Search Results (8,600)

High-precision detection of rice targets in precision agriculture is crucial for yield assessment and field management. However, existing models still face challenges, such as high rates of missed detections and insufficient localization accuracy, particularly when dealing with small targets and dynamic changes in scale and morphology. This paper proposes an accurate rice detection model for UAV images based on Adaptive Aware Dynamic Convolution, named Adaptive Dynamic Convolution YOLO (ADC-YOLO), and designs the Adaptive Aware Dynamic Convolution Block (ADCB). The ADCB employs a “Morphological Parameterization Subnetwork” to learn pixel-specific kernel shapes and a “Spatial Modulation Subnetwork” to precisely adjust sampling offsets and weights—realizing for the first time the adaptive dynamic evolution of convolution kernel morphology with variations in rice scale. Furthermore, ADCB is embedded into the interaction nodes of the YOLO backbone and neck; combined with depthwise separable convolution in the neck, it synergistically enhances multi-scale feature extraction from rice images. Experiments on public datasets show that ADC-YOLO comprehensively outperforms state-of-the-art algorithms in terms of AP50 and AP75 metrics and maintains stable high performance in scenarios such as small targets at the seedling stage and leaf overlap. This work provides robust technical support for intelligent rice field monitoring and advances the practical application of computer vision in precision agriculture. Full article

Background: Individuals with mental health conditions frequently experience stigmatization and discrimination. Among the primary objectives in the fight against stigma is to examine groups that play a crucial role in addressing it, such as healthcare professionals. Although research has examined stigma among healthcare professionals, few studies have specifically addressed how nurses perceive and contribute to the stigmatization of individuals with mental health conditions. Objective: The aim of this review was to compile and compare the scientific literature addressing nurses’ stigma toward individuals with mental health conditions. Methods: Following the methodological guidelines of the Joanna Briggs Institute and in accordance with the PRISMA 2020 guidelines, an integrative review was conducted of MEDLINE (PubMed), EMBASE, APA PsycInfo (EBSCO), and CINAHL Complete (EBSCO). Database-specific indexing terms were combined with the Boolean operators AND/OR. Studies with quantitative or qualitative methodologies, published in Spanish or English and without restrictions by year of publication, were included. Two independent reviewers selected the studies and performed the critical appraisal. Results: The search retrieved 4256 records, of which 32 articles were finally included. A content analysis of the selected studies was conducted. Most studies used validated questionnaires to assess stigma and its associations with various variables, while only a limited number employed qualitative designs. Across the 32 studies (n = 6283 nurses from 29 countries), stigma was observed across settings but tended to be lower among mental health specialists. Insufficient training and limited contact were consistently associated with higher levels of stigma, whereas specialization and positive contact were linked to lower levels. Associative stigma emerged as a recurrent theme with implications for psychiatric nursing identity. Conclusions: Nurses working in mental health settings generally demonstrate more positive attitudes toward individuals with mental health conditions compared with those in other clinical areas; however, stigma persists across all settings. Associative stigma may be influencing the development and advancement of psychiatric nursing. Specific academic training, capacity building, and specialization in mental health are essential to counteract stigma. Further qualitative research is required to achieve a deeper understanding of this phenomenon. Full article

Commercial pedestrian streets serve as vital urban public spaces for residents’ daily leisure and social interaction. However, amid rapid urbanization, many such streets exhibit a tendency towards homogenization, raising practical concerns about the capacity of these environments to consistently deliver rich psychological restorative experiences. Existing research on the restorativeness of urban streets has primarily focused on macro or meso scales, leaving the restorative impacts of micro-scale elements, such as public art within streetscapes, insufficiently explored. To address this research gap, this study takes Tanhualin Historic Cultural Street in Wuhan as its research setting. Employing a streetscape image simulation experiment combined with an online questionnaire survey, it assesses the influence of public art on the perceived restorativeness of commercial pedestrian streets. The results indicate that public art substantially enhances the perceived restorative capacity of commercial pedestrian streets. Further analysis reveals clear independent main effects of both the form and theme of public art on perceived restorativeness, with the influence of form being more pronounced, and no statistically significant interaction effect between the two. These findings offer novel insights for enhancing the restorative potential of commercial pedestrian streets and provide design recommendations for future urban street renewal and sustainable development. Full article

Background: Influencer marketing has become one of the most effective strategies in digital communication due to its capacity to generate trust, credibility and endorsement within segmented online communities. Within the tourism sector, travel influencers have been progressively integrated as key agents in destination and brand promotion, contributing to both the construction of tourism-related perceptions and travel decision-making. This study aims to analyse how travel influencers communicate and promote tourist destinations, focusing on their profiles, content formats, commercial transparency and audience engagement. Methods: The research is based on a quantitative content analysis of publications by leading Spanish travel influencers identified through the Forbes Best Content Creators 2025 ranking. The observation period covered March to July 2025. Analysis was structured around four analytical blocks comprising 17 variables related to influencer profile, format and content, commercial transparency and ethics, and interaction. Results: The results reveal consistent behavioural patterns associated with gender, destination type and narrative style. Male influencers are more frequently linked to adventure-oriented storytelling and natural landscapes, whereas female influencers tend to emphasise urban and cultural experiences. Short-form video emerges as the dominant format, generating higher interaction levels, while engagement proves to be a more informative indicator of effectiveness than follower count. Conclusions: The findings underscore the importance of prioritising specialisation, narrative coherence, authenticity and transparency when integrating influencers into their communication strategies. Full article

The quality of tea bud harvesting directly affects the final quality of the tea; however, due to the small size of tea buds and the complex natural background, accurately detecting them remains challenging. To address this issue, this paper proposes a lightweight and efficient tea bud detection model named TSF-Net. This model adopts the P2-enhanced bidirectional feature pyramid network (P2A-BiFPN) to enhance the recognition ability of small objects and achieve efficient multi-scale feature fusion. Additionally, coordinate space attention (CSA) is embedded in multiple C3k2 blocks to enhance the feature extraction of key regions, while an A2C2f module based on self-attention is introduced to further improve the fine feature representation. Extensive experiments conducted on the self-built WYTeaBud dataset show that TSF-Net increases mAP@50 by 2.0% and reduces the model parameters to approximately 85% of the baseline, achieving a good balance between detection accuracy and model complexity. Further evaluations on public tea bud datasets and the VisDrone2019 small object benchmark also confirm the effectiveness and generalization ability of the proposed method. Moreover, TSF-Net is converted to the RKNN format and successfully deployed on the RK3588 embedded platform, verifying its practical applicability and deployment potential in intelligent tea bud harvesting. Full article

Background/objectives. Emerging viral diseases represent an increasing threat to global health security, driven by environmental change, globalization, and intensified human–animal–environment interactions. The COVID-19 pandemic exposed critical weaknesses in preparedness systems but also demonstrated the transformative potential of recombinant vaccine technologies, which enable rapid, scalable, and safe responses to novel pathogens. We aim to examine the role of recombinant vaccine platforms in the management of emerging viral diseases, emphasizing their contribution to health system preparedness and exploring strategies for their integration into preparedness frameworks. Methods. We synthesized the current evidence on recombinant vaccine platforms (viral vector, protein subunit, DNA, and mRNA) through a targeted review of the scientific literature, regulatory documents, and global health policy reports. Drawing from experiences like COVID-19 (mRNA vaccines) and Ebola (rVSV-ZEBOV), we analyzed the advantages, challenges, and lessons from initiatives such as the CEPI, BARDA, HERA, and WHO frameworks. Results. Recombinant vaccine platforms offer significant advantages for epidemic preparedness through rapid adaptability, standardized production, and strong safety profiles. Nonetheless, challenges remain in manufacturing scalability, cold-chain logistics, regulatory harmonization, and equitable global access. Global initiatives such as the CEPI, WHO-led programs, BARDA, and regional manufacturing networks exemplify this collaborative approach, while regulatory mechanisms have proven to be essential to timely vaccine deployment. Conclusions. Recombinant vaccines have redefined preparedness by coupling scientific innovation with operational agility. Strengthening global coordination, regional production capacity, and public trust is essential to ensure that technological progress translates into equitable and effective public health impacts. Full article

Food supply veterinarians, those who service the dairy, swine, poultry, small ruminant, and beef cattle industries, benefit society by protecting animal and public health and ensuring a safe, wholesome food supply. However, there are not enough entering the workforce to meet current and future demands. Non-formal learning environments can be used as a recruitment tool to provide participants with positive interactions and hands-on experiences. To build awareness of food supply veterinary medicine (FSVM) in youth, we developed an immersion program designed to provide high school students with hands-on experiences with food animal species. Day camps were held during the summers of 2022 and 2023, each coordinated with multiple partners at different locations in central Ohio. Year One camp utilized registration and post-test surveys and Year Two utilized matching pre- and post-test for analysis. Over the two programs, 110 participants engaged in hands-on experiences, including: outbreak investigations, measuring clinical parameters, performing diagnostics, and basic veterinarian procedures. Pre- and post-test evaluations were performed to measure changes in participants’ attitudes and perceptions, and a McNemars test was used to evaluate Year Two data. In Year One, we saw positive shifts in those interested in FSVM careers. In Year Two, we saw positive shifts in knowledge of FVSM careers, with biosecurity knowledge increasing. Outreach activities like day camps can be replicated in other locations to increase interest in FSVM careers. Full article

Between 1940 and 1980, large scale social housing construction in Milan reshaped the city’s modern urban identity, yet how these postwar estates are perceived today remains insufficiently documented. This study analyzes user-generated Google Maps reviews to examine how these neighborhoods are evaluated in terms of sentiment and recurring narratives. We develop a replicable Natural Language Processing (NLP) workflow that combines automated translation, sentiment scoring, thematic keyword extraction, and unsupervised clustering to compare perception patterns across construction decades and architectural typologies, and we synthesize multi dimensional signals into a Perceived Space Quality Index (PSQI). Results show clear differentiation by typology and period: earlier courtyard-based estates are more frequently associated with positive evaluations, while later open block modernist developments exhibit more polarized discourse. Across clusters, positive evaluations most often co-occur with references to well-maintained shared spaces and active everyday life, while negative discourse concentrates on neglect and insecurity. Review narratives also increasingly foreground social experience and reputation over formal description. Overall, the workflow supports comparative reading of public narratives across estates and highlights which themes most shape favorable or unfavorable perception. Full article

Background: We present our experience of carrying out endovascular therapy (EVT) of a pseudo-aneurysm of the posterior tibial artery (PTA) with an associated arteriovenous fistula (AVF). We also present results of a systematic review which was carried out to cast light on endovascular treatment modalities. Methods: A 31-year-old patient with a history of war trauma presented with pain of increasing severity in the lower leg. A CT angiogram confirmed an aneurysm of the PTA with an AVF. With a bidirectional endovascular approach, the aneurysm was occluded with coils and excluded with a Viabahn endoprosthesis. Aspirin and clopidogrel were recommended postoperatively. After 18 months of follow-up, the patient was free of symptoms, with patent endoprosthesis. Multiple databases (Scopus, Pubmed, Medline, OVID) were systematically searched using MeSH terms. The studies were scrutinized, and data on demographics, procedural details, and follow-up were collected and aggregated. Results: A total of 44 studies (56 patients) were eligible and were included. Average age was 50 (15–87 years). The most common etiology was trauma (iatrogenic 29/56 (51.7%); non-iatrogenic 15/56 (26.7%)). EVT strategies included coil embolization (n = 29), stent implantation (n = 25), and a combination of both (n = 2). Median stent diameter was 3 mm (2.5–6). The follow-up period ranged from 1 week to 60 months. Aggregated reported primary patency was 18/27 (66.6%) with no documented complications—an observation that likely reflects reporting and publication bias, rather than a true absence of adverse events. Conclusions: EVT offers a feasible and safe alternative to simple ligation or occlusion of crural aneurysms, to preserve distal flow to the foot. Dedicated stents for crural arteries are not available. Studies with long-term follow-up are lacking. Full article

The identification of community structure is pivotal for understanding the functional characteristics of complex networks. To address the limitations of most existing community detection algorithms, which often require predefining the number of communities and lack robustness, this paper proposes a novel community detection algorithm named D-means (K-means community detection algorithm based on density peaks). This algorithm integrates the concept of density peak clustering with K-means spectral clustering, employing Chebyshev’s inequality to automatically determine the number of community centers, thereby enabling unsupervised identification of community quantities. By designing a multi-dimensional evaluation framework, the comparative experiments were conducted on LFR benchmark networks (Lancichinetti-Fortunato-Radicchi benchmark networks) and real-world social network datasets. The results demonstrate that the D-means algorithm outperforms traditional algorithms in terms of ACC (accuracy), ARI (adjusted rand index), and NMI (normalized mutual information) metrics, while also achieving improvements in runtime efficiency, showcasing strong robustness. Finally, the D-means algorithm was applied to the public transportation network of Urumqi. Empirical analysis identified 12 functionally significant transportation communities, providing theoretical support for urban rail transit optimization and commercial facility layout planning. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

Accurate building instance segmentation from UAV imagery remains a challenging task due to significant scale variations, complex backgrounds, and frequent occlusions. To tackle these issues, this paper proposes an improved lightweight YOLOv13-G-based framework for building extraction in UAV imagery. The backbone network is enhanced by incorporating cross-stage lightweight connections and dilated convolutions, which improve multi-scale feature representation and expand the receptive field with minimal computational cost. Furthermore, a coordinate attention mechanism and an adaptive feature fusion module are introduced to enhance spatial awareness and dynamically balance multi-level features. Extensive experiments on a large-scale dataset, which includes both public benchmarks and real UAV images, demonstrate that the proposed method achieves superior segmentation accuracy with a mean intersection over union of 93.12% and real-time inference speed of 38.46 frames per second while maintaining a compact Model size of 5.66 MB. Ablation studies and cross-dataset experiments further validate the effectiveness and generalization capability of the framework, highlighting its strong potential for practical UAV-based urban applications. Full article

Transdisciplinarity and the co-production of knowledge have become fundamental approaches to addressing complex social problems. However, the sustainability of collaborative partnerships remains underexplored from an empirical perspective. This article examines the mechanisms that shape the continuity of collaborative networks in social innovation projects in the field of social services, particularly those linked to community-based welfare systems in Andalusia (Spain). Drawing on a thematic qualitative analysis of 15 social innovation projects and 14 semi-structured interviews with project coordinators, the study explores how diverse actors (universities, public administrations, third-sector organisations, and citizens) mobilise different types of social capital within local social services. The findings reveal that collaboration success depends on a balance between relational enablers (trust and shared experiences) and structural barriers (bureaucracy, work overload, and lack of time). The analysis also shows that participatory methodologies and connections with pre-existing networks are essential for sustaining collaboration after project completion. The article concludes that the sustainability of transdisciplinary social innovation in social services requires moving beyond project management logics and investing in the care of invisible relational structures, with implications for public policies aimed at consolidating trust ecosystems and long-term collective learning. Full article

Tuberculosis (TB) rarely occurs in isolation; most people with TB experience multiple coexisting conditions, including HIV, diabetes, undernutrition, depression, and substance use disorders, which worsen disease severity and compromise treatment outcomes. Although the World Health Organization has issued disease-specific guidance for managing key comorbidities, TB care remains largely siloed and poorly equipped to address the growing burden of multimorbidity, particularly in African health systems. In this perspective article, we propose a phased framework for multimorbidity-centered TB care. The first phase emphasizes systematic screening for common comorbidities and establishment of basic referral pathways. The second phase focuses on strengthening coordination between TB programs and existing health and social services, including task sharing and longitudinal follow-up. The third phase advances toward fully integrated, co-located, multidisciplinary models of care that embed TB services within broader multimorbidity platforms. Together, this framework offers a pragmatic roadmap for TB programs to deliver more person-centered, equitable, and efficient care, strengthen primary care systems, and accelerate progress toward ending TB as a public health threat in Africa. Full article

As social robots increasingly enter public environments, their acceptance depends not only on technical robustness but also on ethical integrity, accessibility, transparency, and consistent system behaviour across diverse users. This paper reports an in situ pilot deployment of an ARI social robot functioning as a university receptionist, designed and implemented in alignment with the SecuRoPS framework for secure, ethical, and reliable social robot deployment. Thirty-five students and staff interacted with the robot in a real public setting and provided structured feedback on safety, privacy, usability, accessibility, ethical transparency, and perceived reliability. The results indicate strong user confidence in physical safety, data protection, and regulatory compliance while revealing persistent challenges related to accessibility and interaction dynamics. These findings show that reliability in public-facing robotic systems extends beyond fault-free operation to include equitable and consistent user experience across contexts. Beyond reporting empirical outcomes, the study contributes in three key ways. First, it demonstrates a reproducible method for operationalising lifecycle governance frameworks in real-world deployments. Second, it provides new empirical insights into how trust, accessibility, and transparency are experienced by end users in public spaces. Third, it delivers a publicly available, open-source GitHubrepository containing reusable templates for ARI robot applications developed using the PAL Robotics ARI SDK (v23.12), lowering technical entry barriers and supporting reproducibility. By integrating empirical evaluation with practical system artefacts, this work advances research on reliable intelligent environments and provides actionable guidance for the responsible deployment of social robots in public spaces. Full article