High Reliability Research Articles

A visual scale to rate amygdalar atrophy on MRI.

Visual rating scales are routinely used in clinical radiology to assess brain atrophy on scans of patients with suspected neurodegenerative conditions. Limbic predominant age-related TDP-43 encephalopathy (LATE) has recently been described, featuring early and severe atrophy of the amygdala. However, there is currently no scoring system specifically designed to assess amygdalar atrophy on MRI. to develop and validate a visual rating scale for amygdalar atrophy. Stringent criteria were developed for no, mild/moderate, and severe amygdalar atrophy based on axial and coronal volumetric T1-weighted MRI scans. Inter- and intra-rater reliabilities were estimated by three independent expert neuroradiologists in 100 randomly selected scans from the Geneva Memory Center cohort selected to be representative of the variability of medial temporal atrophy. Convergent validity was evaluated versus amygdalar volumes extracted by FreeSurfer on 1943 consecutive patients. Criterion validity versus autopsy-confirmed LATE neuropathologic changes were studied in the pathological subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 96). Intra- and inter-rater agreements of amygdalar visual ratings were between substantial and almost perfect (weighted Cohen's Kappa 0.71 to 0.93). Visual ratings were strongly associated with amygdalar volumes (p ≤ 0.001 on the Kruskal-Wallis test). LATE neuropathologic changes were associated with visual ratings of amygdalar atrophy (p = 0.057 on a test for trend). The proposed visual amygdalar atrophy scale is a reliable and valid tool to assess amygdalar atrophy on MRI and can be a useful adjunct in routine radiological reporting. Question Assessment of amygdalar atrophy is crucial for diagnosing neurodegenerative diseases, as the limbic predominant age-related TDP-43 encephalopathy, yet no validated visual rating scale exists. Findings The proposed amygdalar atrophy scale demonstrated high intra-rater and inter-rater reliability, strong correlation with amygdalar volumetry, and association with limbic predominant age-related TDP-43 encephalopathy (LATE). Clinical relevance The amygdalar atrophy scale provides a reliable practical assessment tool that enhances diagnostic accuracy for dementia-related conditions, particularly aiding in identifying limbic predominant age-related TDP-43 encephalopathy.

Associations of TyG index with coronary heart disease risk and coronary artery sclerosis severity in OSA

BackgroundInsulin resistance (IR) is involved in the pathogenesis and progression of obstructive sleep apnea (OSA) and coronary heart disease (CHD). The triglyceride glucose(TyG) index, an alternative indicator of IR with high reliability, is linked to CHD development and severity. However, the association of TyG index with CHD in OSA cases remains undefined. This study explores the association of TyG index with CHD risk and coronary artery sclerosis severity in the OSA population.MethodsOSA cases with suspected CHD, enrolled at the Department of Hypertension, The First Affiliated Hospital of Xinjiang Medical University between February 2020 and June 2024, were assigned to the OSA and OSA + CHD groups, followed by TyG index assessment. The t-test, Mann-Whitney U-test and one-way analysis of variance were utilized to compare continuous data, while comparisons of categorical data utilized the chi-square test or Fisher’s exact test. Logistic regression analysis was conducted to determine factors independently predicting OSA with CHD and Gensini scores. Restrictive cubic spline (RCS) was used to assess potential non-linear associations of TyG index with CHD risk and Gensini score in OSA patients.ResultsTotally 1059 OSA patients were included, with 514 diagnosed with CHD (48.54%). Multivariable logistic regression analysis upon adjustment for age, gender, hypertension, diabetes, and smoking history revealed TyG index as a risk factor for CHD in OSA cases. CHD risk in the high-TyG index group was 1.977 fold higher versus the low-TyG index group (OR = 1.977, 95% CI 1.424-2.800, P < 0.001). In addition, TyG index had a linear relationship with CHD (P for nonlinearity = 0.0709). In moderate to severe OSA cases, the high-TyG index group had a significantly higher CHD risk (OR = 2.430, 95% CI 1.601–3.690, P < 0.001). Gensini score and TyG index (P for non-linearity = 0.0033) had a non-linear relationship, while high TyG index was a risk factor for high Gensini score.ConclusionTyG index is a risk factor for CHD in OSA cases and reflects the severity of coronary atherosclerosis. TyG, a surrogate indicator for IR evaluation, may help predict CHD in OSA cases, especially in moderate to severe OSA.

STRIAA (Severity TRichoscopy Index Alopecia Areata): Validation of a Novel Trichoscopic Tool for Evaluation of Alopecia Areata.

Alopecia areata (AA) is a non-scarring autoimmune disease characterized by patchy hair loss. The aim of this study was to validate a novel trichoscopic scoring tool, the Severity TRichoscopy Index for Alopecia Areata (STRIAA), for rapid assessment of AA severity. Anonymized images from 340 patients were scored by two independent raters who analyzed four scalp areas (vertex, occipital, and left and right parietal) for trichoscopic signs: black dots, yellow dots, exclamation mark hairs, broken hairs, and short vellus hairs. Scores (0-3) were assigned according to the number of trichoscopic signs per area, resulting in a total STRIAA score out of 60. STRIAA showed high interrater reliability (Cronbach's alpha 0.99) and significant correlation with the Severity of Alopecia Tool (SALT) score (p < 0.001). Yellow and black dots were significantly associated with the SALT score. The STRIAA provides a rapid and comprehensive assessment of AA severity, complementing current assessment tools in clinical practice.

Heart sound recognition using hybrid joint transform correlator

Abstract This work introduces an innovative method for heart sound recognition using hybrid joint transform correlator (JTC). The existing techniques such as convolutional neural networks and machine learning models, often suffer from computational complexity and latency difficulties, resulting in slower processing speeds. The proposed method addresses these challenges by utilizing an optical correlator, capable of operating at significantly accelerated speeds. The heart sound signals are first converted into spectrograms, which are then correlated using hybrid JTC operation for recognition and decision. To enhance the accuracy and robustness of the scheme, distortion-invariant filter fabrication has been incorporated with the JTC. The proposed approach not only enhances the speed of heart sound analysis but also maintains high precision and reliability. The effectiveness of the method has been confirmed through both simulation and experimental results.&amp;#xD;

Adaptation and Psychometric Features of the Korean Medical Academicians Stress Questionnaire for Iranian Medical Academicians

Background: Stress is a major factor affecting the mental health and academic performance of medical academicians, which necessitates the use of valid and reliable tools to measure it within their specific cultural and educational contexts. Given the unique circumstances of Iranian medical academicians, it is essential to adapt and validate the Korean Medical Academicians Stress Questionnaire (KMSSQ) to ensure it accurately captures the stressors they experience. Objectives: This study aimed to adapt and validate the KMSSQ for use among Iranian medical academicians, ensuring its cultural relevance and accuracy in reflecting the unique stressors they face. Methods: This study, focused on psychometric evaluation, included 900 medical academicians selected using stratified sampling from universities in Tehran. Participants completed both the KMSSQ and the occupational stress inventory (OSI). Face validity, content validity, concurrent validity, structural validity, internal consistency, and test-retest reliability were evaluated using SPSS (version 21) and LISREL (version 8.8). The cut-off point of the questionnaire was determined using the ROC curve. Results: The KMSSQ demonstrated high face, content, and concurrent validity (r = 0.91). Exploratory factor analysis (EFA) identified six factors that explained 60.41% of the variance. Confirmatory factor analysis (CFA) supported a six-dimensional construct. The questionnaire exhibited high internal consistency (Cronbach’s alpha = 0.93) and test-retest reliability (r = 0.91). The cut-off point was set at 120, with a sensitivity of 100% and specificity of 85.23%. Conclusions: The KMSSQ is a valid and reliable tool for assessing stress among Iranian medical academicians. It can be used for educational planning and psychological interventions, offering a culturally adapted measure that addresses the unique challenges faced by this group.

Validity and reliability study of the Turkish version of KOOS-ACL questionnaire

BackgroundThis study aimed to adapt the Knee injury and Osteoarthritis Outcome Score- Anterior Cruciate Ligament (KOOS-ACL) into Turkish and to assess its validity and reliability.MethodsThe adaptation process involved translation, back-translation, and cultural adaptation of the questionnaire. Totally 133 patients who had a diagnosis of anterior cruciate ligament tear was recruited. The reliability of the Turkish version of the KOOS-ACL was assessed using Cronbach’s alpha, item-total correlations, and ICC. EFA and CFA were performed to evaluate construct validity, supported by KMO and Bartlett’s test results. Criterion validity was examined through correlations with IKDC scores, and discriminative power was assessed using an independent samples t-test.ResultsIKDC score for evaluating knee function was 59.84 ± 21.74. The composite KOOS-ACL-Tr score was 61.95 ± 25.19. Cronbach’s Alpha value was found to be 0.952 demonstrating high reliability. KOOS-ACL-Tr scores showed a linear positive correlation with the time elapsed since injury and IKDC scores (p < 0.001) establishing concurrent validity.ConclusionsThe adapted Turkish version of the KOOS-ACL is a valid and reliable scale intended for use in monitoring patients with ACL injuries.

Digital technologies as enablers of universal design for learning: higher education students' perceptions in the context of SDG4

Digital technologies are essential for transforming educational environments, particularly in higher education. These tools have revolutionized information access and enable flexible approaches within Universal Design for Learning (UDL). This educational model integrates findings from educational practice, research, learning theories, technological advances, and neuroscience to provide concrete proposals for implementing diverse teaching practice that accommodates different learning needs. Building on universal design concepts, UDL is structured around three neurological learning systems (affective, recognition, and strategic) and advocates three corresponding principles: multiple means of engagement, representation, and action/expression. Aligning with current parameters of quality education for all, UDL incorporates diversity as a fundamental premise in instructional planning to ensure equitable learning opportunities for all students. This framework enables teachers to create flexible curriculum designs that enrich learning, reduce barriers, and provide accessible educational opportunities. This research examined how digital technologies can enable UDL implementation by analyzing higher education students' perceptions. Using a quantitative methodology with a descriptive scope, data was collected through a specially designed Likert-scale questionnaire. The instrument demonstrated high reliability (Cronbachʹs α = 0.97). The sample included students from the Mexican public university system (n = 235) studying pedagogy and education. Results showed favorable perceptions of technology use in UDL across all three principles: representation (what to learn), action and expression (how to learn), and engagement (why to learn). They suggest that digital technologies can expand learning capacity and enhance skill development among university students in the UDL context. However, it should be noted that they need to be carefully integrated into educational frameworks designed and tested for their effectiveness; without such integration, their application may be ineffective or even counterproductive to learning processes. The findings demonstrate the potential of this design as a practical and theoretical model to, in line with current trends, contribute to the achievement of SDG4, promoting quality in education and learning opportunities for all. This will be helpful for institutions seeking to implement technology-enhanced UDL strategies while having broader implications for the body of research for improving higher education.

Preliminary study: Evaluating the reliability of cone-beam computed tomography images for upper airway volume in orthodontics

Objectives: The primary aim of this study was to evaluate the reliability of cone-beam computed tomography (CBCT) in measuring upper airway volume in orthodontics. The secondary aim was to compare the upper airway volumes between laypeople and military personnel (physically fit individuals). Material and Methods: This study included 36 male participants aged 21–29 years who had no upper airway pathology or history of upper airway surgery. CBCT scans had been previously obtained as part of routine dental checkups using a standardized protocol by the same operator. The digital imaging and communications in the medicine files of the subjects were imported into the 3D analysis software SimPlant® 17 Pro (Materialize Dental, Leuven, Belgium) to facilitate the measurement of upper airway volume. The upper airway volume was divided into three sections and measured on days 0 and 14 by the same operator. The intrarater intra-class correlation coefficient (ICC) and Bland-Altman plot were used to assess reliability. The upper airway volumes of the laypeople and military personnel were compared using Tukey’s pairwise test. Results: The variables demonstrated excellent reliability across all sections of the upper airway; the Superior part of the upper airway has an ICC of 0.993, the middle part of the upper airway has an ICC of 0.995, and the inferior part of the upper airway has an ICC of 0.989. The ICC for the entire upper airway was 0.987. The intrarater plot for superior airway measurement showed that 8 out of 8 measurements (100%) fell within the limits of agreement. Similarly, the intrarater plot for the middle airway measurements indicated that 8 out of 8 measurements (100%) fell within the limits of agreement. For inferior airway measurement, the intrarater plot revealed that 7 out of 8 measurements (87.5%) fell within the limits of agreement. No significant differences were found between the upper airway volumes of the laypeople and military personnel. Conclusion: Preliminary evidence suggests that CBCT using a standardized protocol is reliable for measuring upper airway volume. Separating the upper airway into three different parts also demonstrated high reliability and reproducibility among the raters. The ability to accurately measure the upper airway enhances the evaluation of the effects of orthodontic treatment by comparing the pre-and post-upper airway volume.

Geohazard Identification in Underground Mines: A Mobile App

Mining is a critical industry that provides essential minerals and resources for modern society. Despite its benefits, the industry is also recognized as one of the most dangerous occupations, with geotechnical hazards being a primary concern. This study introduces the hazard recognition in underground mines application (HUMApp), a mobile application developed to enhance safety within underground mines by efficiently identifying geotechnical hazards, specifically focusing on roof falls. Employing a convolutional neural network (CNN), HUMApp achieved an accuracy of 90%, with recall and F1-score also reaching 90%, reflecting its high reliability in hazard detection. The application’s effectiveness was validated against expert assessments, showing significant agreement in identifying critical hazards. This validation highlights HUMApp’s potential to enhance proactive risk management in underground mining. This paper details each step of HUMApp’s development, from dataset preparation and model training to performance evaluation and application design, showcasing a scalable solution adaptable to various mining environments.

THE ROLE OF CLOUD COMPUTING IN SCALING E-COMMERCE BUSINESSES

In the rapidly evolving digital landscape, e-commerce has emerged as a cornerstone of global trade, necessitating robust, scalable solutions to accommodate increasing consumer demands. Cloud computing has become integral to this transformation, offering e-commerce businesses the flexibility and resources needed to scale effectively. This paper explores the multifaceted role of cloud computing in enhancing the scalability of e-commerce enterprises, addressing both its benefits and challenges. Cloud computing, defined as the delivery of computing services over the internet, encompasses various models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). These models provide e-commerce businesses with on-demand access to computing resources, allowing for rapid scaling in response to fluctuating market conditions. The inherent flexibility of cloud solutions enables companies to allocate resources dynamically, which is crucial during peak shopping seasons, such as Black Friday or Cyber Monday. This elasticity not only optimizes costs but also enhances the overall customer experience by ensuring high performance and reliability. The adoption of cloud computing can significantly mitigate common challenges faced by e-commerce businesses, including infrastructure limitations and performance bottlenecks. By leveraging cloud services, businesses can achieve load balancing, disaster recovery, and enhanced data security, thereby maintaining operational continuity and protecting sensitive customer information. Furthermore, the compliance features offered by major cloud providers assist businesses in adhering to regulatory requirements, fostering consumer trust. Case studies of prominent e-commerce platforms like Amazon and Shopify illustrate the successful implementation of cloud computing strategies. These companies have demonstrated substantial growth metrics, attributing their scalability to the strategic deployment of cloud technologies. For instance, Amazon’s use of its own cloud service, AWS, has allowed it to seamlessly manage millions of transactions daily while maintaining a robust infrastructure that can adapt to changing demands. Such examples provide valuable insights into best practices and the potential for innovation in cloud-driven e-commerce environments. Despite the myriad benefits, the transition to cloud computing is not without challenges. Businesses must consider potential drawbacks, such as dependency on internet connectivity, concerns regarding data privacy, and the complexity of managing cloud resources. To address these issues, organizations are advised to conduct thorough assessments of their specific needs and to carefully select cloud service providers that align with their operational goals and security requirements. Looking forward, the integration of emerging technologies, such as artificial intelligence and machine learning, with cloud computing is set to revolutionize e-commerce scalability further. These advancements will enable more personalized customer experiences, enhanced data analytics capabilities, and greater operational efficiency. In conclusion, cloud computing serves as a vital enabler for scaling e-commerce businesses, providing them with the necessary tools to navigate the complexities of the digital marketplace. As the e-commerce landscape continues to evolve, the strategic adoption of cloud solutions will remain essential for businesses seeking to maintain competitiveness and drive growth. KEYWORDS : E-Commerce, Cloud Computing, Scalability, Infrastructure As A Service (Iaas), Platform As A Service (Paas), Software As A Service (Saas), On-Demand Resources, Dynamic Resource Allocation, Load Balancing, Disaster Recovery, Data Security, Compliance, Consumer Trust, Amazon Web Services (AWS), Shopify, Peak Shopping Seasons, Black Friday, Cyber Monday, Performance Optimization, Internet Connectivity, Data Privacy, Artificial Intelligence, Machine Learning, Data Analytics, Operational Efficiency, Digital Marketplace, Strategic Deployment, Innovation, Regulatory Requirements, Customer Experience.

Electrochemical-Enhanced Charge State Modulation of Nitrogen-Vacancy Centers for Ultrasensitive Biodetection of MicroRNA-155.

Sensitive and accurate miRNA detection is important in cancer diagnosis but remains challenging owing to the essential features of miRNAs, such as their small size, high homology, and low abundance. This work proposes a novel electrochemical (EC)-enhanced quantum sensor achieving quantitative detection of miRNA-155 with simultaneous EC sensing. Specifically, fluorescent nanodiamonds/MXene nanocomposites were synthesized and modified with dual-mode signal labels, enabling miRNA-155 concentration measurement via T1 relaxation time of nitrogen-vacancy (NV) centers and EC signals. Quantum sensing was enhanced via external voltage during the EC process, which modulated the negatively charged state of the NV centers, thereby improving the sensitivity and accuracy of miRNA-155 detection. EC sensing improved the accuracy and reliability of miRNA-155 detection while enhancing quantum sensing. The limit of detection (LOD) of the EC-enhanced quantum biosensor reached 10.0 aM, nearly 106 and 10 times lower than the reported LODs of a quantum sensor using bulk diamond and fluorescent sensors, respectively. The LOD of EC sensing was 2.6 aM, aligning with previous reports. The findings of the study indicated that quantum sensing combined with EC sensing can achieve ultrasensitive miRNA-155 detection with high accuracy and reliability, providing an advanced approach for early cancer diagnosis.

Thermo-Mechanical Coupled Analysis of Electric Vehicle Drive Shafts

With the growing concerns over global warming and abnormal weather patterns, the development of eco-friendly technologies has emerged as a critical research area in the transportation industry. In particular, the global automotive market, one of the most widely used sectors, has witnessed a surge in research on electric vehicles (EVs) in line with these trends. Compared to traditional internal combustion engine vehicles, EVs require components with high strength and durability to achieve optimal performance. This study focuses on the development of a constant velocity (CV) joint, a critical component for reliably transmitting the maximum output of an electric vehicle motor. Unlike conventional numerical methods, the proposed thermo-mechanical coupled analysis simultaneously accounts for thermal and mechanical interactions, providing more realistic operational performance predictions. This analysis, conducted using the thermal modules of Ls-Dyna and ANSYS Mechanical, effectively simulated field operation scenarios. Prototype testing under simulated conditions showed a 6% discrepancy compared to numerical predictions, validating the high accuracy and reliability of the proposed method. This robust thermo-mechanical coupled analysis is expected to improve the durability and reliability of CV joint designs, advancing electric vehicle component development.

Antecedents For the Adoption of Telemedicine in India: Scale development and validation

Background: Telemedicine is increasingly recognized as a promising solution to healthcare challenges in India, particularly in remote areas. However, the country's vast population and geographic diversity present significant obstacles to providing accessible and high-quality healthcare services to all citizens. By leveraging technology, telemedicine has the potential to bridge this gap and enable remote delivery of medical services. Objective: This study aims to develop and validate a scale that assesses the factors influencing the adoption of telemedicine in the Indian context. Understanding these factors is crucial for identifying key drivers and barriers to telemedicine adoption in India. Design: Quantitative methods were employed for scale development. The instrument development process involved several stages: factor identification, item generation, pre-testing, pilot testing, and scale validation. A structured questionnaire was administered to healthcare professionals, industry experts, and patients who have used or intend to use telemedicine. Setting: Confirmatory factor analysis and subsequent tests, such as reliability and validity tests were conducted to establish the internal consistency of the scales. These statistical analyses aimed to identify underlying factors and ensure accurate measurement of latent variables that affect the adoption of telemedicine. Results: The study established a robust scale to assess the five key factors, which are the vital explanatory variables in telemedicine adoption in India. These meticulously validated scales, encompassing technology, government policy, user attitudes, societal demand, and healthcare professional perspectives, demonstrate high reliability and validity in understanding the adoption dynamics of telemedicine in the context of India. Conclusion: This research offers a validated scale for assessing telemedicine adoption in India, crucial for healthcare service providers, policy makers, and researchers in this field. It enables informed decisions in implementing telemedicine, addressing unique challenges and opportunities, and significantly contributing to optimizing healthcare delivery across India's diverse socio-economic and geographical landscape.

Risk Prioritization from the Crowd-Shipping Provider's Perspective using the CIMAS Method

Crowd-shipping presents a new trend in shipment distribution. It is a process in which the crowd is employed to deliver the items. Effective risk prioritization is essential in city logistics and delivery, especially with the emergence of crowd-shipping. As crowd-shipping platforms grow, they bring uncertainties and challenges that can significantly impact operational efficiency and customer confidence. Emphasizing risk prioritization is crucial for many reasons, including trust and security, improving operational efficiency, and ensuring regulatory readiness. Risk prioritization is more than a mere formality; it is a vital element in successfully managing the intricacies of crowd-shipping. By methodically addressing and mitigating risks, providers can strengthen their operational capabilities, foster better customer connections, and ultimately promote the sustainable advancement of crowd-shipping services. This paper prioritizes the risks in crowd-shipping from the crowd-shipping provider’s perspective, using an MCDM approach such as CIMAS. The risks are prioritized in descending order. Comparative analysis with the BWM indicates the high reliability of the results obtained by the CIMAS method.

Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform

IntroductionIdentity verification plays a crucial role in modern society, with applications spanning from online services to security systems. As the need for robust automatic authentication systems increases, various methodologies—software, hardware, and biometric—have been developed. Among these, biometric modalities have gained significant attention due to their high accuracy and resistance to falsification. This paper focuses on utilizing electrocardiogram (ECG) signals for identity verification, capitalizing on their unique, individualized characteristics.MethodsIn this study, we propose a novel identity verification framework based on ECG signals. Notable datasets, such as the NSRDB and MITDB, are employed to evaluate the performance of the system. These datasets, however, contain inherent noise, which necessitates preprocessing. The proposed framework involves two main steps: (1) signal cleansing to remove noise and (2) transforming the signals into the frequency domain for feature extraction. This is achieved by applying the Wigner-Ville distribution, which converts ECG signals into image data. Each image captures unique cardiac signal information of the individual, ensuring distinction in a noise-free environment. For recognition, deep learning techniques, particularly convolutional neural networks (CNNs), are applied. The GoogleNet architecture is selected for its effectiveness in processing complex image data, and is used for both training and testing the system.ResultsThe identity verification model achieved impressive results across two benchmark datasets. For the NSRDB dataset, the model achieved an accuracy of 99.3% and an Equal Error Rate (EER) of 0.8%. Similarly, for the MITDB dataset, the model demonstrated an accuracy of 99.004% and an EER of 0.8%. These results indicate that the proposed framework offers superior performance in comparison to alternative biometric authentication methods.DiscussionThe outcomes of this study highlight the effectiveness of using ECG signals for identity verification, particularly in terms of accuracy and robustness against noise. The proposed framework, leveraging the Wigner-Ville distribution and GoogleNet architecture, demonstrates the potential of deep learning techniques in biometric authentication. The results from the NSRDB and MITDB datasets reflect the high reliability of the model, with exceptionally low error rates. This approach could be extended to other biometric modalities or combined with additional layers of security to enhance its practical applications. Furthermore, future research could explore additional preprocessing techniques or alternative deep learning architectures to further improve the performance of ECG-based identity verification systems.

Optimal vibration control with considering non-probabilistic time-dependent reliability for hypersonic vehicles

Hypersonic vehicles have complex structures with multiple sources of uncertainties, and the presence of uncertainties may lead to the vehicles’ structural failure. Aiming to ensure the flight safety and structural reliability of a hypersonic vehicle, this study proposes a vibration optimization control method based on non-probabilistic time-dependent reliability. Due to the complex structure of the vehicle, its forebody and aftbody are modeled as two cantilever beams with the center of mass of the vehicle as the fixed fulcrum, respectively, and a vibration control model of cantilever is derived. Considering that the uncertainties in practical engineering are not easy to be counted, the uncertain parameters are quantified by using interval variables, and the dynamic response and time-dependent reliability of the uncertain system are analyzed based on the subinterval method and the non-probabilistic time-dependent reliability analysis method (NTRAM) designed in this paper. Further, to ensure the high reliability of the vehicle, an optimal robust linear quadratic regulator (ORLQR) method with the constraint of time-dependent reliability is proposed. The analysis of the numerical examples shows that the parameter uncertainty has a certain effect on the reliability of the vehicle, and verifies that the proposed ORLQR method can effectively control the vibration and slow down the reliability decline.

A study to assess issues faced during paper evaluation by nursing teachers in selected nursing colleges: A descriptive study

Nursing education is crucial for producing skilled healthcare professionals.However, nursing teachers face various challenges during paper evaluation, which can affect the effectiveness of the assessment process. This study aims to assess these issues and explore their association with demographic variables.A descriptive research design was used, conducted in selected nursing colleges with a sample of 50 nursing teachers.Data was collected using a self-administered structured questionnaire, and analyzed with descriptive and inferential statistics.The questionnaire showed high reliability (0.7593).Nursing teachers face various challenges during paper evaluation. Common issues include unreadable handwriting (12%), irrelevant answers (4%), and incorrect numbering (6%). Long-answer questions are the most difficult to assess (40%), while 80% evaluate 20–40 papers daily, often requiring extra hours (24%). Presentation challenges (58%) and emotional strain (40%) further complicate the process. While 34% prefer digital evaluation, specialization (p = 0.005) and designation (p = 0.0001) significantly influence these challenges, but age (p = 0.48) and MUHS experience (p = 0.60) show no significant association.Nursing teachers face significant challenges during paper evaluation, including difficulty with long-answer questions, presentation issues, and emotional strain. Specialization and designation significantly influence these challenges, while age and experience in MUHS evaluation do not. The findings highlight the need for streamlined evaluation processes, with a preference for digital methods to improve efficiency and reduce stress.

Methodology for determining the technical condition of the pulse power supply unit in dynamic mode

Since the power supply unit is an integral part of any electronic device, to ensure high quality and reliability of its operation, it is necessary to periodically check its operational characteristics and the structural solutions used in it. The lack or untimely verification procedures jeopardizes the functioning of the entire device and leaves the user vulnerable to the likely onset of sudden failures and subsequent potentially unpleasant situations. It should be borne in mind that the element base (workmanship) of the power supply units does not differ from the quality of the element base of the entire device, while this unit is an intermediary between the power supply network (parameters of the characteristics and state of the power supply network) and the main device (technical condition and adequacy of the user when using it). Therefore, the durability of the main equipment depends on the efficiency and correctness of the functioning of the power supply, the stability of its parameters. In case of a critical state of the equipment, the power supply unit itself contains a fuse (automatic emergency shutdown system), while the operation of the fuse does not allow to determine the cause (source) of the critical state (network - power supply unit - main equipment - violation of operating rules). Replacing the fuse, or replacing the power supply does not guarantee the final solution to the problem of technical diagnostics. Given the ever-increasing complexity and high cost of modern electronic devices (including those with elements of artificial intelligence), the issue of timely diagnosis of potential power supply malfunctions is critically important. Therefore, the development of scientifically sound approaches to determining the technical condition of power supplies and localizing its faults is an urgent task

A Cognitive Diagnostic Model of Reading Ability based on China’s Standards of English Language Ability

Traditional English reading assessments in China typically provide a single summative score, without revealing the extent to which students have mastered different aspects of reading comprehension. Cognitive Diagnostic Models (CDMs), as a type of multidimensional latent trait model, offer diagnostic insights by presenting examinees' proficiency levels across a set of predefined skills. Cognitive diagnostic assessments based on these models can deliver multidimensional, fine-grained evaluation results, thus overcoming the limitations of traditional unidimensional score-based assessments. Based on the China's Standards of English Language Ability (CSE), this study constructs a cognitive diagnostic assessment model for English reading. Using college general English reading test papers and the G-DINA model, the English reading abilities of 200 students were diagnosed, and personalized performance reports were generated. A questionnaire survey was conducted to gather students' evaluations and suggestions regarding the feedback reports. The results indicate that the cognitive diagnostic model for English reading, constructed according to the CSE, exhibits good model fit and high diagnostic reliability. The personalized performance reports visually and comprehensively present the strengths and weaknesses of the test takers' reading abilities, providing valuable guidance for students' future remedial learning. This study utilizes a multidimensional assessment approach based on cognitive diagnostic models to evaluate the English reading abilities of Chinese university students, effectively addressing the limitations of traditional assessment methods that typically offer only a single score.

Development and Validation of a Nomogram for Predicting Systemic Inflammatory Response Syndrome Following Percutaneous Nephrolithotomy.

Objective: To develop and validate a nomogram for predicting the occurrence of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL), aiming to enhance clinical decision-making and treatment planning. Methods: Clinical data of 1,047 patients undergoing PCNL at a single-center hospital between 2017 and 2023 were retrospectively analyzed. Independent risk factors influencing SIRS occurrence were identified through multi-variable logistic regression analysis, and a predictive model was constructed. The model's accuracy and reliability were evaluated through internal training and validation set. Results: Multi-variable regression analysis identified six key predictive factors: gender, diabetes, urine culture results, stone surface, staghorn stones, and operative time, leading to the establishment of a nomogram predictive model. Internal validation and validation set data demonstrated the model's high predictive accuracy and reliability, with areas under the receiver operating characteristic curve of 0.718 and 0.723, respectively. Conclusions: A nomogram predictive model for assessing SIRS following PCNL was successfully developed and validated. This model provides clinicians with a valuable tool for personalized treatment planning and implementing preventive measures.