Editor’s Choice Articles

Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD. Full article

Multi-criteria decision analysis (MCDA) is an established methodology to support the decision-making of multi-objective problems. For conducting an MCDA, in most cases, a set of objectives (SOO) is required, which consists of a hierarchical structure comprised of objectives, criteria, and indicators. The development of an SOO is usually based on moderated development processes requiring high organizational and cognitive effort from all stakeholders involved. This article proposes elementary interactions as a key paradigm of an algorithm-driven development process for an SOO that requires little moderation efforts. Elementary interactions are self-contained information requests that may be answered with little cognitive effort. The pairwise comparison of elements in the well-known analytical hierarchical process (AHP) is an example of an elementary interaction. Each elementary interaction in the development process presented contributes to the stepwise development of an SOO. Based on the hypothesis that an SOO may be developed exclusively using elementary interactions (EIs), a concept for a multi-user platform is proposed. Essential components of the platform are a Model Aggregator, an Elementary Interaction Stream Generator, a Participant Manager, and a Discussion Forum. While the latter component serves the professional exchange of the participants, the first three components are intended to be automatable by algorithms. The platform concept proposed has been evaluated partly in an explorative validation study demonstrating the general functionality of the algorithms outlined. In summary, the platform concept suggested demonstrates the potential to ease SOO development processes as the platform concept does not restrict the application domain; it is intended to work with little administration moderation efforts, and it supports the further development of an existing SOO in the event of changes in external conditions. The algorithm-driven development of SOOs proposed in this article may ease the development of MCDA applications and, thus, may have a positive effect on the spread of MCDA applications. Full article

Monetary transactions have advanced into a portable, simple, and comfortable process. Regardless of the advantages of a versatile method of disbursement, the global take-up ratio is inadequate. Consequently, greater insight into the factors that influence acceptance by stakeholders is fundamental for industrialists and scholars. This study analyses the goals and real purpose behind portable disbursement by unifying the definition of its concept, which consolidates the “Mobile Technology Acceptance Model” and the “Theory of Planned Behaviour” as well as an updated profound variable that is more synergistic. The new framework incorporated 12 factors influenced by the user’s insight and was analysed by collecting data based on the PLS-SEM condition mix-mode. Identifying the impact of versatile methods of disbursement will shape essential business choices and the community and will change current lifestyles into contactless ones. This article demonstrates the connection between innovative factors and human preferences concerning the use of portable disbursement. Interestingly, we embrace this uniqueness for an incorporated framework that consolidates MTAM as well as the TPB. Full article

The global vaccination program provides hope for ending the present worldwide pandemic. Due to the current pandemic, COVID-19 vaccines must be delivered and administered to communities once supplies are available. However, there are significant challenges in the sourcing, allocation, distribution, and uptake of vaccinations. A successful vaccination program would necessitate adequate risk management across the vaccination supply chain. This article has collected the predictable risks of the COVID-19 vaccines considered within the vaccine delivery system framework. The risks are presented based on relevant literature. Hence, this work’s framework is expected to contribute to better risk management of vaccination programs and similar future projects. Full article

The COVID-19 pandemic has significantly impacted almost every sector. This impact has been especially felt in the healthcare sector, as the pandemic has affected its stability, which has highlighted the need for improvements in service. As such, we propose a collaborative decision-making framework that is capable of accounting for the goals of multiple stakeholders, which consequently enables an optimal, consensus decision to be identified. The proposed framework utilizes the best–worst method (BWM) and the Multi-Actor Multi-Criteria Analysis (MAMCA) methodology to capture and rank each stakeholder’s preferences, followed by the application of a Multi-Objective Linear Programming (MOLP) model to identify the consensus solution. To demonstrate the applicability of the framework, two hypothetical scenarios involving improving patient care in an intensive care unit (ICU) are considered. Scenario 1 reflects all selected criteria under each stakeholder, whereas in Scenario 2, every stakeholder identifies their preferred set of criteria based on their experience and work background. The results for both scenarios indicate that hiring part-time physicians and medical staff can be the effective solution for improving service quality in the ICU. The developed integrated framework will help the decision makers to identify optimal courses of action in real-time and to select sustainable and effective strategies for improving service quality in the healthcare sector. Full article

Metal matrix composites (MMC) are the materials of tomorrow. This paper presents an in-depth analysis of the MMC research articles published in Web of Science (WoS) during 2001–2020. The study firstly included year on year publications, publication types, sources, research directions as well as the most productive researchers, organizations and nations. Secondly, a detailed analysis of collaborations among various MMC researchers, organizations and countries has been presented. Thirdly, citations based linkages among the published articles, sources, researchers, institutions and places have been discussed relative to their respective collaborative link strengths. A co-occurrence analysis of MMC keywords was also conducted to highlight the most important keywords trending in this area. Finally, burst detection analyses of keywords and references were carried out to unearth sudden citation spikes of keywords and documents. Primary results indicate that research articles formed 80.54% of all MMC publications in WoS. The journal ‘Materials Science and Engineering A: Structural Materials, Properties, Microstructure and Processing’ published maximum MMC articles. Collaboration analysis results indicate that Zhang D, the Chinese Academy of Science and People’s Republic of China, attained topmost collaboration based total link strengths (TLS). Citations based analysis showed that Zhang D, the Shanghai Jiao Tong University (China), People’s Republic of China and the journal ‘Materials Science and Engineering A: Structural Materials, Properties, Microstructure and Processing’ received highest citation TLS values. Keyword ‘Graphene’ scored the highest citation burst strength (2018–2020). The future of MMC research lies in processing and characterization of novel nanocomposites with reinforcements such as graphene and boron carbide for various applications. Full article

A decision support system (DSS) was developed to help reduce food waste at traditional food retailers while selling fresh horticultural products, but also to promote food safety and quality. This computational tool includes two major functions: (1) the prediction of the remaining shelf life of fresh horticultural product, namely lettuce, onion, carrot, and cabbage based on its microbial growth status, governed by extrinsic and intrinsic parameters (temperature, water activity and pH, respectively). The remaining shelf life of the studied horticultural products is determined by using the online predictive food microbiology tool— the Combined Database for Predictive Microbiology (Combase). The time to reach the infectious doses of bacteria considered in the study for each of the four horticultural products are predicted; (2) the calculation of the dynamic price of the produce that should be set each day, depending on the predicted end of the marketing period to increase the demand and potential for sale to the final consumer. The proposed dynamic pricing model assumes a linear relation with the remaining shelf life of the analyzed vegetable to set the selling price. The shelf life determined by the DSS for optimal storage conditions is, in general, conservative, ensuring food safety. The automatic dynamic pricing gives new opportunities to small retailers to manage their business, fostering profit and simultaneously contributing to reduce food waste. Thus, this decision support system can contribute to the sustainable value of reducing food waste by providing information to small grocers and retailers on the safety of their perishable status depending on storage conditions and allowing them to suggest a fair price depending on that quality. Full article

Self-mixing optical coherent detection is a non-contact measurement technique which provides accurate information about the vibration frequency of any test subject. In this research, novel designs of optical homodyne and heterodyne detection techniques are explained. Homodyne and heterodyne setups are used for measuring the frequency of the modulated optical signal. This technique works on the principle of the optical interferometer, which provides a coherent detection of two self-mixing beams. In the optical homodyne technique, one of the two beams receives direct modulation from the vibration frequency of the test subject. In the optical heterodyne detection technique, one of the two optical beams is subjected to modulation by an acousto-optics modulator before becoming further modulated by the vibration frequency of the test subject. These two optical signals form an interference pattern that contains the information of the vibration frequency. The measurement of cardiovascular signals, such as heart rate and heart rate variability, are performed with both homodyne and heterodyne techniques. The optical coherent detection technique provides a high accuracy for the measurement of heart period and heart rate variability. The vibrocardiogram output obtained from both techniques are compared for different heart rate values. Results obtained from both optical homodyne and heterodyne detection techniques are compared and found to be within 1% of deviation value. The results obtained from both the optical techniques have a deviation of less than 1 beat per minute from their corresponding ECG values. Full article

Different software product quality models interpret different amounts of information, i.e., they can capture and address different manifestations of software quality. This characteristic can cause misleading statements and misunderstandings while explaining or comparing the results of software product quality assessments. A total of 23 previously identified distinct software product quality models are analysed on how they handle the abstract notion of quality, and a taxonomy on the quality manifestations that the individual software product quality models are able to capture is established. Quality models that are able to solely describe the quality manifestation of the source code are attractive due to their full automation potential through static code analysers, but their assessment results ignore a huge part of software product quality, which is the one that most impresses the end user. The manifestations of software product quality that address the behaviour of the software while it operates, or the perception of the end user with regard to the software in use, require human involvement in the quality assessment. The taxonomy contributes to interpreting the quality assessment results of different quality models by showing the possible quality manifestations that can be captured by the identified models; moreover, the taxonomy also provides assistance while selecting a quality model for a given project. The quality manifestations used for the quality measurement always need to be presented, otherwise the quality assessment results cannot be interpreted in an appropriate manner. Full article

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology. Full article

The aim of this study is to provide a literature review on biofortified products and their role in the scientific sphere. Despite the large number of studies conducted on biofortified products in the last 20 years, many defining issues are still debated in the literature and several research questions should be clarified. It is therefore relevant to investigate more on this topic, which is considered increasingly important to human health, world hunger reduction strategy, and also for the international marketing strategy of production holdings. The papers were analyzed according to a chronological/conceptual approach, with greater emphasis on research that has added significant value to the literature. The research was carried out using a scientific database from which 1189 scientific papers were extracted. A careful analysis of the abstracts and the text led to the identification of the five dimensions of our greatest interest (Reducing world hunger; Human health; GMOs; Agronomy, herbaceous crops; Economy and the market). The suggestions for future research reported by various authors are organized and structured in order to create an incentive for new studies and insights into biofortification. Full article

Recent studies on chronic obstructive pulmonary disease (COPD) patients in the context of the coronavirus 19 (COVID-19) pandemic have reported two important problems, i.e., high mortality and vulnerability among COPD patients vs. non-COPD patients. The high number of deaths are caused by exacerbations, COVID-19, and other comorbidities. Therefore, the purpose of this article is to reduce the risk factors of COPD in the COVID-19 context. In this article, we propose approaches based on adaptation mechanisms for detecting COVID-19 symptoms, to better provide appropriate care to COPD patients. To achieve this goal, an ontological model called SuspectedCOPDcoviDOlogy has been created, which consists of five ontologies for detecting suspect cases. These ontologies use vital sign parameters, symptom parameters, service management, and alerts. SuspectedCOPDcoviDOlogy enhances the COPDology proposed by a previous research project in the COPD domain. To validate the solution, an experimental study comparing the results of an existing test for the detection of COVID-19 with the results of the proposed detection system is conducted. Finally, with these results, we conclude that a rigorous combination of detection rules based on the vital sign and symptom parameters can greatly improve the dynamic detection rate of COPD patients suspected of having COVID-19, and therefore enable rapid medical assistance. Full article

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor. Full article

Internet-of-Things (IoT) is an enabling technology for numerous initiatives worldwide such as manufacturing, smart cities, precision agriculture, and eHealth. The massive field data aggregation of distributed administered IoT devices allows new insights and actionable information for dynamic intelligent decision-making. In such distributed environments, data integrity, referring to reliability and consistency, is deemed insufficient and requires immediate facilitation. In this article, we introduce a distributed ledger (DLT)-based system for ensuring IoT data integrity which securely processes the aggregated field data. Its uniqueness lies in the embedded use of IOTA’s ledger, called “The Tangle”, used to transmit and store the data. Our approach shifts from a cloud-centric IoT system, where the Super nodes simply aggregate and push data to the cloud, to a node-centric system, where each Super node owns the data pushed in a distributed and decentralized database (i.e., the Tangle). The backend serves as a consumer of data and a provider of additional resources, such as administration panel, analytics, data marketplace, etc. The proposed implementation is highly modularand constitutes a significant contribution to the Open Source communities, regarding blockchain and IoT. Full article

Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%. Full article

The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%. Full article