Search Results (53,058)

Occluded person re-identification (Re-ID) faces significant challenges, mainly due to the interference of occlusion noise and the scarcity of realistic occluded training data. Although data augmentation is a commonly used solution, the current occlusion augmentation methods suffer from the problem of dual inconsistencies: intra-sample inconsistency is caused by misaligned synthetic occluders (an augmentation operation for simulating real occlusion situations); i.e., randomly pasted occluders ignore spatial prior information and style differences, resulting in unrealistic artifacts that mislead feature learning; inter-sample inconsistency stems from information loss during random cropping (an augmentation operation for simulating occlusion-induced information loss); i.e., single-scale cropping strategies discard discriminative regions, weakening the robustness of the model. To address the aforementioned dual inconsistencies, this study proposes the unified Multi-Aligned and Multi-Scale Augmentation (MA–MSA) framework based on the core principle of ”synthetic data should resemble real-world data”. First, the Frequency–Style–Position Data Augmentation (FSPDA) module is designed: it ensures consistency in three aspects (frequency, style, and position) by constructing an occluder library that conforms to real-world distribution, achieving style alignment via adaptive instance normalization and optimizing the placement of occluders using hierarchical position rules. Second, the Multi-Scale Crop Data Augmentation (MSCDA) strategy is proposed. It eliminates the problem of information loss through multi-scale cropping with non-overlapping ratios and dynamic view fusion. In addition, different from the traditional serial augmentation method, MA–MSA integrates FSPDA and MSCDA in a parallel manner to achieve the collaborative resolution of dual inconsistencies. Extensive experiments on Occluded-Duke and Occluded-REID show that MA–MSA achieves state-leading performance of 73.3% Rank-1 (+1.5%) and 62.9% mAP on Occluded-Duke, and 87.3% Rank-1 (+2.0%) and 82.1% mAP on Occluded-REID, demonstrating superior robustness without auxiliary models. Full article

RNA-based therapeutics offer transformative potential for treating devastating diseases. However, current RNA delivery technologies face significant hurdles, including inefficient tissue targeting, insufficient selectivity, and severe side effects, leading to the termination of many clinical trials. This review critically assesses the landscape of RNA-derived medicines, examining world-renowned mRNA vaccines (Spikevax, BNT162b2/Comirnaty) and RNA-based therapeutics like Miravirsen (anti-miR-122). It details the composition and clinical trial results of numerous modified short RNA drugs (e.g., siRNAs, miRNA mimetics/inhibitors) targeting various conditions. Prospects for RNA-based medicines are analysed for diseases with substantial societal impact, such as cancer, autoimmune disorders, and infectious diseases, with a focus on evolving delivery methods, including lipid nanoparticles, viral vectors, and exosomes. RNA-mediated macrophage reprogramming emerges as a promising strategy, potentially enhancing both delivery and clinical efficacy. This review highlights that while approved RNA therapies primarily target rare diseases due to delivery limitations, novel approaches in RNA modification, targeted delivery systems, and enhanced understanding of molecular mechanisms are crucial for expanding their application to prevalent diseases and unlocking their full therapeutic potential. Full article

Content delivery networks (CDNs) face steadily rising, uneven demand, straining heuristic cache replacement. Reinforcement learning (RL) is promising, but most work assumes a fully observable Markov Decision Process (MDP), unrealistic under delayed, partial, and noisy signals. We model cache replacement as a Partially Observable MDP (POMDP) and present the Miss-Triggered Cache Transformer (MTCT), a Transformer-decoder Q-learning agent that encodes recent histories with self-attention. MTCT invokes its policy only on cache misses to align compute with informative events and uses a delayed-hit reward to propagate information from hits. A compact, rank-based action set (12 actions by default) captures popularity–recency trade-offs with complexity independent of cache capacity. We evaluate MTCT on a real trace (MovieLens) and two synthetic workloads (Mandelbrot–Zipf, Pareto) against Adaptive Replacement Cache (ARC), Windowed TinyLFU (W-TinyLFU), classical heuristics, and Double Deep Q-Network (DDQN). MTCT achieves the best or statistically comparable cache-hit rates on most cache sizes; e.g., on MovieLens at $M=600$, it reaches $0.4703$ (DDQN $0.4436$, ARC $0.4513$). Miss-triggered inference also lowers mean wall-clock time per episode; Transformer inference is well suited to modern hardware acceleration. Ablations support $CL=50$ and show that finer action grids improve stability and final accuracy. Full article

Hydrogen-powered unmanned aerial vehicles (UAVs) offer significant advantages, such as environmental sustainability and extended endurance, demonstrating broad application prospects. However, the hydrogen fuel cells face prominent thermal management challenges during flight operations. This study established a numerical model of the fuel cell thermal management system (TMS) for a hydrogen-powered UAV. Computational fluid dynamics (CFD) simulations were subsequently performed to investigate the impact of various design parameters on cooling performance. First, the cooling performance of different fan density configurations was investigated. It was found that dispersed fan placement ensures substantial airflow through the peripheral flow channels, significantly enhancing temperature uniformity. Specifically, the nine-fan configuration achieves an 18.5% reduction in the temperature difference compared to the four-fan layout. Additionally, inlets were integrated with the fan-based cooling system. While increased external airflow lowers the minimum fuel cell temperature, its impact on high-temperature zones remains limited, with a temperature difference increase of more than 19% compared to configurations without inlets. Furthermore, the middle inlet exhibits minimal vortex interference, delivering superior thermal performance. This configuration reduces the maximum temperature and average temperature by 9.1% and 22.2% compared to the back configuration. Full article

Background/Objectives: Modular dual mobility (MDM) cups are constituted by a cobalt-chromium liner inserted into a standard acetabular shell, allowing for intraoperative indication and supplementary screw fixation of the acetabular component. MDM could face mechanical and biological issues, with the associated risk of elevated blood metal ions levels and adverse local tissue reactions. Methods: This is a monocentric retrospective study on a consecutive series of 105 patients who underwent primary unilateral THA with the G7 Dual Mobility Acetabular System cup (Zimmer Biomet, Warsaw, IN, USA) from March 2019 to April 2023, and who were evaluated clinically and radiographically at a minimum two-year follow-up. All complications and revisions were recorded. Survivorship analysis with any revision surgery as endpoint was performed using Kaplan–Meier survival curves. Results: There were eighty-nine patients (follow-up rate 84.8%) who underwent clinical and radiographic follow-up. The mean follow-up was 2.5 ± 0.8 years. Revision-free survival was 98.0%. Three complications (2.8%) were recorded: one case of posterior dislocation, one periprosthetic joint infection and one post-traumatic periprosthetic femur fracture. Dislocation rate and infection rate were less than 1.0%. None of the patients were revised for adverse local tissue reactions. No cup loosening was observed. No cases of intraprosthetic dislocation, liner malseating or femoral notching were observed. Retroacetabular stress shielding was present in 43.0% of patients. Clinical scores significantly improved at the last follow-up compared with preoperative status (p < 0.0001): the final mean mHHS was 87.5 ± 5.3 and the final mean VAS was 0.5 ± 0.9. Conclusions: The Zimmer G7 modular dual mobility cup appears to be a safe and effective option and does not present specific implant-related mechanical and biological issues in primary total hip arthroplasty at a minimum two-year follow-up. Full article

Semi-humid subtropical montane regions face the dual pressures of climate change and water scarcity, making it essential to understand how soil carbon–water coupling varies among forest types. Focusing on seven representative forest types in the central Yunnan Plateau, this study analyzes the spatial distribution, trade-offs, and drivers of soil organic carbon storage (SOCS) and soil water storage (SWS) within the 0–60 cm soil layer, using sloping rainfed farmland (SRF) as a reference. We hypothesize that, relative to SRF, both SOCS and SWS increase across forest types; however, the direction and strength of the SOCS–SWS trade-off differ among plant communities and are regulated by litter traits and soil structural properties. The results show that SOCS in all forest types exceeded that in SRF, whereas a significant increase in SWS occurred only in ACF. Broadleaf stands were particularly prominent: SOCS rose most in the 23 yr SF and the 20 yr ACF (274.44% and 256.48%, respectively), far exceeding the 9–60 yr P. yunnanensis stands (44.01%–105.32%). Carbon–water trade-offs varied by forest type and depth. In conifer stands, SWS gains outweighed SOCS and trade-off intensity increased with stand age (RMSD from 0.48 to 0.53). In broadleaf stands, SOCS gains were larger, with RMSD ranging from 0.21 to 0.45 and the weakest trade-off in SF. Across depths, SOCS gains exceeded SWS in 0–20 cm, whereas SWS gains dominated in 40–60 cm. Regression analyses indicated a significant negative SOCS–SWS relationship in conifer stands and a significant positive relationship in 0–20 cm soils (both p < 0.05), with no significant correlations in other forest types or depths (p > 0.05). Correlation results further suggest that organic matter inputs, N availability, and soil physical structure jointly regulate carbon–water trade-off intensity across forest types and soil depths. We therefore recommend prioritizing native zonal broadleaf species, as well as protecting SF and establishing mixed conifer–broadleaf stands, to achieve synergistic improvements in SOCS and SWS. Full article

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of Things (AIoT). We illustrate the operating procedures of IoHT systems in detail. We highlight their applications in disease management, health promotion, and active ageing. Key enabling technologies, including cloud computing, edge computing architectures, machine learning, and smart sensors, are examined in relation to continuous health monitoring, personalized interventions, and predictive decision support. This paper also indicates potential challenges that IoHT systems face, including data privacy, ethical concerns, and technology transition and aversion, and it reviews corresponding defense mechanisms from perception, policy, and technology levels. Future research directions are discussed, including explainable AI, digital twins, metaverse applications, and multimodal sensor fusion. By integrating IoT and AI, these systems offer the potential to support more adaptive and human-centered healthcare delivery, ultimately improving treatment outcomes and supporting healthy ageing. Full article

Urban community gardens are valued for promoting sustainable food production, yet the accumulation of toxic heavy metals in city soils can present both ecological and public health risks. Therefore, this study was aimed at assessing the environmental and health risks of toxic heavy metals in community gardens soil contaminated by an industrial fire hazard in New Brunswick, Canada. Both top and subsoil soil samples were collected at Carleton community garden. The collected samples were examined for toxic heavy metals using inductively coupled plasma optical emission spectrometry and inductively coupled plasma mass spectrometry. Ecological risks were evaluated through the ecological risk factor and the potential ecological risk index, while human health risks were determined using a standard human health risk assessment approach. The mean concentration of Pb, Zn, Cu, and Sn exceeded permissible limits when compared to the Canadian soil quality guidelines and upper continental crust values. Findings from the ecological risk assessment showed that all metals were associated with low risk, except for nickel, which posed a high ecological risk across both soil layers. PERI results revealed a low overall ecological threat. The human health risk analysis indicated that children could face non-carcinogenic and carcinogenic risks from As exposure, while adults were not at risk from any of the studied metals. These findings identify arsenic as the primary contaminant of concern, with children representing the most vulnerable population, emphasizing the necessity for targeted mitigation strategies and protective measures to reduce their exposure. The results of this study can inform interventions aimed at safeguarding both environmental and public health, while also raising awareness about the presence and risks of toxic heavy metals, ultimately contributing to the protection of human health and the broader ecosystem. Full article

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU’s Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologic–hydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAV-derived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats. Full article

In recent years, frequent wildfires have posed significant threats to both the ecological environment and socioeconomic development. Investigating the mechanisms underlying the influencing factors of forest fires and accurately predicting the likelihood of such events are crucial for effective prevention strategies. However, the field currently faces challenges, including the unclear characterization of influencing factors, limited accuracy in forest fire predictions, and the absence of models for mountain fire scenarios. To address these issues, this study proposes a research framework of “decoupling analysis-model prediction-scenario validation” and employs Principal Component Analysis (PCA) and Shapley Additive Explanations (SHAP) value quantification to elucidate the significant roles of meteorological as well as combustible state indicators through multifactor coupling. Furthermore, the Attention-based Long Short-Term Memory (ALSTM) network trained on PCA-decoupled data achieved mean accuracy, recall, and area under the precision-recall curve (PR-AUC) values of 97.82%, 94.61%, and 99.45%, respectively, through 10-time cross-validation, significantly outperforming traditional LSTM neural networks and logistic regression (LR) methods. Based on digital twin technology, a three-dimensional mountain fire scenario evolution model is constructed to validate the accuracy of the ALSTM network’s predictions and to quantify the impact of key factors on fire evolution. This approach offers an interpretable solution for predicting forest fires in complex environments and provides theoretical and technical support for the digital transformation of forest fire prevention and management. Full article

First responders face adverse health effects because they regularly encounter stressful situations and potentially traumatic events. Peer support programs have emerged as a method to reduce these adverse outcomes. A growing interest in peer programs exists despite a restricted body of research in this field. Additionally, the current research on this topic faces significant conceptual and methodological shortcomings. This paper conducts an extensive analysis of present peer support research gaps before proposing future study directions to improve our understanding of this intervention. Full article

Background: Cardiovascular disease disproportionately affects women in the United States. CVD outcomes are closely tied to a multitude of factors including lifestyle habits and socioeconomic status which create a complex interweb of determinants of health that place certain age and racial groups at higher risk over others. Such inequities highlight the need for targeted prevention strategies to address the unique risks faced by different populations. In this study, a state-wide analysis examining the mortality rates among women aged 35 years or older in Florida from 2018 to 2023 was done in order to identify high risk groups to better inform evidence-based public heath interventions that are tailored to the population’s needs. Mortality rates were extracted from the CDC Wonder Database. Age-adjusted and crude mortality rates, per 100,000 women, were calculated and stratified by ten-year age groups, Hispanic origin, race, and major CVD subtypes based on various ICD-10 codes. From the ICD-10 113 Cause List, “The Diseases of the Heart” were the cause of the majority of the deaths; however, “Other Forms of Chronic Ischemic Heart Diseases” was the leading subtype. Crude rates for the “Ischemic Heart Diseases” decreased 110.9 per 100,000 women in 2018 to 101.5 per 100,000 women in 2023. Yet, the most common cause of CVD-related mortality remains ischemic heart disease. Across the study period, non-Hispanic women experienced more than twice the crude mortality rate of Hispanic women. Racial breakdowns showed that White women had the highest CVD mortality, followed by Black women, who had elevated rates of heart failure and heart attacks in comparison to other minority groups. Asian women had the lowest rates across all cardiovascular subtypes. These findings underscore the persistent age and racial differences in cardiovascular outcomes emphasize the urgent need for culturally informed, community-specific preventative interventions to improve cardiovascular care and reduce mortality within high-risk populations. Full article

This short communication examines the intersection of academic freedom concerns and doctoral student experiences at Hispanic-Serving Institutions (HSIs) during a period of increasing legislative restrictions on diversity, equity, and inclusion (DEI) initiatives. Drawing from faculty observations during doctoral residency weeks at one HSI educational leadership program, this piece explores how doctoral students navigate dissertation topic selection in an environment where diversity-related research may be perceived as entering legal or political “gray areas.” The communication contextualizes these observations within the broader landscape of academic freedom challenges facing higher education, particularly at institutions serving predominantly minoritized populations. Recent data reveals that 91% of faculty across the United States believe academic freedom is under threat, with particularly acute challenges facing institutions designated as Hispanic-Serving Institutions. These observations reveal a fundamental disconnect between educational practitioners pursuing doctoral degrees and policymakers, where research-based inclusive practices designed to benefit all students are misperceived as politically controversial. These observations suggest that doctoral students at HSIs face unique pressures as they balance their commitment to addressing educational equity issues with concerns about professional and academic risks in an increasingly restrictive policy environment. Full article

Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation projects using sustainability-related factors can support more consistent decision-making. Although several models for project selection exist in the literature, few provide a comprehensive approach that incorporates sustainability criteria. This study proposes a model for selecting innovation projects by explicitly considering sustainability aspects, supported by multi-criteria decision support methods. The methodological approach followed the Design Cycle method, grounded in Design Science Research. The main result is a novel, customizable model for evaluating, ranking, and managing innovation projects within a sustainability-oriented context. The model was validated through application in two high-performance organizations recognized for their innovation and sustainability practices. Additionally, this research offered reflections on how sustainability-driven innovation can be implemented in practice. Overall, the findings demonstrated that the proposed model is adaptable to different organizational realities, sectors, and sizes, enhancing the capacity to assess and understand the role of sustainability in innovation projects more effectively. Full article