A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions

The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification, particularly in the Arabic language. The goal is to examine the research trends in Arabic hate speech identification and offer guidance to researchers by highlighting the most significant studies published between 2018 and 2023. This systematic study addresses five specific research questions concerning the types of the Arabic language used, hate speech categories, classification techniques, feature engineering techniques, performance metrics, validation methods, existing challenges faced by researchers, and potential future research directions. Through a comprehensive search across nine academic databases, 24 studies that met the predefined inclusion criteria and quality assessment were identified. The review findings revealed the existence of many Arabic linguistic varieties used in hate speech on Twitter, with modern standard Arabic (MSA) being the most prominent. In identification techniques, machine learning categories are the most used technique for Arabic hate speech identification. The result also shows different feature engineering techniques used and indicates that N-gram and CBOW are the most used techniques. F1-score, precision, recall, and accuracy were also identified as the most used performance metric. The review also shows that the most used validation method is the train/test split method. Therefore, the findings of this study can serve as valuable guidance for researchers in enhancing the efficacy of their models in future investigations. Besides, algorithm development, policy rule regulation, community management, and legal and ethical consideration are other real-world applications that can be reaped from this research.

Image Smart Segmentation Analysis Against Diabetic Foot Ulcer Using Internet of Things with Virtual Sensing

Diabetic foot ulcer (DFU) is a problem worldwide, and prevention is crucial. The image segmentation analysis of DFU identification plays a significant role. This will produce different segmentation of the same idea, incomplete, imprecise, and other problems. To address these issues, a method of image segmentation analysis of DFU through internet of things with the technique of virtual sensing for semantically similar objects, the analysis of four levels of range segmentation (region-based, edge-based, image-based, and computer-aided design-based range segmentation) for deeper segmentation of images is implemented. In this study, the multimodal is compressed with the object co-segmentation for semantical segmentation. The result is predicting the better validity and reliability assessment. The experimental results demonstrate that the proposed model can efficiently perform segmentation analysis, with a lower error rate, than the existing methodologies. The findings on the multiple-image dataset show that DFU obtains an average segmentation score of 90.85% and 89.03% correspondingly in two types of labeled ratios before DFU with virtual sensing and after DFU without virtual sensing (i.e., 25% and 30%), which is an increase of 10.91% and 12.22% over the previous best results. In live DFU studies, our proposed system improved by 59.1% compared with existing deep segmentation-based techniques and its average image smart segmentation improvements over its contemporaries are 15.06%, 23.94%, and 45.41%, respectively. Proposed range-based segmentation achieves interobserver reliability by 73.9% on the positive test namely likelihood ratio test set with only a 0.25 million parameters at the pace of labeled data.

A survey on multi-lingual offensive language detection

The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term "Offensive Language" encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single language, focus and represents more linguistic diversities and cultural factors. By exploring multilingual offensive content, we can broaden our understanding and effectively combat the widespread global impact of offensive language. This survey examines the existing state of multilingual offensive language detection, including a comprehensive analysis on previous multilingual approaches, and existing datasets, as well as provides resources in the field. We also explore the related community challenges on this task, which include technical, cultural, and linguistic ones, as well as their limitations. Furthermore, in this survey we propose several potential future directions toward more efficient solutions for multilingual offensive language detection, enabling safer digital communication environment worldwide.

Effect of thermal and NaOH pretreatment on water hyacinth to enhance the biogas production

Water hyacinth (WH) is used as the substrate for biogas production due to its high lignocellulosic composition and natural abundance. The present study used thermal and chemical (alkali) pretreatment techniques to enhance biogas production from water hyacinth used as a substrate by anaerobic digestion. Thermal pretreatment was done using an autoclave at 121 °C and 15 lb (2 bar) pressure and alkali pretreatment by NaOH at two concentrations (2% and 5% w/v). The inoculum:substrate ratio for biogas production was 2:1, where cow dung was used as inoculum. Results indicated that the pretreatments increased biomass degradability and improved biogas production. Water hyacinth pretreated with 5% NaOH produced the highest amount of biogas (142.61 L/Kg VS) with a maximum methane content of 64.59%. The present study found that alkali pretreatment can modify the chemical structure and enhance WH hydrolysis, leading to enhanced energy production.

Biogas production potential of aquatic weeds as the next-generation feedstock for bioenergy production: a review

Aquatic weeds have exceptionally high reproduction rates, are rich in cellulose and hemicellulose, and contain a negligible amount of lignin, making them an ideal crop for the next generation of biofuels. Previously reported studies proposed that water hyacinth, water lettuce, common duckweeds, and water spinach can be managed or utilized using different advanced techniques; from them, anaerobic digestion is one of the feasible and cost-effective techniques to manage these biowastes. The present study was carried out to investigate the potential of utilizing four common aquatic weed species (water hyacinth, water lettuce, common duckweeds, and water spinach) as substrates for anaerobic digestion in order to produce biogas for use in biofuels. The high reproduction rates and high cellulose and hemicellulose content, coupled with low lignin content, of these aquatic weeds make them ideal candidates for this purpose. The study evaluated the feasibility of using anaerobic digestion as a management technique for these aquatic weeds, which are often considered invasive and difficult to control. The results from various studies indicate that these aquatic weeds are productive feedstock options for anaerobic digestion, yielding a high biogas output. Among the aquatic weeds studied, water hyacinth, water lettuce, and common duckweeds exhibit higher methane production compared to water spinach. The study provides an overview of the characteristics and management strategies of these aquatic weeds in relation to biogas production, with possible future developments in the field.

Marine waters assessment using improved water quality model incorporating machine learning approaches

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends on a number of factors, and one of the most important is the quality of the water. The water quality index (WQI) model is widely used to assess water quality, but existing models have uncertainty issues. To address this, the authors introduced two new WQI models: the weight based weighted quadratic mean (WQM) and unweighted based root mean squared (RMS) models. These models were used to assess water quality in the Bay of Bengal, using seven water quality indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), and molybdate reactive phosphorus (MRP). Both models ranked water quality between "good" and "fair" categories, with no significant difference between the weighted and unweighted models' results. The models showed considerable variation in the computed WQI scores, ranging from 68 to 88 with an average of 75 for WQM and 70 to 76 with an average of 72 for RMS. The models did not have any issues with sub-index or aggregation functions, and both had a high level of sensitivity (R2 = 1) in terms of the spatio-temporal resolution of waterbodies. The study demonstrated that both WQI approaches effectively assessed marine waters, reducing uncertainty and improving the accuracy of the WQI score.

On the post-pandemic travel boom: How capacity building and smart tourism technologies in rural areas can help - evidence from Iran

While there have been numerous studies investigating the impact of the COVID-19 pandemic on tourism, few research projects have examined the impact of the outbreak on using smart tourism technologies (STT), especially in developing countries. This study adopted thematic analysis, with data collected using in-person interviews. The participants for the study were selected using the snow-balling technique. We explored the process of developing smart technologies during the pandemic and its impact on smart rural tourism technology development upon travel restart. The subject was investigated by focusing on five selected villages in central Iran which have tourism dependent economies. Overall, the results indicated that the pandemic partially changed the government's resistance towards the fast development of smart technologies. Thus, the role of smart technologies in curbing the virus spread was officially recognized. This change of policy led to the implementation of Capacity Building (CB) programs to improve digital literacy and minimize the digital gap that exists between urban and rural areas in Iran. Implementing CB programs during the pandemic directly and indirectly contributed to the digitalization of rural tourism. Implementing such programs enhanced tourism stakeholders' individual and institutional capacity to gain access to and creatively use STT in rural area. The results of this study improve our understanding and knowledge of the impact of crises on the degree of acceptability and use of STT in traditional rural societies.

Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review

BACKGROUND

Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients.

OBJECTIVE

This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis.

METHODS

A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included.

RESULTS

The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development.

CONCLUSIONS

There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.

Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model's learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model's feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model's classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model's performance will be compared with state-of-the-art approaches in other existing systems' accuracy, precision, recall, and F1 score.

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today's technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.

A framework for smart construction contracts using BIM and blockchain

Poor payment practices are perceived as one of the biggest challenges facing the construction industry. Since payments are issued according to project contract terms, the project's cash flow is inherently affected by the contract and how parties fulfill their obligations. This research proposes a framework for payment automation in construction projects to achieve smart construction contracts. Payments are automatically issued upon satisfying contract conditions using blockchain. Cryptocurrency is proposed to be utilized in the framework to execute the contract terms with no need for a third party to process project payments. 5D BIM is used to model the geometry of buildings and visualize project progress together with payment status using Autodesk Revit, Navisworks, and Primavera P6. The developed framework has the potential to reduce the consequences of poor payments. An actual case study for a construction project in Cairo, Egypt is worked out to demonstrate the main features of the proposed framework. The results of the case study reveal that project cash flow is secured and payments are instantly issued. Moreover, electronic records of payments are kept on the blockchain.

eHealth literacy was associated with anxiety and depression during the COVID-19 pandemic in Nigeria: a cross-sectional study

Background

Electronic health (eHealth) literacy may play an important role in individuals' engagement with online mental health-related information.

Aim

To examine associations between eHealth literacy and psychological outcomes among Nigerians during the Coronavirus disease-2019 (COVID-19) pandemic.

Methods

This was a cross-sectional study among Nigerians conducted using the 'COVID-19's impAct on feaR and hEalth (CARE) questionnaire. The exposure: eHealth literacy, was assessed using the eHealth literacy scale, and psychological outcomes were assessed using the PHQ-4 scale, which measured anxiety and depression; and the fear scale to measure fear of COVID-19. We fitted logistic regression models to assess the association of eHealth literacy with anxiety, depression, and fear, adjusting for covariates. We included interaction terms to assess for age, gender, and regional differences. We also assessed participants' endorsement of strategies for future pandemic preparedness.

Results

This study involved 590 participants, of which 56% were female, and 38% were 30 years or older. About 83% reported high eHealth literacy, and 55% reported anxiety or depression. High eHealth literacy was associated with a 66% lower likelihood of anxiety (adjusted odds ratio aOR, 0·34; 95% confidence interval, 0·20-0·54) and depression (aOR: 0·34; 95% CI, 0·21-0·56). There were age, gender, and regional differences in the associations between eHealth literacy and psychological outcomes. eHealth-related strategies such as medicine delivery, receiving health information through text messaging, and online courses were highlighted as important for future pandemic preparedness.

Conclusion

Considering that mental health and psychological care services are severely lacking in Nigeria, digital health information sources present an opportunity to improve access and delivery of mental health services. The different associations of e-health literacy with psychological well-being between age, gender, and geographic region highlight the urgent need for targeted interventions for vulnerable populations. Policymakers must prioritize digitally backed interventions, such as medicine delivery and health information dissemination through text messaging, to address these disparities and promote equitable mental well-being.

A systematic review on factors influencing learning management system usage in Arab gulf countries

Although the successful implementation of the Learning Management System (LMS) in most of the universities in the Arab Gulf Countries (AGC), little consideration has been paid to exploring LMS usage. This paper provides a systematic review of the current literature focusing on the most critical factors influencing LMS usage in AGC. The extant literature was identified through six electronic databases from 2013 to 2023. Academic articles were reviewed if they contained a relevant discussion of the factors influencing LMS acceptance and adoption conducted in AGC. Results from a systematic review of 34 studies showed that 15 studies were centred in Saudi Arabia. The results also, revealed that Technology Acceptance Model was the dominant model employed, and students were the main subject of studies. Moreover, the quantitative approach was the preferred design. Overall, forty-one factors were identified, and the results show that the following eight factors appear most frequently: Perceived Ease of Use, Perceived Usefulness, Social Influence, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Self-efficacy, and Attitude. This review will be valuable for future research and helpful for higher education decision-makers who intend to use eLearning to overcome the challenges they face in using LMS effectively.

Characterizing the visualization design space of distant and close reading of poetic rhythm

Metrical and rhythmical poetry analysis is founded on the systematic statistical analysis and comparison of sonic devices (e.g., rhythmic patterns) that emerge from a combination of pre-established aesthetic and structural rules and the poet's abilities and creative genius to convey a given message adhering to the said constraints. These rhythmical patterns, which have been traditionally obtained by means of a careful close reading of the poems, in a process known as "scansion," can now be obtained and made visible by automatic means. However, the visualization literature is still scarce on approaches that allow an insightful close and distant reading of the rhythmical patterns in a poetry corpus. In this work, we report our initial efforts in characterizing of the visualization design space of distant and close reading of poetic rhythm. By employing a digital version of a corpus of 11,268 verses originally written by the Spanish poet and playwright Federico García-Lorca (1898-1936), we could craft several prototypical visualizations representative of the inherent complexity of the problem which we expect to employ in future user studies and that we share here with the rest of the community to foster further discussion around this interesting topic.

Telenursing: The view of care professionals in selected EU countries. A pilot study

Background

With the growth of digital nursing, several studies have focused on recording patients' views on remote care, or specialised nurse staffing aspects. This is the first international survey on telenursing focused exclusively on clinical nurses that analyses the dimensions of usefulness, acceptability, and appropriateness of telenursing from the staff point of view.

Methods

A previously validated structured questionnaire including demographic variables, 18 responses with a Likert-5 scale, three dichotomous questions, and one overall percentual estimation of holistic nursing care susceptible to being undertaken by telenursing, was administered (from 1 September to 30 November 2022) to 225 clinical and community nurses from three selected EU countries. Data analysis: descriptive data, classical and Rasch testing.

Results

The results show adequacy of the model for measurement of the domains of usefulness, acceptability, and appropriateness of telenursing (overall Cronbach's alpha 0.945, Kaiser-Meyer-Olkin 0.952 and Bartlett's p < 0.001). Answers in favour of telenursing ranked 4 out of 5 in Likert scale, both globally and by the three domains. Rasch: reliability coefficient 0.94, Warm's main weighted likelihood estimate reliability 0.95. In the ANOVA analysis, the results for Portugal were significantly higher than those for Spain and Poland, both overall and for each of the dimensions. Respondents with bachelor's, master's and doctoral degrees score significantly higher than those with certificates or diplomas. Multiple regression did not yield additional data of interest.

Conclusions

The tested model proved to be valid, but although the majority of nurses are in favour of telenursing, given the nature of the care, which is mainly face-to-face, according to the respondents, the chances of carrying out their activities by telenursing is only 35.3%. The survey provides useful information on what can be expected from the implementation of telenursing and the questionnaire proves to be a useful tool to be applied in other countries.

Scale Development for COVID-19 Vaccine Hesitancy by Integration of Socio-Demographic and Psychological Factors

Vaccination is the most cost-effective way to maintain population health. However, it can only be effective if widespread acceptance is held. The efficacy of COVID-19 vaccines depends on their favor. When countries start to vaccinate their citizens, there is a certain level of skepticism about the effectiveness of the vaccines. The hesitancy study on vaccines has gained momentum following the pandemic. However, few studies have examined the link between the psychological and sociodemographic factors influencing the fit. This paper proposes integrating the concepts of the information systems success and stimulus-organism-response into a cognitive fit theory framework to explore the integration of psychological and sociodemographic factors in the receivers' reactions (n = 1510). This study analyses the factors that influence the hesitancy of vaccines and the public's refusal in Asia and Europe. Receivers' reactions were assessed to various stimuli and we explored the link between psychological and sociodemographic elements and the concept of fit. Two surveys were conducted following the scale development of Mackenzie. The first was to develop the fit scale, while the second was to validate the fit scale. The results of the second survey were analyzed using structural equation modelling. The results indicate that the scale's fit development is valid and reliable. The quality of the vaccine information, the psychological characteristics of the vaccine system, and vaccine receivers' satisfaction are also beneficial factors for emotional and cognitive fit. Maintaining the vaccines' quality and efficiency can help improve the fit between sociodemographic and psychological characteristics. It can also enhance receivers' satisfaction and encourage continued vaccine administration. This study is regarded as one of the first to examine and develop an emotional and cognitive fit scale for practitioners and researchers.

Social Media and Apps in Urology

Purpose of Review

In this study, we aimed to review the common social media (SoMe) apps used and how they have impacted the practice and exchange of information, as well as the challenges of using SoMe in urology.

Recent Findings

SoMe has become increasingly popular in the urology community. Lay users often turn to SoMe to learn about urological health and share their own experiences, while medical professionals may use it for career development, networking, education, and research purposes.

Summary

It is important to recognize the power of SoMe and to use it responsibly and ethically, particularly given the potential risks of encountering low-quality or misleading information.

Designing mobile health to align with the social determinants of health

The maternal health crisis in the United States is becoming increasingly worse, with disparities continuing to escalate among marginalized populations. mHealth can contribute to addressing the Social Determinants of Health (SDOH) that produce inequities in maternal morbidity and mortality. Reducing inequities through mHealth can be achieved by designing these technologies to align with SDOH. As mHealth developed to support maternal health has primarily supported the extension of clinical care, there is an opportunity to integrate frameworks and methods from human factors/ergonomics and public health to produce thorough comprehension of SDOH through intentional partnerships with marginalized populations. Potential for this opportunity is presented through a case study derived from a community-based participatory research process focused on transportation access to maternal health services. Through multi-faceted, interdisciplinary, and community-based approaches to designing mHealth that attends to the systemic factors that generate and escalate inequities, improvements in the maternal health crisis could be realized.

Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach

Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.

Animal disease surveillance: How to represent textual data for classifying epidemiological information

The value of informal sources in increasing the timeliness of disease outbreak detection and providing detailed epidemiological information in the early warning and preparedness context is recognized. This study evaluates machine learning methods for classifying information from animal disease-related news at a fine-grained level (i.e., epidemiological topic). We compare two textual representations, the bag-of-words method and a distributional approach, i.e., word embeddings. Both representations performed well for binary relevance classification (F-measure of 0.839 and 0.871, respectively). Bag-of-words representation was outperformed by word embedding representation for classifying sentences into fine-grained epidemiological topics (F-measure of 0.745). Our results suggest that the word embedding approach is of interest in the context of low-frequency classes in a specialized domain. However, this representation did not bring significant performance improvements for binary relevance classification, indicating that the textual representation should be adapted to each classification task.

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

With the advent of technology, we are getting more comfortable with the use of gadgets, cameras, etc., and find Artificial Intelligence as an integral part of most of the tasks we perform throughout the day. In such a scenario, the use of cameras and vision-based sensors comes as an escape from many real-time problems and challenges. One major application of these vision-based systems is Indoor Human Activity Recognition (HAR) which serves in a variety of scenarios ranging from smart homes, elderly care, assisted living, and human behavior pattern analysis for identifying any abnormal behavior to abnormal activity recognition like falling, slipping, domestic violence, etc. The effect of HAR in real time has made the area of indoor activity recognition a more explored zone by the industrial segment to attract users with their products in multiple domains. Hence, considering these aspects of HAR, this work proposes a detailed survey on indoor HAR. Through this work, we have highlighted the recent methodologies and their performance in the field of indoor activity recognition. We have also discussed- the challenges, detailed study of approaches with real-world applications of indoor-HAR, datasets available for indoor activity, and their technical details in this work. We have proposed a taxonomy for indoor HAR and highlighted the state-of-the-art and future prospects by mentioning the research gaps and the shortcomings of recent surveys with respect to our work.

Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware

Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe's BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application's pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value.

User Compliance With the Health Emergency and Disaster Management System: Systematic Literature Review

BACKGROUND

Health-related hazards have a detrimental impact on society. The health emergency and disaster management system (Health EDMS), such as a contact-tracing application, is used to respond to and cope with health-related hazards. User compliance with Health EDMS warnings is key to its success. However, it was reported that user compliance with such a system remains low.

OBJECTIVE

Through a systematic literature review, this study aims to identify the theories and corresponding factors that explain user compliance with the warning message provided by Health EDMS.

METHODS

The systematic literature review was conducted using Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. The search was performed using the online databases Scopus, ScienceDirect, ProQuest, IEEE, and PubMed, for English journal papers published between January 2000 and February 2022.

RESULTS

A total of 14 papers were selected for the review based on our inclusion and exclusion criteria. Previous research adopted 6 theories when examining user compliance, and central to the research was Health EDMS. To better understand Health EDMS, based on the literature reviewed, we mapped the activities and features of Health EDMS with the key stakeholders involved. We identified features that require involvement from individual users, which are surveillance and monitoring features and medical care and logistic assistance features. We then proposed a framework showing the individual, technological, and social influencing factors of the use of these features, which in turn affects compliance with the warning message from Health EDMS.

CONCLUSIONS

Research on the Health EDMS topic increased rapidly in 2021 due to the COVID-19 pandemic. An in-depth understanding of Health EDMS and user compliance before designing the system is essential for governments and developers to increase the effectiveness of Health EDMS. Through a systematic literature review, this study proposed a research framework and identified research gaps for future research on this topic.

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review

INTRODUCTION

Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress.

OBJECTIVE

The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face.

METHODS

This study reviewed published works contributing and/or using public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2].

RESULTS

A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and model generalization ability.

CONCLUSION

Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available.

Effectiveness of the SBAR-Based training program in self-efficacy and clinical decision-making of undergraduate anesthesiology nursing students: a quasi-experimental study

BACKGROUND

Structured Situation, Background, Assessment, and Recommendation (SBAR) training technique have been widely utilized in clinical and educational settings. Therefore, the current study investigated the effectiveness of an SBAR-based educational program in students' self-efficacy and clinical decision-making skills.

METHODS

This quasi-experimental study was conducted using a pretest and posttest design and a control group at Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. A total of 70 three- and fourth-year students were recruited for the study using the census method. The students were randomly assigned to the intervention and control groups. The intervention group participated in an SBAR-based educational course consisting of eight sessions held in 4 weeks. Differences in the levels of self-efficacy and clinical decision-making skills before and after participation in the SBAR course were assessed and compared. Data were analyzed using descriptive tests, the Mann-Whiney U test, paired and independent t-tests, and the Wilcoxon test.

RESULTS

The intervention group demonstrated significantly higher levels of self-efficacy with a mean score of 140.66 ± 22.43 (P < 0.001) and clinical decision-making with a mean score of 75.31 ± 7.72 (P < 0.001); while in the control group, the mean score of self-efficacy and clinical decision-making skills was 85.34 ± 18.15 and 65.51 ± 4.49, respectively. Moreover, the Mann-Whitney U test showed that the levels of students' clinical decision-making skills were promoted to the next level after the intervention (P < 0.001); it means the distribution of the level of intuitive-interpretive skill was upgraded from 0 to 22.9%.

CONCLUSION

The SBAR-based training programs can promote the self-efficacy and clinical decision-making skills of anesthesiology nursing students. Considering the weakness of the anesthesiology nursing curriculum at the undergraduate level in Iran, it can be expected that the SBAR-based training course should be included as an educational intervention in the curriculum of anesthesiology nursing students.

Self-Supervised Contrastive Learning to Predict Alzheimer's Disease Progression with 3D Amyloid-PET

Early diagnosis of Alzheimer's disease (AD) is an important task that facilitates the development of treatment and prevention strategies and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET which measures the accumulation of amyloid plaques in the brain - a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET. However, commonly used models are trained in a fully supervised learning manner and they are inevitably biased toward the given label information. To this end, we propose a self-supervised contrastive learning method to predict AD progression with 3D amyloid-PET. It uses unlabeled data to capture general representations underlying the images. As the downstream task is given as classification, unlike the general self-supervised learning problem that aims to generate task-agnostic representations, we also propose a loss function to utilize the label information in the pre-training. To demonstrate the performance of our method, we conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results confirmed that the proposed method is capable of providing appropriate data representations, resulting in accurate classification.

A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping

The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light-electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.

Systematic review and meta-analysis of randomised controlled trials for evaluating the effectiveness of virtual reality therapy for social anxiety disorder

OBJECTIVE

To evaluate the effectiveness of VR therapy (VRT) for symptoms related to social anxiety disorder (SAD), namely fear and avoidance of social interactions and performance situations (FASIP), fear of negative evaluation (FNE), anxiety and depression, a systematic review and meta-analysis were performed.

METHODS

Medline, PubMed, Science Direct, Web of Science, CINAHL, PsychINFO and Scopus were searched to include randomised controlled trials of VRT for SAD that met the criteria. A total of 15 RCTs with 720 participants published between 1998 and 2022 were included. Hedge's g with a 95 % confidence interval (CI) was adopted to compute the effect sizes.

RESULTS

Results showed no difference between the effect of VRT and CBT on FASIP, FNE, anxiety and depression and a large effect size for VRT versus the waitlist control group on FASIP (g = -1.170, 95 % CI: -2.056-0.283; p < 0.010). The moderator analysis demonstrated that VRT was superior to the controlled group in addressing FASIP, FNE and anxiety when the sample size was smaller than 50 and the number of sessions was five or fewer.

LIMITATIONS

Differences in hardware, software and intervention duration for VRT across studies.

CONCLUSION

This study confirmed the feasibility of VRT in alleviating the FASIP in patients with SAD, with the waitlist control group as a comparison. However, the effectiveness of VRT was not significant in FASIP, FNE, anxiety and depression compared to cognitive behavioural therapy (CBT). Additional social interaction scenarios should be developed in VRT, standardised hardware should be used and the proper length of exposure time to VR should be determined to enhance the efficacy of VRT.

Partner-developed electronic health record tools to facilitate social risk-informed care planning

OBJECTIVE

Increased social risk data collection in health care settings presents new opportunities to apply this information to improve patient outcomes. Clinical decision support (CDS) tools can support these applications. We conducted a participatory engagement process to develop electronic health record (EHR)-based CDS tools to facilitate social risk-informed care plan adjustments in community health centers (CHCs).

MATERIALS AND METHODS

We identified potential care plan adaptations through systematic reviews of hypertension and diabetes clinical guidelines. The results were used to inform an engagement process in which CHC staff and patients provided feedback on potential adjustments identified in the guideline reviews and on tool form and functions that could help CHC teams implement these suggested adjustments for patients with social risks.

RESULTS

Partners universally prioritized tools for social risk screening and documentation. Additional high-priority content included adjusting medication costs and changing follow-up plans based on reported social risks. Most content recommendations reflected partners' interests in encouraging provider-patient dialogue about care plan adaptations specific to patients' social needs. Partners recommended CDS tool functions such as alerts and shortcuts to facilitate and efficiently document social risk-informed care plan adjustments.

DISCUSSION AND CONCLUSION

CDS tools were designed to support CHC providers and staff to more consistently tailor care based on information about patients' social context and thereby enhance patients' ability to adhere to care plans. While such adjustments occur on an ad hoc basis in many care settings, these are among the first tools designed both to systematize and document these activities.

Sleep Quality between Nurses and the General Population during the COVID-19 Pandemic in Portugal: What Are the Differences?

Although several studies have described the impact of the COVID-19 pandemic, particularly on sleep quality, there are few studies that, in the same time period and using the same assessment tools, compare sleep quality and mental health status between nurses and the general population. Thus, the aim of this study was to (a) examine whether there were differences between nurses and the general population regarding sleep quality and mental health status during the COVID-19 pandemic and (b) identify which factors may explain sleep quality during the COVID-19 pandemic. To do that, we carried out a cross-sectional study in Portugal. Data were collected using an online survey platform during the first COVID-19 wave, from April to August 2020. Nurses presented poorer sleep quality than the general population, as well as higher anxiety levels. Irritability and worries about the future were two of the factors that might explain those differences. Thus, we can conclude that irritability and worries about the future are dimensions of anxiety that were associated with poor sleep quality during the COVID-19 pandemic. Thus, it would be important to adopt regular anxiety and sleep assessments, particularly for nurses, and to implement strategies to reduce this problem.

Navigating Value Co-Destruction in Open Innovation Communities: An Empirical Study of Expectancy Disconfirmation and Psychological Contracts in Business Analytics Communities

Enterprises seeking to enhance their innovation capabilities are increasingly turning to open innovation communities (OICs), which allow them to leverage the collective knowledge and collaborative potential of external users, providing a powerful source of new and innovative ideas. Despite their potential for value co-creation, recent research suggests that value co-destruction can also occur within OICs. However, the mechanisms underlying value co-destruction in OICs have not yet been fully explored or empirically examined. To address this gap, this study employs expectancy disconfirmation theory and psychological contract theory to investigate the relationship between user expectancy disconfirmation and value co-destruction in OICs. Drawing upon data collected from a questionnaire survey of business analytics OICs, this study reveals that self-interest expectancy disconfirmation has a positive effect on value co-destruction, which is mediated by the transactional psychological contract breach. In addition, social interaction expectancy disconfirmation is found to have a positive impact on value co-destruction, which is mediated by the relational psychological contract breach. The study further reveals that self-worth expectancy disconfirmation of community users positively influences value co-destruction, which is mediated by the ideological psychological contract breach. Moreover, the study demonstrates the crucial role of perceived organizational status in moderating the ideological psychological contract breach resulting from self-worth expectancy disconfirmation. Collectively, these findings contribute valuable insights into the phenomenon of value co-destruction in OICs, and provide practical guidance for enterprises seeking to enhance the development and performance of these innovation paradigms.

Pathology-supported genetic testing for the application of breast cancer pharmacodiagnostics: family counselling, lifestyle adjustments and change of medication

BACKGROUND

Pathology-supported genetic testing (PSGT) enables transitioning of risk stratification from the study population to the individual.

RESEARCH DESIGN AND METHODS

We provide an overview of the translational research performed in postmenopausal breast cancer patients at increased risk of osteoporosis due to aromatase inhibitor therapy, as the indication for referral. Both tumour histopathology and blood biochemistry levels were assessed to identify actionable disease pathways using whole exome sequencing (WES).

RESULTS

The causes and consequences of inadequate vitamin D levels as a modifiable risk factor for bone loss were highlighted in 116 patients with hormone receptor-positive breast cancer. Comparison of lifestyle factors and WES data between cases with vitamin D levels at extreme upper and lower ranges identified obesity as a major discriminating factor, with the lowest levels recorded during winter. Functional polymorphisms in the vitamin D receptor gene contributed independently to therapy-related osteoporosis risk. In a patient with invasive lobular carcinoma, genetic counselling facilitated investigation of the potential modifying effect of a rare CDH1 variant co-occurring withBRCA1 c.66dup (p.Glu23ArgfsTer18).

CONCLUSION

Validation of PSGT as a three-pronged pharmacodiagnostics tool for generation of adaptive reports and data reinterpretation during follow-up represents a new paradigm in personalised medicine, exposing significant limitations to overcome.

Frequency Seismic Response for EEWS Testing on Uniaxial Shaking Table

Earthquake early warning systems are used as important tools in earthquake risk management, providing timely information to residents and both public and private emergency managers. By doing this, the potential impact of large magnitude seismic events is significantly reduced. These systems use seismic sensors in order to acquire real-time data for the weaker but fast moving P wave (usually the first 3-5 s of the earthquake) and specific algorithms to predict the magnitude and the arrival time of the slower but more destructive surface waves. Most of these projection algorithms make use only of the vertical component of the acceleration and need extensive training in earthquake simulators in order to enhance their performance. Therefore, a low-inertial-mass uniaxial shaking table is proposed and analyzed in terms of frequency response in this paper, providing an effective cost/control ratio and high daily duty cycle. Furthermore, with the large variety of prediction algorithms, which use different frequency ranges, a new concept of selective frequency band error is also introduced and discussed in this paper as being a necessary tool for the final assessment of magnitude estimation algorithm error.

Roman Urdu Hate Speech Detection Using Transformer-Based Model for Cyber Security Applications

Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and combating it in real-time. Hate speech detection is a context-dependent problem that requires context-aware mechanisms for resolution. In this study, we employed a transformer-based model for Roman Urdu hate speech classification due to its ability to capture the text context. In addition, we developed the first Roman Urdu pre-trained BERT model, which we named BERT-RU. For this purpose, we exploited the capabilities of BERT by training it from scratch on the largest Roman Urdu dataset consisting of 173,714 text messages. Traditional and deep learning models were used as baseline models, including LSTM, BiLSTM, BiLSTM + Attention Layer, and CNN. We also investigated the concept of transfer learning by using pre-trained BERT embeddings in conjunction with deep learning models. The performance of each model was evaluated in terms of accuracy, precision, recall, and F-measure. The generalization of each model was evaluated on a cross-domain dataset. The experimental results revealed that the transformer-based model, when directly applied to the classification task of the Roman Urdu hate speech, outperformed traditional machine learning, deep learning models, and pre-trained transformer-based models in terms of accuracy, precision, recall, and F-measure, with scores of 96.70%, 97.25%, 96.74%, and 97.89%, respectively. In addition, the transformer-based model exhibited superior generalization on a cross-domain dataset.

Blockchain Technology

To encourage industry discussion of cutting-edge blockchain applications, this chapter was written. This chapter provides an introduction to blockchain technology as well as details on how to secure transactions using technology and how the secure element works. It examines common implementation worries and provides use cases for current commercial or trial projects. The following paragraphs go over the concept of a blockchain, how bitcoin transactions operate, and how to finish a transaction. The network of Marco Polo will be highlighted. In this chapter, the authors show how to use blockchain and cloud technology with Marco Polo to remove corporate pain problems. The chapter goes on to give a full overview of blockchain, including its function, traits, and applications.

Why should we care about social media codes of conduct in healthcare organisations? A systematic literature review

Background

The conduct of healthcare organisation employees on social media can impact both their personal reputation and that of the organisation. However, social media has blurred the lines between professional and personal communication, and what is acceptable and ethical conduct is not always clear. Furthermore, the global COVID-19 pandemic has changed how healthcare organisations and their employees approach the use of social media, expediting the need to ensure that employees communicating health-related information adhere to employee codes of conduct.

Aims

This review aims to investigate the challenges associated with healthcare organisation employees' use of social media for sharing health-related information, identify the crucial elements for inclusion in social media codes of conduct for healthcare organisations, and examine the enablers for good codes of conduct.

Methods

A systematic review of the literature from six research database platforms on articles related to codes of conduct addressing the use of social media for healthcare organisation employees was conducted. The screening process yielded 52 articles.

Results

The key finding in this review focuses on privacy, protecting both patients and healthcare organisation employees. While maintaining separate professional and personal social media accounts is a much-discussed approach, training and education on social media codes of conduct can clarify acceptable behaviour both personally and professionally.

Conclusion

The results raise essential questions about healthcare organisation employees' use of social media. It is evident that organisational support and a constructive culture will enable healthcare organisations to fully realise the benefits of using social media.

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.

Effects of Visual Communication Design Accessibility (VCDA) Guidelines for Low Vision on Public and Open Government Health Data

Since 2019, the Korean government's investments in making data more accessible to the public have grown by 337%. However, open government data, which should be accessible to everyone, are not entirely accessible to people with low vision, who represent an information-vulnerable class. Emergencies, such as the COVID-19 pandemic, decrease face-to-face encounters and inevitably increase untact encounters. Thus, the information gap experienced by low-vision people, who are underprivileged in terms of information, will be further widened, and they may consequently face various disadvantages. This study proposed visual communication design accessibility (VCDA) guidelines for people with low vision. Introduced screens enhanced by accessibility guidelines were presented to 16 people with low vision and 16 people with normal vision and the speed of visual information recognition was analyzed. No statistically significant difference (p > 0.05) was found due to the small sample size; however, this study's results approached significance with improved visual recognition speed for people with low vision after adopting VCDA. As a result of the intervention, the visual information recognition speed of both normal and low-vision people improved. Thus, our results can help improve information recognition speed among people with normal and low vision.

Educational Robotics for Developing Computational Thinking in Young Learners: A Systematic Review

Educational robotics has been adopted to create interactive and engaging learning environments to develop computational thinking (CT) in K-12 learners. This study systematically examined 22 peer-reviewed empirical research articles on the use of educational robotics to develop CT in young learners (pre-kindergarten to 6th grade) published between 2012 and 2021. The findings revealed that using robotics activities to develop CT has mostly been studied in the formal education settings with the duration of robotics curricular activities ranging from 80 minutes to 24 hours. The five CT skills studied most often include Sequencing, Conditionals, Loops, Debugging, and Algorithmic Thinking. The different versions of LEGO Mindstorms are the most frequently adopted robotic kits in the examined studies. The most frequently adopted learning and instructional strategies in the robotics activities include collaborative learning, project-based learning, and embodied learning. This paper identified and discussed developmentally appropriated CT skills, robotics kits, and pedagogical approaches suitable for supporting CT development in young learners. The findings can guide educators and instructional designers for future robotics activity design and development endeavors. This paper also identified gaps in the current research and recommended directions for advancing research in adopting robotics to develop CT in young learners.

Nursing students’ experience of using HoloPatient during the COVID-19 pandemic: A qualitative descriptive study

Background

Due to the COVID-19 pandemic, nursing schools have implemented alternative approaches to teaching clinical competency. This study examined nursing students’ experiences of using HoloPatient to learn COVID-19-related patient care.

Methods

In this qualitative descriptive study, focus group interviews were held virtually with 30 nursing student participants in South Korea. Data were collected from January 25 to February 8, 2022 and analyzed using a mixed content analysis.

Results

Participants shared their experiences of using the HoloPatient, addressing advantages, challenges, and concerns associated with this new approach. Overall, they reported satisfaction associated with having gained patient assessment and critical thinking skills, self-confidence, and knowledge about the care of patients with COVID-19. Barriers identified included the program's novelty, inadequate number of devices, instructions in English, learning environment, and issues such as poor Wi-Fi access.

Conclusion

HoloPatient in nursing education can improve learning motivation, critical thinking skills, and confidence. Efforts should be made to engage users by providing an orientation, supplementary materials, and an environment conducive to learning.

The prognostic value of quality of life in atrial fibrillation on patient value

BACKGROUND

In this study, the prognostic value of AF-related quality of life (AFEQT) at baseline on Major Adverse Cardiovascular Events (MACE) and improvement of perceived symptoms (EHRA) was assessed. Furthermore, the relationship between QoL and AF-related hospitalizations was assessed.

METHODS

A cohort of AF-patients diagnosed between November 2014 and October 2019 in four hospitals embedded within the Netherlands Heart Network were prospectively followed for 12 months. MACE was defined as stroke, myocardial infarction, heart failure and/or mortality. Subsequently, MACE, EHRA score improvement and AF-related hospitalizations between baseline and 12 months of follow-up were recorded.

RESULTS

In total, 970 AF-patients were available for analysis. In analyses with patients with complete information on the confounder subset 36/687 (5.2%) AF-patients developed MACE, 190/432 (44.0%) improved in EHRA score and 189/510(37.1%) were hospitalized during 12 months of follow-up. Patients with a low AFEQT score at baseline more often developed MACE (OR(95%CI): 2.42(1.16-5.06)), more often improved in EHRA score (OR(95%CI): 4.55(2.45-8.44) and were more often hospitalized (OR(95%CI): 4.04(2.22-7.01)) during 12 months post diagnosis, compared to patients with a high AFEQT score at baseline.

CONCLUSIONS

AF-patients with a lower quality of life at diagnosis more often develop MACE, more often improve on their symptoms and also were more often hospitalized, compared to AF-patients with a higher quality of life. This study highlights that the integration of patient-reported outcomes, such as quality of life, has the potential to be used as a prognostic indicator of the expected disease course for AF.

Reminders, reflections, and relationships: insights from the design of a chatbot for college advising

Purpose

In the USA, 22–40% of youth who have been accepted to college do not enroll. Researchers call this phenomenon summer melt, which disproportionately affects students from disadvantaged backgrounds. A major challenge is providing enough mentorship with the limited number of available college counselors. The purpose of this study is to present a case study of a design and user study of a chatbot (Lilo), designed to provide college advising interactions.

Design/methodology/approach

This study adopted four primary data sources to capture aspects of user experience: daily diary entries; in-depth, semi-structured interviews; user logs of interactions with the chatbot; and daily user surveys. User study was conducted with nine participants who represent a range of college experiences.

Findings

Participants illuminated the types of interactions designs that would be particularly impactful for chatbots for college advising including setting reminders, brokering social connections and prompting deeper introspection that build efficacy and identity toward college-going.

Originality/value

As a growing body of human-computer interaction research delves into the design of chatbots for different social interactions, this study illuminates key design needs for continued work in this domain. The study explores the implications for a specific domain to improve college enrollment: providing college advising to youth.

Factors associated with contact tracing compliance among communities of color in the first year of the COVID-19 pandemic

RATIONALE

The disproportionate impact of COVID-19 on communities of color has raised questions about the unique experiences within these communities not only in terms of becoming infected with COVID-19 but also mitigating its spread. The utility of contact tracing for managing community spread and supporting economic reopening is contingent upon, in part, compliance with contact tracer requests.

OBJECTIVE

We investigated how trust in and knowledge of contact tracers influence intentions to comply with tracing requests and whether or not these relationships and associated antecedent factors differ between communities of color.

METHOD

Data were collected from a U.S. sample of 533 survey respondents from Fall (2020) to Spring 2021. Multi-group SEM tested quantitative study hypotheses separately for Black, AAPI, Latinx, and White sub-samples. Qualitative data were collected via open-ended questions to inform the roles of trust and knowledge in contact tracing compliance.

RESULTS

Trust in contact tracers was associated with increased intentions to comply with tracing requests and significantly mediated the positive relationship between trust in healthcare professionals and government health officials with compliance intentions. Yet, the indirect effects of trust in government health officials on compliance intentions were significantly weaker for the Black, Latinx, and AAPI samples compared to Whites, suggesting this strategy for increasing compliance may not be as effective among communities of color. Health literacy and contact tracing knowledge played a more limited role in predicting compliance intentions directly or indirectly, and one that was inconsistent across racial groups. Qualitative results reinforce the importance of trust relative to knowledge for increasing tracing compliance intentions.

CONCLUSIONS

Building trust in contact tracers, more so than increasing knowledge, may be key to encouraging contact tracing compliance. Differences among communities of color and between these communities and Whites inform the policy recommendations provided for improving contact tracing success.

Fake Colorized Image Detection Based on Special Image Representation and Transfer Learning

Nowadays, images have become one of the most popular forms of communication as image editing tools have evolved. Image manipulation, particularly image colorization, has become easier, making it harder to differentiate between fake colorized images and actual images. Furthermore, the RGB space is no longer considered to be the best option for color-based detection techniques due to the high correlation between channels and its blending of luminance and chrominance information. This paper proposes a new approach for fake colorized image detection based on a novel image representation created by combining color information from three separate color spaces (HSV, Lab, and Ycbcr) and selecting the most different channels from each color space to reconstruct the image. Features from the proposed image representation are extracted based on transfer learning using the pre-trained CNNs ResNet50 model. The Support Vector Machine (SVM) approach has been used for classification purposes due to its high ability for generalization. Our experiments indicate that our proposed models outperform other state-of-the-art fake colorized image detection methods in several aspects.

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities

Introduction

Artificial intelligence (AI) applications in healthcare and medicine have increased in recent years. To enable access to personal data, Trusted Research Environments (TREs) (otherwise known as Safe Havens) provide safe and secure environments in which researchers can access sensitive personal data and develop AI (in particular machine learning (ML)) models. However, currently few TREs support the training of ML models in part due to a gap in the practical decision-making guidance for TREs in handling model disclosure. Specifically, the training of ML models creates a need to disclose new types of outputs from TREs. Although TREs have clear policies for the disclosure of statistical outputs, the extent to which trained models can leak personal training data once released is not well understood.

Background

We review, for a general audience, different types of ML models and their applicability within healthcare. We explain the outputs from training a ML model and how trained ML models can be vulnerable to external attacks to discover personal data encoded within the model.

Risks

We present the challenges for disclosure control of trained ML models in the context of training and exporting models from TREs. We provide insights and analyse methods that could be introduced within TREs to mitigate the risk of privacy breaches when disclosing trained models.

Discussion

Although specific guidelines and policies exist for statistical disclosure controls in TREs, they do not satisfactorily address these new types of output requests; i.e., trained ML models. There is significant potential for new interdisciplinary research opportunities in developing and adapting policies and tools for safely disclosing ML outputs from TREs.

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients’ lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.

2D physically unclonable functions of the arbiter type

Objectives. The problem of constructing a new class of physically unclonable functions of the arbiter type (APUF) is being solved, based on the difference in delay times for the inputs of numerous modifications of the base element, due to both an increase in the number of inputs and the topology of their connection. Such an  approach allows building two-dimensional physically unclonable functions (2D-APUF), in which, unlike  classical APUF, the challenge generated for each basic element selects a pair of paths not from two possible, but from a larger number of them. The relevance of such a study is associated with the active development of  physical cryptography. The following goals are pursued in the work: the construction of the basic elements of the APUF and their modifications, the development of a methodology for constructing 2D-APUF.

Methods. The methods of synthesis and analysis of digital devices are used, including those based on  programmable logic integrated circuits, the basics of Boolean algebra and circuitry. 

Results. It is shown that the classical APUF uses a standard basic element that performs two functions,  namely, the function of choosing a pair of paths Select and the function of switching paths Switch, which, due to their joint use, allow achieving high performance. First of all, this concerns the stability of the APUF functioning, which is characterized by a small number of challenge, for which the response randomly takes one of two  possible values 0 or 1. Modifications of the base element in terms of the implementations of its Select and Switch functions are proposed. New structures of the base element are presented in which the modifications of their  implementations are made, including in terms of increasing the number of pairs of paths of the base element from which one of them is selected by the challenge, and the configurations of their switching. The use of  various basic elements makes it possible to improve the main characteristics of APUF, as well as to break the regularity of their structure, which was the main reason for hacking APUF through machine learning. 

Conclusion. The proposed approach to the construction of physically unclonable 2D-APUF functions, based on the difference in signal delays through the base element, has shown its efficiency and promise. The effect of improving the characteristics of such PUFs has been experimentally confirmed with noticeable improvement in the stability of their functioning. It seems promising to further develop the ideas of constructing two-dimensional physically unclonable functions of the arbiter type, as well as experimental study of their characteristics, as well as resistance to various types of attacks, including using machine learning.