Quality Prediction Research Articles

Assessing the Utility of ColabFold and AlphaMissense in Determining Missense Variant Pathogenicity for Congenital Myasthenic Syndromes

Background/Objectives: Congenital myasthenic syndromes (CMSs) are caused by variants in >30 genes with increasing numbers of variants of unknown significance (VUS) discovered by next-generation sequencing. Establishing VUS pathogenicity requires in vitro studies that slow diagnosis and treatment initiation. The recently developed protein structure prediction software AlphaFold2/ColabFold has revolutionized structural biology; such predictions have also been leveraged in AlphaMissense, which predicts ClinVar variant pathogenicity with 90% accuracy. Few reports, however, have tested these tools on rigorously characterized clinical data. We therefore assessed ColabFold and AlphaMissense as diagnostic aids for CMSs, using variants of the CHRN genes that encode the nicotinic acetylcholine receptor (nAChR). Methods: Utilizing a dataset of 61 clinically validated CHRN variants, (1) we evaluated the possibility of a ColabFold metric (either predicted structural disruption, prediction confidence, or prediction quality) that distinguishes variant pathogenicity; (2) we assessed AlphaMissense’s ability to differentiate variant pathogenicity; and (3) we compared AlphaMissense to the existing pathogenicity prediction programs AlamutVP and EVE. Results: Analyzing the variant effects on ColabFold CHRN structure prediction, prediction confidence, and prediction quality did not yield any reliable pathogenicity indicative metric. However, AlphaMissense predicted variant pathogenicity with 63.93% accuracy in our dataset—a much greater proportion than AlamutVP (27.87%) and EVE (28.33%). Conclusions: Emerging in silico tools can revolutionize genetic disease diagnosis—however, improvement, refinement, and clinical validation are imperative prior to practical acquisition.

Complex product quality prediction based on ResNeSt model combining class-aware attention mechanism and dynamically updating class weights

Complex product quality prediction based on ResNeSt model combining class-aware attention mechanism and dynamically updating class weights

A novel hybrid model for long-term water quality prediction with the ‘decomposition–inputs–prediction’ hierarchical optimization framework

A novel hybrid model for long-term water quality prediction with the ‘decomposition–inputs–prediction’ hierarchical optimization framework

A Numerical and Experimental Study to Compare Different IAQ-Based Smart Ventilation Techniques

Maintaining indoor environmental quality in residential buildings is essential for occupants’ comfort, productivity, and health, with effective mechanical ventilation playing a key role in removing or diluting indoor pollutants. A two-week experimental campaign was conducted in an apartment in Lyon, France, known for its poor urban air quality, assessing temperature, relative humidity, CO2, and PM2.5 concentrations. A model verification study was performed to compare experimental measurements against numerical modeling in the living room and bedroom, leading to errors in the accuracy of the sensors. In addition, this study also investigates the impact of different ventilation strategies on indoor air quality. This research evaluates a baseline mechanical exhaust-only ventilation approach with constant air volume against two innovative smart ventilation approaches: mechanical exhaust-only ventilation with humidity control and mechanical exhaust-only ventilation with room-level CO2 and humidity control. A key contribution of this research is the novel coupling of multizone simulation models (DOMUS and CONTAM) with a CFD tool to refine pressure coefficients on the building façade, which enhances the accuracy of indoor air quality predictions. The smart ventilation strategies showed improvements, including a 20% reduction in CO2 concentration and a 5% reduction in the third-quartile PM2.5 concentration, highlighting their effectiveness in enhancing ventilation and pollutant dilution. This research provides valuable insights into advanced ventilation strategies and modeling techniques in urban environments.

Air Quality Prediction Using Machine Learning Models: A Predictive Study in the Himalayan City of Rishikesh

Air Quality Prediction Using Machine Learning Models: A Predictive Study in the Himalayan City of Rishikesh

X-DoF: Automatic Degree of Freedom Subset Selection for Inverse Blocked Force Characterization

Abstract Selecting the proper set of Degrees of Freedom is essential in inverse (Blocked) Force calculation. Including too many Degrees of Freedom in the computation can lead to overfitting, resulting in inaccurate force estimations and poor prediction quality. The discrepancy arises from errors within the dataset, such as measurement noise or other artifacts. This paper presents a solution to the overfitting problem, introducing the X-DoF procedure to automatically identify the relevant subset of Blocked Force Degrees of Freedom. Its effectiveness is showcased through numerical and experimental validation and compared against regularization techniques.

Diversity Analysis and Comprehensive Evaluation of 101 Soybean (Glycine max L.) Germplasms Based on Sprout Quality Characteristics

Soybean sprouts are a common culinary vegetable due to their high nutrition and tasty flavors. To select soybean varieties with excellent sprout quality, 101 soybean materials were collected from different regions of China, and eight sprout quality parameters were determined for overall evaluation. The results showed that eight sprout quality parameters achieved varying degrees of difference and correlation. Based on the principal component analysis (PCA), three principal components were extracted, with a cumulative contribution rate of 78.314%. Further, the comprehensive evaluation value (D) of soybean sprout quality was calculated by membership function analysis based on PCA, and the quality of soybean sprouts was ranked accordingly. Subsequently, a regression equation for the prediction of soybean sprout quality was established using a stepwise regression analysis, and the model showed a good prediction performance (correlation coefficient of prediction > 0.8; residual predictive deviation > 2.0). On these grounds, it was proposed that the quality of soybean sprouts could be comprehensively predicted by four parameters: hypocotyl length, edible rate, 100-seed weight, and total isoflavone content and saponin content. In conclusion, this study provides excellent varieties for soybean sprout production and new variety breeding, and it provides an important reference for the prediction of soybean sprout quality.

Instructed Response Item Attention Checks in Mixed-mode Questionnaires: Evidence from a Probability-based Mail and Online Panel

Mostly in web surveys, attention checks have been proposed to identify inattentive respondents in self-administered surveys as previous research has argued that low-quality answers may introduce severe biases in data analyses. The increasing popularity of mixing survey modes for conducting probability-based surveys amplifies the need for systematic research on the implementation of attention checks in these survey designs. Our study investigates whether an instructed response item (IRI) can be an adequate predictor of response quality in a self-administered mixed-mode panel survey with a probability-based German general population sample. By doing so, we compare the results of the attention check with those of other response quality indicators. However, while the results for the online respondents were somewhat promising, the mail respondents result suggests that this response quality indicator seems reliable, so that we cannot recommend using IRIs in general population mixed-mode surveys, which include a mail option.

Emotional Status, Motor Dysfunction, and Cognitive Functioning as Predictors of Quality of Life in Physically Engaged Community-Dwelling Older Adults: A Structural Equation Modeling Approach

The demographic transition has become a reality, and it demands public policies to promote physical and mental health in aging. Group exercise emerges as a cost-effective and accessible alternative to promote active aging on a large scale, but to optimize the effectiveness of these programs, it is crucial to understand the underlying mechanisms that improve quality of life. This study aimed to explore the associations between emotional status, cognitive functioning, motor dysfunction, and their relationship with quality of life in community-dwelling older adults participating in a group physical exercise program. Structural equation modeling was used to explore these relationships in a sample of 190 older adults. Emotional state directly predicted all domains of quality of life. Motor dysfunction predicts the physical health domain of quality of life. Motor dysfunction and cognitive functioning are strongly correlated with emotional status. The fit indices of the final model are acceptable, and it demonstrates that within group-exercise dynamics, emotional status is the main component of quality-of-life promotion. Therefore, professionals designing group physical exercise programs to promote active aging should consider not only physical fitness, but also the integration of psychosocial elements, offering a holistic approach to enhancing overall well-being.

A Review on Storage Process Models for Improving Water Quality Modeling in Rivers

Water quality is intricately linked to the global water crisis since the availability of safe, clean water is essential for sustaining life and ensuring the well-being of communities worldwide. Pollutants such as industrial chemicals, agricultural runoff, and untreated sewage frequently enter rivers via surface runoff or direct discharges. This study provides an overview of the key mechanisms governing contaminant transport in rivers, with special attention to storage and hyporheic processes. The storage process conceptualizes a ubiquitous reactive boundary between the main channel (mobile zone) and its surrounding slower-flow areas (immobile zone). Research from the last five decades demonstrates the crucial role of storage and hyporheic zones in influencing solute residence time, nutrient cycling, and pollutant degradation. A review of solute transport models highlights significant advancements, including models like the transient storage model (TSM) and multirate mass transport (MRMT) model, which effectively capture complex storage zone dynamics and residence time distributions. However, more widely used models like the classical advection–dispersion equation (ADE) cannot hyporheic exchange, limiting their application in environments with significant storage contributions. Despite these advancements, challenges remain in accurately quantifying the relative contributions of storage zones to solute transport and degradation, especially in smaller streams dominated by hyporheic exchange. Future research should integrate detailed field observations with advanced numerical models to address these gaps and improve water quality predictions across diverse river systems.

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR ERROR PREVENTION AND QUALITY CONTROL IN MIG/MAG WELDING PROCESSES: A COMPREHENSIVE REVIEW

Welding techniques, particularly MIG/MAG (Metal Inert Gas/Metal Active Gas) processes, are essential for various industries, including automotive, construction, aerospace, and defense. Ensuring the quality and accuracy of welds is crucial for efficient production and product reliability. However, numerous issues can arise during MIG/MAG welding, often resulting in suboptimal quality. This study presents a comprehensive review of research efforts employing AI techniques to address errors and defects in MIG/MAG welding. The review systematically analyzes relevant studies published since 2018, focusing on three key aspects: datasets employed, methodologies and approaches adopted, and performance metrics reported. The findings reveal a significant adoption of both machine learning and deep learning techniques, with the choice of approach dependent on factors such as the nature of input data, welding process dynamics, and computational requirements. Deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated superior performance in tasks like image-based defect detection and time-series analysis for quality prediction. However, traditional machine learning algorithms have also been utilized, often in conjunction with dimensionality reduction or feature selection techniques. The review highlights the diverse range of performance metrics employed, including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE), with the selection contingent upon the specific task (classification or regression) and desired trade-off between different performance aspects. While many studies have reported promising results, with accuracy rates frequently exceeding 90%, challenges remain in real-world industrial settings, where factors such as noise, occlusions, and rapidly changing welding conditions can pose significant hurdles. This review serves as a comprehensive guide for researchers and practitioners working in the field of AI-assisted error prevention and quality control for MIG/MAG welding processes, highlighting current trends, methodologies, and future research directions.

Line-shape parameters and their temperature dependence for self-broadened CO2 lines in the 300 K- 1250 K range by requantized classical molecular dynamics simulations

Line-shape parameters for self-broadened CO2 transitions are predicted for temperatures ranging from 296 K to 1250 K, using requantized molecular dynamics simulations (rCMDS). The line broadening coefficient, the speed dependence component and the first-order line-mixing coefficient for lines with rotational quantum number from 2 to 100, have been determined from fits of the rCMDS spectra with the Voigt and speed dependent Voigt profiles. These parameters and their temperature dependences were compared with recent high-quality measurements at both room and high temperatures, showing good agreements for all considered parameters. In particular, this study highlights that the temperature dependence of the speed dependent Voigt line broadening coefficient in the HITRAN database needs to be corrected. Additionally, we demonstrate that the temperature dependence for the speed dependence of the line broadening differs from that of the line broadening, contrary to the assumption widely used in the literature. These findings confirm the quality of theoretical predictions using rCMDS. The data provided can be used to complete and improve spectroscopic databases for various applications.

Spousal Postpartum Social Support and Association with Sexual Function and Sexual Quality of Life among Breastfeeding Women

Background: The present study investigated the relationship between spousal support during postpartum period and the sexual function and sexual quality of life among lactating women. Methods: A cross-sectional study was carried out from October to March 2021 comprising 301 breastfeeding women referred to comprehensive health centers in Qazvin, Iran. The sampling method was performed in two stages. First, via cluster sampling, ten comprehensive health centers were randomly selected. Then based on the list of lactating mothers, 31 mothers in each center were randomly selected and invited to complete the survey. The survey included three psychometric instruments (i.e., Postpartum Partner Support Scale [PPSS], Female Sexual Function Index [FSFI] and Sexual Quality of Life-Female version [SQOL-F]) as well as a reproductive and demographic information questionnaire. The findings were analyzed using univariable and multivariable regression models at a significance level of p<0.05. Results: The participants had a mean age of 30.33 years and a mean length of marriage of 7.99 years. A total of 219 women were exclusively breastfeeding (72.8%), and 82 used a combination of breastfeeding and formula milk to feed their infants (27.2%). The mean (standard deviation) scores in the postpartum period were 61.34 out of 80 (SD=11.41) on the PPSS, 86.52 out of 108 (SD=19.68) on the SQOL-F, and 22.78 out of 36 (SD=6.20) on the FSFI. Based on adjusted multivariable regression models, spousal support in the postpartum period was a significant predictor of sexual quality of life (β=0.39, p<0.001) but not a significant predictor of sexual function. Sexual quality of life was a significant predictor of sexual function (β=0.44, p<0.001). Conclusion: Given the importance of spousal support during the postpartum period in relation to sexual quality of life and sexual function, designing and implementing counseling programs based on the role of spousal support during postpartum sexual life would be helpful.

Cross national patterns in educational inequalities in functional limitations among middle aged and older adults at two time points

IntroductionIn recent decades, the global population has aged rapidly while socioeconomic inequalities in health have widened, with older adults who are most disadvantaged experiencing the poorest health. Functional limitations are key predictors of disability and quality of life and are therefore considered an important measure of how well individuals and populations are aging. We determine if educational inequalities in functioning have widened over time and across countries. MethodsWe used data from five nationally representative surveys of aging, covering 14 high- and middle-income countries, with harmonized measures of functional limitations. We examined change over time in the number of functional limitations among adults aged 50-64 and 65 and older in each country as well as changes in educational inequality in functional limitations over time for both age groups. ResultsIn most high-income countries, the number of functional limitations decreased over time, but they increased in China and Mexico, with the largest increases occurring among adults 65 and older. Educational inequality in functional limitations among those aged 65 and older widened for several countries in our study, but for different reasons. In some countries, it widened due to increased limitations among the least-educated, while in others it widened due to declines in limitations among the most-educated. ConclusionGrowing educational inequality in functional limitations for older adults in several high- and middle-income countries suggests that, despite improvements in health and mortality, socioeconomic differences in functional health among older adults are widening.

Arowana cultivation water quality monitoring and prediction using autoregressive integrated moving average

<p class="western" align="justify"><span>Decorative fish is a fish that humans keep for amusement. There are many decorative fish that exist in this world, one of them is known as the Arowana fish (Scleropages Formosus). This fish is known around Asia including in Indonesia. However, to ensure the Arowana is living well is not easy. The water quality inside a farm must follow a strict balance. The pH of the water must not exceed or below 7 pH. Meanwhile, the total dissolved solid (TDS) salt must not exceed 1000 parts per million. If the balance collapsed, the Arowana fish will not grow. Thus, the owner must monitor the water to make sure that the water is ideal. There were many approaches including internet of things (IoT) solutions. However, they have weaknesses with prediction. Because of this reason, this study designed pH and TDS monitoring with autoregressive integrated moving average (ARIMA) as the algorithm. To achieve the solution, this study used experiment methodology as the research fundamental from top to bottom. According to the evaluation, this study found that the accuracy of ARIMA model is 98.12% for pH and 98.86% for TDS. On the contrary, the seasonal autoregressive integrated moving average (SARIMA) model has an accuracy of 98.52% for pH and 99.89% for TDS.</span></p>

Mindfulness improves psychological health and supports health behaviour cognitions: Evidence from a pragmatic RCT of a digital mindfulness-based intervention.

Mindfulness-based interventions can improve psychological health; yet the mechanisms of change are underexplored. This pre-registered remote RCT evaluated a freely accessible digital mindfulness programme aiming to improve well-being, mental health and sleep quality. Health behaviour cognitions were explored as possible mediators. Participants from 91 countries (N = 1247, Mage = 27.03 [9.04]) were randomized to 30 days of mindfulness practice or attention-matched control condition. Measures of well-being, depression, anxiety, stress, sleep quality, barriers self-efficacy, self-regulation and behavioural predictors (e.g., attitudes and behavioural intentions) were taken at baseline, 1-month (post-intervention) and 2-months (follow-up). Linear regression examined intervention effects between and within groups. Longitudinal mediation analyses explored indirect effects through health behaviour cognitions. Three hundred participants completed post-intervention measures. Those receiving mindfulness training reported significantly better well-being (Mdifference = 2.34, 95%CIs .45-4.24, p = .016), lower depression (Mdifference = -1.47, 95%CIs -2.38 to -.56, p = .002) and anxiety symptoms (Mdifference = -.77, 95%CIs -1.51 to -.02, p = .045) than controls. Improvements in well-being and depression were maintained at follow-up. Intervention effects on primary outcomes were mediated by attitudes towards health maintenance and behavioural intentions. Mediating effects of attitudes remained when controlling for prior scores in models of depression and well-being. Digital, self-administered mindfulness practice for 30 days meaningfully improved psychological health, at least partially due to improved attitudes towards health behaviours and stronger behavioural intentions. This trial found that digital mindfulness is a promising and scalable well-being tool for the general population, and highlighted its role in supporting health behaviours.

Letter to the Editor Regarding “Epidemiological predictors of quality of life and the role of early markers in children with cerebral palsy: A multi-centric cross-sectional study”

Letter to the Editor Regarding “Epidemiological predictors of quality of life and the role of early markers in children with cerebral palsy: A multi-centric cross-sectional study”

Enhancing surface water quality prediction efficiency in northeastern thailand using machine learning

Enhancing surface water quality prediction efficiency in northeastern thailand using machine learning

Enhancing water quality prediction with advanced machine learning techniques: An extreme gradient boosting model based on long short-term memory and autoencoder

Achieving carbon neutrality in the pulp and paper industry necessitates effectively recycling pulp and papermaking wastewater, where continuous monitoring of effluent quality indices is crucial. This study suggests a novel machine learning-based model named LSTMAE-XGBOOST that integrates the feature extraction capabilities of autoencoder, the sequential feature learning capabilities of LSTM, and the high prediction accuracy of XGBOOST. This model is capable of extracting the complex relationships, non-Gaussian characteristics, and time series features from the papermaking wastewater data, and it demonstrates superior predictive performance. Compared to traditional machine learning models, the proposed model exhibits higher prediction accuracy. Specifically, when contrasted with partial least squares regression, LSTMAE-XGBOOST achieves a 40% increase in R2 and a 35% reduction in RMSE. Further comparative assessments against other machine learning-based hybrid models with similar structures confirm the superiority of integrating LSTM and XGBOOST within the hybrid model approach. This study contributes a compelling methodology for modeling effluent quality indices, offering significant implications for environmental management in the pulp and paper industry.

A new method for judging thermal image quality with applications

Infrared thermal imaging, a non-destructive testing technology, measures the surface temperature of objects. Assessing thermal image quality is crucial for image monitoring, system design, algorithm optimization, and benchmarking. However, developing objective metrics that align with human perception is challenging due to the distinct structure of thermal images, which often feature high background temperatures and minimal variance between objects and the background. Existing methods typically target specific local features or overall image contrast, but new measures are needed to bridge the gap between objective performance and the unique characteristics of thermal images.We propose a novel image quality assessment (IQA) method inspired by the human vision system, specifically designed for thermal images, harmonizing local and global data. The primary contributions include (1) innovative local, global, and hybrid thermal quality assessment methods that deliver precise image quality predictions without needing reference images, (2) an experimental analysis evaluating the developed blind thermal IQA measure’s applicability to various thermal images, and (3) a comprehensive analysis of traditional IQA measure-based methods applied to publicly accessible thermal databases. Extensive simulations demonstrate our method’s competitive performance and strong alignment with human perception of image quality.