Usage Data Research Articles

Bilateral flows and rates of international migration of scholars for 210 countries for the period 1998-2020

A lack of comprehensive migration data is a major barrier for understanding the causes and consequences of migration processes, including for specific groups like high-skilled migrants. We leverage large-scale bibliometric data from Scopus and OpenAlex to trace the global movements of scholars. Based on our empirical validations, we develop pre-processing steps and offer best practices for the measurement and identification of migration events. We have prepared a publicly accessible dataset that shows a high level of correlation between the counts of scholars in Scopus and OpenAlex for most countries. Although OpenAlex has more extensive coverage of non-Western countries, the highest correlations with Scopus are observed in Western countries. We share aggregated yearly estimates of international migration rates and of bilateral flows for 210 countries and areas worldwide for the period 1998–2020 and describe the data structure and usage notes. We expect that the publicly shared dataset will enable researchers to further study the causes and the consequences of migration of scholars to forecast the future mobility of academic talent worldwide.

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.

Assessing agreement between population-level administrative pharmaceutical databases and patient-reported medication dispensation in cardiac rehabilitation patients

BackgroundPharmacoepidemiology has emerged as a crucial field in evaluating the use and effects of medications in large populations to ensure their safe and effective use. This study aimed to assess the agreement of cardiac medication use between a provincial medication database, the Pharmaceutical Information Network (PIN), and reconciled medication data from confirmation through patient interviews for patients referred to cardiac rehabilitation. MethodsThe study included data from patients referred to the TotalCardiology Rehabilitation CR program, and medication data was available in both TotalCardiology Rehabilitation charts and PIN. The accuracy of medication data obtained from patient interviews was compared to that obtained from PIN with proportions and kappa statistics to evaluate the reliability of PIN data in assessing medication use. ResultsPatient-reported usage was higher for statins (41.6 %) vs. 38.4 %), ACE/ARB, beta-blockers (75.7 %) vs. 73.7 %), DOAC (3.5 %) vs. 2.6 %), and ADP-receptor antagonists (71.0 %) vs. 68.1 %) than if PIN was used. Patient-reported usage data was lower for Ezetimibe (4.7 vs. 4.8 %), Aldosterone antagonists (5.4 %) vs. 5.5 %), digoxin (0.9 %) vs. 1.0 %), calcium channel blockers (19.2 vs. 19.9 %) and warfarin (7.2 %) vs. 8.1 %). The results indicated that the differences between the two sources were very small, with an average agreement of 95.3 % and a kappa of 0.70. ConclusionThe studyʼs results, which show a high level of agreement between PIN and patient self-reporting, affirm the reliability of PIN data as a source for obtaining an accurate assessment of medication use. This finding is crucial in the context of pharmacoepidemiology research, where the accuracy of data is paramount. Further research to explore the complementary use of both data sources will be valuable.

Archaeological Artefact Database of Finland (AADA)

This paper presents the Archaeological Artefact Database of Finland (AADA) of prehistoric (covering period of almost 11,000 years) artefacts in Finland that are categorised by type and are accompanied with photos of the artefacts. The database is intended to contain all typologically classifiable prehistoric artefacts found in Finland and held in Finnish collections. This dataset provides spatio-temporal context for artefacts across different time periods and regions, as it includes approximately 38,000 single artefacts and approximately 10,000 pottery type identifications from the Early Mesolithic to the end of the Iron Age in Finland (c. 8900 calBC - 1300/1500 calAD). In addition, the artefacts are given period-based (subperiod) dating to allow their chronological affiliation. To facilitate data usage, we also offer an R-script to replicate the data visualisation provided in this paper and a Python script to merge the artefact information to the pictures. We further work towards an interactive user interface for data download and visualization.

BCDA: A blockchain-based dynamic auditing scheme for intelligent IoT

Intelligent Internet of Things (IoT) provides services for machine learning applications by collecting real-time data, where data correctness crucially impacts the training accuracy of models. Data owners host incremental real-time data in the cloud and use blockchain as a public platform for data query, usage, and certification. Malicious attackers may manipulate data through methods like data poisoning, compromising model parameters. Blockchain-based data integrity verification (auditing) schemes can effectively detect and monitor such activities. However, managing the storage demands for massive on-chain data certificates poses significant challenges. Existing compressed certificate storage strategies, such as the Merkle-Hash tree (MHT), incur significant on-chain storage costs because the auxiliary paths and the entire tree structure must be uploaded during verification, resulting in an O(n) storage overhead. Polynomial commitments offer a promising alternative by reducing storage costs to hundreds of bytes. However, current polynomial commitment methods lack effective support for incremental data updates, which are essential for real-time applications. In this paper, we propose a novel polynomial commitment-based dynamic integrity verification scheme that implements cumulative commitment updates for the first time. Our scheme includes a new data block insertion and deletion strategy that maintains the original order of data blocks within polynomial commitments. Theoretical security analysis confirms the robustness of the proposed scheme, while simulation results demonstrate that it meets the efficiency requirements for blockchain-based distributed data integrity verification mechanisms.

Raising Awareness of Mistreatment Policies and Reporting Procedures Using a Novel Medical Student Mobile Application.

Reports of mistreatment are an important first step to improving medical students' learning environment. Students may not report mistreatment due to a lack of awareness of institutional policies, reporting procedures, or for fear of reprisal. We sought to determine if a medical school cross-platform mobile application (app) could be used to improve students' awareness of mistreatment policies and procedures. Participants in this intervention included Drexel University College of Medicine (DUCOM) medical students, faculty, and Student Affairs Deans. We created the DUCOMpass© app to make mistreatment policies and procedures more readily available and to ease mistreatment reporting for medical students. To determine the efficacy of the app at raising mistreatment awareness, we analyzed our institutional GraduationQuestionnaire data before and after the introduction of the app (from 2016 to 2023) as compared with the national average. We verified our students' self-reported data with app usage data. To our knowledge, this is the first instance of a medical school mobile app being implemented to successfully address medical student mistreatment awareness and reporting. We found that reaching students in a familiar and easily accessible mode(s) of communication is a catalyst for lasting change. Not applicable.

Enhancing privacy policy comprehension through Privacify: A user-centric approach using advanced language models

As the digital age advances, the collection, usage, and dissemination of personal data have become critical concerns for users, regulators, and the cybersecurity community. Questions surrounding the extent of identifiable data collection, its usage, sharing, selling, and the mechanisms of consent are increasingly central to discussions on user data privacy. These issues highlight the need for effective management and comprehension of privacy policies. To this end, this paper introduces Privacify— a production-ready web application designed to enhance the accessibility and understandability of privacy policies, thus empowering users to make more informed decisions about their data. At its backend, Privacify leverages a combination of text segmentation, summarization using Large Language Model (LLM), and map-reduce technologies to facilitate BASE analysis for single-document insights and WRT and REV for comprehensive cross-document analysis. Designed with a user-centric approach, Privacify features an intuitive interface that presents all relevant user privacy information in easy-to-understand language, complete with a detailed explainability component. This design not only simplifies privacy policies but also aids users in effortlessly navigating complex privacy terms, significantly boosting their ability to protect and manage their personal information. Our evaluation employs robust methodologies, including reliability and accuracy assessments, alongside rigorous functionality verification through ROUGE metrics and human analysis, validating the system’s efficacy and performance. Privacify’s architecture promotes scalability, replicability, and seamless deployment, advancing the domain of user data protection through improved privacy comprehension.

Exercise Therapy to Reduce Anxiety (ExTRA) in mid-life and later-life adults: study protocol

BackgroundAnxiety in mid-life and later-life is highly prevalent and causes significant distress and disability. There is increasing evidence supporting the potential effectiveness of exercise interventions for reducing clinically significant anxiety. Self-directed online interventions can be scalable and thus reach more people. AimsThe Exercise Therapy to Reduce Anxiety (ExTRA) project aims to assess the feasibility and acceptability of an 8-week web-based exercise intervention with the innovation of being self-directed, and facilitated by wearable activity monitors (WAMs) and tailored behaviour change strategies to reduce anxiety in mid-life and later-life adults with clinically significant anxiety. MethodsExTRA will recruit 20-30 community dwelling adults aged 40-79 years with clinically significant anxiety. The self-directed 8-week web-based exercise intervention includes information and strategies to support participants to meet Australian national physical activity guidelines. The intervention is adapted from previous face-to-face trials and includes strategies to support participants living with anxiety. Feasibility will be measured by screening and retention rates. Acceptability will be measured by website usage data, online outcome measure completion, and an evaluation questionnaire. Other outcome measures include anxiety measured by the Depression Anxiety Stress Scales, and physical activity measured by the Global Physical Activity Questionnaire and a WAM. ResultsFindings will demonstrate whether this intervention is feasible and acceptable, and provide preliminary efficacy data. ConclusionsThe findings of this pragmatic real-world study will be valuable in guiding development of future trials, including randomised controlled trials of scalable and accessible exercise interventions to reduce the distress, disability and cost of anxiety.

Sistem Monitoring Berbasis IoT pada Pembangkit Listrik Tenaga Mikrohidro Tangsijaya

The Micro Hydro Power Plant of Gunung Halu was established in 2007. While electricity from the national power grid is available in the village of Gunung Halu, not all hamlets receive a consistent power supply, particularly those with hilly topography and areas surrounded by forests like Dusun Tangsijaya. The existence of the micro hydro power plant in Tangsijaya is supported by a stable water flow from the river. However, one of the challenges faced by the operator is the lack of a monitoring system for the micro hydro power generator that can provide information about its condition, such as power output and electric current. Until now, the operator has checked the data by visiting the location of the micro hydro power plant directly, which is located outside the residential area. Manual recording relies on the existence of human resources and produces results with a low level of accuracy. In this community service activity, a monitoring system was installed at the Tangsijaya's micro hydro power plant. Leveraging Internet of Things (IoT) technology, this system can display data in real time, so that data visualization can be monitored by relevant parties. With a monitoring system, micro-hydro operators can control the usage and distribution of the generated electricity. In addition to obtaining records of daily electricity usage data, recorded data provide very valuable information in assessing current and potential electricity usage for making decisions related to electricity production. After the implementation of the monitoring system, the main benefits are felt by microhydro operators where the work processes they do become more efficient. Efficiency is obtained in terms of travel distance where operators do not need to visit the PLTMH location directly, and also efficiency in the recording process which no longer needs to be done manually.

Unveiling Data Fairness Functional Requirements in Big Data Analytics Through Data Mapping and Classification Analysis

Aims: In the realm of Big Data Analytics, ensuring the fairness of data-driven decisionmaking processes is imperative. This abstract introduces the Learning Embedded Fairness Interpretation (LEFI) Model, a novel approach designed to uncover and address data fairness functional requirements with an exceptional accuracy rate of 97%. The model harnesses advanced data mapping and classification analysis techniques, employing Explainable-AI (xAI) for transparent insights into fairness within large datasets Methods: The LEFI Model excels in navigating diverse datasets by mapping data elements to discern patterns contributing to biases. Through systematic classification analysis, LEFI identifies potential sources of unfairness, achieving an accuracy rate of 97% in discerning and addressing these issues. This high accuracy empowers data analysts and stakeholders with confidence in the model's assessments, facilitating informed and reliable decision-making. Crucially, the LEFI Model's implementation in Python leverages the power of this versatile programming language. The Python implementation seamlessly integrates advanced mapping, classification analysis, and xAI to provide a robust and efficient solution for achieving data fairness in Big Data Analytics. Results: This implementation ensures accessibility and ease of adoption for organizations aiming to embed fairness into their data-driven processes. The LEFI Model, with its 97% accuracy, exemplifies a comprehensive solution for data fairness in Big Data Analytics. Moreover, by combining advanced technologies and implementing them in Python, LEFI stands as a reliable framework for organizations committed to ethical data usage. Conclusion: The model not only contributes to the ongoing dialogue on fairness but also sets a new standard for accuracy and transparency in the analytics pipeline, advocating for a more equitable future in the realm of Big Data Analytics.

Embedding the Use of Patient Multimedia Educational Resources Into Cardiac Acute Care: Prospective Observational Study.

Multimedia interventions may play an important role in improving patient care and reducing the time constraints of patient-clinician encounters. The "MyStay Cardiac" multimedia resource is an innovative program designed to be accessed by adult patients undergoing cardiac surgery. The purpose of this study was to evaluate the uptake of the MyStay Cardiac both during and following the COVID-19 pandemic. A prospective observational study design was used that involved the evaluation of program usage data available from the digital interface of the multimedia program. Data on usage patterns were analyzed for a 30-month period between August 2020 and January 2023. Usage patterns were compared during and following the lifting of COVID-19 pandemic restrictions. Uptake of the MyStay Cardiac was measured via the type and extent of user activity data captured by the web-based information system. Intensive care unit recovery information was the most accessed information, being viewed in approximately 7 of 10 usage sessions. Ward recovery (n=124/343, 36.2%), goal (n=114/343, 33.2%), and exercise (n=102/343, 29.7%) information were routinely accessed. Most sessions involved users exclusively viewing text-based information (n=210/343, 61.2%). However, in over one-third of sessions (n=132/342, 38.5%), users accessed video information. Most usage sessions occurred during the COVID-19 restriction phase of the study (August 2020-December 2021). Sessions in which video (P=.02, phi=0.124) and audio (P=.006, phi=0.161) media were accessed were significantly more likely to occur in the restriction phase compared to the postrestriction phase. This study found that the use of digital multimedia resources to support patient education was well received and integrated into their practice by cardiac nurses working in acute care during the COVID-19 pandemic. There was a pattern for greater usage of the MyStay Cardiac during the COVID-19 pandemic when access to the health service for nonfrontline, essential workers was limited.

The Impact of User Engagement With Exposure Components on Posttraumatic Stress Symptoms in an mHealth Mobile App: Secondary Analysis of a Randomized Controlled Trial.

Mobile mental health apps are a cost-effective option for managing mental health problems, such as posttraumatic stress disorder (PTSD). The efficacy of mobile health (mHealth) apps depends on engagement with the app, but few studies have examined how users engage with different features of mHealth apps for PTSD. This study aims to examine the relationship between app engagement indices and PTSD symptom reduction using data from an unblinded pilot randomized controlled trial of "Renew" (Vertical Design), an exposure-based app for PTSD with and without coaching support. Because exposure is an effective approach for treating PTSD, we expected that engagement with exposure activities would be positively related to symptom reduction, over and above overall app usage. Participants were veterans (N=69) with clinically significant PTSD symptoms who were recruited online using Facebook advertisements and invited to use the Renew app as often as they wanted over a 6-week period. Participants completed screening and assessments online but provided informed consent, toured the app, and completed feedback interviews via telephone. We assessed users' self-reported PTSD symptoms before and after a 6-week intervention period and collected app usage data using a research-instrumented dashboard. To examine overall app engagement, we used data on the total time spent in the app, the number of log-in days, and the number of points that the user gained in the app. To examine engagement with exposure components, we used data on total time spent completing exposure activities (both in vivo and imaginal), the number of in vivo exposure activities completed, and the number of characters written in response to imaginal exposure prompts. We used hierarchical regression analyses to test the effect of engagement indices on change in PTSD symptoms. Usage varied widely. Participants spent an average of 166.09 (SD 156.52) minutes using Renew, over an average of 14.7 (SD 10.71) mean log-in days. Engagement with the exposure components of the app was positively associated with PTSD symptom reduction (F6,62=2.31; P=.04). Moreover, this relationship remained significant when controlling for overall engagement with the app (ΔF3,62=4.42; P=.007). The number of characters written during imaginal exposure (β=.37; P=.009) and the amount of time spent completing exposure activities (β=.36; P=.03) were significant contributors to the model. To our knowledge, this is the first study to show a relationship between symptom improvement and engagement with the active therapeutic components of an mHealth app (ie, exposure) for PTSD. This relationship held when controlling for overall app use, which suggests that it was engagement with exposure, specifically, that was associated with symptom change. Future work to identify ways of promoting greater engagement with self-guided exposure may help improve the effectiveness of mHealth apps for PTSD.

Harnessing deep reinforcement learning algorithms for image categorization: A multi algorithm approach

Image categorization is an important task in the field of Artificial Intelligence. Deep reinforcement learning (DRL) algorithms are effective for image categorization, especially in the real-time scenarios. In this study, we explore how different DRL algorithms can be used for classifying images in the field of artificial intelligence across multiple benchmark datasets. Our primary goal is to illustrate the way DRL algorithms perform comparable to conventional machine learning(ML) and deep learning(DL) methods. This usage of diverse data allows to evaluate thoroughly how this DRL algorithms will adapt and learn under different situations similar to real-world scenarios with unpredictable data. Our findings suggest that deep reinforcement learning algorithms outperform other algorithms in environments with diverse and complex input. The DRL methods achieved greater accuracies when compared with most of the machine learning and deep learning approaches at 1 Million timesteps. Among the DRL models, Recurrent Proximal Policy Optimization(RPPO) with an accuracy of 97.57% for MNIST dataset, 89% for KMNIST dataset, 89% for EMNIST dataset and Deep Q Network(DQN) on Fashion_MNIST dataset with an accuracy of 87.40% outperformed most of the deep learning and machine learning models and some achieving almost similar to the deep learning and machine learning models. This study not only demonstrates DRL’s capability to handle real-time challenges but also underscores its importance as a valuable tool for computer vision tasks, such as image categorization.

Expressing smells in (American) English

Abstract The paper reports on a study of the usage of smell verbs over the last 200 years by speakers of American English. The focus is on how the expression of smell changes over time and what this reveals about the way speakers conceptualize and assess smells. The study is based on usage data from the COHA (Corpus of Historical American English). They were quantitatively analysed employing the methods of simple and (multiple) distinctive collexeme analysis. The results of our investigations indicate both a general increase over time in the usage of smell-verb constructions and a noticeable diversification of the smell vocabulary used by American English speakers. Moreover, the results of the collexeme analyses reveal more detailed aspects of the types of smell descriptors people use in smell talk. Reflecting what kinds of smell emitters are most typically and especially closely associated with the individual smell-verb constructions at particular times, they are informative about the sources of smells that are salient enough in our culture and (well-)known enough in the speech community to be used as functional smell descriptors and how these may change over time.

A mixed methods evaluation of online discussion tools in higher education

We present a mixed methods comparative assessment of three online discussion tools widely used in higher education. We combine different data types (quantitative, qualitative) and sources (usage data, survey responses) to assess Piazza, Padlet, and Blackboard discussion boards. We highlight and discuss the differences between, and relative merits of, the tools and report the preferences of students on a large introductory Economics unit for them. We summarise lessons learnt from using the tools and make recommendations to engender greater student engagement with online discussion spaces.

Geometric Quality Analysis of Terrestrial Laser Scanning Data for Industrial Usage

Geometric Quality Analysis of Terrestrial Laser Scanning Data for Industrial Usage

Middle School Students' Conceptualizations and Reasoning about the Fairness of Math Tests

Assessment is central to teaching and learning, and recently there has been a substantive shift from paper-and-pencil assessments towards technology delivered assessments such as computer-adaptive tests. Fairness is an important aspect of the assessment process, including design, administration, test-score interpretation, and data utility. The Universal Design for Learning (UDL) guidelines can inform assessment development to promote fairness; however, it is not explicitly clear how UDL and fairness may be linked through students’ conceptualizations of assessment fairness. This phenomenological study explores how middle grades students conceptualize and reason about the fairness of mathematics tests, including paper-and-pencil and technology-delivered assessments. Findings indicate that (a) students conceptualize fairness through unique notions related to educational opportunities and (b) students’ reason about fairness non-linearly. Implications of this study have potential to inform test developers and users about aspects of test fairness, as well as educators data usage from fixed-form, paper-and-pencil tests, and computer-adaptive, technology-delivered tests.

Digital Transformation in HRM: Leveraging AI and Big Data for Employee Engagement and Retention

The Jordanian banking sector is now seeing a significant change in the use of artificial intelligence (AI) and big data in human resource management (HRM). To assess the influence of this era on employee engagement and retention, 300 employees of Jordanian bank were the sample of the study. Using partial least squares (PLS) structural equation modeling, we have a look at investigating how pride mediates and organizational culture modifies those effects. The study found a favorable correlation between AI adoption (r = 0.65, p < 0.01) and Big Data use (r = 0.58, p < 0.05) and work satisfaction, organizational culture, savings technology, staff engagement, and positive connections with Jordanian savings banks. Furthermore, regression research demonstrates that AI adoption and usage of Big Data are predictive. AI and big data can optimize HRM practices and provide insights, leading to higher employee satisfaction (r = 0.72, p < 0.01). This can enhance competitive advantage and strategy in the digital age.

Cycling into the workshop: e-bike and m-bike mobility patterns for predictive maintenance in Barcelona’s bike-sharing system

Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona’s bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system’s predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

An analysis of consumer’s trusting beliefs towards the use of e-commerce platforms

This study evaluates the consumers’ views on various security measures in e-commerce platforms and determines their impact on the trusting beliefs of consumers, which may further lead to an intention to use e-commerce platforms. The study also investigates the moderating effects of gender, age, and the frequency of e-commerce platform use on the proposed model. A total of 780 respondents were used for the study. We used structural equation modeling (SEM) to analyze the proposed trust-based model. The findings indicate a strong positive impact of information integrity and information confidentiality on the consumers’ trusting beliefs. In addition, the study indicates the strong mediating effect of trusting beliefs with the association between information integrity, information confidentiality, and behavioral intention to use e-commerce platforms. A moderating effect of frequency of use on the relationship between perceived information confidentiality and preventing unauthorized secondary data usage on trusting beliefs was found to be significant. This research underwent an in-depth examination of different security aspects that e-commerce firms should consider in order to develop the trusting beliefs of consumers in e-commerce environments.