Good character is an essential factor for a teacher in transferring knowledge to students, alongside mastery of methods and subject matter. The lack of a standardized instrument for assessing the Tazkiyatun Nafs dimension among Madrasah teachers constitutes the primary research problem addressed in this study. This study aimed to develop and validate the Tazkiyatun Nafs instrument for Madrasah teachers using Rasch model analysis. The study population consisted of 405 Madrasah teachers, including 230 female and 175 male teachers. The data collection method used a 1–5 Likert-scale survey. The results of the Rasch analysis, conducted with the Winsteps program, indicated that 13 of 16 items on the Tazkiyatun Nafs instrument met the item-fit criteria and were distributed across four dimensions. The unidimensionality results showed that the Tazkiyatun Nafs instrument measures only a single construct; empirical results indicate that the proportion of variance explained by the measurement exceeds 30%. Item-fit statistics confirm a high level of conformity with the Rasch Model, as reflected by mean-square (MNSQ) values within the acceptable interval of 0.5 < MNSQ < 1.5. Reliability indicators also exhibit strong internal consistency, with item reliability reaching 0.99 and Cronbach’s Alpha recorded at 0.81. The findings of this study indicate a substantive alignment between the theoretical framework and the empirical evidence. The developed measurement model demonstrates coherence with the underlying conceptual structure, thereby offering policymakers practical utility for evaluating teachers’ competencies in the affective domain. Likewise, its reliability value demonstrated a high consistency index. Overall, it can be concluded that the Tazkiyatun Nafs instrument for Madrasah teachers has good psychometric properties and can be used for research and assessment purposes, particularly for evaluating affective characteristics among Madrasah teachers.

Hydrogen Fuel Cells (HFCs) are a central technology for advancing decarbonized mobility in Smart Transportation Networks (STN), yet their durability is limited by advanced electrochemical and thermal degradation. Anticipating such errors requires Predictive Maintenance Models (PMM) capable of extracting health indicators and predicting model behavior under dynamic operating conditions. This study develops Machine Learning (ML)-driven models for Fault Detection (FD), Remaining Useful Life (RUL) prediction, and prognostic reliability test in the Proton Exchange Membrane Fuel Cell (PEMFC) model. A 24-cell PEMFC stack dataset comprising 1500 h of operation under automotive load cycling was employed to analyze Supervised Learning (SL), Deep Temporal Networks (DTN), and a physics-guided hybrid residual model. Model training used cross-entropy and Mean Squared Error (MSE) objectives with causality-preserving temporal partitioning. Results proved that Deep Learning (DL) methods outperformed traditional classifiers, with the hybrid residual LSTM achieving 97.3% classification accuracy, 65.1 h RUL prediction RMSE, and early prognostic stabilization 85 h before error. Robustness analyses verified resilience against sensor noise, and computational profiling confirmed real-time feasibility with implication latency below 50 ms. These results establish that integrating physics-guided constraints into data-driven models yields accurate, deployable predictive maintenance for HFC, thereby enhancing safety, efficiency, and availability in STN.

Chronic spontaneous urticaria (CSU) is an inflammatory disease characterized by recurrent pruritic hives and/or angioedema. Many patients with CSU remain symptomatic despite standard-of-care, histamine 1-receptor antagonist (H1-AH) treatment. Dupilumab blocks IL-4/IL-13 signaling and is approved in multiple disease states associated with type 2 inflammation. In the phase 3 LIBERTY-CSU CUPID-A trial, dupilumab significantly reduced itch and hives severity in anti-immunoglobulin E (IgE)-naive patients with CSU uncontrolled with H1-AH. However, a replicate trial (CUPID-C) was required per US Food and Drug Administration registration requirements. To further evaluate the efficacy and safety of dupilumab vs placebo in anti-IgE-naive patients with CSU uncontrolled by H1-AH. The LIBERTY-CSU CUPID-C (2022-2024) was a randomized, placebo-controlled, double-blind, 24-week phase 3 trial using the same trial design as CUPID-A (2019-2021). CUPID-A and CUPID-C were performed in 10 countries in Asia, Europe, and North and South America with anti-IgE-naive patients aged 6 to 80 years with CSU uncontrolled with H1-AH. Data were analyzed from August to September 2024. Dupilumab or placebo. In CUPID-C and pooled CUPID-A and -C analyses, change from baseline at week 24 in Itch Severity Score over 7 days (ISS7) or Urticaria Activity Score over 7 days (UAS7), with the other as a key secondary end point, per regional regulatory requirements. The CUPID-C analysis included 151 participants (mean [SD] age, 44.7 [16.9] years; 106 females [70.2%]), of whom 77 (51%) were taking H1-AH at a dosage higher than recommended, and 90 (59.6%) had a baseline UAS7 of 28 or greater. Significant improvements in ISS7 and UAS7 were observed with dupilumab vs placebo at week 24. Least squares mean (SE) changes were -8.64 (1.41) vs -6.10 (1.40), respectively (difference: -2.54 points [95% CI, -4.65 to -0.43]; P = .02) for ISS7 and -15.86 (2.66) vs -11.21 (2.65), respectively (difference: -4.65 points [95% CI, -8.65 to -0.65]; P = .02) for UAS7. CUPID-A and -C combined data (289 participants) demonstrated greater improvements in UAS7 and ISS7. Safety outcomes were generally consistent with the known dupilumab safety profile; 77 patients (53.5%) taking dupilumab vs 81 patients (55.9%) taking placebo reported treatment-emergent adverse events in the pooled analysis. The CUPID-C randomized clinical trial confirmed CUPID-A findings, and pooled data demonstrated dupilumab significantly reduced urticaria activity by reducing itch and hives severity in anti-IgE-naive patients with CSU who remained symptomatic despite the use of H1-AH. ClinicalTrials.gov Identifier: NCT04180488.

This analysis aimed to evaluate the efficacy of brexpiprazole 2 or 3 mg/day for the treatment of agitation associated with dementia due to Alzheimer's disease, on the basis of pooled clinical trial data. Data were pooled from two similarly designed, phase 3, 12-week, multicenter, randomized, double-blind, placebo-controlled trials of fixed-dose brexpiprazole in participants in care facilities or community-based settings who had agitation associated with dementia due to Alzheimer's disease. Efficacy outcomes included Cohen-Mansfield Agitation Inventory (CMAI) total score (which measures the frequency of 29 different agitation symptoms), Clinical Global Impression-Severity of illness (CGI-S) score, CMAI factor scores (aggressive behaviors, physically nonaggressive behaviors, and verbally agitated behaviors), and response rates. A sensitivity analysis included a third trial with flexible dosing. In total, 621 participants were randomized (brexpiprazole, 368; placebo, 253), and completion rates were 320/368 (87.0%) and 225/253 (88.9%), respectively. Mean (SD) baseline CMAI total scores were: brexpiprazole 76.9 (17.2) points and placebo 75.5 (18.0) points. Over 12 weeks, CMAI total scores improved by least squares mean (SE) - 22.8 (0.8) points for brexpiprazole and - 18.3 (1.0) points for placebo, with a least squares mean difference between treatment arms of - 4.50 points (95% CI - 6.90 to - 2.10; p < 0.001; Cohen's d 0.30). CGI-S, CMAI factor, and response analyses also showed greater improvement with brexpiprazole versus placebo. The sensitivity analysis was supportive. Brexpiprazole 2 or 3 mg/day reduced agitation symptoms compared with placebo over 12 weeks in this large, pooled sample of participants with dementia due to Alzheimer's disease. ClinicalTrials.gov identifiers: NCT01862640, NCT03548584, and NCT01922258.

Early and reliable detection of bacterial, fungal, and viral infections in fruits and leaves is essential for improving crop productivity, preventing disease spread, and supporting food security. Most existing approaches are domain-specific and struggle to generalize across diverse plant organs or varying image qualities. To address this challenge, we propose a novel domain-independent classification framework that integrates quality-metric features—Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM)—with an adapted lightweight Convolutional Neural Network (CNN). This is the first approach that explores quality measures as features for addressing challenges of classification of fruits and leaves infected by virus, fungus, and bacteria. The method first performs connected-component analysis on K-means clusters generated from R, G, B, and Gray channels to isolate disease-relevant regions and extract quality-based features. These features are fused with visual features extracted from the RGB images using a multimodal CNN architecture. Extensive experiments conducted on the proposed fruit–leaf dataset and four external benchmark datasets demonstrate that the model achieves high accuracy, strong robustness to blur, noise, rotation, and scaling, and superior generalization performance compared with state-of-the-art methods. Cross-domain evaluations further confirm that the proposed method is domain-independent and reliable for the classification of fruits and leaves infected by bacteria, fungi, and viruses. Received: 24 September 2025 | Revised: 2 December 2025 | Accepted: 25 December 2025 Conflicts of Interest Palaiahnakote Shivakumara is the Editor-in-Chief for Artificial Intelligence and Applications, and he is not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Poornima Basatti Hanuma Gowda: Conceptualization, Software, Data curation, Writing – original draft, Visualization. Basavanna Mahadevappa: Formal analysis, Investigation, Supervision, Project administration. Shivakumara Palaiahnakote: Methodology, Writing – review &amp; editing. Muhammad Hammad Saleem: Validation. Niranjan Mallappa Hanumanthu: Resources.

The aim of the study was to analyse the correlation between maternally expressed gene 3 (MEG3) expression and heart rate variability (HRV) in heart failure patients with ventricular arrhythmia (VA). A total of 130 heart failure patients, treated from July 2018 to March 2021, were prospectively selected and divided into a non-VA group (n = 85) and a VA group (n = 45) according to the presence or absence of VA. The correlations of serum MEG3 expression and HRV with cardiac function indicators were investigated by Pearson correlation analysis. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of MEG3, HRV and their combination for the occurrence of heart failure complicated with VA. The VA group had a higher left atrial diameter (LAD) and left ventricular end-diastolic diameter (LVEDD) but lower left ventricular ejection fraction (LVEF) and ratio of mitral early diastolic peak velocity (E) to late peak atrial filling velocity (A) (E/A) than the non-VA group (p < 0.05). The serum MEG3 expression was negatively correlated with: standard deviation of the average RR intervals calculated over five-minute segments in the 24-hour record (SDANN), SDANN index, standard deviation of normal-to-normal RR interval (SDNN) index, percentage of differences between adjacent normal RR intervals exceeding 50 ms (PNN50), root mean square of successive difference (RMSSD), low frequency (LF), high frequency (HF), very low frequency (VLF), LVEF and E/A (r < 0, p < 0.05). The serum MEG3 expression was positively correlated with LAD and LVEDD (r > 0, p < 0.05). The areas under the ROC curves of MEG3, SDANN, SDANN index, SDNN index, PNN50, RMSSD, LF, HF, VLF and their combination for the prediction of the occurrence of heart failure complicated with VA were 0.812, 0.731, 0.737, 0.689, 0.860, 0.783, 0.791, 0.856, 0.769 and 0.966, respectively. MEG3 combined with HRV can effectively predict the occurrence of heart failure complicated with VA.

Functional reference limit: objectively defining the physiological relationship between serum vitamin D and parathyroid hormone.

Improving the efficiency and sustainability of wastewater treatment plants (WWTPs) is essential for protecting the environment and ensuring energy-conscious operations. This research introduces an advanced AI-integrated framework tailored to accurately predict water quality indicators during the aeration stage—one of the most critical phases in the treatment process. At the heart of this approach lies a Parallel Adaptive Neuro-Fuzzy Inference System (PANFIS), which orchestrates multiple ANFIS models running concurrently. This parallelism boosts the system’s scalability, resilience, and predictive accuracy. The architecture follows a structured, multi-step pipeline. The process starts with a Autoencoder-Generative Adversarial Network (AutoGAN) that selects key features and fixes data anomalies, ensuring cleaner inputs. Then, Self-Organizing Maps (SOM) is used to smartly initialize ANFIS membership functions, improving the model’s ability to learn underlying patterns. To further refine model performance, the framework integrates metaheuristic optimization techniques—Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)—that fine-tune the hybrid SOM-PANFIS setup. A defining innovation of this work is the introduction of a three-tier attention mechanism within the PANFIS structure. Attention is strategically applied at the feature level, membership function level, and rule level, enabling the system to focus adaptively on the most critical information at each stage of reasoning. Evaluation across standard performance metrics—Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2—demonstrates the effectiveness of the proposed method. The SOM-PSO-PANFIS model achieved an MSE of 0.0005, RMSE of 0.0233, and R2 of 0.9751. After integrating attention mechanisms, the SOM-PSO-PANFIS-Attention configuration significantly improved performance, reaching an MSE of 0.0003, RMSE of 0.0183, and R2 of 0.9866. These results affirm the model’s capability to handle the complex and nonlinear behavior inherent in wastewater data, delivering a solution that is not only highly accurate but also scalable for real-world WWTP deployment. This study ultimately showcases the transformative potential of AI-driven hybrid models in modernizing wastewater treatment.

Introduction: Understanding the biomechanics of the upper limb is of considerable interest in both clinical and engineering domains. Estimating elbow joint moments and triceps force plays a pivotal role in modelling musculoskeletal function. However, the use of electromyography (EMG) data is often constrained by challenges such as signal noise and calibration complexity. The objective of this study is to determine the elbow joint moment and triceps force during a Rest Pause Triceps Dumbbell Kickback exercise. Methods: This investigation utilized kinematic assessments from a cohort of 14 participants with diverse anthropometric profiles. A range of machine learning and deep learning models were employed to predict joint torque and triceps muscle force, including deep neural networks (DNN), long short-term memory networks (LSTM), convolutional neural networks (CNN), decision trees (DT), linear regression (LR), support vector machines (SVM), and random forests (RF). Model performance was systematically evaluated using multiple statistical metrics: Mean Squared Residuals (MSR), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Correlation Coefficient (R). Results: The analytical outcomes demonstrated that the LSTM model yielded the highest predictive accuracy, achieving a correlation coefficient of R = 0.98374 when six input features (time, mass, forearm mass, upper arm mass, elbow angle, and height) were used. In descending order of R values, the performance of the remaining models was as follows: RF (0.92793), CNN (0.92106), DT (0.88812), DNN (0.75769), SVM (0.70011), and LR (0.44690). These findings underscore the potential of LSTM in capturing the temporal dynamics essential for biomechanical prediction. Conclusion: The findings from this study provide new insights into data-driven biomechanics and suggest that LSTM-based models may offer a promising alternative to EMG-based approaches. Accurate prediction of joint moments has significant implications for the real-time control of assistive technologies, particularly active orthoses in the future.

Real-time retrieval of high-precision ZTD maps using GNSS observation

Understanding of water and nitrogen (NO3-N) dynamics within the crop root zone is important in devising efficient nitrogen management and environmental protection strategies. Field measurement on water and nutrient movements supported with modelling studies have been widely used to predict the water and NO3-N distribution in the crop root zone. The objective of this study was to simulate the water and NO3-N distribution in the soil under drip fertigated tomato irrigated with water of different qualities under polyhouse conditions. Field data were collected on spatial and temporal distribution of water and available NO3-N during growing season. HYDRUS-2D model was calibrated and validated for simulating the water and NO3-N distribution in crop root zone using coefficient of determination (R2), root mean square error (RMSE), index of agreement, and Nash–Sutcliffe model efficiency (NSE) as model performance indicators. The R2 for calibration and validation period ranged from 0.70 to 0.99 for water distribution, and 0.70 to 0.96 for NO3-N distribution implying that observed and predicted values were highly correlated. The value of RMSE ranged from 0.004 to 0.0016 cm.cm-3 for water, and 0.002-0.006 mg.ml-1 for NO3-N distribution. The index of agreement value varied from 0.86-0.98 for water distribution, and 0.89-0.99 for NO3-N distribution. The lower values of NSE (0.17) represented less satisfactory performance, whereas higher values (0.98) for water distribution represented best fit of the observed and predicted values; and (-) 0.09 indicated that the mean observed NO3-N content offered a better predictor than the model. The NSE value of 0.94 for NO3-N distribution, showed that HYDRUS-2D demonstrated acceptable level of accuracy in NO3-N prediction. The study concluded that the HYDRUS model performed well for predicting the water and NO3-N distribution in tomato crop irrigated with water of different qualities under polyhouse conditions.

The aim of the present in vitro study was to investigate the influence of scan paths on the accuracy (trueness and precision) of intraoral scanning of an implant impression on an edentulous patient. An epoxy resin maxillary cast was made with six bone level implants (NobelParallel Conical Connection RP). The implants were placed at the sites of the central incisors, canines, and first molars. The transgingival component (multi-unit) was screwed onto the implants. The scanbodies (Elos Accurate IO 2C-A) were then screwed onto the multi-units. The cast was run through a coordinate measurement machine to obtain a control model. Then, five different scanning paths were applied by a single operator: the zigzag technique (ZZT); the zigzag technique with palatal (ZZTP); the wrap technique (WT); the wrap technique with palatal (WTP); and the big zigzag technique (BZZT). Finally, each scan was compared with the control model. Results were assessed by one-way ANOVA and linear mixed effects models with a significance level of P 0.05. The results showed that scan paths ZZT and ZZTP had significantly lower absolute positioning errors and root mean square errors than the other techniques (P 0.0001). For distances between consecutive implant axes and for absolute vertical errors, their superiority was borderline (P 0.10). Overall, techniques ZZT and ZZTP were equally performant and proved to be the most accurate scan paths. The present in vitro experimental study demonstrates that the scan path can have an influence on the accuracy of the optical impression for full-arch rehabilitations on implants.

Climate change has intensified natural hazards, including droughts, which have caused substantial damage in South Korea. Drought, characterized by prolonged moisture deficiency, is typically assessed using drought indices that reflect meteorological conditions. This study examined the influence of various meteorological and climate indices on drought variability in the Yeongsan and Seomjin River basins. The Standardized Precipitation Index (SPI) was used to represent drought conditions, and its monthly variations were predicted using the Long Short-Term Memory (LSTM) algorithm. To assess model performance, four statistical indicators—Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Nash–Sutcliffe Efficiency (NSE), and the Coefficient of Determination (R2)—were employed. The LSTM model that utilized both precipitation and drought indices as input data showed the best performance, achieving an MSE of 0.2, RMSE of 0.477, NSE of 0.77, and R2 of 0.78. Overall predictive performance ranged from 0.298 to 0.347 (MSE), 0.546 to 0.589 (RMSE), 0.578 to 0.628 (NSE), and 0.580 to 0.675 (R2). This study highlights the effectiveness of LSTM in predicting drought conditions and the value of incorporating meteorological and climatic indices. The results can support improved drought hazard assessment and management strategies in South Korea.

Abstract RATIONALE: Allergic bronchopulmonary aspergillosis (ABPA) is a progressive lung disease driven by type 2 inflammation and characterized by hypersensitivity to the fungus Aspergillus fumigatus after it colonizes the airways in patients with asthma. Asthma patients with ABPA experience more severe clinical outcomes than those without ABPA, highlighting the need for treatments that address the immunological basis of ABPA. Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for interleukins (IL)-4 and IL-13, key and central drivers of type 2 inflammation in multiple diseases. The phase 2 LIBERTY ABPA AIRED (NCT04442269) study evaluated the efficacy of dupilumab in patients with ABPA. METHODS: 62 adult patients received dupilumab 300 mg every 2 weeks or placebo for 24 to 52 weeks. The primary endpoint was change from baseline in pre-bronchodilator forced expiratory volume in 1 second (FEV1) at Week 24, assessed through formal hypothesis testing. Secondary endpoints included annualized rate of severe respiratory exacerbations and change from baseline in St. George's Respiratory Questionnaire (SGRQ) total score, assessed through within-group, descriptive statistics. RESULTS: At Week 24, the least squares mean (SE) change from baseline in pre-bronchodilator FEV1 was 0.203 L (0.0482) in dupilumab-treated patients and 0.002 L (0.0558) in placebo-treated patients, resulting in a mean treatment difference (95% CI) of 0.201 L (0.0768, 0.3256); P = 0.0022. Improvements were seen as early as Week 2. Additionally, ≥100 mL improvement was achieved in dupilumab patients by Week 4 and then maintained at all subsequent time points (Figure). Dupilumab also reduced the rate of severe respiratory exacerbations by 55.2% compared with placebo, with adjusted annual rates (95% CI) of 0.695 (0.35, 1.36) vs 1.551 (0.75, 3.22). While mean SGRQ total scores were lower at each post-baseline timepoint than at baseline in both treatment groups, reductions were greater for dupilumab than for placebo. A mean reduction of &amp;gt;12 points in SGRQ total score was achieved for patients receiving dupilumab at each post-baseline timepoint, compared to the largest mean reduction of 9.46 points at Week 12 in patients receiving placebo. CONCLUSIONS: In the phase 2 LIBERTY ABPA AIRED study, dupilumab significantly improved lung function and substantially reduced severe respiratory exacerbations while enhancing quality of life in patients with ABPA during the 24- to 52-week treatment period.

The application of wireless communication is very complex and there is always a demand for accurate Quality of Service (QoS) for estimating and optimize the spectrum allocation in Cognitive Radio Networks (CRNs). Current machine learning models frequently struggle to adapt effectively to change the network conditions due to significant computational complexity and constrained real-time performance. This paper presents a Hybrid Deep Learning and Ensemble Regression Model (HyDERM) to address these limitations in real-time QoS prediction and spectrum decision-making. The proposed HyDERM model integrates Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN) to enhance accuracy and effectiveness. Key metrics such as Signal-to-Noise Ratio (SNR), bandwidth availability, network load, latency, packet loss, and interference level are evaluated for QoS assessment. The model is assessed using five advanced machine learning techniques: Polynomial Regression, SVR, RF, Gradient Boosting, and ANN. The results demonstrate that HyDERM achieves a R² value of 0.96, exceeding all the compared models. It reduces Mean Squared Error (MSE) by 23% and Mean Absolute Error (MAE) by 19%, illustrating its effectiveness. The results show that the suggested HyDERM can improve frequency efficiency and allow for smooth communication, making it a feasible choice for the next generation of wireless networks.

BackgroundEtrasimod, an oral, selective sphingosine 1‐phosphate 1, 4 and 5 receptor modulator approved for the treatment of ulcerative colitis, has been studied in immune‐mediated inflammatory diseases, including alopecia areata (AA).ObjectivesTo evaluate the efficacy and safety of etrasimod in adults with moderate to severe AA.MethodsThis Phase 2, randomized, double‐blind, placebo‐controlled trial included patients (aged ≥18 years) with moderate to severe AA, defined as a Severity of Alopecia Tool (SALT) score of ≥25. Patients were sequentially enrolled into two cohorts. Cohort 1 included patients (SALT score of ≥50) randomized 2:1 to etrasimod 2 mg or placebo. Cohort 2 included patients (SALT score ≥25 to <95) randomized 4:1:2 to etrasimod 3 mg, 2 mg or placebo. Patients completed a 24‐week double‐blind and 28‐week open‐label extension period. The primary endpoint was percent change from baseline (%CFB) in SALT score at Week 24. Safety was monitored throughout the trial.ResultsEighty patients were randomized to etrasimod 2 mg (n = 31), 3 mg (n = 25) or placebo (n = 24). At Week 24, least squares mean (SE) percent changes from baseline in SALT score for the etrasimod 2 mg, 3 mg and placebo groups were −13.8 (8.6), −21.4 (6.9) and 0.35 (8.9), respectively. The least squares mean difference (95% CI; P value) in SALT score %CFB of etrasimod 2 mg and 3 mg versus placebo was −14.1 (−38.9 to 10.6; p = 0.2579) and − 21.8 (−44.4 to 0.9; p = 0.0592), respectively; statistical superiority was not achieved. The proportions of patients achieving ≥30%, ≥50% or ≥75% improvement in baseline SALT score at Week 24 were generally numerically higher in etrasimod groups versus placebo. Treatment‐emergent adverse events occurred in 67.7%, 80.0% and 78.3% of patients receiving etrasimod 2 mg, 3 mg and placebo, respectively, by Week 24.ConclusionsEtrasimod did not meet the primary and secondary efficacy endpoints, but efficacy was numerically higher with etrasimod than with placebo. The etrasimod clinical programme for AA has been discontinued. Etrasimod was well tolerated, and its safety profile was consistent with other etrasimod studies to date.Trial RegistrationClinicalTrials.gov: NCT04556734.

It is a general practice to use robust estimates to improve ratio estimators using functions of the parameters of an auxiliary variable. In this study, a new class of robust estimators based upon the minimum covariance determinant (MCD) and the minimum volume ellipsoid (MVE) robust covariance estimates have been suggested for estimating population variance in the presence of outlier values in the data set for the simple random sampling. The expression for the mean square error (MSE) of the proposed class of estimators is derived from the first degree of approximation. The efficiency of the proposed class of robust estimators is compared with some competing estimators discussed in the literature and found that proposed estimators are better than other mentioned estimators here. In addition, real data set and simulation studies are performed to present the efficiencies of the estimators. We demonstrate theoretically and numerically that the proposed class of estimators performs better than all other competitor estimators under all situations.

Background:: The quality of water directly or indirectly impacts the health and environmental well-being. Data about water quality can be evaluated using a Water Quality Index (WQI). Computing WQI is a quick and affordable technique to accurately summarise the quality of water. Objective:: The objective of this study is to find strategies for data preparation to categorize a dataset on the water quality in two remote Indian villages in different geographic locations, to predict the quality of water, and to identify low-quality water before it is made accessible for human consumption. Methods:: To accomplish this task, four water quality features Nitrate, pH, Residual Chlorine, and Total Dissolved Solids which are crucial for human consumption, are considered to dictate the quality of water. Methods used in handling these features include five steps that are data preprocessing with min-max normalization, finding WQI, using feature correlation to identify parameter importance with WQI, application of supervised machine learning regression models such as Random Forest (RF), Multiple Linear Regression (MLR), Gradient Boosting (GB) and Support Vector Machine (SVM) for WQI prediction. Then, a variety of machine learning classification models, including K-Nearest Neighbour (KNN), Support Vector Classifier (SVC), and Multi-layer Perceptron (MLP), are ensembled with Logistic Regression (LR), acting as a meta learner, to create a stack ensemble model classifier to predict the Water Quality Class (WQC) more accurately. Results:: The examination of the testing model revealed that RF regression and MLR algorithms performed best in predicting the WQI with mean absolute error (MAE) of 0.003 and 0.001 respectively. Mean square error (MSE), root mean square error (RMSE), R squared (R2), and Explained Variance Score (EVS) findings are 0.002,0.005,0.988 and 0.998 respectively with RF while 0.001,0.031,0.999 and 0.999 respectively with MLR. Meanwhile, for predicting WQC, the stack model classifier showed the best performance with an Accuracy of 0.936, F1 score of 0.93, and Matthews Correlation Coefficient (MCC) of 0.893 for the dataset of Lalpura and Accuracy of 0.991, F1 Score of 0.991 and MCC of 0.981 respectively for the dataset of Heingang. Conclusion:: This study explores a method for predicting water quality that combines easy and feasible water quality measurements with machine learning. The stack model classifier performed best for multiclass classification, according to this study. To ensure that the highest quality of water is given throughout the year, information from this study will motivate researchers to look into the underlying root causes of the quality variations.

Background: The prediction of the stock price is considered to be one of the most fascinating and important research and patent topics in the financial sector. Aims: Making more accurate predictions is a difficult and significant task because the financial industry supports investors and the national economy. Objectives: The DWM is used to adjust the connection weights and biases to enhance prediction precision and convergence rate. DWM was proposed as a method to reduce system error by changing the weights of various levels. The methods for predictable changes in weight were provided together with the computational difficulty. Methods: An extreme learning machine (ELM) is a fast-learning method for training a singlehidden layer neural network (SLFN). However, the model's learning process is ineffective or incomplete due to the randomly chosen weights and biases of the input's hidden layers. Hence, this article presents a deterministic weight modification (DWM) based ELM called DWM-ELM for predicting the stock price. Results: The calculated results showed that DWM-ELM had the best predictive performance, with RMSE (root mean square error) of 0.0096, MAE (mean absolute error) of 0.0563, 0.0428, MAPE (mean absolute percentage error) of 1.7045, and DS (Directional Symmetry) of 89.34. Conclusion: The experimental results showed that, in comparison to other well-known prediction algorithms, the suggested DWM+ELM prediction model offers better prediction performance.

Activity level is a benchmark to document patient recovery; however, there is a lack of instrumentation to measure activity level specific to the foot and ankle. The purpose of this study was to develop a foot and ankle activity level scale (FAALS) instrument that will serve as an effective clinical tool for practitioners by assigning an activity level to patients. This was a 4-phase study with 3 rounds of data collection (n = 1432). Phase 1 was item generation using an expert panel to determine content validity (101 items). In phase 2, all items from phase 1 were piloted (n = 100) to remove poorly performing items (77 items). In phase 3 (n = 505), item reduction, reliability, and validity Rasch analyses were conducted, leaving a total of 22 items. Validity was assessed using outfit mean-square (MNSQ) and infit MNSQ statistics, with acceptable values between 0.5 and 1.5. An additional round of data collection was completed to serve as a validation data set to confirm FAALS instrument structure and psychometric analytics (n = 827). Correlation analysis was performed to assess convergent and divergent validity. Multiple linear regression analysis was conducted to determine whether the FAALS could detect differences in scores between groups with previously proven factors that affect functional status. Person reliability was 0.92 and item reliability was 1.00, demonstrating excellent reliability. There was excellent evidence of validity, with mean-square values between 0.5 and 1.5. The 22 FAALS items are summed for a total score that corresponds to one of 4 activity levels. The FAALS instrument demonstrated sensitivity in the ability to discern between groups with expected foot and ankle functional differences for previous ankle surgery status, t(502) = -7.69, P < .001, and body mass index, t(502) = -3.41, P < .001. The FAALS instrument is a short, clinically useful tool to measure activity level specific to the foot and ankle. FAALS normative values provide valuable information for physician-patient communication, which may serve to facilitate shared decision-making, improve postoperative care, and allow physicians to track recovery progress.