Reliability Analysis Research Articles

Working ability, Location, Intensity, Days of Pain, Dysmenorrhea (WaLIDD): cross-cultural adaptation, reliability, and validity of the Turkish version

BackgroundDysmenorrhea is a gynecological condition that causes painful menstrual cramps in the uterus, most commonly seen in women of reproductive age. The aim was to perform the reliability and validity of the Turkish Working Ability, Location, Intensity, Days of Pain, Dysmenorrhea (WaLIDD) score for evaluating individuals with dysmenorrhea. The evaluation of the reliability and validity of the Turkish version of the WaLIDD tool in the Turkish population is essential to ensure its accuracy, consistency, and cultural relevance in assessing the impact of dysmenorrhea.MethodsCross-sectional, psychometric questionnaire research. 113 individuals with dysmenorrhea were included in the study. The reliability of the WaLIDD was evaluated using internal consistency and test-retest analyses, while its validity (both convergent and divergent) was assessed through correlation analysis. For correlation analysis WaLIDD, Premenstrual Syndrome Impact Questionnaire (PMS-IQ), Pain Disability Index (PDI), and Big Five Inventory-10 (BFI-10) were used.ResultsThe reliability analysis indicated that the questionnaire demonstrated an internal consistency value of 0.875 and an intraclass correlation coefficient (ICC) value of 0.778. Pearson correlation analysis indicated that the WaLIDD had a very well correlation with the PMS-IQ (0.726) and well correlation with the PDI (0.413). WaLIDD had a negligible correlation with the BFI-10 (0.088). There were no floor or ceiling effects observed in the Turkish version of the WaLIDD.ConclusionsWaLIDD was found to be a well-structured, valid, and reliable instrument that can be used to evaluate women with dysmenorrhea. The Turkish version of WaLIDD can be used in Turkish women in clinical practices and research.Trial registrationThis study was registered in ClinicalTrials.gov (NCT05829512).

YouTube as a source of information for stroke rehabilitation: a cross-sectional analysis of quality and reliability of videos

IntroductionDue to YouTube’s meteoric rise in popularity, the quality and reliability of health-related videos on YouTube are being questioned, particularly in specialized fields like stroke rehabilitation. This research aimed to assess the quality and reliability of YouTube videos relevant to stroke rehabilitation.MethodVideo listing was conducted on December 17, 2024, using the keywords “Stroke Rehabilitation”, “Stroke Physical Therapy”, “Stroke Neurophysiotherapy”, and “Stroke Physical Therapy Techniques” as query terms. A final sample of 72 videos was selected upon completion and evaluated according to inclusion and exclusion criteria. The Global Quality Scale (GQS), Modified DISCERN Questionnaire, JAMA Benchmark Criteria, and Patient Education Materials Assessment Tool for Audio/Visual Materials (PEMAT-A/V) were among the evaluation tools used to analyze each video. Researchers captured the videos’ fundamental components and compared the quality classifications.ResultsOf the 72 videos examined, 29.2% (n = 21) were categorized as low quality, 20.8% (n = 15) as intermediate level, and 50% (n = 36) as high quality. Videos generated by academic medical centers (77.8%) and nonphysician healthcare professionals (59.4%) were primarily of high quality, while videos from independent users (100%) and TV channels (66.7%) displayed the lowest quality. Significant differences were observed when comparing quality groups based on daily views, likes, and comments (p < 0.05). The lowest scores were detected in the low-quality group. Significant correlations were identified between GQS and other evaluative instruments (p < 0.001), indicating consistency across evaluation frameworks.ConclusionYouTube possesses considerable potential as an instructional tool for stroke rehabilitation. The inconsistency in video quality underscores the necessity for enhanced content control, editing, and the advocacy of high-quality, evidence-based resources. Promoting collaboration among academics, healthcare professionals, and content producers could augment the platform’s instructional efficacy.

Preservice teachers’ knowledge of math modeling: Initial scale development and validation

Across the world, standards and practices for preparing teachers of mathematics emphasize the importance of math modeling (MM) in developing students’ mathematical thinking. The aim of this research study was to develop the Mathematical Modeling Knowledge Scale (MAMKS), capable of determining preservice teachers’ (PSTs’) knowledge of MM. The study participants consisted of 100 PSTs from a Midwestern state university in the United States. The participants were between the ages of 18–25 and had experience with some form of MM. Using a variety of sources, we developed the MAMKS that included several items with a 6-point Likert response category. Through expert interviews, item analysis, reliability analysis, and factor analysis, the MAMKS was refined to an 8-item version. The 8-item scale exhibited sound psychometric properties, with a moderately high and acceptable reliability (α = .83) for a one-factor structure. The one-dimensional structure from the factor analysis explained 42% of the variation. Additionally, item analysis showed the items were useful and could discriminate among the respondents. Furthermore, the confirmatory factor analysis validated the scale’s factor structure. Therefore, we present a reliable and psychometrically acceptable scale to measure PSTs’ knowledge of MM with broad applicability and implications for teacher education programs and researchers.

A reliability analysis: human trafficking curriculum assessment tool (HT-CAT) for health care provider human trafficking trainings

BackgroundHuman trafficking is an egregious human rights violation with profound physical and mental health sequelae. Healthcare encounters present critical opportunities to identify and support individuals experiencing trafficking. To maximize these opportunities, healthcare professionals must be equipped to recognize the signs of trafficking and respond effectively with trauma-informed, survivor-centered care. High quality training is essential to ensure that healthcare providers have the knowledge and skills to fulfill this role. To address the need for standardized, evidence-based evaluation of trafficking training, the Human Trafficking Curriculum Assessment Tool (HT-CAT) was developed as a structured quality assessment tool. This study evaluates HT-CAT’s reliability.MethodsThe Human Trafficking Curriculum Assessment Tool (HT-CAT) was used to evaluate the quality of a compendium of introductory human trafficking educational resources for health care professionals. Twenty-four trainings were systematically reviewed using the HT-CAT by a minimum of three reviewers. Statistical analyses were conducted to examine interrater reliability and to describe variation across trainings and assessment tool items.ResultsThere was high interrater agreement based on summed HT-CAT domain scores (ICC = 0.88, 95% CI 0.84–0.93). Individual item scores had variable agreement (K = 0.30, 95% CI 0.22–0.38) with high between training variability, as well as variable agreement on how specific items should be scored (alpha = 0.27, 95% CI 0.23–0.31). This variation in scoring was consistent between items, suggesting that there were no items that systematically produced more or less agreement than others.ConclusionsThe HT-CAT is a reliable tool for assessing the overall quality of human trafficking education of health professionals. HT-CAT has important applications for health policy makers and health professions educators. Rigorous assessment of key components of human trafficking trainings for healthcare professionals can help ensure that high quality, relevant education is offered to this group of direct service providers that play an important role in supporting survivors of human trafficking. High-quality training equips providers, a vital lifeline for survivors of human trafficking, with the knowledge and tools to offer compassionate, informed care.

Sensitivity analysis of reliability constrained, eco optimal solar, wind, hydrogen storage based islanded power system

The global energy expansion strategy has incorporated islanded renewable energy-based power generation systems to electrify remote communities. The development of these renewable energy systems (RES) decreases grid dependency and operational costs. Solar photovoltaic power stations (SPPS) and wind-driven power stations (WDPS) are commonly employed technologies in isolated power systems. However, their intermittent nature poses dependability obstacles. Therefore, the incorporation of storage technology is essential to enhance reliability. This paper presents a sensitivity analysis to determine the optimal, reliable, and cost-effective sizing of a SPPS, WDPS, and hydrogen storage systems (HSS) based power system for case study of Jaisalmer, India. The ideal dimensions of each component are determined in two different cases, each having a unique objective function. The optimal sizing is attained through a metaheuristic optimization method called Butterfly-PSO. Reliability assessment is carried out using Monte Carlo Simulation (MCS) and two key reliability indices, namely ENS and LOLE are taken under analysis. Sensitivity analyses are performed to examine the effects of incorporating or excluding RES and storage elements on system reliability and cost-efficiency. The findings presents that increasing SPPS capacity by one unit changes around LOLE by 13%, ENS by 14%, and LCOE/TLCC by 1%. Varying WDPS capacity changes LOLE by 16%, ENS by 19%, TLCC by 3.3%, and LCOE by 1.4%. Adjusting HSS tank size by one unit affects LOLE by 2%, ENS by 2.6%, and TLCC/LCOE by 0.02%. Case 1 (Min TLCC) offers a more reliable and cost-effective solution than Case 2.

Incidence of temporal thumb sign in adult patients with idiopathic seizures with and without elevated opening cerebrospinal fluid pressure.

The temporal thumb sign (TTS) has emerged as a potential distinct magnetic resonance imaging (MRI) finding in pediatric patients with new-onset idiopathic seizures which is characterized by the protrusion of the inferior temporal gyrus into the skull base. This study investigated the prevalence and clinical implications of TTS in adult patients with idiopathic seizures, with or without elevated opening cerebrospinal fluid (CSF) pressure, and those diagnosed with idiopathic intracranial hypertension (IIH) without seizures. A retrospective study was conducted on 157 adults aged 18-40-years-old, divided into four distinct groups. Group 1 consisted of 52 patients diagnosed with idiopathic intracranial hypertension (IIH) without seizures. Group 2 included 27 patients with seizures and elevated opening CSF pressure. Group 3 comprised 39 patients with seizures but normal opening CSF pressure. Group 4 consisted of 39 controls. MRI or CT scans and lumbar puncture data were analyzed to evaluate the presence of TTS. Statistical analysis was performed to assess the prevalence, sensitivity, and specificity of TTS across groups. Significant differences were defined as p < 0.05. Significant differences in TTS prevalence were observed between all groups (p < 0.001). TTS was most frequently detected in patients with seizures and elevated opening pressure (92.6%), followed by IIH patients without seizures (63.5%), and patients with seizures and normal opening pressure (61.5%). The control group exhibited a lower frequency of TTS (12.8%). Logistic regression demonstrated the association of TTS with seizure and elevated intracranial pressure (p < 0.001). Inter-rater reliability analysis showed excellent agreement between observers. The results of this study suggest that the Kamali "Temporal Thumb Sign" is a novel neuroimaging feature associated with seizures and elevated opening CSF pressure in adult patients. High sensitivity (92.6%) of the TTS may yield it as a screening imaging marker in either brain CT or MRI scans in the emergency department, which could aid in suspecting possible elevated opening CSF pressure and proceeding with further workup. The association of TTSwith seizures suggests a potential link between elevated intracranial pressure and structural alterations in the skull basewithseizure presentation. The TTS may represent a distinct manifestation of intracranial hypertension, similar to temporal lobe encephaloceles. Question This study investigates the prevalence and clinical implications of TTS in adults with idiopathic seizures at a single institution. Findings TTS was detected in 92.6% of patients with seizures and elevated CSF pressure. Clinical relevance TTS may serve as a sensitive marker for identifying elevated intracranial pressure in patients presenting with seizures to the emergency department.

Psychometric Properties of the Depression Anxiety Stress Scales (DASS-42 and DASS-21) in Patients with Hematologic Malignancies

Background/Objectives: Patients with hematologic malignancies undergo prolonged, intensive treatments involving frequent hospitalizations and experience debilitating side effects. Consequently, they are at increased risk of developing symptoms of depression, anxiety, and stress, which can undermine their quality of life. However, there is a scarcity of instruments validated for the simultaneous assessment of depression, anxiety, and stress within hematologic cancer populations. The aim of this study is to examine the construct validity, convergent and discriminant validity, and reliability of the Depression Anxiety Stress Scales (DASS-42 and DASS-21) among hematologic cancer patients. Methods: We collected data from 452 hematologic cancer patients across three studies. Results: Confirmatory factor analyses indicated that the theoretical correlated three-factor model and bifactor model for DASS-42 and DASS-21 responses were sufficient to explain the underlying factor structure of the scales in hematologic cancer patients. However, the bifactor model for DASS-42 and DASS-21 fit better with the data compared to the theoretical correlated three-factor model. In addition, we found the correlated three-factor model and the bifactor structure to exhibit scalar measurement invariance across gender for DASS-42 and DASS-21. DASS-42 and DASS-21 subscales demonstrated weak to strong negative correlations with measures of psychological well-being (happiness, well-being, life satisfaction) and strong positive correlations with measures of similar constructs (depression, anxiety, and stress), thereby supporting their convergent and discriminant validity with theoretically and empirically expected correlations with external criteria. The reliability analyses demonstrated that both DASS-42 and DASS-21 subscales exhibited strong internal consistency and test–retest reliability when assessing symptoms of depression, anxiety, and stress among patients with hematologic malignancies. Moreover, the item-scale convergent and discriminant validity analyses demonstrated that items exhibited higher corrected item–total correlations with their intended subscales than with other subscales across the DASS-42 and DASS-21, providing evidence for the distinct measurement properties of each subscale. Conclusions: The findings suggest that the DASS-42 and DASS-21 are psychometrically robust instruments for use in Turkish hematologic cancer patients.

Bayesian updating of the residual structural performance of existing concrete structures based on experimental tests

ABSTRACT Reliability analysis of existing concrete structures requires performance estimations which are affected by uncertainties related to system geometry, material characteristics, loading scenarios, and exposure conditions. A framework for Bayesian model updating with structural reliability methods of concrete structures is presented and validated based on the results of an experimental campaign on prestressed concrete (PC) bridge deck beams extracted from a dismantled 50-year-old viaduct. Subset simulation techniques are applied for structural reliability analysis. The prior knowledge of concrete mechanical properties and residual prestressing levels is updated based on the results of laboratory tests. The statistical residual performance of the PC deck beams is compared with the experimental results of a full-scale load test up to collapse. The structural modelling is developed with bi-dimensional finite elements for plane-stress analysis formulated in accordance with the Modified Compression Field Theory (MCFT). The comparison between experimental and numerical results allows to validate the modelling strategies, the outcomes of the experimental tests, and the Bayesian updating framework.

Innovations in Inductively Coupled Plasma-Mass Spectrometry: Bridging Scientific Fields

Inductively coupled plasma mass spectrometry ICP-MS is a versatile analytical tool with several research uses and regular applications in many domains, including biological materials, environmental analysis, and geochemistry. It is a technique that detects trace components in water, soil and clay, blood, urine, pharmaceutical products, and medicinal cases. Although other techniques, such as atomic absorption and atomic emission, are still used by researchers, there has been a noticeable shift toward ICP-MS, notably over the last decade. Developing accurate and precise methods for measuring components at low concentrations is crucial for detecting abnormalities in the human body and detecting trace amounts of metal in many other species. ICP-MS has been shown to be a viable approach for elemental determination of biological fluids, water, clay, and pharmaceuticals because it allows for reliable analysis at trace and ultra-trace levels while maintaining a wide dynamic range. Many breakthroughs have been made in ICP-MS analytical capabilities over the last years. In this review, we will discuss the most recent works, which use trace element analysis by ICP-MS in several fields.

A Study of the Elements of Corporate Apology Letters on Social Media

With the rise of social media, enterprises are increasingly confronted with challenges in crisis communication, as platforms like Weibo amplify public scrutiny and accelerate the spread of information. In this context, corporate apology letters have become a crucial tool for managing and mitigating the consequences of a crisis. This paper explores the key elements and effects of apology letters within the Chinese food and beverage industry on Weibo. Utilizing a questionnaire survey for data collection, and SPSS27.0 for statistical analysis, the study employs reliability analysis, correlation analysis, and regression analysis to test the research question. The findings reveal that admission of mistakes, emphasis on corporate culture, and solemn commitment exhibit a strong positive correlation with apology satisfaction, thereby being pivotal elements in an effective apology framework. Conversely, while maintaining public relations, repeated apologies, and expressions of gratitude show moderate positive correlations, they have no linear relationship. The research contributes a comprehensive framework for crafting corporate apology letters, offering valuable insights for enhancing crisis management strategies. The findings have significant implications for theoretical development and practical application in crisis communication.

Evaluation of the reliability, usefulness, quality and readability of ChatGPT's responses on Scoliosis.

This study evaluates the reliability, usefulness, quality, and readability of ChatGPT's responses to frequently asked questions aboutscoliosis. Sixteen frequently asked questions, identified through an analysis of Google Trends data and clinical feedback, were presented to ChatGPT for evaluation. Two independent experts assessed the responses using a 7-point Likert scale for reliability and usefulness. Additionally,the overall quality was also rated using the Global Quality Scale (GQS).To assess readability, various established metrics were employed, including the Flesch Reading Ease score(FRE), the Simple Measure of Gobbledygook(SMOG) Index, the Coleman-Liau Index(CLI), the Gunning Fog Index(GFI), the Flesch-Kinkaid Grade Level(FKGL), the FORCAST Grade Level, and the Automated Readability Index(ARI). The mean reliability scores were 4.68 ± 0.73 (Median: 5, IQR 4-5), while the mean usefulness scores were 4.84 ± 0.84 (Median: 5, IQR 4-5).Additionally the mean GQS scores were 4.28 ± 0.58 (Median: 4, IQR 4-5). Inter-rater reliability analysis using the Intraclass correlation coefficient showed excellent agreement: 0.942 for reliability, 0.935 for usefulness, and 0.868 for GQS. While general informational questions received high scores, responses to treatment-specific and personalized inquiries required greater depth and comprehensiveness. Readability analysis indicated that ChatGPT's responses required at least a high school senior to college-level reading ability. ChatGPT provides reliable, useful, and moderate quality information onscoliosis but has limitations in addressing treatment-specific and personalized inquiries. Caution is essential when using Artificial Intelligence (AI) in patient education and medical decision-making.

Reliability analysis of complex multistate systems based on an evidence-based discrete-time Bayesian network

This paper presents an approach to address the uncertainty of each unit’s state information caused by a lack of data, complex structure, and lack of human cognition level in complex multistate systems in practical engineering. The units in the system having the same operating environment and structure can cause common-cause failures. With the change in the working time of the system, the reliability of the system also changes, which is referred to as the dynamic timing problem. This paper first expands the discrete-time Bayesian network based on the Dempster-Shafer evidence theory. This forms the reliability analysis method of the discrete-time Bayesian network of evidence. Then, the β factor method is used to analyze and compare the reliability of system units belonging to multiple common-cause failure groups and each unit separately at different times. Finally, the accuracy and feasibility of the method are proved by the hydraulic system of the pipelayer. This paper solves the problem of analyzing the reliability of multistate systems with uncertain probability information when the failure units belong to multiple common-cause failure groups at different times. The proposed approach of integrating the system multistate, uncertainty of state probability information of each unit, failure correlation among units, and dynamic timing solves the problem of reliability analysis while avoiding large errors in actual engineering.

METHODS OF ROBUST FACTOR ANALYSIS: A COMPARATIVE STUDY WITH APPLICATION TO THE EGYPTIAN BANKING SECTOR

Factor analysis is a vital statistical technique for understanding the underlying structure of complex datasets by identifying latent variables that explain the correlations among multiple observed variables. However, traditional factor analysis methods, such as Principal Factor Analysis (PFA), are sensitive to outliers and noise, which can distort results and limit their applicability in real-world datasets. Robust factor analysis is essential for accurately identifying latent variables in datasets with significant contamination. This study provides a comprehensive evaluation of three robust factor analysis techniques Minimum Covariance Determinant (MCD), -norm Exploratory Factor Analysis -norm EFA), and Principal Component Pursuit (PCP) in comparison to the Principal Factor Analysis (PFA) method. Using a real-world dataset comprising monthly stock market data from the Egyptian banking sector, including stock prices, trading volumes, and 14 key economic indicators over 10 years (2014-2024), the study examines the effectiveness of these methods in identifying meaningful factor structures under challenging conditions of noise and outliers. The findings offer valuable insights into the performance of robust methods in handling data contamination, improving factor estimation, and providing more reliable insights for financial decision-making. This paper contributes to the growing need for robust analytical techniques in financial applications, guiding researchers and practitioners in selecting reliable and appropriate factor analysis methods for complex and noisy data environments.

Development of the Multidimensional Scale Related to Infectious Diseases in Dentistry.

This study aimed to develop and validate an instrument to assess dentists' adherence to infection control protocols and explore psychological factors related to infectious diseases in dental practice. The instrument was developed through phases including context validity, face and semantic analysis, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Internal consistency was evaluated using McDonald's Omega coefficient (ω) and Cronbach's Alpha (α). A total of 405 individuals completed the instrument. EFA (n = 135) revealed a four-factor structure (Work Environmental Protection, Disinfection, Team Training, and Fear and Anxiety) with 23 items with factor loadings above 0.60. The Kaiser-Meyer-Olkin (KMO) value was 0.803, and Bartlett's test of sphericity was significant (χ2: 2501.136, df:253.000, p < 0.001). CFA (n = 270) confirmed an acceptable model fit: χ2/df: 1.75; Comparative Fit Index (CFI): 0.997; Tucker-Lewis Index (TLI): 0.996; Standardized Root Mean Square Residual (SRMR): 0.079; Root Mean Square Error of Approximation (RMSEA): 0.053, supporting the four-factor structure from the EFA. Reliability analysis demonstrated strong internal consistency across all factors (ω and α ≥ 0.70). The developed instrument exhibited appropriate validity, reliability, and internal consistency, providing a suitable tool to evaluate infection control adherence and psychological aspects among dentists facing infectious diseases.

Effect of Leisure Activity Participation on Leisure Attitude, Recreational Specialization, Leisure Satisfaction, and Intention to Re-Participate in South Korea

The importance of leisure in post-COVID-19 society has been underscored by the pandemic, illustrating the need to view leisure not merely as an option but as an essential component of a fulfilling life. This study investigates whether leisure attitudes, considered a novel perspective or belief regarding leisure, along with leisure activities, recreational specialization (the process of becoming proficient in particular leisure activities), and satisfaction derived from these experiences, prompt continued participation. The study surveyed 259 adults, both men and women, residing in Seoul, Gyeonggi, and Chungcheong Province who had engaged in leisure activities for over a year. Data were analyzed using the SPSS 21 program for frequency, reliability, and correlation analyses, while confirmatory factor analysis and structural equation modeling were performed with the AMOS 18.0 program. The research yielded the following findings: Firstly, the leisure attitudes of participants significantly influenced their leisure satisfaction. Secondly, their leisure attitudes did not influence recreational specialization. Thirdly, their attitudes did not affect their intention to re-participate. Fourthly, recreational specialization significantly affected both leisure satisfaction and intention to re-participate. Lastly, leisure satisfaction significantly influenced the intention to re-participate. With the evolving perceptions of leisure post-pandemic, there is a need for policies and infrastructure that support the sustainable engagement of leisure activity participants.

Methods of acoustic data processing affect species detectability in passive acoustic monitoring of multi‐species playback

Passive acoustic monitoring (PAM) efforts have recently been accelerated by the development of automated detection tools, enabling quick and reliable analysis of recordings. However, automated methods are still susceptible to errors, and human processors achieve more accurate results. Our study evaluates the efficacy of three detection methods (auditory, visual and automated using BirdNET) for 43 European bird species (31 diurnal, 12 nocturnal), analysing the impact of various factors on detection probability over different distances. We conducted transmission experiments in two forest types from March to June, examining the effect of call characteristics, weather conditions and habitat features, to assess their impact on detection probability at different distances. Our findings reveal that species detection distance varies with each detection method, with listening to recordings obtaining the highest detectability, followed by the visual method. Although BirdNET is less accurate, it still proves useful for detection, especially for loud species. Large diurnal and small nocturnal species were most detected. Our study emphasizes the importance of considering detection methods to maximize species detectability for effective PAM research.

Cross cultural adaptation and psychometric evaluation of the Turkish PedsQL™ Brain Tumor Module: Assessing health-related quality of life in children with brain tumors

Background Brain tumors are the most common type of pediatric cancer, significantly impacting children’s health-related quality of life (HRQoL). This study aimed to adapt the PedsQL™-BTM to Turkish culture and evaluate its psychometric properties, including reliability, validity, and cross-cultural consistency. Method A total of 108 children with brain tumors (mean age: 9.1 ± 4.99 years) and 120 parents participated in the study. The translation process included forward-backward translation, reconciliation, cognitive debriefing, and pilot testing. Internal consistency (Cronbach’s alpha), test-retest reliability (ICC), exploratory and confirmatory factor analysis were conducted to evaluate the psychometric properties. Correlations between the PedsQL™-BTM and the Generic Core Scales were examined for criterion validity. Results Cronbach’s alpha ranged from 0.83 to 0.97, indicating excellent internal consistency. Test-retest reliability was robust, with ICC values exceeding 0.80 for most subscales. EFA explained 84.2% of the variance in parent reports and 80.4% in child reports, while CFA confirmed the six-factor structure (child-reported outcomes: RMSEA = 0.069, 90% CI: 0.061–0.076; TLI = 0.862; NFI = 0.92; χ2 (147) = 1875, p < 0.001; and parent-reported outcomes: RMSEA = 0.063, 90% CI: 0.059–0.069; TLI = 0.858; NFI = 0.93; χ2 (153) = 1912, p < 0.001). Correlations between the BTM subscales and Generic Core Scales demonstrated strong criterion validity (e.g. Cognitive Problems correlated with School Functioning: r = 0.831, p < 0.05). Conclusions The Turkish PedsQL™-BTM is a reliable and valid tool for assessing HRQoL in children with brain tumors.

Research on computing task scheduling method for distributed heterogeneous parallel systems

With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in achieving energy efficiency through scheduling to realize green computing. This paper investigates the heterogeneous parallel task flow scheduling problem to minimize system energy consumption under response time constraints. First, for a set of independent tasks capable of parallel computation on heterogeneous terminals, the task scheduling is performed according to the computational resource capabilities of each terminal. The problem is modeled as a mixed-integer nonlinear programming problem using a Directed Acyclic Graph as the input model. Then, a dynamic scheduling method based on heuristic and reinforcement learning algorithms is proposed to schedule the task flows. Furthermore, dynamic redundancy is applied to certain tasks based on reliability analysis to enhance system fault tolerance and improve service quality. Experimental results show that our method can achieve significant improvements, reducing energy consumption by 14.3% compared to existing approaches on two practical workflow instances.

Development of an Automated Construction Contract Review Framework Using Large Language Model and Domain Knowledge

Construction contract review demands specialized expertise, requiring comprehensive understanding of both technical and legal aspects. While AI advancements offer potential solutions, two problems exist: LLMs lack sufficient domain-specific knowledge to analyze construction contracts; existing RAG approaches do not effectively utilize domain expertise. This study aims to develop an automated contract review system that integrates domain expertise with AI capabilities while ensuring reliable analysis. By transforming expert knowledge into a structured knowledge base aligned with the SCF classification, the proposed structured knowledge-integrated RAG pipeline is expected to enable context-aware contract analysis. This enhanced performance is achieved through three key components: (1) integrating structured domain knowledge with LLMs, (2) implementing filtering combined with hybrid dense–sparse retrieval mechanisms, and (3) employing reference-based answer generation. Validation using Oman’s standard contract conditions demonstrated the system’s effectiveness in assisting construction professionals with contract analysis. Performance evaluation showed significant improvements, achieving a 52.6% improvement in Context Recall and a 48.3% improvement in Faithfulness compared to basic RAG approaches. This study contributes to enhancing the reliability of construction contract review by applying a structured knowledge-integrated RAG pipeline that enables the accurate retrieval of expert knowledge, thereby addressing the industry’s need for precise contract analysis.

Good or bad? An Analysis of Social Media Participation in Family Relationships

The relationship between social networks and family relationships is an area that has been extensively researched worldwide. Research shows that excessive use of social media can disrupt face-to-face family interactions and weaken relationships. People who have a large number of online relationships may experience a substitution effect compared to face-to-face interactions. The study examines the role of social media in affecting family relations through the lens of uses and gratification theory by studying variables of social media use i.e Social Interaction, Information Seeking and Entertainment. This study examines how the use of social media has affected family relationships among students of Government College University Faisalabad (GCUF). Using a quantitative approach, the study surveyed 348 students and examined correlations between time spent on social media, devices used to access it, purpose of use, and family dynamics. The reliability analysis yielded a moderate Cronbach's alpha of 0.641. Correlation and ANOVA analyses showed that, although time spent on social media has a weak negative relationship with family relationships, the device used for social media (smartphone, tablet or computer) does not significantly influence family relationships. However, the purpose of social media was found to be an important factor: the use of social media for entertainment is correlated with a reduction in family relationships, while the social interactions and goals of information do not have any negative effect. These findings suggest that while device type and time spent on social media are not critical factors, the motivation behind social media use particularly influences family relationships and use for entertainment represents a potential challenge for family cohesion. To address these concerns, policymakers should encourage the development of digital literacy programs to teach families how to use social media responsibly, encourage platforms to develop features that enable meaningful communication and support parental guidance programs.