Validation Data Research Articles

Validation and Utility of Drug-Nutrient Interaction and Dietary Supplement Mechanistic Activity in the Natural Medicines Database

PURPOSE The increasing use of dietary supplements by patients with cancer and other chronic diseases requires the systematized review of potential interactions between prescription drugs and nutrients from supplements by health care and clinical research teams. Dietary supplement interaction databases are positioned to fill a gap in quantifying potential risks for patients, although none have been assessed for reliability in data interpretation. The NatMed database, a source for comprehensive reports on mechanistic and safety data for dietary supplement ingredients, was evaluated for use in future investigations. METHODS Data from NatMed were retrieved using licensed end points for ingredient monographs with drug-nutrient interactions with doxorubicin across five pharmacokinetic and metabolic pathways, and for ingredient monographs with antioxidant activity. Interactions between dietary supplements and doxorubicin treatment and antioxidant monographs were independently reviewed and characterized by clinical pharmacists. Cohen's K was used to measure interrater reliability and the degree of agreement between pharmacists. RESULTS Three hundred fifteen potential interactions with doxorubicin (n = 115 monographs) and 455 other antioxidant ingredients were identified and reviewed by clinical pharmacists. There was substantial to near-perfect agreement for drug-nutrient interactions with doxorubicin (Cohen's K = 0.64-0.85) and for antioxidants (Cohen's K = 0.84). A small proportion of retrieved monographs were not validated by the clinical pharmacists for interactions with doxorubicin (n = 20 occurrences, 6.4%) or for antioxidant activity (n = 28, 6.2%). CONCLUSION A high degree of reliability in data on dietary supplement interactions with doxorubicin and mechanisms of action suggests NatMed may be a dependable source of data for future investigators. Additional procedures including independent data validation and use of multiple dietary supplement interaction databases will strengthen the quality of findings in future studies.

Public Services At the Minahasa District Education Office

The aim of this research is to find out and describe public services at the Minahasa Regency Education Office. This research uses qualitative methods, with the process starting from data collection, data reduction, data display and drawing conclusions. Research informants namely; Head of General and Civil Service Subdivision, General and Civil Service Staff, General and Civil Service Staff, and Teachers. Data collection was carried out through observation, interviews and documentation techniques. Data validity checking techniques include the degree of trust, transferability, dependency and certainty. The analysis was carried out using several indicators, namely; 1). Administrative requirements, 2). Systems, mechanisms and procedures, 3). Completion Time, 4). Costs, and 5). Service Complaints Media. The research results show; 1) Administrative requirements have not been maximized because they have not complied with the applicable SKP; 2) Systems, Mechanisms and Procedures: Not yet optimal because even though they comply with the SOP, there are still delays; 3).Completion Time; Not yet appropriate because delays often occur; 4). Management Fees; It is appropriate that there are no fees for processing promotions; 5). Complaint Media Service. Not yet optimal because only a suggestion box is available, not utilizing existing social media/technology.

Analisis Implementasi Kurikulum Merdeka pada Pembelajaran IPAS di Kelas IV Sekolah Dasar Negeri 28 Pontianak Kota

This research aims to provide an analysis in the realm of planning, implementation and obstacles in the process of implementing an independent curriculum in teaching and learning activities in science or science subjects in class IV 28 Pontianak City Elementary School. This research uses qualitative research. The data collected was obtained through interviews, observation and documentation. The subjects in this research included class IV A teachers and class IV B teachers. Next, the data was analyzed using the Miles and Huberman technique through four stages, including the process of data collection, data reduction, data presentation, and drawing conclusions. Data validity testing was carried out using technical triangulation. This research resulted in a science and science learning planning process where an educator designs teaching modules that have been adapted to independent curriculum procedures which consist of three elements including general information, core components and attachments. In the process of implementing learning, educators have implemented a learning system based on the suitability of the learning flow available in the teaching module so that learning can be carried out based on the syntax of the learning model used. The obstacles felt by educators in the process of implementing their curriculum in natural science and natural science subjects in class IV include adapting material to learning objectives, time management in carrying out teaching and learning activities, and the availability of infrastructure, for example lack of internet or WiFi network facilities and projector.

Validation studies of the FLASH-TV system to passively measure children’s TV viewing

TV viewing is associated with health risks, but existing measures of TV viewing are imprecise due to relying on self-report. We developed the Family Level Assessment of Screen use in the Home (FLASH)-TV, a machine learning pipeline with state-of-the-art computer vision methods to measure children’s TV viewing. In three studies, lab pilot (n = 10), lab validation (n = 30), and home validation (n = 20), we tested the validity of FLASH-TV 3.0 in task-based protocols which included video observations of children for 60 min. To establish a gold-standard to compare FLASH-TV output, the videos were labeled by trained staff at 5-second epochs for whenever the child watched TV. For the combined sample with valid data (n = 59), FLASH-TV 3.0 provided a mean 85% (SD 8%) accuracy, 80% (SD 17%) sensitivity, 86% (SD 8%) specificity, and 0.71 (SD 0.15) kappa, compared to gold-standard. The mean intra-class correlation (ICC) of child’s TV viewing durations of FLASH-TV 3.0 to gold-standard was 0.86. Overall, FLASH-TV 3.0 correlated well with the gold standard across a diverse sample of children, but with higher variability among Black children than others. FLASH-TV provides a tool to estimate children’s TV viewing and increase the precision of research on TV viewing’s impact on children’s health.

Validation Of Matching Nik, Npwp And Assistance In Filling Out Annual Tax Returns With Ibi Kosgoro Tax Volunteers For 2024 Term Of Service

Matching of Population Identification Numbers (NIK) and Taxpayer Identification Numbers (NPWP), as well as assistance in filling out the Annual Tax Return (SPT). Validation of NIK and NPWP is a critical step in identifying and ensuring the validity of individual and company data in various administrative processes, especially those related to tax obligations. NIK validation involves matching a person's personal identity data with an official government database, while NPWP validation verifies the existence and validity of a company's tax identification number. These two processes support the accuracy of information used in various financial transactions and reporting. Apart from validation, assistance in filling out the Annual Tax Return is also important to ensure that the tax documents submitted comply with applicable tax regulations. This assistance can take the form of technical assistance in filling out SPT forms, consultation regarding income classification and processing tax deductions, as well as interpretation of the latest applicable regulations. Validation of NIK and NPWP as well as assistance in filling out the Annual SPT plays an important role in supporting administrative efficiency and tax compliance. By optimizing these processes, it is hoped that it can improve tax compliance, reduce the risk of administrative errors, and increase transparency and accountability in data management and financial reporting.

Artificial Intelligence as a tool for enhancing Building Information Modeling (BIM)

Building Information Modeling (BIM) has revolutionized the Architecture, Engineering, and Construction (AEC) industry, transforming how building lifecycle data is managed and visualized. By consolidating physical and functional characteristics into a shared digital model, BIM facilitates collaboration, minimizes rework, and streamlines planning and execution. In sustainability, BIM supports "Green BIM" practices that reduce carbon footprints by enabling energy modeling and lifecycle analysis. However, despite these advances, BIM faces interoperability issues, largely due to proprietary software formats, leading to isolated data silos that impede efficient data exchange across platforms. The integration of Artificial Intelligence (AI) into BIM introduces groundbreaking solutions to these challenges. AI enhances BIM through automation of design validation, clash detection, and real-time data analysis, transforming BIM into an adaptive system capable of proactive decision-making. AI applications, including predictive maintenance, generative design, and real-time construction monitoring, promise higher safety standards, reduced errors, and improved lifecycle management. However, AI-enhanced BIM adoption is hampered by technical, ethical, and financial challenges, such as data quality, privacy concerns, and high implementation costs. Addressing these obstacles with standardized data protocols, workforce upskilling, and collaborative frameworks can maximize the potential of AI-driven BIM, advancing sustainability, efficiency, and resilience within the construction industry.

Examination of Scales for Test Anxiety with Thematic Content Analysis

This study aims to examine the descriptive and psychometric properties of test anxiety scales in Türkiye through thematic content analysis. A total of 12 scales were obtained by means of the review of studies conducted since 2013. Two studies were not evaluated because they failed to meet the requisite criteria. Document analysis was used as the data collection method. Many of the scales examined consisted of articles. The majority of scale development and adaptation studies were conducted between 2016 and 2018. Most of the scales were of the Likert-type and the number of items ranged from 11 to 30. The multidimensional structure of test anxiety was not considered in some scales, while a number of scales were developed to measure the unidimensional structure of test anxiety. In reliability estimates, the Cronbach alpha coefficient was calculated for all scales, but different reliability estimation methods were also used, including Composite reliability, Spearman-Brown Split half reliability, and test-retest. Cronbach's alpha coefficient was calculated as a minimum of 0.70 and a maximum of 0.94. When the validity data of the scales were reviewed, it was observed that construct validity was the most frequently examined construct, followed by criterion validity. However, content validity was not investigated in any of the studies. Findings regarding EFA and CFA values in the studies on construct validity and the scales used in the studies on criterion validity are presented. At the same time, the analysis of the psychometric properties revealed certain deficiencies, and suggestions were made accordingly.

Evaluation of Load Response in Elastomeric Bearings Through Link Element and Finite Element Modelling

Abstract: The Finite element modeling (FEM) and link element analysis of elastomeric bearings were conducted in this study to evaluate load behaviour and ensure compliance with design standards. A detailed FEM was developed, where material properties and boundary conditions were incorporated to simulate load transfer mechanisms, which were enhanced through link element analysis. Validation of results was performed against design codes and analysis software data, confirming the model's accuracy. A reliable method for optimizing elastomeric bearing design, improving structural safety, and meeting essential design checks was presented. Ten models in total were analyzed with variations, firstly the models were selected as per the design checks criteria. Then selected parameter’s output values are compared with each passed model case and then to finalize the research conducted, the data validation table has created with providing recommendations to show the suitability, aiming to improve design practices and address challenges in modern bridge engineering

Gender Identity Formation of Single Parent Children in Pinang Sebatang Barat Village, Tualang District, Siak Regency (Study Case: Single Parent Families and Gender Identity Formation)

This study was conducted in Pinang Sebatang Barat Village, Tualang District, Siak Regency. This study aims to identify how single mothers and fathers shape the gender identity of children aged 3-7 years through their roles as caregivers, educators, and breadwinners. This study involved 6 subjects, consisting of 5 single mothers and 1 single father, selected using purposive sampling techniques. The theory used in this study is Albert Bandura's social learning theory, which looks at the process of forming children's gender identity through observation, imitation, and behavioral reinforcement. This study uses a qualitative method with a descriptive approach, as well as a case study research type. Data collection techniques are carried out through in-depth interviews, observation, and documentation. To ensure data validity, researchers also use triangulation methods to compare results from different methods and sources. The results of this study indicate that single mothers and fathers have challenges in shaping children's gender identity, especially in modeling masculine or feminine gender roles. Limited time and economic resources are often obstacles for single parents in carrying out parenting and education functions, so that some children are cared for more by other family members, such as siblings or grandparents. Nevertheless, the role of single mothers in providing positive reinforcement for children's behavior still seems important in the social learning process. Based on these findings, researchers suggest that single parents need social and economic support to help them shape their children's gender identity in the most effective way.

Calibration and Validation of NOAA-21 Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper Sensor Data Record Data

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

Character Education Values in the Legend of "Mount Lewotobi" in East Flores, East Nusa Tenggara, Indonesia

This study examines the integration of character education values in the Indonesian legend Mount Lewotobi from East Flores, East Nusa Tenggara. Character education is a fundamental aspect of human development, shaping morals and personalities to create more humane individuals, and local legends serve as valuable mediums for imparting ethical lessons. Employing a qualitative approach, this research utilized documentation as the primary data collection technique and applied Spradley’s cultural theme analysis model to analyze the narrative. Data validation was ensured through triangulation techniques. The findings reveal that the Mount Lewotobi legend conveys significant character education values, particularly in teaching ethics and manners. The study underscores the rich correlation between education and literature, emphasizing the potential of local wisdom as an effective medium for character education. By incorporating such narratives into educational curricula, students can connect with their cultural heritage while fostering moral and ethical growth, making local legends a vital resource for holistic education.

Validity of vaccination information in the COVID-19 surveillance system in Japan: Implications for developing efficient and highly valid data collection systems in future pandemics

Validity of vaccination information in the COVID-19 surveillance system in Japan: Implications for developing efficient and highly valid data collection systems in future pandemics

Assessment of dispute causes in government building maintenance projects through adjusted importance rating and risk mapping

Despite the reliance of Saudi Arabian government facilities on private contractors for maintenance, there is a persistent rise in disputes between these contractors and government entities. This ongoing issue raises concerns about the efficiency and effectiveness of contract management, ultimately affecting the quality of maintenance services and operational continuity. This study investigates the primary causes of disputes in government building maintenance projects, using both the Frequency Adjusted Importance Index (FAII) and risk mapping methods to analyze and prioritize the causes. A literature review and a designed questionnaire survey were employed to rank dispute causes. Thirty-six responses were analyzed. Cronbach's alpha and coefficient of variation were utilized to assess the collected data's validity and reliability. Three case studies then were utilized as a validation tool, compared against the findings of the survey to integrate practical insights with the theoretical results from the survey. The findings reveal that issues such as (1) the lack of original as-built drawings, (2) constant government interference, (3) delays in decision-making, (4) poorly defined scope of work, and (5) mistakes in bidding or cost estimation are the most critical causes of disputes in these projects. Cross-method validation with real-world case studies further strengthens the reliability of these findings, as these top-ranked causes consistently align with issues encountered in practice. The findings of this study could assist local authorities adopt alternative approaches in managing and executing maintenance contracts, thereby mitigating the causes of disputes.

Association of attachment with level of physical activity among dog owners: A cross-sectional study.

This cross-sectional study examined the association of attachment with the frequency of dog walking and physical activity level among dog owners. We analyzed data collected in an internet survey conducted by the Japan Pet Food Association in 2023. Valid data were obtained from 1041 dog owners. The mean (SD) age of participants was 52.5 (14.9) years, with 57.5% being women. Ordinal logistic regression models showed that a higher attachment score was associated with a higher frequency of dog walking after controlling for important confounders (B = 0.04, SE = 0.02, p<0.01, Adjusted R2 = 0.03). Linear regression models showed that a higher attachment score was associated with a higher moderate-vigorous physical activity level (B = 1.43, SE = 0.44, p<0.01, Adjusted R2 = 0.01). These results suggest that dog owners with strong attachment maintain physical activity levels through caring for their dogs. Higher levels of physical activity might have protective effects against adverse health outcomes.

Flexural Strength of Structural Beams Cast Using Combined Normal-Weight and Lightweight Concrete Mixtures

Limited investigations have evaluated the potential of using layered sections of normal-weight and lightweight concrete (NWC and LWC) mixtures in structural beams and slabs. The main objective of this paper is to assess the flexural strength properties of layered reinforced concrete (RC) beams, which help conserve natural resources and reduce construction weight. Six RC beams cast with different NWC/LWC combinations are tested to determine the damage patterns, concrete strains, ultimate load, displacements at failure, and ductility. The test results showed that the LWC cast in the tension zone (and up to the neutral axis) has a negligible effect on the beam’s stiffness and ultimate load since the overall behavior remains governed by the yielding of tensile steel reinforcement. Nevertheless, the deflection at failure and ductility seem to gradually curtail when the NWC is partially replaced by LWC at different elevations across the beam’s cross-section. A finite element analysis using ABAQUS software 6.14 is performed, and the results are compared with experimental data for model validation. Such data can be of interest to structural engineers and consultants aiming for optimized design of slabs and beams using layered concrete casting, which helps reduce the overall construction weight while maintaining the structural integrity of members.

Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships

The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making.

Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model.

Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean section (CS) rates after induction of labor (IOL) demand for improved counseling on delivery mode within this context. We aim to develop a prognostic model for predicting vaginal delivery after labor induction using computational learning. Secondary aims include elaborating a prognostic model for CS due to abnormal fetal heart rate and labor dystocia, and evaluation of these models' feature importance, using maternal clinical predictors at IOL admission. The best performing model was assessed in an independent validation data using the area under the receiver operating curve (AUROC). Internal model validation was performed using 10-fold cross-validation. Feature importance was calculated using SHAP (SHapley Additive exPlanation) values to interpret the importance of influential features. Our main outcome measures were mode of delivery after induction of labor, dichotomized as vaginal or cesarean delivery and CS indications, dichotomized as abnormal fetal heart rate and labor dystocia. Our sample comprised singleton term pregnant women (n = 2434) referred for IOL to a tertiary Obstetrics center between January 2018 and December 2021. Prediction of vaginal delivery obtained good discrimination in the independent validation data (AUROC = 0.794, 95% CI 0.783-0.805), showing high positive and negative predictive values (PPV and NPV) of 0.752 and 0.793, respectively, high specificity (0.910) and sensitivity (0.766). The CS model showed an AUROC of 0.590 (95% CI 0.565-0.615) and high specificity (0.893). Sensitivity, PPV and NVP values were 0.665, 0.617, and 0.7, respectively. Labor features associated with vaginal delivery were by order of importance: Bishop score, number of previous term deliveries, maternal height, interpregnancy time interval, and previous eutocic delivery. This prognostic model produced a 0.794 AUROC for predicting vaginal delivery. This, coupled with knowing the features influencing this outcome, may aid providers in assessing an individual's risk of CS after IOL and provide personalized counseling.

Integrating isotope mixing and hydrologic models towards a more accurate riverine nitrate source apportionment

Nitrate (NO3–) is the major form of bioavailable nitrogen in most rivers, and its dramatic increase may lead to a number of serious environmental issues, such as eutrophication and biodiversity decline. Quantification of NO3– sources and fluxes in rivers is essential for effective management of NO3– pollution. Isotope mixing and hydrological models are proven practical tools for quantifying the sources of NO3– in rivers, yet both methods have severe limitations. To improve the accuracy and reliability of riverine NO3– source apportionment, this study proposed a protocol that integrates the strengths of both tools, capable of solving issues such as the calculation bias caused by isotope endmember overprinting in isotope mixing model and the lack of validation data for hydrological models. We applied the framework to a typical agricultural river, the Qihe River, and illustrated the effectiveness of the method in quantifying riverine NO3– sources. The proportional contribution of sewage simulated by the SWAT model approximated that estimated by the isotopic mixing model, yet the SWAT-based contributions of soil organic nitrogen and chemical fertilizer were 16.3 % lower and 14.0 % higher, respectively than the MCMC-based results. After integrating both models, we adopted the proportions of NO3– sources in the river from chemical fertilizer, sewage, and soil organic nitrogen as 45.1 %, 30.9 %, and 23.9 %, respectively. This study showed that coupling isotopic and hydrological models provided a new dimension for the accurate quantification of N sources in rivers.

Early-stage identification of indoor leakage sources in factories

Identifying the leakage source in industrial factories as soon as an incident happens is crucial for controlling the leak in time and safeguarding lives and property. This study developed a rapid source identification method for factories, aiming to identify the characteristics of leakage sources at the early stage of the leakage event. The approach integrates pollutant diffusion theory, sliding window method, correlation coefficient method, and data fitting method. Based on an experimental cabin, 162 verification conditions were established, encompassing constant sources as well as two time-varying sources: linear and non-linear ones. Additionally, various aspects of the method were evaluated. It was found that considering the error and noise in sensor monitoring data, the method exhibits identification accuracies of 83.3%, 79.6%, and 74.1% for constant sources, linear sources, and non-linear sources, respectively. In the case of non-linear sources, the coefficient “b” must be taken into account when generating the dimensionless concentration vector. A sliding window containing 50 to 60 valid data points can achieve the highest source identification accuracy. This research can provide help in quickly locating leakage sources in factories, help with the timely control of leaks, and protect workers’ health and property safety.

Pengelelolaan Fasilitas Laboratorium Komputer di SMA Islam Al Amalul Khair Palembang

This study aims to analyze the implementation of computer lab facility procurement, the factors influencing it, and the solutions for the procurement process at SMA Islam Al Amal Khair Palembang. The study was conducted at SMA Islam Al Amalul Khair Palembang using a descriptive qualitative research method. The informants in this study were the school principal and the vice principal in charge of facilities and infrastructure. Data were collected through observations, interviews, and documentation. The data analysis techniques used include data reduction, data presentation, and conclusion drawing, while data validity was ensured through source triangulation and technique triangulation. The research findings indicate that the implementation of computer lab facility procurement at SMA Islam Al Amalul Khair Palembang has been successfully carried out. However, there are several aspects that need further improvement, particularly in the procurement process, to enhance the school's quality and competitiveness with other institutions