Data Requests Research Articles

GRIMS: global and regional ionosphere monitoring system

The ionosphere shows regular changes such as daily, 27 days, seasonal, semi-annual, annual, and 11 years. These changes can be modeled and their effects largely determined. However, in addition to regular changes, irregular changes occur in the ionosphere due to space weather conditions, natural disasters, and human-induced irregularities. GNSS is one of the instruments along with many others that can give a piece of information on the ionospheric state. Various indices/parameters are used to determine the effect of space weather conditions. The well-known ones are solar activity indices, geomagnetic storm indices, magnetic field components, proton density, and proton flux parameters. It is important to take all of these indices into consideration when investigating the source of the anomaly. Considering only some of them may lead to incorrect inferences about the source of possible anomalies. To carry out comprehensive research in this field, it is necessary to analyze a very large data set. This indicates the requirement for an automatic system. With the Global and Regional Ionosphere Monitoring System (GRIMS) designed within the scope of this study, the ionosphere can be monitored globally and regionally. The GRIMS is online at https://www.online-grims.com/. By using Global ionospheric maps and GNSS receiver data, global, regional, and station-specific anomalies can be detected regularly through methods such as HDI (Highest Density Interval) and ARIMA (Autoregressive Integrated Moving Average). GRIMS gathers space weather-related parameters from ionospheric data centers to help users interpret the situation, and it allows users to download the results and request data for specific days. The details of the experimental results and output products of the system designed during the geomagnetic active days of March 17, 18, 2015 are given in this paper. Moreover, geomagnetic active days that occurred between 2000 and 2023 are given in the GRIMS.

GRIMS: global and regional ionosphere monitoring system

The ionosphere shows regular changes such as daily, 27 days, seasonal, semi-annual, annual, and 11 years. These changes can be modeled and their effects largely determined. However, in addition to regular changes, irregular changes occur in the ionosphere due to space weather conditions, natural disasters, and human-induced irregularities. GNSS is one of the instruments along with many others that can give a piece of information on the ionospheric state. Various indices/parameters are used to determine the effect of space weather conditions. The well-known ones are solar activity indices, geomagnetic storm indices, magnetic field components, proton density, and proton flux parameters. It is important to take all of these indices into consideration when investigating the source of the anomaly. Considering only some of them may lead to incorrect inferences about the source of possible anomalies. To carry out comprehensive research in this field, it is necessary to analyze a very large data set. This indicates the requirement for an automatic system. With the Global and Regional Ionosphere Monitoring System (GRIMS) designed within the scope of this study, the ionosphere can be monitored globally and regionally. The GRIMS is online at https://www.online-grims.com/. By using Global ionospheric maps and GNSS receiver data, global, regional, and station-specific anomalies can be detected regularly through methods such as HDI (Highest Density Interval) and ARIMA (Autoregressive Integrated Moving Average). GRIMS gathers space weather-related parameters from ionospheric data centers to help users interpret the situation, and it allows users to download the results and request data for specific days. The details of the experimental results and output products of the system designed during the geomagnetic active days of March 17, 18, 2015 are given in this paper. Moreover, geomagnetic active days that occurred between 2000 and 2023 are given in the GRIMS.

Differences in driver takeover performance and physiological responses in conditionally automated driving: Links to emotional instability

When the automated driving system reaches its technical limit, drivers will have to take over control from the vehicle within a limited time. The drivers’ emotional instability may affect whether they can safely control the vehicle during takeover transitions. Accordingly, this paper explores the association between emotional instability and physiological responses and driver takeover performance. 42 drivers engaged in negative emotion induced by movie clips were involved in a sequence of takeover events in a driving simulator. Personality questionnaires were used to assess the driver’s level of emotional instability. Their physiological data before the takeover request (TOR) and operational behavior data fragments for the takeover process were extracted. The results demonstrated that integration of subjective evaluations with electroencephalogram (EEG) and electrocardiogram (ECG) signals allows for more objective measurement of drivers’ level of emotional instability. The statistical analyses indicated that emotional instability has a significant effect on drivers’ heartbeat interval, heart rate, the frequency bands of θ(4–8 Hz), α(8–12 Hz), β(13–30 Hz), γ (30–50 Hz) and lateral takeover performance. Those findings of differences in driver physiological responses and takeover performance under emotional instability may provide additional support for the design of driver state monitoring and adaptive warning systems.

Psychotropic medications and mortality from cardiovascular disease and suicide for individuals with depression in Taiwan

BackgroundPolypharmacy for treatment of depression has been increasing in Taiwan. MethodsIndividuals having depressive disorders were identified in a national database for healthcare services and followed up for 5 years. The mean dosage of antidepressants, antipsychotics, mood stabilizers, and sedative-hypnotics was calculated; the associations between the exposure dosage to different psychotropic medications and patients’ overall death and death due to cardiovascular diseases (CVD) and suicide were examined. ResultsA total of 400,042 individuals with depressive disorders (63.8% women) were identified. Compared with those with no exposure to antidepressants, patients prescribed antidepressants had decreased mortality. Use of antipsychotics had a dose-related increase in overall mortality risk compared to no exposure group. Contrarily, depressed patients taking sedative-hypnotics had decreased overall and CVD mortality compared to no exposure group, with the most prominent decrease in CVD mortality of up to 54.9% for those in the moderate exposure group (hazard ratio: 0.451, 95% confidence interval: 0.405–0.503). A moderate or high dose of antidepressants or sedative-hypnotics was shown to be associated with a significantly increased mortality for suicide compared to those with no exposure. ConclusionsAntidepressant and sedative-hypnotic use was associated with decreased all-cause and CVD-related mortality and use of antipsychotics was associated with a dose-related increase in mortality risk. Future studies are needed to further clarify the involved mechanisms and benefits and risks should be carefully weighed when prescribing psychotropic medications in patients with depressive disorders.

TWOFOLD SUBAREA MODEL FOR ESTIMATING COMMUTER PROPORTION IN 10 METROPOLITAN AREAS

The metropolitan area is a major contributor to national GDP. The metropolitan area is a center of attraction for many people who come to earn income as commuters. Commuters are people who carry out work activities in the center of the metropolitan area, which are carried out by residents who live in suburban areas around the center of the metropolitan area and commute regularly every day. The availability of commuter statistics from surveys for presentation level down to the smallest administrative level, such as regencies/municipalities, is unreliable. This happens because this level of presentation has poor precision due to insufficient samples due to the Statistics Indonesia survey design for making estimates at the national and provincial levels. It can be done using small area estimation (SAE) to meet increasing data needs, but existing SAE models can often estimate only at one level. To meet data requests more effectively, a model is needed that can estimate several small areas simultaneously. In SAE, one of the SAE models that can do this is the twofold subarea model. The twofold subarea model produces estimates of the proportion of commuters with good precision at the subarea level (regencies/municipalities) and area level (metropolitan area), with the RRMSE percentage value of the estimated proportion of commuters being below 25% for all regions. The results of this research can be used to present commuter data at the regencies/municipalities level and metropolitan area level where there is a lack of samples and become a new opportunity for Statistics Indonesia to increase statistical production in small areas, which is more effective compared to other SAE methods which have so far been used only to estimate one area level.

Towards a Formal MultipathP2P Protocol

Peer-To-Peer networks are becoming increasingly common as a mean of transferring files over Internet. In this paper, we describe, first, the design and implementation of our P2P system (MultiPathP2P). This latter is based on the social networks concepts where nodes are identified through their virtual addresses, we have designed our protocol based on 1) An architecture exploiting the principle of social networks where nodes are identified by virtual addresses and are able to randomly change their neighbors 2) A process of data request and file sharing differing from those supported in other P2P networks. Second, we present a formal validation of our proposal in order to prove its optimality and completeness.

The Royal Marsden BRIDgE TRE Transparency Project

BackgroundThe BRIDgE platform is the Royal Marsden’s Trusted Research environment, which aims to transform clinical practice and improve outcomes for patients by enabling data analytics and AI development using real-world cancer data within secure, collaborative, cloud-based workspaces. This project evaluated the BRIDgE website against the new UK Health Data Research UK Transparency Standards, leading to changes to implement the following: Open Access Application form and Guidance (Standard 1), Transparent Access Process and Criteria (Standard 2), Clear website navigation (Standard 3), Consider Target Audience (Standard 4), Regular Review of Website Content (Standard 5), and Transparency of Data Use and Auditing (Standard 6). Our primary aim was to improve the content and sign posting so that patients and researchers would be better informed, resulting in increased website traffic and requests via our Contact Us page. MethodsInformation about the transparency project and the updates to the website were presented to a patient panel for feedback and a leaflet was co-designed with patient representatives. ResultsChanges included a new BRIDgE home page, which was re-designed to make navigation simpler and clearer, improved signposting, so both patients and researchers were aware of the sections meant for their consumption, such as the new Researchers’ Frequently Asked Questions (FAQ) page, which contained detailed instructions on how to request data, the governance workflow and costing structure. Finally, we expanded the Research Projects and Publications page, and added a Case Study from one of the Researchers, detailing their personal experience on the platform. ConclusionsWe anticipate that the impact of this project will be that patients will be more informed about Trusted Research Environments in general and what BRIDgE specifically enables in terms of innovative research and data security, leading to greater assurance for patients that their data is being used securely for their benefit. Researchers will get the benefit of the new FAQ page, which includes a great deal more information than was previously available about the platform, the data access methods, and the governance processes. We envisage that this will lead to more traffic on the site and a greater number of requests for information and workspaces via the Contact Us page. Finally, the patient leaflet will be the first time that patients in our waiting areas will be able to read about research informatics at the Royal Marsden and the work of our data scientists, in paper form. The resources developed using the funding from the transparency project are the revamped BRIDgE website https://www.cancerbrc.org/BRIDgE and (soon to be published) patient leaflet.

Skylark Wiki: Accessing research data of the MRC National Survey of Health and Development

Background & IntroductionThe Medical Research Council National Survey of Health and Development (NSHD) is the oldest and longest-running of the British birth cohort studies. In 2018 we launched the Skylark Wiki, a central metadata repository and the first port of call for any data sharing queries. In 2023 we utilised the HDRUK funding initiative to work with our advisory group and research community to improve the transparency of our data access process and make the content more accessible. MethodsWe conducted a focus group session with the NSHD Participant Advisory Panel to understand their views on the data sharing process and whether the information we provided them, sufficiently explained how their data are shared. We also sent out a questionnaire to all Skylark Users to identify areas of improvement. Lastly, we undertook an audit of the website’s content and used Google Analytics to see how Users navigated across the site. Combining all this information we identified areas requiring improvements, from the point of view of our stakeholders, and comparing these to the requirements indicated in the HDRUK Transparency Standards we mapped out updates to the Skylark Wiki. The work was carried out by an external agency (Aubergine262 Ltd). ResultsTo increase transparency and searchability, we improved the flow diagram of the data sharing process to include links to the relevant web pages. We also created guidance to explain why the information in the data request application form is being asked. As feedback from Skylark Users suggested difficulties in finding the required information, the navigation has been updated. We have also changed the layout of the front page to include navigation boxes that takes users straight to the content. The NSHD Advisory Panel reinforced the notion of trust that study participants place in the study and its approach to data sharing. They confirmed the information provided on data sharing was adequate and appreciated that this information was placed on the main study website (https://nshd.mrc.ac.uk/) for their benefit. Initial feedback from Users indicates that it is easier to find information on the website. We will keep monitoring access to the website, the number of queries, and data access requests to evaluate long-term benefits. We have changed the sitemap and added in large link boxes, to make it easier to find the different sections. We hope this will improve the searchability of the site so users can find information they required. We hope this will therefore lead to fewer emails and correspondence between the data sharing team and users. We have also updated the flow diagram of the data sharing process to include links to the web pages which are related to that section. We hope this will help the users find the information they require. ConclusionsThe new Skylark data-sharing website supports easier navigation providing a better User experience. In return, this should lead to fewer queries from Users. The website will be easier to maintain having changed the content management system from a Wiki to WordPress. WordPress requires less technical knowledge, allowing more staff to contribute to regular maintenance. References Skylark data-sharing website: https://skylark.ucl.ac.uk/NSHD/ Aubergine: https://www.aubergine262.com/

The stateoftheart in secondary pharmacology and its impact on the safety of new medicines.

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

Federated learning in smart home: A dynamic contract-based incentive approach with task preferences

Federated Learning (FL) is a new type of distributed machine learning paradigm aimed at addressing data privacy and security issues in the Internet of Things (IoT) context. In the training process of federated learning model of smart home, data owners need to constantly update cached data, which lead to resources consumption and service latency. Meanwhile, the update cost of the data owners will change with the passage of time when the model owner requests data update. Therefore, this paper first puts forward an incentive scheme based on two-period dynamic contract theory under the information asymmetry. The scheme can balance the weighted preference of the model owner for age of information (AoI) and service latency, as well as encourage more data owners to participate in model training to increase the utility of model owner. Then we formally prove the feasibility of the proposed dynamic contract, which satisfies the constraints of individual rationality and intertemporal incentive compatibility. The experimental results on MNIST dataset show that the accuracy of the proposed dynamic contract is improved by at least 4% compared with the existing contracts. Additionally, compared with traditional contract and consistent pricing strategy, the model owner obtains more profit from the proposed contract.

137Cesium (137Cs) assessment in wild boars from northwestern Italy.

Radionuclide contamination is a serious health issue caused by nuclear experiments and plant accidents, as seen for the Chernobyl and Fukushima nuclear plants. Italy has been especially interested in northwestern alpine regions, as have several other nations. The aim of this work was to indagate 134Cs and 137Cs contamination in wild boars, which were considered bioindicators sampled in the Chisone/Germanasca Valley and the Pellice Valley districts (Piedmont, Italy) in two hunting seasons (2014 and 2016). In the 2014 season, only the livers of the animals (n = 48) were sampled, whereas in 2016, five different anatomical sampling sites were sampled for each animal (n = 16). The analyses were conducted in an accredited laboratory (Agenzia Regionale per la Protezione dell'Ambiente-ARPA) by the aid of an HPGe detector (Ortec) with a relative efficiency of 50%. In general, the contamination levels registered in 2014 were under the detection limit for 134Cs and low for 137Cs (Chisone/Germanasca valley: min: 0.0, max: 23.9 median 11.0 Bq/kg vs Pellice valley: min 0, max: 31.7, median: 9.6 Bq/kg) and no health concern can be supposed. In the first-year samples, the liver showed a negative correlation between age and contamination level. In the second year of sampling, low levels were confirmed (min: 3.1 Bq/kg, max: 113.3; median 17.7 Bq/kg). Multiple sampling from the same animal showed that the diaphragm (median = 27.7 Bq/kg) kidney (27.4) and tongue (27.6) were more contaminated than the liver (17.7) and spleen (15.3). Moreover, a linear mixed model revealed a negative organ-by-age interaction, meaning that interorgan differences in contamination level were greater in younger (5-11 months) than in older (18-36 months) animals. Different feeding habits can be the explanation. Our paper shows that muscle sites (diaphragm and tongue) can be useful for radionuclide pollution surveillance in wild boar populations and that younger animals show more interorgan variability in contamination levels than older animals. More investigations are needed to confirm this correlation and to fulfill the request for more data to achieve better risk assessment.

Expanding the data Ark: an attempt to make the data from highly cited social science papers publicly available

Access to scientific data can enable independent reuse and verification; however, most data are not available and become increasingly irrecoverable over time. This study aimed to retrieve and preserve important datasets from 160 of the most highly-cited social science articles published between 2008–2013 and 2015–2018. We asked authors if they would share data in a public repository—the Data Ark—or provide reasons if data could not be shared. Of the 160 articles, data for 117 (73%, 95% CI [67%–80%]) were not available and data for 7 (4%, 95% CI [0%–12%]) were available with restrictions. Data for 36 (22%, 95% CI [16%–30%]) articles were available in unrestricted form: 29 of these datasets were already available and 7 datasets were made available in the Data Ark. Most authors did not respond to our data requests and a minority shared reasons for not sharing, such as legal or ethical constraints. These findings highlight an unresolved need to preserve important scientific datasets and increase their accessibility to the scientific community.

Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection in Wireless Sensor Networks

There has been a rise in the use of Wireless Sensor Networks (WSNs) in several fields. Depending on the malicious actions, attacks on WSN can take many forms. This research demonstrates how to leverage request and response data from each node to pinpoint the location of malicious nodes. If a node is malevolent, it will prohibit data from being sent to other nodes. They do an excellent job of pretending to be someone else. To enhance route quality, packet dropping can be used to perform a Denial of Service (DoS) assault by rejecting data packets and cutting them off from their destinations. The primary goal of this research is to expose malicious nodes within the network. To avoid being attacked by the false information fed by the adversary through compromised nodes, it is crucial to discover and isolate the nodes that have been compromised. Unfortunately, flat topology networks are difficult to keep safe because of their limited scalability and substantial communication overhead. By computing the packet drop and throughput values with valid nodes, efficient security architecture for the WSN can be proposed, allowing for the detection of malicious activity. If sensor networks are susceptible to attacks, they will be unable to fulfill their duty in a variety of event detection applications. Malicious nodes can cause unacceptable drops in event detection effectiveness and spikes in false alarm rates by producing fake readings and deceptive information. In this research, a method for identifying malicious nodes in WSNs is proposed. This research considers a Trusted Head Node (THN) for monitoring the nodes behaviour in the network to detect the malicious actions in the network. This research proposes a Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection (THN-NBA-MND) in WSN. The proposed model based on node behavior analysis in frequent time intervals; detect the malicious nodes to improve the network performance in data transmission. The proposed model when contrasted with the existing model performs better in malicious node detection.

Analisis Putusan Banding Pengadilan Pajak Sengketa Nilai Pabean: Studi Kasus di KPU BC Tipe A Tanjung Priok

This study aims to evaluate and mitigate the factors causing the low victory of KPU BC Type A Tanjung Priok in the customs value dispute at the Tax Court. This study is qualitative with triangulation method of content analysis and interview. Data is obtained from the tax court decision on the customs value dispute for the period 2018 to 2020. The study found that factors that cause the low winnings on customs value disputes are the low effectiveness of collecting transaction evidence at the time of objection and the limitations of testing the validity of the evidence submitted by the Appellant during the appeal hearing process. This study provide a practical implication that KPU BC Type A Tanjung Priok can conduct further action to mitigate the loss of the appeal hearing for the customs value dispute by for example optimizing data requests to the applicant during the objection process, coordinating with the Document Examiner Officer (Pejabat Fungsional Pemeriksa Dokumen - PFPD) to improve the objection decision, and evaluating tax court decisions. Keywords: Tax Dispute, Tax Court, Tax Appeal, Customs

Distributed realtime rendering in decentralized network for mobile web augmented reality

Low-latency, real-time rendering of 3D objects is critical for mobile web-based augmented reality (MWAR) applications. While cloud-based or edge-based server rendering offloading can reduce the latency for mobile devices, a high volume of user requests in user aggregation scenarios can overload servers and transmission channels. It can result in a poor user experience due to resource consumption and high latency. This paper presents a decentralized and collaborative real-time rendering offloading network architecture (CRCDnet) to address this issue. CRCDnet makes contributions in the following three areas: (1) In the user aggregation scenarios, mobile devices that perform the same services are used as service nodes to perform the offloading of rendering computing and a collaborative rendering computing network is established. (2) For data exchange in decentralized networks, a data sharing middle layer based on blockchain key indexes separates sensitive service data and ensures a secure, reliable exchange mechanism and efficient data exchange. (3) A data request and computing offloading scheduling approach is proposed for the collaborative rendering computing network to optimize the rendering computation delay.

DEVELOPMENT OF A WEBGIS FOR SOLAR PV RESOURCE AND INSTALLATION ASSESSMENT USING GEOSPATIAL TECHNOLOGIES

Abstract. The SINAG Web Portal, a web-based GIS platform that hosts spatial data and a forecasting model for solar PV installation assessment, serves as a repository for the products and outputs of the SolarPot and OutSolar components of a research project called Solar PV Resource and Installation Assessment Using Geospatial Technologies (SINAG). The website was developed with frontend and backend services which involved the use of a content management system, database systems, cloud virtual machines, Linux operating systems, programming languages, specifically Python and JavaScript, and a style scripting framework. All outputs generated by the two components of Project SINAG are stored on the dedicated cloud server of the website. It includes processed spatial data, a graphical summary of the created models, the ability to explore historical data, data requests and downloads, and the use of mapping tools. It is anticipated that the web portal will play a significant role in the assessment and decision-making process for selecting candidate sites for installing solar PV systems across the Philippine archipelago.

E073 The use of magnetic resonance imaging to assess disease activity in axial spondyloarthritis- data from a tertiary rheumatology centre

Abstract Background/Aims The role of MRI of the spine and sacroiliac joints to aid the diagnosis of axial spondyloarthritis (axSpA) is now well established. However, its utility in disease monitoring, in particular assessment of treatment non-response is unclear with limited and conflicting data on the relationship between inflammatory changes suggestive of disease activity on MRI and outcome measures such as ASDAS-CRP and BASDAI. Although the most recent ASAS/EULAR guidelines recommend against routine use of MRI in the assessment of treatment response in axSpA, this is a pragmatic approach often undertaken in NHS clinics with substantial cost implications. We aimed to explore the current use of MRI to assess disease activity in axSpA in a large tertiary centre, in particular its utility in exploring treatment non-response and its impact on treatment switching decision. Methods As part of a service evaluation audit, MRIs performed under the “inflammatory back pain protocol” at Leeds Teaching Hospitals Trust between December 2021 and May 2023 were identified from radiology request data. Patients with axial spondyloarthritis (nr-axSpA, r-axSpA or axial psoriatic spondyloarthritis [axPsA]), were identified. Demographic data, baseline MRI findings, current medications, repeat MRI findings for ongoing axial symptoms, outcome measures and inflammatory markers were extracted from the clinical notes. Data on MRI findings consistent with inflammatory or structural changes related to axSpA or alternative findings, were extracted from the radiologist’s report. Results Overall, 111 patients had an MRI to assess disease activity, 93 had axSpA (49 r-axSpA, 44 nr-axSpA) and 18 axPsA. Patients were predominantly male (58.5%) and HLA-B27 positive (65.8%), 25.2% and 27.9% current smokers and ex-smokers respectively. A history of psoriasis was documented in 25.2% and peripheral arthritis in 42.3%. Baseline MRI was available on 76 patients (68.9%), of which 61 (80.3%) had spinal or sacroiliac inflammation as per the radiologist report. In total, 79 patients were on bDMARD therapy, with TNFi the commonest class (n = 59, 52.7%). Repeat MRI demonstrated active inflammation in the spine or sacroiliac joints in 58.6% (n = 65) as per the radiologist report. Patients with active inflammation in the repeat scan were more likely to have their bDMARD switched or treatment escalated (54% vs 36%). Other important findings included spinal stenosis (n = 4), disc disease (n = 15), vertebral fractures (n = 2) and potential malignancy (n = 1). Conclusion In our practice, MRI is of utility in the monitoring and assessment of non-response in axSpA in two-thirds of cases, aiding treatment decision making by leading to a switch of bDMARD in half of patients and excluding alternative pathologies, such as vertebral fractures and disc disease which may also affect components of disease activity scores. Further research exploring association of MR findings with clinical outcomes, including economic evaluation of routine MRI to assess disease activity in axSpA is warranted. Disclosure J. Weddell: None. R. Shah: None. P. Robinson: None. A. Barr: None. C. Vandevelde: None. J. Freeston: None. D. McGonagle: None. H. Marzo-Ortega: None.

A Blockchain-Based Secure Sharing Scheme for Electrical Impedance Tomography Data

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing.

Mathematical Modeling on Integrated Vehicle Assignment and Rebalancing in Ride-hailing System with Uncertainty Using Fuzzy Linear Programming

The general public frequently uses taxis as local transportation to get from one location to another. Ride-hailing is an innovation in taxi services that lets customers use their smartphones to find drivers, find prices, and submit requests. The two parts of ride-hailing are vehicle assignment and rebalancing. The task of the assignment is to allocate resources as efficiently as possible to fulfil demand. Demand and supply in the area are brought into balance through the rebalancing process. The rebalancing step is frequently carried out independently of the assignment process in ride-hailing pickup systems. However, it can be integrated into a single optimization process to improve system performance. The linear programming approach can integrate assignment and rebalancing. The batch assignment is an assignment algorithm in which each supply and demand is collected within a specific time window. The assignment is done after the vehicles and requests are collected. Fuzzy linear programming is used to deal with environmental uncertainty. Uncertain demand can reduce the reliability of ride-hailing pickup systems in addressing the problem of allocating empty vehicles to areas of high demand. The pickup time may change over time due to traffic conditions. Assignment integration and rebalancing modelling with fuzzy parameters are carried out to obtain a ride-hailing pickup system model that can handle demand uncertainty, pickup travel time, and travel delay times and increase the ride-hailing effectiveness of the pickup system. Numerical simulations were carried out on the assignment and rebalancing integration model without and with uncertainty parameters—numerical simulations based on publicly available taxi travel request data. The model selection for the ride-hailing system can be determined based on the numerical simulation results. This research can potentially advance and expand knowledge, especially in transportation.

92 Honest Broker Tool to Automate Data Extraction from Clinical Research Data Warehouse

OBJECTIVES/GOALS: To describe an Honest Broker (HB) tool and workflow integrated with the Institutional Review Board (IRB) to automate requests, approvals and delivery of both de-identified and identified data extractions from a clinical research data warehouse (CRDW). METHODS/STUDY POPULATION: The HB tool has predefined domain tables and is closely integrated IRB for quick and easy review and approval. Investigators can access patient data using query tools, barcodes from biospecimens or build a query in TriNetX and provide the patient list as an input for the HB tool. For de-identified data extraction, the required data domain tables and date ranges can be selected and submitted in the HB tool. For identified data extractions, investigators with an approved IRB protocol can enter the protocol number and the approved date range in the HB tool. This request is automatically forwarded to the IRB for review. RESULTS/ANTICIPATED RESULTS: For de-identified data extraction, an email alert is automatically sent to the investigator once the data extract is completed. For identified data extraction, if IRB approves the request, an HB is immediately notified to release the data. Data release triggers two emails to the investigator: (1) a link to an encrypted zipped file with the requested data, and (2) a password to unlock the encrypted file. If the request is denied, the IRB sends an email to the investigator with the reason for denial and options for remediation. The entire HB workflow is accomplished in a secure environment with an audit trail from the initial data request to data download by the investigator. Since the launch of the HB tool, the time from data request to delivery is approximately an hour for deidentified data and 24 hrs for identified data. DISCUSSION/SIGNIFICANCE: The HB tool has increased successful data delivery in support of publications, grant submissions, and clinical trial recruitment. Optimization of data extraction from the CRDW through automation and integration with the IRB can minimize interaction with data analysts and IRB staff, thus accelerating the conduct of clinical research.