Reliable Data Research Articles

Bridging the United States population diversity gaps in clinical research: roadmap to precision health and reducing health disparities.

Precision medicine promises improved health outcomes by tailoring treatments to individual genetic and environmental factors. However, achieving this potential is hindered by persistent health disparities and the underrepresentation of racially and ethnically diverse populations in clinical trials. Limited diversity in research exacerbates health inequities, reducing the generalizability of findings and widening gaps in access to effective treatments. This review outlines a multi-faceted strategy to bridge diversity gaps in clinical trials, focusing on community engagement, clinical pharmacology, and regulatory science. Key approaches include decentralized trials, targeted recruitment, advanced data modeling, and comprehensive integration of genetic and social determinants of health data. Regulatory frameworks, such as diversity action plans, play a crucial role in ensuring equitable access to precision health innovations. Increasing representation in research enhances the reliability of clinical data and fosters health equity by addressing differences in disease prevalence, treatment responses, and healthcare access. By leveraging technological advancements and inclusive research methodologies, this framework aims to transform clinical trials into a roadmap for equitable healthcare. Ensuring diverse participation in research is essential for the successful implementation of precision medicine and realizing the full potential of precision health, ultimately reducing health disparities and promoting fair access to medical advancements across all populations.

How to measure Integrated Reporting Adoption, compliance, and Quality: A Systematic Review of taxonomies, constructs, and measures toward a research agenda

Abstract This systematic literature review examines Integrated Reporting (IR) constructs, identifying four distinct categories to enhance the understanding of IR measurement in research. Analyzing 84 seminal studies published in 28 high-quality journals, we systematize the existing IR literature to clarify how different constructs serve diverse research objectives while varying in their adherence to the IR Framework’s guiding principles. Our findings reveal that quantitative IR constructs, often based on readily available databases, face challenges related to data opacity and reliability, whereas qualitative constructs, while addressing these limitations, remain difficult to replicate and scale for large datasets. We provide guidelines for aligning research objectives with suitable constructs and highlight the shortcomings of IR measurement, particularly its predominant focus on report outputs rather than the underlying reporting processes. To address these gaps, we propose a research agenda that integrates input and process-oriented IR constructs and explores the potential of emerging technologies to advance IR research. By bridging the gap between theoretical principles and practical applications, our study contributes to the development of more rigorous and meaningful IR measurement approaches.

IoT based Data-Driven Methodology for Real Time Production Optimization and Supply Chain Visibility in Smart Manufacturing and Logistics

This research looks at how data methods and IoT technologies can be used effectively for planning and improving supply chain transparency. It seeks to explain the importance of measurements namely cycle time, lead time, on-time delivery, inventory turns, and fill rate. Analyzing the company objectives and requirements based on the identified ones, the study underscores the paramount importance of KPI visualization in helping the users comprehend organizational processes and seek improvement. The study also explores how the effectiveness of the IoT infrastructure is assessed and how the IoT devices are chosen and subsequently deployed for strategic purposes and the building of real-time data acquisition systems. In addition, the article also covers the approaches with regard to data acquisition and assimilation; more focus is given to the understanding of the performance of the machine, conditions of the environment, and the logistical aspects by means of data visualization of the IoT. The study also emphasizes data quality governance mechanisms to ensure the accuracy and comprehensiveness of IoT data, and thus make people more confident in data reliability.

Dynamic weighted cluster-sampling: An optimized cohesive method for improving data quality in the context of big data

In the field of data mining, imbalanced big data has emerged as a critical challenge, characterized by a disproportionate distribution of classes within large datasets. This phenomenon often results in biased models that underperform on minority classes, compromising the overall effectiveness of predictive analytics. Standard machine learning algorithms may struggle to accurately classify underrepresented instances, leading to predictions that reflect majority class tendencies rather than the true underlying patterns. To effectively address these challenges, it is imperative to employ advanced methods. This work presents a novel hybrid approach designed to mitigate the challenges of imbalanced big data classification effectively by employing clustering and sampling methods. Our proposed approach aims to reduce data volume, enhance veracity (improving performance metrics), and accelerate execution time, all while preserving essential attributes and ensuring data reliability. The results demonstrate that our approach achieves superior accuracy, AUC, F1-score, and G-means metrics compared to scenarios lacking data balancing strategies. Furthermore, we evaluate our proposed method against current methods in the field using large imbalanced datasets. Notably, our method exhibits an impressive accuracy rate approaching 100%, with improvements ranging from 17% to 22% across all performance metrics assessed, thus underscoring its effectiveness in addressing the challenges associated with imbalanced big data classification.

The Effect of Filtering on Signal Features of Equine sEMG Collected During Overground Locomotion in Basic Gaits

In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering–induced changes in basic signal features in relation to the balance between signal loss and noise attenuation. Raw sEMG signals were collected from the quadriceps muscle of six horses during walk, trot, and canter and then filtered using eight filtering methods with varying cut–off frequencies (low–pass at 10 Hz, high–pass at 20 Hz and 40 Hz, and bandpass at 20–450 Hz, 40–450 Hz, 7–200 Hz, 15–500 Hz, and 30–500 Hz). For each signal variation, signal features—such as amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal–to–noise ratio (SNR)—along with signal loss metrics and power spectral density (PSD), were calculated. High–pass filtering at 40 Hz and bandpass filtering at 40–450 Hz introduced significant filtering–induced changes in signal features while providing full attenuation of low–frequency noise contamination, with no observed differences in signal loss between these two methods. Other filtering methods led to only partial attenuation of low–frequency noise, resulting in lower signal loss and less consistent changes across gaits in signal features. Therefore, filtering–induced changes should be carefully considered when comparing signal features from studies using different filtering approaches. These findings may support cross-referencing in equine sEMG research related to training, rehabilitation programs, and the diagnosis of musculoskeletal diseases, and emphasize the importance of applying standardized filtering methods, particularly with a high–pass cut–off frequency set at 40 Hz.

Measuring Progress in Equitable Urban Sustainability: Six Key Questions from European Cities

There is mounting evidence and concern that humanity is failing to equitably meet social needs while overshooting the Earth’s ecological boundaries. Efforts to monitor progress towards equitable urban sustainability have expanded significantly over the years; however, challenges remain in comprehensively assessing and comparing the progress made in different settings. To stimulate critical thinking and guide capacity-building efforts, we assessed the main dimensions and indicators used to monitor urban sustainability and equity in a selection of European cities. We analysed city reports to identify major recurring underlying themes, which we framed as guiding questions, and suggested areas for further development. The purpose was not to highlight the strengths and limitations of specific cities’ efforts. Our critical assessment identified several areas that require attention: the need for the more explicit use of theories or conceptual frameworks to select dimensions and indicators and to frame problems (and subsequently to guide intervention design); the standardisation of indicators; and improved data availability, reliability, and disaggregation to support data capturing, reporting, and comparability across settings. Despite meaningful progress, further efforts are needed to strengthen cities’ capacities to measure, monitor, and report on equitable urban sustainability. These efforts should be complemented with educational initiatives to foster the socio-cultural and behavioural changes necessary to achieve more equitable, sustainable, and healthy urban environments.

КОРЕЛЯЦІЙНИЙ АНАЛІЗ ПАРАМЕТРІВ КЛІМАТИЧНИХ ГЕОІНФОРМАЦІЙНИХ СИСТЕМ ДЛЯ ВІДНОВЛЮВАНОЇ ЕНЕРГЕТИКИ

The paper examines the technical aspects of the integration of distributed renewable generation, in particular solar energy, into the energy system of Ukraine, which is undergoing a large-scale transformation with the aim of increasing reliability, sustainability and efficiency. The relevance of the transition to renewable energy sources in the context of global environmental challenges and Ukraine's obligations to reduce greenhouse gas emissions is considered. Special attention is paid to the analysis of meteorological data as a key factor for accurate forecasting of electricity generation by solar power plants. The main part of the research is focused on the correlation analysis of data from NASA POWER and Open Meteo open climate geoinformation systems. These resources provide access to a wide range of data, including parameters of insolation, air temperature and wind speed, which are critical for modelling and forecasting the operation of solar and wind farms. A comparison of these data with data obtained from weather stations installed at an operating solar power plant was carried out, which made it possible to assess the accuracy and reliability of data from each source. Combining data from NASA POWER, known for its high overall accuracy, and Open Meteo, characterised by higher spatial and temporal resolution, has been found to significantly improve forecast accuracy. This is especially important in the context of operational power system management and load planning. A conclusion was made about the need for a systematic and interdisciplinary approach to solving the tasks. The implementation of modern forecasting methods using machine learning and artificial intelligence algorithms for processing large volumes of meteorological data is recommended. The importance of the development of the national data collection and analysis infrastructure is emphasised, which will increase the reliability and efficiency of the energy system in the face of a growing share of renewable generation. Keywords: distributed generation, renewable energy sources, geographic information system, GIS, solar power plant, wind power plant, meteorological data, forecasting, integration, energy system.

Intelligent bridge monitoring system operational status assessment using analytic network-aided triangular intuitionistic fuzzy comprehensive model

The extensive construction of bridge health monitoring (BHM) systems has made it challenging for the authorities to manage them centrally. The reliable operational status of BHM systems is vital to obtaining accurate monitoring data and evaluating the condition of bridges. To evaluate the operational status of these systems, this study established an assessment model that integrates the triangular intuitionistic fuzzy analytic network process (TIFANP) and the triangular intuitionistic fuzzy comprehensive evaluation (TIFCE) method. Firstly, an evaluation index system was established for the operational status of a BHM system. Factors such as system stability, data reliability, system maintenance, early warning, and human-computer interaction were comprehensively considered. Secondly, the evaluation indicator weights were assigned using TIFANP. The system evaluation rating levels were divided into four grades, and the membership and non-membership functions of the evaluation indicators for these rating levels were constructed based on TIFCE. Finally, the effectiveness of the proposed method was verified based on a case study. This is the first time that an operational status assessment method suitable for in-service BHM systems has been proposed. The results show that the TIFANP better accounts for the relationships for non-independence and interactions among the evaluation indicators. Hesitations in the decision-making process were quantified, making the weight allocations more accurate. The proposed method outperforms other comparison methods and can be used to evaluate the operational status of BHM systems in a more scientific and objective manner.

Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset

High-quality data are foundational to reliable environmental monitoring and urban planning in smart cities, yet challenges like missing values and outliers in air pollution and meteorological time series data are critical barriers. This study developed and validated a dual-phase framework to improve data quality using a 60-month gas and weather dataset from Jubail Industrial City, Saudi Arabia, an industrial region. First, outliers were identified via statistical methods like Interquartile Range and Z-Score. Machine learning algorithms like Isolation Forest and Local Outlier Factor were also used, chosen for their robustness to non-normal data distributions, significantly improving subsequent imputation accuracy. Second, missing values in both single and sequential gaps were imputed using linear interpolation, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and Akima interpolation. Linear interpolation excelled for short gaps (R2 up to 0.97), and PCHIP and Akima minimized errors in sequential gaps (R2 up to 0.95, lowest MSE). By aligning methods with gap characteristics, the framework handles real-world data complexities, significantly improving time series consistency and reliability. This work demonstrates a significant improvement in data reliability, offering a replicable model for smart cities worldwide.

Estimated Modern Use: Employing A Service Statistics-Based Indicator to Monitor Family Planning Programs

Estimated Modern Use (EMU) is a novel, service statistics-based indicator designed to complement Couple Years of Protection (CYP) in assessing the scale of family planning use and the first widely used metric since CYPs. Developed by the Track20 project, EMU offers a population-based proportional metric that facilitates cross-country comparisons and temporal trend analysis. By leveraging existing family planning service statistics, EMU provides a more accessible and interpretable measure of contraceptive use.The associated SS-to-EMU tool used to calculate EMU incorporates rigorous data quality review mechanisms, including data visualizations and validated review processes, to enhance the reliability and utility of family planning data for decision-making. The standardization of EMU across countries and projects promotes its integration into routine data review practices, fostering a more comprehensive approach to family planning monitoring and evaluation. Since 2014, all countries that prepare annual estimates for the FP2030 global initiative utilize the SS to EMU tool, to assess data quality and produce EMU estimates.Moreover, the EMU serves as a valuable input for the Family Planning Estimation Tool (FPET), contributing to the refinement of modeled estimates of modern contraceptive prevalence. Since its introduction, EMU has gained widespread adoption at various levels, demonstrating its effectiveness in informing global, regional, and country-level monitoring efforts. Ongoing refinements to the EMU calculation further enhance its accuracy and utility as a supplementary data source for understanding contraceptive use patterns.

High-quality full stokes polarimetric spectroscopy reconstruction using a model-compensated physics-informed neural network for channeled spectropolarimetry

Channeled spectropolarimetry (CSP) enables the simultaneous acquisition of full Stokes parameters spectra in a single-shot, providing a robust solution for dynamic and complex optical measurements. However, accurate spectral reconstruction is often hindered by systematic errors and the limitations of a simplified CSP physical model, resulting in reduced reliability of its applications. To address these challenges, this paper introduces a model-compensated physics-informed neural network (MC-PINN), which integrates an improved physical model with deep learning to enhance reconstruction performance. The MC-PINN framework incorporates a prediction network to approximate data and physical laws, together with a compensation network to correct discrepancies between the observed data and the physical model. This approach significantly reduces errors caused by model inaccuracies, enabling high-quality reconstruction even with limited and noisy data. Simulations and experiments confirm that MC-PINN outperforms traditional methods, achieving superior accuracy and robustness in reconstructing high-frequency spectral features and complex polarization states. This work demonstrates the potential of MC-PINN to improve data reliability, thereby expanding the applicability of CSP to diverse and challenging scenarios.

Pengaruh Independensi dan Pengalaman Kerja terhadap Kualitas Audit pada Kantor Akuntan Publik di DKI Jakarta Timur

This study aims to test and determine how important the influence of Independence and Work Experience is on audit quality at Public Accounting Firms (KAP) in East Jakarta. The research method used is quantitative because it has conducted tests using SPSS such as respondent identity, gender, age, last level of education, length of service and also conducted classical assumption tests, namely data validity, data reliability, data linearity, multicollinearity, heteroscedasticity, autocorrelation test, simple linear regression, multiple linear regression, coefficient test, correlation coefficient test and also partial and simultaneous hypothesis tests. This study proves that the influence of independence and work experience as variables that are very influential, an auditor must have these two integrities with a responsible basis in order to provide good and quality audit results.

Mechanistic insights into granulocyte-macrophage colony-stimulating factor in combating fungal infections: Diverse fungal pathogens.

Granulocyte-macrophage colony-stimulating factor (GM-CSF) has been used for its immunomodulatory properties to enhance therapeutic outcomes and improve cure rates in fungal infections. However, the mechanisms of GM-CSF action in various fungal infections have not been systematically summarized in current literature, and the reliability and broad effectiveness of clinical data remain uncertain. This review provides a comprehensive analysis of how GM-CSF supports host defense against infections caused by specific fungal pathogens. These pathogens include yeasts (Candida spp., Cryptococcus spp.), filamentous fungi (Aspergillus spp., Mucorales, dematiaceous fungi), and thermally dimorphic fungi (Histoplasma capsulatum, Talaromyces marneffei, Paracoccidioides brasiliensis, and Blastomyces dermatitidis). These insights underscore the potential of GM-CSF as a promising adjunctive therapy in combating challenging fungal infections.

Comprehensive Assessment of Coalbed Methane Content Through Integrated Geophysical and Geological Analysis: Case Study from YJP Block

The study block is located on the eastern edge of the Ordos Basin and is one of the typical medium coalbed methane blocks in China that have previously been subjected to exploration and development work. The rich CBM resource base and good exploration and development situation in this block mean there is an urgent need to accelerate development efforts, but compared with the current situation for tight sandstone gas where development is in full swing in the area, the production capacity construction of CBM wells in the area shows a phenomenon of lagging to a certain degree. In this study, taking the 4 + 5 coal seam of the YJP block in the Ordos Basin as the research object, we carried out technical research on an integrated program concerning CBM geology and engineering and put forward a comprehensive seismic geology analysis method for the prediction of the CBM content. The study quantitatively assessed the tectonic conditions, depositional environment, and coal seam thickness as potential controlling factors using gray relationship analysis, trend surface analysis, and seismic geological data integration. The results show that tectonic conditions, especially the burial depth, residual deformation, and fault development, are the main controlling factors affecting the coalbed methane content, showing a strong correlation (gray relational value greater than 0.75). The effects of the depositional environment (sand–shale ratio) and coal bed thickness were negligible. A weighted fusion model incorporating seismic attributes and geological parameters was developed to predict the gas content distribution, achieving relative prediction errors of below 15% in validation wells, significantly outperforming traditional interpolation methods. The integrated approach demonstrated enhanced spatial resolution and accuracy in delineating the lateral CBM distribution, particularly in structurally complex zones. However, limitations persist due to the seismic data resolution and logging data reliability. This method provides a robust framework for CBM exploration in heterogeneous coal reservoirs, emphasizing the critical role of tectonic characterization in gas content prediction.

The Influence of Salary, Work Environment, and Working Hours on Employee Turnover Intention at Bintang Pulubala Farm

This study aims to analyze the effect of salary, work environment, and working hours on employee turnover intention at Bintang Pulubala Farm. Turnover intention is an essential indicator in human resource management that can affect the stability of a company's workforce. This research uses a quantitative method with data collection techniques through questionnaires distributed to 93 permanent employees at Bintang Pulubala Farm. The data obtained were analyzed using multiple linear regression analysis with classical assumption tests to ensure data validity and reliability. The independent variables in this study are salary, work environment, and working hours, while the turnover intention is the dependent variable. The results showed that salary had a negative and significant effect on turnover intention, which means that the higher the salary, the lower the employee's desire to change jobs. The work environment also negatively and significantly affects turnover intention, indicating that a conducive work environment can reduce employee intentions to leave. Meanwhile, working hours have a positive and significant effect on turnover intention, which means that the higher the workload, the more likely employees are to look for other jobs. This study concludes that salary and work environment factors can be the main strategies for reducing turnover intention. At the same time, more flexible working hour management can be a solution to increase employee retention. Therefore, company management is advised to improve employee welfare through more competitive wage policies and a better work environment to reduce turnover intention.

Occupancy-Based Predictive AI-Driven Ventilation Control for Energy Savings in Office Buildings

Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO2 concentrations below 1000 ppm, and up to 12.5% if CO2 concentrations are slightly above 1000 ppm for 1.5% of the time.

Molecular interactions in binary mixtures: a study using 2-ethyl-1-hexanol and various amines, correlation with the Jouyban–Acree model, FTIR, and DFT at different temperatures

ABSTRACT The viscous flow of 2-ethyl-1-hexanol with substituted benzyl amines (N,N-dimethyl benzyl amine (J1), dibenzyl amine (J2), and N-methyl benzyl amine (J3)) was demonstrated to be activated by binary mixtures with excess properties such as excess molar volume( V E ) , excess isentropic compressibility( K S E ) , deviation in viscosity ( Δ η ) , and excess Gibbs free energy ( Δ G ∗ E ) , at specific compositions and temperatures between 303.15 K, 308.15K, and 313.15 K. These properties were calculated using experimentally determined densities, viscosities, and sound speeds. The main molecular interactions were found using the Prigogine-Flory-Patterson (PFP) speculation. The measured density and speed of sound data were correlated using the Jouyban-Acree approach model, and the accuracy was evaluated using mean relevant deviation (MRD) and independent relation deviations (IRD). The measurement data reliability was further corroborated by FTIR studies. The characteristics of chemical, kinetic, and electrical reactivity were examined using B3LYP/6–31 G**(d,p) density functional theory.

Constructing an adaptive blended teaching model through big data analytics and machine learning

This research investigates the development of an adaptive blended teaching model (ABTM) that employs customized instructional strategies to optimize learning outcomes. The approach harnesses data-driven insights derived from student performance, behavior, and engagement to provide a personalized educational experience tailored to each student’s requirements. The integration of big data analytics and machine learning (ML) in education presents significant potential to transform traditional teaching methodologies. Data is collected from open sources, including engagement scores, assessment results, forum participation, attendance, and study hours. Preprocessing steps include data cleaning, normalization, and handling missing values to ensure data reliability. The term frequency-inverse document frequency (TF-IDF) text mining technique is utilized to extract features from student-generated content, highlighting essential phrases. TF-IDF enables the identification of critical learning themes and areas requiring additional support. A hybrid method, namely, the snow leopard optimized-tuned intelligent CatBoost (SLO-ICatBoost), is deployed for predicting student grades, assessing performance, and enhancing the educational process. The SLO improves the selection of relevant features, while the ICatBoost algorithm classifies students based on their performance patterns and learning behaviors. When compared to conventional teaching techniques, the proposed SLO-ICatBoost method significantly improves precision (0.990), accuracy (0.991), F1-score (0.990), and recall (0.991). Due to its flexibility in accommodating various learning environments and individual requirements, this approach can be applied in diverse educational settings.

Data Quality of Resident Documentation in Long-Term Care: A Systematic Review and Meta-analysis.

Data Quality of Resident Documentation in Long-Term Care: A Systematic Review and Meta-analysis.

Calibration and validation-based assessment of low-cost air quality sensors.

Calibration and validation-based assessment of low-cost air quality sensors.