Search Results (126,807)

Background: Obesity is frequently accompanied by metabolic dysfunction and micronutrient deficiencies, yet data on individuals with severe obesity (BMI ≥ 50 kg/m²) remain limited. Understanding how vitamin and mineral status changes across the adiposity continuum may optimize personalized nutritional management in severe obesity. Methods: We analyzed anthropometric, metabolic, hepatic, and micronutrient parameters in a retrospective, single-center cohort of adults evaluated before bariatric surgery, with a BMI range of 30 to 91 kg/m² (mean 48.7 ± 8.7). Measurements included bioimpedance-derived body composition, liver elastography (CAP and stiffness), and fasting laboratory parameters, vitamins B1, B6, B12, D, and folate and minerals (iron, selenium, and magnesium). Correlation analysis, principal component analysis (PCA), and random forest (RF) classification were applied to identify the variables most strongly associated with BMI and metabolic phenotypes. Results: Several micronutrient concentrations, most notably vitamin D, folate and vitamin B6, declined progressively with increasing BMI. Vitamin D additionally correlated inversely with HbA1c. PCA revealed a separation of participants with extreme obesity driven by anthropometric and hepatic parameters, while micronutrients clustered inversely. RF classification identified HbA1c, hepatic enzymes (ALAT and γ-GT) and body-composition measures (waist circumference and visceral fat) as the strongest discriminators of T2DM, MASLD and high BMI, respectively. Combined MASLD and T2DM was associated with the most pronounced vitamin B6 depletion, whereas vitamin D was low across metabolic subgroups. Conclusions: Severe obesity is associated with distinct micronutrient depletion patterns that mirror metabolic deterioration. Integrating nutritional and metabolic profiling enables refined risk stratification and may, pending prospective validation, inform targeted supplementation strategies in individuals with severe obesity. Full article

Background/Objectives: Diabetic retinopathy is a major cause of preventable vision loss worldwide, making early and accurate disease grading crucial for timely treatment. Although convolutional neural network (CNN)- and transformer-based architectures have demonstrated promising performance for retinal image analysis, comprehensive comparisons within a unified experimental framework remain limited. This study systematically compares representative standard and lightweight CNN- and transformer-based architectures for multi-class DR grading. Methods: Six ImageNet-pretrained deep-learning models, including ResNet50, EfficientNet-B0, MobileNetV2, Vision Transformer (ViT), Swin-Tiny, and Swin Transformer, were evaluated on the APTOS 2019 retinal fundus image dataset under a unified experimental configuration with consistent preprocessing, data augmentation, training, and evaluation settings. All models were fine-tuned and evaluated independently over five runs with different random seeds. Their performance was assessed using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), Quadratic Weighted Kappa (QWK), per-class analysis, computational efficiency, and statistical analysis. Results: Transformer-based models generally achieved higher mean classification performance than the evaluated CNN-based models. Swin-Tiny achieved the highest mean accuracy (82.3%), macro F1-score (64.4%), weighted F1-score (82.1%), and QWK (89.8%) across the five runs. Among the CNN-based models, EfficientNet-B0 achieved the strongest overall classification performance, whereas MobileNetV2 provided the lowest computational complexity. The results also highlighted differences in learning behavior and computational requirements across the evaluated architectures. Repeated experiments demonstrated stable performance across different random seeds, supporting the reliability of the proposed evaluation. Conclusions: Overall, this study provides a comprehensive comparison of representative CNN- and transformer-based architectures under consistent experimental settings and offers practical guidance for selecting suitable deep learning models for automated diabetic retinopathy screening. Full article

Introduction: MRI is the gold-standard imaging modality for rectal cancer (RC) local staging, but the ability to determine tumor invasion (pT) and nodal status (pN) remains limited in clinical practice. The main objective of this study is to evaluate the performance of different machine learning (ML) models based on clinical–radiological and radiomic variables for predicting these categories from preoperative MRI. Methods: A retrospective observational study was conducted involving 152 patients with RC (70 without neoadjuvant therapy and 82 with neoadjuvant therapy). Radiomic features were extracted from high-resolution T2 sequences using two independent segmentations: tumor and tumor + mesorectum. Twenty-three ML algorithms were evaluated using cross-validation to predict pT and pN. For each combination of outcome, cohort, data source and segmentation, an optimal model was selected based on the area under the curve (AUC). Results: Models based on clinical and radiological variables showed the most consistent performance, particularly in the overall cohort, with AUCs of 0.767 for pT and 0.764 for pN. The radiomic and combined models achieved a moderate and heterogeneous performance, with maximum AUCs of 0.770 for pT and 0.732 for pN. Conclusions: The clinico-radiological variables analyzed using ML showed a predictive performance similar to that of a radiologist. Radiomics did not show significant improvement in this setting. Full article

Segmenting brain tumor subregions in multimodal MRI is difficult due to severe class imbalance and scarce annotated data, and current state-of-the-art models require 21.3–31 million parameters to reach a whole-tumor Dice of 0.906–0.921. We propose a parameter-efficient 2D encoder–decoder coupling a Fourier Neural Operator (FNO) and a Vision Transformer (ViT), trained with BraTSPipeline, which raises throughput from 257 to 16,040 slices per epoch, and two composite loss functions penalizing false negatives 2.3× more than false positives. On BraTS 2020, the 2D variant (5.6 M parameters) achieves whole-tumor (WT), tumor-core (TC), and enhancing-tumor (ET) Dice of 0.8985, 0.8263, and 0.7568 on the 56-case held-out test set, using 3.8–5.6× fewer parameters than published architectures. Encoding three consecutive axial slices as 12 channels (2.5D, 20.5 M parameters) raises WT to 0.9117, within 0.010 of the best published 2D result on BraTS 2020, Mod-R2AU-Net (WT = 0.921); a 4.6 M-parameter ablation without the ViT reaches WT of 0.9106 and TC of 0.8428, while the ViT adds 0.043 ET Dice. Full article

Metro feeder activity is commonly summarized by trip counts, although equal counts can correspond to different occupied distances and emissions. Using occupied taxi-trip records from 19 days in June 2023 at six purposively selected peripheral stations on Lanzhou Metro Line 1, this study links directional feeder activity, occupied distance, occupied feeder vehicle-kilometers traveled (VKT), and occupied-phase CO₂. Egress dominates in the evening peak and off-peak under the three entrance-buffer specifications, whereas morning balance is buffer-sensitive; Access is more spatially concentrated than Egress. Under the 150 m reference specification, 200,493 feeder trips average 5.451 km and generate 1,092,957 km of occupied VKT. Count-proportional allocation produces station-level VKT errors from −26.7% to +22.3%; five of the six station rankings change under VKT, and top-10 count and attributed-VKT grids overlap by 6.67 of 10. At the same occupied VKT, the all-battery-electric scenario yields 82.10 t of occupied-phase CO₂, 37.6% below the proxy-fleet base of 131.57 t; contextual non-occupied-VKT assumptions raise the estimate to 177.80–197.35 t. Counts and occupied VKT capture different operational dimensions. Low-carbon sustainability assessment should combine service frequency, occupied distance, and vehicle technology to support station-area carbon monitoring. Full article

Accurate characterization of pore-fluid mobility is essential for evaluating tight sandstone reservoirs. This study investigates ten tight sandstone samples from the Sangonghe Formation in the Junggar Basin using petrophysical measurements, X-ray diffraction, scanning electron microscopy, low-field nuclear magnetic resonance (NMR), and multifractal analysis. Saturated–centrifugation NMR results show that the conventional single-T₂-cutoff model cannot fully separate bound and movable fluids. A dual-cutoff framework was therefore used to classify pore fluids into totally bound, partially movable, and totally movable states. The experimentally determined T_2C1 and T_2C2 values range from 0.127 to 0.582 ms and 155.340 to 265.210 ms, respectively. An adaptive second-order difference method was further applied to the fully saturated T₂ spectrum to estimate the dual cutoffs. Within the investigated dataset, the model-derived values show strong agreement with the centrifugation-derived results, with MAPE values of 3.040% for T_2C1 and 3.820% for T_2C2. Correlation, multifractal, and grey relational analyses indicate that T_2C1 is more strongly associated with clay-mineral-related fluid retention and pore heterogeneity, whereas T_2C2 is more closely associated with porosity and permeability. These results demonstrate the potential of the proposed approach for NMR-based evaluation of fluid mobility in tight sandstone, although further validation using larger and more diverse datasets is required. Full article

This study presents a comparative in vitro evaluation of 24 natural sesquiterpene γ-lactones and selected semisynthetic derivatives against three trypanosomatid parasites responsible for neglected tropical diseases: Trypanosoma brucei brucei, Trypanosoma cruzi, and Leishmania amazonensis. The compounds were screened for antiparasitic activity across different parasite life stages, and their cytotoxicity was assessed in mammalian cells. Several compounds demonstrated high inhibitory activity (>95%) at 10 µg/mL, including estafiatin (1), grossheimin (4), cynaropicrin (5), argolide (2) derivatives, and arglabin (23). However, a marked decrease in activity was observed at lower concentrations and in intracellular parasite models, particularly for T. cruzi and L. amazonensis amastigotes. Among the tested compounds, dihydroargolide (3) and pulchellin C (19) exhibited comparatively lower cytotoxicity and were further evaluated, yielding IC₅₀ values of 10.51 ± 1.72 µg/mL and 2.51 ± 1.13 µg/mL against T. b. brucei, respectively. Against L. amazonensis promastigotes, estafiatin (1), 8α-chloroacetoxygrossheimin (12), and arglabin (23) showed IC₅₀ values of 0.16 ± 0.13, 0.35 ± 0.18, and 0.58 ± 0.11 µg/mL, respectively, although their selectivity indices remained limited. Comparative analysis of the dataset indicates that specific structural elements, such as the α-methylene-γ-lactone moiety and selected substituents (e.g., epoxy and acetyl groups), may influence antiparasitic activity in a species-dependent manner. At the same time, the generally low selectivity indices and reduced activity in intracellular models highlight important limitations of these compounds as direct drug leads. Overall, this work provides a systematic comparative dataset and identifies preliminary structure–activity trends that may guide further chemical optimization of sesquiterpene γ-lactones as antiparasitic hit compounds. Full article

Background/Objectives: Morphological signs of non-carious tooth surface loss are clinically heterogeneous, and their prevalence depends strongly on the diagnostic framework and threshold used. This exploratory secondary analysis evaluated a study-specific combined morphological outcome restricted to second permanent molars and its association with cumulative caries experience in a regional clinical sample of adolescents. Methods: The analysis included 231 adolescents aged 13–19 years from Dolj, Gorj, and Olt counties, selected from a parent clinical database of 638 participants. The study-specific outcome was coded when a clearly discernible non-carious hard-tissue change compatible with an abrasive, erosive, or abfraction-related morphology was identified on any clinically accessible surface of tooth 17, 27, 37, or 47. This study did not use BEWE, the Smith and Knight Tooth Wear Index, or another validated severity-based tooth wear index; subtype, surface, and severity were not retained separately in the analytical dataset. Dental caries experience was measured using DMF-T. Group comparisons, correlations, and multivariable logistic regression were performed in JASP version 0.96.0. Sensitivity analyses included a participant-level coding audit and a logistic model using DMF-T recalculated after excluding the contribution of teeth 17, 27, 37, and 47. Results: The study-specific morphological outcome was present in 50/231 participants (21.6%). A coding audit confirmed that 181 participants had no positive second molar and 50 had exactly one positive second molar; tooth-specific counts were 14, 15, 12, and 9 for teeth 17, 27, 37, and 47, respectively. In the primary model, male sex (adjusted OR = 3.34, 95% CI 1.67–6.71) and DMF-T (adjusted OR = 1.15 per point, 95% CI 1.01–1.31) were associated with the outcome. After excluding the four evaluated second molars from DMF-T, the caries experience association remained positive but borderline (OR = 1.198, 95% CI 1.000–1.435, p = 0.049), while male sex remained associated (OR = 3.310, 95% CI 1.647–6.652, p < 0.001). Conclusions: A modest exploratory association was observed between the study-specific morphological outcome and cumulative caries experience in this clinical sample. The result should not be interpreted as a standardized NCDL prevalence estimate, a causal relationship, or a prediction model. Confirmation using validated whole-mouth tooth wear measures, outcome-specific reliability assessment, and population-based longitudinal data is required. Full article

Land disturbance and ecological degradation are widespread in abandoned mining areas, and their redevelopment is shaped by multiple factors. Identifying construction suitability patterns and key influencing factors can provide a quantitative basis for the reuse of these areas. Focusing on an abandoned mining area in Luhe District, Nanjing, this study selected 16 influencing factors. K-means clustering and a self-organizing map (SOM) were used to identify construction suitability types. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were used to visualize the cluster structure. Random forest (RF) and SHapley Additive exPlanations (SHAP) were then combined to analyze the overall contributions and interaction patterns of the individual factors. The results showed the following: (1) The study area was classified into five construction suitability types with no inherent ranking: the Soil-Location Suitability Type, Entrance Accessibility–Public Service Suitability Type, Vegetation Conservation Suitability Type, Terrain-Location Suitability Type, and Ecological Restoration Suitability Type. Among them, the Entrance Accessibility–Public Service Suitability Type and Vegetation Conservation Suitability Type were the most widely distributed. (2) The influence of the central urban area made the greatest overall contribution, followed by soil texture and vegetation coverage. Vegetation coverage had the strongest effect on the model’s ability to reproduce the cluster-derived classes under spatial validation. Vegetation diversity, entrance accessibility, soil texture, and pollution level also showed high permutation importance. (3) Factor contributions were type-specific, with soil, location, accessibility, vegetation, and pollution conditions showing distinct contribution and interaction patterns across the five types. Differentiated strategies should therefore be tailored to each type, including location and service optimization, vegetation protection and low-disturbance use, terrain management, and pollution control and ecological restoration. Full article

Background: This study examines the relationships among resource reduction awareness (RRA), AIoT-enabled Cold Chain System (CCS) implementation, supply–demand control (SDC), perceived operational performance (OPF), and ISO certification from a Lean Logistics perspective. Method: Data were collected from 250 Korean agricultural and marine product firms. PLS-SEM was used to examine the hypothesized relationships and indirect associations, while MICOM and multi-group analysis assessed differences between firms holding both ISO 9001 and ISO 14001 certifications and other firms. Result: RRA was positively associated with ACI and OPF. ACI was positively associated with SDC and OPF, and SDC was positively related to OPF. The indirect association between ACI and OPF through SDC was also significant. The ACI–OPF relationship differed significantly across ISO certification groups and was stronger in the high-certification group. Conclusions: The findings suggest that AIoT-enabled cold chain implementation is associated with perceived operational performance directly and through supply–demand control, while standardized management practices may be relevant to the ACI–OPF relationship. This study therefore clarifies organizational and operational conditions associated with digital cold chain practices and provides implications for research. Full article

Tool–prepreg friction contributes to process-induced residual stress and distortion in thermoset composite manufacturing, but its variation with the resin cure state remains insufficiently characterized. This study experimentally characterizes the friction response of untreated and post-gel pretreated T800/bismaleimide prepregs using a pull-out apparatus. The effects of slip velocity, temperature, and normal pressure are evaluated, and the measured trends are interpreted using Coulomb-type contact, viscous film shearing, and mixed-lubrication concepts. The friction coefficient of the untreated prepreg increases with the slip velocity and decreases with temperature and pressure, indicating a substantial contribution from viscous resin-film shearing. In contrast, the post-gel pretreated prepreg is nearly insensitive to the slip velocity and pressure, while its friction coefficient increases with temperature, consistent with a predominantly solid-like interfacial response. During a cure cycle, the friction increases slowly at low cure levels and more rapidly during the later stages of curing. The results provide experimental friction data for a high-temperature bismaleimide prepreg system and suggest that the gelation state may serve as a useful reference for distinguishing early- and later-stage interfacial behavior. Full article

Background: Unilateral hearing loss (UHL) is associated with speech-in-noise difficulties, impaired spatial hearing, and reduced social participation, yet conventional audiological assessments may not capture these real-world consequences. This study characterised the multidimensional functional impact of UHL using an ecologically oriented test battery and explored whether accessible behavioural and self-reported measures accounted for individual differences in speech-in-noise comprehension. Methods: Fifteen adults with moderate to profound sensorineural UHL and sixteen adults with normal hearing completed standardised questionnaires assessing hearing disability and social participation, an auditory reaction-time task in quiet and noise, the National Acoustic Laboratories Dynamic Conversations Test, head-orientation tracking, and post-task self-reports. Group differences were assessed using mixed-effects models and independent-samples t-tests. An exploratory regression examined speech-in-noise performance within the UHL group. Results: Participants with UHL reported greater hearing difficulties and social participation restrictions, and showed modestly poorer speech-in-noise comprehension although the UHL–NH difference in speech-in-noise comprehension was no longer statistically significant after adjustment for age. Reaction times were significantly longer in noise than in quiet but did not differ significantly between hearing groups. The UHL group also showed a significantly more pronounced head orientation towards the poorer-hearing side and greater within-participant head-orientation variability. An exploratory model combining reaction time in noise with three post-task self-report items showed strong apparent in-sample fit (adjusted $R^2$ = 0.71). However, internal cross-validation of the complete model-selection procedure yielded substantially lower predictive performance ($R_{\mathrm{CV}}^2$ = 0.19). Conclusions: UHL is associated with differences across complementary perceptual, behavioural, and self-reported dimensions of everyday communication. Multimodal functional assessment may provide information beyond conventional audiometry, although the exploratory model requires validation in larger, age-matched independent cohorts. Full article

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks. Full article

Multi-objective optimization plays an important role in modern design and complex engineering applications. However, achieving an effective balance between the convergence and diversity of Pareto-optimal solutions remains challenging. This paper proposes a Sobol-driven Multi-objective Whale Migration Algorithm (SMOWMA), which extends the Whale Migration Algorithm within a non-dominated sorting and elite-selection framework. A maximin scrambled Sobol initialization scheme is first employed to improve the distribution of the initial population. An archive-guided adaptive Student-t flight mechanism is then incorporated into the leader-whale position update to dynamically balance global exploration and local exploitation. In addition, archive crowding information and archive-entry success feedback are jointly used to adjust the search behavior according to both environmental diversity and recent search performance. SMOWMA is evaluated on five widely used multi-objective benchmark suites, namely ZDT, DTLZ, WFG, UF, and CF, using four performance indicators: generational distance (GD), inverted generational distance (IGD), spacing (SP), and hypervolume (HV). The results, together with Friedman tests and Holm-adjusted Wilcoxon tests, demonstrate that SMOWMA achieves competitive overall performance in terms of convergence, diversity, and objective-space coverage, although its relative advantage remains problem-dependent. The practical applicability of SMOWMA is further examined using multi-objective welded-beam design formulations, a bi-objective four-bar truss design problem, and a five-objective car side-impact design problem. The engineering results show that SMOWMA can obtain competitive and stable approximation sets for constrained design problems with different numbers of objectives, supporting its effectiveness and applicability in multi-objective engineering optimization. Full article

Background/Objectives: Class II malocclusion in growing patients may be associated with altered mandibular posture, occlusal instability, and adaptive changes in masticatory muscle function. This prospective, controlled, non-randomized longitudinal study evaluated changes in masseter and anterior temporalis muscle activity during Andresen functional appliance therapy and compared them with untreated Class I and Class II controls. Methods: Seventy children with skeletal and dental Class II malocclusion were treated with a customized Andresen appliance; 50 untreated Class II children and 30 untreated children with physiological Class I occlusion served as comparison groups. All participants were in mixed dentition and at cervical stage CS3 at baseline. Standardized bilateral sEMG recordings were obtained at T0, T1 (6 months), and T2 (12 months). Longitudinal sEMG outcomes were analyzed using mixed-design repeated-measures ANOVA, with time as the within-subject factor and group as the between-subject factor; prespecified T0-to-T2 contrasts were accompanied by Cohen’s d effect sizes. Results: In the treated Class II cohort, IMPACT increased from 108.64 ± 3.40 at T0 to 111.58 ± 3.13 at T1 and 113.82 ± 3.27 at T2 (within-group time effect p < 0.001). ATTIV changed from −0.88 ± 0.42 at T0 to +0.96 ± 0.55 at T1 and +1.65 ± 0.78 at T2 (p < 0.001). In contrast, neither the untreated Class II nor the physiological Class I group showed statistically significant within-group time effects for IMPACT (p = 0.96 and p = 0.58, respectively) or ATTIV (p = 0.78 and p = 0.93, respectively). Significant T0-to-T2 differences between treated and untreated Class II groups were observed for ATTIV (p < 0.001; d = +5.28) and IMPACT (p < 0.001; d = +2.00). Conclusions: Andresen therapy was associated with statistically supported longitudinal differences in masticatory muscle recruitment compared with untreated Class II controls. The observed pattern may reflect treatment-associated neuromuscular adaptation; however, causal attribution remains limited by the non-randomized design, and the sEMG changes should be interpreted as modifications in muscle recruitment rather than as evidence of physiological normalization or clinical improvement. Full article