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Cyber Physical System (CPS) has provided an outstanding foundation to build advanced industrial systems and applications by integrating innovative functionalities through Internet of Things (IoT) and Web of Things (WoB) to enable connection of the operations of the physical reality with computing and communication infrastructures. A wide range of industrial CPS-based applications have been developed and deployed in Industry 4.0. In order to understand the development of CPS in Industry 4.0, this paper reviews the current research of CPS, key enabling technologies, major CPS applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art CPS in Industry 4.0 from Web of Science (WoS) database (including 595 articles) and proposes a potential framework of CPS systematically.

At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2

Medical 4.0 is now emerging as the fourth medical revolution. It represents the applications of electronically supported Information Technology, microsystem, high level of automation, personalized therapy, and Artificial Intelligence (AI)-enabled intelligent devices enabled through the Internet of Medical Things (IoMT). In the current scenario, the COVID-19 pandemic has a significant effect on global healthcare, and this impact is also observed in associated fields. There is a requirement for proper telehealth management and remote monitoring systems in healthcare. Medical 4.0, if implemented, can adequately handle the ongoing situation in the medical field as it will provide applications of advanced technologies to take care of the challenges of the COVID-19 outbreak. This paper studies Medical 4.0 exclusively and also in the context of COVID-19. The paper provides a brief of the significant medical revolution that has happened so far and identifies the significant supporting technologies of Medical 4.0. It also discusses the primary capabilities of Medical 4.0 for healthcare during the COVID-19 pandemic crisis. The roles of Medical 4.0 in healthcare during the COVID-19 pandemic are studied, and finally, this paper identifies 10 significant applications of Medical 4.0 in healthcare during COVID-19-type pandemics. We observe that the contemporary phase of development and mass-level production of intelligent medical devices has not happened in the same way as it has happened for smart electronic devices and application devices. Engineers will have a prominent role in taking up the healthcare challenges that can reach the common man.

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for which patients the graph is most accurate. We identify sample size and unmeasured confounders as major sources of error in the health knowledge graph. We introduce a method to leverage non-linear functions in building the causal graph to better understand existing model assumptions. Finally, to assess model generalizability, we extend to a larger set of complete patient visits within a hospital system. We conclude with a discussion on how to robustly extract medical knowledge from EHRs.

Mental illnesses are becoming increasingly prevalent, in turn leading to an increased interest in exploring artificial intelligence (AI) solutions to facilitate and enhance healthcare processes ranging from diagnosis to monitoring and treatment. In contrast to application areas where black box systems may be acceptable, explainability in healthcare applications is essential, especially in the case of diagnosing complex and sensitive mental health issues. In this paper, we first summarize recent developments in AI research for mental health, followed by an overview of approaches to explainable AI and their potential benefits in healthcare settings. We then present a recent case study of applying explainable AI for ADHD diagnosis which is used as a basis to identify challenges in realizing explainable AI solutions for mental health diagnosis and potential future research directions to address these challenges.

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.

The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis. Recently, many deep learning methods have been shown with impressive successes in predicting disease status with raw input data. However, the “black-box” nature of deep learning and the highreliability requirement of biomedical applications have created new challenges regarding the existence of confounding factors. In this paper, with a brief argument that inappropriate handling of confounding factors will lead to models’ sub-optimal performance in real-world applications, we present an efficient method that can remove the inuences of confounding factors such as age or gender to improve the across-cohort prediction accuracy of neural networks. One distinct advantage of our method is that it only requires minimal changes of the baseline model’s architecture so that it can be plugged into most of the existing neural networks. We conduct experiments across CT-scan, MRA, and EEG brain wave with convolutional neural networks and LSTM to verify the efficiency of our method.

Health information technology (HIT) refers to the concept of applying cloud-based services, internet, connected network, etc. in healthcare. It mainly utilizes electronic health records, information, and data related to the patients for providing the treatment and services in a more effective and advanced manner. This study attempts to propose roles and applications and identify the impact of the concept of HIT on serving the patients during the ongoing COVID-19 pandemic period. This paper also assesses the significant impact of HIT in the healthcare sector during the COVID-19 crisis. The medical decision support, e-sign-off tools, bar-coding approach, advanced medicine dispensing, e-patient portals, etc. are numerous data-sharing and network system-based HIT services. It has many capabilities to shift the working culture of medical facilities while serving and treating patients with a higher care level and impressive satisfaction, especially so during this COVID-19 pandemic. Interoperability and tele-healthcare have also become practicable with the proposed HIT approach.

This paper looks at the management of service innovation. In particular, it explores the challenge of public services and argues that there is a need for new approaches to the ways which engage users as more active co-creators within the innovation process. It draws on wider research on radical innovation being carried out as part of a long-term international programme and reports on a series of case studies of experiments in the health sector in the UK using tools like ethnography and prototyping to enable innovation.

The paper argues that a potentially valuable toolkit can be found in the field of design methods. By their nature, design tools are used to help articulate needs and give them shape and form; as such they are critical to the "front end" of any innovation process. Methods like ethnography allow for deep insights into user needs, including those not clearly articulated whilst prototyping provides the possibility of creating a set of "boundary objects" around which design discussions which include users and their perspectives can be carried out.

One of the major areas where social robots are finding their place in society is for healthcare-related applications. Yet, very little research has mapped the deployment of socially assistive robots (SARs) in real settings. By using a documentary research method, we traced back 279 experiences of SARs deployments in hospitals, elderly care centers, occupational health centers, private homes, and educational institutions worldwide that involved 52 different robot models. We retrieved, analyzed, and classified the functions that SARs develop in these experiences, the areas in which they are deployed, the principal manufacturers, and the robot models that are being adopted. The functions we identified for SARs are entertainment, companionship, telepresence, edutainment, providing general and personalized information or advice, monitoring, promotion of physical exercise and rehabilitation, testing and pre-diagnosis, delivering supplies, patient registration, giving location indications, patient simulator, protective measure enforcement, medication and well-being adherence, translating and having conversations in multiple languages, psychological therapy, patrolling, interacting with digital devices, and disinfection. Our work provides an in-depth picture of the current state of the art of SARs’ deployment in real scenarios for healthcare-related applications and contributes to understanding better the role of these machines in the healthcare sector.

Robotics is a disruptive technology that has already revolutionized patient healthcare globally. This technology is presently helping to perform various essential tasks such as conducting operations via numerous specializations and managing the entire operating room. Robot surgery is, in reality, available worldwide for knee substitution, correction of the hernia, and colon resection. Surgical robots entered the operating theatres far before entering other medicine-related robotics applications and now facilitate better outcomes for a whole range of healthcare products. In the COVID-19 pandemic, some robots were used in hospitals to deliver medicines, screen, perform odd jobs, and maintain hygienic conditions. This paper provides an overview about robotics and its various applications useful for healthcare. Significant enhancement, quality services, and advancements in healthcare services are also discussed. Here, we have identified the role of robotics in healthcare as a technology that dramatically changes the healthcare field. An artificial intelligence robot can duplicate creativity via algorithms, and its programming too plays a crucial role. Hospitals can now save time and money by removing the need for physical chores for different jobs. It is helpful for surgical training, exoskeletons, intelligent prostheses and bionics, robotic nurses, treatment, medicines, logistics, telepresence, and cleaning services. Robotics technologies such as gesture control, machine view, voice recognition, and touch sensor technology are also available. The future is bright with lower installation and maintenance costs.

The coronavirus (COVID-19) pandemic is one of the biggest challenges in the field of healthcare. Nanomedicine is a developing area that has the potential to treat various diseases and control infections. Now, its applications are open for the treatment of COVID-19. We have studied relevant papers through Scopus, Google Scholar, Science Direct and ResearchGate on nanomedicine in context of COVID-19. This paper provides detailed information about nanomedicine in the context of healthcare. It further identifies the primary challenges faced in the current situation. This study provides details about the advancements in the area of nanomedicine in healthcare for fighting the COVID-19 pandemic. Finally, we have identified and discussed various significant applications of nanomedicine in solving challenges thrown by the COVID-19 pandemic. Researchers can work on developing applications of nanoparticles with the size of the novel Coronavirus. Nanomedicine is helpful to repair the cells of an infected patient the help of repair proteins. It also plays a vital role in testing medicine and helps many clinical trials get approval from healthcare agencies. In the future, nanomedicine will be helpful for fighting against this pandemic and creating advancements in healthcare.

3D printing applications help solve challenges in the field of healthcare. These technologies evolved to produce custom-made medical devices and implants for patients and enhance medical education and research. This paper aims to make readers aware of the role of 3D printing in the field of medical education. 3D printing technologies are part of additive manufacturing (AM) technologies. 3D printing shows excellent potential with unconventional materials like different types of plastic, ketones, wood, human cells, metal powder, ceramics, composites, smart material, etc. This manufacturing method is suitable for producing complex and intricate shaped medical objects of the required property with lesser wastage of material. This paper introduces 3D printing technology and the need to carry out this study related to medical education and research. A brief literature review of 3D printing has been carried out. The paper further discusses the capabilities of 3D printing in the field of medicine. Patient-specific 3D models are being designed and then manufactured and implanted. 3D models of defective body parts help surgical planning and better part designing. Finally, the paper discusses significant roles of 3D printing in healthcare education in a tabular form. For the future, this technology has immense potential for medical education, surgical planning and support including for a clear understanding of the disease.

Delay or queue length information has the potential to influence the decision of a customer to join a queue. Thus, it is imperative for managers of queueing systems to understand how the information that they provide will affect the performance of the system. To this end, we construct and analyze two two-dimensional deterministic fluid models that incorporate customer choice behavior based on delayed queue length information. In the first fluid model, customers join each queue according to a Multinomial Logit Model, however, the queue length information the customer receives is delayed by a constant $\mathrm{\Delta }$. We show that the delay can cause oscillations or asynchronous behavior in the model based on the value of $\mathrm{\Delta }$. In the second model, customers receive information about the queue length through a moving average of the queue length. Although it has been shown empirically that giving patients moving average information causes oscillations and asynchronous behavior to occur in U.S. hospitals, we analytically and mathematically show for the first time that the moving average fluid model can exhibit oscillations and determine their dependence on the moving average window. Thus, our analysis provides new insight on how operators of service systems should report queue length information to customers and how delayed information can produce unwanted system dynamics.

Healthcare-associated infections are a significant concern in acute care facilities across the US. Studies have shown the importance of a hygienic patient environment in reducing the risk of such infections. This has caused an increased interest in ultraviolet (UV-C) light disinfectant technology as an adjunct technology to provide additional pathogen reduction to environmental surfaces and patient care equipment (i.e., surfaces). It is also well known that UV-C light can cause premature degradation of materials, particularly certain plastic materials. However, there is little information in the literature regarding characterizing this degradation of plastics and other materials used for surfaces in healthcare. This study aims to evaluate multiple characterization techniques and propose a systematic approach to further understand early onset degradation of plastics due to UV-C exposure. Susceptibility and modes of degradation of multiple plastic materials were compared using the techniques evaluated. Ten grades of plastic materials were exposed to UV-C light in a manner consistent with standards given in the healthcare and furniture industry to achieve disinfection. These materials were characterized for visual appearance, chemical composition, surface roughness and hardness using light microscopy, spectrophotometry, contact angle analysis, infrared spectroscopy, profilometry and nanoindentation. All characterization methods were able to identify one or more specific degradation features from UV-C exposure covering different aspects of physicochemical properties of the surfaces. However, these methods showed different sensitivity and applicability to identify the onset of surface damage. Different types of surface materials showed different susceptibility and modes to degradation upon UV-C light exposure. UV-C disinfection can cause detectable damage to various surfaces in healthcare. A characterization approach consisting of physical and chemical characterizations is proposed in quantifying surface degradation of a material from UV-C exposure to address the complexity in modes of degradation and the varied sensitivity to UV-C from different materials. Methods with high sensitivity can be used to evaluate onset of damage or early stage damage.

Bioengineering (BE) technology has significant influence on the healthcare environment. This has grown steadily particularly since the medical practice has become more technology based. We have tried to assess the impact of bioengineering in tackling the COVID-19 pandemic. The use of bioengineering principles in healthcare has been evaluated. The practical implications of these technologies in fighting the current global health pandemic have been presented. There has been a shared drive worldwide to harness the advancements of bioengineering to combat COVID-19. These efforts have ranged from small groups of volunteers to large scale research and mass production. Together the engineering and medical fields have worked to address areas of critical need including the production and delivery of personal protective equipment, ventilators as well as the creation of a viable vaccine. The fight against COVID-19 has helped highlight the work and contributions of so many professionals in the bioengineering fields who are working tirelessly to help our health services cope. Their innovation and ingenuity are paving the way to successfully beat this virus. We must continue to support these fields as we evolve our health systems to deal with the challenges of healthcare in the future.

The revolutionary digital technology of drones, also known as unmanned aerial vehicles (UAVs), has altered healthcare. This technology proved highly effective for healthcare, research, start-ups, and large corporations. Various drones are used across the industries such as infrastructure, transportation, insurance, telecommunications, agriculture, media and entertainment, security, and mining. Drones have been utilized to help the medical industry for several years, with numerous start-up firms with considerable investment-testing innovative methods. This new drone delivery network will give healthcare practitioners and the communities they serve better access to critically needed therapeutic goods. Compared to traditional approaches, drones to map disaster zones give better cost savings and faster reaction times. Drones can immediately deploy, provide high-resolution and three-dimensional (3D) mapping, identify hotspot locations with the most damage, and upload data in real-time to coordinate rescue operations. This study is mainly about drones and their primary functions. Devoted features and various aspects associated with drone technology for healthcare are briefly discussed and, finally, significant applications of drones for healthcare are identified and discussed. The COVID-19 pandemic has brought to light several long-hidden health disparities worldwide. Drones have resulted in innovation, such as the unprecedented success of developing new vaccinations at record speed. Drones can distribute vaccinations in low-income countries, lowering transportation costs and increasing immunization rates. People are now receiving good care, and the medical infrastructure is also improving, which is made possible by drone technology.

Software-defined network (SDN) is a new network structure, which has the characteristics of centralized management and programmable, and is widely used in the field of Internet of things. Distributed denial of service (DDoS) attack is one of the most threatening attacks in SDN network. How to effectively detect DDoS attacks has become a research hotspot in the field of SDN security management. Aiming at the above problems, this paper proposes a DDoS attack detection method based on Deep belief network (DBN) in SDN network architecture. By extracting the characteristics of OpenFlow switch flow table entries, DBN algorithm is trained to detect whether there are DDoS attacks. The experimental results show that the method is better than the other algorithms in accuracy, precision and recall.

The current demographic development puts even greater demands on the healthcare sector which is already struggling with scarce resources and constant pressure of cost reductions. This paper, through multiple case studies, aims to describe how automation of hospital internal logistics can be a tool in improving efficiency. The results include several potential implementations for patient transports, waste handling and small goods transports. However, organizational issues as lack of ownership and a strategic view render difficulties and need to be dealt with. The authors conclude that transfer of knowledge and technology used in the manufacturing industry would be beneficial.

In spite of the continued importance of an innovation's attributes to research methodologies, and the increasing tendency toward multidimensional conceptualizations, the lack of a theoretically derived and empirically developed classification of innovations, conceived in terms of these perceived characteristics, continues to deter substantive research in the area. The absence of a stable descriptive framework has constrained researchers' facility to develop cross-case and cumulative research. In this paper, in which innovations are conceptualized as complex and multi-dimensional, we report on a mixed-method, exploratory study addressing the question of innovation classification. Data from a rigorous thematic investigation of the literature and four case studies, are synthesized into a descriptive framework incorporating 13 variables (innovation attributes). Following operationalization of the framework, we conduct a cluster analysis of the returns from a post-adoption survey of 310 innovations. Three distinct innovation types are identified: readily-adopted, challenging and under-cover. The attributes disruption, observability, profile and risk were found to be particularly important in distinguishing clusters that offer opportunities for new theoretical development. The UK National Health Service (NHS) forms the context for the study. Implications for theory and practice are examined.