Search Results (3,893)

Background: Veterinarians are pivotal to wildlife health surveillance and game-meat safety, yet these topics receive limited emphasis in many veterinary curricula. Understanding students’ perceptions can inform targeted educational improvements. Methods: We conducted a cross-sectional, anonymous online survey among students enrolled in the Integrated Master’s in Veterinary Medicine at ICBAS-UP (Portugal). The questionnaire covered sociodemographic, meat and game-meat consumption, perceived appeal of working with game animals, and multi-select views on hunting, game-meat consumption and the veterinary role. We computed descriptive statistics and tested associations between categorical variables using Pearson’s Chi-square or Fisher’s exact tests (Monte Carlo correction when appropriate). Results: Of the 391 eligible students, 152 responded (39%). The majority (76%) associated hunting with veterinary inspection of game meat and research in epidemiology and emerging diseases, and many (72%) recognized as core roles monitoring the health of game animals and the contribution to public health and environmental sustainability. Significant associations included: prior game-meat consumption with finding game animals appealing/interest in learning more; year of enrolment with recognising hunting as an economic activity and acknowledging veterinary inspection and public health contributions; and perceiving game animals as appealing with associating hunting with population control (all p < 0.05; Cramer’s V indicating weak–moderate effects). Conclusions: Students show awareness of veterinary roles in game-animal health and meat inspection, but interest in working with game animals is low and knowledge gaps persist (e.g., inspection of game meat). Findings support curricular integration of wildlife health, game-meat inspection and One Health. Multicenter studies and evaluation of educational interventions are warranted. Full article

This paper addresses the problem of improving the robustness of image steganographic methods under lossy compression while preserving high perceptual quality and low computational complexity. The paper proposes an adaptive code-controlled steganographic method that enables spectrally selective embedding in the spatial domain through structured codewords. The proposed approach introduces block-level adaptivity in which the energy of the embedding codeword is dynamically selected according to the robustness characteristics of each image block. Instead of applying uniform embedding strength, the method determines the minimal codeword energy required to guarantee reliable message extraction under a predefined worst-case JPEG compression level. Experimental evaluation demonstrates that the proposed adaptive strategy significantly improves robustness to compression attacks while preserving high perceptual reliability and strong resistance to statistical steganalysis techniques. In particular, for JPEG quality factor (QF) = 50, the bit error rate is reduced to 1.25% while a high perceptual quality of 52.07 dB peak signal-to-noise ratio (PSNR) is achieved. For stronger attack conditions, QF = 20, the method achieves 6.6% bit errors with a PSNR of 47.7 dB. Overall, the proposed adaptive energy selection provides up to 22.68% fewer errors or up to 6.05 dB higher PSNR compared to the classical code-controlled steganographic method, confirming its effectiveness for practical steganographic applications. Full article

With the proliferation of edge computing in IoT and smart security, there is a growing demand for large-scale encrypted traffic anomaly detection. However, the opaque nature of encrypted traffic makes it difficult for traditional detection methods to balance efficiency and accuracy. To address this challenge, this paper proposes FMTF, a Multi-Scale Feature Fusion method based on Federated Learning for encrypted traffic anomaly detection. FMTF constructs graph structures at three scales—spatial, statistical, and content—to comprehensively characterize traffic features. At the spatial scale, communication graphs are constructed based on host-to-host IP interactions, where each node represents the IP address of a host and edges capture the communication relationships between them. The statistical scale builds traffic statistic graphs based on interactions between port numbers, with nodes representing individual ports and edge weights corresponding to the lengths of transmitted packets. At the content scale, byte-level traffic graphs are generated, where nodes represent pairs of bytes extracted from the traffic data, and edges are weighted using pointwise mutual information (PMI) to reflect the statistical association between byte occurrences. To extract and fuse these multi-scale features, FMTF employs the Graph Attention Network (GAT), enhancing the model’s traffic representation capability. Furthermore, to reduce raw-data exposure in distributed edge environments, FMTF integrates a federated learning framework. In this framework, edge devices train models locally based on their multi-scale traffic features and periodically share model parameters with a central server for aggregation, thereby optimizing the global model without exposing raw data. Experimental results demonstrate that FMTF maintains efficient and accurate anomaly detection performance even under limited computing resources, offering a practical and effective solution for encrypted traffic identification and network security protection in edge computing environments. Full article

This study introduces a deterministic fractional-penalty refinement of Vogel’s Approximation Method (VAM) for generating high-quality initial basic feasible solutions (IBFS) in classical transportation problems. Unlike the traditional additive regret measure employed in VAM, the proposed method uses a multiplicative contrast ratio between the two smallest admissible costs in each row and column. This modification preserves the allocation structure of VAM while introducing scale-invariant prioritization that improves sensitivity to relative cost differences.The method was evaluated on thirty-four benchmark transportation problems drawn from the literature and self-constructed large-scale instances (up to $10\times 20$). Performance was assessed using percentage optimality gaps relative to optimal solutions obtained via the Stepping–Stone and MODI procedures. Across all instances, the proposed approach achieved a mean optimality gap of $2.78\%$, compared to $5.22\%$ for classical VAM, $14.97\%$ for the Least Cost Method (LCM), and $45.78\%$ for the Northwest Corner Method (NWCM). Dispersion of deviations was also reduced, indicating improved robustness across heterogeneous cost structures Statistical validation confirms the improvement over VAM: the paired t-test yielded $t=-3.17$ ($p=0.00163$, one-sided), and the Wilcoxon signed-rank test produced $p=6.10\times {10}^{-5}$. Computational experiments further show that the refinement does not increase runtime relative to classical IBFS procedures.The proposed method therefore constitutes a structured enhancement of VAM that improves initial solution quality while maintaining computational simplicity. Full article

Background/Objectives: Sex estimation from craniofacial morphology is a fundamental component of biological profile construction in forensic anthropology. Population-specific reference data for Thai individuals derived from computed tomography (CT) remain limited, and direct comparisons between discriminant function analysis (DFA) and machine learning classifiers are frequently complicated by inconsistent validation protocols. This study aimed to characterize sexual dimorphism in CT-derived craniofacial measurements, compare the classification performance of DFA, support vector machine (SVM), and random forest (RF) under a unified validation protocol, and demonstrate their practical application in a forensic context. Methods: CT images from 300 Thai adults (150 males, 150 females; age range 20–90 years) were obtained from Srinagarind Hospital, Khon Kaen University. Eight linear craniofacial measurements spanning the cranial vault, facial skeleton, nasal aperture, and orbital region were obtained from each case. DFA, SVM, and RF were developed and compared under a unified leave-one-out cross-validation protocol. Classification performance was assessed using accuracy, AUC, and Matthews correlation coefficient (MCC). Results: Seven of eight measurements exhibited statistically significant sexual dimorphism, with facial breadth and nasal height demonstrating the greatest dimorphism. DFA achieved the highest classification accuracy of 85.7%, AUC of 0.924, and MCC of 0.713, incorporating five measurements into the canonical function. SVM and RF achieved comparable accuracy of 84.7% and 84.0%, respectively. All three classifiers correctly classified both forensic application cases with high confidence. Conclusions: CT-derived craniofacial measurements provide a reliable basis for sex estimation in Thai adults. The convergence of performance across all three classifiers under a unified internal validation protocol strengthens confidence in the internally validated performance estimates. The derived discriminant function equation and saved machine learning models constitute a complementary and immediately applicable toolkit for CT-based forensic sex estimation in the Thai population. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Background/Objectives: Tuberculosis remains one of the preventable causes of mortality among liver transplant recipients. The prevalence of tuberculosis in solid organ transplant recipients is higher than in the general population. The aim of this study was to evaluate the incidence of latent tuberculosis infection (LTBI) and active tuberculosis after liver transplantation. Methods: This is a retrospective, single-center, case–control study. Adult liver transplant candidates who were evaluated between 1 January 2016 and 31 December 2022 were retrospectively assessed. Patients with pre-transplant tuberculin skin test (TST) and/or interferon-gamma release assay (IGRA) results who underwent transplantation were included in this study. Results: A total of 111 liver transplant recipients with available IGRA and/or TST results were included; 70 were men (63.1%) and 41 were women (36.9%), with a mean age of 53.5 ± 11.3 years. Demographic, clinical, and laboratory characteristics were evaluated. The most common indication for liver transplantation was viral hepatitis (33.3%), followed by cryptogenic cirrhosis (19.8%) and hepatocellular carcinoma (10.8%). All patients had a Bacillus Calmette–Guérin (BCG) vaccination scar. Ten patients received grafts from deceased donors, while 101 underwent living-donor liver transplantation. No patient received LTBI treatment before transplantation, whereas LTBI treatment was initiated in four patients after transplantation. None of the patients had a diagnosis of active tuberculosis prior to transplantation. Thoracic computed tomography revealed findings compatible with tuberculosis sequelae in 11 patients (9.9%). During a median follow-up period of 49 [27–64] months after transplantation, no cases of active tuberculosis were observed among patients with positive TST and/or IGRA results. Patients were divided into two groups according to their TST and IGRA results. Group 1 consisted of patients with IGRA positivity and/or a TST ≥ 5 mm, while Group 2 included patients with a TST < 5 mm and negative IGRA results. The only statistically significant difference between the groups was the administration of LTBI treatment (p = 0.027); four patients in Group 1 received LTBI therapy. None of these patients were able to continue prophylaxis due to treatment-related adverse effects. Conclusions: Prophylaxis with hepatotoxic agents poses a substantial risk in liver transplant candidates. Since the hepatotoxicity may cause early cessation of LTBI treatment, the risk–benefit ratio of post-transplant LTBI therapy should be carefully assessed. In situations where LTBI treatment is deferred, close clinical monitoring is strongly recommended. Full article

Background: This study aims to evaluate the prevalence, spectrum, and coexistence of anatomical variations in the major branches of the abdominal aorta using Multidetector Computed Tomography (MDCT) angiography, with a specific emphasis on analyzing sex-related differences in a large-scale cohort. Methods: A retrospective analysis was conducted on 1174 patients (63.8% male, 36.2% female; mean age 60.54) who underwent abdominal CT angiography between January 2023 and June 2024. Images were acquired using a 128-slice MDCT scanner and reconstructed for detailed vascular assessment. Statistical comparisons between genders were performed using Chi-square and Fisher–Freeman–Halton tests, with p < 0.05 considered significant. Results: The celiac trunk (93.3%), superior mesenteric artery (SMA) (97.1%), and inferior mesenteric artery (IMA) (98.5%) predominantly showed classical patterns. However, significant sex-related differences were identified. Females exhibited significantly higher rates of classical patterns for the celiac trunk (96.2% vs. 91.7%), IMA (99.1% vs. 98.1%), right hepatic artery (RHA) (91.5% vs. 82.6%), and left hepatic artery (LHA) (95.8% vs. 85.4%). Conversely, males showed a higher prevalence of complex variations, including replaced/accessory hepatic arteries and the absence of the common hepatic artery. The number of right and left renal arteries was similar between sexes and did not show a significant difference, while horseshoe kidney was detected only in males. Conclusions: Abdominal vascular structures adhere to classical anatomy more frequently in females, while males exhibit greater morphological variability. These findings emphasize the necessity of gender-specific preoperative vascular mapping to optimize surgical outcomes and reduce morbidity. Full article

The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely recognized Permutation Flowshop Scheduling Problem (PFSP) with the makespan criterion as the optimization target. Our study aims to assess the effectiveness and robustness of this cutting-edge metaheuristic through computational experiments and statistical analysis. The proposed SSA is a hybrid variant that incorporates the Variable Neighborhood Search (VNS) algorithm along with a Path Relinking Strategy. The effectiveness of the proposed method is evaluated through computational experiments on PFSP benchmark instances. The performance of the hybrid SSA is compared against several well-established swarm-intelligence metaheuristics, namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Tuna Swarm Optimization Algorithm (TSO), Particle Swarm Optimization Algorithm (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and the Artificial Bee Colony (ABC). To ensure fair comparison, all methods are implemented within the same computational framework as the hybrid SSA. The experimental results show that the proposed hybrid SSA achieves the lowest average mean error compared with the competing methods in solving the PFSP. The results were further validated through a comprehensive non-parametric statistical analysis using Friedman, Aligned Friedman, and Quade tests, followed by post-hoc analysis with p-adjusted values, as well as Kruskal–Wallis and Wilcoxon post-hoc tests. Full article

Peripheral facial palsy (PFP) causes pronounced facial asymmetry and functional impairment, highlighting the need for reliable, objective assessment. This study presents a novel, fully automated, reference-free method for quantifying facial symmetry using artificial intelligence (AI)-based facial landmark detection. A total of 405 datasets from 198 PFP patients were analyzed, each including nine standardized facial expressions covering both resting and dynamic movements. AI detected 478 landmarks per image, from which 225 paired landmarks were used to compute local asymmetry angles. Systematic evaluation identified 91 highly informative landmark pairs, primarily around the eyes, nose and mouth, which simplified the analysis and enhanced discriminatory power, while also enabling region-specific assessment of asymmetry. Statistical evaluation included Kruskal–Wallis H-tests across clinical scores and Spearman correlations, showing moderate to strong associations (0.32–0.73, p < 0.001). The fully automated pipeline produced reproducible results and demonstrated robustness to head rotation. Intuitive full-face angle maps allowed direct assessment of asymmetry without a reference image. This AI-driven approach provides a robust, objective, and visually interpretable framework for clinical monitoring, severity classification, and treatment evaluation in PFP, combining quantitative precision with practical applicability. Full article

Background/Objectives: Transarterial chemoembolization (TACE) is included in international guidelines for the treatment of hepatocellular carcinoma (HCC), but it is still not a standardized intervention in terms of vector and chemotherapy. This study aims to report on clinical and radiological outcomes of degradable starch microspheres TACE (DSM-TACE) with idarubicin and compare with DSM-TACE with an epirubicin protocol after a single session. Methods: This is a single-center retrospective study analyzing cirrhotic patients affected by HCC in early or intermediate stages. Primary objectives were to assess the safety and efficacy of a single DSM-TACE with 10 mg idarubicin in terms of adverse event (AE) occurrences evaluated via the CTCAE 5.0 system and mRECIST criteria with computed tomography (CT) at 3 months. The secondary purpose was to compare the procedural outcomes with those from patients treated with DSM-TACE with 50 mg epirubicin. Results: Thirty-seven patients were included, 19 treated with idarubicin (IDA group) and 18 with epirubicin (EPI group); demographic data and lesion characteristics were comparable. No major AE (grade ≥ 3) occurred overall. In the IDA group, the minor AE incidence was 52.7%: one patient presented with mild ascites, eight developed hyperbilirubinemia and one leucopenia. At the 3-month CT follow-up, mRECIST criteria reported an overall response rate (ORR) of 63.2% and a disease control rate (DCR) of 84.2%. No statistically significant differences were appreciable comparing both AE occurrence and mRECIST findings with the EPI group (50% minor AE, 77.8% ORR, 88.9% DCR). Conclusions: In this sample of cirrhotic patients with HCC, DSM-TACE with 10 mg idarubicin was safe and effective comparable to DSM-TACE with 50 mg epirubicin. Full article

The paper introduces a computationally efficient semantic-aware route planning framework for indoor mobile robots, designed for real-time execution on resource-constrained edge hardware (Raspberry Pi 5, CPU-only). The proposed architecture fuses monocular object detection with 2D LiDAR-based range estimation and integrates the resulting semantic annotations into the Nav2 Route Server for penalty-weighted route selection. Object localization in the map frame is achieved through the Angular Sector Fusion (ASF) pipeline, a deterministic geometric method requiring no parameter tuning. The ASF projects YOLO bounding boxes onto LiDAR angular sectors and estimates the object range using a 25th-percentile distance statistic, providing robustness to sparse returns and partial occlusions. All intrinsic and extrinsic sensor parameters are resolved at runtime via ROS 2 topic introspection and the URDF transform tree, enabling platform-agnostic deployment. Detected entities are classified according to mobility semantics (dynamic, static, and minor) and persistently encoded in a GeoJSON-based semantic map, with these annotations subsequently propagated to navigation graph edges as additive penalties and velocity constraints. Route computation is performed by the Nav2 Route Server through the minimization of a composite cost functional combining geometric path length with semantic penalties. A reactive replanning module monitors semantic cost updates during execution and triggers route invalidation and re-computation when threshold violations occur. Experimental evaluation over 115 navigation segments (legs) on three heterogeneous robotic platforms (two single-board RPi5 configurations and one dual-board setup with inference offloading) yielded an overall success rate of 97% (baseline: 100%, adaptive: 94%), with 42 replanning events observed in 57% of adaptive trials. Navigation time distributions exhibited statistically significant departures from normality (Shapiro–Wilk, p < 0.005). While central tendency differences between the baseline and adaptive modes were not significant (Mann–Whitney U, p = 0.157), the adaptive planner reduced temporal variance substantially (σ = 11.0 s vs. 31.1 s; Levene’s test W = 3.14, p = 0.082), primarily by mitigating AMCL recovery-induced outliers. On-device YOLO26n inference, executed via the NCNN backend, achieved 5.5 ± 0.7 FPS (167 ± 21 ms latency), and distributed inference reduced the average system CPU load from 85% to 48%. The study further reports deployment-level observations relevant to the Nav2 ecosystem, including GeoJSON metadata persistence constraints, graph discontinuity (“path-gap”) artifacts, and practical Route Server configuration patterns for semantic cost integration. Full article

Glass artworks represent a significant component of cultural heritage, yet their surfaces are highly vulnerable to physicochemical weathering resulting from composition-dependent interactions with environmental factors. Understanding the complex and nonlinear relationships between glass composition and deterioration remains challenging using conventional, often invasive, analytical techniques. To address this issue, this study proposes an interpretable and non-destructive computational framework to analyze weathering patterns in historical glass based on oxide composition data. The framework combines statistical hypothesis testing (Chi-squared analysis), metric-based machine learning (Prototypical Networks), probabilistic modeling (Gaussian Mixture Models), multivariate statistical analysis (orthogonal partial least squares discriminant analysis), and information-theoretic methods (mutual information analysis) to identify key compositional features and inter-elemental relationships associated with surface degradation. The results show that lead-barium glass exhibits a higher susceptibility to weathering compared with high-potassium glass, with PbO, BaO, and SiO₂ identified as the most discriminative components. The Prototypical Network achieved 100% accuracy on most specific data partitions within the analyzed dataset, demonstrating its effectiveness in small-sample compositional classification. Meanwhile, mutual information network analysis revealed the complex interrelationships among chemical components involved in surface weathering behavior. These findings indicate that interpretable machine learning and statistical modeling can provide meaningful insights into composition-dependent patterns and support reproducible analysis for the sustainable conservation of cultural heritage glass. Full article

Background: Gait analysis offers a comprehensive assessment of locomotion and postural control, which are often altered in individuals with spinal deformities. After validating a stereophotogrammetric protocol for whole-body kinematics, including spinal motion in healthy subjects, its application to clinical populations is needed to assess its clinical relevance. Patients treated with spinal arthrodesis for scoliosis may show reduced trunk mobility and compensatory gait strategies. Methods: The validated spinal protocol was applied to 10 patients with scoliosis who underwent arthrodesis and 5 healthy controls. For each participant, the range of motion (ROM) of the upper thoracic, lower thoracic, and lumbar districts was computed. Group differences were assessed with the Mann–Whitney U test, and time-normalized angular curves were compared using Statistical Parametric Mapping (SPM1d). Results: In the pathological group, the protocol showed moderate-to-excellent intra- and inter-operator reliability (ICC > 0.594). Compared with controls, patients exhibited a significant reduction in ROM in fused or adjacent districts. SPM analysis identified altered upper thoracic flexion–extension patterns, particularly relative to the lower thoracic segment, throughout the gait cycle. Conclusions: The protocol demonstrated preliminary feasibility and sensitivity in identifying segmental and phase-dependent changes in spinal motion after arthrodesis, indicating that it may serve as a useful tool for exploratory postoperative gait evaluation. Full article

This review paper synthesizes the application and evolution of environmentally extended input–output (EEIO) analysis in agricultural research, drawing on 647 publications (Scopus and Web of Science, 1978–2025) following the PRISMA method and using the Bibliometrix package in the R statistical computing environment. EEIO has become a leading method for assessing system-level environmental impacts by quantifying direct and indirect flows across complete supply chains. Bibliometric and thematic analyses reveal accelerated growth since 2015 and four principal domains of enquiry: emissions embodied in trade, water-resource management, energy and climate impacts, and the sustainability of agri-food supply chains. EEIO’s principal value lies in its capacity to support production- versus consumption-based accounting and to reveal intersectoral trade-offs that single-sector approaches overlook. However, standard EEIO frameworks remain constrained by fixed technical coefficients, coarse sectoral aggregation, and uncertainty in environmental extensions, which limit their capacity to resolve farm-scale processes, structural change, and feedbacks. To enhance analytical rigor and policy relevance, we advocate hybridization with life-cycle and farm-level data, development of higher-resolution multi-regional EEIO tables, incorporation of stochastic and scenario analyses, dynamic formulations to capture technological change, and adoption of open-data standards with transparent reporting. Advancing these priorities will improve comparability, reproducibility and the practical uptake of EEIO for evidence-based transitions in agricultural systems. Full article