Data Transfer Research Articles

Legal Issues of Cross-Border Data Transfer in the Era of Digital Government

Legal Issues of Cross-Border Data Transfer in the Era of Digital Government

High‐throughput in‐memory bitwise computing based on a coupled dual‐SRAM array with independent operands

AbstractThe successful implementation of artificial intelligence algorithms depends on the capacity to execute numerous repeated operations, which, in turn, requires systems with high data throughput. Although emerging computing‐in‐memory (CIM) eliminates the need for frequent data transfer between the memory and processing blocks and enables parallel activation of multiple rows, the traditional structure, where each row has only one identical input value, significantly limits its further application. To solve this problem, this study proposes a dual‐SRAM CIM architecture in which two SRAM arrays are coupled such that all operands are different, thus rendering the use of CIM considerably more flexible. The proposed dual‐SRAM array was implemented through a 55‐nm process, essentially delivering a frequency of 361 MHz for a 1.2‐V supply and energy efficiency of 161 TOPS/W at 0.9 V supply.

Impact of digital cataract workflow on time and resource efficiencies in cataract surgery: time and motion study.

To compare time and resource-saving with integration of digital cataract workflow to the existing workflow in high-volume cataract surgery clinics. L V Prasad Eye Institute, Hyderabad, India (Site 1) and Narayana Nethralaya, Bengaluru, India (Site 2). Prospective, time-and-motion. The total time to complete each step (preoperative measurements, surgical planning, and surgical procedures) of the cataract workflow, number of data fields entered, and support staff required for both workflows were recorded. All study measurements were determined first for existing EMR cataract workflow followed by digital workflow (integrated data management system with data reviewer, surgical planner, and data transfer to OR) at both sites. A total of 85 (Site 1, 44; Site 2, 41) cataract workflows were analyzed. The integration of digital workflow into the site's existing EMR workflow reduced the mean time for preoperative measurements by 25.3% (P = .006), surgical planning by 55.1% (P = .008), and surgical procedures by 22.6% (P = .002). The mean±SD overall time for the surgery was significantly shorter in the digital group (887.3±103.3 vs 1271.3±300.7 seconds; P < .0001). For both sites, the number of data fields recorded and the number of support staff needed was significantly lesser for the digital workflow (P < .0001, for both). Integration of digital workflow significantly reduced the overall cataract surgery time, variability of overall time, number of data fields recorded, and resource utilization. Complete digitalization has important implications for improving the efficiency and standardization of cataract surgery workflow.

Reproducible and Attributable Materials Science Curation Practices: A Case Study

While small labs produce much of the fundamental experimental research in Material Science and Engineering (MSE), little is known about their data management and sharing practices and the extent to which they promote trust in, and transparency of, the published research. In this research, we conduct a case study of a leading MSE research lab to characterize the limits of current data management and sharing practices concerning reproducibility and attribution. We systematically reconstruct the workflows, underpinning four research projects by combining interviews, document review, and digital forensics. We then apply information graph analysis and computer-assisted retrospective auditing to identify where critical research information is unavailable or at risk. We find that while data management and sharing practices in this leading lab protect against computer and disk failure, they are insufficient to ensure reproducibility or correct attribution of work — especially when a group member withdraws before project completion. We conclude with recommendations for adjustments to MSE data management and sharing practices to promote trustworthiness and transparency by adding lightweight automated file-level auditing and automated data transfer processes.

Patient Experience and Feasibility of a Remote Monitoring System in Parkinson's Disease.

Remote monitoring systems have the potential to measure symptoms and treatment effects in people with Parkinson's disease (PwP) in the home environment. However, information about user experience and long-term compliance of such systems in a large group of PwP with relatively severe PD symptoms is lacking. The aim was to gain insight into user experience and long-term compliance of a smartwatch (to be worn 24/7) and an online dashboard to report falls and receive feedback of data. We analyzed the data of the "Bringing Parkinson Care Back Home" study, a 1-year observational cohort study in 200 PwP with a fall history. User experience, compliance, and reasons for noncompliance were described. Multiple Cox regression models were used to identify determinants of 1-year compliance. We included 200 PwP (mean age: 69 years, 37% women), of whom 116 (58%) completed the 1-year study. The main reasons for dropping out of the study were technical problems (61 of 118 reasons). Median wear time of the smartwatch was 17.5 h/day. The online dashboard was used by 77% of participants to report falls. Smartphone possession, shorter disease duration, more severe motor symptoms, and less-severe freezing and balance problems, but not age and gender, were associated with a higher likelihood of 1-year compliance. The 1-year compliance with this specific smartwatch was moderate, and the user experience was generally good, except battery life and data transfer. Future studies can build on these findings by incorporating a smartwatch that is less prone to technical issues.

A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

Experimental demonstration of magnetic tunnel junction-based computational random-access memory

The conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence because much of the power and energy is consumed by constant data transfers between logic and memory modules. A new paradigm, called “computational random-access memory (CRAM),” has emerged to address this fundamental limitation. CRAM performs logic operations directly using the memory cells themselves, without having the data ever leave the memory. The energy and performance benefits of CRAM for both conventional and emerging applications have been well established by prior numerical studies. However, there is a lack of experimental demonstration and study of CRAM to evaluate its computational accuracy, which is a realistic and application-critical metric for its technological feasibility and competitiveness. In this work, a CRAM array based on magnetic tunnel junctions (MTJs) is experimentally demonstrated. First, basic memory operations, as well as 2-, 3-, and 5-input logic operations, are studied. Then, a 1-bit full adder with two different designs is demonstrated. Based on the experimental results, a suite of models has been developed to characterize the accuracy of CRAM computation. Scalar addition, multiplication, and matrix multiplication, which are essential building blocks for many conventional and machine intelligence applications, are evaluated and show promising accuracy performance. With the confirmation of MTJ-based CRAM’s accuracy, there is a strong case that this technology will have a significant impact on power- and energy-demanding applications of machine intelligence.

Data sovereignty and data transfers as fundamental elements of digital transformation: Lessons from the BRICS countries

When talking about digital transformation, data sovereignty considerations and data transfers cannot be excluded from the discussion, given the considerable likelihood that digital technologies deployed along the process collect, process and transfer (personal) data in multiple jurisdictions. An increasing number of nations, especially those within the BRICS grouping (Brazil, Russia, India, China, and South Africa) are developing their data governance and digital transformation approaches based on data sovereignty considerations, deeming specific types of data as key strategic and economic resources, which deserve particular protection and that must be leveraged for national development. From this perspective, this paper will try to shed light on how data sovereignty and data transfers interplay in the context of digital transformations. Particularly, we will consider the various dimensions that compose the concept of data sovereignty and will utilise a range of examples from the BRICS grouping to back some of the key considerations developed with empirical evidence. We define data sovereignty as the capacity to understand how and why (personal) data are processed and by whom, develop data processing capabilities, and effectively regulate data processing, thus retaining self-determination and control. We have chosen the BRICS grouping for three reasons. First, research on the grouping's data policies and digital transformation is still minimal despite their leading role. Second, BRICS account for over 40 % of the global population, or 3.2 billion people (which can be seen as 3.2 billion “data subjects” or data producers, depending on perspective, thus making them key players in data governance and digital transformation. Third, the BRICS members have realised that digital transformation is essential for the future of their economies and societies and have shaped specific data governance visions which must be considered by other countries, especially from the global majority, to understand why data governance is instrumental to foster thriving digital environments.

Stenopool: A Comprehensive Platform for Consolidating Diabetes Device Data.

The growing adoption of diabetes devices has highlighted the need for integrated platforms to consolidate data from various vendors and device types, enhancing the patient experience and treatment. This shift could pave the way for a transition from conventional outpatient diabetes clinics to advanced home monitoring and virtual care methods. Overall, we wished to empower individuals with diabetes and healthcare providers to interpret and utilize information from diabetes devices more effectively. Stenopool integrates most diabetes devices for glucose monitoring and insulin administration in our clinic. The platform was initially developed with inspiration from open-source software, and the current version is a unique digital platform for managing and analyzing diabetes device data. The development process, outcomes, and status are described. Since November 2021, Stenopool has been used in our outpatient clinic to integrate over 30 different diabetes devices from around 7000 individuals. Data are primarily uploaded via wired connections, but also using semi-automated and automated cloud-to-cloud data transfers. The platform offers a streamlined workflow for healthcare providers and displays data from various glucose meter, insulin pump, and continuous glucose monitor (CGM) vendors on a single screen in a manner that healthcare providers can modify. A data warehouse with data from Stenopool and electronical health records is nearing completion, preparing the development of tools for population health management, quality assessment, and risk stratification of patients. Using Stenopool, we aimed to enhance diabetes device data management, facilitate the future for virtual patient care pathways, and improve outcomes. This article outlines the platform's development process and challenges.

BIM/BES INTEROPERABILITY: A CASE STUDY BETWEEN REVIT®, DESIGNBUILDER®, IES-VE®, AND E-QUEST®

Objective: This study proposes a BIM/BES interoperability test through a case study involving Revit® and three BES software programs: DesignBuilder®, IES-VE®, and e-Quest®. Theoretical Framework: For thermo-energy analyses, a recognized combination is the BIM (Building Information Modeling) / BES (Building Energy Simulation) pair. This requires data transfer from one software to another. To export the virtual model, it is necessary to generate the energy analytical model in BIM and use transfer protocols for BES software. Various studies have indicated failures in this process. Method: A case study was used to analyze the interoperability between the software programs, through a 58m² single-family residence. The geometric model was created with its dimensions and layers, input data such as location, thermal properties of materials, and usage data, generating the energy analytical model and exporting it to each BES software. Interoperability was tested based on data transfer analysis, establishing seven criteria. Results and Discussion: The results indicated failures in interoperability, as none of the software programs exported all the criteria correctly. The BES software with the best results was IES-VE®, with 70.90% of the criteria exported, followed by e-Quest® (65.45%) and DesignBuilder® (45.45%). Research Implications: The results of this study demonstrate that interoperability challenges limit the potential of the BIM/BES pair. Originality/Value: Despite studies on this topic, interoperability challenges remain, representing a gap to be filled.

Small data challenges for intelligent prognostics and health management: a review

Prognostics and health management (PHM) is critical for enhancing equipment reliability and reducing maintenance costs, and research on intelligent PHM has made significant progress driven by big data and deep learning techniques in recent years. However, complex working conditions and high-cost data collection inherent in real-world scenarios pose small-data challenges for the application of these methods. Given the urgent need for data-efficient PHM techniques in academia and industry, this paper aims to explore the fundamental concepts, ongoing research, and future trajectories of small data challenges in the PHM domain. This survey first elucidates the definition, causes, and impacts of small data on PHM tasks, and then analyzes the current mainstream approaches to solving small data problems, including data augmentation, transfer learning, and few-shot learning techniques, each of which has its advantages and disadvantages. In addition, this survey summarizes benchmark datasets and experimental paradigms to facilitate fair evaluations of diverse methodologies under small data conditions. Finally, some promising directions are pointed out to inspire future research.

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.

The Interplay Between Cybersecurity in Europe and the Development of the General Data Protection Regulation: A Study of Lateral Pressure

The securitization of cyberspace has been a specter to the political studies in various countries. This article focuses on the dynamic of speech act on implementation consent and its relation to the theory of lateral pressure formulated by Nazli in studying cyber politics. Since 2018, the European Commission has implemented GDPR as a basic law related to data transfer, collection and process (TCP). This policy has brought a fundamental problem to the activity conducted by the state and private sector. Moreover, it was also criticized that this policy has brought to the discrimination of algorithm that came to bias to the minority group. In speak of Methodology, we used qualitative research of 418 articles with the keyword ‘consent’ focusing on Social and Humanities Science in Journal Sage; after manual analysis, we found 146 articles related to the consent study and social science. The final project is to find out the significant tendency of how Europe has securitized GDPR through speech acts by correlating it to the Lateral Pressure Index. The potential implications of the finding could give theoretical preference in formulating a data protection base policy.

TURKISH COURT OF ACCOUNTS: ANALYZING FINANCIAL AUDIT, DIGITALIZATION, AI IMPACT

Countries’ audit institutions play a critical role in financial management and accountability. These institutions perform tasks such as financial accountability, detection of corruption and errors, evaluation of government performance, accountable public administration, transparency and legal oversight, and prevention of legal violations, contributing to public trust. The Court of Accounts Presidency is the supreme audit institution of the Republic of Türkiye. This study sequentially addresses the audit process and procedure of the Court of Accounts, the impact of digitization, information technologies, and artificial intelligence on the audit process. Additionally, an evaluation of the institution’s technology and information infrastructure is conducted. A detailed assessment is presented regarding the Court of Accounts’ use of the Court of Accounts Data Analysis System (VERA), Unified Data Transfer System (BVAS), and Audit Management Program (SayCap). The institution’s technology and information infrastructure related to big data and big data analytics are evaluated based on the institution’s reports and documents. Finally, evaluating the potential integration of artificial intelligence into these systems is considered highly beneficial. For human resources in the field to be more proficient in current technologies, implementing artificial intelligence, big data, and big data analytics in the audit domain of public institutions is seen as highly advantageous. Additionally, it is strongly recommended that educational institutions training audit professionals incorporate courses on data analytics, big data, big data analytics, machine learning, and artificial intelligence into their curricula.

SDN architecture‐based secure data transfer with cooperative searching scheme for AUV‐based underwater wireless networks

SDN architecture‐based secure data transfer with cooperative searching scheme for AUV‐based underwater wireless networks

Data-driven prediction of tool wear using Bayesian regularized artificial neural networks

The prediction of wear in cutting tools is pivotal for boosting productivity and reducing manufacturing costs. Although current data-driven models in machine learning and deep learning have advanced predictive capabilities, they often lack generality and demand substantial data training. This paper presents a novel approach using Bayesian Regularized Artificial Neural Networks (BRANNs) to precisely forecast wear in milling tools. Unlike conventional machine learning models, BRANNs merge the strengths of artificial neural networks (ANNs) and Bayesian regularization, yielding a more robust and generalized predictive model. We utilized three open-access datasets from the literature alongside an in-house dataset generated by our milling setup. Initially, we assessed the model’s predictive ability by training and testing it against individual open-access datasets. We investigated the impact of input features, training data size, hidden units, training algorithms, and transfer functions on the model’s predictive capability. Subsequently, we trained the model using three open-access datasets and tested it against our in-house data. Our findings demonstrate that the developed model surpasses existing state-of-the-art models in accuracy and transferability

Use of the CardioMEMS Device in Children and Patients with Congenital Heart Disease: A Literature Review.

The CardioMEMS HF System (Abbott, Abbott Park, IL) is the first FDA- and CE-Mark-approved device for monitoring patients with heart failure, significantly reducing hospitalizations and improving the quality of life for NYHA class III non-congenital adult patients. This device, implanted percutaneously, allows the direct monitoring of pulmonary arterial pressure with the wireless transfer of pressure data to the clinician, who can adjust the therapy remotely. Limited experience exists regarding its use in patients with congenital heart disease (CHD). CardioMEMS device implantation is feasible and safe in selected adults and children with CHD. The potential of the device to reduce heart failure hospitalizations in this population is enormous, but further multi-center studies are needed to demonstrate its efficacy.

Adapting UWB AoA estimation towards unseen environments using transfer learning and data augmentation

Ultra-wideband technology has become increasingly prevalent in localization systems, particularly with the emergence of multi-antenna systems capable of estimating both distance and angle of arrival (AoA) for incoming signals. However, most scientific research analyzes the accuracy of AoA in one specific environment for which the estimator is trained. In this paper, we analyze the performance of various AoA estimation algorithms, such as deep convolutional neural networks (DCNN), multiple signal classification (MUSIC), and phase difference of arrival (PDoA), in unseen environments. Prior work already demonstrated the superior performance of ML for AoA estimation compared to PDoA or MUSIC. We show that MUSIC, PDoA and ML solutions suffer from degradation in unseen environments at the 90th percentile of error, with ML-based AoA estimation degrading by about 14 degrees in unseen environments compared to 4 degrees for PDoA. We demonstrate that while PDoA more effectively corrects AoA at the median level in unseen environments, ML-based methods excel at correcting higher-percentile AoA errors, including outliers. Finally, we propose a novel framework to further improve the correction of AoA outliers for DCNN-based AoA estimators using data augmentation and transfer learning, resulting in a median angular error of only 5 degrees in unseen environments, even considering a field of view up to 90 degrees.

Smart AODV Routing Protocol Strategies Based on Learning Automata to Improve V2V Communication Quality of Services in VANET

The Adhoc On-Demand Distance Vector (AODV) protocol faces challenges in selecting the best relay nodes, which requires optimization to improve performance in Vehicular ad-hoc networks (VANETs). This study aims to enhance Vehicle-to-Vehicle (V2V) communication in VANETs by implementing the Learning Automata-Driven Ad-hoc On-Demand Distance Vector (LA-AODV) routing protocol. LA-AODV is designed to achieve higher packet delivery ratios and optimize data transfer rates, even under congested network conditions, by dynamically adjusting to changing network scenarios. The performance evaluation includes six key metrics analyzed under varying node densities and time intervals, comparing LA-AODV against the standard AODV protocol. Results indicate that LA-AODV consistently outperforms AODV, demonstrating improved efficiency in flood identifier management, reduced data loss, higher packet delivery ratios, better throughput, and reduced end-to-end delay and jitter. Specifically, under a 20-node scenario, LA-AODV exhibits lower flood ID scores (54 vs. 88), reduced packet loss (11% vs. 12%), higher PDR (88.0% vs. 87.0%), and superior throughput (85.34 Kbps vs. 47.26 Kbps). Additionally, LA-AODV achieves lower end-to-end delay (6.84E+09 ns vs. 3.76E+10 ns) and jitter (2.52E+09 ns vs. 2.15E+10 ns). These findings suggest that LA-AODV significantly enhances Quality of Service (QoS) in vehicular ad-hoc networks, positioning it as a promising solution for optimizing V2V communication performance.

Advancing IOT Interoperability: Dynamic Protocol Translation through Machine Learning For Enhanced Communication Efficiency

This study presents a pioneering methodology for Dynamic Protocol Translation in the Internet of Things (IoT), aiming to overcome challenges posed by diverse communication protocols among IoT devices. The primary objective is to develop a two-fold approach: first, acquiring data from IoT devices through their specific protocols, preprocessing it for consistency, and employing Natural Language Processing (NLP) techniques for semantic extraction and normalization; second, implementing a machine learning model, incorporating neural networks, to discern correlations between normalized representations and target protocol structures. The emphasis is on rigorous testing, validation, &amp; real-time translation capabilities. The main conclusions of the study demonstrate how well the suggested Logistic Regression model performed, with an accuracy of 96.76%, in contrast to an existing model (XML-JSON) that had an accuracy of 82.41%. The detailed evaluation metrics, which include F1 score, precision, and recall, demonstrate how well the suggested method works to solve protocol translation issues. The iterative feedback loop, real-time translation, and secure data transfer of the proposed system improve its adaptability and reliability. This research enhances the field of IoT communication by offering a comprehensive solution for smooth interoperability &amp; communication efficiency in a range of IoT applications.