Quality Prediction Research Articles

The Correlation between Cardiac Magnetic Resonance Findings and Post-COVID-19: The Impact of Myocardial Injury on Quality of Life.

In the post-COVID-19 era, there is growing concern regarding its impact on cardiovascular health and the following effects on the overall quality of life of affected individuals. This research seeks to investigate cardiac magnetic resonance (CMR) findings following COVID-19 and their impact on the quality of life of affected individuals. An observational, cross-sectional study was conducted in consecutive patients with persistent cardiovascular symptoms after COVID-19 who were referred to CMR due to suspected myocardial injury. In addition, patients completed a questionnaire about symptoms and the quality of life during the post-COVID-19 period. In this study, 85 patients were included. The study population consisted of patients with a mean age of 42.5 ± 13.4 years, predominantly women, who made up 69.4% of the study population, while men made up 30.6%. CMR findings showed non-ischemic myocardial injury in 78.8% of patients and myocardial edema in 14.1% of patients. Late pericardial enhancement was present in 40% of patients and pericardial effusion in 51.8% of patients. Pericardial effusion (p = 0.001) was more prevalent in patients who reported more pronounced symptoms in the post-COVID-19 period compared to the acute infection phase. Predictors of lower quality of life in the post-COVID-19 period were the presence of irregular heartbeat (p = 0.039), cardiovascular problems that last longer than 12 weeks (p = 0.018), and the presence of pericardial effusion (p = 0.037). Acute myocarditis was observed in a minority of patients after COVID-19, while non-ischemic LGE pattern and pericardial effusion were observed in the majority. Quality of life was worse during the post-COVID-19 period in patients with CMR abnormalities, primarily in patients with pericardial effusion. Also, irregular heartbeat, cardiovascular symptoms that last longer than 12 weeks, as well as pericardial effusion were independent predictors of lower quality of life during the post-COVID-19 period.

Hybrid deep learning based prediction for water quality of plain watershed

Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R2 by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.

Cross cutting symptoms, liver function and perceived immune status as predictors of quality of life in patients with Hepatitis B and C: the mediating role of social support and resilience

Cross cutting symptoms, liver function and perceived immune status as predictors of quality of life in patients with Hepatitis B and C: the mediating role of social support and resilience

Evaluating the impact of urban traffic patterns on air pollution emissions in Dublin: a regression model using google project air view data and traffic data

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

Evaluation of whether commonly used risk assessment tools are applicable to women in forensic psychiatric institutions

ObjectiveBy providing a structured assessment of specific risk factors, risk assessment tools allow statements to be made about the likelihood of future recidivism in people who have committed a crime. These tools were originally developed for and primarily tested in men and are mainly based on the usual criminological background of men. Despite significant progress in the last decade, there is still a lack of empirical research on female offenders, especially female forensic psychiatric inpatients. To improve prognosis in female offenders, we performed a retrospective study to compare the predictive quality of the following risk assessment tools: PCL-R, LSI-R, HCR-20 v3, FAM, and VRAG-R. MethodData were collected from the information available in the medical files of 525 female patients who had been discharged between 2001 and 2017. We examined the ability of the tools to predict general and violent recidivism by comparing the predictions with information from the Federal Central Criminal Register. ResultsOverall, the prediction instruments had moderate to good predictive performance, and the study confirmed their general applicability to female forensic psychiatric patients. ConclusionThe LSI-R proved to be particularly valid for general recidivism, and both, LSI-R and HCR-20 v3, for violent recidivism.

AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis

The industrial sector is undergoing a transformation thanks to artificial intelligence (AI), which is bringing revolutionary changes to a number of areas like robots and automation, supply chain efficiency, predictive maintenance, and quality control and assurance. This thorough analysis investigates AI's significant influence on contemporary manufacturing procedures. Artificial Intelligence (AI) improves machine capabilities in robotics and automation, creating more intelligent and flexible systems. Robots can now complete complicated tasks with more flexibility and precision thanks to AI-driven developments, which boosts manufacturing efficiency and human-robot cooperation. Another crucial area where AI has a big impact is predictive maintenance. With the use of machine learning algorithms and real-time data analysis, artificial intelligence (AI) helps manufacturers anticipate equipment faults before they happen. By taking a proactive stance, unplanned downtime is decreased, resource usage is optimized, and machinery longevity is increased. AI has a significant positive impact on quality assurance and control because to cutting-edge technologies like data analytics and computer vision. Artificial intelligence (AI) solutions facilitate predictive quality management, improve fault identification, and offer real-time monitoring. Higher quality standards, less waste, and more customer happiness are the outcomes of this. Artificial Intelligence (AI) tackles issues related to supplier performance, accurate forecasting, and inventory management in supply chain optimization. Automation and analytics powered by AI simplify supply chain processes, increase transparency, and facilitate improved decision-making, which lowers costs and increases flexibility. All things considered, integrating AI into manufacturing processes offers a strategic advantage by promoting increased accuracy, flexibility, and efficiency. The continued developments in AI technology have the potential to significantly influence how manufacturing develops in the future by creating new avenues for creativity and excellence in the sector.

Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature

Water quality assessment and prediction play crucial roles in ensuring the sustainability and safety of freshwater resources. This study aims to enhance water quality assessment and prediction by integrating advanced machine learning models with XAI techniques. Traditional methods, such as the water quality index, often require extensive data collection and laboratory analysis, making them resource-intensive. The weighted arithmetic water quality index is employed alongside machine learning models, specifically RF, LightGBM, and XGBoost, to predict water quality. The models' performance was evaluated using metrics such as MAE, RMSE, R2, and R. The results demonstrated high predictive accuracy, with XGBoost showing the best performance (R2 = 0.992, R = 0.996, MAE = 0.825, and RMSE = 1.381). Additionally, SHAP were used to interpret the model's predictions, revealing that COD and BOD are the most influential factors in determining water quality, while electrical conductivity, chloride, and nitrate had minimal impact. High dissolved oxygen levels were associated with lower water quality index, indicative of excellent water quality, while pH consistently influenced predictions. The findings suggest that the proposed approach offers a reliable and interpretable method for water quality prediction, which can significantly benefit water specialists and decision-makers.

Predictors of quality of life, economic impact, and loss to follow-up after open tibial shaft fractures in Ghana.

Open tibia fractures are associated with substantial morbidity and impact on quality of life. Despite increasing incidence in low-resource settings, most open tibia fracture research comes from high-resource settings. This study aimed to assess the impact of socioeconomic status on treatment modality and evaluate predictors of health-related quality of life following open tibia fractures in Ghana. A single-center prospective observational study was conducted in Kumasi, Ghana, from May 2020 to April 2022. Adults with open tibial shaft fractures presenting within 2 weeks of injury were eligible. Demographics, comorbidities, socioeconomic factors, and hospital course were collected at enrollment. Follow-up was scheduled at 8, 12, 26, and 52 weeks. A telephone survey assessing reasons for loss to follow-up was initiated on enrollment completion. A total of 180 patients were enrolled. Most patients were employed before injury (79.9%), had government insurance (67.2%), and were from rural areas (59.4%). Fracture classification was primarily Gustilo-Anderson type 3A (49.1%). No relationship between socioeconomic predictors and treatment modality was identified. The largest barriers to follow-up were preference for bonesetter treatment (63.1%), treatment cost (48.8%), and travel cost (29.8%). Of the lost to follow-up patients contacted, 67 (79.8%) reported receiving traditional bonesetter care. Reasons for seeking traditional bonesetter care included ease of access (83.6%), lower cost (77.6%), and familial influence (50.7%). No association was identified between socioeconomic predictors and choice of treatment. Bonesetter treatment plays a substantial role in the care of open tibia fractures in Ghana, largely because of ease of access and lower cost.

Prediction of mine water quality by the Seq2Seq model based on attention mechanism

In recent years, as China's industrialization level has advanced, the issue of environmental pollution, particularly mine water pollution, has become increasingly severe. Water quality prediction is a fundamental aspect of water resource protection and a critical approach to addressing the water resource crisis. For improvement in water quality prediction, this research first analyzes the characteristics of mine water quality changes and provides a brief overview of water quality prediction. Subsequently, the Long Short-Term Memory and Sequence to Sequence (Seq2Seq) models, derived from Artificial Neural Networks, are introduced. The Seq2Seq water quality prediction model is implemented, incorporating the attention mechanism. Experimental validation confirms the effectiveness of the proposed model. The results demonstrate that the attention mechanism-based Seq2Seq model accurately predicts parameters such as pH value, Dissolved Oxygen, ammonia nitrogen, and Chemical Oxygen Demand, exhibiting a high degree of consistency with actual results. They play a vital role in assessing the health of the water and its ability to support aquatic life. The change of these indicators can reflect the degree and type of water pollution. Moreover, the Seq2Seq + attention model stands out with the lowest predicted Root Mean Square Error of 0.309. Notably, in comparison to the traditional Seq2Seq model, the incorporation of attention mechanisms in the Seq2Seq model results in a substantial 2.94 reduction in Mean Absolute Error. This research on the Seq2Seq water quality prediction model with attention mechanism provides valuable insights and references for future endeavors in water quality prediction.

1683P Self-care confidence as a predictor of symptom burden and quality of life in people living with myeloproliferative neoplasms

1683P Self-care confidence as a predictor of symptom burden and quality of life in people living with myeloproliferative neoplasms

Predictors of quality of life (QOL) and treatment adherence among children with nephrotic syndrome.

Any significant changes in the growth and development of any aspect of life will ultimately affect the health-related quality of life (HRQOL) of children with nephrotic syndrome. Various factors can influence treatment adherence, which can promote or decline the child's quality of life (QOL). Objective: To assess the quality of life and adherence to treatment regimen and to identify the predictors of QOL and adherence to treatment regimen among children with nephrotic syndrome. A cross-sectional study was conducted among 59 children with nephrotic syndrome who attended the Paediatric Department of AIIMS, Bhubaneswar, using the PedsQL 4.0 generic core scale to assess the quality of life and adherence response scale. Both descriptive and inferential statistics were performed using the SPSS 20 version. The overall QOL median score was 93.48, with an IQR of 18.48, which indicated good QOL. A median score of treatment adherence was 16 with IQR 5, which indicated that the overall treatment adherence was good. Type of nephrotic syndrome (P = 0.014), developmental stage (P = 0.018), education of mother (P = 0.026), and occupation of mother (P = 0.026) were the variables predicted QOL. Duration of disease (P = 0.006) and duration of therapy (P = 0.005) significantly predicted treatment adherence. Children need continuous reinforcement on treatment adherence strategies to attain and maintain good QOL so that it can help to reduce the disease severity. Controlling the predictors that influence the QOL and treatment adherence has to be counseled among the parents so that their adherence can be sustained throughout the disease process to maintain good QOL.

Predictors of quality of professional life and well-being of medical residents and interns: Role of gender and workplace spirituality.

The well-being of physicians in their professional lives has a major influence on the quality of medical care and patient outcomes. This study explored how Professional Quality of Life (ProQOL) and psychological well-being are connected to workplace spirituality (WPS) and demographic factors like age and gender. It also compared the average levels of ProQOL, psychological well-being, and WPS between medical residents and interns. In a cross-sectional study, 230 medical residents and interns completed three questionnaires, including ProQOL, Ryff Scale Psychological Wellbeing-Short Form, and WPS. The data was analysed using Pearson correlation, independent t-test, ANOVA, and multiple linear regression modeling. Residents and interns had moderate ProQOL levels and higher work spirituality. However, medical residents scored lower in occupational spirituality and compassionate care fulfillment compared to interns while experiencing higher levels of emotional exhaustion and secondary mental strain. Gender disparities were significant, with women scoring higher in compassionate care fulfillment but lower in secondary mental strain. Regression analysis showed that occupational spirituality positively predicted professional, compassionate care fulfillment (B = 0.910, p < 0.001) but negatively predicted emotional exhaustion in both groups. These findings underscore the importance of understanding factors like gender and work spirituality to enhance the well-being and quality of care provided by medical residents and interns.

No-reference stereoscopic image quality assessment based on binocular collaboration

Stereoscopic images typically consist of left and right views along with depth information. Assessing the quality of stereoscopic/3D images (SIQA) is often more complex than that of 2D images due to scene disparities between the left and right views and the intricate process of fusion in binocular vision. To address the problem of quality prediction bias of multi-distortion images, we investigated the visual physiology and the processing of visual information by the primary visual cortex of the human brain and proposed a no-reference stereoscopic image quality evaluation method. The method mainly includes an innovative end-to-end NR-SIQA neural network with a picture patch generation algorithm. The algorithm generates a saliency map by fusing the left and right views and then guides the image cropping in the database based on the saliency map. The proposed models are validated and compared based on publicly available databases. The results show that the model and algorithm together outperform the state-of-the-art NR-SIQA metric in the LIVE 3D database and the WIVC 3D database, and have excellent results in the specific noise metric. The model generalization experiments demonstrate a certain degree of generality of our proposed model.

Self-Tuning Transfer Dynamic Convolution Autoencoder for Quality Prediction of Multimode Processes With Shifts

Self-Tuning Transfer Dynamic Convolution Autoencoder for Quality Prediction of Multimode Processes With Shifts

Approach to Aircraft Handling Qualities Prediction

For the decades that followed the publication of the Cooper–Harper report that formalized a standard and universally recognized handling qualities pilot rating scale, researchers have sought to correlate pilot compensation—as well as physical and mental workload—with the assigned rating. A quantitative correlation remains elusive. In recent years, new physiological measurement devices have been developed that, together with software processing tools, can provide accurate measures of psychophysiological measures, including cognitive workload, distraction, and high/low engagement, based on electroencephalogram and electrocardiogram measures (i.e., brain waves and heart rate variability). The pilot compensation referred to in the Cooper–Harper scale is also a function of task performance measures that reflect aircraft characteristics and inceptor activity that reflects upon physical workload. Using a new piloted simulation test database generated in Manned Flight Simulator’s containerized rotary-wing simulator with 10 experienced test pilots, a machine-learning-based software algorithm that integrates a disparate mix of pilot–vehicle system, physiological, and task performance measures was used to further develop an approach to predict handling qualities levels and ratings.

Water Quality Prediction using Random Forest Algorithm and Optimization

Water Quality Prediction using Random Forest Algorithm and Optimization

Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree of bran layer remaining (DOR) datasets is established with image capturing and processing (grain color, texture, and shape features extraction). The mapping relationship between DOR and the DOM is in-depth analyzed. Rice grain DOR typical machine learning and deep learning prediction models are established. The results indicate that the optimized Catboost model can be established with cross-validation and grid search method, with the best accuracy improving from 84.28% to 91.24%, achieving precision 91.31%, recall 90.89%, and F1-score 91.07%. Shapley additive explanations analysis indicates that color, texture, and shape feature affect Catboost prediction accuracy, the feature importance: color>texture>shape. The YCbCr-Cb_ske and GLCM-Contrast features make the most significant contribution to rice milling quality prediction. The feature importance provides theoretical and practical guidancefor grain DOM prediction model. PRACTICAL APPLICATION: Rice milling degree prediction and detection are valuable for rice milling process in practical application. In this paper, image processing and machine learning methods provide an automated, nondestructive, and cost-effective way to predict the quality of rice. The study may serve as a valuable reference for improving rice milling methods, retaining rice nutrition, and reducing broken rice yield.

Ensemble of naive Bayes, decision tree, and random forest to predict air quality

&lt;p&gt;Air quality prediction is an important research issue because air quality can affect many areas of life. This study aims to predict air quality using the ensemble method and compare the results with the prediction results using a single method. The proposed ensemble method is built from three singlesupervised methods: naïve Bayes, decision trees, and random forests. The results show that the ensemble method performs better than the single methods. The ensemble method achieves the highest performance with scores of 99.89% accuracy, 79.6% precision, 79.81% recall, and 79.7% F1-score. The performance comparison between single and ensemble models is expected to provide information on the percentage increase in predictive model performance metrics from the single to ensemble methods.&lt;/p&gt;

Quality Prediction Algorithm for Flow Drilling Screw Joining of 590 MPa High-Strength Steel and 6xxx Series Aluminum Alloy

Flow drilling screw (FDS) process is applied to various components such as car bodies and battery cases due to its advantage of enabling one-sided joining. Various studies have been conducted on the correlation between monitored process parameters and joint quality. These correlations suggest the potential for non-destructive classification or prediction of joint quality using process monitoring signals. In this study, the effect of FDS process parameters on joint quality was analyzed, and a decision tree-based quality prediction model for classifying joint quality was developed. The material combination consisted of a 1.8 mm thick SGAFC 590DP as the upper plate and a 3.0 mm thick Al 6061 as the lower plate. To develop the joint quality prediction algorithm, the effect of process parameters in each process step on joint quality was analyzed. It is used as input data to identify various features. The output data were generated by classifying the products into three categories from class 0 to class 2. Based on the extracted feature data, a machine learning algorithm was trained to develop the joint quality prediction model.

Probability based dynamic soft label assignment for object detection

By defining effective supervision labels for network training, the performance of object detectors can be improved without incurring additional inference costs. Current label assignment strategies generally require two steps: first, constructing a positive sample candidate bag, and then designing labels for these samples. However, the construction of candidate bag of positive samples may result in some noisy samples being introduced into the label assignment process. We explore a single-step label assignment approach: directly generating a probability map as labels for all samples. We design the label assignment approach from the following perspectives: Firstly, it should be able to reduce the impact of noise samples. Secondly, each sample should be treated differently because each one matches the target to a different extent, which assists the network to learn more valuable information from high-quality samples. We propose a probability-based dynamic soft label assignment method. Instead of dividing the samples into positive and negative samples, a probability map, which is calculated based on prediction quality and prior knowledge, is used to supervise all anchor points of the classification branch. The weight of prior knowledge in the labels decreases as the network improves the quality of instance predictions, as a way to reduce noise samples introduced by prior knowledge. By using continuous probability values as labels to supervise the classification branch, the network is able to focus on high-quality samples. As demonstrated in the experiments on the MS COCO benchmark, our label assignment method achieves 40.9% AP in the ResNet-50 under 1x schedule, which improves FCOS performance by approximately 2.0% AP. The code has been available at https://github.com/Liyi4578/PDSLA.