Search Results (8,736)

Dry gas reservoirs play a pivotal transitional role in meeting the net-zero target worldwide. Accurate modelling and simulation of this energy source require fast and reliable prediction of the gas compressibility factor (Z-factor). The experimental measurements of Z-factor are the most reliable source; however, they are expensive and time-consuming. This makes developing accurate predictive models essential. Traditional methods, such as empirical correlations and Equations of States (EoSs), often lack accuracy and computational efficiency. This study aims to address these limitations by leveraging the predictive power of machine learning (ML) techniques. Hence in this study three ML models of Artificial Neural Network (ANN), Group Method of Data Handling (GMDH), and Genetic Programming (GP) were developed. These models were trained on a comprehensive dataset comprising 1079 samples where pseudo-reduced pressure (Ppr) and pseudo-reduced temperature (Tpr) served as input and experimentally measured Z-factors as output. The performance of the developed ML models was benchmarked against two cubic EoSs of Peng–Robinson (PR) and van der Waals (vdW), and two semi-empirical correlations of Dranchuk-Abou-Kassem (DAK) and Hall and Yarborough (HY), and recent developed ML based models, using statistical metrics of Mean Squared Error (MSE), coefficient of determination (R²), and Average Absolute Relative Deviation Percentage (AARD%). The proposed ANN model reduces average prediction error by approximately 70% relative to the PR equation of state and by over 35% compared with the DAK correlation, while maintaining robust performance across the full Ppr and Tpr of dry gas systems. Additionally paired t-tests and Wilcoxon signed-rank tests performed on the ML results confirmed that the ANN model achieved statistically significant improvements over the other models. Moreover, two physical equations using the white-box models of GMDH and GP were proposed as a function of Ppr and Tpr for prediction of the dry gas Z-factor. The sensitivity analysis of the data shows that the Ppr has the highest positive effect of 88% on Z-factor while Tpr has a moderate effect of 12%. This study presents the first unified, statistically validated comparison of ANN, GMDH, and GP models for accurate and interpretable Z-factor prediction. The developed models can be used as an alternative tool to bridge the limitation of cubic EoSs and limited accuracy and applicability of empirical models. Full article

Accurate prediction of soil shear strength is critical for safe and cost-effective geotechnical design. This study investigates the application of four machine learning (ML) models—Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Regression (SVR)—to predict the shear strength of soils from Bahir Dar city using laboratory-obtained geotechnical data. A total of 298 soil samples and 13 geotechnical parameters were collected from depths of 0.13–35 m, encompassing both disturbed and undisturbed conditions. The dataset was divided into training (80%) and testing (20%) sets, and models were trained with optimized hyperparameters. The RF model achieved the highest accuracy (R² = 0.9992, RMSE = 0.0983), followed by DT (R² = 0.9974, RMSE = 0.1812). ANN and SVR showed lower predictive accuracy, with SVR demonstrating the largest maximum errors. Predicted vs. actual plots, kernel density estimates, and absolute error per sample analysis confirmed that tree-based models provide the most reliable predictions, while ANN and SVR exhibited sporadic large deviations. SHAP analysis revealed that Cohesion, Clay content, and Plasticity Index are the most influential factors in predicting shear strength. The results demonstrate that ensemble tree-based ML models offer a robust and accurate tool for geotechnical prediction, capturing complex nonlinear relationships in soil behavior. Full article

Accurate prediction and optimized control of shield tunneling attitude are critical for ensuring tunneling quality and construction safety. In karst and other highly heterogeneous strata, complex geological conditions and construction parameters exhibit significant nonlinear coupling, greatly increasing the difficulty of attitude regulation. To address this challenge, this study proposes a machine learning-based approach for the automatic control of shield tunneling attitude. First, a Tree-structured Parzen Estimator-optimized Light Gradient Boosting Machine predictive model is employed to construct a nonlinear mapping model between construction parameters and shield tunneling attitude. Subsequently, the SHapley Additive exPlanations (SHAP) interpretability model is introduced to identify the core tunneling factors influencing attitude stability. On this basis, the developed predictive model is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework as a fitness function to achieve multi-objective optimization of key construction parameters. Using field data from shield tunneling construction in the karst strata of Shenzhen Metro Line 16, the proposed model achieved prediction accuracies of R² = 0.959 for pitch and R² = 0.936 for roll, outperforming XGBoost, Random Forest, Long Short-Term Memory, and Transformer baselines. SHAP analysis identified the partitioned propulsion thrust, partitioned chamber pressure, cutterhead rotational speed, and advance rate as key parameters influencing attitude. Further, MOEA/D optimization generated a Pareto set of construction parameters, from which the optimal solution was selected using the ideal point method, resulting in reductions of 26.45% and 39.47% in pitch and roll deviations, respectively. These findings demonstrate the feasibility and effectiveness of the proposed method in achieving high-precision prediction and intelligent optimization control of shield tunneling attitude under complex geological conditions, providing a reliable technical pathway for metro and tunnel construction projects. Full article

Gait analysis plays a critical role in assessing mobility and identifying risks such as frailty and falls, where accurate spatiotemporal measurements are essential for early intervention, particularly in aging populations and clinical screening contexts. However, robust gait characterization remains challenging due to noise contamination and variability in sensor-based signals. To address these limitations, this study presents a stride-length estimation framework formulated as a modified processing-and-estimation pipeline integrated with Long Short-Term Memory (LSTM) networks. The pipeline includes wavelet-based denoising and cubic-spline interpolation as front-end preprocessing, followed by a Kalman-filtering stage with dynamic gain regulation guided by acceleration zero-crossing events to mitigate transient errors around abrupt turning points. Experimental data were collected from twelve healthy participants (seven females, mean age: 26.76 ± 3.01 years; five males, mean age: 25.81 ± 1.63 years) walking at self-selected speeds on a treadmill, using both an inertial sensor-based gait monitoring system and a motion capture system as the ground-truth reference. The proposed framework demonstrated a substantial improvement in stride-length estimation accuracy, reducing the absolute mean error from 29.78% to 7.77% and the standard deviation from 20.31 to 7.17. Furthermore, the LSTM models trained on Modified EKF-preprocessed data achieved superior performance metrics, with a Mean Absolute Error (MAE) of 0.0376 and a coefficient of determination (R²) of 0.7066. These results highlight the effectiveness of combining Modified EKF preprocessing with LSTM learning to enhance stride-length estimation reliability. This integrated approach offers a robust, noise-resilient solution for wearable gait analysis, providing valuable insights for clinical diagnostics, rehabilitation monitoring, and health management applications. Full article

Data quality issues in hydrological time series directly affect hydrological modelling applications, including flood forecasting and water resource management. A critical challenge in hydrological monitoring is distinguishing erroneous outliers caused by sensor malfunctions or data transmission errors from natural extreme events such as floods, which exhibit similar statistical characteristics but require opposite treatments in forecasting models. Current detection practices rely on generic algorithms without systematic validation or adaptation to hydrological temporal dependencies, limiting their effectiveness in operational contexts. This study addresses these gaps through a comprehensive framework for detecting erroneous outliers in daily hydrological time series. We engineered 19 features that capture temporal dependencies and hydrological patterns, and reduced them to six key features that capture raw measurements, temporal patterns, and hydrological dynamics. We evaluated 13 detection algorithms across three categories: statistical methods (e.g., Extreme Studentised Deviate and Hampel filter), ML approaches (e.g., Isolation Forest, and Local Outlier Factor), and feature-enhanced variants. Three data-driven ensemble strategies were developed: Accurate (maximising F1-score), Diverse (balancing performance with method diversity), and Fast (prioritising computational efficiency). By injecting controlled outliers into the recorded hydrological data from five-gauge stations (in the Tweed River catchment, Australia), the outlier detection framework was validated. The outcomes showed that the ensemble methods achieved satisfactory F1 scores (0.6–0.9) in detecting the erroneous outliers. Statistical testing also identified the top-performing detection algorithms. The framework developed in this paper provides a validated tool for quality control in hydrological analysis, with potential applications in drought monitoring and flood forecasting systems. Full article

Accurate current–voltage (IV) characterization is essential for assessing photovoltaic (PV) module performance, yet conventional IV tracing requires physical contact and controlled conditions, limiting large-scale deployment. Electroluminescence (EL) imaging, while highly effective for detecting localized defects, remains largely qualitative and indirect in estimating actual PV module power loss. This study introduces a deep learning framework that directly predicts complete IV curves from EL images, transforming EL inspection into a quantitative, non-contact diagnostic tool. In this work, we propose a convolutional neural network (CNN) that learns the nonlinear mapping between paired EL images captured at 20% and 80% of the short-circuit current and the corresponding IV response. A total of 438 PV modules were used for model development, with performance evaluated on unseen data. The trained CNN reconstructs IV curves with high fidelity, achieving a validation accuracy of approximately 95% and low parameter deviations (<2% for key metrics such as maximum power point and fill factor). The model maintains consistent accuracy even when a single EL image is provided, supporting flexible field operation. Inference is rapid, requiring less than 0.5 s per PV module inspection, enabling real-time analysis. Full article

Objectives: Nasal obstruction is a common presenting symptom in otolaryngology practice. Frequent etiologies include allergic and non-allergic rhinitis, inferior turbinate hypertrophy (HIT), and nasal septal deviation (DNS). This study aimed to evaluate the relationship between major causes of nasal obstruction and their effect on patient-reported quality of life (QoL). Methods: We conducted a retrospective analysis of patients presenting with nasal obstruction who completed the 22-item Sino-Nasal Outcome Test (SNOT-22), the Nasal Obstruction Symptom Evaluation (NOSE) scale, and a visual analog scale (VAS). Patients were categorized into three groups based on etiology: rhinitis, anatomical obstruction, or combined pathology. Results: The study included 170 patients (62% male), with a mean age of 38.4 years. Mean SNOT-22, NOSE, and VAS scores were 38, 61, and 6.5, respectively, with no statistically significant differences observed among the three etiologic groups. QoL outcomes were also comparable across anatomical subgroups, including isolated DNS, HIT, or combined findings. Among SNOT-22 domains, rhinologic symptoms demonstrated the highest burden. Patients with rhinitis exhibited significantly higher rhinologic and ear/facial symptom scores compared with patients with isolated anatomical obstruction (p = 0.04 and p = 0.005, respectively). Strong correlations were observed between SNOT-22, NOSE, and VAS scores across the entire cohort. Conclusions: Nasal obstruction is associated with substantial impairment in multiple domains of quality of life, independent of the underlying etiology. These findings highlight the broad impact of nasal obstruction on patient well-being. Larger prospective studies are warranted to further assess changes in quality of life following medical and surgical interventions. Full article

This study presents an operational approach to atmospheric wind profiling using a purpose-built meteorological uncrewed aerial vehicle (UAV) and an orientation-based wind estimation method that does not rely on dedicated onboard anemometers. The quadrotor platform, designed and developed by our team, has a maximum take-off mass of 2.45 kg and is capable of acquiring vertical atmospheric profiles up to 3000 m under a wide range of weather conditions. Within the framework of the World Meteorological Organization’s (WMO) global demonstration campaign for evaluating the use of uncrewed aircraft systems in operational meteorology and associated field activities, twelve vertical wind profiles were collected in parallel with radiosonde observations. UAV-based wind estimates were evaluated against radiosonde data using the WMO OSCAR (Observing Systems Capability Analysis and Review) performance framework. Across most wind speed regimes, the central 50% of UAV–radiosonde wind speed differences remain within OSCAR threshold requirements, indicating operationally relevant accuracy. Systematic deviations are physically interpretable and arise primarily in strongly sheared boundary-layer flows. A representative low-level jet case is used as a stress test, demonstrating that the UAV system remains safe and that wind estimates remain reliable even under extreme wind conditions, supporting robust performance in less demanding regimes. These results establish UAV-based wind profiling as a viable and complementary observing technique in the lower atmosphere and provide a practical pathway toward high-resolution, operational boundary-layer wind measurements. Full article

Background/Objectives: Anastomoses under tension are associated with anastomotic leaks and strictures. In experimental surgery, anastomoses are frequently tested for their resistance to traction forces, but without the surgically untouched organ as a comparator. We therefore investigated whether and to what extent the breaking forces along the gastrointestinal tract differed in the intact intestinal organs to provide some data for the comparison of anastomoses to it and guide sample size estimation in the mouse. Methods: We included 54 mice of the Crl:CD1(ICR) stock and, as a comparator, 10 mice of the C57Bl/6J and 10 mice of the C57Bl/6NCrl strain of both sexes. We determined breaking forces using a motorised test stand. Results were compared via estimated marginal means with a control of the false-discovery rate by the Benjamini–Hochberg procedure. Results: In all mice strains, the resistance to traction forces was in a descending manner: stomach (mean (µ) ≥ 1.87 Newtons, standard deviation (σ) ≤ 0.63) > rectum(µ > 1.31 Newtons, σ ≤ 0.63) > caecum (µ > 1.1 Newtons, σ ≤ 0.37) > colon(µ > 0.93 Newtons, σ ≤ 0.31) > duodenum (µ > 0.65 Newtons, σ ≤ 0.28) > jejunum (µ > 0.5 N, σ ≤ 0.16) > ileum (µ ≥ 0.43 Newtons, σ ≤ 0.13). The analysis of variance showed a statistically significant effect of the mouse strain on breaking forces (F(2,497) = 16.81, p < 0.001). This was also the case for the investigated organ (F(6,497) = 104.18, p < 0.001) and the interaction between strain and organ (F(12,497) = 2, p = 0.022), indicating a difference between strains. Only the stomachs differed between the included strains; the stomach of Crl:CD1(ICR) sustained −0.81 Newtons (t = −6.23, p < 0.001) compared to those of C57Bl/6J, and −0.37 Newtons (t = −2.88, p = 0.006) compared to those of C57Bl/6NCrl. Other statistically significant differences were absent. Conclusions: Differences in breaking forces between inbred strains and outbred stock were only present for the stomach. Our results may provide a first baseline of breaking force measurements for other studies investigating anastomoses and the respective sample size analyses. Full article

Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand Cat Optimization (SCO), is proposed to enhance fault identification and predictive maintenance capabilities. The model utilized multivariate sensor data from cyber-physical and IoT-enabled robotic platforms to learn operational patterns and predict failures with enhanced reliability. The At-GRU provides deeper temporal feature extraction, thereby improving classification performance. The robustness of the proposed model is validated through analysis of a benchmark dataset for industrial robots, and the results demonstrate that the proposed model exhibits impressive predictive capacity, surpassing other prediction methods and predictive maintenance approaches. Additionally, the performance evaluation indicates a lower computational cost due to the lightweight gating architecture of GRU, combined with attention. The robotic motion is further optimized by the SCO algorithm, which reduces energy usage, execution delay, and trajectory deviations while ensuring smooth operation. Overall, the proposed work offers an intelligent and scalable solution for next-generation industrial automation systems. Furthermore, the proposed model demonstrates the real-world applicability and significant benefits of incorporating hybrid artificial intelligence models into real-time robot control applications for smart manufacturing environments. Full article

Background: Rheumatoid arthritis (RA) is a systemic, pro-inflammatory, autoimmune disease that mainly affects the joints in a symmetrical manner. Differential proteomic profiling through Sequential Window Acquisition of all Theoretical Fragment Ion Mass Spectra (SWATH-MS/MS) helps in a better understanding of the RA pathogenesis. In this study, we compared the differentially upregulated proteins with those associated with fibrosis to gain a deeper understanding of the fibrotic aspect of RA. Methods: We analyzed plasma proteomics data, previously obtained by SWATH-MS/MS. Our focus was on proteins associated with Leucine Rich Alpha2glycoprotein1 (LRG1) and we employed an in silico method. Results: We identified common proteins between RA and fibrosis. Among them, LRG1 and Serine Protease Inhibitor Clade A, Member 1 (SERPINA1) showed a high co-expression score in the gene clusters. LRG1 is both pro-inflammatory and pro-fibrotic, while SERPINA1 is an anti-inflammatory protein that inhibits pro-inflammatory and pro-fibrotic molecules (Elastase). Further, docking studies and a simulation study of the docked complexes with the analysis of Hydrogen bonds, Solvent Accessible Surface Area (SASA), Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) and Radius of gyration (Rg), suggested a strong interaction between the two partners, LRG1 and SERPINA1. Conclusions: Our study suggests that LRG1 may inhibit SERPINA1 and promote inflammation and fibrotic processes by disrupting SERPINA1’s primary function. Full article

Objectives: This prospective cohort study aimed to evaluate the accuracy and precision of static computer-guided, flapless implant surgery in partially edentulous patients, comparing the virtually planned and clinically achieved implant positions. Materials and Methods: From 2017 to 2022, 40 patients (20 males and 20 females) received a total of 129 implants across 59 partial rehabilitations, with 62 implants placed in the maxilla and 67 in the mandible. All interventions were performed by a single experienced operator using dental-supported stereolithographic guides and a flapless protocol. The discrepancy between planned and actual implant positions was measured using reverse engineering software, assessing linear deviations at the implant Platform (coronal) and apex, as well as angular deviations. Subgroup analyses were conducted based on the jaw (maxilla vs. mandible) and the type of surgical guide support (Kennedy classes I–IV). Results: The mean linear deviation was 1.16 ± 0.58 mm at the apex and 0.80 ± 0.41 mm at the implant Platform (coronal). The mean angular deviation was 3.23° ± 1.86°. Slightly higher deviations were observed in the mandible than in the maxilla. Group-wise analysis showed minor variations depending on the type of guide support. Conclusions: Static computer-guided surgery demonstrated measurable linear and angular deviations between planned and achieved implant positions. These discrepancies should be considered during treatment planning, especially in narrow ridges or Class I configurations. Full article

Considering the demand for nutritional assessment and product quality control in the tea industry, this work develops an effective electrochemical sensor based on gold nanoparticles electrodeposited onto a zeolitic imidazolate framework (Au/MOF(Zn)) for evaluating the antioxidant activity of tea subjected to microwave-assisted drying (MAD) through hydrogen peroxide (H₂O₂) scavenging. The MOF(Zn) enables uniform deposition of AuNPs, which significantly enhances the electrocatalytic oxidation of H₂O₂. The fabricated sensor exhibits a wide linear detection range from 400 μM to 1.8 mM for H₂O₂ with a correlation coefficient of 0.9983. The experimental results demonstrate acceptable selectivity, with signal interference <5% from common tea compounds like inorganic ions, sugars, and organic acids. Electrochemical methods, including cyclic voltammetry (CV) and differential pulse voltammetry (DPV) analysis, were employed to quantify H₂O₂ by measuring oxidation currents in phosphate-buffered saline (PBS, pH 7.0). The relative standard deviation (RSD) for repeatability and reproducibility was 5.1% and 6.8%, respectively, confirming high reliability. This sensor was successfully applied to assess antioxidant capacity in tea extracts obtained from fresh leaves subjected to microwave-assisted drying under varying power and duration. Results indicate that increasing microwave power enhances antioxidant activity, while prolonged drying at low power initially increases activity (peaking at 120 s) but reduces it upon extended exposure. Optimal antioxidant preservation was achieved at 120 s. This real-time, reliable sensing strategy offers theoretical foundations for optimizing tea processing parameters to preserve bioactive compounds, particularly polyphenols like catechins, thereby improving tea quality and health benefits. Full article

Background: Growing antibiotic resistance and the limited availability of key components in standard Helicobacter pylori treatments have driven the search for effective alternatives. Minocycline, with its broad-spectrum activity and favorable pharmacokinetics, has emerged as a promising substitute. This meta-analysis compares the safety and efficacy of minocycline-containing bismuth quadruple therapy (MBQT) to conventional first-line BQT regimens, incorporating data from the recent study by Lin et al. Methods: The inclusion criteria were randomized controlled trials (RCTs) with a target population of both treatment-naïve and previously treated patients diagnosed with Helicobacter pylori (H. pylori) infection. The intervention received by eligible patients was a minocycline–bismuth quadruple therapy (MBQT) regimen containing bismuth, minocycline, proton pump inhibitors (PPI), and any additional antibiotic with a minimum period of 2 weeks of administration. We excluded study designs other than RCT and clinical trials that include patients without confirmed H. pylori infection, animal populations, in vitro experiments, and reports of other outcomes that did not include a minimum intervention duration of 2 weeks. A comprehensive literature search was conducted on PubMed, EMBASE, Cochrane Library, and ScienceDirect from inception to 20 May 2025. After screening via Rayyan, data were extracted on an Excel spreadsheet. Quality was assessed using the Cochrane RoB 2.0 tool. Eligible randomized controlled trials (RCTs) were included and analyzed using RevMan 5.4. Outcomes assessed were intention-to-treat and per-protocol eradication rates. Adverse effects were compared among therapies. A random-effects model was used; an I² < 50% and p-value < 0.05 indicated homogeneity and significant results respectively. Results: Five RCTs with 7 interventions involving 2812 patients were included. The pooled odds ratio (OR) for MBQT in intention-to-treat (ITT) analysis was 1.25 (95% CI: 0.96–1.61), showing a non-significant trend. No heterogeneity was detected (I² = 0.0%). In the modified ITT (mITT) analysis (2 studies), MBQT showed higher eradication (OR: 1.70, 95% CI: 0.00–1042.90), but wide CI and high heterogeneity (I² = 70.7%) limited interpretation. All studies were included in the per-protocol (PP) analysis, which showed a statistically significant improvement with MBQT (OR: 1.67, 95% CI: 1.14–2.45) and low heterogeneity (I² = 5.2%), suggesting consistent results. Although not statistically significant, MBQT was associated with a slightly lower rate of adverse events compared to standard therapy (OR: 0.81, 95% CI: 0.59–1.12). I² = 50.6% showed moderate heterogeneity in safety outcomes. Discussion: the number of included RCTs was modest, with only five studies meeting eligibility criteria, and only two contributing to the modified intention-to-treat analysis. The risk-of-bias assessment showed variation in methodological quality across the included studies. Several studies exhibited high risk judgments in critical domains. particularly randomization, deviations from intervention, and selective reporting. Patients who completed the treatment benefited more from MBQT, which also had a comparable safety profile to conventional BQT regimens. In the treatment of H. pylori infection, MBQT may be considered a safe alternative for first-line treatment. Full article

This study presents a comprehensive thermal analysis, design, and optimization framework for electrothermal heating systems integrated into composite wing structures. Thermal behavior is first investigated using finite volume simulations conducted with a commercial solver. An in-house thermal solver is then developed based on the governing heat transfer equations and a second-order finite difference discretization scheme. The in-house solver is validated against the commercial solver, showing a maximum deviation of less than 1%. The validated solver is subsequently coupled with a genetic algorithm to perform multi-objective optimization of the electrothermal heating system. A novel correlation for the convection heat transfer coefficient over airfoil surfaces is developed based on extensive turbulent flow simulations and a genetic algorithm. The developed correlation equation has significantly lower percent relative error (from 34% to 6%) compared to flat plate correlations. The developed convection coefficient is incorporated into the optimization process. Key design variables, including heat generation intensity, heater strip dimensions, and the thermal conductivity of composite and surface protection materials, are included in the optimization process. An original objective function is formulated to simultaneously minimize electrical power consumption, prevent ice formation on the external surface, and limit internal temperatures to safe operating ranges for composite materials. The optimized design is evaluated under both spatially varying and constant convection heat transfer coefficients to assess the impact of convection modeling assumptions. The proposed methodology provides a unified and extensible framework for the optimal design of electrothermal ice protection systems and can be readily extended to three-dimensional composite wing configurations. Full article