Rapid Integration Research Articles

The impact of trust, comfortability, usability and technophobia factors on acceptance of health information technology

In the contemporary landscape of healthcare, the rapid integration of digital technologies is reshaping traditional practices. As healthcare systems increasingly embrace digitalization, there arises a critical need to comprehend the dynamic and ever-evolving role played by Information and Communication Technologies (ICTs). Amidst the rapid digitization of healthcare systems, understanding the evolving role of Information and Communication Technologies (ICTs) is paramount. This study explores the impact of Information and Communication Technologies (ICTs) on healthcare, with a focus on Health Information Technology (HIT) and its role in enhancing patient-centered care. Specifically, the research centers on Personal Health Records (PHRs), which empower patients to control their health information securely. The study aims to identify key features influencing patients’ behavioral intention to adopt HIT, utilizing a structured questionnaire and a conceptual framework based on the Technology Acceptance Model (TAM). Feature selection analysis, conducted using the Weka tool, employed various classifiers to predict users’ perceptions and behavioral intentions towards HIT adoption. Results highlight the significance of trust, comfort, usefulness, and technophobia in shaping patients’ acceptance of HIT, with Random Forest and Bayes Net classifiers emerging as key predictors. This research provides valuable insights into the critical factors driving patients’ acceptance of HIT, contributing to the ongoing discourse on the adoption of healthcare technologies.

Rapid integration of face detection and task set in visually guided reaching.

The superior colliculus (SC) has been increasingly implicated in the rapid processing of evolutionarily relevant stimuli like faces, but the behavioural relevance of such processing is unclear. The SC has also been implicated in the generation of express visuomotor responses (EVR), which are very short-latency (~80 ms) bursts of muscle activity time-locked to visual target presentation. These observations led us to investigate the influence of faces on EVRs. We recorded upper limb muscle activity from healthy participants as they reached toward targets in the presence of a distractor. In some experiments, faces were used as stimuli. Across blocks of trials, we varied the instruction as to which stimulus served as the target or distractor. Doing so allowed us to assess the impact of instruction on muscle recruitment given identical visual stimuli. We found that responses were uniquely modulated in tasks involving high-contrast faces, promoting reaches toward or away from a face depending on instruction. Follow-up experiments confirmed that the phenomenon required highly salient repeated faces and was not observed to non-facial stimuli nor to faces expressing different affects. This study extends the hypothesis that the SC mediates the EVR by demonstrating that faces impact muscle recruitment at short latencies that precede cortical activity for face perception. Our results constitute direct evidence for the behavioural relevance of face detection in the brainstem, and also implicate a role for top-down cortical pre-setting of the EVR depending on task context.

Adapting to AI: exploring the implications of AI integration in shaping the accounting and auditing profession for developing economies

The study investigates the adaptability of the accounting and auditing profession in the face of rapid AI integration, focusing on developing economies with Ghana as a case study. It employs a quantitative approach, using a cross-sectional survey design to collect data from accounting and auditing professionals across various sectors in Ghana. A stratified random sampling technique ensures diverse representation, and the data is analysed using structural equation modelling (SEM) tools. The research identifies six key domains and twelve adaptability attributes, including forces of change, leadership qualities, readiness, approach, and resistance to change, which are critical for the profession’s response to disruptive AI technologies. The extracted model, based on these attributes, was validated through a survey, revealing a good model fit with the Standardized Root Mean Square Residual (SRMR) below 0.08 and a Normed Fit Index (NFI) within an acceptable range. The findings suggest that to remain relevant in the AI era, accounting professionals in developing economies must continuously upskill and reskill, alongside cultivating strong business acumen, interpersonal skills, critical thinking, value-added services, and communication abilities. The study emphasizes the importance of policymakers, professional bodies, and leadership within the profession aligning their strategies with these adaptability attributes to enhance resilience and competitiveness in an AI-driven landscape. This research significantly contributes to the understanding of how the accounting and auditing profession in developing economies can navigate the challenges and opportunities presented by AI.

The Multilateral Integration of Blockchain, Ethical Packet Sniffing and AI for Cybersecurity in BRICS Countries

This policy brief proposes that the BRICS (Brazil, Russia, India and South Africa) should plan and coordinate on ways to mitigate cybersecurity risks on a multilateral level. The BRICS countries are ideal for such a meta-governance mechanism which could coordinate toward the circumvention of some cyber risks. This Policy brief proposes that there should be an integration of blockchain technologies, packet sniffing technologies and AI detection tools to help mitigate emerging cybersecurity risks. The policy brief outlines the risks and challenges prevalent in this current climate, whilst also proposing potential remedies sourced from rigorous academic studies. The precise amendments include the synthesised mechanism of blockchain-based internet registries, packet sniffing technologies AI verification tools and capacity building to aid in the mitigation of cybersecurity risks. In 2007, a cyber-attack in Brazil plunged more than 3 million people into darkness. Cyberattacks collectively cost the BRICS countries $50.3bn in 2013. These funds could have been reinvested into the economy. BRICS needs the rapid technological integration of cyber-crime mitigation tools, tools which this policy brief will outline.

The Mediating Role of Work Motivation in the Impact of Training on Teachers’ Digital Literacy

It is importance of improving the digital literacy of teachers is crucial with the rapid integration of technology in education, effective training programs are critical. However, their success largely depends on the motivation of the participants. The purpose of this study is to determine the mediating role of the work motivation variable on the effect of the training variable on the digital literacy variable so that there are two independent variables and one dependent variable. The number of samples used was 73 teachers in Palembang. To collect data, a questionnaire technique was used. Using the Smart PLS 3.0 structural equation modeling technique, the hypothesized relationships in this study were tested. This study found that: 1) training had a positive and significant effect on teacher motivation; 2) work motivation had a positive and significant effect on teacher digital literacy; 3) training had a positive and significant effect on teacher digital literacy; and 4) work motivation had a mediating effect on the relationship between training and teacher digital literacy. These results highlight the importance of fostering a motivational climate to maximize the benefits of training programs. Implications for educational policy and practice are discussed, emphasizing the need for strategies that not only provide training but also actively engage and motivate teachers to enhance their digital competencies.

Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value.In studies of two deregulated markets (California ISO and the Electric Reliability Council of Texas), the integrated Natrium-style system performed better in CAISO than in ERCOT, given higher and more consistent electricity prices during peak-demand periods. The potentially enlarged cost associated with the variable operation and maintenance of the TES system also plays a significant role in driving the system sizing, thus its impacts on the system are investigated in detail through comparison against a baseline case. The study also finds that the bi-level optimization results based on stochastic gradient descent closely match the grid search results. The uncertainty quantification of the stochastic signals provides further NPV-related insights and probability distributions for the case studies. The normal standard error of the mean of NPV for the case with and without TES VOM for CAISO were found to be 7.73M ± 1.09M USD and 104.99M ± 1.25M USD, respectively based on a 95% confidence. Given the relatively small NPV variance based on 150 samples, the analysis affords the most robust possible prediction of the techno-economic performance of the integrated Natrium-style energy systems.

Implementing virtual primary care: experiences, perspectives and identification of improvement opportunities in an academic primary care setting

BackgroundOne of the biggest changes to primary care triggered by the COVID-19 pandemic was the rapid integration of virtual care (VC). VC offers benefits to patients and providers but implementation...

Bibliometric analysis of AI and Fintech: Mapping the intersection of artificial intelligence and financial technologies

This paper presents a comprehensive bibliometric analysis aimed at mapping the convergent landscapes of artificial intelligence (AI) and financial technology (Fintech), fields poised for significant disruptive potential in global financial services. Given the rapid integration of AI within financial operations, this research investigates the publication trends, key contributing nations, and prevalent themes within this intersection, crucial for understanding the trajectory of Fintech innovations and their alignment with AI advancements. Employing a robust methodology utilizing VOSviewer and the Scopus database, the analysis distilled insights from 298 selected papers, focusing on co-authorship, bibliographic coupling, and keyword occurrences to highlight the global influential works and primary research hubs. Key findings reveal that AI and Fintech are not only predominant themes but are also the nucleus of emerging scholarly discussions, with the United States, China, and India leading in contributions. These nations, alongside others like France and the United Kingdom, form critical nodes in our analysis, indicating a robust interconnection of global research efforts. This study introduces the novel application of advanced bibliometric techniques to dissect dense academic outputs, offering a granular view of how AI influences financial technologies. The implications of this research are manifold; it provides a strategic blueprint for academics, industry practitioners, and policymakers to understand the focal areas of AI in Fintech, suggesting an amplified focus on collaborative innovations and policy-making that aligns with technological advancements. Future research should expand the analysis to include diverse databases and explore the integration of AI across various financial sectors, emphasizing the socioeconomic impacts, ethical considerations, and regulatory challenges posed by AI-driven financial services. This extended focus will enhance our understanding of AI’s role in shaping the future of Finance, ensuring comprehensive coverage of this dynamically evolving field.

Educational paradigm shift: Long-term ramifications of COVID-19 in the Indian context

The COVID-19 pandemic has brought about a significant transformation in the global educational landscape, which has far-reaching implications for learning systems. This study focuses on the impact of the pandemic on India’s education sector, exploring its long-term consequences. The research delves into the enduring effects of COVID-19 and how it has reshaped traditional educational methods. The Indian education system has undergone a dynamic evolution, from the rapid integration of digital technologies to the redefinition of pedagogical approaches. The study employs surveys to comprehensively assess the multifaceted dimensions of adaptation. By synthesizing qualitative insights from 386 respondents, the research aims to contribute to a better understanding of the challenges, opportunities, and long-term implications of COVID-19. The hypothesis results show a significant difference among genders for factors 2 (consequences of COVID-19 on teachers, students, and teaching), 3 (impact of COVID-19 on education), and 4 (education system responses to COVID-19). In contrast, factor 3 is significant for education. Likewise, awareness factors 1 and 3 show that factors 1 (policies and strategies on education during COVID-19) and 5 (possible solutions and implementation) are significant for an application. Finally, binary logistic regression demonstrates that for gender factors three, education factors four, and five, online participation is significant for awareness factors one, three, and four. However, all of the factors are insignificant for an application. These findings can guide policy decisions, inform educational practices, and lay a valuable foundation for future research endeavours to shape a resilient, equitable, and innovative educational future.

Flexible multifunctional sensing platform based on a rapid lateral integration strategy for health monitoring with ultralow spatial crosstalk

There has been a widespread demand for the adaptability of flexible strain sensors in the field of human health monitoring. However, the currently manufactured flexible sensors are difficult to adapt to various working conditions since the strain range or the size of the sensors is limited to be altered after production. Additionally, there is an urgent need to quickly integrate flexible multimode sensors to perceive multiple stimuli simultaneously. Thus, a lateral extension strategy is proposed to develop a flexible sensing platform by coupling biomimetic strain-insensitive connectors and individual sensors, making it laterally extended and freely assembled with ultralow spatial crosstalk. The process allows for rapid integration (<300 s) without the additional adhesives. The integrated sensing platform exhibits a fast response time (34 ms) and excellent stability (over 1000 cycles). Furthermore, this technique also enables the quick integration of multimode flexible sensors (pressure and temperature) into a multifunctional sensing platform with accurate signal decoupling capabilities. The proposed lateral extension strategy is expected to provide a new approach for the design and fabrication of a multifunctional sensing platform, as well as advance the application in fields of human-machine interaction and medical diagnostics.

Impact of Mobile Learning on Self-Regulated Learning Abilities of Higher Education Students

This study examines the influence of mobile learning (m-learning) on the self-regulated learning skills of college students using the Z-number AROMAN decision-making approach. The rapid integration of mobile technologies into educational practices has transformed traditional learning environments, offering unprecedented opportunities to enhance student autonomy and engagement. This study introduces a novel method for evaluating complex and uncertain educational scenarios through the Z-number AROMAN decision-making process. This approach provides a robust framework for assessing various intricate aspects of self-regulated learning, including goal setting (GS), self-monitoring, and reflective practices. The study underscores the transformative potential of m-learning in education, underscoring the importance of leveraging such technology to foster student-centered learning environments. This study contributes to the body of literature on educational technology, offering valuable insights for educators, policymakers, and scholars seeking to effectively implement m-learning strategies to enhance student learning outcomes.

Multi-timescale neural dynamics for multisensory integration.

Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms thatmediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered.

Hyperacute ischemic stroke care-Current treatment and future directions.

A decade on from the first positive thrombectomy trials, hyperacute therapies for ischemic stroke continue to rapidly advance. Effective treatments remain limited to reperfusion, although several cytoprotective approaches continue to be investigated. Intravenous fibrinolytics are now demonstrated to be beneficial up to 24 h in patients selected using perfusion imaging, but their role in patients with non-disabling symptoms appears very limited. Tenecteplase is superior to alteplase in meta-analysis of the latest trials, and adjuvant thrombolytics are an area of active investigation. Endovascular thrombectomy is beneficial in a wide range of anterior and posterior circulation large vessel occlusions up to 24 h after onset with the more distal occlusions, mild presentations, and >24 h window being the main frontiers to be tested in ongoing trials. Imaging parameters are prognostic but appear not to modify the relative treatment benefit of thrombectomy versus standard medical care. Therefore, deciding who not to treat with thrombectomy is a key clinical challenge that requires careful but rapid integration of clinical, imaging, and patient preference considerations. Systems of care to accelerate delivery of these highly effective therapies will maximize benefits for the greatest number of patients with stroke.

Empowerment of Digital Technology for the Resilience of the Logistics Industry: Mechanisms and Paths

Digital technology, acting as an engine for industrial development, propels the rapid integration of data elements and the swift iteration of digital logistics technology, significantly enhancing logistic resilience. Exploring the impact of digital technology on the resilience of logistics helps strengthen the latter’s ability to withstand external shocks. Based on the correlation between digital technology and the resilience of the logistics industry, this study measured their levels in 275 Chinese prefecture-level cities from 2011 to 2020, showing that the former significantly improved the latter, a conclusion which remained valid after robustness tests. The mechanism analysis results showed that promoting industrial collaborative agglomeration was an important part of this process, while the threshold model analysis found that the impact on resilience had nonlinear characteristics. The heterogeneity test results showed that digital technology had a significant resilience-promoting effect in the eastern and northern coastal regions, as well as in the middle reaches of the Yellow and Yangtze Rivers. Accordingly, it is necessary to accelerate digital technology integration with the logistics industry and formulate differentiated development strategies. This study provides a theoretical foundation for exploring the relationship between digital technology and logistics resilience, as well as policy recommendations.

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry.

Generative AI Adoption in Postsecondary Education, AI Hype, and ChatGPT’s Launch

The rapid integration of generative artificial intelligence (AI) into postsecondary education and many other sectors resulted in a global reckoning with this new technology. This paper contributes to the study of the multifaceted influence of generative AI, with a particular focus on OpenAI's ChatGPT within academic settings during the first six months after the release in three specific ways. First, it scrutinizes the rise of ChatGPT as a transformative event construed through a study of mainstream discourses exhibiting AI hype. Second, it discusses the perceived implications of generative AI for writing, teaching, and learning through the lens of critical discourse analysis and critical AI studies. Third, it encourages the necessity for best practices in the adoption of generative AI technologies in education.

Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review.

The rapid progression and integration of digital technologies into public health have reshaped the global landscape of health care delivery and disease prevention. In pursuit of better population health and health care accessibility, many countries have integrated digital interventions into their health care systems, such as web-based consultations, electronic health records, and telemedicine. Despite the increasing prevalence and relevance of digital technologies in public health and their varying definitions, there has been a shortage of studies examining whether these technologies align with the established definition and core characteristics of digital public health (DiPH) interventions. Hence, the imperative need for a scoping review emerges to explore the breadth of literature dedicated to this subject. This scoping review aims to outline DiPH interventions from different implementation stages for health promotion, primary to tertiary prevention, including health care and disease surveillance and monitoring. In addition, we aim to map the reported intervention characteristics, including their technical features and nontechnical elements. Original studies or reports of DiPH intervention focused on population health were eligible for this review. PubMed, Web of Science, CENTRAL, IEEE Xplore, and the ACM Full-Text Collection were searched for relevant literature (last updated on October 5, 2022). Intervention characteristics of each identified DiPH intervention, such as target groups, level of prevention or health care, digital health functions, intervention types, and public health functions, were extracted and used to map DiPH interventions. MAXQDA 2022.7 (VERBI GmbH) was used for qualitative data analysis of such interventions' technical functions and nontechnical characteristics. In total, we identified and screened 15,701 records, of which 1562 (9.94%) full texts were considered relevant and were assessed for eligibility. Finally, we included 185 (11.84%) publications, which reported 179 different DiPH interventions. Our analysis revealed a diverse landscape of interventions, with telemedical services, health apps, and electronic health records as dominant types. These interventions targeted a wide range of populations and settings, demonstrating their adaptability. The analysis highlighted the multifaceted nature of digital interventions, necessitating precise definitions and standardized terminologies for effective collaboration and evaluation. Although this scoping review was able to map characteristics and technical functions among 13 intervention types in DiPH, emerging technologies such as artificial intelligence might have been underrepresented in our study. This review underscores the diversity of DiPH interventions among and within intervention groups. Moreover, it highlights the importance of precise terminology for effective planning and evaluation. This review promotes cross-disciplinary collaboration by emphasizing the need for clear definitions, distinct technological functions, and well-defined use cases. It lays the foundation for international benchmarks and comparability within DiPH systems. Further research is needed to map intervention characteristics in this still-evolving field continuously. PROSPERO CRD42021265562; https://tinyurl.com/43jksb3k. RR2-10.2196/33404.

Who’s afraid of AI? socio-technological perspectives on artificial intelligence in the workforce: an Israeli case study

PurposeThis study aimed to investigate the perception and acceptance of artificial intelligence (AI) technologies among the Israeli workforce. More specifically, it examined how age, income, and education level are related to employees’ fears of being replaced by AI technologies and their willingness to adopt these technologies in their personal and professional lives.Design/methodology/approachData were collected by surveying 502 adults from the Jewish population of Israel in February 2023 via an Internet panel. Stratified sampling was performed to ensure a representative cross-section of the population.FindingsContrary to the expectations from a technologically advanced society, the findings indicated varied levels of enthusiasm and apprehension. Age was found to be negatively correlated with the fear of being replaced by AI technologies and the willingness to adopt these technologies. Income was negatively correlated with the fear of being replaced by AI technologies. Education level was negatively correlated with the fear of being replaced and positively correlated with the willingness to adopt.Practical implicationsThe findings provide valuable guidance for policymakers, educators, and business leaders in shaping AI integration strategies. They emphasize the need for targeted educational and policy initiatives to bridge the gap in AI readiness.Originality/valueThis study offers unique insights into the perceptions toward AI in a leading technological hub, contributing to the understanding of how advanced societies are adapting to rapid AI integration.

Evaluations of medical device usability during clinical investigations: a scoping review of clinical study protocols

ABSTRACT Objective Combining clinical investigations with usability studies provides valuable information for medical devices evaluation. But both types of study are very different in terms of objectives and methodologies. How are usability studies integrated into clinical investigations in practice? Methods We searched the ClinicalTrials.gov database for clinical investigation protocols that included usability outcome(s) and analyzed them. Results 77 study protocols were identified for the analysis, including 102 outcomes related to usability in total. The most frequently assessed outcomes were satisfaction (53/102) and ease of use (33/102). The questionnaire was the most frequently planned technique (85/102) followed by interviews (24/102). Other methods were used, such as observation (9/102), mostly when the end users was a healthcare professional, and diary (6/102), mostly with patients. Conclusion Our study results showed that the collection of usability data can be included in a clinical investigation, with various levels of investment. Resource-light, rapid integration via a questionnaire will enable the collection of subjective data on the users’ perceptions. When more resources are available, observation in accessible environments can be set up (especially during use by healthcare professionals in hospital) or interviews and/or diaries for home-based environments (especially by patients).

Understanding the Intergenerational Impact of Migration: An Adult Mortality Advantage for the Children of Forced Migrants?

Children of immigrants often have excess mortality rates, in contrast to the low mortality typically exhibited by their parents' generation. However, prior research has studied children of immigrants who were selected for migration, thereby rendering it difficult to isolate the intergenerational impact of migration on adult mortality. We use semiparametric survival analysis to carry out a total population cohort study estimating all-cause and cause-specific mortality among all adult men and women from age of 17 years among all men and women born in 1953-1972 and resident in Finland in 1970-2020. We compare children of forced migrants from ceded Karelia, an area of Finland that was ceded to Russia during the Second World War, with the children of parents born in present-day Finland. Children with two parents who were forced migrants have higher mortality than children with two parents born in Northern, Southern, and Western Finland, but similar or lower mortality than the subpopulation of children whose parents were born in the more comparable areas of Eastern Finland. For women and men, a mortality advantage is largest for external causes and persists after controlling for socioeconomic factors. Our findings suggest that forced migration can have a beneficial impact on the mortality of later generations, at least in the case where forced migrants are able to move to contextually similar locations that offer opportunities for rapid integration and social mobility. The findings also highlight the importance of making appropriate comparisons when evaluating the impact of forced migration.