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The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to the new era of big data, where different sensors collect massive medical data every day. This special issue explores the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and, security and privacy. The biological wearable sensors can enhance the decision-making and early disease diagnosis processes by intelligently investigating and collecting large amounts of biomedical data (i.e., big health data). Hence, there is a need for scalable advanced learning, and intelligent algorithms that lead to reliable and interoperable solutions to make effective decisions in emergency medicine technologies. The optimization algorithms can be used in order to acquire the sensor data from multiple sources for fast and accurate health monitoring.

Precision medicine (PM) is an emerging approach for disease prevention, diagnosis, treatment, and management that holds promise for improving the future of sleep healthcare. This concept of PM is guided by evidence-based medicine for treatment decisions by taking into account individual variability in genes, environment, and lifestyle factors. Chronic sleep loss affects millions of people each year, representing an important public health issue. The same approach can be applied in sleep medicine to treat sleep disorders where the physicians and scientists can make accurate predictions concerning the best strategy for an individual/groups in the prevention, diagnosis, treatment, and management. This review aims to explore the emerging role of PM and its application in sleep medicine through the integration of innovative technology such as artificial intelligence (AI), machine learning (ML), blockchain technology which aids in the diagnosis and therapeutic modalities. An extensive literature search was done in all the major databases such as PubMed/MEDLINE, Web of Science, Scopus, and Google scholar search engines for the keywords “Precision Medicine” AND “Sleep disorders” OR “Sleep medicine,” and the information is extracted and discussed in this review. Though the PM in Sleep Medicine application is in nascent stages, it is very promising in sleep medicine and is expected in many areas of sleep healthcare in the near future. The application of AI and ML in the concept of PM has revamped the testing, diagnosis, and treatment modalities for sleep disorders which hold a promise to translate these benefits to the global population through sustained efforts of innovation.

The paper aims to be to identify how dashboards can be used to help Information and Communications Technology (ICT) production support teams to manage systems better and giving business stability and performance to be able to be more competitive. This includes forecasting potential problems that can occur as well as prescribe possible solutions before there are any major system outages. Dashboards will track and monitor behaviour of systems to allow for faster turnaround time in resolving issues when it occurs. The analysis and design show how the technologies work together to create a prescriptive dashboard, e.g. alerts sent through to mobile devices (phones) which create a quick reaction time and staff not on site (vendors, remote locations, etc.) can be informed and reduce downtime to the business. The proposed method provides knowledge management requests/incidents/problems and existing monitoring tools can all be integrated to provide the support staff with possible solutions. This paper will explain how dashboards can assist ICT, with the focus on production support areas, to help resolve ICT incidents, problems, and requests. A few concerns were highlighted however these concerns form part of the continuous improvements to the prescriptive dashboards. The system model will help minimize the run around between various ICT teams for troubleshooting. The flow diagram shows how it would provide early warnings and recommended solutions based on calculations of previous ICT incidents and problems. This model is complementary in the sense that it will work/integrate with existing system monitoring tools. Various types of technologies have been identified and researched through the review of the literature. The research was conducted to identify the feel for dashboards, how they can be used, and the various technologies that can complement each other to get the success of a prescriptive dashboard.

This work proposes a novel multivariate-multiscale approach for computing the spectral and temporal entropies from the multichannel electroencephalogram (EEG) signal. This facilitates the recognition of three human emotions: positive, neutral, and negative. The proposed approach is based on the application of the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT). We have extended the existing FBSE-EWT method for multichannel signals and derived FBSE-EWT based multivariate Hilbert marginal spectrum (MHMS) for computing spectral Shannon and K-nearest neighbor (K-NN) entropies. The multivariate FBSE-EWT decomposes the multichannel EEG signals into narrow band subband signals. The multiscaling operation adapted in the spectral domain is based on the selection of successive joint instantaneous amplitude and frequency functions of the subband signals. On the other hand, the time domain multiscale K-NN entropy is computed from the cumulatively added multidimensional subband signals. The extracted spectral and temporal entropy features are smoothed and fed to the sparse autoencoder based random forest (ARF) classifier architecture for emotion classification. The proposed approach is tested using multichannel EEG signals available in a public database (SJTU emotion EEG dataset (SEED)). The bivariate EEG signals from different channel pairs with distinct spatial locations over the scalp are considered as input to our proposed system. The obtained overall classification accuracy of 94.4% reveals that the proposed approach is useful in classifying human emotions. The method is also validated using DREAMER emotion EEG public database. The method outperforms the existing state-of-the-art methods evaluated in these databases.

In the COVID-19 pandemic situation, the need to adopt cloud computing (CC) applications by education institutions, in general, and higher education (HE) institutions, in particular, has especially increased to engage students in an online mode and remotely carrying out research The adoption of CC across various sectors, including HE, has been picking momentum in the developing countries in the last few years In the Indian context, the CC adaptation in the HE sector (HES) remains a less thoroughly explored sector, and no comprehensive study is reported in the literature Therefore, the aim of the present study is to overcome this research vacuum and examine the factors that impact the CC adoption (CCA) by HE institutions (HEIs) in India The scope of the study is limited to public universities (PUs) in India There are, in total, 465 Indian PUs and among these 304 PUs, (i e , 65% PUs) are surveyed using questionnaire-based research The study has put forth a novel integrated technology adoption framework consisting of the Technology Acceptance Model (TAM), Technology-Organization-Environment (TOE), and Diffusion of Innovation (DOI) in the context of the HES This integrated TAM-TOE-DOI framework is utilized in the study to analyze eleven hypotheses concerning factors of CCA that have been tested using structural equation modelling (SEM) and confirmatory factor analysis (CFA) The findings reveal that competitive advantage (CA), technology compatibility (TC), technology readiness (TR), senior leadership support, security concerns, government support, and vendor support are the significant contributing factors of CCA by Indian PUs The study contends that whereas the rest of the factors positively affect the PUs’ intention towards CCA, security concerns are a significant reason for the reluctance of these universities against adopting CC The findings demonstrated the application of an integrated TAM-TOE-DOI framework to assess determining factors of CCA in Indian PUs Further, the study has given useful insights into the successful CCA by Indian PUs, which will facilitate eLearning and remote working during COVID-19 or similar outbreak

In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identification. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.

According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semiautomated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research develops a deep convolutional neural network (deep CNN) models for predicting the reason behind the driver’s distraction. The deep CNN models are trained using numerous images of distracted drivers. The performance of deepCNNmodels, namely theVGG16,ResNet, and Xception network, is assessed based on the evaluation metrics, such as the precision score, the recall/sensitivity score, the F1 score, and the specificity score. The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’ activities.

Internet of Things (IoT), Information and Communication Technology (ICT), and data mining technologies are three prime pillars of any smart city project. The intelligent urban transportation system is an indispensable part of any smart city and acts as a major contributor to a city's vitality. An extensive amount of vehicular information is being amassed by Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), and Radio-Frequency Identification (RFID) based Intelligent Transportation System (ITS), which can provide an insightful image of various ITS issues concerning safety, security, and management. Merely depending on WSN and RFID predicated technologies for the construction of ITS is not adequate to realise smart city dream in developing countries due to technological and monetary barriers. Considering this, an Intelligent Vigilare System (IVS) for ITS is proposed, which sanctions network implementation without disrupting the current vehicular infrastructure through multifaceted modeling.

This article reviews the literature to understand the current state of corporate social responsibility (CSR) application and communication in the healthcare sector. First, the authors provide a bibliometric analysis that quantifies the number of scientific publications regarding CSR in healthcare of the last ten years (2007-2016). Then, they present the theoretical framework. Starting from a brief summary of CSR literature, the authors explain peculiarities of the healthcare sector and contextualize the CSR debate in the healthcare context, identifying its current application and gaps. The authors review not only the application of CSR in healthcare but also analyze the topic of CSR communication, as well as the tools adopted by healthcare organizations to communicate their CSR activities in this sector.

Health systems are currently facing a series of challenges dealing with continuous technology advances and social demands, which require changes at managerial and policy level that fully incorporate social responsibilities within healthcare organizations' strategy. Thus, communicating corporate social responsibility (CSR) takes an important role in today's health contexts worldwide. This work aims to investigate CSR communication in healthcare through the use of both traditional and interactive technologies by adopting a mixed qualitative-quantitative research approach. To this extent, a comparative research was conducted in two different countries with different health systems and contexts, namely Malta and India. Findings show that healthcare organisations of both countries are increasing their awareness towards their social responsibilities and the different ways of communicating their CSR activities. A mixed strategy—including both digital (interactive technologies) and traditional tools—was identified as the most effective way of communicating CSR in a healthcare context.

This article aims to analyze corporate social responsibility (CSR) communication in today's health care systems. The authors investigate the importance of CSR communication and the use of both traditional and interactive technologies for healthcare CSR communication, adopting quantitative research methodologies. In order to understand different applications of the topic, the article examines and compares best practices regarding CSR communication between two extremely different countries and contexts: namely Malta and India. Results emphasize a growing attention on healthcare CSR communication in both countries. The key finding observed is that healthcare organizations are using a mixed strategy to communicate their CSR activities. Indeed, interactive technologies enable several advantages to organizations than traditional tools (such as better dialogue and interaction with different stakeholders, speed, low costs, easy access to information), as well as they require investments and costs in infrastructures and skills/competencies for their implementation.

In an uncontrolled environment, many of the face detection algorithms lack robustness due to their design. The present research on unconstrained face detection is focused on handcrafted and visual features in isolation. We propose a novel approach to use handcrafted as well as visual features together for improvement in face detection to achieve robustness. The algorithm uses a side-view face detector, which divides the problem space into two: side view face detection and frontal face detection. For frontal faces, Discrete Wavelet Transform (DWT) followed by the encoding of Eyes like landmarks (ELL) pixels is proposed in this work. A Human trait that helps to make decisions even better when they are taken together with the help of more than one decision makers is modeled in this work. To achieve this, a combination of handcrafted and visual features is used. Further to improve classification and to provide a better decision, a faster second stage classification scheme is introduced. The result shows an improvement when handcrafted and visual features are combined instead of using them separately.

This article reviews the literature to understand the current state of corporate social responsibility (CSR) application and communication in the healthcare sector. First, the authors provide a bibliometric analysis that quantifies the number of scientific publications regarding CSR in healthcare of the last ten years (2007-2016). Then, they present the theoretical framework. Starting from a brief summary of CSR literature, the authors explain peculiarities of the healthcare sector and contextualize the CSR debate in the healthcare context, identifying its current application and gaps. The authors review not only the application of CSR in healthcare but also analyze the topic of CSR communication, as well as the tools adopted by healthcare organizations to communicate their CSR activities in this sector.

This study aims at understanding how a user's emotions fluctuate when undertaking certain tasks on a social media platform such as Facebook or other software products which may have emotional effects on its user. Specifically, we explored the difference in the usability aspect of Facebook concerning frequent and new Facebook users. The study involves a qualitative study on eighteen participants, nine of whom were Facebook users and nine non-Facebook users who had never used Facebook before participating in this study. During the testing procedure, users were asked to complete several tasks on Facebook, while the electrophysiological activity of their brain was recorded using an EEG (electroencephalogram) acquisition system. Certainly, this study can be applied to any software product, before its release, to improve its user interface by acquiring insight into how user-friendly it is for new users when compared to frequent users. Additionally, a correlation in user friendliness between new users and frequent users is investigated. Furthermore, the study will help us discern which parts of the brain had the most significant difference between groups and discuss the motives behind an individual's emotional state, concerning user experience. Based on the analysis of the power spectrum of the characteristic brain waves, this research establishes that there is a substantial statistical difference between new and frequent Facebook users. Also, it resulted that there is a significant difference between the central, temporal and occipital lobes of new and frequent users. These results will assist developers in creating optimal and user-friendly software products.

Since the early 1990s, there has been a lot of enthusiasm for using high-speed connectivity to develop local community links through education, employment possibilities, fostering community events, and enhancing overall sociability within a local region. 5G is the 5th iteration of a broadband network operating on cellular systems. 5G is not only for mobile phones, but it is also the foundation for virtual reality (VR); the internet of things (IoT); and autonomous transport, immersive services, and public infrastructure; and connecting many electronic devices to the internet. In this chapter, first, the authors have discussed the evolution of 1G network to 6G networks by focussing on its potential impact on the quality of life. Further, 5G applications in IoT, autonomous transport, immersive services, and public infrastructure have been discussed. Then the chapter discusses popular advantages, limitations in the current technologies, implementations, and future perspective.

5G is a new universal wireless standard, a new form of mobile network engineered to bring everyone and everything virtually together. 5G is not only for mobile phones, but it is also the foundation for virtual reality (VR), the internet of things (IoT), and autonomous driving, connecting many electronic devices to the internet. Having good healthcare is very important as it affects all parts of human life and social well-being. Moreover, it is crucial to have a great healthcare system if we want economic growth, workforce productivity, and society to advance. Despite all the hard work done by scientists and medical professionals, today's healthcare is mainly inefficient, and a significant overhaul is required. This chapter discusses the primary advantages, including the 5G's main features in healthcare and their limitations and probable solutions and applications to the latest scenario.

Background Teenage pregnancy remains high with low contraceptive prevalence among adolescents (aged 15-19 years) in Sierra Leone. Stakeholders leverage multiple strategies to address the challenge. Mobile technology is pervasive and presents an opportunity to reach young people with critical sexual reproductive health and family planning messages. Objective The objectives of this research study are to understand how mobile health (mHealth) is used for family planning, understand phone use habits among young people in Sierra Leone, and recommend strategies for mobile-enabled dissemination of family planning information at scale. Methods This formative research study was conducted using a systematic literature review and focus group discussions (FGDs). The literature survey assessed similar but existing interventions through a systematic search of 6 scholarly databases. Cross-sections of young people of both sexes and their support groups were engaged in 9 FGDs in an urban and a rural district in Sierra Leone. The FGD data were qualitatively analyzed using MAXQDA software (VERBI Software GmbH) to determine appropriate technology channels, content, and format for different user segments. Results Our systematic search results were categorized using Grading of Recommended Assessment and Evaluation (GRADE) into communication channels, audiovisual messaging format, purpose of the intervention, and message direction. The majority of reviewed articles report on SMS-based interventions. At the same time, most intervention purposes are for awareness and as helpful resources. Our survey did not find documented use of custom mHealth apps for family planning information dissemination. From the FGDs, more young people in Sierra Leone own basic mobile phones than those that have feature capablilities or are smartphone. Young people with smartphones use them mostly for WhatsApp and Facebook. Young people widely subscribe to the social media–only internet bundle, with the cost ranging from 1000 leones (US $0.11) to 1500 leones (US $0.16) daily. Pupils in both districts top-up their voice call and SMS credit every day between 1000 leones (US $0.11) and 5000 leones (US $0.52). Conclusions mHealth has facilitated family planning information dissemination for demand creation around the world. Despite the widespread use of social and new media, SMS is the scalable channel to reach literate and semiliterate young people. We have cataloged mHealth for contraceptive research to show SMS followed by call center as widely used channels. Jingles are popular for audiovisual message formats, mostly delivered as either push or pull only message directions (not both). Interactive voice response and automated calls are best suited to reach nonliterate young people at scale.

BACKGROUND UNFPA launched two one-month campaigns to reach Sierra Leoneans at scale with critical Sexual Reproductive Health and gender-based violence messages during the Coronavirus 2019 pandemic. OBJECTIVE The intervention objective was to deliver Sexual Reproductive Health (SRH) and gender-based violence (GBV) messages to mobile phone subscribers in Sierra Leone. This paper presents the intervention’s messaging campaign design, deployment methodologies, and design-decisions; shows campaign effectiveness; and share lessons learned, including call pickup rates and listening-duration. METHODS We designed and deployed a two-phased campaign – phase one targeted Freetown (urban) residents, and phase two targeted Sierra Leoneans nationwide (urban and rural). Phase one delivered Family Planning, Maternal Health, Gender Based Violence (GBV), and Coronavirus 2019 messages through automated voice calls, SMS, radio jingles, and social media. Phase two of the campaign delivered national GBV only campaign messages through SMS and Radio jingles. RESULTS In phase one, only 31% of the 1,093,606 initiated automated calls to 290,000 subscribers were picked up, and this dropped significantly at 95% confidence (p=1) after each of the four weeks. Also, at 95% confidence levels, a significant number of subscribers did not listen to the complete messages when repeated (p=1). Thirty-one million two hundred (31.2 million) SMS messages were sent to all 3.9 million active Africell subscribers in Sierra Leone during the second phase. Also, SRH and GBV messages were aired on thirteen national radio stations in Sierra Leone during the second phase. The national toll free helpline for GBV cases reported an increase in calls and attributed it to the campaign. Automated call interventions are cost and human resource intensive. Call pickup rates, listening duration, language, and consideration for users’ ability to re-reference messages are key factors when selecting scalable messaging campaign channels. The drop in the number of subscribers picking up automated calls from the first to fourth week was significant at a confidence level of 95%. According to the GBV helpline operators, the increase in calls reporting GBV was attributable to our campaign. CONCLUSIONS Only a third of subscribers called with pre-recorded messages picked up their calls. When automated calls are repeated, it leads to a significant drop in call completion rates. There was an increase in demand for service to the GBV helpline in the month following the campaign. A multi-channel messaging campaign helped reach different groups of young people.

This paper reviews the existing technologies for the development of cross-platform applications. Both Server-side as well as client-side applications for web, desktop and mobile devices are considered which mainly consists of smart phones and tablets. Further, based on the review process and research issues a web-based development approach is recommended for the development of truly cross-platform applications across devices and operating systems.

Cloud computing is not associated with a specific technology, instead it as an alternative method to deliver technology as a service. This article investigates current cloud computing adoption in the United States (USA) and United Kingdom (UK) hedge fund industry. Hedge fund technologists, prime service consultants, technology service providers, industry application vendors, investors and an independent information security consultant participated were surveyed for this article. The article acknowledges the growth of cloud computing in the hedge fund sector. This research work also highlights that the private cloud definition is vague and requires further classification, elaborating on the variants of private cloud. This is important as the variants of private cloud computing offer varying benefits and risk which the hedge fund sector has proven to be sensitive. Equally, this article argues that some of the current security concerns are over-stated and perhaps reflect a conservative decision making framework rather than a realistic consideration of the options.

Background Liver failure (LF) is a serious complication of transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). This could be influenced by the hemodynamic and functional status of the underlying cirrhotic liver. We evaluated baseline hepatic venous pressure gradient (HVPG) and indocyanine green (ICG) clearance as predictive factors for the development of LF in patients with liver cirrhosis undergoing TACE for HCC. Methods Forty-two patients with cirrhosis and HCC, referred for TACE, were clinically evaluated including the assessment of Child Turcotte Pugh score (CTP), Model for End-Stage Liver Disease (MELD), HVPG measurement, and ICG retention test. Predictors of development of hepatic failure after TACE were determined. Results In our study population, the mean age of the patients was 58 years, with mean CTP of 6.60 ± 1.149 and mean MELD score of 9.57 ± 2.923. The mean HVPG and ICG retention at 15 min was 13.57 ± 4.64 mmHg and 21.571 ± 12.434, respectively. Post-TACE Liver Failure (PTLF within 1 month after TACE) developed in 23.80% patients, whereas 76.19% patients did not have PTLF. The statistically significant preprocedure variables that might predict hepatic failure after TACE using univariate analysis were found to be high CTP, MELD score, ICG retention, HVPG, serum bilirubin, serum creatinine, alfa-fetoprotein levels, large tumor size, and low baseline serum albumin. On multivariate analysis, ICG was an independent factor predictive of hepatic failure after TACE. Conclusion Pretreatment evaluation of routine liver function is of fundamental importance before TACE. Baseline ICG retention test (ICG-R15) is a marker indicating the state of liver function in patients undergoing TACE and is an independent predictor for PTLF. Our study concludes that with a cutoff of 25, ICG-R15 has 92.9% accuracy, 90% sensitivity, and 87.5% specificity to predict hepatic failure after TACE.