, help from others, or navigational aids). Additionally, environmental factors such as weather conditions, crowdedness, and familiarity of the environment influence if, how, and which sensory modalities are employed. These preferences and strategies might be mediated by individual differences in priorities and needs pertaining to energy management. Conclusions: We discuss how the current findings could be of interest to orientation and mobility instructors when choosing a training strategy for individual clients. Full article

Extensive goat farming is the dominant livestock system in the Mediterranean region, where woody rangelands represent essential forage resources for goats. Understanding how goats move and select vegetation within these heterogeneous landscapes–and how these patterns are shaped by herding decisions-is critical for improving grazing management. This study investigated the spatio-temporal movement behavior of a goat flock in a complex woody rangeland using GPS tracking combined with GIS-based vegetation and land morphology mapping. The influence of seasonal changes in forage availability and the shepherd’s management on movement trajectories and vegetation selection was specifically examined over two consecutive years. Goat movement paths, activity ranges, and speed differed among seasons and years, reflecting changes in resource distribution, physiological stage, and herding decisions. Dense oak woodland and moderate shrubland were consistently the most selected vegetation types, confirming goats’ preference for woody species. The shepherd’s management—particularly decisions on grazing duration, route planning, and provision or withdrawal of supplementary feed—strongly affected movement characteristics and habitat use. Flexibility in adjusting grazing strategies under shifting economic conditions played a crucial role in shaping spatial behavior. The combined use of GPS devices, GIS software, vegetation maps, and direct observation proved to be an effective approach for assessing movement behavior, forage selection and grazing pressure. Such integration of technological and classical methods provides valuable insights into diet composition and resource use and offers strong potential for future applications in precision livestock management. Real-time monitoring and decision support tools based on this approach could help farmers optimize grazing strategies, improve forage utilization, and support sustainable rangeland management. Full article

Accurate mapping of hurricane-induced damage is essential for guiding rapid disaster response and long-term recovery planning. This study evaluates the Three-Dimensional Multi-Attributes, Multiscale, Multi-Cloud (3DMASC) framework for semantic classification of pre- and post-hurricane Light Detection and Ranging (LiDAR) data, using Mexico Beach, Florida, as a case study following Hurricane Michael. The goal was to assess the framework’s ability to classify stable landscape features and detect damage-specific classes in a highly complex post-disaster environment. Bitemporal topo-bathymetric LiDAR datasets from 2017 (pre-event) and 2018 (post-event) were processed to extract more than 80 geometric, radiometric, and echo-based features at multiple spatial scales. A Random Forest classifier was trained on a 2.37 km² pre-hurricane area (Zone A) and evaluated on an independent 0.95 km² post-hurricane area (Zone B). Pre-hurricane classification achieved an overall accuracy of 0.9711, with stable classes such as ground, water, and buildings achieving precision and recall exceeding 0.95. Post-hurricane classification maintained similar accuracy; however, damage-related classes exhibited lower performance, with debris reaching an F1-score of 0.77, damaged buildings 0.58, and vehicles recording a recall of only 0.13. These results indicate that the workflow is effective for rapid mapping of persistent structures, with additional refinements needed for detailed damage classification. Misclassifications were concentrated along class boundaries and in structurally ambiguous areas, consistent with known LiDAR limitations in disaster contexts. These results demonstrate the robustness and spatial transferability of the 3DMASC–Random Forest approach for disaster mapping. Integrating multispectral data, improving small-object representation, and incorporating automated debris volume estimation could further enhance classification reliability, enabling faster, more informed post-disaster decision-making. By enabling rapid, accurate damage mapping, this approach supports sustainable disaster recovery, resource-efficient debris management, and resilience planning in hurricane-prone regions. Full article

Object detection plays a pivotal role in safety-critical applications, including autonomous driving, intelligent surveillance, and unmanned aerial systems. However, many state-of-the-art detectors remain highly resource-intensive; their large parameter sizes and substantial floating-point operations make it difficult to balance accuracy and efficiency, particularly under constrained computational budgets. To mitigate this accuracy–efficiency trade-off, we propose FE-YOLOv8, a lightweight yet more effective variant of YOLOv8 (You Only Look Once version 8). Specifically, two architectural refinements are introduced: (1) C2f-Faster (Cross-Stage-Partial 2-Conv Faster Block) modules embedded in both the backbone and neck, where PConv (partial convolution) prunes redundant computations without diminishing representational capacity; and (2) an EfficientHead detection head that integrates EMSConv (Efficient Multi-Scale Convolution) to enhance multi-scale feature fusion while simplifying the head design and maintaining low computational complexity. Extensive ablation and comparative experiments on the SODA-10M dataset show that FE-YOLOv8 reduces the parameter count by 31.09% and the computational cost by 43.31% relative to baseline YOLOv8 while achieving comparable or superior mean Average Precision (mAP). Generalization experiments conducted on the BDD100K dataset further validate these improvements, demonstrating that FE-YOLOv8 achieves a favorable balance between accuracy and efficiency within the YOLOv8 family and provides new architectural insights for lightweight object detector design. Full article

Floods rank among the most destructive natural hazards worldwide. In Canada’s capital region—Ottawa and its surrounding areas—flood prediction is crucial, especially in flood-prone zones, to improve flood mitigation strategies, given its historical record-breaking events in 2017 and 2019, which resulted in substantial damage to homes and infrastructure in the region. Previous studies in these regions typically did not use remote sensing techniques or advanced methods to enhance flood susceptibility prediction and extent mapping. This study addressed the gap by incorporating 18 flood conditioning factors and integrating high-performance machine learning algorithms such as Random Forest, Support Vector Machines and XGBoost to develop ensemble flood susceptibility models. The HEC-RAS 2D model was used to simulate hydrodynamic variables based on a 100-year flood scenario. The developed ensemble model for flood susceptibility prediction achieved strong performance (Kappa, F1-score, and AUC all above 0.979) and demonstrated model transferability, maintaining high accuracy (Kappa > 0.850, F1-score > 0.920, AUC > 0.990) when applied to other sub-regions. The hydraulic model reveals that flood velocity and depth differ across sub-regions, reaching maximums of 15 m/s and 15 m, respectively. SHAP analysis indicates Elevation, Handmodel, MNDWI, NDWI, and Aspect are key factors influencing floods. These findings and methods help Natural Resources Canada develop tools and policies for effective flood risk reduction in the Ottawa River watershed and similar regions. Full article

Background: Gynostemma pentaphyllum (Thunb.) Makino is an important medicinal plant within the Cucurbitaceae family. Despite its economic and pharmacological importance, genomic resources for this species remain limited. Methods: We sequenced and assembled the complete mitochondrial genome of G. pentaphyllum. Comparative analyses were conducted to investigate the genomic structure, gene content, RNA editing events, and intracellular gene transfer (IGT) from chloroplasts. Additionally, phylogenomic relationships, synteny, and the selective pressure on mitochondrial genes were evaluated against related species within Cucurbitaceae. Results: The ~324 kb mitogenome has a multipartite architecture of six circular-mapping molecules. It encodes the typical complement of mitochondrial protein-coding genes, tRNAs, and rRNAs found in angiosperms. Extensive C-to-U RNA editing, including events that generate functional start and stop codons, points to substantial post-transcriptional regulation. We also detected multiple chloroplast-derived fragments, including several intact genes, indicating active intracellular gene transfer. Phylogenomic analyses of conserved mitochondrial genes place G. pentaphyllum firmly within Cucurbitaceae, clustering it with Thladiantha cordifolia and Momordica charantia, whereas synteny comparisons reveal pronounced structural rearrangements with respect to these close relatives. While most genes evolve under strong purifying selection, rps1, sdh3, and sdh4 show signatures of accelerated evolution; furthermore, haplotype networks based on conserved loci further corroborate the close affinity with T. cordifolia. Conclusions: This study provides the first high-resolution mitogenome resource for G. pentaphyllum and candidate mitochondrial markers for species authentication, evolutionary studies, and breeding in Gynostemma and related cucurbits. Full article

Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential omega-3 polyunsaturated fatty acids (n-3 PUFAs) with well-established health benefits. They occur primarily in marine resources, while their quantitative distribution within the glycerolipidome is rarely analyzed. Therefore, we investigated major commercial sources, including 12 microalgal species, the protist Schizochytrium sp., four fish species, and nine commercial n-3 supplements (fish, krill and Schizochytrium-derived “algal” oils) by high-performance thin-layer chromatography–gas chromatography–mass spectrometry (HPTLC–GC–MS). The class-resolved mapping of EPA and DHA revealed signature lipid profiles across all sources. In microalgae, 60–80% of EPA was localized in glycolipids, whereas in Schizochytrium and fish, >90% of DHA occurred in triacylglycerols. Krill oils exhibited phospholipid-rich profiles with ~70% of phosphatidylcholine-bound DHA. Nutritional indices also highlighted major differences: fish and fish oils showed favorable PUFA-to-saturated FA ratios (>0.45) and hypocholesterolemic-to-hypercholesterolemic ratios (>1), while Schizochytrium-based “algal” oils even surpassed these values. The microalgae Nannochloropsis granulata contained the highest EPA content in biomass form, combined with favorable nutritional indices. Beyond total n-3 content in relation to recommended daily intake values, the lipid-class distribution and nutritional indices should be considered decisive metrics for evaluating the health relevance of n-3 resources in the human diet. Full article

In communication environments with limited computing resources, securely and efficiently transmitting image data has become a challenging problem. However, most existing image data protection schemes are based on high-dimensional chaotic systems as key generators, which suffer from issues such as high algorithmic complexity and large computational overhead. To address this, this paper presents new designs for a 1D Sine Fractional Chaotic Map (1D-SFCM) as a random sequence generator and provides mathematical proofs related to the boundedness and fixed points of this model. Furthermore, this paper improves the traditional 2D compressive sensing (2DCS) algorithm by using the newly designed 1D-SFCM map to generate a chaotic measurement matrix, which can effectively enhance the quality of image recovery and reconstruction. Moreover, referring to the principle of gene mutation in biogenetics, this paper designs an image encryption algorithm based on DNA base substitution. Finally, the security of the proposed encryption scheme and the quality of image compression and reconstruction are verified through indicators such as key space, information entropy, and Number of Pixel Change Rate (NPCR). Full article

How can the interaction between the seismological community and society contribute to the exploitation and usage of renewable energy resources? We try to provide an answer by describing the seismic experiment realized in March–April 2025 in the hydrothermal area close to Contursi Terme municipality (Southern Italy). We deployed a 29-station seismic array thanks to the availability of local citizens, civic administrations, schools, and accommodation facilities, which provided hosting and power for six-component seismological instruments over a one-month period. By computing the Probabilistic Power Spectral Densities (PPSD) and spectrograms, we assessed the noise level and the quality of the dataset. The seismic recordings were also used for studying the local seismic response of the area by the HVSR method and detecting small magnitude (1.4–4.2) local and regional earthquakes. We thus described some solutions to tackle the challenges of a possible geothermal exploitation project in the area: (a) to map the energy resource through a tomography on good-quality ambient-noise data; (b) to manage the seismic risk related to the resource exploitation by installing a proper local seismic network; (c) to increase the acceptance by the population through a citizen-science action for instituting a fruitful alliance between different actors of civil society. Full article

This article explores the pedagogical potential of soundscapes as creative learning tools for advancing environmental education in Chilean primary schools. Drawing on the Soundlapse project, we designed and implemented a school workshop that combined activity sheets, an online bird-sound repository, structured soundwalks, and immersive audio concerts with teachers and students in Valdivia. The study employed a qualitative, participatory design, analyzing teacher interviews through reflexive thematic analysis. Four themes emerged: (1) listening as pedagogical practice, (2) learning through place and the senses, (3) creativity and cross-disciplinarity, and (4) implementation challenges and opportunities. Teachers emphasized the transformative role of attentive listening, which reconfigured classroom dynamics through shared silence and cultivated students’ capacity for self-regulation. Soundwalks and sensory encounters with local wetlands positioned the environment as a ‘living laboratory,’ fostering ecological awareness, attachment to place, and intergenerational knowledge. Creative activities such as sound mapping legitimized symbolic and artistic modes of representation, while interdisciplinary collaborations between science and music expanded curricular possibilities. At the same time, institutional rigidity and lack of resources highlighted the importance of teacher agency, co-designed materials, and flexible frameworks to sustain these practices. We argue that soundscape-based education offers a timely opportunity to integrate sensory, creative, and ecological dimensions into school curricula, aligning with national and international calls for interdisciplinary sustainability education. By treating listening and creativity as core rather than peripheral, such approaches may open new pathways for cultivating ecological awareness, cultural belonging, and pedagogical innovation. Full article

The global production of electronic waste (e-waste) has increased due to the quick turnover of electronic devices, creating urgent problems for resource management and environmental sustainability. As a result, e-waste-derived materials (EWDMs) are being explored in pavement engineering research as sustainable substitutes in line with Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). Therefore, to assess global research production and the effectiveness of EWDMs in asphalt applications, this review combines scientometric mapping and systematic evidence synthesis. A total of 276 relevant publications were identified via a thorough search of Web of Science, Scopus, and ScienceDirect (2010–2025). These were examined via coauthorship structures, keyword networks, and contributions at the national level. The review revealed that China, India, and the United States are prominent research hubs. Additionally, experimental studies have shown that EWDMs, such as printed circuit board powder, fluorescent lamp waste glass, high-impact polystyrene, and acrylonitrile–butadiene–styrene, improve the fatigue life, Marshall stability, rutting resistance (up to 35%), and stiffness (up to 28%). However, issues with long-term field durability, microplastic release, heavy metal leaching, and chemical compatibility still exist. These restrictions highlight the necessity for standardised toxicity testing, harmonised mixed-design frameworks, and performance standards unique to EWDMs. Overall, the review shows that e-waste valorisation can lower carbon emissions, landfill build-up, and virgin material extraction, highlighting its potential in the circular pavement industry and promoting sustainable paving practices in accordance with SDGs 9, 11, 12, and 13. This review suggests that further studies on large-scale field trials, life cycles, and technoeconomic assessments are needed to guarantee the safe, long-lasting integration of EWDMs in pavements. It also advocates for coordinated research, supportive policies, and standardised methods. Full article