Search Results (124,784)

Bait costs constitute 40–50% of the total expenditure in river crab aquaculture, highlighting the critical need for accurately assessing crab growth and scientifically determining optimal feeding regimes across different farming stages. Current traditional methods rely on periodic manual sampling to monitor growth status and artificial feeding platforms to observe consumption and adjust bait input. These approaches are inefficient, disruptive to crab growth, and fail to provide comprehensive growth data. Therefore, this study proposes a machine vision-based monitoring system for river crab feeding platforms. Firstly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is applied to enhance underwater images of river crabs. Subsequently, an improved YOLOv11 (You Only Look Once) model is introduced and applied for multi-target detection and counting in crab ponds, enabling the extraction of information related to both river crabs and bait. Concurrently, underwater environmental parameters are monitored in real-time via an integrated environmental information sensing system. Finally, an information processing platform is established to facilitate data sharing under a “detection–processing–distribution” workflow. The real crab farm experimental results show that the river crab quality error rate was below 9.57%, while the detection rates for both corn and pellet baits consistently exceeded 90% across varying conditions. These results indicate that the proposed system significantly enhances farming efficiency, elevates the level of automation, and provides technological support for the river crab aquaculture industry. Full article

Microbial biosurfactants have emerged as natural and sustainable alternatives to synthetic surfactants used in the food industry, due to the growing demand for biodegradable and safe ingredients. Produced by bacteria, fungi, and yeasts, these compounds exhibit important physicochemical properties, such as emulsifying capacity, surface tension reduction, foam stabilization, and favorable interaction with different food matrices. In addition to their technological function, they exhibit relevant biological activities, including antioxidant and antimicrobial action, which contribute to the control of lipid oxidation and microbiological deterioration. These characteristics make biosurfactants attractive for applications in emulsions, fermented beverages, aerated products, probiotic systems, and bioactive packaging. The objective of this work is to provide a narrative literature review that integrates recent advances in the production, functionality, safety, sustainability, and application perspectives of biosurfactants in the food sector. In the field of production, biotechnological advances have made it possible to overcome historical limitations such as high cost and low yield. Strategies such as the use of agro-industrial waste, metabolic engineering, microbial co-cultures, continuous fermentations, and in situ removal techniques have increased efficiency and reduced environmental impacts. Despite the advances, significant challenges remain. Future prospects and advances tend to facilitate industrial adoption and consolidate biosurfactants as strategic ingredients for the development of more sustainable, functional, and technologically advanced foods. Full article

Background: Multiple sclerosis (MS) is a neuroinflammatory disease characterized by autoimmune-driven inflammation in the central nervous system that damages axons and destroys myelin. It is difficult to diagnose multiple sclerosis due to its complexity, and different people may react differently to different treatments. While the exact cause of multiple sclerosis (MS) and the reasons for its increasing prevalence remain unclear, it is widely believed that a combination of genetic predisposition and environmental influences plays a significant role. Methods: Finding biomarkers for complicated diseases like multiple sclerosis (MS) is made more promising by the emergence of network and system biology technologies. Currently, using tools like Network Analyst to apply network-based gene expression profiling provides a novel approach to finding potential medication targets followed by molecular docking and MD Simulations. Results: There were 1200 genes found to be differentially expressed, with CD44 showing the highest degree score of 15, followed by CDC42 and SNAP25 genes, each with a degree score of 14. To explore the regulatory kinases involved in the protein–protein interaction network, we utilized the X2K online tool. The present study examines the binding interactions and the dynamic stability of four ligands (Obeticholic acid, Chlordiazepoxide, Dextromethorphan, and Hyaluronic acid) in the Hyaluronan binding site of the human CD44 receptor using molecular docking and molecular dynamics (MD) simulations. Docking studies demonstrated a significant docking score for Obeticholic acid (−6.3 kcal/mol), underscoring its medicinal potential. MD simulations conducted over a 100 ns period corroborated these results, revealing negligible structural aberrations (RMSD 1.3 Å) and consistent residue flexibility (RMSF 0.7 Å). Comparative examinations of RMSD, RMSF, Rg, and β-factor indicated that Obeticholic acid exhibited enhanced stability and compactness, establishing it as the most promising choice. Conclusions: This integrated method underscores the significance of dynamic validations for dependable drug design aimed at CD44 receptor-mediated pathways. Future experimental techniques are anticipated to further hone these findings, which further advance our understanding of putative biomarkers in multiple sclerosis (MS). Full article

Food waste generated by hospitalized patients represents a significant challenge with environmental, economic, and social implications. In this context, Sustainable Development Goal 12, which promotes responsible consumption and production patterns, highlights the urgency of reducing this waste as an essential measure to mitigate climate change, optimize resource use, and improve the sustainability of health and food systems. This study presents a narrative review of the literature, complemented by a bibliometric analysis, aimed at synthesizing the available evidence on food waste in hospitals. Based on the identification of 746 records in different databases published between 2019 and 2024, studies focusing on the determining factors, quantification methods, and sustainable strategies to mitigate hospital food waste were included. The lack of menu personalization, the perceived low quality of food, operational disorganization, and reduced patient appetite are identified as relevant factors associated with waste at the hospital level, while direct weighing remains the most accurate quantification method. The sustainable strategies reviewed can reduce food waste and improve hospital sustainability; however, there remains limited assessment of their long-term impact. Our results highlight the urgent need to address food waste in hospitals through the implementation of comprehensive, evidence-based strategies. Full article

Background and Objectives: Early identification of cardiac dysfunction in multi-system inflammatory syndrome in children (MIS-C) is crucial for effective management. Our primary objective was to predict left ventricular systolic dysfunction (LVSD) through a multicenter collaborative assessing admission laboratory data and echocardiogram findings. Methods: Laboratory and clinical data were collected by retrospective chart review from a cohort of pediatric patients admitted and treated for MIS-C in our institutions. Laboratory data including absolute lymphocyte count, albumin, sedimentation rate, C-reactive protein, procalcitonin, d-dimer, fibrinogen, ferritin, interleukin-6 level, and lymphocyte subsets (T, B and NK quantitation, TBNK) were collected. We built a LASSO logistic regression model to predict which MIS-C patients would have left ventricular systolic dysfunction LVSD using only laboratory data obtained within the first 24 h of admission. Results: Of the 1474 MIS-C patients evaluated, 297 had LVSD. The linear kinetic analysis found differences in albumin, lymphocyte count, C-reactive proteins and fibrinogen for systolic dysfunction patients, and of these C-reactive proteins, fibrinogen and procalcitonin were more predictive earlier. The best model for coronary artery abnormalities (CAAs) performed poorly, with a mean cross-validated AUC of 0.57. The model performed well with a cross-validated AUC of 0.845. Conclusions: This model identified widely available biomarkers to successfully predict systolic dysfunction in MIS-C patients. Those at high risk of systolic dysfunction had higher peak laboratory values for C-reactive protein, fibrinogen, and procalcitonin early on. A regularized logistic regression model was validated to provide excellent discrimination for LVSD. Full article

Elderly patients often experience multimorbidity and long-term polypharmacy, making medication safety a critical challenge in disease management. In China, the concurrent use of Western medicines and proprietary Chinese medicines (PCMs) further complicates this issue, as potential drug interactions are often implicit, increasing risks for physiologically vulnerable older adults. Although large language model-based medical question-answering systems have been widely adopted, they remain prone to unsafe outputs in medication-related contexts. Existing retrieval-augmented generation (RAG) frameworks typically rely on static retrieval strategies, limiting their ability to appropriately allocate retrieval and verification efforts across different question types. This paper proposes FusionGraphRAG, an adaptive RAG framework for geriatric disease management. The framework employs query classification-based routing to distinguish questions by complexity and medication relevance; integrates dual-granularity knowledge alignment to connect fine-grained medical entities with higher-level contextual knowledge across diseases, medications, and lifestyle guidance; and incorporates explicit contradiction detection for high-risk medication scenarios. Experiments on the GeriatricHealthQA dataset (derived from the Huatuo corpus) indicate that FusionGraphRAG achieves a Safety Recall of 71.7%. Comparative analysis demonstrates that the framework improves retrieval accuracy and risk interception capabilities compared to existing graph-enhanced baselines, particularly in identifying implicit pharmacological conflicts. The results indicate that the framework supports more reliable geriatric medical question answering while providing enhanced safety verification for medication-related reasoning. Full article

Accurate prediction, forecasting and interpretability of air pollutant concentrations are important for sustainable environmental management and protecting public health. An integrated artificial intelligence (AI) framework is proposed to predict, forecast and analyse six major air pollutants, such as particulate matter concentrations (PM_2.5 and PM₁₀), ground-level ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂), using a combination of ensemble and deep learning models. Five years of hourly air quality and meteorological data are analysed through correlation and Granger causality tests to uncover pollutant interdependencies and driving factors. The results of the Pearson correlation analysis reveal strong positive associations among primary pollutants (PM_2.5–PM₁₀, CO–nitrogen oxides NO_x and VOCs) and inverse correlations between O₃ and NO_x (NO and NO₂), confirming typical photochemical behaviour. Granger causality analysis further identified NO₂ and NO as key causal drivers influencing other pollutants, particularly O₃ formation. Among the 23 tested AI models for prediction, XGBoost, Random Forest, and Convolutional Neural Networks (CNNs) achieve the best performance for different pollutants. NO₂ prediction using CNNs displays the highest accuracy in testing (R² = 0.999, RMSE = 0.66 µg/m³), followed by PM_2.5 and PM₁₀ with XGBoost (R² = 0.90 and 0.79 during testing, respectively). The Air Quality Index (AQI) analysis shows that SO₂ and PM₁₀ are the dominant contributors to poor air quality episodes, while ozone peaks occur during warm, high-radiation periods. The interpretability analysis based on Shapley Additive exPlanations (SHAP) highlights the key influence of relative humidity, temperature, solar brightness, and NO_x species on pollutant concentrations, confirming their meteorological and chemical relevance. Finally, a deep-NARMAX model was applied to forecast the next horizons for the six air pollutants studied. Six formulas were elaborated using input data at times (t, t − 1, t − 2, …, t − n) to forecast a horizon of (t + 1) hours for single-step forecasting. For multi-step forecasting, the forecast is extended iteratively to (t + 2) hours and beyond. A recursive strategy is adopted for this purpose, whereby the forecast at (t + 1) is fed back as an input to generate the forecasts at (t + 2), and so forth. Overall, this integrated framework combines predictive accuracy with physical interpretability, offering a powerful data-driven tool for air quality assessment and policy support. This approach can be extended to real-time applications for sustainable environmental monitoring and decision-making systems. Full article

Obstructive sleep apnea (OSA) is a common sleep disorder that impacts patient health and imposes a burden on families and healthcare systems. The diagnosis of OSA is usually performed through overnight polysomnography (PSG) in a hospital setting. In recent years, OSA detection using a single-lead electrocardiogram (ECG) has been explored. The advantage of this method is that patients can be measured in home environments. Thus, the aim of this study was to predict occurrences of sleep apnea with parameters extracted from previous single-lead ECG measurements. The parameters were the R-R interval (RRI) and R-wave amplitude (RwA). The dataset was the single-lead ECG Apnea-ECG Database, and a stacking hybrid architecture (SHA) including three gated recurrent neural network architectures (GRNNAs) and logistic regression was proposed to improve the accuracy of OSA detection. Three GRNNAs used three different recurrent neural networks: Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). The challenge of this method was in exploring how many minutes of previous RRI and RwA measurements (n minutes) have the best performance in predicting occurrences of sleep apnea in the future (h minutes). The results showed that the SHA under an n of 20 min had the best performance in predicting occurrences of sleep apnea in the following 10 min: the SHA achieved a precision of 95.79%, sensitivity of 94.74%, specificity of 97.48%, F₁-score of 95.26%, and accuracy of 96.45%. The proposed SHA was successful in predicting future sleep apnea occurrence with a single-lead ECG. Thus, this approach could be used in the development of wearable sleep monitors for the management of sleep apnea. Full article

The urgent need to reduce greenhouse gas emissions and enhance resource circularity is driving the cement and construction industry to explore alternatives to clinker-based binders. Electric arc furnace slag (EAFS), a major steelmaking by-product, is currently underutilised as a binder due to its low intrinsic reactivity. This study provides a comparative evaluation of three distinct valorisation pathways for the same EAFS—use as a supplementary cementitious material (SCM), as a precursor for alkali-activated binders, and as a component in accelerated carbonation systems—thereby highlighting its multifunctional and more ecological binding potential. A comprehensive physicochemical characterisation was conducted, followed by mechanical performance assessment under different curing regimes. When used as an SCM, partial cement replacement resulted in no loss of mechanical performance and a compressive strength increase of up to 8.9% at 10% replacement, demonstrating its suitability for structural applications. Under accelerated carbonation, specimens with 50% replacement of cement and sand achieved compressive strengths of 46.7 MPa, comparable to the non-carbonated reference (47 MPa), indicating full strength recovery despite high substitution levels. Full replacement systems based on alkali activation or carbonation of EAFS achieved moderate compressive strengths (~10 MPa), suitable for non-structural applications, with clear potential for improvement through optimisation of activation and curing conditions. Overall, this work demonstrates that EAFS can be effectively valorised through multiple reaction routes, supporting its role as a versatile and low-carbon resource for sustainable cementitious materials. Full article

With its benefits of high efficiency and cheap cost, solar photovoltaic is rebuilding the energy supply and demand system as the world’s energy structure shifts to a clean one. This research investigates the wind-induced vibration response of a multi-row flexible photovoltaic system using large eddy simulation and the two-way fluid–solid coupling approach. Firstly, the two-way coupling and the standard shape coefficient are compared to verify the reliability of the simulation method. Then, the model of multi-row flexible photovoltaics is analyzed to determine the natural frequency and vibration mode of the photovoltaic system. Finally, the vertical displacement of the photovoltaic system and the internal force of the cable are studied by investigating different wind direction angles and initial pretension. It is discovered that the natural frequency of the flexible photovoltaic system exhibits a stepwise increase in three distinct stages. Both the internal force in the load-bearing cable and the vertical displacement of the photovoltaic system decrease with increasing wind direction angle, with the cable force lagging behind at the peak time. The internal force and vertical displacement of the first row of load-bearing cables are at their highest at the 0° direction angle. The difference between the cable’s internal force’s peak and valley values grows when the pretension is low. The cable pretension significantly affects the vibration response of the flexible photovoltaic more than the angle of direction. The response law of direction angle and pretension to multi-row flexible photovoltaic wind-induced vibration is revealed, which provides a basis for wind-resistant design. Full article

Significant volumes of wastewater and solid by-products are produced by olive oil industries worldwide, posing serious environmental challenges. This study presents an innovative circular economy and environmental sustainability approach that simultaneously valorizes liquid (olive mill wastewater, OMW) and solid by-products (crushed olive pits) rom olive oil production through hydroponic lettuce cultivation. The OMW was pretreated and supplemented with nutrients (OMW-N) to create a hydroponic solution for lettuce (Lactuca sativa) cultivation using crushed olive pits as growing substrate. A hydroponic system fed with a nutritive solution was used as a control. Lettuces grown in the OMW-N system achieved a 100% survival rate with no signs of phytotoxicity, although they exhibited a significant reduction in fresh mass (approx. 66%) and size, compared to the control. The sensory analysis revealed no significant differences in consumer acceptance, except for slightly lower color intensity, with 40% of participants explicitly indicating a purchase preference for the OMW-N lettuce, validating its commercial feasibility. Results demonstrated that OMW-N system functioned as a tertiary treatment, achieving additional removal of nutrients. Overall, integrating treated OMW and olive pits into hydroponics is a feasible strategy to convert agro-industrial waste into value-added food products, reducing the environmental footprint of the olive sector. Full article

The Mediterranean pond turtle (Mauremys leprosa) is a native semi-aquatic species of the Iberian Peninsula, southern France, and North Africa, widely distributed across Mediterranean aquatic systems. Within these environments, M. leprosa inhabits a mosaic of lotic (flowing) and lentic (still) habitats, whose structure and connectivity may influence its spatial use, behavior, and ontogenetic development. How morphometry and age-class distribution differ between these habitat types, however, remains unclear. This study analyzed morphometric differences among individuals from both habitat types to explore potential ontogenic habitat preferences. Lotic habitats were primarily used for dispersal and breeding by adults, while lentic habitats served as foraging and residency areas for juveniles. Morphometric differences between habitat types support this functional differentiation. These findings highlight the ecological importance of habitat heterogeneity and underscore the need to preserve both habitat types to support the full life cycle of M. leprosa in Mediterranean ecosystems and suggest potential evolutionary and ecological consequences of habitat-related morphometric and demographic variation. Full article

Kenaf (Hibiscus cannabinus) is a versatile fiber crop known for rapid growth and high biomass productivity that is often cultivated in warm-season regions where root-knot nematodes (RKNs) are prevalent. Here, we compared eight kenaf genotypes with Hibiscus acetosella and Hibiscus sabdariffa to evaluate resistance versus susceptibility to five RKN (Meloidogyne spp.) populations in two replicated greenhouse trials. The nematode panel comprised globally dominant species (M. incognita races 2 and 4, M. javanica) and emerging high-impact threats in warm-season systems (M. floridensis and M. enterolobii), which overlap geographically with current and potential kenaf production. Reproduction and galling were quantified using eggs per system, eggs per gram of root, egg masses, gall index, and reproduction factor, and genotypic differences were assessed by nonparametric rank-based tests at α = 0.05. Across nematode species, H. acetosella and H. sabdariffa showed minimal reproduction and galling, whereas most kenaf genotypes were highly susceptible. Susceptibility was most pronounced to M. enterolobii and M. floridensis, and several kenaf lines (‘Whitten’, ‘G 14’, ‘G 32’, ‘Yue 74-3’) had the highest egg counts and near-maximal egg masses and galling. M. incognita race 2 and race 4 produced strong contrasts, with H. acetosella and H. sabdariffa remaining resistant while multiple kenaf lines exhibited heavy reproduction and severe galling. M. javanica followed a similar pattern, with ‘G 32’, ‘Yue 74-3’, ‘Whitten’, ‘G 14’, and ‘74200 I4’ being highly susceptible. These results identify H. acetosella ‘PI 500707’ and H. sabdariffa ‘X17’ as robust donors of RKN resistance and highlight the susceptibility of cultivated kenaf genotypes, underscoring urgent breeding and integrated management needs for kenaf in warm-season production regions. Full article

To provide essential information on geoheritage and geotourism potential in Jiangxi Province—a key region for geoheritage distribution in China—this study summarizes and categorizes the types, grades, and distribution characteristics of geoheritage within local communities. The primary analytical methods included average nearest neighbour analysis, kernel density estimation, and spatial autocorrelation to explore spatial distribution patterns. A total of 202 significant geoheritage sites were identified in Jiangxi Province. Furthermore, an evaluation index system was established using the entropy weight TOPSIS model to assess the geotourism potential of each city. The findings reveal the following: (1) Geoheritage sites in Jiangxi Province exhibit an overall aggregated spatial distribution, although clustering intensity varies among different geoheritage types and grades. (2) Considering both grade and category, the core distribution area of geoheritage is located in eastern Shangrao City, while global-level geoheritage sites are mainly concentrated in the Poyang Lake Plain. (3) Spatial autocorrelation analysis indicates that, except for global-level geoheritage sites, other geoheritage sites display significant spatial agglomeration with positive spatial correlation. Moreover, local-scale spatial association characteristics differ notably according to geoheritage type and grade. (4) The geotourism development potential across Jiangxi Province shows clear spatial differentiation, with higher potential concentrated in the eastern and southern regions. Full article

Model-Based Systems Engineering (MBSE) originated in aerospace engineering and has emerged as a promising approach in other fields for designing, analyzing, and managing complex interdisciplinary systems throughout their entire life cycle. While MBSE is applicable to various engineering domains, its applications remain closely tied to software engineering. As software becomes a critical component of physical systems, such as vehicles, appliances, and production plants, bridging the gap between software engineering and other disciplines, such as mechanical, electrical, and civil engineering, becomes essential. Despite its potential, MBSE is still in its early stages when it comes to integrating executable models of physical systems into engineering environments. The purpose of this research is to assess the present capabilities of MBSE by identifying existing missing links, thereby enabling prospective users to make well-informed decisions about its integration into organizational processes. In this analysis, it is important to have a comprehensive view of the complexity of MBSE across different disciplines to obtain an overall picture. In addition to identifying open challenges, we present three critical gaps in the MBSE practice through a comprehensive demonstration case: limited tool interoperability and model integration, modeling language limitations, and dependence on a specialized workforce. Current studies largely view MBSE as the most applicable and effective for the design phase of the system life cycle. Yet, to capture MBSE in its entirety, its principles must be applied throughout the whole system life cycle. Full article