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Uncapping Ultimate Machine Learning for Advanced Manufacturing Optimization: Steering Supply Chain Projecting Sustainability
Abstract
The disruptive impact of Machine Learning presents an opportunity to rethink the optimization of industrial processes, especially in the complex supply chain. The need to reduce environmental effect is driving a paradigm change in the industrial sector towards sustainability. The world struggles associated with sustainable development, the manufacturing industry is leading the charge in pursuing efficiency and environmentally responsible methods. The revolutionary potential of the machine learning to revolutionize factory optimization especially in the supply chain is examined. This chapter focuses on the understanding of current challenges in manufacturing optimization for sustainability; explore the fundamentals of Machine Learning and it's application to manufacturing; analyze diverse aspects and examples of Machine Learning in supply chain optimization; discuss the potential impact of Machine Learning on sustainability within the manufacturing sector while reducing its environmental impact and advancing global sustainability.
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This book chapter delves into the profound synergy between Biomedical Engineering and the burgeoning realm of augmented reality (AR) and virtual reality (VR) applications. Through a meticulous examination of state-of-the-art advancements and a forward-looking analysis of future prospects, this chapter endeavors to offer a comprehensive resource for a diverse audience, including dedicated researchers, seasoned healthcare practitioners, discerning educators, and aspiring students. The integration of these technologies has the potential to revolutionize medical training, patient education, surgical planning, and therapy delivery, ushering in an era of more personalized and effective healthcare solutions. This chapter serves as a guiding beacon in navigating this transformative landscape, ensuring that the adoption of AR/VR technologies is not only innovative but also ethically grounded and attuned to the highest standards of patient care.
The rapid evolution of emerging technologies in healthcare is reshaping the field of medical practices and patient outcomes, ushering in an era of unprecedented innovation. This narrative review touches upon the transformative impacts of various technologies, including virtual reality (VR), augmented reality (AR), the internet of medical things (IoMT), remote patient monitoring (RPM), financial technology (fintech) integration, cloud migration, and the pivotal role of machine learning (ML). It emphasizes the collaborative impact of these technologies, which is reshaping the healthcare landscape. Virtual reality and AR revolutionize medical training, IoMT extends healthcare boundaries, RPM facilitates proactive care, and fintech integration enhances financial processes. Cloud migration ensures scalable and efficient data management, while ML harnesses algorithms for diagnostic precision and personalized treatment.
This systematic review explores the evolving landscape of mental health advocacy through the lens of technology, investigating its role, impact, and future directions. Tracing historical perspectives, from grassroots movements to digital revolutions, the review analyzes the integration of social media, mobile applications, virtual reality, and artificial intelligence in advocacy efforts. Identified factors influencing effectiveness include accessibility, user engagement, privacy, cultural sensitivity, integration with traditional approaches, and collaborative partnerships. Privacy protection, inclusivity, quality assurance, stigma reduction, and user autonomy are ethical considerations. Future directions emphasize personalized AI interventions, gamification, VR/AR applications, telehealth integration, and community-centric platforms. Balancing innovation with ethical practice is critical for realizing technology's potential in fostering a more connected, informed, and supportive mental health advocacy landscape. Keywords: Mental Health Advocacy, Technology, Digital Innovation, Ethical Considerations, Inclusivity.
The evidence for the use of Augmented Reality (AR) in treating specific phobias has been growing. However, issues of accessibility persist, especially in developing countries. The current study examined a novel, but relatively simple therapist guided smartphone-based AR Exposure Treatment (ARET) of spider phobia. Participants who reported symptoms of Arachnophobia were randomized into one of three comparison groups: ARET ( n = 20), traditional in vivo exposure therapy (IVET; n = 18) and a waitlist control group ( n = 17). Behavioral approach, subjective symptom measures, and galvanic skin response were assessed pre- and post-treatment. The study was concluded with a one-month follow up assessment. Results indicated that both treatment groups showed statistically significant and clinically meaningful improvements in behavioral approach at post-test that were maintained at 1 month follow- up, compared to the wait-listed group. Moreover, the treatment groups demonstrated significant improvements in subjective symptom report at 1-month follow up. Given its utility and potential accessibility, our findings suggest that future AR evaluation research could be conducted in therapy settings with minimal resources.
Breast cancer stands as the most prevalent cancer globally, necessitating comprehensive care. A multidisciplinary approach proves crucial for precise diagnosis and treatment, ultimately leading to effective disease management. While surgical interventions continue to evolve and remain integral for curative treatment, imaging assumes a fundamental role in breast cancer detection. Advanced imaging techniques not only facilitate improved diagnosis but also contribute significantly to the overall enhancement of breast cancer management. This review article aims to provide an overview of innovative technologies such as virtual reality, augmented reality, and three-dimensional imaging, utilized in the medical field to elevate the diagnosis and treatment of breast cancer. Additionally, the article delves into an emerging technology known as the metaverse, still under development. Through the analysis of impactful research and comparison of their findings, this study offers valuable insights into the advantages of each innovative technique. The goal is to provide physicians, surgeons, and radiologists with information on how to enhance breast cancer management.
Uncertainties caused by many internal and external factors can lead to supply-chain disruptions, increasing the vulnerability and cost of operations. In particular, the COVID-19 pandemic, whose worldwide emergence was not foreseen, has become a major threat to supply-chain resilience and has caused the disruption of global network connections. The purpose of this study is to examine in depth the impact of uncertainty on supply-chain resilience and to determine whether information sharing has a moderating effect on this interaction. The relationships proposed in the research model are tested through empirical analyses in SEM applied to 244 survey data points from internationally operating manufacturing firms in Turkey. The findings reveal several key insights. First, it is concluded that all dimensions of uncertainty, except technological uncertainty, negatively affect supply-chain resilience. Second, although no direct effect of technological uncertainty on supply-chain resilience is found, technological uncertainty has a negative effect on resilience when the moderating role of internal and supplier information sharing is taken into account. Low-level information sharing, as opposed to high-level, creates variation in the severity of supply-chain resilience at different levels of technological uncertainty. In addition, it is worth noting that a high level of information sharing with suppliers under high-uncertainty conditions negatively affects supply-chain resilience. The results of this study, conducted within the framework of the Contingent Resource-Based Theory, demonstrate compatibility with the theory. Based on all the findings, this study suggests that managers should adopt proactive strategies to maintain high supply-chain resilience, considering today’s highly uncertain conditions.
In today’s world, crises like the COVID-19 pandemic and ongoing global changes pose significant challenges for manufacturing companies. Resilience, the ability to withstand and recover from disruptions, is essential for survival. To make resilience actionable, the discussion introduces a four-step Circular Resilience Assessment Tool. To assess their resilience score, companies undergo a risk and vulnerability assessment, a qualitative resilience factor assessment, a suitable strategies identification phase, and a quantitative performance assessment. This tool guides companies in evaluating their resilience before, during, and after hypothetical or occurred crises. The balance among qualitative and quantitative aspects, encompassing technical, social, and organizational considerations, ensures that an omni comprehensive point of view is adopted in evaluating the overall resilience score of a company. This innovative approach empowers companies to not only survive crises but also to gain a competitive advantage and expand their market share in the long term. The work provides a thorough description of each of the four steps, accompanied by examples. The Circular Resilience Assessment Tool is designed to be as specific as necessary and as general as possible, thus making it a valuable resource for a variety of enterprises.
Today, with the development of technology, the concept of the virtual world is gaining more and more importance. "Metaverse", which has become popular in recent years, aims to provide an environment where people can interact, do business, have fun and even live in the digital world. Metaverse, which has become increasingly popular in recent years, offers many different uses in the field of mental health. However, we do not yet have enough information about the effects of this technology on mental health. The effect of Metaverse on mental health has been the subject of many researches that it can be used in areas such as the treatment of psychological disorders, stress reduction, self-confidence, and development of social interaction skills. The biggest advantage of using Metaverse for psychiatric treatment is that the virtual world is separated from the real world. Therefore, the stress, anxiety and other emotional reactions experienced during treatment have no real-life consequences. In addition, the therapist is easier to access in the virtual world and can help patients adapt to treatment more easily. However, the use of Metaverse for psychiatric treatment also has some disadvantages. For example, this method of treatment may be an unrealistic experience for some patients and not effective enough for others. Also, this treatment modality has limited accessibility as it requires high cost equipment and technology. In particular, there has not been enough research on the relationship between the use of Metaverse in the diagnosis and treatment of psychiatric diseases. More research is needed on the effect of Metaverse on mental health. In particular, more studies are needed on its long-term effects and effects on different people. In this review, studies investigating the potential relationship between Metaverse technology and psychiatry and how this technology can be used in the psychiatric field are reviewed.
The resilience and reliability of healthcare supply chain models were put to the test by the Corona Virus Disease 2019 (COVID-19). This study investigated the application of supply chain systems in South African healthcare institutions during the COVID-19 pandemic. A systematic literature review (SLR) was employed to explore the performance of existing supply chain systems, followed by a case study that tested and compared the acquisition and distribution of COVID-19 resources. The SLR revealed that most of the flare-ups were exacerbated by the acquisition of insufficient resources and speculative shortages as the supply chain systems got overwhelmed by the unprecedented demand. The simulation of the real-world data of South Africa revealed gaps in the distribution of resources, allocation of medical staff to administer COVID-19 vaccines, and shortages of vaccines. The study recommends development of effective contextual (SA) healthcare supply chain systems to support the containment of pandemic flare-ups. The study was conducted in South Africa and only reported data was used.
Digital twin (DT), referring to a promising technique to digitally and accurately represent actual physical entities, has attracted explosive interests from both academia and industry. One typical advantage of DT is that it can be used to not only virtually replicate a system’s detailed operations but also analyze the current condition, predict the future behavior, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate the remote monitoring, diagnosis, prescription, surgery and rehabilitation, and hence significantly alleviate the heavy burden on the traditional health- care system. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and the conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT.
Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In Wireless Body Area Network (WBAN), for example, the patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity rendering the data transmission slower. Cognitive Radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors rendering the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.
According to WHO and other key statistics, cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. By identifying cancer early, it is more likely to respond to treatment, resulting in a better chance of survival as well as a lower cost of treatment. Early detection of cancer and avoiding delays in treatment can significantly improve the lives of cancer patients. Nanomaterial-based sensors with the ability to extract and identify tumor-specific biomarkers, circulating tumor cells, or extracellular vesicles secreted by the tumor have the potential to diagnose cancer considerably earlier and enhance patient long-term survival. In this review, we provide the application of nanosensors for the early detection of cancer and the recent techniques for clinically diagnosing cancer patients' biomarker levels. Furthermore, the presented works are compared based on their sensitivity and selectivity in the detection of different kinds of cancer biomarkers.
Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.
The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities. In this paper, an intelligent healthcare system is proposed which consists of nano sensors that collect real-time health status and transfer it to the doctor’s server. Time consumption and various attacks are major concerns, and some existing techniques contain stumbling blocks. Therefore, in this research, a genetic-based encryption method is advocated to protect data transmitted over a wireless channel using sensors to avoid an uncomfortable data transmission environment. An authentication procedure is also proposed for legitimate users to access the data channel. Results show that the proposed algorithm is lightweight and energy efficient, and time consumption is 90% lower with a higher security ratio.
An assimilated navigation model based on advanced optical systems involves the integration of optical sensors and processing algorithms to provide accurate and reliable navigation information. These sensors gather information about the surrounding environment, such as the location of landmarks, the position of other objects, and the topography of the terrain. The processing algorithms would use this information to create a detailed model of the environment and the vehicle's position within it. This model could be updated in real-time as the vehicle moves and new information is gathered. The proposed IoT and AI enabled navigation model can provide several benefits, including increased accuracy and reliability compared to traditional GPS-based systems, the ability to operate in areas where GPS signals are weak or unavailable, and the potential for greater autonomy and automation in navigation tasks. The ability of integrated navigation systems can be enhanced with the aid of advanced optical systems (AOS), AI and IoT. Therefore, this research is proposing an assimilated navigation system based on AOS, AI and IoT. Assimilated navigation models based on AOS and smart technologies can provide high-precision navigation solutions for various applications such as aviation, maritime and land-based transportation systems. Firstly, the output signal model of the navigation system based on AOS is designed. Then the reliability parameter estimation and target location tracking of the integrated navigation of objects are designed by using the entropy weight characteristic analysis method. The integrated development of reliability parameter estimation and target location tracking system of navigation system is developed innovatively by using machine intelligence techniques. The simulation test analysis shows the superior performance of the proposed method in improving the intelligent operation and maintenance control ability of the navigation output signal with the aid of advanced optical technologies. By integrating components such as optical sensors, machine intelligence based mechanisms, and GPS backup, the proposed AOS based framework for smart navigation system provides accurate, reliable, and user-friendly navigation solutions for various applications.
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The recent advancements in the field of Internet of Things (IoT), AI, Big Data, Blockchain, AR/VR, Cloud platform, Quantum computing, Cybersecurity, and Telecommunication Technology (ICT) enabled the promotion of conventional computer-aided industry to Metaverse ecosystem that is powered by AR/VR-driven technologies. In this paradigm shift, the integrated technologies of IoT and AI play a vital role to connect the cyberspace of computing systems and virtual environments. AR/VR supports a huge range of industrial applications such as logistics, the food industry, and manufacturing utilities. IoT aims to improve production throughput, operation efficiency and mitigate machine downtime. Decentralization of AR/VR systems, heterogeneity of AR/VR data, diversity of systems and AR/VR devices, and network complexity are the characteristic features of IoT. These results in numerous challenges including resource constraints of AR/VR devices, poor interoperability, heterogeneity of AR/VR systems, and several privacy and security vulnerabilities in the Metaverse ecosystem.
Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and bibliometric analysis was performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the results of the keyword co-occurrence network, IoT healthcare applications, blockchain applications, artificial intelligence (AI) techniques, 5G telecommunications, as well as data analytics and computing technologies emerged as important topics. The co-citation network analysis reveals other important themes, including authentication schemes, fog computing, cloud-IoT integration, and cognitive smart healthcare. Overall, the review offers scholars an improved understanding of the current status of IoT research in healthcare and identifies knowledge gaps for future research. This review also informs healthcare professionals about the latest developments and applications of IoT in the healthcare sector.
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
Malfunction of human liver happens due to non-alcoholic fatty liver. Fatty liver measurement is used for grading hepatic steatosis, fibrosis and cirrho-sis. The various imaging techniques for measuring fatty liver are Magnetic Reso-nance Imaging, Ultrasound and Computed Tomography. Imaging modalities lead to the exposure of harmful radiation of electromagnetic waves because of frequent measurement. The continuous monitoring of fatty liver is never achieved through imaging techniques. In this paper, the human fatty liver measured through a Fatty Liver Sensor (FLS). The continuous monitoring of the fatty liver is achieved through the FLS. FLS is fabricated through the screen-printing with materials such as graphene and polyacrylic. The fatty liver sensor is placed around the liver surface for continuous measuring fatty liver. The signal acquired from the fatty liver sensor is processed using blind source separation, a filtering technique removes the random noise from the acquired signal. The denoised signal is processed with tunable Q wavelet transform (TQWT), of FLS based fatty liver measurement fatty liver signals. The continuous fatty liver volume measured and analysis are performed through Long-short term memory and internet of medical things (IoMT). The experimental results are validated with ultrasound lab values.
Cardiovascular events occurring in the bloodstream are responsible for about 40% of human deaths in developed countries. Motivated by this fact, we present a new global network architecture for a system for the diagnosis and treatment of cardiovascular events, focusing on problems related to pulmonary artery occlusion, i.e., situations of artery blockage by a blood clot. The proposed system is based on bio-sensors for detection of artery blockage and bio-actuators for releasing appropriate medicines, both types of devices being implanted in pulmonary arteries. The system can be used by a person leading an active life and provides bidirectional communication with medical personnel via nano-nodes circulating in the bloodstream constituting an in-body area network. We derive an analytical model for calculating the required number of nano-nodes to detect artery blockage and the probability of activating a bio-actuator. We also analyze the performance of the body area component of the system in terms of path loss and of wireless links budget. Results show that the system can diagnose a blocked artery in about 3 hours and that after another 3 hours medicines can be released in the exact spot of the artery occlusion, while with current medical practices the average time for diagnosis varies between 5 to 9 days.
In the healthcare industry, where concerns are frequently and appropriately focused on saving someone’s life, access to interfaces and computer systems storing sensitive data, such as medical records, is crucial to take into account. Medical information has to be secretive and protected by the laws of privacy with restrictions on its access. E-health security is a holistic notion that encompasses available medical data’s integrities and confidentiality which ensures that data are not accessed by unauthorized people and allow doctors to offer proper treatment. The patients’ data need to be secured on servers holding medical data. This work adds new features for ensuring storage and access safety through ITPKLEIN-EHO (integrated transformed Paillier and KLEIN algorithms) that use EHOs (elephant herd optimizations) to provide lightweight features. The key space affects lightweight encryption techniques in general. The EHOs (elephant herd optimizations) optimize key spaces by adjusting iteration rounds. The main goal is to encrypt EEGs (electroencephalographic signals) in healthcare and send it to end users using the proposed ITPKLEIN-EHO approach. This suggested technique utilizes MATLAB for its tests on various EEG data sets for implementation. The simulations of the proposed IRPKLEIN-EHO technique are evaluated with other existing techniques in terms of MSEs, PSNRs, SSIMs, PRDs, and encryption/decryption times.
The quality and completeness of the master data has a direct impact on the accuracy of purchasing processes in supply chains. Today, manufacturers and retail chains have both centralized catalogue solutions and distributed repositories supported by appropriate standards at their disposal. Despite the popularization of digitization of the synchronization processes of data describing products, research conducted around the world indicates basic errors that concern packaging at various levels, from the basic item, through cartons, to pallets. Therefore, incompleteness and unreliability of the data force the parties involved in the processes to remove errors, which leads to a deterioration of the sales economic parameters. However, the master data used in both B2B and B2C relations are not only the identifiers, classifiers, and dimension and weight information, but also a set of information on the composition and content of products, e.g. food products, which may affect the safety of consumers.
Therefore, a detailed verification of the information content provided by suppliers and producers for individual participants in the supply chain is required. Such activities require the work of specialized teams of expert auditors who must deliver a verdict on timeliness, quality, and completeness. The elements of Artificial Intelligence (AI), which can take over most of the controlling activities, are the perfect solution for this role. This paper identifies important factors as the places where important decisions are made regarding the approval or rejection of product/master data.
AI will be an important element of content verification in terms of consumer safety. The role of these mechanisms is particularly important in the context of the sale of food products and cosmetics, i.e. items that come into contact with the human body. Automation of these processes using this methodology and self-learning mechanisms will enable mass checking of entire databases in search of places that do not meet user safety requirements. The implementation of such mechanisms, whether in catalogue systems or distributed systems, will improve the substantive quality of product descriptions, and thus increase their usability and safety and build customer trust in the brands of individual manufacturers.
Financial portfolio management is a very time-consuming task as it requires the continuous surveying of the market volatility. Investors need to hire potential financial advisors to manage portfolios on their behalf. Efficient hiring of financial advisors not only facilitates their cooperation with investors but also guarantees optimized portfolio returns and hence, optimized benefits for the two entities. In this paper, we propose to tackle the portfolio optimization problem by efficiently matching financial advisors to investors. To this end, we model the problem as an automated crowdsourcing platform to organize the cooperation between the different actors based on their features. The recruitment of financial advisors is performed using a Generative Adversarial Network (GAN) that extrapolates the problem to an image processing task where financial advisors’ features are encapsulated in gray-scale images. Hence, the GAN is trained to generate, based on an investor profile given as an input, the ’ideal’ financial advisor profile. Afterwards, we measure the level of similarity between the generated ideal profiles and the existing profiles in the crowdsourcing database to perform a low complexity, many-to-many investor-to-financial advisor matching. In the simulations, intensive tests were performed to show the convergence and effectiveness of the proposed GAN-based solution. We have shown that the proposed method achieves more than 17% of the average expected return compared to baseline approaches.
Internet of Things (IoT) based wearable healthcare data monitoring is an emerging application of smart medicines. IoT-based communication and information exchange methods make it adaptable for the recent sixth-generation computing systems. The terahertz and large-scale processing of the 6th generation communication and computation technology is assimilated in the wearable sensor data exchange process. In this article, a fault-tolerant data processing method (FTDPM) is proposed to handle uneven sensor data. The non-uniform and unsynchronised observation interval-based sensor data is analyzed for its impact on healthcare recommendations. The reliability in the recommendation ensures the precise need for sensor data processing, mitigating the faults. In this reliability estimation process, a support vector machine classifier is used. This learning classifier differentiates the uniform and non-uniform sensor data traffic for providing reliable recommendations. The data from the uniform process is replicated in the non-uniform sequence for recommendation filling. This is carried out based on marginal classification and near-to-reliable data as classified using SVM. The IoT wearable alliance is exploited using the 6G communication paradigms in handling monitored data. The proposed method's performance is verified using the metrics processing time, recommendation failure, processing complexity, and recommendation response time.
An important goal of medical diagnosis is the ability to diagnose medical problems rapidly, enabling clinicians to treat patients before any irreversible or long-lasting damage can occur. This can be achieved by point-of-care testing which ensures fast detection of analytes near to the patient, facilitating a better disease diagnosis, monitoring, and management. It also enables quick medical decisions since the diseases can be diagnosed at an early stage leading to improved health outcomes for the patients and enable them to start early treatment. Nanotechnology has improved the sensitivity and linear ranges of various detection methods improving assessment of the disease onset and its progression and help to plan for treatment of many diseases. Therefore, the development of nanosensors for point-of-care diagnostics is an important area of research. Nanosensors have shown promise as theranostic tools and as nanoprobes for diagnostic imaging, disease monitoring, and the detection of pathogens and can provide real-time information of a patient’s condition which can be of significant benefit. This chapter discusses the current status of innovative nano-sensor arrays for medical diagnosis. Specifically, the chapter discusses various methods used in the development and fabrication of nanosensor arrays and their application in medical diagnosis.
Recent advances in mental healthcare technology, especially in virtual reality (VR) and augmented reality (AR), have revolutionized psychiatry and psychology. This chapter critically examines these innovations' potential in treating various mental disorders, from anxiety and depression to PTSD and ADHD. It emphasizes the controlled and customizable nature of VR environments, which empower individuals to confront fears within personalized settings. Interdisciplinary collaboration is stressed, showcasing applications in psychology, from assessment to clinical treatment. The chapter explores neuropsychological impacts, detailing how VR modulates emotions, enhances neural plasticity in stroke rehabilitation, and alters neural networks. Despite promise, challenges like treatment standardization and ethical concerns persist, demanding careful consideration. The chapter advocates for ongoing research and global interdisciplinary cooperation to fully exploit these technologies' potential in mental healthcare.
By producing immersive, individualized, and captivating therapeutic experiences, augmented reality (AR) and virtual reality (VR) may significantly transform mental health treatment. These technologies provide efficacious resolutions for exposure therapy, augmenting conventional methodologies, mitigating social disapproval, and fortifying the therapeutic alliance. Virtual and augmented reality increase the accessibility and convenience of therapy by enabling highly individualized interventions. Training for mental health professionals, rigorous research, compliance with data privacy regulations, and adherence to ethical standards are essential for responsible use. Augmented reality (AR) and virtual reality (VR) can expand the accessibility of mental health services as costs decrease, thereby ultimately enhancing the welfare of those in search of assistance and recovery. Incorporating augmented reality and virtual reality into clinical practice may make mental health treatment more engaging, effective, and individualized.
Objective: Falling is considered one of the major problems that may affect the elderly, leading to multiple health issues. Walking adaptability to environmental demands is essential for safe walking in the elderly. The aim of this study was to evaluate the efficacy of virtual reality (VR)/augmented reality (AR) treadmill training on balance performance and the risk of falls in the elderly. Materials and Methods: Sixty Saudi elderly individuals of both genders, aged between 60 and 70 years, participated in the study. The participants were categorized into two groups: the experimental and the control groups. Both groups received 1 hour of training: 30 minutes of conventional exercises and 30 minutes of gait training on the C-Mill VR/AR treadmill. The experimental group used the C-Mill treadmill with VR and AR games therapy. The control group had gait training on the C-Mill treadmill without VR and AR. The training for both groups was conducted for 6 successive weeks/three times a week. The changes in the scores of the following variables were recorded at baseline, after 6 weeks of training, and 4 weeks after the completion of training. These variables involved the time needed for completing the Timed Up and Go (TUG) test, overall stability indices of the Fall Risk (FR) test and Limit of Stability (LOS) test evaluated using the Biodex Balance System (BBS), and the time required for completing the LOS test. Results: Both groups demonstrated significant improvement in all measured variables immediately post-training, and this improvement persisted for 4 weeks after completing the training. The experimental group exhibited greater improvement in the recorded values of all measured variables compared with the control group following the training. Conclusions: This study concluded that C-Mill VR/AR treadmill training is effective in improving balance control and reducing the fall risk in the elderly.
Virtual reality (VR) and augmented reality (AR) stand on the edge of reality, transforming sectors like gaming, organization management, education, promotions, health services, and veterans' recovery. This chapter meticulously explores their therapeutic potential, underscoring their vital role in mental health assessment. The literature review identifies research gaps and spans applications in rehabilitation, emotional regulation, Autism, ADHD, PTSD, coping skills, cognitive behavioral therapy, stress reduction, pain management, chronic diseases, and mental health apps. A well-crafted entrepreneurial plan addresses technical, physical, social, and financial challenges, integrating ethical considerations, device limitations, cost efficiency, and data security. Forward-looking, it delves into future research trends, estimating their impact on the health industry and pivotal fields, concluding with a summary of transformative prospects in mental health through VR and AR. The future promises enriched well-being via cutting-edge technologies and innovative interventions.
Nanosensors are nanoscale devices that measure physical attributes and convert these signals into analyzable information. In preparation, for the impending reality of nanosensors in clinical practice, we confront important questions regarding the evidence supporting widespread device use. Our objectives are to demonstrate the value and implications for new nanosensors as they relate to the next phase of remote patient monitoring and to apply lessons learned from digital health devices through real-world examples.
Introduction:
The amount of research exploring the use of virtual reality [VR] and augmented reality [AR] technologies in health care has exploded. This has resulted in a massive body of work, making it difficult to obtain all of the research. The objective of this study was to map out and put together the scientific output of research and global trends in virtual and AR in pediatrics.
Method:
Publications were collected from the Web of Science [WoS] database. The R tool was used to categorize and evaluate the research outputs, as well as the most productive and influential countries, journals, institutions, authors, articles, subject areas, and the latest research themes. The most utilized and co-occurring keywords were also examined. Texts, tables, and images were used to assess and describe the retrieval of findings.
Results:
The research was based on information from 7423 publications. The strongest growth in publications occurred in 2020. The most productive and influential country was the USA. The journal was Pediatrics, the author was G Riva, and the institution was the University of Washington. The most frequently occurring keywords were simulation, rehabilitation, and stroke. The main research themes were therapy, surgical education, and rehabilitation. Pain, stroke, anxiety, depression, fear, dementia, and neurodegenerative illnesses were all common medical issues investigated.
Conclusion:
VR studies have mainly focused on surgical education or procedures, simulation technologies, and neurological conditions. Neurological conditions are linked to balance, gait, and rehabilitation, reflecting the prevalence of these disease groups. This article provides a thorough overview of VR and AR studies in the healthcare field. This work will allow academics, policymakers, and practitioners to gain a deeper understanding of the evolution of VR and AR studies in the healthcare field and its potential practical implications. Future VR and AR research should focus on bridging the gap between VR and AR healthcare research and clinical applications. Emerging trends in related fields, such as navigation, rehabilitation, stroke, dementia, and VR exposure therapy, should be given special attention.
Purpose
The Sustainable Lean Six Sigma (SLSS) adoption approach, advancements in Internet technologies and the use of Industry4.0 technologies has resulted in faster customer need fulfilment. The Industry4.0 technologies have resulted in a new paradigm where strategic and operational decisions are in favour of profitability and long-term viability. The purpose of this study is to identify Industry4.0-SLSS practices and sustainable supply chain performance metrics, as well as to develop a framework for decision-makers and managers to make supply chains more sustainable.
Design/methodology/approach
The 33 Industry4.0-SLSS practices and 24 performance metrics associated with the sustainable supply chain are shortlisted based on extensive literature review and expert opinion. The Pythagorean Fuzzy Analytical Hierarchy Process (PF-AHP) approach is used to evaluate the weights of Industry4.0-SLSS practices after collecting expert panel opinions. The Weighted Aggregated Sum Product Assessment (WASPAS) methodology used these weights to rank performance metrics.
Findings
According to the results of PF-AHP, “Product development competencies (PDC)” are first in the class of major criteria, followed by “Advanced technological competencies (ATC)” second, “Organisational management competencies (OMC)” third, “Personnel and sustainable competencies (PSC)” fourth and “Soft Computing competencies (SCC)” fifth. The performance metric “Frequency of NPD” was ranked first by the WASPAS method.
Research limitations/implications
The proposed paradigm helps practitioners to comprehend Industry4.0 technology and SLSS practices well. The identified practices have the potential to boost the sustainability and supply chain's performance. Organizational effectiveness will benefit from practices that promote a sustainable supply chain and the use of developing technology. Managers can evaluate performance using performance metrics that have been prioritized.
Originality/value
The present study is one of the unique attempts to establish a framework for enhancing the performance of the sustainable supply chain. The idea of establishing Industry4.0-SLSS practices and performance measures is the authors' original contribution.
Notice of Violation of IEEE Publication Principles
"Affirmative Fusion Process for Improving Wearable Sensor Data Availability in Artificial Intelligence of Medical Things,"
by P. M. Kumar, L. U. Khan and C. S. Hong,
in IEEE Sensors Journal, Early Access
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.
The submitting author, Priyan Malarvizhi Kumar, added the coauthors Latif U. Khan and Choong Seon Hong without their consent. Due to the nature of this violation, the Editor in Chief has decided the article will not be published in an issue of IEEE Sensors Journal.
Artificial Intelligence of Medical Things (AIoMT) is a hybridized outcome of Internet of Things (IoT), machine learning (ML) paradigms, and data analytics procedures for sophisticated healthcare services and applications. However, the fluctuating or lacking wearable sensors (WSs) data cause trivial computing errors that lead to incomplete diagnosis/ recommendation in healthcare applications. This article proposes a novel Affirmative Fusion Process (AFP) to enable high quality WS data with fewer fluctuations in in medical diagnosis. The proposed process assimilates sensed data with the existing datasets for avoiding discrete availability of WS data during the analysis. In this fusion process, based on the dataset inputs, the discreteness in the sensed data is identified. The discreteness is mitigated through precise replacement consideration from the existing datasets, preventing computational errors. The fusion process is monitored using simulated annealing and neural learning for output approximation and identification. The fused output with and without discreteness is identified for which annealing-based approximation is performed. In this process, the recurrence of the learning iterates is confined to identifying the final best solution. The proposed process is assessed using an activity dataset for the metrics fusion ratio, time delay, complexity, and data availability.
Investments in infrastructure platforms aim to enhance supply chain performance. We build on network‐based models of platform growth to characterize configurations of digital and physical supply chain infrastructure. Within these configurations, we define cross platform effect as the value gain accrued by a platform on one side of a multi‐platform configuration as network capability for the opposite side platform evolves. We posit that aligning cross platform effect across digital and physical infrastructure enhances system outcomes. Our arguments are informed by evidence on private‐public‐partnerships for developing production and distribution infrastructure in the pharmaceutical industry. We identify enablers and inhibitors for cross platform effects within these data in terms of product and process technology readiness, path dependent standards creation, organizational alignment, regulatory fragmentation, and the desire towards such infrastructure yielding resilient outcomes. Implications of findings, in shaping analytical and empirical research on managing supply chain infrastructure, at the levels of firms, industries, and public policies are discussed. This article is protected by copyright. All rights reserved
The smartphone has been broadly combined with nanosensors, including sensor chips, test strips, and handheld detectors for biochemical applications because of their ubiquitous and portability and availability. The smartphone-based nanosensors can primarily be classified through transducers. They combine superiorities of nanomaterials and sensing platforms that are used for selective, quick, and sensitive disease determination and are of great interest in the biology, chemistry, and medical communities. The prompt and precise diagnosis of infected people is the most important action in controlling diagnosis and monitoring of health services. Nanosensors must provide important requirements such as response accuracy, reproducibility, high selectivity, nontoxicity, sensitivity, and cost-effectiveness. This chapter provides a wide survey of a variety of smartphone nanosensors for disaster prevention using various approaches. This chapter also highlights the smart sensing methods and mentions their usage in disaster detection. It is also finalized with opportunities and challenges for diagnosis of diseases employing smartphone-based nanosensors.
The Internet of Things (IoT) has an amazing role in the development of numerous fields like health care, smart grids, disaster management, supply chain management, etc. It also simplifies the publics’ day-to-day lives and improves their collaboration with one another, environment as well as neighbors in a wide area. IoT achieves this by using devices, tools, fixed sensors, and carryable sensors/devices for computerized systems. Conversely, IoT systems are developing in dimension, intricacy, and the total number of linked devices due to this, several experiments and difficulties arise like safety, legitimacy, consistency, and scalability, it is very important to preserve and rise assurance in IoT systems by addressing the problems mentioned above. Blockchain’s features like safety, limpidity, consistency, as well as tracking mark it a flawless applicant to develop IoT systems, by resolving their complications and maintaining their forthcoming growth. The chapter outlines the main issues faced by IoT organizations and recommends the role of blockchain to resolve those issues. Likewise, it examines the status of recent research in integrating blockchain with IoT systems and the updated phases of implementation. Moreover, it deliberates numerous problems interlinked to the integration of IoT and blockchain. Furthermore, the paper highlights many benefits and needs of integration of both technologies.KeywordsInternet of ThingsBlockchainSecurityConnectivityInternetReliabilityTransmissionBlockDistributedTechnology
Substantial quantities of agri-fresh produce especially fruits and vegetables are lost and wasted at various channels and operational levels in the agri-fresh produce supply chain. This post-harvest loss and waste (PHLW) create an imbalance in demand–supply of fruits and vegetable which hinders the provision of a healthy and nutritious diet for the people. This research article aims to develop a cause-effect model by identifying and analyzing the key factors under five major operational issues (Demand forecasting, Production planning, Transportation, Inventory, and Inefficient harvesting) that leads to post-harvest loss and waste of fruits and vegetables in the agri-fresh produce supply chains (AFPSCs) in developing economies, specifically India. The empirical study evaluates sixteen key factors and is further analyzed by using the fuzzy Decision-Making Trial and Evaluation of Laboratory (F-DEMATEL) technique to understand cause-effect relationships among factors and identify significant causal factors. The results revealed a lack of coordination between production, processing, and fresh market (PHLW6), lack of seasonal demand forecasting for non-producing regions (PHLW1), poor knowledge sharing about demand and supply (PHLW2), insufficient logistics in the catchment area (PHLW10), limited availability of cold chain facility (PHLW16), improper planting and sowing time duration (PHLW5), no change in mindsets for the diversification of crops (PHLW3), and lack of specialized vehicles (PHLW8) are the eight significant causal factors which have high influence to lead the post-harvest loss and waste of fruits and vegetables in the supply chain, that needs special attention. At last, the sensitivity analysis was performed to test the robustness of the result and decrease the biases in the decision-making process. The insights of this outcome will assist in forming policies and making an operational and strategic decision for the reduction of PHLW of fruits and vegetables in the agri-fresh produce supply chain to attain sustainable development goals.
Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory.