Search Results (9,604)

The lymphocystis disease virus (LCDV) is a widespread disease in Mediterranean aquaculture and could lead to losses in fry as well as prevent the sale of adult gilthead seabream (Sparus aurata), affecting both hatchery and on-growing stages. Although LCDV infections are often considered self-limiting, they can lead to severe outcomes due to skin microbiome alterations that promote secondary infections, while also reducing growth and marketability, causing substantial economic losses. Basic biosecurity measures are not successful, and there is no available commercial vaccine. This study evaluated diets supplemented with Origanum vulgare and Cinnamomum zeylanicum essential oils (1% and 2%) in gilthead seabream experimentally infected with LCDV. Preventive feeding (90 days before infection) and therapeutic feeding (initiated at infection) were compared across 11 experimental groups, including infected, recovered, and control groups. Results showed that essential oils were more effective prophylactically than therapeutically, highlighting their protective role when incorporated into diets. Cinnamon-supplemented groups consistently exhibited lower prevalence and mortality than oregano groups. High DNA damage values linked to reduced mortality, particularly in the CIN90.1 group, demonstrated that viral dissemination was most restricted. In conclusion, essential oils modulated LCD progression by influencing viral interactions with DNA damage repair mechanisms, supporting their potential for disease control in intensive aquaculture. Full article

Small-object detection (SOD) remains an important and growing challenge in computer vision and is the backbone of many applications, including autonomous vehicles, aerial surveillance, medical imaging, and industrial quality control. Small objects, in pixels, lose discriminative features during deep neural network processing, making them difficult to disentangle from background noise and other artifacts. This survey presents a comprehensive and systematic review of the SOD advancements between 2023 and 2025, a period marked by the maturation of transformer-based architectures and a return to efficient, realistic deployment. We applied the PRISMA methodology for this work, yielding 112 seminal works in the field to ensure the robustness of our foundation for this study. We present a critical taxonomy of the developments since 2023, arranged in five categories: (1) multiscale feature learning; (2) transformer-based architectures; (3) context-aware methods; (4) data augmentation enhancements; and (5) advancements to mainstream detectors (e.g., YOLO). Third, we describe and analyze the evolving SOD-centered datasets and benchmarks and establish the importance of evaluating models fairly. Fourth, we contribute a comparative assessment of state-of-the-art models, evaluating not only accuracy (e.g., the average precision for small objects (AP_S)) but also important efficiency (FPS, latency, parameters, GFLOPS) metrics across standardized hardware platforms, including edge devices. We further use data-driven case studies in the remote sensing, manufacturing, and healthcare domains to create a bridge between academic benchmarks and real-world performance. Finally, we summarize practical guidance for practitioners, the model selection decision matrix, scenario-based playbooks, and the deployment checklist. The goal of this work is to help synthesize the recent progress, identify the primary limitations in SOD, and open research directions, including the potential future role of generative AI and foundational models, to address the long-standing data and feature representation challenges that have limited SOD. Full article

With the growing global demand for mineral resources, enhancing the transport capacity of heavy-haul railways (HHR) has emerged as a key area of research. As an emerging train formation technology, the virtual coupling train system (VCTS) has the potential to substantially increase the traffic density of heavy-haul trains (HHT) and thereby improve transport efficiency. However, the stable operation of virtually coupled fleets relies on train-to-train (T2T) communication, which is vulnerable to jamming attacks (JAs) within the complex operational environments of HHR. To address issues such as train decoupling and emergency braking in the VCTS that may be caused by JAs, this study proposes a stochastic game-based anti-jamming control (SGAC) strategy aimed at ensuring the stability and operational safety of the VCTS operating within HHR. The proposed approach models both JAs and defensive actions as a stochastic game and employs an $H^{\infty }$-based cross-layer control method to mitigate their adverse effects. The control performance is analyzed through frequency-domain mapping, and a quantitative evaluation is conducted using the $H^{\infty }$ norm. The simulation results demonstrate that the SGAC scheme significantly enhances the resilience of VCTS cooperative control under JAs, offering a robust solution for ensuring the stable operation of HHR. Full article

This study investigates the impact of equipment geometry on residence time distribution (RTD) using computational fluid dynamics (CFD) methods in a wastewater treatment tank with different configurations of static mixer elements. With growing environmental concerns, optimizing wastewater treatment processes is crucial. Proper mixing in these units can be achieved by optimal placement of static mixer elements such as baffle walls to create circulation zones and increase residence time of the fluid within the control volume. A CFD model of a wastewater treatment tank was developed and validated using experimental RTD data under three distinct mixer configurations.The experimentally validated model was subsequently enhanced by investigating the degradation of methylene blue (MB) during ozonation in the system. The results of the model allowed for the analysis of how tank geometry—specifically, the number and placement of baffles—affects the flow field and MB conversion. RTD was characterized using expectancy and standard deviation of residence time, revealing a link between RTD and MB degradation efficiency. Results showed that constructional parameters significantly influence residence time and mixing efficiency, with a potential 60% increase in expectancy. The model demonstrated high predictive accuracy, ranging from 75% in the worst case to nearly 90% in the best case. Full article

To address severe seasonal drought affecting over 60% of China’s olive-growing regions, this study evaluates whether arbuscular mycorrhizal fungi (AMF) can enhance drought tolerance in elite olive cultivars (Arbequina and Koroneiki) under simulated arid conditions. A controlled pot experiment inoculated seedlings with two AMF strains (Rhizophagus intraradices [AMF1], Funneliformis mosseae [AMF2]) under full irrigation or a 32-day water deficit. Biomass, root colonization, photosynthesis, PSII efficiency, osmolytes, antioxidants, and lipid peroxidation were measured. Data were analyzed via two-way ANOVA, Pearson’s correlation, and principal component analysis (PCA). Under optimal hydration, both AMF strains colonized >60% of roots, significantly increasing Arbequina biomass by 25–35% (p < 0.05) and Koroneiki biomass. Drought reversed benefits in Arbequina but triggered resilience: AMF1 restored photosynthesis (18%), Fv/Fm (37%), and water potential (18%) (p < 0.05) while reducing lipid peroxidation (79%) (p < 0.01). In Koroneiki, AMF2 restored Ψw to 47% of pre-irrigation levels and increased root volume (137%), PSII efficiency (43%), osmolytes (100%), and carotenoids (28%) (p < 0.01). PCA ranked Arbequina–drought–AMF1 as the most resilient combination. Pairing AMF strains with specific cultivars offers a scalable, chemical-free strategy to stabilize olive productivity in southwest China’s aridifying climate, advancing climate-smart agriculture for drought-prone regions. Full article

As climate disasters intensify, their financial shockwaves increasingly threaten residential stability and the resilience of the U.S. mortgage market. While prior research links natural disasters to payment delinquency, far less is known about foreclosure—the terminal outcome of housing distress. We construct a novel property-level panel covering 55 flood, wildfire, and hurricane events, integrating transactional, mortgage, and insurance data. A difference-in-differences framework compares foreclosure rates for damaged parcels with nearby undamaged controls within narrowly defined hazard perimeters. Results show that flooding substantially increases foreclosure risk: inundated properties experience a 0.29-percentage-point rise in foreclosure likelihood within three years, with effects concentrated outside federally mandated flood-insurance zones. In contrast, wildfire and hurricane wind damage are associated with lower foreclosure incidence, likely reflecting standard insurance coverage and rapid post-event price recovery. These findings suggest that physical destruction alone does not drive credit distress; rather, insurance liquidity and post-disaster equity dynamics mediate outcomes. Policy interventions that expand flood insurance coverage, stabilize insurance markets, and embed climate metrics in mortgage underwriting could reduce systemic exposure. Absent such measures, climate-driven foreclosures could account for nearly 30% of lender losses by 2035, posing growing risks to both household wealth and financial stability. Full article

The goal of the Multimodal Named Entity Recognition (MNER) job is to identify and classify named entities by combining various data modalities (such as text and images) and assigning them to specified categories. The growing prevalence of multimodal social media posts has spurred heightened interest in MNER, particularly due to its pivotal role in applications ranging from intention comprehension to personalized user recommendations. In the MNER task, the inconsistency between image information and text information and the difficulty of fully utilizing the image information to complement the text information are the two main difficulties currently faced. In order to solve these problems, this study proposes a Multilevel Predictive Cross-Fusion Network (MPCFN) approach for Multimodal Named Entity Recognition. First, textual features are extracted using BERT and visual features are extracted using ResNet, then irrelevant information in the image is filtered using the Correlation Prediction Gate. Second, the hierarchy of visual features received by each Transformer block is controlled by the Dynamic Gate and aligned between image and textual features using the Cross-Fusion Module to align the image and text features. Finally, the hidden layer representation is fed into the CRF layer optimized for decoding using Flooding. Through experiments on TWITTER-2015, TWITTER-2017, and WuKong datasets, our method achieves F1 scores of 76.74%, 87.61%, and 82.35%, outperforming the existing mainstream state-of-the-art models and proving the effectiveness and superiority of our method. Full article

With growing demand for functional foods, mushroom-based ingredients are gaining popularity. The typical white mushroom (Agaricus bisporus) is particularly valued for its bioactive compounds and shows promise as a nutritional enhancer in widely consumed products, such as chocolate. This study examined the fortification of dark, milk, and white chocolates with freeze-dried, sous-vide processed A. bisporus powder at 4%, 6%, and 8% levels. Analyses focused on protein content, dietary fiber, essential minerals, texture, and sensory characteristics. Mushroom addition notably improved nutritional values. In white chocolate, protein increased from 6.04% to 8.92%, while dark chocolate with 8% fortification reached 13.25%, compared to 11.09% in the control. The magnesium content also increased significantly, from 2579 mg/kg to 3184 mg/kg. Total dietary fiber also showed a significant improvement. Texture analysis revealed a reduction in firmness, with the 8% A. bisporus powder fortified dark chocolate formulation softening from 24,685 g·s to 10,633 g·s. Despite these changes, sensory evaluation confirmed that taste and appearance remained acceptable. Overall, incorporating A. bisporus powder into chocolate improved its nutritional profile while introducing moderate changes to texture. These findings highlight its potential as a functional ingredient in the development of healthier confectionery products. Full article

This study analyzes global data on human exposure to pesticides, focusing on glyphosate, POPs, carbamates, and organophosphates, which are among the most widely used in agricultural and urban environments, providing an overview of global human contamination by these substances. Current research has increasingly focused on the unintended consequences of pesticide use, including food, water, and soil contamination, biodiversity loss (especially beneficial insects such as pollinators), and the growing evidence of adverse impacts on human health (neurological, reproductive, endocrine, and carcinogenic effects). Therefore, we compiled information from several existing studies that evaluated pesticide residues in human biological samples, specifically urine, blood, and breast milk, to assess the extent of exposure. The analysis takes a global perspective, highlighting the importance of monitoring exposure in countries that demonstrate exceptionally high pesticide use (in terms of absolute volume), such as Brazil, the United States, and China, which are among the largest global consumers. The data cover both contemporary pesticides, whose consumption is driven by intensive agriculture in these and other countries, and persistent legacy compounds (POPs) that continue to circulate in nature and accumulate in the human body decades after their ban in many countries. Globally, there is a wide disparity in global regulations, and many developing countries continue to use pesticides that have been banned or severely restricted in more developed nations. Finally, it provides a critical overview of global data on human pesticide contamination. The data reinforce the critical importance of establishing preventive initiatives and strengthening surveillance and monitoring systems to detect and control pesticide residues in human populations globally, ultimately aiming to mitigate the harms of chronic pesticide exposure to human health and well-being. Full article

This study examines the exposure of the U.S. insurance sector to climate-related risks using a two-step approach combining factor modeling and Extreme Value Theory. The analysis first constructs a climate risk factor from transition-sensitive sectors and estimates its impact on the SPDR S&P Insurance ETF using a standard factor model. The resulting residual, termed Insurance Climate Risk, isolates climate-driven excess returns by controlling for market-wide effects. To assess the sector’s sensitivity to extreme events, the study applies both the Peaks Over Threshold method using the Generalized Pareto Distribution and the Block Maxima Method using the Generalized Extreme Value distribution. The findings reveal statistically significant climate sensitivity, especially in daily and weekly data, and confirm the presence of heavy tails in the loss distribution. VaR and CVaR estimates indicate heightened risk over longer horizons and under block maxima modeling. Notably, peak over threshold daily returns yield a 95% VaR of 1.33% and CVaR of 2.28%, while block maxima CVaR exceeds 5%. These results show the importance of incorporating tail-risk-aware metrics in insurance risk management and highlight the growing influence of climate-related financial shocks. Full article

Given the growing demand for sustainable energy solutions, this study addresses the challenge of improving the efficiency and environmental performance of residential water heating systems. This work presents the design and implementation of a controller aimed at regulating the outlet water temperature of a direct-expansion solar-assisted heat pump operating with propane. A dynamic model was experimentally identified using the AutoRegressive with eXogenous input methodology and used to design a Proportional–Integral–Derivative controller via the direct synthesis method. To regulate the outlet water temperature, the controller acts on the water flow rate. The effectiveness of the controller was evaluated through computer simulations and experimental tests. Its robustness was assessed by considering parametric variations of ±15%, during which the system maintained stability and performance. The controller demonstrated good accuracy and performance, keeping the desired temperature stable even in the presence of disturbances, both in simulations and experimental evaluations. Full article

The human endometrium, previously considered a sterile environment, is now recognized as a low-biomass but biologically active microbial niche critical to reproductive health. Advances in sequencing technologies, particularly shotgun metagenomics, have provided unprecedented insights into the taxonomic and functional complexity of the endometrial microbiome. While 16S rRNA sequencing has delineated the distinction between Lactobacillus-dominant and non-dominant microbial communities, shotgun metagenomics has revealed additional diversity at the species and strain level, uncovering microbial signatures that remain undetected by amplicon-based approaches. Current evidence supports the association of Lactobacillus dominance with endometrial homeostasis and favorable reproductive outcomes. Dysbiosis, characterized by increased microbial diversity and enrichment of anaerobic taxa such as Gardnerella, Atopobium, Prevotella, and Streptococcus, is linked to chronic endometritis, implantation failure, and adverse IVF results. Beyond compositional differences, the endometrial microbiome interacts with the host through immunological, metabolic, and epigenetic mechanisms. These interactions modulate cytokine signaling, epithelial barrier integrity, and receptivity-associated gene expression, ultimately influencing embryo implantation. However, discrepancies between published studies reflect the lack of standardized protocols for sampling, DNA extraction, and bioinformatic analysis, as well as the inherent challenges of studying low-biomass environments. Factors such as geography, ethnicity, hormonal status, and antibiotic exposure further contribute to interindividual variability. Culturomics approaches complement sequencing by enabling the isolation of viable bacterial strains, offering perspectives for microbiome-based biotherapeutics. Emerging 3D endometrial models provide additional tools to dissect microbiome–host interactions under controlled conditions. Taken together, the growing body of data highlights the potential of endometrial microbiome profiling as a biomarker for reproductive success and as a target for personalized interventions. Future research should focus on integrating multi-omics approaches and functional analyses to establish causal relationships and translate findings into clinical practice. This review gives a new insight into current knowledge on the uterine microbiome and its impact on implantation success, analyzed through the lenses of microbiology, immunology, and oxidative stress. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly attracting growing concern around the world. While there has been progress in the development of pharmacologic treatments, lifestyle and dietary interventions remain as the first-line approach for management. This scoping review aimed to identify dietary strategies for managing MASLD and to highlight current research gaps and challenges. Methods: A systematic search of PubMed and Science Direct was conducted up to 10 July 2025, for relevant studies on dietary modifications and MASLD. Data extracted included types of interventions, outcomes related to liver health, and research limitations. Results: Dietary interventions were shown to consistently improve hepatic and metabolic outcomes. In a randomized controlled trial of 12 weeks (n = 259), a Mediterranean diet reduced hepatic steatosis by 39% and improved insulin sensitivity. A calorie-restricted lifestyle program in adults with MASLD (n = 196) reduced liver fat by 25% over 52 weeks. Resistant starch supplementation (n = 200) lowered intrahepatic triglyceride content by 8% through gut microbiome modulation. A pilot RCT of medically tailored meals in cirrhosis (n = 40) reduced ascites symptoms and improved quality of life. Finally, prebiotic supplementation in MASLD (n = 200) lowered systemic inflammation and increased immune-regulating microbes. In contrast, Western dietary patterns and ultra-processed foods were consistently linked to lipotoxicity and inflammation. Conclusions: Dietary interventions remain critical for the management of chronic liver disease and continue to play a vital role even as pharmacotherapy options emerge. Further research should explore precision nutrition and microbiome-based therapies while also addressing the methodological limitations like the underutilization of causal inference frameworks. Finally, it is also important to consider culturally tailored interventions to account for barriers in access and equity in underserved populations. Full article

With the growing global demand for renewable energy, the pump as turbine (PAT) exhibits significant potential in the micro-hydropower sector. To reveal its internal unsteady flow characteristics and energy loss mechanisms, this study analyzes the internal flow field of an ultra-low specific speed pump as turbine (USSPAT) by employing a combined approach of entropy generation theory and dynamic mode decomposition (DMD). The results indicate that the outlet pressure pulsation characteristics are highly dependent on the flow rate. Under low flow rate conditions, pulsations are dominated by low-frequency vortex bands induced by rotor-stator interaction (RSI), whereas at high flow rates, the blade passing frequency (BPF) becomes the absolute dominant frequency. Energy losses within the PAT are primarily composed of turbulent and wall dissipation, concentrated in the impeller and volute, particularly at the impeller inlet, outlet, and near the volute tongue. DMD reveals that the flow field is governed by a series of stable modes with near-zero growth rates, whose frequencies are the shaft frequency (25 Hz) and its harmonics (50 Hz, 75 Hz, 100 Hz). These low-frequency modes, driven by RSI, contain the majority of the fluctuation energy. Therefore, this study confirms that RSI between the impeller and the volute is the root cause of the dominant pressure pulsations and periodic energy losses. This provides crucial theoretical and data-driven guidance for the design optimization, efficient operation, and stability control of PAT. Full article

Black smoke emitted from diesel vehicles contains substantial amounts of hazardous substances. With the increasing annual levels of such emissions, there is growing concern over their detrimental effects on both the environment and human health. Therefore, it is imperative to strengthen the supervision and control of black smoke emissions. An effective approach is to analyze the smoke emission status of vehicles. Conventional object detection models often exhibit limitations in detecting black smoke, including challenges related to multi-scale target sizes, complex backgrounds, and insufficient localization accuracy. To address these issues, this study proposes a multi-dimensional detection algorithm. First, a multi-scale feature extraction method was introduced by replacing the conventional C2F module with a mechanism that employs parallel convolutional kernels of varying sizes. This design enables the extraction of features at different receptive fields, significantly improving the capability to capture black smoke patterns. To further enhance the network’s performance, a four-layer adaptive feature fusion detection head was proposed. This component dynamically adjusts the fusion weights assigned to each feature layer, thereby leveraging the unique advantages of different hierarchical representations. Additionally, to improve localization accuracy affected by the highly irregular shapes of black smoke edges, the Inner-IoU loss function was incorporated. This loss effectively alleviates the oversensitivity of CIoU to bounding box regression near image boundaries. Experiments conducted on a custom dataset, named Smoke-X, demonstrated that the proposed algorithm achieves a 4.8% increase in precision, a 5.9% improvement in recall, and a 5.6% gain in mAP50, compared to baseline methods. These improvements indicate that the model exhibits stronger adaptability to complex environments, suggesting considerable practical value for real-world applications. Full article