Data Management Research Articles

Statistical analysis plan for the Robotic-Assisted Surgery and Kinematic ALignment in total knee arthroplasty (RASKAL) registry-nested randomized trial.

There is uncertainty over whether robotic-assisted surgery (RAS) or kinematic alignment (KA) improves outcomes in total knee arthroplasty (TKA). The Robotic-Assisted Surgery and Kinematic ALignment in TKA trial (RASKAL) is a registry-nested, multicentre, randomized, blinded, 2 × 2 factorial trial involving approximately 300 TKA patients. RASKAL will assess the effectiveness of RAS, KA, or both to improve clinical, operative, functional, radiological, and survivorship outcomes when compared to computer-assisted surgery (CAS) and mechanical alignment (MA), respectively. We describe the statistical analysis plan (SAP) for the RASKAL trial to ensure transparency of this study. Our purpose is to minimize analysis bias to bolster the internal validity of our findings. We created a SAP for the RASKAL trial to define the analysis of primary and secondary outcomes. The SAP will also state our approach to protocol deviations, withdrawals, management of missing data, and any loss to follow-up. General statistical analysis principles are outlined for the main outcomes, along with statistical tests required for each outcome. The RASKAL SAP pre-specifies the statistical plan for the primary outcome of knee-specific function using the Knee injury and Osteoarthritis Outcome Score-12 comparing RAS to CAS and KA to MA up to one year postoperatively. The primary outcome will be reported as a modified intention-to-treat analysis, with secondary per-protocol analyses. Secondary outcomes will include other patient-reported outcome measures, early recovery, operative outcomes, functional outcomes, radiological precision, prosthetic survivorship, and cost-effectiveness analyses.

Understanding the legal status of online logistics platforms in Europe: a decision framework for service providers improving legal certainty in the platform context

The legal framework in logistics depends on a liability exposure that is attached to service providers (legal status). However, mandatory (which cannot be avoided with the parties’ agreement) and facultative (which can be replaced with the parties’ agreement) contract laws impose fundamentally different liability exposures on service providers. Moreover, depending on jurisdiction, the same service provider can acquire different liabilities. The reason for this is that national judges consider operational context differently. The rise of platforms in logistics (understood as internet logistics service providers that use various types of procurement mechanism to optimise flows of goods) has made the set of rules that can be applied even more problematic mainly because more legal frameworks should be considered. The uniqueness of this paper is two-fold. Firstly, the paper adds theoretical implications that justify further exploration in the topic of legal classification of logistics intermediaries. Particularly, the paper expands the discussions on the concept of a transport intermediary by investigating the legal status of online logistic platforms in the context of the provision of online services and management of transport data. Furthermore, the paper provides a practical support for contract creation in a digitalized context.

Streamlining data recording through optical character recognition: a prospective multi-center study in intensive care units

BackgroundThe manual entry of data into large patient databases requires significant resources and time. It is possible that a system that is enhanced with the technology of optical character recognition (OCR) can facilitate data entry, reduce data entry errors, and decrease the burden on healthcare personnel.MethodsThis was a prospective multi-center observational study across intensive care units (ICU) in 3 countries. Subjects were critically-ill and required invasive mechanical ventilation and extracorporeal life support. Clinical photos from various medical devices were uploaded using an OCR-enhanced case record form. The degree of data completeness, data accuracy, and time saved in entering data were compared with conventional manual data entry.ResultsThe OCR-based system was developed with 868 photos and validated with 469 photos. In independent validation by 8 untrained personnel involving 1018 data points, the overall data completeness was 98.5% (range 98.2–100%), while the overall data accuracy was 96.9% (range 95.3–100%). It significantly reduced data entry time compared to manual entry (mean reduction 43.9% [range 27.0–1.1%]). The average data entry time needed per patient were 3.4 (range 1.2–5.9) minutes with the OCR-based system, compared with 6.0 (range 2.2–8.1) minutes with manual data entry. Users reported high satisfaction with the tool, with an overall recommendation rate of 4.25 ± 1.04 (maximum of 5).ConclusionAn OCR-based data entry system can effectively and efficiently facilitate data entry into clinical databases, making it a promising tool for future clinical data management. Wider uptake of these systems should be encouraged to better understand their strengths and limitations in both clinical and research settings.

MedAccessX: A Blockchain-Enabled Dynamic Access Control Framework for IoMT Networks

The integration of Internet of Things (IoT) devices in healthcare has enhanced medical efficiency but poses challenges such as data privacy risks and internal abuse. Traditional IoT data access frameworks suffer from centralization, limited scalability, and static permission controls. To address these issues, we propose MedAccessX, a blockchain-based access control framework combining attribute-based access control (ABAC) and role-based access control (RBAC). MedAccessX utilizes four types of smart contracts: a user management contract (UMC) for managing user operations, a medical data management contract (MDMC) for handling data, a policy contract (PC) for managing access rights, and an access control contract (ACC) for enforcing permissions and facilitating data sharing. Our evaluation, conducted on a private Ethereum blockchain network with multiple nodes, assesses security, deployment cost, gas consumption, throughput, and response time. Comparative analysis demonstrates that MedAccessX achieves lower deployment costs and higher throughput, outperforming existing solutions.

A lightweight data management and visualization platform for structural health monitoring based on BIM and augmented reality

A lightweight data management and visualization platform for structural health monitoring based on BIM and augmented reality

Secure healthcare data management using multimodal image fusion and dual watermarking

This study introduces a robust framework to address critical security requirements in digital healthcare systems, ensuring the confidentiality and integrity of medical data. It employs Laplacian redecomposition to fuse MRI and SPECT/PET images, creating a host image for embedding Aadhaar card image and a computed hash value. The embedding process integrates lifting wavelet transform, Hessenberg decomposition, and singular value decomposition to balance imperceptibility and robustness. A pseudo magic cube technique conceals the hash value, while an encryption scheme secures the watermarked image during transmission. Performance evaluations highlight strong results, with PSNR reaching 37.7895 dB, SSIM up to 0.9993, and NC up to 0.9998, demonstrating resilience against various image processing attacks. This framework provides a reliable and effective solution for safeguarding medical data, addressing the pressing need for secure digital healthcare systems in an era of increasing reliance on telehealth and electronic health records.

Development of an Online Power Distribution Information Management System

This research focuses on designing and implementing an Online Power Distribution Information Management System (PIMS) to address the growing complexity and demands of modern power grids. Traditional manual approaches to power distribution often lead to inefficient operations, delayed fault detection, and poor data management. By integrating real-time monitoring, data analytics, and secure online platforms, the proposed system significantly enhances reliability, reduces downtime, and improves overall operational efficiency. Employing the Structured Systems Analysis and Design Methodology (SSADM), the study systematically gathered requirements, modeled system functionalities, and developed a robust architecture comprising a user-friendly interface, scalable database management, and strong security protocols. Key components include an analytics engine for load forecasting and fault prediction, as well as seamless integration with existing systems such as SCADA. Evaluation metrics demonstrate the system’s effectiveness in swiftly identifying network anomalies—achieving a 97% fault detection accuracy—and reducing outage durations by 30% compared to older solutions. Advanced analytics achieved predictive accuracies above 95%, thereby optimizing resource allocation and informing proactive maintenance. Real-time data capture and storage enable operators to promptly respond to changing conditions, while stringent security measures and encryption protocols bolster cyber-resilience. The Online Power Distribution Information Management System paves the way for a more sustainable, efficient, and secure power distribution framework, positioning utilities to better handle emerging challenges such as renewable energy integration and growing consumer demands.

The status quo of research data management in the German-speaking sports sciences—Results of an online pilot survey

Abstract In October 2022, the German Society of Sport Science (dvs) set up an ad hoc committee “Research Data Management” (RDM) with the aim, among others, to develop research data management guidelines for the German sports science community. Recent surveys focusing on various other scientific disciplines suggests barriers for the implementation of sustainable and open RDM practices. Here, guidelines formulated by a professional society might help to overcome potential restraints. On this account, as a first step within the dvs ad hoc committee, a pilot survey was conducted in August to September 2023 to assess the status quo in RDM practices in the community. The online survey was structured into three parts: (1) characteristics of the survey participants, followed by questions about the participants’ (2) current RDM practices, and (3) general knowledge about RDM. The survey was completed by 122 sports scientists from all sections within the dvs. The outcomes provide some interesting first insights into the current RDM practices in data collection, the management of research data access, and the status quo in data publication of researchers in the community. This will guide actions within the dvs ad hoc committee, focusing on three core topics: guidelines, sensitive data, and best practices, but may also serve as a starting point for future research on facilitators and barriers for the implementation of sustainable and open research data practices.

RANCANG BANGUN SISTEM INFORMASI PERSEDIAAN BARANG BERBASIS WEBSITE

The advancement of technology has significantly facilitated various aspects of human life, including business, health, education, and more. One such technological application is the use of computer-based inventory management systems that streamline data management processes, from recording to quick and accurate data retrieval, while also monitoring assets. PT. SAC Nusantara, established in 1971, faces challenges in its Bandar Lampung branch, where inventory management is still done manually, leading to slow data input, frequent errors in stock calculations, and delayed reporting. To address these issues, a computerized inventory management system is needed to improve efficiency, accuracy, and monitoring. This study aims to design, build, and develop a web-based inventory management system to replace the manual processes. The research utilizes the Framework for the Applications of System Thinking (FAST) method, which is flexible and allows for rapid development with user involvement in each stage. The system is designed to automate the recording and reporting process, reduce errors, and improve real-time inventory tracking. The FAST method, along with Data Flow Diagram (DFD) and Entity Relationship Diagram (ERD), will be used to analyze and design the system. The system's functionalities, such as item entry, exit records, and reporting, are evaluated through black-box testing. The expected outcome of this research is a web-based inventory management system that increases the efficiency and effectiveness of inventory management, providing accurate and timely data that will support decision-making processes. The findings indicate that the implementation of this system will significantly improve inventory handling at PT. SAC Nusantara's Bandar Lampung branch.

Mixed covers: optimizing updates and queries using minimal keys and functional dependencies

Covers for a set of functional dependencies (FDs) are fundamental for many areas of data management, such as integrity maintenance, query optimization, database design, and data cleaning. When declaring integrity constraints, keys enjoy native support in database systems while FDs need to be enforced by triggers or at application level. Consequently, maximizing the use of keys will provide the best support. We propose the new notion of mixed cover for a set of FDs, comprising the set of minimal keys together with a cover for the set of non-key FDs implied by the FD set. We establish sequential and parallel algorithms for computing mixed covers from a given set of FDs, and illustrate that they complement each other in terms of their performance. Even though FD covers are typically smaller in number or size than their corresponding mixed cover, the latter generate orders of magnitude lower overheads during integrity maintenance. We also quantify how mixed covers improve the performance of query, refresh and insert operations on the TPC-H benchmark under different constraint workloads and scaling factors. Finally, we illustrate the growth of performance improvement for optimal over minimal-reduced covers, justifying the significant time required for computing the former.

Student Acadmic Data Management

Inculcative institutions manage astronomical magnitudes of student data, including academic records, attendance, and performance tracking. Traditional methods, such as paper-predicated systems and fundamental spreadsheets, are prone to inefficiencies, errors, and data loss. This paper presents the development of a Student Academic Data Management System (SADMS), a centralized digital platform designed to streamline academic data handling. The system leverages web technologies such as HTML, CSS, JavaScript, PHP, and SQL to provide functionalities like data ingress, grade tracking, attendance management, and report generation. Security measures such as role- predicated access control (RBAC) and data encryption ascertain privacy and integrity. The methodology includes system design, database structuring, utilizer interface development, integration, and rigorous testing to ascertain efficiency and reliability. The proposed system enhances data accessibility, minimizes human errors, and ameliorates institutional efficiency. Future enhancements may include AI-predicated performance analytics and cloud integration for scalability. This research contributes to the perpetual digital transformation in edification by providing a robust, automated solution for student academic record management. Keywords— Student Data Management, Academic Records, Digital Inculcation, Web-predicated System, Scholastic Technology, Data Security.

A scoping review of digital health technologies in multimorbidity management: mechanisms, outcomes, challenges, and strategies

IntroductionMultimorbidity amplifies healthcare burdens due to the intricate requirements of patients and the pathophysiological complexities of multiple diseases. To address this, digital health technologies play a crucial role in effective healthcare delivery, requiring comprehensive evidence on their applications in managing multimorbidity. Therefore, this scoping review aims to identify various types of digital health technologies, explore their mechanisms, and identify barriers and facilitators within the context of multimorbidity.MethodsThis scoping review follows the Preferred Reporting Items for Scoping Reviews guidelines. PubMed, Scopus, Web of Science, EMBASE, and Google Scholar were used to search articles. Data extraction focused on study characteristics, types of health technologies, mechanisms, outcomes, challenges, and facilitators. Results were presented using figures, tables, and texts. Thematic analysis was employed to describe mechanisms, impacts, challenges, and strategies related to digital health technologies in managing multimorbidity.ResultsDigital health technology encompasses smartphone apps, wearable devices, and platforms for remote healthcare (telehealth). These technologies work through care coordination, collaboration, communication, self-management, remote monitoring, health data management, and tele-referrals. Digital health technologies improved quality of care and life, cost efficiency, acceptability of care, collaboration, streamlined healthcare delivery, reduced workload, and bridging knowledge gaps. Patients’ and healthcare providers’ resistance and skills, lack of support (technical, financial, and infrastructure), and ethical concerns (e.g., privacy) barred digital health technologies implementation. Arranging organization, providing technical support, employing care coordination strategies, enhancing acceptability, deploying appropriate technology, considering patient needs, and adhering with ethical principles facilitate digital health technologies implementation.ConclusionsDigital health technology holds significant promise in improving care for individuals with multimorbidity by enhancing coordination, self-management, and monitoring. Successful implementation requires addressing challenges such as patient resistance and infrastructure limitations through targeted strategies and investments. It is also essential to consider usability, privacy, and trustworthiness when adopting these tools.

Security Challenges and Performance Trade-Offs in On-Chain and Off-Chain Blockchain Storage: A Comprehensive Review

Blockchain-based data storage methods offer strong data integrity, decentralized security, and transparent access control but also face scalability challenges, high computational costs, and complex data management. This study provides a comprehensive review of on-chain, off-chain, and hybrid storage architectures, analyzing their security vulnerabilities, performance trade-offs, and industry-specific applications. On-chain data storage ensures immutability, data integrity, and high security by storing data directly on the blockchain; however, it is associated with high transaction costs and scalability limitations. In contrast, off-chain solutions reduce costs and enhance performance by storing data outside the blockchain but introduce risks related to data integrity and access security in decentralized environments. Hybrid approaches aim to balance security, cost, and scalability by integrating the strengths of both on-chain and off-chain solutions. This study examines the fundamental components of blockchain-based data storage systems, their sector-specific applications, and the technical challenges they present. Additionally, it explores the trade-offs between security, cost, and decentralization, offering insights into blockchain storage optimization strategies. As a result, this study evaluates the optimization of security protocols, the efficiency of hybrid systems, and the sustainability of distributed storage solutions, contributing to future research in this field.

Accounting Information System and Financial Performance of Manufacturing Firms in Kenya

The integration of financial technologies has become a critical factor in enhancing the financial performance of firms across various industries. This study explores the role of financial systems, including cash ledger systems, data management platforms, automated payroll systems, and accounting information systems (AIS), in improving operational efficiency, accuracy, and financial decision-making. Through an extensive literature review, the study assesses both the benefits and challenges associated with adopting these technologies. Findings indicate that financial technologies contribute to streamlined financial reporting, improved compliance, and enhanced strategic planning. However, small and medium-sized enterprises (SMEs) in the manufacturing sector face significant challenges, including high implementation costs, technical skill gaps, and resistance to technological change. Additionally, existing research tends to focus on large corporations, leaving gaps in understanding how SMEs can effectively implement and benefit from financial technologies despite resource limitations. The study underscores the need for industry-specific research on financial technology adoption, particularly in developing economies, where firms often struggle with financial constraints and regulatory complexities. Future studies should explore phased implementation strategies, cost-effective technological solutions, and the long-term financial impact of adopting digital financial systems. By addressing these gaps, firms can develop tailored approaches to technology adoption, ensuring sustainable growth and improved financial performance.

Tentative renderings: Describing local data infrastructures that support the implementation and evaluation of national evaluation Initiatives.

Tentative renderings: Describing local data infrastructures that support the implementation and evaluation of national evaluation Initiatives.

AlphaPulldown2-a general pipeline for high-throughput structural modeling.

AlphaPulldown2 streamlines protein structural modeling by automating workflows, improving code adaptability, and optimizing data management for large-scale applications. It introduces an automated Snakemake pipeline, compressed data storage, support for additional modeling backends like UniFold and AlphaLink2, and a range of other improvements. These upgrades make AlphaPulldown2 a versatile platform for predicting both binary interactions and complex multi-unit assemblies. AlphaPulldown2 is freely available at https://github.com/KosinskiLab/AlphaPulldown. Supplementary information is available at Bioinformatics online.

FlowHON: Representing Flow Fields Using Higher-Order Networks.

Flow fields are often partitioned into data blocks for massively parallel computation and analysis based on blockwise relationships. However, most of the previous techniques only consider the first-order dependencies among blocks, which is insufficient in describing complex flow patterns. In this work, we present FlowHON, an approach to construct higher-order networks (HONs) from flow fields. FlowHON captures the inherent higher-order dependencies in flow fields as nodes and estimates the transitions among them as edges. We formulate the HON construction as an optimization problem with three linear transformations. The first two layers correspond to the node generation and the third one corresponds to edge estimation. Our formulation allows the node generation and edge estimation to be solved in a unified framework. With FlowHON, the rich set of traditional graph algorithms can be applied without any modification to analyze flow fields, while leveraging the higher-order information to understand the inherent structure and manage flow data for efficiency. We demonstrate the effectiveness of FlowHON using a series of downstream tasks, including estimating the density of particles during tracing, partitioning flow fields for data management, and understanding flow fields using the node-link diagram representation of networks.

The Pathway to Increase Standards and Competency of eDNA Surveys (PISCeS) 2023 Conference - Towards standardization and data management in the field of eDNA.

The second iteration of the international conference 'Pathway to Increase Standards and Competency of eDNA Surveys (PISCeS)' was held at the University of Guelph, Guelph, Ontario, Canada from June 18-20th, 2023. During this environmental DNA (eDNA) conference, 60 oral and 25 poster presentations from academia, government, industry, NGOs, and Indigenous partners discussed the latest developments in eDNA research, explored strategies to inform public policy, and presented future directions in the field. The conference also included three panel discussions focused on prominent themes in the eDNA space, and five workshops dedicated to practical eDNA tools and methods. Recordings of presentations at the conference have been made available on YouTube. Here we summarize the major themes covered during the conference, provide our concluding remarks, and share the conference abstracts.

The Effects of Artificial Intelligence Applications in Natural Resource Management

Artificial intelligence methods have been increasingly used in natural resource management as an alternative to classical methods. Three computational challenges in natural resource management are data management and communication, data analysis, and optimization and control. Artificial intelligence methods can be a solution to these problems due to their ability to manage dynamic activities in natural resources. There are several artificial intelligence algorithms that have found various applications in various fields. In this article, some artificial intelligence methods, including artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, collective intelligence, and hybrid systems, are introduced, and some of their applications in natural resource management are listed.

IoT and AI for smart agriculture in resource-constrained environments: challenges, opportunities and solutions

Climate change is an undeniable reality with far-reaching and profound implications for agriculture and, subsequently, global food security. The highly vulnerable farming communities in underdeveloped and developing countries can overcome resource and capacity constraints with the help of technology, particularly the Internet of Things (IoT) and Artificial Intelligence (AI), making agriculture smarter and more resilient to climate change. This work aims to enrich existing research on smart agriculture by focusing on its applicability in resource-constrained environments. In this context, it presents a detailed overview of the technologies enabling smart agriculture, highlights the challenges and opportunities for farming communities in developing countries, and proposes a framework for climate change-resilient smart agriculture. The framework is loosely based on McKinsey’s 7S model for change management, consisting of hard and soft elements that are defined and adapted for the desired context. The hard elements include IoT sensors, network communications, and data management and analysis using AI, whereas the soft elements consist of policies and regulations, capacity building measures, and a supportive developmental ecosystem. This novel approach has not been employed before in this context. Furthermore, the framework’s efficacy for environmental and crop growth monitoring is demonstrated through its implementation in a low-cost, open-source IoT system within a greenhouse using Edge-Cloud architecture. Here raw, extracted, and derived features are monitored to estimate irrigation requirements and crop maturity date. We conclude with an analysis of the results, recommendations for implementing climate change-resilient smart agriculture in resource-constrained environments, and the identification of areas for future research.