Search Results (78)

Background: Diabetic foot ulcers (DFUs) carry high risks of infection, amputation, and mortality. We systematically reviewed randomized controlled trials (RCTs) of negative-pressure wound therapy (NPWT), including single-use systems, for clinically uninfected DFUs (with sensitivity analyses for mixed/infected cohorts). Methods: We searched PubMed and Scopus (1 January 2004–30 June 2024). Dual reviewers performed screening and extraction; risk of bias was assessed with Cochrane Risk of Bias 2 (RoB 2) and certainty of evidence with GRADE. When ≥2 trials reported comparable outcomes, we used random-effects meta-analysis. The DiaFu cohort reported in two publications was counted once across analyses. Results: Eleven RCT publications (n = 1699; 10 unique cohorts) met criteria; eight trials (n = 1456) informed the primary endpoint. Trials largely excluded severe ischemia; findings therefore apply mainly to neuropathic or mixed-etiology DFUs with adequate perfusion. NPWT increased complete healing at 12–16 weeks (risk ratio [RR] 1.46, 95% CI 1.21–1.76; I² = 48%) and shortened time to healing (mean difference –18 days, 95% CI −28 to −8). Effects were similar for conventional and single-use NPWT. Outcomes did not vary systematically within commonly used pressure ranges (approximately −80 to −125 mmHg). Only two RCTs reported direct cost data (exploratory). Moderate heterogeneity (Higgins’ I² 48–68%) reflected variation in ulcer severity, device type/settings, dressing-change frequency, and off-loading protocols. Conclusions: NPWT probably improves short-term healing of clinically uninfected DFUs compared with standard care and may reduce minor amputations, without increasing adverse events. Certainty is moderate for healing and low for most secondary outcomes. Benefits appear consistent across device classes and may support earlier discharge and community-based care. Evidence gaps include ischemia-dominated ulcers, long-term outcomes (recurrence and limb preservation), adherence mechanisms, and contemporary cost-effectiveness. Full article

This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments can span many kilometers and are loaded with challenges such as limited to no communication, hazardous terrain, blocked passages due to collapses, and vertical structures. The time-sensitive nature of these operations inherently requires solutions that are reliably deployable in practice. Moreover, a human-supervised multi-robot team is required to ensure that mobility and cognitive capabilities of various agents are leveraged for efficiency of the mission. Therefore, this article attempts to propose a solution that is suited for both air and ground vehicles and is adapted well for information sharing between different agents. This article first details a sampling-based autonomous exploration solution that brings significant improvements with respect to the current state of the art. These improvements include relying on an occupancy grid-based sample-and-project solution to terrain assessment and formulating the solution-search problem as a constraint-satisfaction problem to further enhance the computational efficiency of the planner. In addition, the demonstration of the exploration planner by team MARBLE at the DARPA Subterranean Challenge finals is presented. The inevitable interaction of heterogeneous autonomous robots with human operators demands the use of common semantics for reasoning across the robot and human teams making use of different geometric map capabilities suited for their mobility and computational resources. To this end, the path planner is further extended to include semantic mapping and decision-making into the framework. Firstly, the proposed solution generates a semantic map of the exploration environment by labeling position history of a robot in the form of probability distributions of observations. The semantic reasoning solution uses higher-level cues from a semantic map in order to bias exploration behaviors toward a semantic of interest. This objective is achieved by using a particle filter to localize a robot on a given semantic map followed by a Partially Observable Markov Decision Process (POMDP)-based controller to guide the exploration direction of the sampling-based exploration planner. Hence, this article aims to bridge an understanding gap between human and a heterogeneous robotic team not just through a common-sense semantic map transfer among the agents but by also enabling a robot to make use of such information to guide its lower-level reasoning in case such abstract information is transferred to it. Full article

In a hospital ward, transmission of airborne pathogens can occur in any area where people breathe the same air. These areas include patient rooms and specialised treatment rooms, as well as corridors and common areas. Numerous studies have been conducted to investigate the risk of airborne transmission within hospital rooms where patient care activities take place; however, studies assessing the risk of exposure to airborne pathogens in common areas such as nurse stations and corridors, in which healthcare workers spend up to 63% of their time, are very rare. In this study, we addressed this gap by simulating aerosol transport in the common area of a real inpatient ward encompassing different types of patient rooms and equipped with a mixing ventilation system. The risk of airborne transmission of COVID-19 in the ward was evaluated using a spatially resolved risk model, coupled with the clinical and pathological data on SARS-CoV-2 infection. The results showed that the central-return ventilation system causes directional air flows in the corridors, which enhanced long-distance aerosol transport and were conducive to infection transmission between different rooms. An improved ventilation system was proposed that aimed to reduce air mixing and minimise directional air flows. The improvement involved only rearrangement of air supply and exhaust vents, but led to significant reductions in both particle residence time and travelling distance within the ward, contributing to a nearly two-fold increase and 60% decrease in the areas of low-risk and high-risk zones, respectively, resulting in a 34% reduction in the overall infection probability in the studied area. This study demonstrated the potential of preventing hospital-acquired infection (HAI) via engineering controls and provided recommendations for future studies to assess novel ventilation configurations to reduce transmission risk. Full article

Soybean (Glycine max) is a predominantly self-pollinating crop; however, its flowers exhibit traits associated with insect pollination. While several studies report yield benefits from floral visitation, others suggest little or no effect, and few have assessed pollen limitation through direct hand-pollination experiments. Here, we assess pollinator contribution and pollen limitation through two manipulative common garden experiments using different soybean cultivars. First, we assessed the contribution of pollinators by comparing reproductive variables between caged (pollinator excluded) and open-pollinated plants over two growing seasons. Second, we supplemented flowers with cross-pollen to test for pollen limitation, evaluating pollen-tube growth, pod set, seed number per pod, and seed weight. Pollinator exclusion did not significantly reduce total pod or seed production per plant, but open pollination increased seed set (seeds per flower) by ~16%. In contrast, hand supplementation substantially improved reproductive success at the flower level, tripling pod set probability and increasing seed number per pod by 40%. Additionally, both open-pollinated and hand-pollinated flowers exhibited higher pollen-tube growth relative to autonomous selfing. These findings highlight that even in largely self-compatible crops like soybean, additional pollen input can enhance reproductive success and help bridge the gap between the ecological and agronomic dimensions of pollination. Full article

Critical Infrastructures (CIs) are the backbone of modern societies, providing essential services whose disruption can have severe consequences. The interdependencies among the CIs, across sectors and national borders, add significant complexity to risk and resilience management. While various EU Directives and EU-funded projects have addressed CI risk management, most efforts have focused on individual infrastructures rather than systemic cross-sector and cross-border approaches. In the EU-funded project ATLANTIS, we address this gap by advancing CI risk and resilience assessment towards a fully integrated European protection framework. We emphasise a holistic, multi-level approach that transcends individual assets, enabling coordination across operators, sectors, and national borders. To this end, we introduce a comprehensive risk assessment methodology that explicitly accounts for interdependencies among CIs and evaluates potential impacts and probabilities of disruptive events. This methodology is underpinned by the tailored data management framework, structured across three integrated layers. To validate the approach, novel tools and methods were implemented and tested in three large-scale pilot exercises, conducted through a series of stakeholder workshops. Results indicated measurable improvements in CI preparedness and awareness, ranging from approximately 5% to 55%, depending on the threat scenario and stakeholder group. The findings demonstrate that our approach delivers added value by supporting enhanced decision-making and fostering consistent, cross-CI communication through a shared platform. This paper presents the key components, cross-CI and multi-threat risk assessment methodology, and testing outcomes of the ATLANTIS project, highlighting its contribution to advancing European CI resilience. Full article

The valorization of agri-food by-products represents a promising approach to advancing sustainable agriculture while contributing to climate resilience efforts. Leguminous crops, cultivated extensively across diverse agroecological zones, play a central role in global food systems and soil fertility dynamics. Waste from leguminous crops can contribute essential nutrients to the soil, such as nitrogen, helping the growth of associated or subsequent crops, thereby reducing the need for inorganic fertilizers. Additionally, they can help improve soil biological activity, physical soil properties, and increase nutrient availability. As nitrogen-fixing crops, the waste obtained after threshing pulses probably still contains large amounts of nutrients, which can replenish part of the nutrient needs required for other crops. However, there is little information available about the amount of nutrients these residues may contain, as well as their decomposition rate and release. In this review, we explore the role of agri-food waste, particularly leguminous residues, in promoting sustainable agricultural practices, identifying main knowledge gaps in legume crop residue characterization (i.e., nutrient content and decomposition rates). We also identify potential risks in using leguminous waste and discuss mitigation strategies for using these residues safely. Additionally, we propose new strategies to promote more sustainable agricultural practices and highlight future research directions. Full article

The lack of acknowledgment of children as victims of gender-based violence hinders the support they receive. This study aimed to identify the psychological consequences of children’s exposure to gender-based violence and gaps in knowledge. This work used a scoping review approach, based on the PRISMA quality criteria. The search was conducted in the 14 databases included in the Web of Science platform. A total of 13 open-access articles published in English between 2015 and 2025 that focus on gender-based violence psychological consequences in children met the inclusion criteria. The results of the review indicate that gender violence has significant negative psychological, emotional, and social effects on children exposed to it. Thus, symptoms of internalizing, externalizing, and post-traumatic stress disorder may appear. Additionally, there is a high probability of experiencing difficulties in school, interpersonal relationships, and identity development. These effects may have long-term consequences affecting well-being and development later in life. It is crucial to recognize children as direct and significant victims of gender-based violence and promote their protection through psychological, educational, and social support. Full article

This study aims to bridge the research gap emerging from the relationships between Visual electronic Word-of-Mouth (VeWOM) and brand factors, and their impact on consumers’ behavior by exploring the causal effects of eWOM attributes on hotel brand factor spreading through Brand Awareness (BA) and Brand Perceived Value (BV) and its consequences on Purchase Decisions (PD) in the hospitality context. Attribution Theory was extended to incorporate brand-mediated effects and crisis-specific factors. The study investigates the impact of VeWOM on consumer Purchase Decisions (PD) in terms of hotel room bookings in the British hospitality market, emphasizing the mediating role of brand-related constructs. Drawing on Attribution Theory, the research proposes a structural model to assess both direct and indirect pathways through which VeWOM influences behavioral outcomes. A stratified, non-probability sampling approach yielded 443 valid responses from hotel bookers who engaged with user-generated visual content prior to booking. The Partial Least Squares Structural Equation Model (PLS-SEM) was employed to test the hypothesized relationships. The findings reveal that VeWOM significantly influences Brand Value (BV), eWOM Credibility, and Information Quality, which in turn shape consumer purchase behavior. Crucially, Brand Value emerges as a key mediating variable, bridging VeWOM and Purchase Decisions, while VeWOM alone does not directly affect booking behavior. Moreover, Brand Awareness showed no significant mediating effect. The study underscores the indirect attribution process in visual review contexts, demonstrating that the influence of VeWOM is channeled primarily through brand perception mechanisms rather than direct persuasion. These insights extend Attribution Theory by highlighting the distinct cognitive pathways activated by visual content compared to text-based reviews. Practically, the research suggests that hoteliers should focus on enhancing Brand Value via bundled offerings and relationship-based marketing rather than relying solely on visual appeal or awareness to drive bookings. The study contributes to the growing body of VeWOM literature by clarifying its nuanced effects on decision-making in digital hospitality environments. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article

Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. The experimental results confirm that the proposed algorithm model surpasses most existing recent methods. Compared with the baseline, the intersection over union (IoU) and probability of detection $P_d$ of MEFA-Net on the IRSTD-1k dataset are increased by 2.25% and 3.05%, respectively, achieving better detection performance and a lower false alarm rate in complex scenarios. Full article

The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address this gap, we introduce MT-tracker (microbiome transition tracker), a novel algorithm designed to capture the transitional trajectories of microbial communities. Grounded in diversity and phylogenetic principles, MT-tracker reconstructs the virtual common ancestors of microbiomes at the community level. By calculating distances between microbiomes and their ancestors, MT-tracker deduces their transitional directions and probabilities, achieving a substantial speed advantage over conventional approaches. The accuracy and robustness of MT-tracker were first validated by a phylosymbiosis analysis using samples from 28 mammals and 24 nonmammal animals, describing the co-evolutionary pattern between hosts and their associated microbiomes. We then expanded the usage of MT-tracker to 456,702 microbiomes sampled world-wide, uncovering the global transitional directions among 21 ecosystems for the first time. This effort provides new insights into the macro-scale dynamic patterns of microbial communities. Additionally, MT-tracker revealed intricate longitudinal transition trends in human microbiomes over a sampling period exceeding 400 days, capturing temporal dynamics often overlooked by normal diversity analyses. In summary, MT-tracker offers robust support for the qualitative and quantitative analysis of microbial community diversity, offering significant potential for studying and utilizing the macrobiome variation. Full article

In the context of autonomous driving, ensuring safe passage at intersections is of significant importance. An effective method is necessary to optimize the passage rights of autonomous vehicles at intersections to enhance traffic safety and operational efficiency. This paper proposes an analytical model for assigning the right-of-way to autonomous vehicles approaching intersections from different directions. Assuming that fully autonomous vehicles equipped with advanced Vehicle-to-Everything (V2X) communication and real-time data processing can utilize gaps to proceed at unsignalized intersections in the future, the Crossing Risk Degree (CRD) indicator is introduced for safety assessment. A higher CRD value indicates a higher crossing risk. CRD is defined as the product of the kinetic energy loss from collisions between vehicles in the priority and conflicting fleets, and the probability of conflict between these two fleets. By comparing CRD values, the passage priority of vehicles at intersection entrances can be determined, ensuring efficient passage and reduced conflict risks. SUMO microsimulation modeling is employed to compare the proposed traffic optimization method with fixed signal control strategies. The simulation results indicate that under a traffic demand of 1200 vehicles per hour, the proposed method reduces the average delay per entry approach by approximately 20 s and decreases fuel consumption by about 50% compared to fixed-time signal control strategies. In addition, carbon emissions are significantly reduced. The findings provide critical insights for developing intersection safety management policies, including the establishment of CRD-based priority systems and real-time traffic monitoring frameworks to enhance urban traffic safety, symmetry, and efficiency. Full article

Managing emissions and congestion in urban transportation systems is a growing challenge, particularly when traffic dynamics are influenced by real-time conditions and infrastructure constraints. This study addresses this issue by proposing a cellular automata-based model to analyze traffic emissions and flow dynamics in two-route traffic systems under one-directional flow conditions, incorporating various real-time information feedback strategies. Unlike previous studies, the proposed model integrates key components of urban infrastructure, such as lane-changing dynamics, traffic signalization, and vehicle-type heterogeneity, along with operational factors including entry rates, exit probabilities, and the number of waiting vehicles. The model aims to fill a gap in existing emission studies by capturing the dynamics of heterogeneous, multi-lane systems with integrated feedback mechanisms. These considerations provide valuable insights into traffic management and emission mitigation strategies. The analysis reveals that prioritizing information feedback from the system entrance, rather than relying on feedback from the entire system, more effectively reduces traffic emissions. Additionally, the Vehicle Number Feedback Strategy (VNFS) proved to be the most effective, reducing the number of waiting vehicles and consequently lowering CO₂ emissions. Furthermore, simulation results indicate that for entry rate values below approximately 0.4, asymmetrical lane-changing generates higher emissions, whereas symmetrical lane-changing yields elevated emissions when entry rate surpasses this threshold. Overall, this research contributes to advancing the understanding of traffic management strategies and offers actionable insights for emissions mitigation in two-route systems, with potential applications in intelligent transportation infrastructure. Full article

In the evolving landscape of healthcare, telemedicine has emerged as a transformative solution, effectively bridging gaps in medical service delivery across diverse geographic regions. Particularly in rural areas, where access to immediate and specialized care remains limited, store-and-forward telemedicine provides a powerful and practical approach. In rural emergency healthcare settings, resource limitations, specialist shortages, and unreliable connectivity frequently delay critical medical interventions. To address these challenges, this study proposes a store-and-forward telemedicine framework optimized through the use of queueing networks, aiming to enhance emergency response efficiency. The proposed model is structured as a four-node system comprising initial registration, consultation, diagnosis, and treatment. Each node operates as a service queue where patient data are sequentially collected, prioritized, and forwarded. By employing an open queueing network structure, the model devises steady-state probabilities for the number of patients at each node, facilitating a detailed performance analysis of patient flows. Symmetry plays a critical role in maintaining patient flow balance and system stability within the store-and-forward telemedicine model. When the routing probabilities between nodes are balanced, the queueing network exhibits probabilistic symmetry, ensuring consistent transition behavior. Moreover, the directed graph representation of the system demonstrates structural symmetry, reflecting identical service times at all nodes and uniform transition probabilities between nodes. Incorporating the concept of symmetry enables a simplified analytical approach, reduces computational complexity, and provides a more accurate approximation model for evaluating system performance. Full article