Search Results (13,862)

The growing availability of genomic resources is changing how genetic diversity is studied in Vitis vinifera L. At the same time, it has become increasingly clear that a single reference genome cannot fully represent the complexity of a species characterised by high heterozygosity, clonal propagation and a long history of diversification. Recent grapevine pangenomes, super-pangenomes and graph-based resources have revealed forms of variation that are often overlooked in conventional reference-based analyses, including structural variants and gene presence–absence variation. Rather than providing another inventory of available datasets, this review examines how continued reliance on a single reference genome may influence the interpretation of grapevine diversity and what can be gained from a broader pangenomic perspective. Drawing on recent studies in grapevine and other crops, we discuss how these approaches are beginning to improve the representation of genetic diversity, uncover biologically relevant variation and strengthen links between genomic information and adaptive traits. We also examine the challenges that still limit their practical use, particularly the integration of genomic resources with functional studies and breeding programmes. In the end, the value of pangenomics will probably depend not only on generating additional genomic resources, but also on how effectively these can be translated into tools that support grapevine conservation, climate adaptation and varietal improvement. Full article

Operational labor shortages have become a pressing challenge for hospitality organizations, especially in highly seasonal tourism destinations such as Greece, where service experiences are deeply tied to cultural identity and authentic hospitality. While much of the existing research has examined understaffing from operational or human resource management perspectives, limited attention has been paid to its impact on the organizational capacity to sustain authentic hospitality experiences. Using Service-Dominant Logic (SDL) as an interpretive framework, this study views authentic hospitality as an organizational process shaped by employee interaction, cultural transmission, and service delivery practices. Drawing on survey data from 201 hotel employees in Greece, it investigates the relationship between operational labor shortages, organizational pressures, and perceived threats to authentic hospitality within hotel operations. The findings reveal significant positive relationships between work stress and service quality decline, as well as between cultural knowledge and perceived challenges in maintaining authentic hospitality. Multiple regression analysis further shows that reactive hiring, serious understaffing, and payroll cost pressure are significantly linked to perceived challenges in sustaining authentic hospitality, while service quality decline exhibits a positive but statistically non-significant effect in the final model. The study contributes to hospitality authenticity literature by emphasizing employee perceptions of authenticity as an organizationally supported process rather than merely a guest-centered outcome. The results also highlight the importance of workforce planning, recruitment quality, and cultural onboarding in supporting authentic hospitality within Greek hotel operations. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article

Extracellular vesicles (EVs) are key mediators of intercellular communication across biological kingdoms, with central roles in immune regulation and disease processes. Despite shared structural features, EVs derived from bacteria, plants, and mammalian cells differ substantially in their biogenesis, molecular composition, and immunological functions. EV formation pathways generate vesicles with distinct cargo profiles, including pathogen-associated molecular patterns (PAMPs) in bacterial EVs, regulatory small RNAs in plant-derived vesicles, and cytokines, microRNAs, and antigen-presenting complexes in mammalian EVs. Differences in cargo result in divergent immune outcomes. Bacterial EVs predominantly activate innate immunity via pattern recognition receptors such as Toll-like receptors, whereas plant-derived EVs exhibit low immunogenicity and mediate cross-kingdom RNA interference. In contrast, mammalian EVs primarily regulate immune responses by modulating antigen presentation and cytokine signaling. These findings support a framework in which EV origin determines immunological function and therapeutic applicability. This perspective highlights the importance of selecting appropriate EV sources for vaccine development, regenerative medicine, and targeted delivery strategies, while addressing current challenges related to heterogeneity, standardization, and safety. Full article

The rapid emergence of antimicrobial resistance has intensified the need for advanced therapeutic platforms capable of improving the efficacy, stability, and targeted delivery of antimicrobial agents. Electrospun nanofibers have emerged as highly promising materials for biomedical applications due to their large surface area, high porosity, tunable morphology, and ability to incorporate a broad range of bioactive compounds. This review provides a comprehensive overview of the design, fabrication, and biomedical applications of electrospun bioactive nanofibers functionalized with antimicrobial drugs. It presents the main nanofiber fabrication techniques, with particular emphasis on electrospinning and the influence of solution, process, and environmental parameters on fiber morphology and drug-loading efficiency. Natural, synthetic, and hybrid polymer systems commonly employed in electrospun antimicrobial nanofibers are analyzed in relation to their physicochemical properties, biocompatibility, and therapeutic performance. In addition, the review highlights different drug incorporation strategies, including encapsulation, immobilization, and surface coating, as well as the mechanisms of action of antimicrobial agents. Recent advances in nanotechnology-based antimicrobial systems and their role in overcoming analytical, biopharmaceutical, and drug-delivery limitations are also examined. Furthermore, the review addresses current challenges related to scalability, reproducibility, stability, and clinical translation of electrospun nanofibers. Finally, future perspectives focusing on multifunctional, stimuli-responsive, and personalized antimicrobial nanofiber systems are discussed as promising directions for combating bacterial infections and reducing the global burden of antimicrobial resistance. Full article

Background/Objectives: Medication-related harm is a leading global patient safety challenge, yet patients’ lived experiences of medication safety remain underexplored in Central and Eastern European healthcare systems, where structural constraints significantly shape everyday medication use. Methods: This study provides an in-depth qualitative analysis of Hungarian patients’ online narratives, building on a prior netnographic mixed-methods study. Using grounded theory-informed principles and a patient-centred medication safety framework, we inductively analysed 5174 publicly accessible Hungarian-language comments posted on health forums and social media platforms between August 2020 and August 2023. The COM-B model was applied as a secondary lens to map findings onto modifiable behavioural determinants. Results: Access to services and communication emerged as the dominant medication safety concerns. Patients reported long waiting times, limited rural emergency services, and brief consultations leading to delayed or inadequate treatment. Communication gaps included insufficient information on medication duration, side effects, and follow-up, as well as conflicting advice from multiple sources, all of which eroded trust and prompted treatment discontinuation or reliance on informal online communities. Community pharmacists were largely absent from patients’ mental models of care, representing a significant missed opportunity given their accessibility. Less frequently mentioned were medication shortages, healthcare professional workload, and systemic safety culture. Conclusions: Clear, respectful communication and timely access to care are central to medication safety from the patient perspective. Netnography combined with a grounded theory-informed methodology offers a valuable approach for capturing authentic patient perspectives in structurally burdened healthcare systems, with findings relevant beyond the Hungarian context. Full article

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

High-entropy alloys (HEAs), composed of five or more principal elements in near-equimolar ratios, have emerged as a groundbreaking class of materials for next-generation flexible electronics. This review systematically examines the unique potential of HEAs as sensing materials, moving beyond their traditional role as structural components. We first elucidate the fundamental mechanisms—core effects including lattice distortion, sluggish diffusion, and the cocktail effect—that endow HEAs with an exceptional synergy of high strength, good ductility, tunable electrical resistivity, and superior electrocatalytic activity. Subsequently, we critically analyze the state-of-the-art strategies for processing HEA-based micro/nano structures, including mechanical alloying, wet-chemical synthesis, and non-equilibrium deposition techniques, with an emphasis on their compatibility with flexible substrates. The core of the review categorizes and discusses the latest advances in HEA-based flexible sensors for strain/stress, gas, and electrochemical (e.g., glucose, biomarkers, heavy metals) detection, highlighting the structure–property–performance relationships. Representative studies have demonstrated that HEA flexible strain sensors achieve a temperature coefficient of resistance as low as 45.59 ppm/K with no signal drift over 6000 stretching cycles; room-temperature hydrogen sensors reach a detection limit down to 31 ppb with a response time of 19 s; and non-enzymatic glucose sensors deliver a sensitivity up to 3043 μA·mM⁻¹·cm⁻². Finally, we summarize the key challenges—such as manufacturing scalability, long-term stability under dynamic deformation, and cost-effectiveness—and provide a forward-looking perspective on promising research directions, including high-throughput compositional screening, multi-functional sensor arrays, and the integration of machine learning for rational material design. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

The growth of Intelligent Transportation Systems (ITS) has made high-quality perception data from multi-sensor setups essential. Pairing millimeter-wave (mmW) radar with a monocular camera is a common way to recover three-dimensional information about the environment, but aligning the two is difficult because sparse radar point clouds and dense camera images differ sharply in how they sense a scene. The problem grows more severe in roadside infrastructure, where the high mounting elevation introduces perspective distortion that vehicle-mounted systems rarely face. This paper presents a systematic review, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, of radar-camera extrinsic calibration for fixed roadside infrastructure, organizing existing work into a taxonomy that separates traditional two-stage pipelines from recent end-to-end learning frameworks. Because methods designed specifically for roadside units remain scarce, the review also covers vehicle- and robot-mounted methods whose static-sensor formulation carries over to fixed roadside deployment. For the two-stage pipeline, the analysis covers target-based and targetless correspondence registration along with the optimization techniques and algorithmic assumptions behind parameter estimation. The end-to-end learning literature shows a clear shift toward self-supervised and fusion-based models, some of which report real-time performance. The review also compares the metrics and procedures used to quantify calibration accuracy. Progress is evident, but robustness in cluttered urban environments remains an open challenge, and the paper closes by outlining future directions, arguing that standardized roadside benchmarks are needed before scalable, targetless calibration can mature. Full article

Population ageing is reshaping the assumptions on which succession law has traditionally rested. This article examines how Spanish succession law responds to this demographic shift through two closely connected dimensions: testamentary capacity and the growing role of succession agreements. The analysis adopts a doctrinal and comparative perspective within the Spanish legal system, taking account of the coexistence of the Spanish Civil Code and several autonomous succession regimes. It argues that testamentary capacity remains governed by a deliberately low and functional threshold, centred on the testator’s actual ability to form and express a testamentary intention at the time of execution, and that notarial ex ante control is central to preserving both autonomy and legal certainty. At the same time, relational vulnerability in later life requires distinct safeguards aimed at preserving testamentary freedom. The article further shows that succession agreements, often viewed as restrictions on testamentary freedom, may also operate as instruments of anticipatory autonomy. The central challenge is to make autonomy effective across time without confusing vulnerability with incapacity, or protection with constraint. Full article

Unmanned Aerial Vehicle (UAV) image object detection holds significant application value in the low-altitude economy, traffic monitoring, intelligent agriculture, and disaster rescue. However, due to the top-down perspective, UAV images typically suffer from challenges such as small target scales, dense object distribution, severe occlusions, and complex backgrounds. These issues often limit the recall and localization accuracy of general-purpose detectors when they are directly applied to UAV small-object detection scenarios. To address these aforementioned challenges, this paper proposes an Adaptive Dynamic Aggregation YOLO network, termed ADA-YOLO. The novelty of ADA-YOLO lies in its highly efficient combinatorial design specifically tailored for UAV small object detection, while retaining the efficient backbone of YOLOv8, we systematically reconstruct the neck and detection head to improve accuracy. Specifically, a high-resolution P2 detection branch is incorporated to construct a P2–P5 multi-scale prediction structure. Furthermore, the lightweight DySample dynamic upsampling module is adopted to replace traditional upsampling methods, and an Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to alleviate semantic conflicts and noise interference during multi-scale feature fusion. This synergistic combination explicitly addresses multi-scale representation challenges and enhances small-object detection performance in complex scenes. Comparative experiments with the baseline YOLOv8n on the VisDrone2019 dataset demonstrate that ADA-YOLO achieves an improvement of 11.3% in mAP@0.5 and 8.2% in mAP@0.5:0.95. The improved model achieves these performance gains with a modest parameter increase and acceptable computational complexity. Finally, ablation experiments further validate the effectiveness of each individual module and their synergistic gains. Full article

Aqueous magnesium-ion batteries (AMIBs) are promising for next-generation energy storage technologies due to their high safety, low cost, high theoretical energy density, and environmental friendliness. In particular, manganese-based oxides have attracted much attention due to the abundant resources, high theoretical capacity, and environmental friendliness. This paper provides a comprehensive overview of manganese-based oxide cathode materials for AMIBs, including the crystal structure, electrochemical performance, optimization strategies, and electrode reaction mechanisms. Meanwhile, recent research progress of AMIB full cells based on Mn-based oxide cathode materials is summarized. Finally, the challenges and future perspectives of Mn-based oxide cathode materials for AMIBs are discussed. This review will provide a valuable reference and source of inspiration for future research of manganese-based oxide cathode materials for AMIBs. Full article

This research investigates the development of a biophilic conceptual design proposal tailored to dementia care environments in South Korea, responding to the country’s rapidly aging population and the projected rise in dementia prevalence. The study integrates spatial and aesthetic strategies grounded in established biophilic design principles, including visual and non-visual connections with nature, thermal and airflow variability, dynamic and diffuse lighting, and the presence of water. Drawing on comparative case study analysis, the research emphasizes the therapeutic potential of nature-oriented environments in reducing stress, enhancing mood, improving physical health, and supporting cognitive function among residents with dementia. Emphasizing a human-centric perspective, the study also considers the experimental and behavioral needs of elderly users within the design process. In addition, it critically examines the challenges and limitations associated with implementing biophilic design strategies in architectural practice. Full article

Numerous space missions are advancing our understanding of the origin and evolution of planetary bodies and the potential for the emergence of life throughout the Solar System and beyond. Investigations across the inner Solar System have revealed contrasting planetary environments: Venus offers insights into runaway greenhouse processes, while Mars remains a primary target for studying climate evolution, atmospheric loss, past water activity, and extinct life, with sample return missions planned in the next decade. Beyond the traditional habitable zone, attention has shifted to the icy moons of Jupiter and Saturn. Data from space missions have identified subsurface oceans and possibly active geology on moons such as Europa, Ganymede, Titan, and Enceladus, highlighting their astrobiological potential. Among others, Europa’s ocean, possibly interacting with a silicate mantle and sustained by tidal heating, Enceladus plumes and Titan’s complex organic chemistry make these worlds compelling targets. Current and upcoming missions will further explore these environments and refine our understanding of habitability. This work also emphasizes the importance of planetary protection to prevent biological contamination, particularly for sample return missions. Continued exploration, supported by international collaboration and technological innovation, will be essential to address engineering challenges and to expand our knowledge of potentially habitable environments across the Solar System. Full article