Reliable Data Research Articles

The Effect of Uploads on Folkative on the Fulfillment of Information for Z Generation

A rapid development, especially in the technology industry, led to a big impact on media development. A lot of media companies switched from conventional media to digital media because of fear of being eroded and disability to adapt with the digital advances. not only that, there were also a lot of new online media appearance based on social media, one of which is Folkative. We cannot deny that social media is a new platform which is loved by lots of people, especially Z generation, so the appearance of new online media such as Folkative created a uniqueness of it. Folkative itself is a new online media platform based on Instagram which focused on news, art, culture, and local products, Folkative was called as new media since it was established on 2018. This research aims to determine the effect of uploads on Folkative on the fulfillment of information for Z generation. This research is quantitative research which defines phenomenon with numbers. This research also focused on communication major student at Universitas Negeri Jakarta especially on class of 2020 as the population. This research tests the validity and reliability of data, simple linear regression test to check the effect, and hypothesis test using IBM SPSS (Statistical Product and Service Solutions) version 25. Based on the results of this research, it was determined that there was a positive impact of 15,6%, so it was concluded that there was an effect of uploads on Folkative on the fulfillment of information for Z generation.

Fraud Factor Analysis Hexagon in Detecting Financial Report Fraud In Listed Companies in Indonesia: A Systematic Literature Approach

Financial reporting fraud is a significant challenge across industries, undermining stakeholder trust and the reliability of financial data. The Fraud Model Hexagon expands upon earlier models, like the Fraud Triangle and Fraud Diamond, by introducing six elements pressure, opportunity, rationalization, capability, ego, and collusion that offer a comprehensive view of the factors enabling fraud. This literature review systematically examines the model's effectiveness in detecting financial fraud within Indonesian corporations, especially those listed on the Indonesia Stock Exchange or connected to political and governmental entities. Ten studies from 2018 to 2024 were analyzed, focusing on each Fraud Hexagon element's role in fostering fraud. Findings highlight that pressure, opportunity, and collusion are primary drivers of fraud in politically connected sectors, with capability and rationalization further facilitating fraud, while ego emboldens managers to act without fear of repercussions. The Fraud Hexagon provides a nuanced framework for risk assessment, prompting recommendations for stronger internal controls and ethical standards. It also serves as a valuable diagnostic tool for auditors and regulators, particularly in politically sensitive sectors. This study contributes to understanding the social and psychological dimensions of financial fraud and offers a foundation for future cross-industry research.

Application of AI to minimize information risk in tax control: Evidence from Bulgaria

The application of artificial intelligence (AI) in tax control is becoming more and more relevant in the conditions of globalization and digitalization of the economy, which creates a need for effective mechanisms for managing information risk. With the growing volume of data and the complexity of the tax processes, the risk of deviations, fraud and inconsistencies increases significantly. This article aims to examine applied AI tools and approaches that can minimize information risk in tax control, as well as to analyze their effectiveness through the prism of practice in Bulgaria. The methodology is based on correlation analysis, which measures the strength and direction of the linear relationship between pairs of variables. A Pearson correlation coefficient was used to evaluate the variable "Artificial Intelligence" and the different forms of tax control. Kendall's tau-b correlation coefficient was used to assess the linear relationship between the variables of collection, processing, verification, and management of tax information related to the application of AI. The main results show that AI has a positive and statistically significant effect on risk management and cybersecurity, suggesting the potential to improve data protection and reliability. However, the verification and revision of tax information needs to significantly impact AI, which points to the need for further development of these technologies for better integration. In conclusion, the application of AI offers significant opportunities to minimize information risk in tax control but requires targeted adaptation and refinement in specific aspects of control processes.

The crucial role of 5G, 6G, and fiber in robotic telesurgery.

This paper explores the role of 5G-and future 6G networks-in advancing robotic telesurgery by minimizing latency and enhancing data reliability for real-time remote operations. With robotic telesurgery gaining prominence as a tool to democratize access to specialized surgical care, telecommunications infrastructure has become central to its feasibility and safety. Key elements include 5G's capacity for ultra-low latency and high data transfer rates, which support critical modalities such as kinesthetic, audiovisual, and tactile feedback in telesurgery. The paper outlines the differing latency demands of these modalities, noting that kinesthetic data are particularly sensitive, requiring ultra-low latency for effective surgeon feedback. In addition, the paper discusses the importance of network reliability and Quality-of-Service (QoS) agreements, alongside the potential for 6G networks to further reduce latency and integrate AI-driven predictive analytics. These advancements are positioned to not only broaden telesurgery's reach but also to enhance the precision and safety of procedures, setting the stage for a new paradigm in remote surgical care.

NMR Phase Error Correction with New Modelling Approaches

Nuclear Magnetic Resonance (NMR) spectroscopy is a highly sensitive analytical technique essential for precise molecular identification and quantification. However, accurate results depend on effective pre-processing to correct for various types of errors. Phase error correction, in particular, is crucial for ensuring the reliability of NMR data. Current methods often rely on a single linear model, which may not adequately address all types of phase errors. As a result, this limitation frequently requires manual intervention, making the process both time-consuming and prone to errors. To address these limitations, we propose three modelling approaches for NMR phase error correction: nonlinear shrinkage, multiple models, and a new optimization function called delta absolute net minimization (DANM). Our comparison of seven methods revealed that nonlinear shrinkage outperformed others in both simulated spectra and a diabetes study, followed by multiple models with DANM. Additionally, our spike-in experiments demonstrated that DANM performed quite well in both single and multiple models. Our nonlinear shrinkage approach is a simple yet effective solution. We provide an open-source R package, NMRphasing, available on CRAN (https://cran.r-project.org/web/packages/NMRphasing/) and on GitHub (https://github.com/ajiangsfu/NMRphasing).

The desire path: unleashing expectations, discussing apprehensions, and proposing a way forward for GAI use in higher education

Purpose The purpose of this study is to demonstrate the desire path of using GAI in higher education, including expectations, apprehensions, and the way forward. Design/methodology/approach This qualitative study employs thematic analysis, scrutinizing 11 interviews with innovative higher education faculty. The methodology section details the utilization of GAI (Chat GPT) for conducting thematic analysis on interviews, showcasing academics' practical application of this technology for research purposes. Findings Stakeholders expect continuous improvement in technology, overdependence, advocate for gradual adjustment, and emphasize context-dependent technology utilization. Concerns encompass issues such as data reliability, ethical considerations, risks of undermining fundamental aspects, limitations in fully replacing human involvement, and worries about personal responsibility. Practical implications Recommendations include flexible regulations, data-driven decision-making, professional development, diverse stakeholder engagement, and promoting distributed responsibility. Originality/value This study offers valuable insights into the use of GAI in higher education, formulating policies that encourage innovation without hampering effectiveness.

Capturing Differential Quality of Experimental Evidence in a Predictive Quantum-Mechanical Model for Respiratory Sensitization.

Asthma is of concern in occupational toxicology with significant public-health and economic costs. In the absence of benchmark in vivo and in vitro tests, the use of mechanistically sound in silico models is critical to inform hazard and to protect workers from exposure to potentially harmful substances. We recently reported on the computer-aided discovery and REdesign (CADRE) model for respiratory sensitization, which relies on a tiered structure of expert rules, molecular simulations, quantum-mechanics calculations and advanced statistics to accurately identify respiratory sensitizers from first principles. Here, we present an update to this model based on two years of testing in the pharmaceutical space, where we captured the heterogeneity of the underlying experimental evidence in two predictive tiers, thus allowing the practitioner to select an outcome based on their expert assessment of the data reliability and relevance. This user-based tuning of predictive models is critical for end points that lack consensus on what constitutes satisfactory evidence to support a decision in the handling of chemicals for occupational safety.

Charting a way forward for the use of data science in competition enforcement and platform regulation

ABSTRACT Competition authorities and other regulators already rely on data science to monitor markets and check compliance with applicable rules. The Digital Markets Act and the Digital Services Act have made the use of data science by regulators even more relevant, given the scale and complexity of the oversight required. The existing use of data science shows that regulators are able to adapt their organizations and processes to realize the potential of using technology tools in their activities. At the same time, the use of data science poses challenges in terms of data reliability and individual privacy. Beyond involvement in specific investigations, data science also has the potential to reform the work of regulators by moving towards a more proactive form of enforcement. The sharing of data science expertise and tools between regulators deserves to be further facilitated to increase collaboration and share resources in monitoring the platform economy.

Application of electromagnetic method to seaward slope: Case study from Saemangeum Seawall in South Korea

As dams and seawalls around the world age, there has been increasing attention towards the safety of these structures and monitoring of their condition using various geophysical methods. However, assessing seaward slopes, which are directly impacted by waves and tides and a seawater-filled riprap stone deposit structure, has been challenging due to the highly conductive environment and the difficulty in installing contact equipment. This study examines the feasibility of using electromagnetic (EM) surveys to monitor seawall slopes by analyzing and interpreting multi-frequency EM data measured over 2-year period along the seawall slope of the Saemangeum Seawall in South Korea, which is recognized as the world’s longest seawall. The repeatability of data measured at the same survey line over the 2-years substantiates the reliability of EM survey data. The field data are inverted using a 2.5-dimensional regularized Gauss–Newton method. EM responses calculated from the inverted resistivity model closely mirrors the field data. The inversion results consistently indicate the presence of an isolated conductive anomaly with seawater resistivity below the survey line on the seaward slope. This suggests scouring erosion by seawater and affirms the applicability of the EM surveys to seaward slope monitoring. Additionally, the EM responses are affected significantly by sea level changes during the survey; these variations are quantified in this study. Thus, our results demonstrate the effectiveness of the EM method in monitoring the seaward slope to assess the safety of seawalls and provides a comprehensive overview of the entire data analysis and inversion processes, as a valuable reference for those using EM technology in their work.#xD;

“It Just Galls Me, as a Taxpayer”: Trust Implications of School Closure Decision-Making in Ontario

More than 400 publicly funded schools in Ontario, Canada, permanently closed between 2011 and 2017. This study examines the impact of highly criticized closure decisions on trust, guided by the OECD competence-values framework for citizens’ trust in public institutions. Interviews with key informants reveal factors affecting trust, including timing and format of public consultations, reliability of data informing closure recommendations, silencing of community concerns, timeliness of government coordination, asset management horizons, and system-wide approaches to planning. This study underscores the need for an alternative school-closure decision-making model for Ontario.

Panchakarma fitness form for clinical practice and research in Ayurveda

Background: The Panchakarma Fitness Form is an essential tool in Ayurveda, used to assess a patient’s suitability for specific Panchakarma treatments. By evaluating Dashavidha Pareeksha, Sama-Nirama Pareeksha, assessment of Agni, assessment of Koshta, examination of vital parameters, medical history, and other relevant factors, this form ensures that treatment decisions are informed, ethical, and patient-centered. Objectives: To develop and implement a Panchakarma Fitness Form for evaluating patient fitness for Panchakarma therapies and to assess its effectiveness in enhancing treatment safety, personalization, and compliance with clinical and ethical standards over a five-year period. Methods: A comprehensive Panchakarma Fitness Form was designed, incorporating sections on classical advocation on fitness for different Panchakarma therapies, medical history, clinical examination of patient, allergies, medication, and lifestyle factors. The form was used in both routine healthcare and clinical research settings, with a particular emphasis on its role in screening for study eligibility. The implementation of the Panchakarma Fitness Form enhanced patient safety by identifying contraindications and optimizing treatment decisions. This form contributed to improved treatment efficacy by tailoring therapies to individual health conditions and supporting personalized care. In clinical research settings, the form facilitated participant selection, improving study integrity and data reliability. Conclusions: The Panchakarma Fitness Form is a valuable tool that supports safe, effective, and personalized Ayurvedic treatment. By systematically assessing patient readiness, it reduces risks, enhances treatment outcomes, and aids in legal and ethical compliance in both clinical practice and research. This form sets a standard for quality and safety in Ayurveda, supporting patient-centered and informed healthcare.

Abstract 4119094: Claims-Based versus Registry-Reported Stroke Events in the NCDR Left Atrial Appendage Occlusion Registry

Background: Administrative claims data are increasingly used for post-marketing surveillance of drugs and medical devices, though data reliability remains uncertain. Given their associated morbidity and mortality, stroke events are particularly important to monitor for cardiovascular therapies such as transcatheter left atrial appendage occlusion (LAAO). However, there are limited data comparing stroke events in contemporary claims data to events reported in large nationwide registries with comprehensive data collection. Aim: To compare stroke events in Medicare claims data with adjudicated registry-reported events in the National Cardiovascular Data Registry Left Atrial Appendage Occlusion (LAAO) Registry. Methods: LAAO Registry data were linked with 2016-2022 Medicare inpatient claims. Primary diagnosis ICD-10 codes from inpatient hospitalizations were compared to adjudicated registry-reported ischemic and hemorrhagic stroke events occurring after discharge with up to two years of follow-up. In multiple secondary analyses, primary and secondary diagnosis codes were counted as events in claims data and patients with prior history of stroke were also excluded. Results: The cohort included 71,043 adults ≥65 years old in the LAAO Registry with inpatient Medicare data. Following LAAO hospitalization discharge, there were 1405 claims-based and 1308 registry-reported total stroke events. Sensitivity and positive predictive value (PPV) of claims for identifying registry-reported events were 60.8% and 50.5% for ischemic stroke (kappa statistic 0.55), 42.7% and 50.5% for hemorrhagic stroke (kappa 0.46), and 58.1% and 54.1% for all stroke (kappa 0.55), respectively. Inclusion of secondary diagnosis codes improved sensitivity of claims (70.3% for ischemic stroke, 53.9% for hemorrhagic stroke and 67.0% for all stroke), but PPV decreased (44.8% for ischemic stroke, 34.8% for hemorrhagic stroke and 47.8% for all stroke). Specificity and negative predictive values of claims data were >98% for all stroke types. Findings were similar after excluding patients with prior history of stroke ( Table ). Conclusions: Among Medicare patients in the LAAO Registry, administrative claims data had moderate agreement with adjudicated registry-reported stroke events. Claims data may not reliably capture all stroke events in real-world practice, and additional event ascertainment methods may be needed to ensure accurate assessment of stroke in post-marketing surveillance efforts.

Comment on Zhou et al. Adsorption Behaviors of Lanthanum (III) and Yttrium (III) Ions on Gibbsite. Minerals 2023, 13, 1530

We compare trends in experimental observations reported by Zhou et al. to relevant results from numerous previously published studies. Based on these comparisons, we question the reliability of the gibbsite titration data and consider the cation uptake curves to be incomplete and questionable as well. The adsorption edges in Zhou et al. are too flat compared to the typical adsorption edges reported in hundreds of studies. Finally, we are convinced that trends of cation adsorption with NaNO3 content not only disagree with relevant, previously published results, but are even opposite to trends expected with increasing ionic strength, whenever such effects are observed. Finally, we discuss other inconsistencies in the experimental data.

Self-Sovereign Identity-Based E-Portfolio Ecosystem

In Taiwan, traditional student assessments, covering academic and extracurricular achievements, have shifted from paper to electronic portfolios (e-portfolios). However, limited trust among institutions restricts students from freely sharing and using their educational data. This paper introduces a self-sovereign identity-based infrastructure aimed at enhancing personal data security within the e-portfolio ecosystem. The proposed system includes two core components: (1) a decentralized identity chain, aligning user identities across platforms and granting users full self-sovereign control; and (2) an e-portfolio application chain to manage user interactions and access permissions within the ecosystem. A trusted educational authority also audits data sources, ensuring data integrity and reliability. This infrastructure empowers users to control who can access their data, safeguarding their security, with the identity chain preventing unauthorized access and the application chain recording authorization statuses to restrict data visibility to approved parties only.

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education.

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesurgery and telementoring. However, its potential to enhance data transmission efficiency and reliability in these fields remains unclear. While previous reviews have explored the general applications of telesurgery and telementoring in specific surgical contexts, this review uniquely focuses on AI models designed to optimise data transmission and mitigate delays. We conducted a comprehensive literature search on PubMed and IEEE Xplore for studies published in English between 2010 and 2023, focusing on AI-driven, surgery-related, telemedicine, and delay-related research. This review includes methodologies from journals, conferences, and symposiums. Our analysis identified a total of twelve AI studies that focus on optimising network resources, enhancing edge computing, and developing delay-robust predictive applications. Specifically, three studies addressed wireless network resource optimisation, two proposed low-latency control and transfer learning algorithms for edge computing, and seven developed delay-robust applications, five of which focused on motion data, with the remaining two addressing visual and haptic data. These advancements lay the foundation for a truly holistic and context-aware telesurgical experience, significantly transforming remote surgical practice and education. By mapping the current role of AI in addressing delay-related challenges, this review highlights the pressing need for collaborative research to drive the evolution of telesurgery and telementoring in modern robotic surgery.

Blockchain in digital notary services

The purpose of the study is to determine the future application of blockchain technology. Protection of rights, as well as legal interests of citizens and legal entities is the main postulate of notary activity. Only through the prism of this provision should one consider the possible application of blockchain technology in the notary. This includes studying the possibilities of the blockchain for verifying the authenticity of documents, automating registration actions. The relevance of the study is related to the need for modern notaries to meet the requirements of the digital world. The research is based on the application of an interdisciplinary approach, civil law and information technology, which combines the analysis of legal aspects with modern technological research. To study legislative aspects, methods of comparative analysis and synthesis were used, which includes an analysis of the legislation of Ukraine and other countries. The empirical method consists in researching existing examples of implementation of blockchain projects in EU and US notary offices. Analysis and modeling were carried out taking into account the architecture of blockchain networks, their capabilities in terms of information protection, authentication and legal validity of digital records. The study found that blockchain technology has significant potential for the notary industry due to its decentralized nature and high level of data protection. It has been established that the implementation of the blockchain allows for the creation of a reliable database for recording notarial actions, ensuring the impossibility of forgery or loss of information. The results of the study confirmed the possibility of using blockchain for digital recording of documents and automation of notarial procedures, which allows to reduce time costs and the risk of human errors. The experience of countries that have already implemented blockchain in the notary office indicates an increase in the level of trust in notary services and a reduction in bureaucratic procedures. The application of blockchain in the notarial sphere can fundamentally change the mechanisms of notarial actions, increasing security, convenience and speed of service. The use of decentralized networks ensures the reliability of data, which makes it impossible to forge them and makes procedures transparent. However, for the full implementation of such decisions, it is necessary to create an appropriate regulatory framework, adapt Ukrainian legislation and ensure the training of specialists for work in the conditions of digital notary.

BIOS-08. CISD: A NOVEL, USER-FRIENDLY MULTIFACETED ALGORITHM FOR DETECTING STITCHING ARTIFACTS IN LARGE-SCALE CODEX IMAGING DATASETS

Abstract BACKGROUND CODEX (CO-Detection by indEXing) imaging has emerged as a pivotal tool in cellular biology, providing unprecedented insights into cellular distributions and interactions within the tissue microenvironment, crucial for advancing computational pathology. Despite its advantages, CODEX is susceptible to stitching errors during the merging of image tiles, which can significantly distort spatial analysis by introducing artifacts that obscure true cellular architecture. METHODS Addressing this challenge, we introduce CISD (CODEX Image Stitch Detection), a novel, user-friendly algorithm optimized for the detection of stitching artifacts in large-scale CODEX datasets. Developed and validated using a comprehensive dataset from 7 matched glioblastoma patients—encompassing over 2800 images across 29 immune protein markers—CISD utilizes a combination of image preprocessing, statistical measures, and thresholding to detect stitching errors. RESULTS CISD demonstrates robust performance, achieving a median Area Under Curve (AUC) value in the gold standard dataset where 73% of markers exceed 70% AUC, and 17% surpass 90%. Further validation shows that in a secondary dataset, 45% of markers maintain AUC values over 70% with 38% exceeding 90%, validating the algorithm’s consistent efficacy across varied datasets. CONCLUSION CISD’s significance lies not only in its computational achievements but also in its accessibility, facilitating high-quality analysis of extensive datasets without complex pattern recognition requirements. This democratizes high-precision computational pathology, enabling more researchers to accurately analyze spatial data. By reducing technical barriers, CISD sets a new benchmark in CODEX imaging, enhancing data reliability and accelerating research discoveries in cellular biology. This innovation stands to substantially refine spatial analysis in digital pathology, ensuring dependable interpretation of complex biological data.

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.

Role of Interpersonal Skills, Communication Capabilities and Information System on Learning Capabilities of Higher Educational Institutions in China: Mediating Role of Students’ Satisfaction

The growth of higher educational institutions much depends upon various factors among which interpersonal skills, communication capabilities and information system are prominent ones. The current study aimed to examine the impact of these factors viz., interpersonal skills, communication capabilities and information system, on the learning capabilities of higher educational institutions in China. The study also checks the mediating role of student satisfaction among these three constructs. The study gathered the data from final year students of higher educational institutions using surveys. The study also examines the data reliability along with association among variables using smart-PLS. The outcomes revealed that the interpersonal skills, communication capabilities and information system have a positive association with learning capabilities. The outcomes also exposed that student satisfaction significantly mediated among exogenous constructs of the study. The study findings would act as a guide to the policymakers regarding enhancing the learning capabilities by focusing on interpersonal skills, communication capabilities and information system.

Long-term monitoring of citizen science: driving factors and pandemic impacts

ABSTRACT In recent years, the importance of citizen science in biodiversity monitoring has become increasingly evident. Citizen science provides valuable data to understand the dynamic changes in biodiversity across different cities. Although long-term monitoring is crucial for understanding these dynamic changes, previous research has primarily focused on short-term data analysis. Therefore, this study analysed data from the iNaturalist platform for 11 Japanese cities from 2016 to 2023, focusing on the driving factors of observation changes and the impact of the COVID-19 pandemic using the Logarithmic Mean Divisia Index (LMDI) method. The results revealed that the population (total number of active users) metric had the greatest impact, followed by intensity (the number of observations per active day), frequency (active days per active user), and structure (proportion of regular users). To ensure data continuity and reliability, optimizing user structure by increasing the proportion of regular users is essential. Although observation volumes fluctuated during the pandemic, they rebounded significantly post-pandemic, demonstrating the resilience, and adaptability of citizen science projects. The iNaturalist platform provides a stable foundation for long-term data collection, aiding in the comprehensive understanding of biodiversity dynamics. Our study also highlighted differences in citizen science participation and biodiversity monitoring across cities, indicating the need for tailored biodiversity conservation policies. To effectively address similar crises in the future, developing contingency plans is recommended to provide continuous support for biodiversity conservation and public engagement in environmental management.