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BACKGROUND: The American College of Surgeons requires continuous process improvement review to maintain trauma center verification. Paper-based systems to monitor and track performance improvement are commonly used but can be inadequate to monitor concurrent ongoing improvement effectively. OBJECTIVE: To describe the implementation of an electronic process to capture and monitor performance improvement in near real time. METHODS: In 2020, a Midwestern U.S. Level I adult trauma center and a Level II pediatric trauma center's trauma programs transitioned from a paper to an electronic file-sharing system for performance improvement. We converted our primary, secondary, and tertiary review documentation into a single electronic performance improvement file stored on the institution's virtual hard drive, accessible to designated staff, allowing continuous real-time updates. RESULTS: The electronic file-sharing and monitoring process reinvigorated the team and enhanced performance improvement efforts, leading to increased efficiency through documentation and effective loop closure. Real-time monitoring allowed the trauma program to identify opportunities for improvement and enact timely action plans, including targeted performance improvement projects, department education, and ongoing training. CONCLUSION: We found that implementing an electronic file-sharing system enhanced the trauma team's ability to monitor and trend performance improvement in real time. [ABSTRACT FROM AUTHOR]

Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data. [ABSTRACT FROM AUTHOR]

Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community. [ABSTRACT FROM AUTHOR]

Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patientcentric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions. [ABSTRACT FROM AUTHOR]

There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research. [ABSTRACT FROM AUTHOR]

This paper outlines an initial analysis of 20 years of data held on an electronic bridge management database for approximately 3500 arch bridges across Northern Ireland (NI) by the Department for Infrastructure. Arch bridges represent the largest group of bridge types, making up nearly 56% of the total bridge stock in NI. This initial analysis aims to identify trends that might help inform maintenance decisions in the future. Consideration of the Bridge Condition Indicator (BCI) average value for the overall arch bridge stock indicates the potential for regional variations in the overall condition and the potential for human bias in inspections. The paper presents the most prevalent structural elements and associated defects recorded in the inspections of arch bridges. This indicated a link to scour and undermining for the worst-conditioned arch bridges. An Analysis of Variance (ANOVA) analysis identified function, number of spans, and deck width as significant factors during the various deterioration stages in a bridge's lifecycle. [ABSTRACT FROM AUTHOR]

Simple Summary: Large datasets concerning childhood cancers are rare. Therefore, it is important to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), datasets can be merged, without disclosing the patients' identities. Although PPRL is already implemented or described in various settings, use case descriptions are fragmented and incomplete. The present paper gives an overview of current and future use cases of PPRL in childhood cancer. We screened the literature, projects, and trial protocols, analysed a hypothetical patient journey, and discussed use cases with experts. All the identified use cases were structured along six key dimensions. We conclude that PPRL is a key concept in childhood cancer. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. Large datasets in paediatric oncology are inherently rare. Therefore, it is paramount to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), personalised or pseudonymised datasets can be merged, without disclosing the patients' identities. Although PPRL is implemented in various settings, use case descriptions are currently fragmented and incomplete. The present paper provides a comprehensive overview of current and future use cases for PPRL in paediatric oncology. We analysed the literature, projects, and trial protocols, identified use cases along a hypothetical patient journey, and discussed use cases with paediatric oncology experts. To structure PPRL use cases, we defined six key dimensions: distributed personalised records, pseudonymisation, distributed pseudonymised records, record linkage, linked data, and data analysis. Selected use cases were described (a) per dimension and (b) on a multi-dimensional level. While focusing on paediatric oncology, most aspects are also applicable to other (particularly rare) diseases. We conclude that PPRL is a key concept in paediatric oncology. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. [ABSTRACT FROM AUTHOR]

Digital health technologies (DHTs) at the intersection of health, medical informatics, and business aim to enhance patient care through personalised digital approaches. Ensuring the efficacy and reliability of these innovations demands rigorous clinical validation. A PubMed literature review (January 2006 to July 2023) identified 1250 papers, highlighting growing academic interest. A focused narrative review (January 2018 to July 2023) delved into challenges, highlighting issues such as diverse regulatory landscapes, adoption issues in complex healthcare systems, and a plethora of evaluation frameworks lacking pragmatic guidance. Existing frameworks often omit crucial criteria, neglect empirical evidence, and clinical effectiveness is rarely included as a criterion for DHT quality. The paper underscores the urgency of addressing challenges in accreditation, adoption, business models, and integration to safeguard the quality, efficacy, and safety of DHTs. A pivotal illustration of collaborative efforts to address these challenges is exemplified by the Digital Health Validation Center, dedicated to generating clinical evidence of innovative healthcare technologies and facilitating seamless technology transfer. In conclusion, it is necessary to harmonise evaluation approaches and frameworks, improve regulatory clarity, and commit to collaboration to integrate rigorous clinical validation and empirical evidence throughout the DHT life cycle. [ABSTRACT FROM AUTHOR]

In a wide area environment, leveraging a unified interface for the management of diverse databases is appealing. Nonetheless, variations in access and operation across heterogeneous databases pose challenges in abstracting a unified access model while preserving specific database operations. Simultaneously, intricate deployment and network conditions in wide-area environments create obstacles for forwarding database requests and achieving high-performance access. To address these challenges, this paper introduces a technology for unified access to heterogeneous databases in wide-area environments, termed Global Virtual Data Space SQL (GvdsSQL). Initially, this paper implements a unified data access mechanism for heterogeneous databases through metadata extraction, abstracts the unified access model, and accomplishes identification and forwarding of fundamental database operations. Secondly, the paper introduces a mechanism for expanding database operations through code generation. This mechanism achieves compatibility for special database operations by injecting rules to generate code. Lastly, this paper implements a multilevel caching mechanism for query results in wide-area databases utilizing semantic analysis. Through intelligent analysis of operation statements, it achieves precise management of cache items, enhancing wide-area access performance. The performance is improved by approximately 35% and 240% compared to similar methods. [ABSTRACT FROM AUTHOR]

Population Health Management – often abbreviated to PHM – is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system's data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services. [ABSTRACT FROM AUTHOR]

Continuous improvement in the clinical performance of neonatal intensive care units (NICU) depends on the use of locally relevant, reliable data. However, neonatal databases with these characteristics are typically unavailable in NICUs using paper-based records, while in those using electronic records, the inaccuracy of data and the inability to customize commercial data systems limit their usability for quality improvement or research purposes. We describe the characteristics and uses of a simple, neonatologist-centered data system that has been successfully maintained for 30 years, with minimal resources and serving multiple purposes, including quality improvement, administrative, research support and educational functions. Structurally, our system comprises customized paper and electronic components, while key functional aspects include the attending-based recording of diagnoses, integration into clinical workflows, multilevel data accuracy and validation checks, and periodic reporting on both data quality and NICU performance results. We provide examples of data validation methods and trends observed over three decades, and discuss essential elements for the successful implementation of this system. This database is reliable and easily maintained; it can be developed from simple paper-based forms or used to supplement the functionality and end-user customizability of existing electronic medical records. This system should be readily adaptable to NICUs in either high- or limited-resource environments. [ABSTRACT FROM AUTHOR]

Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching. [ABSTRACT FROM AUTHOR]

'Open Science' advocates for open access to scientific research, as well as sharing data, analysis plans and code in order to enable replication of results. However, these requirements typically fail to account for methodological differences between quantitative and qualitative research, and serious ethical problems are raised by the suggestion that full qualitative datasets can or should be published alongside qualitative research papers. Aside from important ethical concerns, the idea of sharing qualitative data in order to enable replication is conceptually at odds with the underpinnings on most qualitative methodologies, which highlight the importance of the unique interpretative function of the researcher. The question of whether secondary analysis of qualitative data is acceptable is key, and in this commentary we argue that there are good conceptual, ethical and economic reasons to consider how funders, researchers and publishers can make better use of existing data. [ABSTRACT FROM AUTHOR]

Healthcare is a critical area where blockchain technology (BT) is being heralded as a potential game-changer for facilitating secure and efficient data sharing. The purpose of this review is to examine BT applications, performance challenges, and solutions in healthcare. To begin, This review paper explores popular blockchain networks for data exchange, encompassing both public and permissioned platforms, such as Ethereum and Hyperledger Fabric. This paper analyzes the potential applications of BT's decentralized, immutable, and smart contract capabilities in healthcare settings, including secure and interoperable health data exchange, patient consent management, drug supply chain oversight, and clinical trial management. The healthcare industry might greatly benefit from the increased privacy, transparency, and accessibility that these technologies provide. Despite BT's promising medical uses, the technology is not without its drawbacks. High energy consumption, throughput, and scalability are all concerns. We wrapped up by discussing the solutions that have been implemented, including consensus processes, scalability measures like sharding, and off-chain transactions that are designed to mitigate the drawbacks. [ABSTRACT FROM AUTHOR]

In 2016 WHO launched the priority assistive products list (APL) consisting of 50 products and recommended that using this as a reference, countries should develop their own contextually relevant national APLs. This paper describes the development of Malawi's APL. Two hundred and ninety-six persons with disabilities participated in a rapid Assistive Technology Assessment (rATA) survey. Six focus group discussions (FGDs) with people with various types of disabilities were conducted. The rATA questionnaire and FGDs collected data on assistive products (APs) participants used, APs they needed and the challenges they experienced. Data collection was done in six districts spread across the three regions in Malawi. All age groups were included in the survey. Persons with disabilities aged less than 18 participated but went with their guardians. All persons who participated in this study provided consent. Survey and FGDs results were presented at an APL consensus meeting with policymakers, service providers, disabled peoples' organizations and development partners in the disability sector. Based on the results and further discussions, a consensus was reached on the priority APs for Malawi. More than a third of respondents used wheelchairs (32%), followed by auxiliary crutches (25%), walking sticks (13%), reading glasses (11%), prosthesis (10%), elbow crutches (9%) and orthosis (8%). There is also a high demand for products such as pull-up underwear (incontinence products) (79%), hearing aids (70%), reading glasses (59%) and diapers (63%). After intensive discussions during a consensus meeting, an agreement was reached on the 22 priority APs for Malawi. There is a wide range of APs being used by people with different functional limitations in Malawi. There is also a demand for APs that are not readily available. When developing an APL, the list should include products in use, those in demand, and those recommended by service providers. Following the development of the priority assistive products list (APL) by WHO, member states should develop their own contextually based APL. The development of the APL should be based on research evidence. All key stakeholders including persons with disabilities and other functional limitations, government, and development partners should participate in this process. The APL should be part of the national health system or community services. The Department of Disability and Elderly Affairs in the Ministry of Gender, Community Development, being the Government of Malawi line ministry coordinating disability issues participated actively in this study including inviting participants in the stakeholders' validation workshop. [ABSTRACT FROM AUTHOR]

Copyright of Zeszyty Teoretyczne Rachunkowości is the property of Stowarzyszenie Ksiegowych w Polsce Rada Naukowa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, the Internet of Things (IoT) is used in every domain, especially in the healthcare system it plays a very important role in our life and made our life easier by providing various facilities in every area. On the Internet of Medical Things (IoMT), IoT devices collect real-time data from patients and send it to the cloud through the Internet. These gathered data are extorted to centralized processing and computational power. Centralization can lead to a single point of failure, data modification, snooping, security, and privacy evasion. Blockchain can resolve such issues by providing decentralized computational power and better storage capacity for IoT data. Therefore, integrating IoT and blockchain technologies can be an intelligent way to implement a decentralized IoT-based smart healthcare system. We find that integration of the blockchain and IoT is persuasive and can cause remarkable transformations across the healthcare system. We discussed blockchain and its applicability in the healthcare industry. The paper explores blockchain's dimensions, making the innovative healthcare system more secure and stable. We provide a comparative analysis of well-known recent research on IoT-based smart healthcare system security using blockchain based on different parameters such as architecture, data integrity, medical information sharing, distributed electronic health records, patient encryption key, access control, hardware implementation, etc. The paper focuses on the current security issues in IoT-based smart healthcare systems, architectures, taxonomy, and research gap and their possible solutions we point out various research challenges that should be considered before the deployment of a blockchain network in an IoT setting. This paper will be helpful to new researchers to get an overview of IoT-based healthcare system security. [ABSTRACT FROM AUTHOR]

The database management system (DBMS) for any application is crucial for developers because each application needs high performance to run efficiently. Therefore, database Benchmarking is the process of performing several Defined tests on those databases to evaluate their performance. The electronic benchmarking System (EBS) facilitates and improves human resource management (HRM) in all aspects of real life. EBS designed by four different database backends and three different web technology. This paper presents a comparative evaluation of the performance of the top DBMS systems namely (MySQL, SQL Server, Oracle, and MS Access). The middleware is designed using three dynamic web technologies (PHP, ASP, and PYTHON). In order to evaluate the backend performance for the four mentioned databases system by using two Parameters Response Time (RT) and Throughput (TT) over different Tire Architectures namely: One-Tier Architecture (1TA), Two-Tier Architecture (2TA). This paper will show which of the database has a better Response time (RT) and Throughput (TT). [ABSTRACT FROM AUTHOR]

It is considered best practice to conduct a functional analysis and visually inspect data collected to determine the function of problem behavior, which then informs the intervention approaches applied. Visual inspection has been described as a "subjective" process that may be affected by factors unrelated to the data. Structured decision-making guidelines have been established to address some of these shortcomings. The current paper is a follow-up to earlier work describing positive outcomes related to the viability of a decision support system based on structured criteria from Roane et al. Here, we demonstrate important improvements in a computer script's interpretation of functional analysis data, including improvement in agreement between the updated computer script version and experienced human raters (89%) compared to our original agreement outcomes (81%). This paper further supports the use of decision support systems for functional analysis interpretation. [ABSTRACT FROM AUTHOR]

UV light is a potent mutagen that induces bulky DNA damage in the form of cyclobutane pyrimidine dimers (CPDs). Photodamage and other bulky lesions occurring in nuclear genomes can be repaired through nucleotide excision repair (NER), where incisions on both sides of a damaged site precede the removal of a single-stranded oligonucleotide containing the damage. Mitochondrial genomes (mtDNAs) are also susceptible to damage from UV light, but current evidence suggests that the only way to eliminate bulky mtDNA damage is through mtDNA degradation. Damage-containing oligonucleotides excised during NER can be captured with antidamage antibodies and sequenced (XR-seq) to produce high-resolution maps of active repair locations following UV exposure. We analyzed previously published datasets from Arabidopsis thaliana, Saccharomyces cerevisiae , and Drosophila melanogaster to identify reads originating from the mtDNA (and plastid genome in A. thaliana). In A. thaliana and S. cerevisiae , the mtDNA-mapping reads have unique length distributions compared to the nuclear-mapping reads. The dominant fragment size was 26 nt in S. cerevisiae and 28 nt in A. thaliana with distinct secondary peaks occurring in regular intervals. These reads also show a nonrandom distribution of di-pyrimidines (the substrate for CPD formation) with TT enrichment at positions 7–8 of the reads. Therefore, UV damage to mtDNA appears to result in production of DNA fragments of characteristic lengths and positions relative to the damaged location. The mechanisms producing these fragments are unclear, but we hypothesize that they result from a previously uncharacterized DNA degradation pathway or repair mechanism in mitochondria. [ABSTRACT FROM AUTHOR]

Background The extensive and continuous reuse of sensitive health data could enhance the role of population health research on public decisions. This paper describes the design principles and the different building blocks that have supported the implementation and deployment of Population Health Information Research Infrastructure (PHIRI), the strengths and challenges of the approach and some future developments. Methods The design and implementation of PHIRI have been developed upon: (i) the data visiting principle—data does not move but code moves; (ii) the orchestration of the research question throughout a workflow that ensured legal, organizational, semantic and technological interoperability and (iii) a 'master–worker' federated computational architecture that supported the development of four uses cases. Results Nine participants nodes and 28 Euro-Peristat members completed the deployment of the infrastructure according to the expected outputs. As a consequence, each use case produced and published their own common data model, the analytical pipeline and the corresponding research outputs. All the digital objects were developed and published according to Open Science and FAIR principles. Conclusion PHIRI has successfully supported the development of four use cases in a federated manner, overcoming limitations for the reuse of sensitive health data and providing a methodology to achieve interoperability in multiple research nodes. [ABSTRACT FROM AUTHOR]

In September 2017, two strong earthquakes hit the central region of Mexico, producing substantial damage to the historical buildings. A retroactive analysis for assessing the pre-event seismic vulnerability of these constructions allowed for testing the suitability of an existing parameter-based approach based on material and geometrical features. More than 160 adobe buildings in four municipalities of the State of Morelos were surveyed and included in a vulnerability-oriented GIS database. Data were collected on-site and managed by resorting to open-source GIS software combined with a Python-based database management tool and a cloud-based platform for onsite data collection using mobile devices. The parameter-based approach was used for assessing the analytical seismic vulnerability of the buildings and implementing a secondary, more conservative assessment that considers uncertainties associated with the data acquisition process. The capabilities of the database were further used to train a Machine Learning algorithm aimed at overcoming some representativeness limitations of the parameter-based analytical method. This third approach was found to be suitable for assessing the vulnerability of the building typologies addressed in this investigation. Although the implementation discussed in this paper is limited to a specific vernacular typology, it can be used to conduct customized local calibrations. [ABSTRACT FROM AUTHOR]

Background: Nursing interventions in the post‐operative time period including psychological and emotional support, adverse event education, and instructions for follow‐up care contribute patient satisfaction, safety, and quality of life. However, the time spent in the post‐anesthesia care unit (PACU) and hospital continues to shorten around the world to reduce health care spending and improve patient outcomes. Nurses conducting research during the important post‐operative recovery period need to utilize unique techniques and emerging technologies to contact, recruit and collect data outside of the hospital setting including the Research Electronic Data Capture (REDCap) platform. Aims: This paper describes the feasibility and acceptability, facilitators and barriers of the software application, REDCap, to complete a repeated‐measures, descriptive correlational study in patients undergoing outpatient breast cancer surgeries. Methods & Materials: The recruitment, data collection and storage were completed utilizing the secure REDCap Platform. The Institutional Research Board (IRB)‐approved study was a repeated‐measures, descriptive, correlational study with data collection at three time points. The data points aligned with important transitions and routine visits to improve data collection feasibility and increase relevance to clinical practice. Results: The sample consisted of women diagnosed with breast cancer undergoing breast conserving surgery between August 15 and October 15, 2020. There were 123 potential participants, of which 76 started the surveys and 75 participated (61%) responded and participated in the study on Post‐operative Day 1. Fifty‐nine participants (78%) completed the surveys on post‐operative Day 14. Discussion: As the frequency of outpatient treatment increases, nurses conducting post‐operative research will need to collect the data outside of the hospital setting. Conclusion: Email provides a method of studying new phenomena by recruiting participants, providing information about the study, and collecting results in a non‐traditional setting. REDCap provides a method to facilitate nursing research through a securely encrypted integrated process. [ABSTRACT FROM AUTHOR]

Introduction: In recent decades, numerous large survey projects have been initiated to enhance our understanding of the cosmos. Among these, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) stands out as a flagship project of the Stage IV cosmology imaging surveys, offering an open-source framework for data management and processing adaptable to various instruments. Methods: In this paper, we introduce the 'obs_mccd' software package, designed to serve as a bridge linking raw data from generic mosaic-CCD instruments to the LSST data management framework. The package also facilitates the deployment of tailored configurations to the pipeline middleware. To validate our data processing pipeline, we processed a batch of realistic data from a commissioning wide-field telescope. Results: We established a prototype of the quality control (QC) system capable of assessing image quality parameters such as PSF size, ellipticity, and astrometric calibration. Our findings indicate that using a fifth-order polynomial for astrometric calibration effectively characterizes geometric distortion, achieving a median average geometric distortion residual of 0.011 pixel. Discussion: When comparing the performance of our pipeline to our inhouse pipeline applied to the same dataset, we observed that our new 'obs_mccd' pipeline offers improved precision, reducing the median average geometric distortion residual from 0.016 pixel to 0.011 pixel. This enhancement in performance underscores the benefits of the obs_mccd package in managing and processing data from wide-field surveys, and it opens up new avenues for scientific exploration with smaller, flexible survey systems complementing the LSST. [ABSTRACT FROM AUTHOR]

Recently, numerous machine learning (ML) techniques have been applied to address database performance management problems, including cardinality estimation, cost modeling, optimal join order prediction, hint generation, etc. In this paper, we focus on query optimizer hints employed by users in their queries in order to mask some Query Optimizer deficiencies. We treat the query optimizer hints, bound to previous queries, as significant additional query metadata and learn to automatically predict which new queries will pose similar performance challenges and should therefore also be supported by query optimizer hints. To validate our approach, we have performed a number of experiments using real-life SQL workloads and we achieved promising results. [ABSTRACT FROM AUTHOR]

Generation of reliable data underpins the effectiveness of Occupational Health and Safety (OHS) surveillance systems. Despite the importance of understanding OHS data systems, there are few papers that provide overviews of their structure and/or content. This paper introduces a basic framework for assessing OHS data systems that will be of use to researchers internationally. We applied this approach to assess the Irish OHS data system by undertaking a data mapping exercise. We developed a checklist based on recommendations of monitoring and measurement of OHS proposed by the National Academies of Sciences, Engineering, and Medicine (USA). An assessment of published reports that present systematic OHS surveillance data was undertaken to identify the institutions or organisations responsible for collecting and curating the data, their remit, and, associated with this, their respective case definitions. We then provide an overview of the variables collected and these are then mapped against the checklist. The assessment highlights that whilst the farm fatalities dataset provides complete coverage of all fatalities, regardless of age or employment status, the same is not true of the three non-fatal injuries datasets reviewed. There are important differences in the data collection methods and, associated with this, which populations are covered. The assessment approach provides valuable insights into the strengths and weaknesses of a critical element of OHS surveillance systems, namely the production of datasets. This knowledge is important for researchers as understanding the data that informs their research is fundamental to good science. It is critical for policy-makers and other stakeholders to understand the strengths and weaknesses on which OHS policy, strategies, or education and training interventions are developed. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Transactions B: Applications is the property of International Journal of Engineering (IJE) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimates of the price or value of the individual journals within a full-text database may be useful to librarians engaged in serials reviews or other collection development projects, to scholars investigating the determinants of journal prices, and to publishers seeking to rationalize their pricing strategies. This paper evaluates six methods of apportioning the cost of a full-text database among the individual journals in the database--methods based on variables such as journal size, total citations, Journal Impact Factor (JIF) percentile, and single-journal list price. Each method is evaluated based on how well the resulting prices can be predicted by the determinants of journal prices identified in previous research. Although the six methods yield similar results, the single best option is to use price estimates that account for JIF percentile. If citation data are not available and cannot be estimated, the best alternative is to rely on the equal-value assumption--to split the total price equally among the wanted journals in the database. [ABSTRACT FROM AUTHOR]

Purpose: The National Child Abuse and Neglect Data System (NCANDS) Child File, the only national dataset cataloging child maltreatment reports. It includes variables representing economic distress frequently used in published research. At the national level, these variables are demonstrably implausible, substantially underestimating economic distress. Method: This paper reviews recent work using these variables, analyzes the NCANDS data directly, demonstrates why the economic variables in NCANDS are unusable at a national level, and provides recommendations for incorporating economic measures using NCANDS. Results: We find 19 articles that have used these variables within the past 10 years. Most states provide implausible estimates. Economic measures can be incorporated into NCANDS data by either subsetting to s states with plausible estimates of these variables in given years, or appending county-level economic Census data. Discussion: Without addressing these variables' issues in plausibility, use of them will yield biased estimates. [ABSTRACT FROM AUTHOR]

The paper reviews the evolution of Interactive Management, later referred to as Structured Democratic Dialogue, starting from the early 1970s up to this date. The authors propose a generational classification scheme consisting of five periods based primarily on whether some or all stages of the process were implemented synchronously or asynchronously and whether the participants' presence was physical, virtual or hybrid. Other aspects such as modifications in the steps of the process; the evolution of the software; domains of applications; file management; methods of collecting or recording contributions, votes, clarifications and preparation of reports; and key players are also considered and reported within the context of the primary scheme. The paper considers key advances achieved at each generational stage in terms of process or software, discusses associated challenges and concludes with a view towards the future of the emerging fifth generation. [ABSTRACT FROM AUTHOR]

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes. [ABSTRACT FROM AUTHOR]

The secure access and reliable access revocation methods of modern digital systems are based on access control mechanisms. Access policies, which are used in access control mechanisms, are very important in safeguarding security and ensuring data protection. It is evident that the protection and tamper-proofing of such policies are very important. In addition, efficient access revocation schemes are required to promptly remove access privileges when users are no longer needed or authorized. The shortcomings of existing systems in ensuring efficient, streamlined access revocation and tamper-proof protection of access control policies underscore the need for innovative solutions. In this paper, we have introduced the novel Blockchain Attribute-Based Secure Data Management Model (BABSDMM). Our model is the first to integrate attribute-based encryption (ABE), AttributeBased Access Control (ABAC), and blockchain to achieve multiple security features as well as provide partial and complete revocation at the same time. The experimental results and analysis, performed using the Ethereum blockchain network, demonstrated the enhanced performance of the proposed BAB-SDMM compared to existing research works. [ABSTRACT FROM AUTHOR]

Creative industries, by their nature of innovation through creativity, add value to any territory by giving it spatial functionality, and at the same time, they favour sustainable development through the sustainability of the urban centres in which they operate. This paper develops a GIS methodology based on the implementation of a spatial analysis model that captures, in a comparative way, the spatial functionality of two different urban centres—(Cluj-Napoca (Romania) and Pécs (Hungary))—in terms of spatial extent based on the impact of the spatial distribution of creative industries. The spatial analysis model is based on the management of alpha-numeric databases representing the addresses of the creative industries, based on geolocation, for the development of spatial analyses of pedestrian accessibility. The main aim is to identify the specific areas of 15 and 5 min model cities. The comparative analysis was carried out by implementing a deterministic methodology through which the areas with certain degrees of accessibility were analysed in relation to the total analysed area, with the main aim of standardising the scale of analysis due to the difference between the two urban centres, thus facilitating the use of qualitative boning in the final comparison process. The results obtained highlight the local behaviour, at the same time making it possible to conduct a realistic deterministic comparative analysis of the two urban centres, which are also identifiable from the point of view of local territorial functionality, thereby validating the GIS model implemented in the present study. [ABSTRACT FROM AUTHOR]

Background: Age is a key factor when dealing with language and speech disorders, as it entails a progressive loss of neuroplasticity even in healthy individuals. Apart from this, ageing also affects our word‐retrieval abilities, and thus, our discursive skills, particularly in people suffering from neurodegenerative diseases. Therefore, descriptions and/or measures of communicative performance always need to be interpreted through the lens of variation across the lifespan. Aim: This paper's main objective is to create a general tutorial for researchers willing to start delving into discourse analysis, both in healthy and pathological ageing. Methods: An eight‐step tutorial on discourse analysis in the elderly is presented. Each of these steps starts with general recommendations and progresses to more specific topics that may be relevant when conducting this type of research. All of the steps have been extrapolated from an extensive literature review on discourse analysis. Main Contributions: This work presents an easy‐to‐follow, step‐by‐step tutorial on discourse analysis in the elderly. It is aimed at clinical researchers who are taking their first steps in discourse analysis.It may also be useful for those who are already familiar with the methodology but may be interested in reading a general overview on the topic. Moreover, it offers new insights into the following topics: types of research questions, advantages and disadvantages of the different research methodologies and ethical considerations for data production in clinical linguistics. Conclusions: Discourse analysis in the elderly is a highly complex issue that may require researching from different approaches and disciplines. This implies following a well‐planned and thorough process, which we have detailed through the following eight steps: (i) reviewing literature; (ii) formulating the research question; (iii) designing the study; (iv) producing data; (v) selecting technological tools for data treatment; (vi) transcribing the corpus; (vii) annotating the corpus and (viii) analysing and interpreting the results. What This Paper Adds: What is already known on the subject: Approaches in discourse analysis in elderly adults, and particularly, in people suffering from dementia have already been analysed by previous researchers and categorised into three main trends: the quantitative‐experimental approach, the qualitative‐naturalistic approach and an in‐between path, the quantitative‐naturalistic approach. Also, several handbooks on general discourse analysis have presented comprehensive revisions on potential resources and methodologies that can be applied to researching discourse in elderly populations. What this paper adds to existing knowledge: This paper takes these three main approaches and analyses how the most recent research on language in ageing and dementia fits into them. Furthermore, it reviews the advantages and disadvantages each of them may bring for beginners in the field of discourse analysis. Moreover, it adds some studies that may fit into a fourth approach: the qualitative‐experimental. This article also presents information about several of the main steps when analysing data from the pragmatic perspective: the formulation of the research question, data production and the transcription/annotation process. What are the potential or actual clinical implications of this work?: This work has been devised for linguists who may want to read a systematization of the steps for analysing discourse in elderly populations. It may also be of interest to specialists from different fields such as speech therapy, psychology, gerontology or neurology who desire to start applying methods from discourse analysis in their work and aim to have a comprehensive scope of the main research trends within the field of clinical pragmatics. [ABSTRACT FROM AUTHOR]

Developing a solid online presence for a business in the commercial sector is vital to both its survival and growth. The culmination of all elements, strategies, tactics, and effort exerted by a company in selling products reaches its completion only at the moment of finalizing the sale of the product. An e-commerce business relies on a webcentered information system, built around the website that includes the online store. The purpose of this article is to present an IT solution for creating an e-commerce website as a working tool that incorporates the desire of businesses, consumers, and management to limit costs while simultaneously improving the quality of products and increasing the speed of service delivery. The e-commerce platform presented in this work constitutes a modern solution for implementing an online store, intending to sell agricultural products and equipment. For database management, a relational open-source software, MySQL, has been chosen, preferred for its numerous advantages and its ability to run on almost all software platforms. For fast processing and easy page loading, PHP has been used, which is one of the most popular open-source programs and has the advantage of running on almost all operating systems. The website features a modularized structure, consisting of administration, authentication, payment, and order launch/cancellation modules. All these components contribute to the optimal functioning of the e-commerce application. [ABSTRACT FROM AUTHOR]

This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction. [ABSTRACT FROM AUTHOR]

Background: There is currently a limited understanding of the identification, nature, and treatment of Atypical Anorexia Nervosa (AAN). Recent systematic reviews have identified only small numbers of candidate papers, and some areas lack any meaningful review so far – particularly treatment outcomes. A key issue is the lack of clarity in the literature regarding the definition of weight loss criteria. Objectives: This scoping review aimed to determine the nature and extent of our knowledge of AAN, in order to assist in the development of future systematic reviews and meta‐analyses, as well as indicating what further research is needed. Method: Following the identification of 6747 records, 317 records using the term AAN or a defined equivalent were identified from six databases, including the 'grey' literature. Results: Of the 317 studies, 111 provided participant characteristics, and only 10 provided discrete treatment outcomes. Each of these subsets of the data are tabulated and supported with supplementary material, so that future systematic reviewers can access this resource. Discussion: The pattern and content of the existing studies allows recommendations to be made regarding future reviews, research and clinical practice. There is a particular need for clear weight/weight loss criteria and adequate interventions. Highlights: Our identification of Atypical Anorexia Nervosa (AAN) and associated interventions lacks the evidence‐based clarity that can be provided by effective reviews of the field.Relatively few studies provide discrete characteristics or treatment outcomes of AAN.Guidance is outlined regarding reporting criteria for such studies for authors and journals. [ABSTRACT FROM AUTHOR]

Background: Data literacy (DL) is vital for teachers, as it enables them to build on data and improve teaching and learning. Therefore, developing DL among pre‐service teachers is critical. Objectives: The purpose of this study is threefold: to evaluate whether a feedback visualisation of peer assessment‐based teaching approach (FVPA‐based teaching approach) can (1) promote pre‐service teachers' DL; (2) enhance their learning motivation; and (3) improve their cognitive load. Methods: The research was conducted based on a pre‐test‐post‐test control group quasi‐experimental design. With 20 participants in the experimental group and 21 in the control group, a total of 41 pre‐service teachers were included in the study. The pre‐service teachers in the experimental group adopted the FVPA‐based teaching approach, and those in the control group adopted the traditional peer assessment‐based learning approach. Results and Conclusions: The experimental group participants outperformed the control group participants in DL, learning motivation, and cognitive load. FVPA was conducive to helping pre‐service teachers critically interpret data, understand their teaching and learning issues, and improve self‐reflection. The findings indicate a reciprocal relationship between learning motivation and DL; improving the learning motivation of pre‐service teachers could promote their DL. Implications: This study contributes to current knowledge by providing empirical evidence on the benefits of an FVPA‐based teaching approach in improving pre‐service teachers' DL, motivation, and cognitive load. The study findings, limitations, and prospects for future research are discussed. Lay Description: What is already known about this topic?: Teacher data literacy has emerged as a crucial competency for the forthcoming generation of educators, gaining attention from education institutions globally.Peer assessment, a well‐established practice in pre‐service teacher education, has proven effective in enhancing the learning experience for disadvantaged students by fostering motivation and improving academic performance.Despite the acknowledged benefits of feedback visualisation in facilitating self‐reflection, motivation, and academic performance, creating meaningful visualisations remains a challenge for educators and learners. What this paper adds?: This study introduces feedback visualisation in peer assessment to enhance comprehension of feedback information among pre‐service teachers.Feedback visualisation augments the benefits derived from peer assessment, refining data literacy, improving learning motivation, and alleviating cognitive load among pre‐service educators.The analysis of data directly relevant to pre‐service teachers and the provision of timely feedback can motivate critical analysis and utilisation of data, facilitating the identification of pedagogical and learning challenges.The study explores intricate interactions among learning motivation, data literacy, and cognitive load within the context of peer assessment visual feedback. The implications of study findings for practitioners: We encourage teacher educators to implement peer assessment instruction grounded in visual feedback for a comprehensive understanding of learning outcomes, identification of pedagogical challenges, determination of causes, and evidence‐informed instructional decision‐making.Teacher educator should diversify strategies to augment the motivation of pre‐service teachers, cultivating their proactive engagement in data analysis, interpretation, and application to enhance overall data literacy.We recommend that teacher educators provide rich visual feedback or present complex data in a visually accessible format for pre‐service teachers who need additional assistance to mitigate cognitive load and facilitate successful learning outcomes. [ABSTRACT FROM AUTHOR]

In June 2021, FDA released a Draft Guidance on Sponsor Responsibilities for IND Safety Reporting and cited components of a recommended Safety Surveillance Plan (SSP). To meet the expectations of the 2021 FDA guidance, sponsors should document their plan for aggregate safety assessment. The Drug Information Association-American Statistical Association Interdisciplinary Safety Evaluation scientific working group has proposed an Aggregate Safety Assessment Plan (ASAP) that addresses this recommendation. The 2021 FDA guidance also discusses potential strategies for unblinded review of safety data from ongoing studies by an independent Assessment Entity, which could occur via planned periodic evaluations or "triggered" reviews based on blinded data assessments. The Assessment Entity reviewing unblinded data makes recommendations as to whether the threshold has been met for submission of an aggregate IND safety report. In this paper, we discuss how the ASAP supports IND aggregate safety reporting decisions, including elements to be included in a proposed SSP appendix to the ASAP. In addition, the authors advocate for the benefits of developing a charter (or specific section of the Data Monitoring Committee charter, if applicable) that describes the responsibilities and conduct of the Assessment Entity. With these components in place, study sponsors will meet the objective of having clearly defined processes for the monitoring of clinical trial safety data in aggregate and making IND safety reporting decisions. [ABSTRACT FROM AUTHOR]

Today's society has entered the pace of information development era. All kinds of information are digitized, while the loss of information data also directly affects the normal operation of the application system and has become the biggest obstacle to the development of information technology. The existing missing filling methods do not take into account the time-series information of the data set. Based on the neural network method for filling missing data in time series, an end-to-end missing data filling method for time series based on the residual of regression equation is proposed. Under the assumption that the activation function and the noise diffusion coefficient function are satisfied, the exponential stability of the complex-valued stochastic inertial neural network systems with additive time-varying delays is studied. The bipartite graph model of the time series data missing value filling method of the countermeasure network and the filling cyclic neural unit are generated. According to the end-to-end time series data missing value filling method of the self encoder, the corresponding low-dimensional feature vector can be automatically generated for each time series data. It avoids overfitting by using nonlinear methods directly in low-dimensional space. Experiments show that the method proposed in this paper uses Monte Carlo to fill data missing at different proportions. In data gap filling process, the coefficient variance is less than 0.05, which enhances the rationality of filling data. It is of great research value and practical significance to reasonably fill in the missing values of time series data. The research can accelerate the improvement of big data system, improve the level and effectiveness of database management, and is an important means of computing capacity of existing data centers. [ABSTRACT FROM AUTHOR]

The custom GPT is the latest powerful feature added to ChatGPT. Non-programmers can create and share their own GPTs ("chat bots"), allowing Health Professions Educators to apply the capabilities of ChatGPT to create administrative assistants, online tutors, virtual patients, and more, to support their clinical and non-clinical teaching environments. To achieve this correctly, however, requires some skills, and this 12-Tips paper provides those: we explain how to construct data sources, build relevant GPTs, and apply some basic security. [ABSTRACT FROM AUTHOR]

Visual impairment-related disabilities have become increasingly pervasive. Current reports estimate a total of 36 million persons with blindness and 217 million persons with moderate to severe visual impairment worldwide. Assistive technologies (AT), including text-to-speech software, navigational/spatial guides, and object recognition tools have the capacity to improve the lives of people with blindness and low vision. However, access to such AT is constrained by high costs and implementation barriers. More recently, expansive growth in mobile computing has enabled many technologies to be translated into mobile applications. As a result, a marketplace of accessibility apps has become available, yet no framework exists to facilitate navigation of this voluminous space. We developed the BLV (Blind and Low Vision) App Arcade: a fun, engaging, and searchable curated repository of app AT broken down into 11 categories spanning a wide variety of themes from entertainment to navigation. Additionally, a standardized evaluation metric was formalized to assess each app in five key dimensions: reputability, privacy, data sharing, effectiveness, and ease of use/accessibility. In this paper, we describe the methodological approaches, considerations, and metrics used to find, store and score mobile applications. The development of a comprehensive and standardized database of apps with a scoring rubric has the potential to increase access to reputable tools for the visually impaired community, especially for those in low- and middle-income demographics, who may have access to mobile devices but otherwise have limited access to more expensive technologies or services. A wide array of assistive mobile applications now serve as low cost, convenient, and effective alternatives to standard tools in the rehabilitation domain. Given an extensive (and growing) marketplace of assistive apps, we highlight the importance of developing standardized evaluation frameworks that serve to assess the merit, functionality, and accessibility of tools in respective rehabilitation fields. To provide an introduction to a novel resource accessible to the public to exhibit verified and reliable assistive apps for the visually impaired community, especially for those in low- and middle-income demographics who may not have access to common technologies and services. [ABSTRACT FROM AUTHOR]

Background: Central monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials. Material and Methods: Quality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal. Results: The SDM quality metrics showed improvement in 83% (95% CI, 80–85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41–70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct. Conclusion: The results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites. [ABSTRACT FROM AUTHOR]

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data. [ABSTRACT FROM AUTHOR]

In this paper, a network model named DCC-Net based on infrared image sensor is developed for sports data management. Optical imaging plays an important role in the collection and analysis of sports data, but the traditional methods have limitations in the aspects of illumination change and motion blur. To solve these problems, we propose a DCC-net network model based on infrared image sensor. The background of this study is to improve the efficiency of collection and management of sports data in order to promote the improvement of athletes' training and competitive performance. We use a combination of convolutional neural networks and recurrent neural networks to extract and understand motion information in infrared images. The experimental results show that the DCC-net network model has better performance than the traditional optical method in the aspects of illumination variation and motion blur, and provides more accurate and stable motion data. Therefore, DCC-net network model based on infrared image sensor is an effective sports data management tool, which can provide more comprehensive and accurate training and competitive support for athletes and coaches. [ABSTRACT FROM AUTHOR]

The lack of sex-specific variables, such as reproductive system history (RSH), in cardiovascular research studies is a missed opportunity to address the cardiovascular disease (CVD) burden, especially among women who face sex-specific risks of developing CVD. Collecting RSH data from women enrolled in research studies is an important step towards improving women's cardiovascular health. In this paper, we describe two approaches to collecting RSH in CVD research: extracting RSH from the medical record and participant self-report of RSH. We provide specific examples from our own research and address common data management and statistical analysis problems when dealing with RSH data in research. [ABSTRACT FROM AUTHOR]

The U.S. FDA has permanently removed the in-person prescribing requirements that previously safeguarded the use of mifepristone/misoprostol medical abortions, allowing prescribing through telemedicine or on-line ordering and distribution through the mail and pharmacies, without standard pre-abortion testing. This will increase the risk of complications due to failure to adequately determine the gestational age or rule out ectopic pregnancy by ultrasound or physical exam, failure to perform labs to document whether RhoGAM is indicated, and failure to obtain appropriate informed consent to prevent unwanted abortions, among other concerns. The FDA justified this action by referencing flawed studies with significantly undercounted complications. The details of these study deficiencies are examined in this paper. [ABSTRACT FROM AUTHOR]

The United States (US) Food and Drug Administration (FDA) Investigational New Drug (IND) Final Rule (US FDA, Final rule: Investigational new drug safety reporting requirements for human drug and biological products and safety reporting requirements for bioavailability and bioequivalence studies in humans, 2010) applies to all human drugs and biological products being studied under an IND. The Final Rule specifies that a sponsor must file an IND safety report for any Suspected Unexpected Serious Adverse Reaction (SUSAR) of a medicinal product being investigated. To make a proper SUSAR classification, sponsors need to go beyond conventional Data Monitoring Committees (DMCs) with an interdisciplinary effort, using all relevant data (including data outside clinical trials), to make judgments on the possibility of serious adverse events being caused by the study drug—rather than the underlying condition of the patient or a concomitant therapy. Ball et al. (Ball et al. in Ther Innov Regul Sci 55:705–716, 2021) have reported on how the Final Rule has been implemented by large pharmaceutical companies. This paper explores the experiences of small sponsor companies regarding the Final Rule, to understand the current challenges that they have been facing to meet aggregate IND safety reporting requirements. [ABSTRACT FROM AUTHOR]

In recent years, there is growing interest for evaluation of urban flood risks of cities over the past decade due to rapid urbanization and climate change. The optimal flood risk assessment is strategically achieved not only with classical risk modelling approaches but also with holistic and comprehensive framework. This paper focuses on a detailed flood assessment providing risk database for policymakers and urban planners to decide the flood prone areas in Turkey. In this context, the Entropy based VIKOR (VIseKriterijumska Optimizacija Kompromisno Resenje) was provided to evaluate a range of flood risk criteria named number of floods, population density and number of buildings, flood protection area which are under the concept of risk dimension including "hazard, exposure and vulnerability" aspects. Computational results demonstrate that the provinces of Şanlıurfa, Ordu, Zonguldak and Van are assigned with higher urban risk values, respectively and the ranking of the cities was presented with different q values. The findings should support practitioners and researchers for land use planning and risk reduction works as the detailed flood risk evaluation was presented in terms of the flood management. [ABSTRACT FROM AUTHOR]