Search Results (35)

Underground distribution networks increasingly rely on aging cable assets whose parameter drift modifies propagation velocity, attenuation, and fault-initiated transient signatures, thereby reducing the reliability of conventional traveling-wave (TW) fault location and purely data-driven diagnosis. This paper proposes a physics-regularized hybrid learning framework for joint fault-type classification, feeder-area identification, and continuous fault localization in aging underground distribution feeders. The methodology integrates (i) an aging-aware simulation pipeline driven by a normalized aging-stress index $\alpha \in [0,1]$ that perturbs the per-unit-length cable matrices within a controlled domain; (ii) synchronized multi-sensor time–frequency representations of three-phase voltage and current transients; (iii) an area-aware multi-task architecture with fault-type and area-classification heads and area-specific local regression heads; and (iv) a propagation-consistency loss that depends explicitly on the model-predicted fault position and therefore contributes gradients during training. A branched underground feeder is evaluated using five synchronized sensing locations and a stratified dataset of $N_{\mathrm{tot}}=\mathrm{36,000}$ simulated fault events covering 11 fault classes (SLG-A/B/C, LL-AB/BC/CA, DLG-ABG/BCG/CAG, LLL, and LLLG), six non-overlapping feeder areas, fault resistance $R_f\in [0.1,50]\Omega$, measurement noise $\mathrm{SNR}\in [20,40]\mathrm{dB}$, and a $20\mathrm{ms}$ transient window sampled at $200\mathrm{kHz}$. On a held-out test set of 7200 previously unseen event records drawn from the same simulation domain, the Hybrid model achieves a fault-type accuracy of $0.93$, an area-identification accuracy of $0.96$, a localization MAE of $0.011$ p.u., and a 95th-percentile absolute error of $0.027$ p.u. The proposed configuration outperforms the TW-TOA, purely data-driven Baseline, and physics-regularized PINN references across the reported diagnostic tasks within the prescribed simulator and parameter ranges. Time–frequency attribution is included only as a qualitative interpretability illustration and is not treated as quantitative evidence of explanation faithfulness. Accordingly, the results demonstrate comparative in-domain simulation performance rather than field or cross-simulator deployment readiness. Full article

Background/Objectives: Prognostication in ICU patients with documented coma or unresponsiveness is a high-stakes task that informs escalation of care, goals-of-care discussions, and family counselling. Conventional scores are often based on static snapshots and may not reflect early physiological evolution in heterogeneous real-world ICU populations. Routine arterial blood gases (ABG) and SpO₂ are repeatedly measured during early ICU care and may capture clinically meaningful trajectories that can be leveraged by explainable machine learning. To develop and internally validate exploratory, time-updated explainable machine-learning models for ICU outcome in ICU patients with clinically documented coma or unresponsiveness using routine ABG/SpO₂ measurements and physiological trajectories available at admission, 24 h, and 72 h, and to evaluate whether trajectory information adds prognostic information within a staged internal-validation framework. Methods: We conducted a retrospective single-centre study of 108 adult ICU patients with clinically documented coma or unresponsiveness. Predictors included demographics, comorbidity burden, COVID-19 status, baseline ABG/SpO₂ at ICU admission, inflammatory and coagulation biomarkers, and derived ABG/SpO₂ trajectory variables at 24 h and 72 h. Trajectory variables were defined as changes from admission to 24 h and to 72 h and were retained as missing when follow-up measurements were unavailable. The primary ICU-course outcome was ICU death versus transfer to ward. Three staged models were evaluated: Model A using baseline variables, Model B adding 24 h trajectory features, and Model C adding 72 h trajectory features. For each stage, models were analyzed with and without the derived respiratory_support index; models excluding respiratory_support were treated as the main interpretive analyses. Logistic regression, random forest, and gradient boosting (XGBoost) classifiers were assessed using repeated stratified 5-fold cross-validation with 20 repeats and aligned out-of-fold predictions. Performance was reported using AUC-ROC, precision–recall AUC, Brier score, and operating-point metrics; clinical utility was examined with decision-curve analysis. Model interpretation used SHAP and partial dependence plots. Robustness analyses included feature-exclusion sensitivity analysis for respiratory_support and a label-permutation sanity check. Results: ICU mortality was 65.7% (71/108). Follow-up ABG completeness was 75.9% at 24 h and 61.1% at 72 h. Because respiratory_support summarized the highest support level during the first 72 h and strongly separated outcome groups, models excluding respiratory_support were treated as the primary interpretive analyses. In the primary NoRS logistic-regression models, discrimination was moderate-to-strong, with AUC-ROC 0.822 for Model A_noRS, 0.848 for Model B_noRS, and 0.895 for Model C_noRS; bootstrap 95% confidence intervals were 0.739–0.897, 0.766–0.919, and 0.830–0.951, respectively. Measurement-availability sensitivity analyses and simple benchmark models were added to contextualize trajectory-related performance. Respiratory_support-enriched models were retained only as secondary severity-aware analyses, not as admission-only prediction models. Label permutation reduced discrimination toward chance (AUC ≈ 0.55). SHAP and partial-dependence analyses identified oxygenation variables, inflammatory burden, acid–base status, and ΔPaO₂ at 72 h as clinically coherent contributors to predicted risk; when included, respiratory_support dominated feature attribution, consistent with its role as an organ-support intensity marker. Conclusions: In ICU patients with clinically documented coma or unresponsiveness, explainable machine-learning models using routine ABG/SpO₂ trajectories within the first 72 h are feasible and may provide time-updated prognostic information, but the incremental value of trajectory-enriched models over simpler admission-only benchmarks remains unproven. Trajectory-enriched NoRS models retained meaningful discrimination after removing organ-support severity, suggesting a possible physiologically meaningful signal beyond support intensity alone, although definitive incremental value over parsimonious admission-only benchmarks was not established. These findings should be interpreted as exploratory and internally validated only; they do not establish a deployable ICU mortality score, do not demonstrate superiority over established ICU severity scores, and require external validation in larger multicentre cohorts before clinical deployment. Full article

Background/Objectives: Accurate quantification of alveolar cleft defects for bone grafting remains difficult due to inconsistent anatomical boundaries. This study established an expert consensus on boundary landmarks for alveolar bone graft (ABG) planning and validated the accuracy and reliability of digital volumetric measurement methods. Methods: Three cleft specialists performed repeated simulated graft procedures in seven patient-specific 3D-printed models, first according to the operator’s clinical judgment, and subsequently according to panel-derived consensus boundaries. Two radiologists independently conducted digital volumetric assessments in 3D X-ray imaging using four measurement approaches (axial tracing, interpolated axial tracing, landmark-based mirroring, and mesh-based mirroring), generating 56 independent digital segmentations to be evaluated against the consensus-based physical reference standard. Volumes of the defects were recorded, intra- and inter-rater reliabilities were calculated using the intraclass correlation coefficient (ICC), and differences among methods were analyzed. Results: Operator-defined plans showed significant inter-operator differences (p < 0.001) with poor-to-excellent reliability (intra-rater ICC 0.060–0.967; inter-rater ICC 0.300–0.635). Consensus established standardized boundaries: tilted plane from base of anterior nasal spine to hard palate, cemento-enamel junctions, incisive canal, and alveolar contour. Consensus-based filling showed non-significant inter-rater differences (p = 0.139) and substantially improved reliability (intra-rater ICC 0.904–0.988; inter-rater ICC 0.622–0.861). Among the four digital methods evaluated, axial tracing demonstrated excellent reliability (intra-rater ICC 0.971–0.99; inter-rater ICC 0.965) and high accuracy (mean difference 0.001–0.026 cm³), with no significant difference (p = 0.999) from the physical reference standard. Conclusions: These proposed consensus-based boundary definitions and validated volumetric measurement methods improved the accuracy and reproducibility of personalized alveolar bone graft planning. Full article

Human Resource Information Systems (HRIS) are often introduced as platforms expected to deliver strategic value through workforce analytics, decision support, and alignment with organizational goals. Yet evidence consistently shows that line managers’ use remains confined to administrative functions. This paper addresses this paradox by reframing it through the lens of the attitude-behavior gap (ABG), a concept established in consumer research to describe the disconnect between favorable attitudes and actual behaviors. Drawing on qualitative interviews with 25 line managers in five UK organizations, the study identifies three themes: HRIS as an Administrative Rather than Strategic Tool, Organizational Identity and Role Expectations, and Confidence Gaps and Habitual Routines. Together, these themes illustrate how supportive attitudes toward HRIS coexist with restricted behavioral engagement, sustained by cultural scripts, situational barriers, and ingrained routines. Theoretically, the study extends the ABG beyond consumer contexts into organizational technology use, challenging the linear assumptions of dominant adoption models such as TAM and UTAUT. Practically, it highlights the need for cultural reframing of HR’s role, user-centered system design, and sustained training and integration efforts to enable more strategic engagement. By framing HRIS adoption as a context-dependent practice shaped by organizational roles and behavioral patterns, the paper offers deeper insight into why favorable attitudes toward innovation frequently fall short of producing substantive engagement. Full article

Introduction: Patients with an intracerebral hemorrhage (ICH) have been shown to have a high incidence of acute respiratory distress syndrome (ARDS). We aimed to determine the incidence of ARDS following ICH in the era of lung-protective ventilation. We also examined risk factors associated with ARDS following ICH. Materials and Methods: A retrospective cohort study of adults admitted to a single health system’s intensive care units with acute, spontaneous ICH from 1 March 2015 to 28 February 2018, using univariate and multivariable logistic regression models to evaluate the associations of patient characteristics with the diagnosis of ARDS. Results: In total, 269 patients were included, with 155 patients requiring invasive mechanical ventilation. The overall incidence of ARDS was 6.7% (18/269), and the incidence in intubated patients was 10% (16/155), as the median time of ventilation with >8 cc/mL of ideal body weight was low. For the entire ICH population, severity of hypoxemia on initial arterial blood gas (ABG; Odds Ratio [OR] per 10 mmHg 0.855 [95% Confidence Interval [CI] 0.74–0.987]) and total minutes of mechanical ventilation (OR per 60 min 1.018 [95% CI 1.007–1.029]) were both associated with the diagnosis of ARDS. In intubated patients, ventilation, younger age (OR per 10 years 0.655 [95% CI 0.431–0.997]), and total minutes of mechanical ventilation (OR per 60 min 1.028 [95% CI 1.006–1.049] increased the odds of developing ARDS. Conclusions: ARDS was found to be significantly lower in the present cohort of ICH patients when compared to prior studies, with younger age and hypoxemia associated with an increasing risk. Full article

This paper presents a novel DEVA-ConvNeXt model to address challenges in beef freshness grading, including data collection difficulties, complex backgrounds, and model accuracy issues. The Alpha-Background Generation Shift (ABG-Shift) technology enables rapid generation of beef image datasets with complex backgrounds. By incorporating the Dynamic Non-Local Coordinate Attention (DNLC) and Enhanced Depthwise Convolution (EDW) modules, the model enhances feature extraction in complex environments. Additionally, Varifocal Loss (VFL) accelerates key feature learning, reducing training time and improving convergence speed. Experimental results show that DEVA-ConvNeXt outperforms models like ResNet101 and ShuffleNet V2 in terms of overall performance. Compared to the baseline model ConvNeXt, it achieves significant improvements in recognition Accuracy (94.8%, a 6.2% increase), Precision (94.8%, a 5.4% increase), Recall (94.6%, a 5.9% increase), and F1 score (94.7%, a 6.0% increase). Furthermore, real-world deployment and testing on embedded devices confirm the feasibility of this method in terms of accuracy and speed, providing valuable technical support for beef freshness grading and equipment design. Full article

Background and Objectives: Interpreting acid–base disorders is challenging, particularly in complex or mixed cases. Given the growing potential of large language models (LLMs) to assist in cognitively demanding tasks, this study evaluated their performance in interpreting arterial blood gas (ABG) results. Materials and Methods: In this single-center retrospective study, 200 ABG datasets were curated to include 40 cases in each of five diagnostic categories: metabolic acidosis, respiratory acidosis, metabolic alkalosis, respiratory alkalosis, and no acid–base disorder. Three medical students, each assigned to one LLM (ChatGPT GPT-4o, Copilot GPT-4, or Gemini 1.5-flash/2.5-flash), perform ABG interpretation using two evaluation methods: interpretation (LLM-I) and interpretation with supervision model (LLM-S). Two clinical pathologists independently performed the conventional evaluation to serve as the reference standard. Results: Agreement for identifying the primary acid–base (APD) disorder was strong across all approaches (Cohen’s κ ≥ 0.88). For identifying both primary and secondary disorders regardless of order (APSD), LLM-I showed moderate agreement (ChatGPT κ = 0.65, Copilot κ = 0.61, Gemini κ = 0.62), whereas LLM-S achieved strong agreement (ChatGPT κ = 0.91, Copilot κ = 0.81, Gemini κ = 0.81). Conclusions: LLM-assisted ABG interpretation demonstrates strong concordance with expert interpretation in detecting primary acid–base disorders. These tools may enhance the understanding of acid–base disorders while reducing calculation-related errors among medical students. Full article

Background/Objectives: This study evaluated the feasibility of predicting bone conduction (BC) thresholds and classifying air–bone gap (ABG) status using only air conduction (AC) data obtained from pure tone audiometry (PTA). Methods: A total of 60,718 PTA records from five tertiary hospitals in the Republic of Korea were utilized. Input features included AC thresholds (0.25–8 kHz), age, and sex, while outputs were BC thresholds (0.25–4 kHz) and ABG classification based on 10 dB and 15 dB criteria. Five machine learning models—deep neural network (DNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), random forest (RF), and extreme gradient boosting (XGB)—were trained using 5-fold cross-validation with Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated based on accuracy, sensitivity, precision, and F1 score under ±5 dB and ±10 dB thresholds for BC prediction. Results: LSTM and BiLSTM outperformed DNN in predicting BC thresholds, achieving ~60% accuracy within ±5 dB and ~80% within ±10 dB. For ABG classification, all models performed better with the 10 dB criterion than the 15 dB. Tree-based models (RF, XGB) achieved the highest classification accuracy (up to 0.512) and precision (up to 0.827). Confidence intervals for all metrics were within ±0.01, indicating stable results. Conclusions: AI models can accurately predict BC thresholds and ABG status using AC data alone. These findings support the integration of AI-driven tools into clinical audiology and telemedicine, particularly for remote screening and diagnosis. Future work should focus on clinical validation and implementation to expand accessibility in hearing care. Full article

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article

This study investigates, for the first time, using finite element analysis (FEA), the differential impact of middle ear prosthesis diameter on hearing gain in two distinct surgical techniques: stapedotomy and partial stapedectomy. The model represented the cochlea as two fluid-filled straight channels separated by the basilar membrane and considered pistons of 0.4 mm and 0.6 mm diameters. The results demonstrated that in stapedotomy, a 0.6 mm diameter piston yielded a significantly better reduction in ABG (8.31 dB) compared to the 0.4 mm piston (10.67 dB), indicating improved hearing gain. Conversely, in partial stapedectomy, the smaller 0.4 mm piston was more effective, reducing ABG to 11.2 dB versus 12.12 dB with the larger piston. These findings highlight that the optimal prosthesis diameter varies according to surgical technique, emphasizing the need for tailored prosthesis selection. Full article

Background: Early defibrillation improves outcomes in cardiac arrest, but the optimal defibrillation strategy and energy requirements remain debated. This study investigated whether arterial blood gas (ABG) parameters could predict optimal defibrillation energy requirements for achieving the highest first-shock success rates in an animal model. Our study focused on clinical scenarios where ABG measurements are readily available, such as ventricular tachycardia and ventricular fibrillation storms requiring multiple shock deliveries. Materials and Methods: In the experimental setting, ventricular fibrillation was induced by 50 Hz direct current (DC), and the defibrillation threshold (DFT) was determined using a stepwise defibrillation protocol. ABG parameters were measured before each defibrillation attempt, recording partial arterial pressure of carbon dioxide (PaCO₂) and oxygen (PaO₂), pH, hematocrit (Hct), sodium (Na⁺), potassium (K⁺), and bicarbonate (HCO₃⁻) levels. The relationships between ABG parameters and the DFT were analyzed for 15 subjects using classical data analysis techniques and machine learning (ML) algorithms. Multiple ML models were trained and tested to predict the higher energy needed for successful defibrillation based on the ABG parameters. Results: Statistically significant differences were found in Hct and Na⁺ levels between the two DFT categories, above 130 Joules (J) and below 40 J (p < 0.01). The DFT negatively correlated with PaO₂ and positively correlated with Hct and Na⁺. However, other ABG parameters did not show significant correlations with DFT. Using ML, we predicted cases requiring higher defibrillation E. Our best-performing model, the Extra Trees Classifier, achieved 83% overall accuracy, with 100% and 67% precision rates for higher and lower DFT categories, respectively. We validated the model using bootstrap resampling and 10-fold cross-validation, confirming consistent performance. We identified Hct, PaCO₂, and PaO₂ as significant contributors to model prediction based on the feature importance value. Conclusions: Modern data analysis techniques applied to ABG parameters may guide personalized defibrillation energy selection, particularly in controlled clinical environments such as catheterization laboratories and intensive care units where ABG measurements are readily available. Full article

This review systematically examines the critical mechanisms and process optimization strategies of algal–bacterial granular sludge (ABGS) technology in wastewater treatment. The key findings highlight the following: (1) enhanced pollutant removal—ABGS achieves >90% COD removal, >80% total nitrogen elimination via nitrification–denitrification coupling, and 70–95% phosphorus uptake through polyphosphate-accumulating organisms (PAOs), with simultaneous adsorption of heavy metals (e.g., Cu²⁺, Pb²⁺) via EPS binding; (2) energy-saving advantages—microalgal oxygen production reduces aeration energy consumption by 30–50% compared to conventional activated sludge, while the granular stability maintains >85% biomass retention under hydraulic shocks; (3) AI-driven optimization—machine learning models enable real-time prediction of nutrient removal efficiency (±5% error) by correlating microbial composition (e.g., Nitrosomonas abundance) with operational parameters (DO: 2–4 mg/L, pH: 7.5–8.5). This review further identifies EPS-mediated microbial co-aggregation and Chlorella–Pseudomonas cross-feeding as pivotal for system resilience. These advances position ABGS as a sustainable solution for low-carbon wastewater treatment, although challenges persist in scaling photobioreactors and maintaining symbiosis under fluctuating industrial loads. Full article

Background/Objectives: Stapes surgery is a well-established treatment for conductive hearing loss caused by otosclerosis, with the choice of prosthesis playing a pivotal role in audiological outcomes and safety. Heat-activated and superelastic nitinol prostheses are widely used, but their comparative effectiveness and complication profiles remain debated. This systematic review and meta-analysis aimed to evaluate the audiological outcomes, complication rates, and overall performance of these two prosthesis types. Methods: A systematic review was conducted following PRISMA guidelines. Seven studies involving 273 patients were included. Data on mean air–bone gap (ABG) reduction, complication rates, and follow-up outcomes were extracted. Meta-analyses were performed using a random-effects model, and odds ratios (OR) with 95% confidence intervals (CI) were calculated for comparative analysis. Results: The pooled mean ABG reduction was 20.2 dB (95% CI: 19.47–20.95), indicating substantial and comparable improvements in hearing for both prosthesis types. Heat-activated prostheses achieved slightly higher ABG reduction in individual studies, while superelastic prostheses offered advantages in procedural simplicity. Complication rates were 6.0% for heat-activated and 5.6% for superelastic prostheses. The most common complications included sensorineural hearing loss (2.0–2.4%) and incus-related issues such as necrosis and lateralization. Conclusions: Both prosthesis types provide comparable audiological outcomes and surgical success rates. However, the slightly lower complication rate observed with superelastic prostheses emphasizes their safety and predictability. The choice of prosthesis should be guided by patient anatomy, surgeon expertise, and procedural considerations. Future studies should prioritize long-term outcomes and standardized reporting to further refine prosthesis selection. Full article

In the field of smart grid monitoring, real-time defect detection for overhead transmission lines is crucial for ensuring the safety and stability of power systems. This paper proposes a defect detection model for overhead transmission lines based on an improved YOLOv8n model, named ABG-YOLOv8n. The model incorporates four key improvements: Lightweight convolutional neural networks and spatial–channel reconstructed convolutional modules are integrated into the backbone network and feature fusion network, respectively. A bidirectional feature pyramid network is employed to achieve multi-scale feature fusion, and the ASFF mechanism is used to enhance the sensitivity of YOLOv8n’s detection head. Finally, comprehensive comparative experiments were conducted with multiple models to validate the effectiveness of the proposed method based on the obtained prediction curves and various performance metrics. The validation results indicate that the proposed ABG-YOLOv8n model achieves a 4.5% improvement in mean average precision compared to the original YOLOv8n model, with corresponding increases of 3.6% in accuracy and 2.0% in recall. Additionally, the ABG-YOLOv8n model demonstrates superior detection performance compared to other enhanced YOLO models. Full article

Chronic inflammation is a recognized risk factor for various cancers, including prostate cancer (PCa). We aim to explore the potential protective effects of aged black garlic extract (ABGE) against inflammation-induced prostate damage and its impact on prostate cancer cell lines. We used an ex vivo model of inflammation induced by Escherichia coli lipopolysaccharide (LPS) on C57BL/6 male mouse prostate specimens to investigate the anti-inflammatory properties of ABGE. The gene expression levels of pro-inflammatory biomarkers (COX-2, NF-κB, and TNF-α, IL-6) were measured. Additionally, we evaluated ABGE’s therapeutic effects on the prostate cancer cell lines through in vitro functional assays, including colony formation, tumorsphere formation, migration assays, and phosphorylation arrays to assess the signaling pathways (MAPK, AKT, JAK/STAT, and TGF-β). ABGE demonstrated significant anti-inflammatory and antioxidant effects in preclinical models, partly attributed to its polyphenolic content, notably catechin and gallic acid. In the ex vivo model, ABGE reduced the gene expression levels of COX-2, NF-κB, TNF-α, and IL-6. The in vitro studies showed that ABGE inhibited cell proliferation, colony and tumorsphere formation, and cell migration in the prostate cancer cells, suggesting its potential as a therapeutic agent. ABGE exhibits promising anti-inflammatory and anti-cancer properties, supporting further investigation into ABGE as a potential agent for managing inflammation and prostate cancer. Full article