Search Results (1,318)

Regional water systems in rapidly urbanizing hilly basin cities are affected by hydrological variability, population concentration, industrial water demand, and water-use efficiency. This study evaluated the water resources carrying capacity (WRCC) of Jinhua City, southeastern China, from 2011 to 2023 using an integrated 15-indicator system covering water resources support, water-use and population pressure, economic structure and water-use efficiency, and ecological and environmental support. Indicator definitions, units, directions, and data sources were harmonized using official water resources bulletins and statistical records. A combined weighting method integrating the modified Analytic Hierarchy Process and the entropy weight method was coupled with a fuzzy matter-element model and the Hamming closeness measure. WRCC grades were assigned using standard-derived Hamming closeness thresholds based on pooled-reference membership transformation. Obstacle degree, leave-one-indicator-out sensitivity, and redundancy diagnostics were further used for interpretation and robustness assessment. The combined weights were mainly concentrated in water-use and population pressure (35.85%), water resources support (26.77%), and economic structure and water-use efficiency (26.10%). Industrial water use, per capita comprehensive water use, population density, water consumption per 10,000 yuan industrial value added, and water consumption per 10,000 yuan GDP had the highest indicator weights. Annual Hamming closeness ranged from 0.2621 to 0.6391. Jinhua’s WRCC reached Grade II in 2015, 2019, 2020, and 2021, while the remaining years were classified as Grade III. The highest closeness occurred in 2019, whereas 2022 and 2023 declined to Grade III and were close to the II/III threshold. Obstacle diagnosis showed that water-use and population pressure were the dominant subsystem obstacles. Sensitivity analysis showed that the peak year and the lowest year remained unchanged across all leave-one-indicator-out scenarios, whereas the boundary years showed grade sensitivity. The results provide a transparent annual assessment and diagnostic evidence for WRCC management. Full article

Background/Objectives: Given the limited evidence internationally, this qualitative study employed discovery interviews to explore the lived experience of patients who developed Hospital-Acquired Malnutrition (HAM) and that of their carers. Methods: Seven (two patients [(n = 1 female] and five carers [n = 3 female]) completed discovery interviews with an experienced independent interviewer. Carers were either spouses or parents. Responses were thematically analyzed using a constant comparative approach. Results: A key theme was ‘It’s not about the food, it’s the hospital system’ with the needs of the system dominating, including when patients were feeling at their worst. Subthemes were ‘integration of care’ and ‘patient acuity’, including symptoms that impacted food intake. Another theme was ‘Who is looking out for the patient?’, exploring ‘reliance on carer advocacy’, and ‘variation in staff involvement’. One carer said, “… the girl that delivered the meal tray was the only one in our hospital stay who actually said to [the patient], ‘I’m so glad you’re sitting up. I was worried about you because you hadn’t eaten for so long?” A persistent but comparatively less strong theme was ‘When it is about the food’ which explored ‘the quality of the food’ and ‘receiving information on eating and drinking’. Conclusions: The three key themes identified from carers and patients were hospital system impacts, care co-ordination and, less strongly, experiences with food quality and information. The key opportunities to prevent, or better support the nutritional care of patients with, HAM may be through improving systems and care co-ordination. Full article

Hypobaric hypoxia poses a serious threat to growth and development and can induce pronounced inflammatory responses. These effects are closely associated with the gut microbiota. However, the underlying mechanisms, particularly the role of gut microbiota in regulating hepatic metabolism under chronic hypoxic conditions, remain poorly understood. In this study, SD rats were used as recipients and assigned to three groups: a hypobaric hypoxia group (H), an antibiotic-treated group (HA), and an antibiotic-treated group receiving fecal microbiota transplantation from plateau zokors (HAM). All rats were maintained in a hypobaric hypoxia chamber simulating an altitude of 6000 m for 30 days. Subsequently, growth performance, routine hematological parameters, and multi-omics profiles were evaluated. Compared with the H group, both the HAM and HA groups showed significantly increased average daily gain (ADG) (p < 0.05), while the ADG/ADFI ratio was significantly higher in the HAM group than in the H group (p < 0.05). Monocyte count (Mon#) and monocyte percentage (Mon%) were significantly higher in the HA group than in both the H and HAM groups (p < 0.05). Microbiota analysis revealed significant enrichment of Lachnospiraceae_NK4A136_group in the HAM group, whereas Desulfovibrio was significantly enriched in the HA group (p < 0.05). Fecal metabolomics showed that ursodeoxycholic acid (UDCA) was significantly increased in the HAM group (p < 0.05). In the liver metabolome, the anti-inflammatory lipid FAHFA 18:1/20:3 was significantly elevated in the HAM group, whereas pro-inflammatory factors, including uric acid and leukotriene D4, were significantly reduced (p < 0.05). Correlation analysis further demonstrated that the abundance of Lachnospiraceae was positively correlated with FAHFA 18:1/20:3 and negatively correlated with uric acid and creatinine (p < 0.05). Collectively, these findings indicate that the gut microbiota can modulate gut–liver metabolism, alleviate inflammatory responses, and enhance the adaptation of rats to hypoxic environments. This study provides valuable insights into potential strategies for promoting sustainable animal health and adaptation under hypoxic conditions. Full article

Feature selection is crucial for high-dimensional sensor and biomedical data because it reduces redundancy, improves generalization, and supports interpretable biomarker discovery. In this study, we propose a Binary Chaos-Enhanced Newton–Raphson-Based Optimizer (BCNRBO) for wrapper-based feature selection. The method integrates chaotic search dynamics, a Hamming-distance-based Dynamic Potential mechanism, and a new binary transfer function to enhance exploration and prevent premature convergence. BCNRBO was evaluated on 26 benchmark datasets using a variety of classifiers, including K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers. The proposed method consistently achieved competitive or superior classification performance while selecting fewer features than competing binary metaheuristic methods. In particular, BCNRBO consistently achieved the best feature reduction performance across all classifiers and secured the top Friedman rankings for DT, NB, and SVM, demonstrating its overall effectiveness. Statistical tests confirmed significant improvements over competing methods in most pairwise comparisons. These results suggest that BCNRBO is a promising feature selection strategy for sensor-derived biomedical and neurorehabilitation data, where compact and reliable digital biomarkers are needed. Full article

Ionosphere status plays an important role in satellite communication and navigation systems. In this study, we developed an ANN model to predict the ionosphere status regarding the signal-to-noise ratio at non-line-of-sight, near-vertical incidence (NLOS-NVIS) at frequencies within the HF band. The channel sounding was performed by using two software-defined radios placed at a distance of 29 km apart. The databases regarding signal-to-noise ratio (SNR) data were collected for three ham radio bands: 30 m (10.140203 MHz), 40 m (7.040101 MHz) and 80 m (3.570101 MHz). Subsequently, each database was split into a 70% training set and a 30% testing set. In this configuration, the input vectors were represented by the exact time of day (hour and minute) at which the SNR value was predicted, which functioned as an output variable. Also, three error figures were used as indicators for predicting capability and comparing our ANN with other models. Full article

The amniotic membrane is widely recognized in regenerative medicine due to its rich content of extracellular matrix proteins and growth factors that confer anti-inflammatory and pro-regenerative properties. However, its rapid degradation restricts its standalone clinical use. To overcome these limitations, we developed biofilms by incorporating decellularized human amniotic membrane matrix (dHAM) into polycaprolactone (PCL) and polyvinylpyrrolidone (PVP) matrices using spin-coating. Bone marrow-derived mesenchymal stem cells (BM-MSCs) were used to evaluate film biocompatibility through cell viability, proliferation, and wound healing migration assays. Surface characterization was performed using contact angle measurements, Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy, and scanning electron microscopy. Soluble dHAM extracts (4–6 mg/mL) significantly enhanced BM-MSC proliferation at 48 h compared to controls (p ≤ 0.01 and p ≤ 0.0001). Both PCL-dHAM and PVP-dHAM biofilms exhibited high cell viability (>90%) and improved initial adhesion. Notably, dHAM incorporation significantly increased wound closure rates at 24 h, reaching 98.47% for PCL-dHAM and 93.13% for PVP-dHAM, compared to 76.56% and 64.20% for pure polymers (p = 0.0001). All scaffolds maintained hydrophilic surfaces (<90°), favorable for cell interaction. The integration of dHAM into PCL and PVP by spin-coating produces biofilms biocompatible with enhanced regenerative potential, representing promising candidates for wound healing applications. In conclusion, these coatings support BM-MSC adhesion, proliferation, and migration, while significantly accelerating wound closure, underscoring their value as advanced bioactive coatings for regenerative medicine. Full article

Scene understanding is a fundamental task in autonomous driving, requiring effective integration of semantic and geometric information from heterogeneous sensors. Although vision–language models (VLMs) provide powerful semantic representations, their integration with LiDAR-based geometric perception remains challenging. This paper proposes a multimodal late-fusion framework for multi-label scene classification that combines semantic embeddings extracted from camera images using a frozen CLIP (ViT-B/32) encoder with geometric features derived from LiDAR Bird’s-Eye-View (BEV) representations. To improve multimodal compatibility, modality-specific adaptation networks are employed to refine visual and geometric features before fusion. The proposed framework was evaluated on an annotated subset of the nuScenes dataset containing synchronized camera–LiDAR samples and nine scene-level labels. Experimental results show that the proposed late-fusion architecture outperforms both unimodal and early-fusion baselines, achieving a Hamming Accuracy of 0.950, a Micro-F1 score of 0.925, and a mean Average Precision (mAP) of 0.908. Additional experiments using a CLIP-based early-fusion baseline demonstrate that the observed performance gains are primarily attributable to the proposed modality-specific refinement and late-fusion strategy rather than the visual encoder alone. These findings indicate that modality-aware late fusion of pretrained semantic representations and LiDAR geometric information provides an effective and scalable solution for multimodal perception in autonomous driving. Full article

Fruit and vegetable processing by-products, such as peels and pomace, are rich in antioxidant polyphenols and represent promising sources of functional ingredients, but how their galloyl-based polyphenols interact with starch remains insufficiently understood. In this study, corilagin with three non-free galloyl moieties and 1,2,3,4,6-O-pentagalloyl glucose with five free galloyl moieties were used as model polyphenols to clarify how galloyl moiety number and accessibility modulate their complexation with high-amylose maize starch (HAMS). Size-exclusion chromatography showed that both polyphenols preferentially complexed with amylose, while FTIR confirmed that complex formation occurred mainly through non-covalent interactions. The two polyphenols induced distinct changes in HAMS structure. Corilagin disrupted short-range order and produced no detectable crystalline structure, whereas 1,2,3,4,6-O-pentagalloyl glucose enhanced molecular order and induced V-type crystallization. Isothermal titration calorimetry revealed more binding sites but weaker affinity for corilagin, with thermodynamic signatures indicating hydrogen bonding and van der Waals interactions. By contrast, 1,2,3,4,6-O-pentagalloyl glucose showed stronger affinity and hydrophobic interaction-dominated binding. Molecular dynamics simulations further confirmed that 1,2,3,4,6-O-pentagalloyl glucose formed a more stable association with the amylose helix than corilagin. These results indicate that galloyl moiety characteristics markedly influence starch–polyphenol interaction mechanisms, providing guidance for the utilization of polyphenol-rich agro-processing by-products in functional starch-based foods. Full article

Background: Treatment-resistant depression (TRD) poses a major clinical challenge, particularly when accompanied by suicidal behavior. Intranasal esketamine has demonstrated rapid antidepressant effects in TRD, but real-world comparative evidence remains limited. Methods: We conducted a six-month naturalistic prospective cohort study in two Spanish mental health centers, including 62 TRD patients with high suicide risk undergoing fourth-line treatment. Thirty patients received intranasal esketamine and thirty-two alternative pharmacological interventions. Suicidal ideation (C-SSRS), depressive symptoms (HAM-D-17) and functional status (FAST) were assessed at baseline and at 1-, 3- and 6-month follow-ups. Results: Both groups showed significant improvement during follow-up; however, esketamine-treated patients exhibited a faster and greater reduction in suicidal ideation and depressive symptoms than those receiving alternative pharmacological strategies. The number needed to treat to prevent one case of high suicide risk was 1.35. Functional improvement was comparable between groups. Conclusions: In real-world clinical settings, intranasal esketamine was associated with a faster and greater reduction in suicidal ideation and depressive symptoms among TRD patients with high suicide risk, supporting its role as a rapid-acting therapeutic option within comprehensive and closely monitored care. Full article

Background/Objectives: Thyroid dysfunction is frequently associated with mood and anxiety disorders, yet the directionality and diagnostic specificity of this relationship remain debated. Although numerous studies have examined major depressive disorder (MDD) and bipolar disorder (BD) separately, comparative data including generalized anxiety disorder (GAD) and accounting for non-thyroidal illness (NTI) effects remain scarce. This study aimed to evaluate thyroid function and its correlation with affective symptom severity across MDD, GAD, and BD in an inpatient cohort. Methods: Eighty-eight hospitalized patients with MDD, GAD, or BD were included in the study (MDD = 30, GAD = 30, BD = 28). Serum levels of TSH, FT4, and FT3 were measured 24–48 h after admission to minimize the influence of NTI. Psychiatric assessment included the Montgomery-Åsberg Depression Rating Scale (MADRS), Hamilton Anxiety Scale (HAM-A), and Young Mania Rating Scale (YMRS). Between-group differences were analyzed using ANOVA, and associations between thyroid parameters and symptom severity were examined using correlation and regression analyses. Results: ANOVA revealed that patients with MDD had significantly higher TSH levels compared with those with GAD and BD (p < 0.01). MDD patients also showed a higher prevalence of subclinical hypothyroidism (33.3%) than patients with GAD (13.3%) and BD (7.1%), as well as a higher prevalence of overt hypothyroidism (13.3%) compared with GAD (0%) and BD (7.1%). TSH levels correlated positively with MADRS scores (r = 0.45, p < 0.05) and HAM-A scores (r = 0.38, p < 0.05), particularly within the MDD group. In BD, FT4 and FT3 levels were elevated and positively correlated with YMRS scores (FT4: r = 0.30, p < 0.05; FT3: r = 0.42, p < 0.05). In regression analysis within the MDD subgroup, both hypothyroidism and male sex were independently associated with higher MADRS scores, indicating greater depressive symptom severity. Conclusions: These findings suggest diagnosis-specific patterns of thyroid dysfunction among psychiatric inpatients. Higher TSH levels and increased rates of hypothyroidism were most prominent in MDD and were associated with greater depressive and anxiety symptom severity, whereas elevated FT4 and FT3 levels in BD were associated with manic symptom severity. The results support systematic thyroid screening in depressive admissions, hormone-informed monitoring in bipolar disorder, and a more integrated endocrine–psychiatric approach to clinical care. Full article

Deep learning-based automated classification of dermatoscopic skin lesions has exhibited promising potential in diagnostics. However, two prominent issues need to be addressed before achieving high-quality diagnostic tools: inconsistent performance in the case of imbalanced classes and poor clinical interpretability of models. Even though some studies have attempted to leverage both deep and shallow learning by combining pretrained convolutional neural networks (CNNs)-based feature extraction with classical machine learning (ML) models, very few of them systematically explore several model combinations based on various clinically important metrics, such as F1-score, precision, recall, accuracy, etc., and utilize decision threshold calibration techniques. In this research, we present an evaluation of a systematic framework with threshold calibration for the comparison of several hybrid models on seven-class skin lesion classification (multi-class) on the HAM10000 dataset. In particular, we used deep features extracted from three pretrained CNN architectures, i.e., DenseNet201, InceptionV3 and EfficientNet-B4. These deep features were used as inputs for six different classical classifiers. As a result, we obtained 18 comparable hybrid models that were then systematically compared by multiple clinically relevant metrics: accuracy, macro-precision, macro-recall, macro-F1, ROC-AUC, Precision-Recall-AUC, and log loss. Also, fold-wise optimization of decision thresholds was performed, which was based on the maximization of the macro-F1 score. Finally, we found out that DenseNet201 with an SVM-RBF classifier yielded the highest performance among all 18 tested models, showing 90.88% accuracy, 90.7% macro-precision, and 0.921 ROC-AUC. To analyze the clinical plausibility, top-performing models were further explained with explainable artificial intelligence (XAI) techniques: Grad-CAM, LIME and Occlusion Sensitivity. Results show that the most successful models concentrated mostly on lesion-specific areas. Overall, this study contributes a reproducible hybrid-XAI model-selection framework rather than a single black-box classifier, supporting more transparent and clinically meaningful skin lesion diagnosis. Full article

The purpose of side-channel leakage detection is to determine whether or not there is side-channel leakage in the target cryptographic chip. The application of grouping (i.e., dividing the collected power traces into groups based on a property of the intermediate value, such as the Hamming weight of a byte or the bit value of an S-box output) in side-channel leakage detection is a research hotspot. The bit-level grouping mode and the byte-value grouping mode are proposed by previous scholars. However, the bit-level grouping mode does not match the byte operation architecture of cryptographic chips, resulting in an overly fine detection granularity and a high computational complexity. Although the byte-value grouping mode takes into account the byte operation architecture of cryptographic chips, it will cause unequal sizes of traces contained in two groups, reducing the test efficiency. In light of this, we propose the Interval-Optimized Hamming-Weight-Oriented Grouping (IHOG) Mode. IHOG groups data according to the Hamming weight (HW) of byte, dividing them into two groups with Hamming weights of {0, 1, 2, 3} and {5, 6, 7, 8}. In this way, it solves the problem of overly fine detection granularity and high computational complexity caused by bit-level grouping, and it also addresses the issue of unequal sample sizes and low test efficiency caused by the byte-value grouping mode. This paper verifies the effectiveness of the proposed IHOG method using four datasets, namely DPA v4, AES HD, Custom Dataset 1, and Custom Dataset 2. The results show that, compared with three existing grouping schemes such as HW value, bit value, and byte value, the IHOG scheme proposed in this paper increases the accuracy of leakage detection by 37.2%, 18.5%, and 146.3% respectively at the selected leakage points. Full article

Future Internet applications such as intelligent transportation, immersive services, and edge-assisted artificial intelligence require latency-sensitive service provisioning at the network edge. In containerized mobile edge computing (MEC), service orchestration is not only a task-offloading problem, but also a task–container–image constrained decision problem: an offloaded task can be executed only when the required runtime container is active, and a newly activated container must be supported by a locally cached service image. This dependency couples task placement, runtime container caching, and persistent image caching under limited RAM and ROM resources. To address this challenge, this paper proposes HAM-MADDPG, a dependency-aware hierarchical action-masked multi-agent reinforcement learning algorithm for joint task offloading and image–container caching in containerized MEC networks. HAM-MADDPG decomposes the monolithic orchestration decision into three causally ordered policy layers: task offloading, runtime container caching, and persistent image caching. Each layer learns a structured subproblem conditioned on upstream realized decisions, while dynamic action masking and feasibility-aware action realization guide the learned policies toward executable decisions satisfying task–container and container–image constraints. Extensive simulations under dynamic service demands and heterogeneous edge resources show that HAM-MADDPG achieves more stable convergence than non-hierarchical reinforcement learning baselines and reduces long-term system latency by approximately 14–25% compared with representative heuristic and flat DRL baselines. Full article

Ochratoxin A (OTA) is a toxic metabolite produced by fungi, that can be present on various food products, cereals and plant-derived (raw) feed (materials). It was demonstrated that OTA has toxic effects after consumption by both animals and humans. Therefore, the European Food Safety Authority (EFSA) concluded that contribution to human exposure of OTA from (processed) meats should not be ignored. The objective of this study was to assess the occurrence of OTA; thus, data on OTA in cured meats were collected. An in-house validated analytical method using methanol extraction, clean-up with an immunoaffinity columns and LC-MS/MS detection was applied. Quantification was done through the external calibration of standards in solvent using ¹³C₂₀ OTA internal standard, with a reporting limit of 0.2 µg/kg and LOQ of 0.04 µg/kg. A total of 50 cured meat products were obtained from Dutch supermarkets. OTA was detected at or above the reporting limit in four samples of cured ham (range 0.30 µg/kg to 79.8 µg/kg) and two samples of sausages (0.2 µg/kg and 0.41 µg/kg). Overall, OTA was detected in twenty samples, and it was concluded that OTA occurred above the LOQ in 40% of cured meats analyzed. Full article

Manual arc welding is highly dependent on operator skill, leading to variability in weld quality and an increased risk of defects; therefore, reliable monitoring methods for Shielded Metal Arc Welding (SMAW) are required, particularly in manual environments where process variability and environmental noise are inherent. This study proposes a monitoring approach for classifying SMAW process parameters using airborne acoustic signals generated by the welding arc. Welding experiments were conducted on carbon steel plates of different thicknesses (3, 6, and 12 mm) using E6010, E6011, E6013, and E7018 electrodes under Alternating Current (AC) and Direct Current (DC) configurations; acoustic signals were recorded in real time and processed using Mel-Frequency Cepstral Coefficients (MFCCs) and deep audio embeddings from pre-trained VGGish and YAMNet models as inputs to artificial neural network classifiers for multi-output classification of welding process parameters. Model performance was evaluated using per-target metrics (accuracy and macro F1-score) and joint multi-output metrics (Exact Match and Hamming Accuracy). MFCC-based models significantly outperformed embedding-based approaches, achieving up to 94.51% Exact Match and 97.88% Hamming Accuracy, while reducing computational costs. These results demonstrate the feasibility of SMAW monitoring using arc sound, suggesting that spectral features are an effective solution for welding-process monitoring and a promising foundation for future weld-quality monitoring systems. Full article