Search Results (2,556)

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

Background: Predicting atrial fibrillation (AF) recurrence remains a major clinical challenge, as conventional echocardiographic parameters often fail to capture the complex electro-mechanical substrate of the arrhythmia. The prognostic significance of right ventricular (RV) function and atrial mechanical–structural coupling in paroxysmal AF (PAF) remains underexplored. Methods: We prospectively enrolled patients with PAF in sinus rhythm undergoing comprehensive echocardiography. A wide range of conventional left-sided, right-sided, and novel coupling indices was assessed. Univariable analysis was performed to screen for potential AF recurrence predictors. Based on the initial findings, receiver operating characteristic (ROC) analysis was used to determine the optimal cutoff for RV fractional area change (RV FAC). Finally, multivariable logistic regression identified independent predictors of AF recurrence over a 12-month follow-up. Results: A total of 73 patients were included, of whom 31 (42.5%) experienced AF recurrence during 12-month follow-up. Conventional left atrial (LA) indices, including LA volume index (LAVI) and reservoir strain, showed no significant association with recurrence. In univariable analysis, RV FAC, LA contraction strain, and the novel LA contraction strain/LAVI ratio were all significant predictors. ROC analysis identified an RV FAC cutoff of 42.5%, with lower values associated with significantly higher recurrence rates. In multivariable analysis, lower RV systolic performance determined by RV FAC ≤ 42.5% emerged as a primary independent predictor of recurrence (p = 0.003), while the LA contraction strain/LAVI ratio demonstrated a strong trend towards significance (p = 0.076). Conclusions: In this exploratory study of patients with PAF, atrial mechanical–structural mismatch emerged as a primary marker of the arrhythmic substrate. Additionally, an exploratory signal suggested that a subclinical reduction in RV performance may also correlate with recurrence, though this warrants further investigation in larger cohorts. Full article

Accurate prediction of truck stop behavior is essential for freight transportation analysis and logistics management. However, many existing methods neglect the operational decision logic that governs the transport activities. For example, they treat stop location and stop duration as separate prediction targets, or they rely on uniform sequence modeling architectures that cannot adequately represent multi-scale temporal patterns or freight-specific operational semantics. To overcome these challenges, this paper introduces DTAE-CCP, a decision-aligned framework for truck stop behavior prediction that embeds freight operational logic into the representation and sequential prediction process through domain-aware truck activity encoding and structured sequential prediction. The framework uses a decoupled truck activity encoder that integrates heterogeneous temporal features and periodic operational patterns to characterize both long-term behavioral regularities and short-term driving dynamics, alongside a causal cascade prediction architecture that explicitly models the sequential dependence from driving time to stop location and then to stop duration while ensuring spatial feasibility. Experiments on large-scale real-world freight trajectory datasets show that the proposed method achieves the best observed performance among the compared representative baselines across the reported evaluation metrics, and ablation plus sensitivity studies indicate that aligning the architecture with freight decision logic, reinforced by domain-specific representation learning, is the primary contributor to the performance gains. Full article

Aim: This study aimed to detect the maxillary dental midline region on orthodontic frontal photographs using a YOLOv8-based deep learning approach and to evaluate how the detection outputs affect the classification performance of various machine learning algorithms in distinguishing symmetric from asymmetric midline conditions. Materials and Methods: A total of 146 standardized frontal photographs (72 with midline deviation ≥ 2 mm from the facial midline, defined by the soft-tissue nasion–subnasal line; 74 symmetric) were analyzed. YOLOv8 was used to obtain bounding-box and keypoint predictions, which were converted into a numerical feature vector and used to train 11 classifiers (including Naive Bayes, Logistic Regression with L1 and ElasticNet penalties, Support Vector Machine, AdaBoost, and others). Performance was assessed using accuracy (with 95% Wilson confidence intervals), precision, recall, F1-score, and ROC-AUC. Optimization of hyperparameters for the downstream classifiers employed five-fold cross-validation along with grid search inside the training data set (n = 126) while final classifier assessment was done using a reserved test data set (n = 20). As the YOLOv8 object detector was trained using the full image dataset before extracting features, the classification metrics presented here should be considered as exploratory results only. Results: YOLOv8 achieved mAP@0.5 = 0.995 for midline detection. Naive Bayes attained the highest classification accuracy of 75% (95% CI: 53–89%) with ROC-AUC = 0.75. AdaBoost achieved 65% (95% CI: 43–82%). Several models defaulted to majority-class prediction (accuracy = 40%), indicating insufficient feature discriminability. Conclusions: YOLOv8 detected the maxillary dental midline under the present internal experimental conditions. However, because leakage-free outer k-fold validation of the complete detection-plus-classification pipeline was not performed, the classification results should be considered preliminary. Future work should address information leakage, incorporate facial reference frame normalization, include inter-observer reliability assessment, and validate the approach on larger datasets. Full article

This study investigates the impact of logistics performance on international trade by comparing Lithuania and Turkey within a gravity model framework. Using a bilateral panel dataset of 984 observations covering trade with 26 European Union member states over the period 2007–2025, the study incorporates the six sub-indicators of the World Bank’s Logistics Performance Index (LPI) as explanatory variables. The results confirm that logistics performance significantly influences bilateral trade, but through markedly different channels for the two economies. For Lithuania, the quality and competence of logistics services emerges as the dominant trade-enhancing factor (4.726, p < 0.01), reflecting its position as a small open EU economy. For Turkey, infrastructure quality is the primary driver of trade (2.782, p < 0.01), consistent with its status as a large emerging economy. The Turkey dummy variable becomes statistically insignificant when LPI variables are included, indicating that logistics performance substantially explains the trade differential between the two countries. Export–import disaggregation reveals that imports are more sensitive to logistics dimensions such as timeliness and service quality than exports. Robustness checks using pooled OLS, random effects, and fixed effects estimations, along with the Hausman test, broadly support the baseline findings. The study provides differentiated policy recommendations: Lithuania should prioritize logistics service quality, while Turkey should focus on infrastructure development and customs reform. Full article

Background: Crohn’s disease-associated intestinal strictures represent a major source of morbidity and frequently require endoscopic or surgical intervention. However, patients with stricturing Crohn’s disease demonstrate substantial clinical heterogeneity regarding disease localization, penetrating complications, systemic manifestations, metabolic alterations, and treatment exposure. This study aimed to explore phenotypic heterogeneity within patients with Crohn’s disease-associated intestinal strictures. Methods: In this retrospective exploratory cohort study, 96 patients with Crohn’s disease-associated intestinal strictures treated at a tertiary referral center between 2014 and 2024 were included. Clinical, structural, metabolic, and treatment-related variables were analyzed. Univariate analyses were performed using chi-square, Fisher’s exact test, Student’s t-test, or Mann–Whitney U test as appropriate. Exploratory multivariable logistic regression models were constructed to explore relationships between different clinical phenotypes and disease-related characteristics, including extraintestinal manifestations (EIMs), smoking status, penetrating disease manifestations, hepatic steatosis, stenosis localization, and abscess formation. Given the limited sample size and event numbers in several subgroup analyses, all multivariable analyses were considered exploratory and hypothesis-generating. Results: The cohort demonstrated a heterogeneous clinical presentation with a high prevalence of perianal disease, penetrating complications, prior intestinal surgery, and biologic therapy exposure. Female sex (OR 4.63, p = 0.044), autoimmune disease (OR 23.5, p = 0.049), rectal stenosis (inverse association; OR 0.08, p = 0.041), and exposure to multiple biologic therapies (OR 20.11, p = 0.036) remained associated with EIMs after multivariable adjustment. Smoking status was associated with anastomotic stenosis (OR 3.16, p = 0.023) and inversely associated with female sex (OR 0.35, p = 0.036). Phenotype-oriented analyses further suggested clustering of penetrating disease manifestations, including associations between intestinal fistulas, perianal fistulas, and abscess formation. Hepatic steatosis demonstrated exploratory associations with intestinal fistulas, intestinal resection, and appendectomy. Several analyses demonstrated wide confidence intervals and should therefore be interpreted cautiously. Conclusions: This exploratory retrospective cohort study highlights the substantial clinical heterogeneity observed among patients with Crohn’s disease-associated intestinal strictures. Different structural, systemic, penetrating, behavioral, and metabolic disease manifestations may indicate potentially overlapping phenotypic patterns within stricturing Crohn’s disease. Given the retrospective design, limited sample size, and exploratory statistical approach, these findings should be interpreted cautiously and require validation in larger prospective studies. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Agricultural supply chains operating under rural infrastructure constraints face persistent logistical inefficiencies that reduce producer income and weaken territorial sustainability. This paper assesses how collaborative and coordinated distribution architectures reshape economic performance, efficiency, and equity in dispersed networks of cocoa producers in El Carmen de Bolívar, Colombia. The unified optimization framework compares three regimes: decentralized non-collaborative individual shipments, collaborative consolidation based on distribution centers, and coordinated distribution with time-window synchronization. The findings show a reduction in average logistics costs from $0.688/kg in decentralized distribution to $0.323/kg with collaborative distribution centers, and even further to $0.282/kg in coordinated distribution, representing an overall reduction of approximately 59%. A sensitivity analysis across 64 accessibility configurations shows that the advantage of coordination increases as time rigidity increases. These structural improvements translate into a 13.97% increase in total producer utility, raising average utility from $278 to $317 per producer. In addition, the distributional assessment based on Lorenz curves and Gini coefficients indicates that inequality remains stable despite gains in welfare. These results demonstrate that spatial consolidation combined with temporal synchronization is a decisive lever for resilient and inclusive rural supply systems. Full article

Background/Objectives: Anthropometric measurements provide essential normative datasets that form the foundation for clinical practice and forensic identification. The human ear is a highly informative structure due to its complex morphology and individual specificity, making it a valuable tool for biometric systems. This study aimed to estimate biological sex based on auricular morphometric measurements, develop a logistic regression model for this purpose, and validate its performance using ROC analysis. Materials and Methods: This cross-sectional study included 120 adult participants (60 males, 60 females). Standardized digital photographs were analyzed in ImageJ to record 22 linear and 6 angular measurements using established anatomical landmarks. LASSO logistic regression was employed for variable selection and model shrinkage. The final model’s discriminative performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer–Lemeshow test, and the Brier score. Results: A comparative analysis revealed that most linear and angular measurements showed significant sexual dimorphism. Almost all linear dimensions (A1–A22) were significantly larger in males (p < 0.001). Auricular width (A2) and width at the level of the tragus (A3) emerged as the most robust indicators, demonstrating “very large” effect sizes. Conversely, the angle between the preauricular line and the vertical plane (A28) was significantly greater in females, providing a unique inverse relationship for sex estimation. A parsimonious 5-predictor model (incorporating A2, A3, A5, A10, and A28) achieved exceptional discriminative performance with an AUC of 0.980. Conclusions: Auricular morphometry is a highly effective tool for sex estimation. The findings confirm significant sexual dimorphism in the external ear, particularly in linear dimensions. The developed model may serve as a preliminary morphometric reference for future automated biometric recognition studies, although no artificial intelligence-based classification model was developed in the present study. Full article

The sweet potato weevil, Cylas formicarius (Fabricius) (Coleoptera: Brentidae), is one of the most destructive pests of sweet potato [Ipomoea batatas (L.) Lam.] crops worldwide. Current management of the sweet potato weevil relies heavily on conventional pesticides, raising concerns about pesticide residues, environmental impacts, and the development of pesticide resistance. This study evaluated the effects of seven essential oils (EOs): eucalyptus (Eucalyptus globulus), garlic (Allium sativum), marigold (Calendula officinalis), mustard seed (Sinapis alba), peppermint (Mentha piperita), rosemary (Rosmarinus officinalis), and thyme (Thymus gobicus) against different life stages of C. formicarius under laboratory conditions. Feeding activity, oviposition, larval mortality, pupal mortality, and adult survival were evaluated using EO concentrations of 1%, 5%, and 10%, with acetone and distilled water as control treatments. Each treatment consisted of six replicates, with 10 insects per replicate. Statistical analyses were performed using logistic regression models with a binomial distribution. Significant effects of oil type and concentration were observed as lethal to weevil larval and pupal stages. Similarly, the feeding and oviposition were significantly impacted (p < 0.0001). Peppermint oil exhibited the highest efficacy, causing complete or near-complete mortality of second- and third-instar larvae and pupae at 10%. This also substantially reduced adult survival, feeding activity, and oviposition. Rosemary, thyme, and eucalyptus, at higher concentrations. Most oils almost completely suppressed oviposition. These findings demonstrate that plant-derived essential oils (EOs) exhibit significant insecticidal activity against Cylas formicarius, indicating their promise as sustainable tools for integrated pest management programs in sweet potato production systems. Full article

Algorithmic prediction models are increasingly used to support decision-making in high-stakes environments, including emergency departments (ED). However, aggregate performance metrics may obscure systematic differences in classification errors or calibration across subgroups. This study presents a stage-wise, multi-metric, and interpretable fairness auditing framework for ED prediction. The framework compares mitigation strategies across data-, model-, and decision-level interventions, evaluates subgroup fairness using complementary classification and calibration criteria including equalized odds difference (EOD) and expected calibration error (ECE) disparity, and incorporates interpretability analyses based on SHapley Additive exPlanations (SHAP) and the calibration adjustment difference (CAD) to characterize changes in feature-contribution patterns and subgroup-specific probability adjustments after mitigation. The framework was applied to 126,819 ED encounters from MIMIC-IV-ED using measurements recorded within the first 2 h after arrival, and penalized logistic regression and random forest models were compared under reweighting, reduction, and multicalibration. Baseline AUROC values were 0.748 ± 0.028 for random forest and 0.746 ± 0.028 for penalized logistic regression. Reduction and multicalibration largely preserved discrimination performance, whereas reweighting was associated with reduced AUROC and AUPRC. Reweighting most clearly reduced EOD-based classification disparity, particularly for age, yielding reductions of 80.6% in random forest and 86.4% in penalized logistic regression. By contrast, multicalibration most consistently reduced ECE-based calibration disparity for sex and age but did not consistently improve EOD-based classification disparity. In the interpretability analyses, SHAP indicated that data- and model-level mitigation altered feature-contribution patterns, whereas CAD showed that decision-level mitigation produced subgroup-specific probability adjustments that varied in direction and magnitude across groups. These findings reveal trade-offs among discrimination performance, classification fairness, and calibration fairness, indicating that fairness mitigation should be guided by a clearly defined target fairness objective. Pre-deployment fairness auditing should therefore combine complementary fairness metrics with interpretability analyses to evaluate both subgroup-level outcomes and unintended changes in model behavior. Full article

Background/Objectives: This study aims to estimate the prevalence of MASLD in a general outpatient population, describe associated metabolic risk factors, and evaluate liver fibrosis. Methods: We conducted a prospective, cross-sectional study at a tertiary hospital that included adults referred there for abdominal ultrasound for non-hepatic indications. Exclusion criteria were significant alcohol intake or known liver disease. Hepatic steatosis was assessed by ultrasound in all patients, and vibration-controlled transient elastography (VCTE) was performed in a subgroup. Clinical and laboratory data were collected. Comparisons used the chi-square test, Fisher’s exact test, and the Wilcoxon test, and logistic regression identified associated factors. Results: Hepatic steatosis was identified by ultrasound in 57.6% of the 182 patients, with most (93%) fulfilling the MASLD criteria. MASLD was diagnosed in 58.2% of patients based on ultrasound or VCTE findings of steatosis. Hepatic steatosis by ultrasound was associated with higher BMI (OR 1.30; 95% CI 1.18–1.43), hypertension (OR 1.92; 95% CI 1.04–3.53), glucose disorders (OR 3.33; 95% CI 1.60–6.92), and triglycerides (OR 1.01; 95% CI 1.00–1.03), while physical activity was protective (OR 0.86; 95% CI 0.26–0.99). Among 74 patients evaluated by VCTE, 8% had fibrosis (≥F1), which was more frequent in those with higher BMI and a number of cardiometabolic risk factors. Conclusions: This study reveals a high prevalence of MASLD and fibrosis among outpatients, supporting the use of abdominal ultrasound for opportunistic screening of MASLD and emphasizing the need for early risk stratification and referral. Full article

Background/Objectives: This analysis aims to examine the correlations involving glucose levels, serum lipid parameters, and lipid ratios in adults diagnosed with type 2 diabetes mellitus (T2DM) in Southern Thailand. Methods: A cross-sectional investigation included 360 individuals diagnosed with T2DM. Associations among fasting blood glucose (FBG), HbA1c, lipid variables, TG/HDL, TyG, and TyG-BMI were assessed using Spearman correlation analysis. Linear and logistic regression models were applied to identify factors related to TG/HDL and elevated TG/HDL (≥3.5). Receiver operating characteristic analysis evaluated marker performance for poor glycemic control. Results: HbA1c showed more consistent associations with atherogenic lipid indices than FBG. TyG demonstrated the strongest association with glycemic status and the highest discriminatory performance for HbA1c ≥ 7%. In adjusted analyses, HbA1c and BMI remained independently associated with higher TG/HDL and increased odds of TG/HDL. Conclusions: HbA1c was more strongly associated with atherogenic lipid abnormalities than FBG. Among the evaluated lipid-related indices, TG/HDL and TyG showed the closest relationships with glycemic status, while TyG demonstrated the highest ability to discriminate poor glycemic control. Full article

Objectives: This study aimed to compare the aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and fibrosis-5 index (FIB-5) between women with threatened miscarriage and healthy pregnant controls, and to evaluate their discriminative performance. Methods: This single-center retrospective case–control study included 100 women with threatened miscarriage and 100 gestational-age-matched healthy pregnant controls within the first 12 weeks of gestation. Demographic, obstetric, ultrasonographic, and laboratory data were retrieved from electronic records. APRI, FIB-4, and FIB-5 were calculated from routine laboratory parameters. Group comparisons, binary logistic regression, and ROC analyses were performed. Results: AST, ALT, ALP, APRI, and FIB-4 were higher, while hemoglobin, platelet count, albumin, and FIB-5 were lower in the threatened miscarriage group (all p < 0.001). APRI (OR = 6.937), FIB-4 (OR = 89.114), and FIB-5 (OR = 0.766) were independently associated with case status. FIB-4 showed the highest discriminative performance (AUC = 0.929), followed by APRI (AUC = 0.903) and FIB-5 (AUC = 0.761). Conclusions: APRI and particularly FIB-4 showed good apparent discrimination between women with threatened miscarriage and healthy controls in this retrospective dataset. However, these indices should be interpreted as exploratory laboratory-derived markers rather than disease-specific biomarkers until validated in prospective multicenter studies. Full article

Background and Objectives: Post-thyroidectomy hypoparathyroidism (hypoPTH) is the most common complication of total thyroidectomy. Transient hypoPTH was defined as postoperative day 1 (POD1) intact parathyroid hormone (PTH) < 15 pg/mL and/or symptomatic hypocalcemia (<8.0 mg/dL), requiring supplementation, resolving within six months. We evaluated POD1 calcium, PTH, and their combination; identified preoperative predictors; and compared absolute with percent-change metrics. Materials and Methods: Participants comprised a retrospective single-center cohort of 380 consecutive adults undergoing total thyroidectomy between January 2023 and December 2025. Multivariable logistic regression identified preoperative predictors, and receiver operating characteristic (ROC) analysis evaluated POD1 biomarkers. Because both biomarkers are part of the outcome definition, a pre-specified sensitivity analysis re-evaluated POD1 PTH and ΔPTH against PTH-independent outcomes (POD1-calcium-defined hypocalcemia and permanent hypoPTH). Subgroups examined malignancy and central neck dissection (CND). Results: The cohort comprised 193 males (50.8%) and 187 females (49.2%), with a median age of 53 years (IQR 38–69). Indications were multinodular goiter (45.0%), differentiated thyroid cancer (37.9%), Graves’ disease (15.0%) and recurrent disease (2.1%). CND was performed in 9.5% of patients. Transient and permanent hypoPTH occurred in 132 (34.7%) and 11 (2.9%) patients. Thyroid gland weight was the sole independent preoperative predictor (OR 0.982, 95% CI 0.969–0.995, p = 0.008), with smaller glands conferring higher risk. Against the composite outcome, POD1 calcium and PTH yielded AUCs of 0.997 and 0.991 (combined 1.000), reflecting partial circularity. In the decoupled-outcome sensitivity analysis, POD1 PTH retained good-to-excellent discrimination for severe hypocalcemia (AUC 0.943) and permanent hypoPTH (AUC 0.976). Malignant cases showed a greater relative PTH decline than benign cases (−53.7% vs. −38.5%, p = 0.013) despite comparable absolute POD1 values, and CND did not increase risk. Conclusions: Combined POD1 calcium and PTH provided strong biochemical confirmation of transient hypoPTH, but the composite-outcome AUCs reflect internal definitional consistency rather than independent predictive performance; the decoupled-outcome AUCs (0.93–0.98) are the conservative benchmark. Thyroid gland weight was an inverse risk modifier with limited stand-alone utility. Multicenter prospective validation is required. Full article