High Reliability Research Articles

Depression recognition over fusion of visual and vocal expression using artificial intelligence

<div class="WordSection1"><p>Depression is a mental illness that usually goes untreated in people and can have catastrophic consequences, including suicidal thoughts. Counselling services are widely available, but because depression is a stigmatized illness, many people who are depressed decide not to seek help. Therefore, it is essential to develop an automated system that can recognize depression in individuals before it worsens. In this study, a novel approach is proposed for identifying depression using a combination of visual and vocal emotions. Long short-term memory (LSTM) is used to assess verbal expressions and convolutional neural networks (CNN) to analyze facial expressions. The proposed system is trained using features of depression from the distress analysis interview corpus (DAIC) dataset and tested on videos of college students with frontal faces. The proposed approach is effective in detecting depression in individuals, with high accuracy and reliability.</p></div>

Friedreich Ataxia Caregiver-Reported Health Index: Development of a Novel, Disease-Specific Caregiver-Reported Outcome Measure.

The Friedreich ataxia (FRDA) scientific community needs access to patient-centered outcome measures that satisfy regulatory guidelines and are capable of tracking clinically meaningful changes in FRDA disease burden. The objective of this research was to develop a novel, disease-specific caregiver-reported outcome measure for use in FRDA research and clinical care. In prior work, we conducted qualitative interviews and a cross-sectional study of FRDA caregivers and patients to determine the symptoms of greatest importance to individuals with FRDA. We designed the Friedreich Ataxia Caregiver-Reported Health Index (FACR-HI) to serially measure the symptoms of greatest importance to patients and utilized factor analysis, beta testing, reliability testing, and cross-sectional subgroup analysis to further evaluate and optimize this disease-specific outcome measure. The FACR-HI was designed to measure total disease burden and disease burden in 18 symptomatic domains. The FACR-HI total score demonstrated high internal consistency (Cronbach's α = 0.98) and test-retest reliability (intraclass correlation coefficient = 0.96). Beta interview participants found the FACR-HI to be highly relevant, comprehensive, and easy to use. FACR-HI total and subscale scores were associated with functional staging for ataxia scores and speech impairment. Initial evaluation of the FACR-HI supports its content validity, test-retest reliability, and construct validity as a caregiver-reported outcome measure for assessing how pediatric individuals with FRDA feel and function. The FACR-HI provides a potential mechanism to quantify changes in multifactorial FRDA disease burden during future clinical trials.

Power converters analyzed in energy storage systems to enhance the performance of the smart grid application

This paper implements and compares the existing power supply converters for an energy storage system to determine the best suited for the smart grid application. A survey of different DC-DC converters is carried out to analyze the battery's overall performance. The main objective is to identify this application's most appropriate energy storage device. The advantages of this technology have high efficiency and reliability, which can connect various energy sources and reduce conduction losses in the power converters. Through the converter control, reference currents are imposed to charge the battery. The battery nominal voltage needs to change to see which type of converter is the most suitable and robust. Simulation results show that the operating ranges of boost-buck, buck-boost, and buck-boost converters with negative output voltage enhance the efficiency of battery and renewable energy sources and compared the DC converters to know the functional voltage for the energy storage system. The power converter's efficiency and control facility will allow us to link the energy storage system with the power grid. The overall installation is established using MATLAB/Simulink software.

The validity and reliability of professional learning community instruments in small schools peninsular Malaysia

A professional learning community (PLC) instrument was developed to determine the level of PLC practice in small schools in Peninsular Malaysia. This study was conducted in Perak and Negeri Sembilan to determine the instrument's reliability and validity. Exploratory factor analysis (EFA) and item reliability analysis were used to determine the questionnaire’s reliability and validity. Next, the average congruence percentage (ACP) is used to determine the reliability test between expert assessors. Experts approved the validity and reliability of the instrument before the EFA test was conducted. All five constructs have high-reliability index values between 0.86–0.95. Next, the EFA analysis shows five dimensions in the PLC instrument with factor loadings ranging from 0.61–0.84. The findings also show that the variance explained in the data is 68.99% with an Eigenvalue greater than 1. This result indicates that all items are received with high approval. In addition, a very high-reliability coefficient value, α=0.96. The results prove that this PLC instrument has high validity and reliability and can measure the level of PLC implementation practices in small schools in Peninsular Malaysia.

Xác định acetamiprid bằng phương pháp quang học sử dụng dung dịch nano vàng được ổn định bằng Polydiallyldimethylammoni clorua

On this research, the development of a simple, cost-efective and highly sensitive spectrophotometric method for acetamiprid detection based on the localized surface plasmon resonance of polydiallyldimethylammonium chloridestabilized gold nanoparticles. By naked eyes, one can detect acetamiprid by the visual change of gold solution color from red to purple, corresponding to the shift of maximum absorbance wavelength in the UV-Vis spectrum. Quantification of acetamiprid is possible through a linear relationship between the maximum absorbance wavelength and the acetamiprid concentrations in the range of 8×10-8 -2×10-6 M. Under the optimum conditions including pH 5, NaCl concentration of 0.02 M and incubation time 20 min, the present method has high precision (RSD < 5%), very low limit of detection (LOD) of 2.7×10-8 M, limit of quantification (LOQ) of 8.2×10-8 M. This method has been successfully applied to determine acetamiprid in several pesticide samples and vegetable samples. The measurement results of acetamiprid in the pesticide samples of the research method with the HPLC-UV method showed a difference of < 2%, while for the vegetable sample, the difference between the research method and the LC-MS/method method/ MS < 5% shows that the method has high accuracy and reliability.

Advanced Image Analysis and Machine Learning Models for Accurate Cover Factor and Porosity Prediction in Knitted Fabrics: Tailored Applications in Sportswear, Swimwear, and Casual Wear

This paper presents a study focused on developing robust algorithms for cover factor and porosity calculation through digital image analysis. Computational models based on machine learning for efficient cover factor prediction based on fabric parameters have also been developed. Five algorithms were devised and implemented in MATLAB: the single threshold algorithm (ST); multiple linear threshold algorithms, ML-1 and ML-2; and algorithms with multiple thresholds obtained by the Otzu method, MT-1 and MT-2. These algorithms were applied to knitted fabrics used for football, swimming, and leisure. Algorithms ML-1 and MT-1, employing multiple thresholds, outperformed the single threshold algorithm. The ML-1 variant yielded the highest average porosity value at 95.24%, indicating the importance of adaptable thresholding in image analysis. Comparative analysis revealed that algorithm variants ML-2 and MT-2 obtain lower cover factors compared to ML-1 and MT-1 but can detect potential void areas in fabrics with higher reliability. Algorithm MT-1 proved to be the most sensitive when it came to distinguishing between different fabric samples. Computational models that were developed based on random tree, random forest, and SMOreg machine learning algorithms predicted cover factor based on fabric parameters with up to 95% accuracy.

Geospatial Artificial Intelligence for Estimating Daytime and Nighttime Nitrogen Dioxide Concentration Variations in Taiwan: A Spatial Prediction Model

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Research on a capacitive particle analysis smoke detector.

Smoke detectors face the challenges of increasing accuracy, sensitivity, and high reliability in complex use environments to ensure the timeliness, accuracy, and reliability of very early fire detection. The improvement and innovation of the principle and algorithm for smoke particle concentration detection provide opportunities for improving the performance of the detector. This study represents a new refinement of the smoke concentration detection principle based on capacitive detection of cell structures, and detection signals are processed by a multiscale smoke particle concentration detection algorithm to calculate smoke concentration. Through experiments, it was found that the detector provides effective detection of smoke particle concentrations ranging from 0 to 10% obs/m; moreover, when the detection accuracy is greater than a certain number of parts per million (PPM), the sensitivity of the detector can reach the PPM level; furthermore, the detector can detect smoke particle concentrations higher than the PPM level accuracy even in an environment with a certain concentration of petroliferous and dust particles of different sizes.

Ambulatory Quality Improvement Despite COVID-19: Blueprint for a Successful System for Continuous Improvement.

In this article, we describe our experience developing and implementing a multipronged approach to improve performance across a strategic subset of quality measures within primary care. Detailed techniques include data visualization and analytics, process reengineering, team engagement, visual project management, continuous improvement methods and training, and incentives and recognition. We achieved positive change across 12 high priority measures which we deemed the "High Value Framework (HVF)" by fostering a collaborative, nonpunitive, problem-solving culture. We focused on measures that had the greatest potential for impact from a clinical, reimbursement, and reputational perspective. More importantly, we sustained gains despite the challenges posed by the COVID-19 pandemic, thereby demonstrating programmatic resilience and high process reliability. This systematic approach serves as a practical blueprint for other healthcare entities seeking to navigate the complexities of quality improvement in a dynamic environment. The model provides a strategic framework for prioritizing and standardizing quality measures, effectively engaging stakeholders, and managing organizational change. Our model emerged from a need to address real-world operational challenges, rather than as an academic or theoretical exercise, and was developed independently of existing literature on measure prioritization and standardization at the time of its inception.

Predictive Analysis of Campus Placement of Student Using Machine Learning Algorithms

In today's competitive job market, student campus placement process becomes very important role in the academic journey of students and the reputation of educational institutions alike. As the demand for skilled professionals continues to rise, the ability to accurately predict and optimize student placement outcomes becomes increasingly essential. This research paper proposes a machine learning algorithms analysis to forecast the campus placement of students with the help of various academic factors. The dataset contains historical placement records of students received from open-source repository named as kaggle. It includes information such as student academic achievements, internship, project, workshop, soft skill, extracurricular activities, and placement outcomes. Through data preprocessing and feature engineering, we transform raw data into a structured format suitable for predictive modeling. One hot encoding (Dummy encoding) applied on dataset to represent categorical variables as numerical values to provide more information to the model about the categorical variables. Predictive analysis work on various machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines (SVM), XGBoost, AdaBoost, KNN and Naive Byes. The results of analysis demonstrate in predicting student placement outcomes with high accuracy and reliability. Accuracy is calculated in the form of precision, recall, f1-score, and support. Furthermore, we conduct feature importance analysis to identify the importance of different predictors in determining placement success. This research paper study can help to academic institutions that can identify at-risk students early in their academic journey and to improve their employability. In conclusion, research underscores the potential of predictive analysis and machine learning in optimizing student placement outcomes. By harnessing the power of data-driven insights, institutes can make use of placement prediction model to check the likelihood of campus placement of students.

A versatile ratiometric fluorescence nanoprobe for the determination of clonazepam in patients' plasma samples.

Despite the necessity of the study of therapeutic drug monitoring of clonazepam (CLZ), there are only a few fast detection methods available for determining CLZ in biological media. This study aims to develop a cost-effective and ratiometric probe for the quantification of CLZ in plasma samples. Fluorescent polydopamine nanoparticles were produced through a self-polymerization process at a pH of 8.5. Rhodamine B molecules were employed as a fluorescent reference material, emitting stable fluorescence in the visible range. The fabricated probe exhibited a specific detection capability for CLZ. The fluorescence emission of the probe was enhanced in two concentration ranges: from 50 ng/mL to 1.0 μg/mL and from 1.0 to 15.0 μg/mL with a lower limit of quantification of 50 ng/mL, indicating the sensitivity of the probe for detecting CLZ plasma levels. The accuracy of the probe is favorable which could be recommended for CLZ monitoring in the biological media. Furthermore, this probe is highly specific towards CLZ in the presence of various interfering agents which is mainly caused by its ratiometric nature. The developed platform showed high reliability in quantifying CLZ concentrations in patients' plasma samples. Hence, the fabricated probe could be recommended as a reliable method for the routine detection of CLZ in clinical settings.

Cohort profile of an early life observational cohort in China: Bone and MicroBiOme onset (BAMBOO) study.

The Bone And MicroBiOme Onset (BAMBOO) study is an ongoing prospective observational cohort study conducted in Tianjin, China, aiming to determine age-appropriate trajectories for microbiome maturation and bone development and to identify the influence of dietary factors in the process. The recruitment started in September 2021 and was completed in February 2023. A total of 1380 subjects were recruited, 690 at birth (group 1) and 690 at 6 months of age (group 2). Groups 1 and 2 will be followed up for 12 months and 36 months, respectively. The age of the mothers was 31.1±3.7 (mean±SD), and the birth weight of infants was 3.3±0.5 kg with an incidence of caesarean section 50.4%. Food diary information of the first 100 subjects showed that 64 food items were introduced by 6 months. A pilot microbiome analysis revealed that at the species level, bacterial communities were composed of mostly Bacteroides dorei, Bacteroides vulgatus and Escherichia coli, which were consistent with that of previous reports. Feasibility assessments of breast milk vitamin D and human milk oligosaccharides were validated through certified reference measurements. The early data assessment showed a high reliability of the data generated from this study. Data collection will be completed in August 2025. Four stage-statistical analyses will be performed as the cohort reaches certain age thresholds before the final report. Analysis of BAMBOO data will be used to develop age-appropriate trajectories for microbiome maturation and bone development for children aged 0-3 years and investigate the contribution of dietary factors in the process. ChiCTR2100049972.

Enhancing Probabilistic Solar PV Forecasting: Integrating the NB-DST Method with Deterministic Models

Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy.

Impedance-Based Method for Predictive Stability Assessment

Impedance-based analysis methods enable a more specific and earlier foundation for assessing harmonic stability in decentralized converter-based power plants compared to the conventional compliance testing in the grid connection process. Essentially, they can be implemented as black-box model approaches without the necessity to disclose internal control models. Initially, only knowledge of the input impedances of the planned grid connection point and the planned PV system is required for application. For this purpose, the method of impedance spectroscopy for inverters has already been developed as a means to determine the effective impedance profile and the internal harmonic sources of inverters, allowing for the description of the frequency-dependent behavior of individual units. The time- and frequency-dependent grid impedance at the grid connection point (GCP) has also been successfully measured in several campaigns on medium and low-voltage grids. Through the coordinated application of both measurement methods, a predictive harmonic assessment is intended in the future, ensuring high planning reliability and grid quality even in grids with a high penetration of power electronics-coupled systems. This paper provides an overview of the current state of research on impedance-based stability criteria and presents measurement methods for practical implementation. Furthermore, it outlines remaining open questions until application in the field.

A Chinese Version of the Aesthetics Scale: Cultural Adaptation and Psychological Measurement

The Aesthetics Scale is one of the most commonly used aesthetics evaluation scale in HCI. In this study, a Chinese version of the Aesthetics Scale (C-AS) was translated and validated using the forward–backward translation method. The C-AS passed the content validity and face validity tests. We then recruited 310 Chinese users and tested the psychological properties of the C-AS using the China Railway Customer Service Center ticketing platform as the test material. The experimental results showed that the C-AS has high reliability, validity, and sensitivity. Following rigorous testing, the results of this work may provide Chinese UX practitioners with a standardized aesthetics scale that researchers and practitioners can use with confidence.

Chinese Emotional Speech Audiometry Project (CESAP): Establishment and Validation of a New Material Set With Emotionally Neutral Disyllabic Words.

The Chinese Emotional Speech Audiometry Project (CESAP) aims to establish a new material set for Chinese speech audiometry tests, which can be used in both neutral and emotional prosody settings. As the first endeavor of CESAP, this study demonstrates the development of the material foundation and reports its validation in neutral prosody. In the development step, 40 phonetically balanced word lists consisting of 30 Chinese disyllabic words with neutral valence were first generated. In a following affective rating experiment, 35 word lists were qualified for validation based on the familiarity and valence ratings from 30 normal-hearing (NH) participants. For validation, performance-intensity functions of each word list were fitted with responses from 60 NH subjects under six presentation levels (-1, 3, 5, 7, 11, and 20 dB HL). The final material set was determined by the intelligibility scores at each decibel level and the mean slopes. First, 35 lists satisfied the criteria of phonetic balance, limited repetitions, high familiarity, and neutral valence and were selected for validation. Second, 15 lists were compiled in the final material set based on the pairwise differences in intelligibility scores and the fitted 20%-80% slopes. The established material set had high reliability and validity and was sensitive to detect intelligibility changes (50% slope: 6.20%/dB; 20%-80% slope: 5.45%/dB), with small covariance of variation for thresholds (15%), 50% slope (12%), and 20%-80% slope (12%). Our final material set of 15 word lists takes the initiative to control the emotional aspect of audiometry tests, which enriches available Mandarin speech recognition materials and warrants future assessments in emotional prosody among populations with hearing impairments. https://doi.org/10.23641/asha.25742814.

Validation of the Fibromyalgia Rapid Screening Tool in patients with axial spondyloarthritis: Polish version adaptation and value in clinical practice.

Fibromyalgia frequently co-occurs with axial spondyloarthritis. The Fibromyalgia Rapid Screening Tool (FiRST) is a well-recognized screening tool for fibromyalgia and has been translated into multiple languages. Yet, it has not been adapted into Polish, nor has it been validated in the context of axial spondyloarthritis. This study aimed to create a Polish version of FiRST, evaluate its psychometric properties, and conduct its validation among patients with axial spondyloarthritis. We translated and performed the cross-cultural adaptation of FiRST into Polish, followed by its validation on a cohort of 174 patients with axial spondyloarthritis. For criterion validity, we employed the ACR 2016 fibromyalgia diagnostic criteria as the 'gold standard' for fibromyalgia diagnosis. The Polish version of FiRST demonstrated marginally acceptable internal consistency with a Cronbach's α coefficient of 0.644, but exhibited high test-retest reliability, with a global score correlation coefficient of 0.75 (P <0.001). ROC analysis indicated a good performance of the translated questionnaire (AUC of 0.803). The accuracy of the derived cut-off value for the global score (5+ points, consistent with the original instrument) was 75.3%, featuring higher specificity (82.6%) than sensitivity (63.1%), and a fair level of diagnostic agreement, as indicated by Cohen's κ coefficient of 0.46. Our study successfully produced a validated Polish version of FiRST. Although it may not be the ideal tool for screening in axial spondyloarthritis cases and should be used cautiously in research, it proves to be a useful instrument in daily clinical settings.

The interrelationship among trust, cooperativity and reliability: an analysis of doctor-patient interaction

Trust-related issues have attracted much attention from researchers recently. In the studies on trust, little attention has been paid to its interrelations with cooperativity and reliability. This paper sheds light on the relationship among trust, cooperation, and reliability in a medical context. Through a discourse analysis of the conversations between doctors and patients (including the patients’ relatives) in China, this paper finds that: (i) cooperativity will not directly lead to trust in a medical context; (ii) being cooperative or using strategies to disclaim responsibility will lead to high reliability and will indirectly construct trust; (iii) reliability is a premise for trust in the medical context, and it can be repaired through rapport management strategies, such as empathy discourse if that reliability has been broken down. The research findings in this paper possess both theoretical and practical significance because they will refresh our understanding of the interrelationship among trust, cooperation, and reliability and contribute to the maintenance or enhancement of trust relations between doctors and patients.

A Scale to Measure the Perception of Graduates towards Student READY Programme

A scale was developed to measure the perception of graduates towards Student READY Programme (SRP). The Likert’s summated rating scale was followed in the construction of scale. Based on the review of literature and discussion with the expert’s, 49 statements were enlisted. The relevancy rating were sent to 160 scientists and extension specialists working in research institutes of Indian Council of Agricultural Research (ICAR) and State Agricultural University (SAU) for critical evaluation of statements on a 5 point continuum. Out of 160 judges 66 responded within a period of one month. Based on their judgment 40 statements were isolated in the first stage of screening by finding the relevancy weightage scores. Statements having relevancy weightage of more than 0.70, relevancy percentage of more than 70 per cent and mean relevancy score of more than 3.70 were considered for final selection. By this process, 32 statements were isolated in the first stage of screening, which were suitably modified and rewritten wherever applicable. Eight statements were added as per the comments of experts. Thus finally 40 statements were selected for item analysis. In item analysis the selected statements were administered to 80 agriculture graduates of non-sample area of Tamil Nadu and Karnataka. Finally a total of 28 statements were selected for the study based on ‘t’ values (&gt; 1.75) resulted from the item analysis and were included in the final scale. The ‘r’ value of the scale was found to be 0.922, which was highly significant at one per cent level indicating the high reliability of the scale. Hence, the scale developed was found to be reliable and valid. The instrument developed to measure the perception of graduates towards Student READY Programme (SRP) can be used by the researchers.