To describe the flexible coping strategies and measures made by Crohn's disease patients during the COVID-19 epidemic to determine their experiences with COVID-19 and epidemic management. A descriptive phenomenology study based on interviews. Twelve patients with Crohn's disease were interviewed from two Grade III and Grade A hospitals in Nanjing from February 2022 to June 2022. Twelve interviews were audio-taped or recorded live and content was analysed following Colaizzi's seven-step method. Transcripts were verbatim transcribed and analysed using Colaizzi's seven-step descriptive phenomenological method. Data management was supported by NVivo 11 software. The analysis of interviews identified five themes and 14 subthemes. Crohn's disease patients demonstrated a capacity for adapting to the evolving landscape of medical care during the COVID-19 epidemic, underscoring the importance of self-management. Patients took voluntary measures to cope with the continuous adjustment of epidemic management, such as flexible adjustment of medical treatment plans. Improving existing management measures may help both healthcare professionals and patients in effectively responding to infectious disease epidemics, which suggests that we should pay attention to humanized management and hospital construction in the future. Additionally, we should pay more attention to the psychological impact of COVID-19, both positive and negative. No patient or public contribution.

A web-based inventory system integrated with Barcode/QR Code technology was developed to enhance stock management at Hassya Hijab Store. The system, constructed using PHP and MySQL, facilitates centralized management of item data, suppliers, stock-in and stock-out transactions, reports, and user access rights based on specific roles. By incorporating Barcode/QR Code technology, the system significantly improves the speed and accuracy of item data recording and retrieval through code scanning, reducing reliance on manual data input and minimizing human error. Furthermore, the system allows for seamless integration between the inventory management process and real-time updates, ensuring data consistency across all transactions. Black-box testing revealed that essential functionalities such as managing master data, processing stock transactions, generating and scanning barcodes/QR codes, and producing automated reports operated as anticipated, confirming the system's reliability and performance. Based on these results, the system proves to be an efficient and effective solution for digital inventory management, making it highly suitable for use at Hassya Hijab Store, with potential for broader application in similar retail environments.

Aminotransferases are widely used for metabolic dysfunction-associated steatotic liver disease (MASLD) evaluation, especially in type 2 diabetes mellitus (T2DM). Whether within-person seasonal variation affects classification near commonly used thresholds or relates to long-term metabolic outcomes remains unclear. This registry-based cohort analysed monthly aspartate aminotransferase (AST) and alanine aminotransferase (ALT) measurements from 6039 adults with T2DM in the Japan Diabetes Clinical Data Management registry (2014-2020). Classification discordance across the 30 IU/L threshold was compared between winter-mean and summer-mean values. Individual seasonal amplitude was derived from seasonal-trend decomposition of multiply imputed monthly time series. Final glycated haemoglobin (HbA1c) and non-achievement of HbA1c < 7% were analysed using multivariable regression. Both AST and ALT showed significant seasonal variation, with the highest values in late autumn to early winter and the lowest in summer (p < 0.001). Approximately one in nine patients with borderline ALT values showed discordant winter-summer classification. Each 1-SD increase in AST amplitude was associated with 0.06 percentage points higher final HbA1c (95% CI, 0.04-0.08) and higher odds of not achieving HbA1c < 7% (odds ratio, 1.14; 95% CI, 1.08-1.21; p < 0.001). ALT amplitude showed a similar but weaker, less consistent association. AST and ALT exhibited reproducible seasonal variation peaking in late autumn to early winter, with approximately one in nine patients near the MASLD screening threshold reclassified depending on season. Greater seasonal amplitude, especially AST, was independently associated with poorer glycemic control, supporting season-aware interpretation in routine clinical practice.

Account opening administrative process is one of the essential activities in banking operations as it relates to accurate customer data management and service quality. This study aims to analyze the account opening administrative process in supporting customer service effectiveness at Bank BJB Syariah KCP UIN Sunan Gunung Djati Bandung. This research employs a descriptive qualitative method with data collection techniques through observation and documentation during the Field Work Practice (PKL) program. Data analysis was conducted through data reduction, data display, and conclusion drawing. The results show that the account opening administrative process is carried out through several stages, including form preparation, document completeness checking, customer data verification, approval process, and document archiving. The implementation of a structured administrative process has been proven to support service effectiveness by improving service speed, reducing administrative errors, and facilitating the overall customer service flow.

Next-generation sequencing (NGS) technology has revolutionized forensic DNA analysis by increasing sensitivity to detect low amounts of DNA, expanding throughput capacity, allowing multiplexed marker panels, and improving sequencing resolution. However, the diversity of marker types and the complexity of report formatting have made managing and utilizing genetic statistics for large volumes of NGS data challenging. To address these challenges, advancements in bioinformatics tools and data management systems are essential. Implementing a public database can facilitate more effective popular statistics, ultimately enhancing the application of NGS in forensic investigations. We introduce “diversID,” a forensic DNA database ( https://www.diversid.org ) focused on autosomal STRs (short tandem repeats), X- and Y-chromosomal STRs, and identity SNPs (single nucleotide polymorphisms). The database software provides comprehensive population statistics, including allele frequencies, Hardy-Weinberg equilibrium, heterozygosity, and more. Making use of cloud computing, diversID simplifies data upload and download processes to improve user convenience while maintaining the highest data quality through stringent filtering criteria. To further enrich the database’s value, we invite researchers to encourage global collaboration. We believe that collective efforts will continually refine and advance this invaluable resource, thereby benefiting the field of forensic DNA analysis.

Simplification of Hematopoietic Stem Cell Transplantation and Continued FACT Accreditation: Strategies for Low- and Middle-Income Countries.

The growing complexity of clinical trials, particularly in oncology, has significantly increased the operational burden on research staff. However, standardized and validated instruments to measure trial-related workload remain scarce. This study aimed to adapt and validate the Italian version of Ontario Protocol Assessment Level (I-OPAL) tool for the Italian context, providing a reliable framework for workload planning and feasibility assessment. A cross-sectional, multicenter study was conducted across Italian research institutions. The OPAL tool was translated and culturally adapted following established validation procedures. Content validity was assessed using the Content Validity Ratio (CVR), while inter-rater reliability was evaluated with the intraclass correlation coefficient (ICC). Discriminant validity was examined through non-parametric tests, effect size measures, and multiple correspondence analysis. A total of 513 clinical trials were included. The OPAL tool showed excellent inter-rater reliability (ICC = 0.93) and all items met the minimum CVR threshold (0.78-1.00). Significant differences in OPAL scores were observed across study type, clinical setting, sample size, and duration (all p < 0.001), with large effect sizes (ε2 up to 0.645). Higher OPAL scores were positively correlated with greater allocation of research staff, particularly nurses and data managers. Sensitivity analyses confirmed the robustness of findings, and internal consistency checks revealed full alignment with the model's classification rules. The Italian validation of OPAL confirmed its reliability, validity, and practical relevance as a tool for standardized workload assessment in clinical research. Its integration into feasibility analyses and trial planning could enhance resource allocation, regulatory compliance, and sustainability of research activities in Italy.

Animal health surveillance is essential for early disease detection, outbreak response, and the prevention of zoonoses. In June 2024, Malawi conducted its first national assessment of its animal health surveillance system using the FAO Surveillance Evaluation Tool (SET), specifically the 2024 SET Evaluation Guide, version 2 to evaluate institutional, operational, and technical capacities. A participatory assessment was undertaken by experts from the Department of Animal Health and Livestock Development (DAHLD) and FAO, involving document reviews, stakeholder interviews, and site visits in seven districts. The SET was used to score 96 indicators across various domains on a scale of 1 (no capacity) to 4 (advanced). The results indicated low overall performance. The weakest domains were active surveillance (5%), internal communication (12.6%), and risk assessment (16.7%). Relative strengths were observed in external communication (50%) and information systems (44.3%), though these were still below optimal levels. The system suffers from significant workforce shortages, with approximately 33% of officer positions vacant, placing a heavy burden on inadequately resourced community-based assistants. Laboratory capacity was found to be centralized and limited, with regional laboratories offering only basic diagnostic services. Data management is predominantly paper-based, and formal risk-based surveillance plans and feedback mechanisms are lacking. In conclusion, while Malawi's animal health surveillance system has an established institutional framework, it is weakly structured and inconsistently operationalized. This is characterized by limited proactive surveillance, significant workforce gaps, under-resourced laboratory diagnostics, and fragmented data and communication channels. Nonetheless, a foundation for improvement exists, including legal provisions, a dedicated Epidemiology Unit within DAHLD, and a tiered surveillance workforce. Prioritized recommendations include developing and operationalizing a national surveillance strategy, strengthening laboratory and data systems, addressing critical workforce gaps, and integrating animal health surveillance into broader One Health and emergency preparedness frameworks.

The emergence of the Unified Payments Interface (UPI) has transformed the digital payment ecosystem by enabling seamless, instant, and interoperable financial transactions across India. Its widespread acceptance has accelerated the shift toward cashless payments and increased the availability of digital financial services for millions of users. However, the rapid growth in transaction volume and user adoption has also created new opportunities for cybercriminals to exploit vulnerabilities within the digital payment infrastructure.Financial fraud associated with UPI platforms has become increasingly sophisticated, involving techniques such as phishing campaigns, fraudulent QR codes, identity impersonation, account hijacking, social engineering attacks, and the misuse of mule accounts. These evolving threats generate complex transaction patterns that are often difficult to detect using conventional rule-based security mechanisms. As fraud strategies continue to change, static detection systems struggle to provide accurate and timely identification of suspicious activities.Recent progress in Artificial Intelligence, Machine Learning, and Deep Learning technologies has significantly enhanced the capability of fraud detection systems. One of these developments has caught a lot of interest from researchers is hybrid deep learning methods that are able to integrate the benefits of several computational models. Several techniques can be combined in a unified approach for enhancing fraud detection, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, Graph Neural Networks (GNNs), Explainable Artificial Intelligence (XAI), and Federated Learning. These architectures enable real-time analysis, adaptive learning, privacy protection, and efficient management of large-scale transactional data. This review is based on the recent research and studies regarding the detection of fraudulent activities in UPI-based payment environment using hybrid deep learning techniques. It offers detailed evaluations of the current models, their strengths and weaknesses, explores current issues and challenges, and suggests areas of future work. These results suggest that hybrid deep learning architectures are promising to improve the security of digital payment systems through better detection accuracy, fewer false alarms, safeguarding sensitive user data, and greater transparency of automated decision-making. Thus, these wise frameworks are a valuable basis for developing safe, reliable, and trustworthy digital financial ecosystems

Previously, the Anak Angin Dojo Karate Team registered members manually by writing data on paper and sending proof of payment via WhatsApp. During this process, various problems arose, such as data irregularities and long administrative times. The objective of this research is to create a web-based registration information system that helps manage dojo member data more effectively. Requirements analysis, design, implementation, and testing are the steps in the waterfall method sistem development. This sistem is built with the CodeIgniter framework based on PHP and the MySQL database. Member registration, management of coach and athlete data, achievements, training schedules, and submission of biodata changes are the main features developed. The User Acceptance Test (UAT) method is used to assess how well the sistem functions meet user needs. The research results show that the information sistem can replace manual processes with digital processes, speed up the registration process, reduce recording errors, and overall improve the efficiency and accuracy of dojo administrative management.

This study aims to address issues in employee leave data management at the Deli Serdang District Agricultural Office, where leave records were previously processed manually using separate documents, resulting in data duplication, inaccuracies, and delays in preparing reports. To solve this problem, a web-based Employee Leave Record Management System was developed using the Waterfall model through stages of requirements analysis, system design, implementation, testing, and maintenance. System evaluation was conducted using Black-Box testing to verify functional performance. The system supports structured recording of employee leave information, employee profile data such as position and department, and provides real-time reporting features to support administrative decision-making. The application was developed using the CodeIgniter-based MVC architecture and MySQL database, which enhances data accuracy, information accessibility, and administrative efficiency. The results demonstrate that the system successfully minimizes recording errors and improves the effectiveness of leave data management. Future development opportunities include integrating automated online leave request and approval workflows, mobile access features, and notification services to maximize usability and system automation.

Clinical data management (CDM) is central to the quality of clinical research. In Japan, CDM faces a shortage of qualified personnel, particularly in academic research organizations (AROs), as well as increasing data volume and complexity. Rapid advances in artificial intelligence (AI), especially large language models, have therefore attracted attention as a way to support CDM. This review summarizes domestic and international examples of AI utilization in CDM-related tasks, including data cleaning, medical coding, and query generation. Across the cases reviewed, a common implementation principle emerged: a human-in-the-loop design in which AI performs initial processing or detection, while final judgment remains with human personnel. This design is especially relevant to AROs, where high data quality must be maintained with limited CDM human resources. Regulatory frameworks, including ICH E6 (R3) and the FDA-EMA Guiding Principles, are beginning to address AI use, but how AI-aided processes should be handled under Good Clinical Practice remains under discussion. Comprehensive risk mitigation is therefore essential. AI and data are interdependent: better data improve AI performance, and better AI can further improve data quality. The shift from manual processes to human-AI collaborative workflows is likely to accelerate, and CDM must develop the technical, regulatory, and risk-management frameworks needed to support that transition.

This study aims to analyze the integration of the Family Hope Program (Program Keluarga Harapan/PKH) into regional development planning and its influence on the effectiveness of poverty alleviation in Tangerang City. Urban poverty is characterized by complex and dynamic challenges, requiring integrated, adaptive, and sustainable policy interventions. This research employs a qualitative approach using a descriptive method. Data were collected through interviews, observations, and document analysis, and were systematically analyzed to examine the implementation of PKH within the regional development planning framework. The findings indicate that PKH has become an important instrument in the local poverty reduction strategy; however, its integration into regional development planning documents remains suboptimal. Coordination among government agencies through the Regional Poverty Alleviation Coordination Team (TKPK) has been implemented, although challenges persist in data synchronization, beneficiary data validity, and program implementation. The program has contributed to improving beneficiaries’ access to education and healthcare services. Therefore, strengthening policy integration, improving data management systems, and promoting innovation in regional development planning are essential to enhance the long-term effectiveness and sustainability of poverty alleviation efforts.

The article deals with the creation and implementation of the Digital Analytical Platform of the Federal State Statistics Service (Rosstat DAP) as the core of the National Data Management System in the context of the digitalization of the economics. The authors have revealed and systematized the problems of the national statistics: fragmented data, high bur den on small and medium-sized businesses, lack of efficiency and evidence of information. The analysis of the normative evolution is used to formulate priorities of the development of statistics up to 2023: the transition to predictive analytics, ensuring trust in data, integration of sources based on the “one-stop shop” principle, personalization of services, staff development, international harmonization, transparency. The authors have described functional components of DAP: sample constructor, interactive visualization, statistical and network analysis, machine learning-based forecasting, application programming interface. It has been shown that the platform ensures the interaction of the state, business, science and society in a single digital environment on the principles of interdepartmental automation using the System of Inter departmental Electronic Interaction (SIEI), Unifi ed Identification and Authentication System (ESIA) and the Gostech platform. The results will be useful for the government authorities, large business and small and medium-sized enterprises, researchers and teachers.

ABSTRACT As consumers increasingly adopt privacy-enhancing technologies (PETs) to protect personal information, firms face growing challenges in preserving consumer data integrity and the reliability of downstream analytics. By intervening at the point of data collection, end-user PETs introduce systematic distortions that reshape the data environment on which business analytics depend, yet their implications remain insufficiently understood. To address this gap, this study develops two complementary conceptual frameworks. The Data Integrity Framework characterizes how different end-user PETs generate missing values and measurement errors across attributes, entities, and relationships, offering a structured lens for conceptualizing privacy-induced data distortions. Building on this foundation, the Analytics Adaptation Framework provides guidance on how firms can assess and adapt their data analytics in response to these data distortions. To demonstrate their applicability, an illustrative simulation case study in product recommendation shows how key characteristics of end-user PET adoption—adoption rate and pattern, protection mechanism and intensity—systematically shape analytics outcomes. Together, the study advances IS research on data management by linking consumer privacy protection to data integrity and analytics adaptation, highlighting how consumer-driven privacy technologies fundamentally alter firms’ data and analytical environments.

The development of information technology in the digital era provides a great opportunity for business actors to improve service quality, operational efficiency, and expand marketing reach through the use of e-commerce platforms. However, Bakso Koko as a culinary business still uses a conventional ordering and payment system, which often causes various obstacles such as slow service processes, a high risk of transaction recording errors, difficulties in managing order data, and limitations in reaching a wider customer base. These problems indicate the need for a more effective, integrated, and easily accessible system for customers. This study aims to design and build an Android-based Bakso Koko e-commerce application integrated with a payment gateway so that the ordering and payment process can be carried out online more easily, quickly, safely, and efficiently. The system development method used in this study is the Waterfall method which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The analysis technique used is system requirements analysis through observation and interviews to understand the ongoing business process, then modeled using the Unified Modeling Language (UML) in the form of use case diagrams and activity diagrams. The research results show that the application was successfully designed and built with user registration, a product catalog, a shopping cart, ordering, online payment, order status tracking, and integrated product and transaction data management. This system is expected to improve service efficiency, transaction efficiency, and expand the business's marketing reach.

This study investigates how well the scholarly communication course addresses the student learning objectives designed based on the competencies for SC librarianship established by North American Serials Interest Group (NASIG). Literature review shows a wide range of roles and responsibilities that scholarly communication librarians must learn on the job. Thus, this course was developed with the goal of preparing the LIS students for these emerging new roles and responsibilities. The self-assessment questionnaire was used to assess students’ comprehension of the scholarly communication competencies at the beginning and end of the semester. The paired t-test shows a statistically significant difference between pre- and post- course scores. The ANOVA analysis reveals that after the course completion the students learned the most about Institutional Repository management, with 1.57 points difference between pre- and post- scores, followed by publishing services (1.14), assessment metrics (.71), copyright services (.57), and data management (.43). These findings suggest that the course does cover most of the roles and responsibilities for scholarly communication librarians as defined by NASIG and it’s an important step forward in preparing students for scholarly communication librarianship.

Introduction Village Health Volunteers (VHVs) constitute a crucial community health workforce in communicable disease prevention and control. Despite generally strong performance, persistent gaps in digital literacy, data management, and risk communication indicate a need for a structured competency development model. Methods A mixed-methods Multiphase Research design was employed from March 2022 to July 2024, integrating quantitative surveys with qualitative focus group discussions and in-depth interviews. Phase 1 comprised quantitative ( n = 416) and qualitative ( n = 100) data collection using multi-stage stratified random sampling and purposive sampling, respectively. Phase 2 ( n = 34) employed the Plan–Act–Observe–Reflect cycle to develop the competency model. Phase 3 ( n = 33) evaluated the intervention. Results Baseline assessment indicated that most VHVs were female (80.53%), aged 51–60 years (43.03%), and had 11–20 years of experience (45.19%). Overall self-reported competency was at a high level (mean = 3.71, SD = 0.46), with strong performance in practices (mean = 4.34, SD = 0.42), moderate-to-high skills (mean = 3.60, SD = 0.45), and the lowest scores in knowledge (mean = 3.59, SD = 0.49). Qualitative findings identified substantive competency gaps in epidemiological reasoning, digital literacy, systematic data recording, risk communication, and leadership. The SMART VHV Plus Model, comprising five components (communicable disease control, management, technology, leadership and teamwork, and community health planning), was subsequently developed and delivered through five structured training programmes. Post-intervention assessment demonstrated a statistically significant improvement in overall competency scores: from a pre-intervention mean of 86.14% (SD = 7.65, classified as moderate) to a post-intervention mean of 98.16% (SD = 1.95, classified as high), representing a mean difference of 12.02 percentage points (95% CI: 9.84–14.20, p &amp;lt; 0.05). Discussion The SMART VHV Plus Model was associated with meaningful improvements in VHV competencies in communicable disease prevention and control. Its participatory design and integration of digital literacy, leadership, and community health planning provide a potentially sustainable framework for strengthening community health workforce capacity.

Fragmentation of electronic health records in oncology hinders coordinated care, delays diagnoses, and limits therapeutic personalization. Blockchains promise to promote secure, interoperable, and patient-centered data governance; however, patient perceptions of blockchains remain underexplored, particularly in middle-income countries such as Brazil. We assessed opinions, attitudes, and willingness among patients with cancer to digitally share clinical information and the feasibility of applying blockchains to restructuring secure health data sharing in the Brazilian public health context. We had three research questions: (1) What is the level of digital health tool acceptance among patients with cancer in Brazil? (2) Which sociodemographic factors are associated with willingness to share health data? (3) Are blockchains feasible and acceptable for restructuring secure oncology data sharing? An exploratory, descriptive, cross-sectional self-report survey was conducted at Hospital Santa Izabel, a national oncology reference center in Salvador, Bahia, Brazil, between September and November 2023. A convenience sample of 110 outpatients with cancer was recruited systematically; data were collected via a self-administered questionnaire. The 20-item instrument, developed de novo and validated via expert panel and pilot testing, covered 5 content domains yielding 3 composite scoring domains: self-management, adherence, and governance. We used Cronbach α to assess internal consistency, independent 2-tailed t tests, 1-way ANOVA, and Pearson correlations to compare domain scores across sociodemographic groups, and a chi-square goodness-of-fit test to examine trust proportions across recipient types. We received sufficiently complete responses from 94.5% (104/110) of patients. The sample was predominantly female (63/98, 64.3%), self-identified as pardo (mixed-race; 64/98, 65.3%), and lower income (55/96, 57.3% earned less than twice the minimum wage). Acceptance of technology was high: 86.4% (95/110) would use health apps and 89.1% (98/110) expressed interest in prevention-focused applications. Trust in data sharing varied significantly across recipient types (χ23=210.4; P<.001): 79.1% (87/110) trusted health care professionals, 51.8% (57/110) hospitals, 15.5% (17/110) the pharmaceutical industry, and 10% (11/110) the government. Anonymization and encryption significantly increased willingness to share (92/110, 83.6%). Younger patients (18-59 years) showed significantly higher adherence scores than those aged ≥60 years (mean 76.49, SD 19.16 vs mean 65.83, SD 26.66; t95=2.29; P=.02). Domain reliability was good to excellent (Cronbach α=0.8807 [adherence], 0.8504 [self-management], and 0.7576 [governance]). Patients with cancer in Brazil demonstrated high acceptance of digital health tools and openness to data sharing when privacy, security, and governance are guaranteed. This supports the feasibility of blockchain-based health data management systems, provided they incorporate patient-centered principles, digital inclusion strategies, and robust governance aligned with Brazilian regulations (the General Data Protection Law) and the Unified Health System (Sistema Único de Saúde) infrastructure. Importantly, patient support reflected acceptance of blockchain's functional principles, data security, anonymization, and auditability rather than familiarity with the technology itself, a distinction with direct implications for future implementation studies.

In response to funder and institutional requirements for data sharing and reuse of research data in support of the open-access and open-science movement, a comprehensive open distance e-learning (CODeL) institution in South Africa implemented a research data management policy. This article employs statistical analysis and interviews with purposively selected researchers to investigate the curation of research data at a CODeL institution in South Africa. The CODeL institution utilizes Figshare software to curate datasets. Researcher uptake remains low, with fewer than 100 datasets submitted since the software was introduced in 2019. However, the number of views and downloads is high, highlighting the demand for accessible datasets and the need for researchers to submit their data. It is hoped that the findings will inform the development of best practices for data curation that CODeL institutions can adopt to enhance the visibility and impact of their research outputs.