Novel flexible sensing technology for nondestructive detection on live fish health/quality during waterless and low-temperature transportation

Fish health/quality issues are increasingly attracting attention during waterless and low-temperature transportation. Nondestructive detection has become a great need for an effective method to improve fish health/quality. Currently, emerging Internet of Things, novel flexible electronics and data fusion technology have received great interest for nondestructive detection on live fish health/quality. This paper analysized nondestructive detection mechanisms using novel flexible sensing technology to achieve high-precision sensing of key parameters, and machine learning based data fusion modeling to achieve live fish health/quality nondestructive evaluation during waterless and low-temperature transportation. Recent studies on novel flexible electrochemical and physiological biosensors development and application for solving key ambient and physiological parameter sensing were summarized. The ML based data fusion modeling framework and application for live fish health/quality nondestructive evaluation was also highlighted. The future perspective is also proposed to provide promising solutions for accurate sensing of multi-parameter and real applications of live fish health/quality nondestructive detection during waterless and low-temperature transportation.

Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches

The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues.

IoT-based disease prediction using machine learning

COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform.

Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloud

Purpose

Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully.

Methods

This article introduces the VitalSense model, which provides a hierarchical multi-tier remote health monitoring architecture in smart cities by combining edge, fog, and cloud computing.

Results

Although using a traditional composition, our contributions appear in handling each infrastructure level. We explore adaptive data compression and homomorphic encryption at the edge, a multi-tier notification mechanism, low latency health traceability with data sharding, a Serverless execution engine to support multiple fog layers, and an offloading mechanism based on service and person computing priorities.

Conclusions

This article details the rationale behind these topics, describing VitalSense use cases for disruptive healthcare services and preliminary insights regarding prototype evaluation.

Prediction Framework on Early Urine Infection in IoT-Fog Environment Using XGBoost Ensemble Model

Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoT-based sensors for data collection, followed by data encoding and infectious risk factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the health-related information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out, and results are calculated based on real-time patient data. The statistical findings of accuracy (91.45%), specificity (95.96%), sensitivity (84.79%), precision (95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.

Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing

Cloud-based Healthcare 4.0 systems have research challenges with secure medical data processing, especially biomedical image processing with privacy protection. Medical records are generally text/numerical or multimedia. Multimedia data includes X-ray scans, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, etc. Transferring biomedical multimedia data to medical authorities raises various security concerns. This paper proposes a one-of-a-kind blockchain-based secure biomedical image processing system that maintains anonymity. The integrated Healthcare 4.0 assisted multimedia image processing architecture includes an edge layer, fog computing layer, cloud storage layer, and blockchain layer. The edge layer collects and sends periodic medical information from the patient to the higher layer. The multimedia data from the edge layer is securely preserved in blockchain-assisted cloud storage through fog nodes using lightweight cryptography. Medical users then safely search such data for medical treatment or monitoring. Lightweight cryptographic procedures are proposed by employing Elliptic Curve Cryptography (ECC) with Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Digital Signature (ECDS) algorithm to secure biomedical image processing while maintaining privacy (ECDSA). The proposed technique is experimented with using publically available chest X-ray and CT images. The experimental results revealed that the proposed model shows higher computational efficiency (encryption and decryption time), Peak to Signal Noise Ratio (PSNR), and Meas Square Error (MSE).

Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation

This data article describes a dataset collected in 2022 in a domestic household in the UK. The data provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plugs and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (°C), relative indoor humidity (RH%), and occupancy (binary). The dataset also includes outdoor weather conditions based on data from The Norwegian Meteorological Institute (MET Norway) including temperature (°C), outdoor humidity (RH%), barometric pressure (hPA), wind bearing (deg), and windspeed (m/s). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems.

Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19

The connection between humans and digital technologies has been documented extensively in the past decades but needs to be evaluated through the current global pandemic. Artificial Intelligence(AI), with its two strands, Machine Learning (ML) and Semantic Reasoning, has proven to be a great solution to provide efficient ways to prevent, diagnose and limit the spread of COVID-19. IoT solutions have been widely proposed for COVID-19 disease monitoring, infection geolocation, and social applications. In this paper, we investigate the usage of the three technologies for handling the COVID-19 pandemic. For this purpose, we surveyed the existing ML applications and algorithms proposed during the pandemic to detect COVID-19 disease using symptom factors and image processing. The survey includes existing approaches including semantic technologies and IoT systems for COVID-19. Based on the survey result, we classified the main challenges and the solutions that could solve them. The study proposes a conceptual framework for pandemic management and discusses challenges and trends for future research.

COVID-19 clinical waste reuse: A triboelectric touch sensor for IoT-cloud supported smart hand sanitizer dispenser

Earth's plastic pollution has increased due to the COVID-19 pandemic, and the world is on the doorstep of an enormous waste pandemic. The extensive use of mandatory personal protectives like masks, gloves, and PPE kits and the lack of proper waste management systems lead to a rise in the plastic pollution content of the earth. Such disposable and non-biodegradable personal protectives are thrown out to the environment after use. These distributed wastes pollute land, soil, and water bodies and effects their ecosystems. This research work establishes the concept of a waste-to-energy conversion approach to reuse COVID-19 scraps for green and sustainable development. Three-layered surgical masks and nitrile gloves were reused in this work after sterilization for energy harvesting and sensing applications by fabricating a 3D-printed contact-separation-based triboelectric nanogenerator. A piece of three-layered mask and nitrile gloves were placed inside the 3D structure as the top negative and bottom positive triboelectric materials with copper and aluminum as corresponding electrodes (MG-CS TENG). It can convert external mechanical motions into electrical energy. The maximum voltage, current, and power density obtained from the device are 50.7 V, 4.8 µA, and 6.39 µW/cm2, respectively, for a mechanical force of 9 N. The harvested energy was sufficient to power small-scale electronic devices like digital tally counters, wristwatches, lumex displays, and series connected 25 LEDs. MG-CS TENG was also performed as a pedal-operated touch sensor to dispense hand sanitizer. MG-CS TENG was pedal pressed to trigger a microcontroller and control the solenoid valve's opening and closing to regulate sanitizer flow. The setup was integrated using the internet of things (IoT) and Blynk cloud services for the remote monitoring and controlling of the sanitizer dispenser using a smartphone. This work contributes a substantial role in disaster management to suppress microplastic environmental pollution by reusing pandemic wastes for energy harvesting and sensing applications and preventing the spread of coronavirus through proper hand sanitization.

A lung sound recognition model to diagnoses the respiratory diseases by using transfer learning

Respiratory disease is one of the leading causes of death in the world. Through advances in Artificial Intelligence, it appears possible for the days of misdiagnosis and treatment of respiratory disease symptoms rather than their root cause to move behind us. The traditional convolutional neural network cannot extract the temporal features of lung sounds. To solve the problem, a lung sounds recognition algorithm based on VGGish- stacked BiGRU is proposed which combines the VGGish network with the stacked bidirectional gated recurrent unit neural network. A lung Sound Recognition Algorithm Based on VGGish-Stacked BiGRU is used as a feature extractor which is a pre-trained model used for transfer learning. The target model is built with the same structure as the source model which is the VGGish model and parameter transfer is done from the source model to the target model. The multi-layer BiGRU stack is used to enhance the feature value and retain the model. While fine-tuning of the parameter of VGGish is frozen which successfully improves the model. The experimental results show that the proposed algorithm improves the recognition accuracy of lung sounds and the recognition accuracy of respiratory diseases.

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper's main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.

Fog computing-assisted path planning for smart shopping

A Smart City (SC) is a viable solution for green and sustainable living, especially with the current explosion in global population and rural-urban immigration. One of the fields that is not getting much attention in the Smart Economy (SE) is customer satisfaction. The SE is a component of SC that is concerned with using Information and Communication Technology (ICT) to improve stages of the traditional economy. In this paper, we propose a fog computing-based shopping recommendation system. Our simulations used Al-Madinah city as a case study. It aims to improve the customer shopping experience. Customers in shopping malls can connect to the system via Wi-Fi. Then the system recommends products to the shoppers according to their preferences. It optimizes shoppers' schedules using price, the distance between the shops, and the congestion. It also improves customers' savings by up to 30%. It also increases the shopping speed by up to 6.12% compared to the system proposed in the literature.

A Novel Blockchain and Internet of Things-Based Food Traceability System for Smart Cities

Rapid urbanization has recently caused serious problems for cities all around the world. Smart cities have drawn much interest from researchers in the present research paradigm to manage the expanding urban population. Frameworks for smart cities are planned and implemented using platforms based on blockchain and the Internet of Things (BIOT). Smart cities may use the BIoT platform to provide improved transportation, food traceability, and healthcare services. Food safety is one of the sectors where less research has been done than the others. The importance of food safety is now more widely recognized, making it essential to improve the traceability and transparency of the food supply chain. In this paper, a novel BIOT-based layered framework using EOSIO has been proposed for effective food traceability. The proposed system first identifies the suitable traceability units to provide better transparency and traceability and then defines and implements a layered architecture using Ethereum and EOSIO blockchain platforms. The performance of the proposed EOSIO-based model is evaluated using the practicality of the consensus algorithm, block production rate, throughput, and block confirmation time. The proposed traceability system attains a block production rate of 0.5 s and a block confirmation time of 1 s, which is much lower than the Ethereum-based traceability system. Hence, from the experimental evidence, the superiority of the proposed EOSIO-based food traceability can be observed.

Revisiting the Digital Plumber: Modifying the Installation Process of an Established Commercial IoT Alarm System

The 'digital plumber' is a conceptualisation in ubicomp research that describes the work of installing and maintaining IoT devices. But an important and often understated element of commercial IoT solutions is their long-term socio-technical infrastructural nature, and therefore long-term installation and maintenance needs. This adds complexity to both the practice of digital plumbing and to the work of design that supports it. In this paper we study a commercial company producing and installing IoT alarm systems. We examine video recordings that capture how a digital plumbing representative and software development team members make changes to both the installation process and supporting technology. Our data enables us to critically reflect on concepts of infrastructuring, and uncover the ways in which the team methodically foreground hidden elements of the infrastructure to address a point of failure experienced during field trials of a new version of their product. The contributions from this paper are twofold. Firstly, our findings build on previous examples of infrastructuring in practice by demonstrating the use of notions of elemental states to support design reasoning through the continual foregrounding and assessment of tensions identified as key factors at the point of failure. Secondly, we build on current notions of digital plumbing work. We argue that additional responsibilities of 'reporting failure' and 'facilitation of change' are part of the professional digital plumbing role and that commercial teams should support these additional responsibilities through collaborative troubleshooting and design sessions alongside solid communication channels with related stakeholders within the product team.

Design of a low-cost active and sustainable autonomous system for heating agricultural greenhouses: A case study on strawberry (fragaria vulgaris) growth

The utilization of solar energy is a vital strategy in the agricultural sector's efforts to address and reduce greenhouse gas emissions. This renewable resource can greatly decrease the industry's carbon footprint and play a significant role in combating climate change. In this context, this study examines an automatic solar system installed in a south-facing agricultural greenhouse and its effect on the growth of strawberry plants in winter. Heat is transferred from the environment during the day to the structure at night using this technology, which uses water flowing in a closed circuit that is put on the greenhouse roof. This system includes a battery, a photovoltaic solar panel to power some accessories, a copper coil placed within double glass on the greenhouse's roof, a water pump circulator, and storage tanks. Two greenhouses-one experimental with a solar heating system and the other reference without one-were used for the comparative experimental investigation. Both greenhouses are constructed on the terrace of Mohammed V University's Solar Energy and Environment Laboratory in Rabat, Morocco. An environmental monitoring system was built to automatically measure environmental parameters. Real-time data visualization and analysis may be done from any place via a website with the Internet of Things integrated into the system. The greenhouse's microclimate was improved by this low-cost technology, which also allowed for winter heating. Compared to the control greenhouse, this improvement allowed for a 17-day earlier harvest and a 30% reduction in irrigation water usage. The economic analysis findings show that the system is profitable for investments and energy savings.

Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study

Objective

The popularity of the ​"bring your own device (BYOD)" ​concept has grown in recent years, and its application has extended to the healthcare field. This study was aimed at examining nurses' acceptance of a BYOD-supported system after a 9-month implementation period.

Methods

We used the technology acceptance model to develop and validate a structured questionnaire as a research tool. All nurses (n ​= ​18) responsible for the BYOD-supported wards during the study period were included in our study. A 5-point Likert scale was used to assess the degree of disagreement and agreement. Statistical analysis was performed in SPSS version 24.0.

Results

The questionnaire was determined to be reliable and well constructed, on the basis of the item-level content validity index and Cronbach α values above 0.95 and 0.87, respectively. The mean constant values for all items were above 3.95, thus suggesting that nurses had a positive attitude toward the BYOD-supported system, driven by the characteristics of the tasks involved.

Conclusions

We successfully developed a BYOD-supported system. Our study results suggested that nursing staff satisfaction with BYOD-supported systems could be effectively increased by providing practical functionalities and reducing clinical burden. Hospitals could benefit from the insights generated by this study when implementing similar systems.

Moving toward smart biomedical sensing

The development of novel biomedical sensors as highly promising devices/tools in early diagnosis and therapy monitoring of many diseases and disorders has recently witnessed unprecedented growth; more and faster than ever. Nonetheless, on the eve of Industry 5.0 and by learning from defects of current sensors in smart diagnostics of pandemics, there is still a long way to go to achieve the ideal biomedical sensors capable of meeting the growing needs and expectations for smart biomedical/diagnostic sensing through eHealth systems. Herein, an overview is provided to highlight the importance and necessity of an inevitable transition in the era of digital health/Healthcare 4.0 towards smart biomedical/diagnostic sensing and how to approach it via new digital technologies including Internet of Things (IoT), artificial intelligence, IoT gateways (smartphones, readers), etc. This review will bring together the different types of smartphone/reader-based biomedical sensors, which have been employing for a wide variety of optical/electrical/electrochemical biosensing applications and paving the way for future eHealth diagnostic devices by moving towards smart biomedical sensing. Here, alongside highlighting the characteristics/criteria that should be met by the developed sensors towards smart biomedical sensing, the challenging issues ahead are delineated along with a comprehensive outlook on this extremely necessary field.

First report of aeroponically grown Bambara groundnut, an African indigenous hypogeal legume: Implications for climate adaptation

Global agricultural production is currently limited by negative climate-related hazards such as drought, uneven rainfall and rising temperatures. Many efforts have been put in place by government and non-government agencies to mitigate the challenges of climate change in the sector. However, the approaches do not seem feasible due to the growing demand for food. With these challenges, climate-smart agricultural technologies such as aeroponics and underutilised crops have been projected as the future of agriculture in developing African countries to reduce the risk of food insecurity. In this paper, we present the cultivation of an underutilised indigenous African legume crop, Bambara groundnut, in an aeroponics system. Seventy Bambara groundnut landraces were cultivated in a low-cost climate-smart aeroponics system and in sawdust media. The results showed that Bambara groundnut landraces cultivated in aeroponics performed better than those cultivated in a traditional hydroponics (sawdust/drip irrigation) technique in terms of plant height and chlorophyll content, where the landraces cultivated in sawdust had a higher number of leaves than those cultivated in aeroponics. This study also demonstrated the feasibility of introducing a generic Internet of Things platform for climate-smart agriculture in developing countries. The proof-of-concept and the successful cultivation of a hypogeal crop in aeroponics can be useful for cost-effective adaptation and mitigation plans for climate change, particularly for food security in rural African agricultural sectors.

An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

BACKGROUND

During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems.

OBJECTIVE

To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients.

METHODOLOGY

We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal.

RESULTS

Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments.

CONCLUSION

Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.

Deep learning: survey of environmental and camera impacts on internet of things images

Internet of Things (IoT) images are captivating growing attention because of their wide range of applications which requires visual analysis to drive automation. However, IoT images are predominantly captured from outdoor environments and thus are inherently impacted by the camera and environmental parameters which can adversely affect corresponding applications. Deep Learning (DL) has been widely adopted in the field of image processing and computer vision and can reduce the impact of these parameters on IoT images. Albeit, there are many DL-based techniques available in the current literature for analyzing and reducing the environmental and camera impacts on IoT images. However, to the best of our knowledge, no survey paper presents state-of-the-art DL-based approaches for this purpose. Motivated by this, for the first time, we present a Systematic Literature Review (SLR) of existing DL techniques available for analyzing and reducing environmental and camera lens impacts on IoT images. As part of this SLR, firstly, we reiterate and highlight the significance of IoT images in their respective applications. Secondly, we describe the DL techniques employed for assessing the environmental and camera lens distortion impacts on IoT images. Thirdly, we illustrate how DL can be effective in reducing the impact of environmental and camera lens distortion in IoT images. Finally, along with the critical reflection on the advantages and limitations of the techniques, we also present ways to address the research challenges of existing techniques and identify some further researches to advance the relevant research areas.

Digital Intervention For The Management Of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive, multifactorial, chronic, neurodegenerative disease with high prevalence and limited therapeutic options, making it a global health crisis. Being the most common cause of dementia, AD erodes the cognitive, functional, and social abilities of the individual and causes escalating medical and psychosocial needs. As yet, this disorder has no cure and current treatment options are palliative in nature. There is an urgent need for novel therapy to address this pressing challenge. Digital therapeutics (Dtx) is one such novel therapy that is gaining popularity globally. Dtx provides evidence based therapeutic interventions driven by internet and software, employing tools such as mobile devices, computers, videogames, apps, sensors, virtual reality aiding in the prevention, management, and treatment of ailments like neurological abnormalities and chronic diseases. Dtx acts as a supportive tool for the optimization of patient care, individualized treatment and improved health outcomes. Dtx uses visual, sound and other non-invasive approaches for instance-consistent therapy, reminiscence therapy, computerised cognitive training, semantic and phonological assistance devices, wearables and computer-assisted rehabilitation environment to find applications in Alzheimer's disease for improving memory, cognition, functional abilities and managing motor symptom. A few of the Dtx-based tools employed in AD include "Memory Matters", "AlzSense", "Alzheimer Assistant", "smart robotic dog", "Immersive virtual reality (iVR)" and the most current gamma stimulation. The purpose of this review is to summarize the current trends in digital health in AD and explore the benefits, challenges, and impediments of using Dtx as an adjunctive therapy for the management of AD.

Care4AD: A Technology-Driven Platform for Care Coordination and Management: Acceptability Study in Dementia

INTRODUCTION

The technology-driven solution can reduce the caregiving burden; however, the needs of dementia caregiving are unique, and attitudes towards adopting technology from the perspectives of all the stakeholders involved in dementia caregiving are unclear. This study aims to assess the acceptability and feasibility of a technology-driven platform to facilitate care coordination platform, Care4AD, from the end-user perspective.

METHODS

Care4AD includes three components: (1) Care4AD app: the app is used by caregivers to coordinate care, monitor physical activity, and schedule reminders; (2) Care4AD tablet: a smart tablet is used by the care recipient to display scheduled reminders; and (3) Care4AD tags: a series of wireless sensor tags attached to various objects of daily care to facilitate monitoring instrumental activities of daily living (IADL) and adherence to scheduled tasks. Stakeholders in caregiving, including 11 experts in dementia care (age: 53.3 ± 8, 73% female), 10 individuals with dementia (IWD) (age: 76.1 ± 7.3, 50% female), and 14 caregivers (age: 66.9 ± 10.6, 75% female) were interviewed to determine perceived ease of use, attitude towards use, and perceived usefulness, based on the technology acceptance model (TAM) questionnaire. Additionally, we assessed technology anxiety and concerns with data sharing by caregivers and IWD. The interviews were conducted through videoconferencing or in-person meetings. The interview was composed of open-ended questions, a demonstration of the proposed Care4AD platform, and a survey based on TAM.

RESULTS

Compared to the neutral response, stakeholders showed significantly higher acceptance (70-100% satisfied to highly satisfied, p < 0.05) for all components of the TAM. Among IWD, age (r = -0.68, p = 0.03) and for caregivers the perceived ease of use (r = 0.73, p < 0.01) were significant predictors of attitude towards using the technology. Interestingly, neither concerns about data sharing nor educational level were limiting factors in the acceptability of the system in our sample.

CONCLUSION

Overall, the results support a high perception of usefulness, ease of use, and attitude towards using Care4AD. The key barriers to adopting such technology are the age of IWD and the caregiver's perception of ease of use. Future studies are warranted to explore the effectiveness of such a platform to reduce caregiver stress and improve the quality of life and independence of IWD.

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

Reshaping healthcare supply chain using chain-of-things technology and key lessons experienced from COVID-19 pandemic

The COVID-19 (Corona virus disease 2019) pandemic continues to slash through the entire humanity on the earth causing an international health crisis and financial uncertainty. The pandemic has formed a colossal disruption in supply chain networks. It has caused piling higher mortality in patients with comorbidities and generated a surging demand for critical care equipment, vaccines, pharmaceuticals, and cutting-edge technologies. Personal protective equipment, masks, ventilators, testing kits, and even commodities required for daily care have been scarce as lockdown and social distancing guidelines have kicked in. Amidst COVID-19, implementing and executing key processes of the healthcare supply chain (HSC) in a secured, trusted, effective, universally manageable, and the traceable way is perplexing owing to the fragile nature of the HSC, which is susceptible to redundant efforts and systemic risks that can lead to adverse impacts on consumer health and safety. Though the crisis shone a harsh light on the cracks and weaknesses of the HSC, it brings some significant insights into how HSC can be made more resilient and how healthcare industries figure out solutions to mitigate disruptions. While there are innumerable experiences learned from the disruption of this crisis, in this paper, five important areas to analyze the most vital and immediate HSC enhancements including building a resilient supply chain, thinking localization, implementing reliable reverse logistics, breaking down extant silos to achieve end-to-end visibility, and redesigning HSC using digitalization are emphasized. This work identifies important features related to CoT and HSC. Also, this study links these lessons to a potential solution through Chain of Things (CoT) technology. CoT technology provides a better way to monitor HSC products by integrating the Internet of Things (IoT) with blockchain networks. However, such an integrated solution should not only focus on the required features and aspects but also on the correlation among different features. The major objective of this study is to reveal the influence path of CoT on smart HSC development. Hence, this study exploits (i) fuzzy set theory to eliminate redundant and unrelated features; (ii) the Decision-Making and Experimental Evaluation Laboratory (DEMATEL) method to handle the intricate correlation among different features. This fuzzy-DEMATEL (F-DEMATEL) model attempts to direct CoT technology towards smart HSC by identifying the most influencing factors and investors are recommended to contribute to the development of application systems. This work also demonstrates how CoT can act a vital role in handling the HSC issues triggered by the pandemic now and in the post-COVID-19 world. Also, this work proposes different CoT design patterns for increasing opportunities in the HSC network and applied them as imperative solutions for major challenges related to traditional HSC networks.

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning

Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.