Industry 5.0 and digital innovations: Antecedents to sustainable business model

The concept of Industry 5.0 is not just one more revolution but calls for a tectonic shift in digitization and operationalizing technology with connected value chain across sectors. It is human centric that promotes talents, diversity and empowerment coupled with resilience leading to agile and adaptable technologies with prime focus on sustainability. The COVID-19 pandemic has given impetus to digital transformation and accelerated the focus on other challenges of present time and with extended importance on people, planet and societal concepts. This study shall attempt to examine the nature of association between revolution of Industry 5.0 with perspectives to digital innovation and its implications toward bringing sustainable business model. The main objective of this chapter shall be to uncover interrelated questions in terms of sustainability perspectives of industries in framing business models. This study shall serve as a primer to significance of digital transformation with relevance to businesses that can lead to efficient use of scarce resources and optimal feasible solutions to the business models, given the institutional and organizational frameworks. Further, an attempt shall also be made to underpin the key facets of effects of Industry 5.0 on the knowledge economy. It shall delve into how digital innovations can yield benefits to industry in terms of competitiveness and sustainability with focus on Society 5.0 that attempts to balance economic development with the resolution of societal and environmental problems. It is not restricted to the manufacturing sector but addresses larger social challenges based on the integration of physical and virtual spaces. © 2023 by Parag Shukla and Surabhi Singh. All rights reserved.

Self-rated health implications of noise for open-plan office workers: An overview of the literature

In open-plan offices (OPO), workspaces without ground-to-ceiling dividers, noise is one of the most complained about aspects, causing physical and psychological impacts. With the increasing interest for a human-centric design, notably after the publication of ISO 22955, this review aims to identify the main noise sources in this office layout and the employees' perception of related health effects, evaluating the interventions proposed to overcome their impacts. Following the PRISMA guidelines, a review was conducted using the Scopus and PubMed databases, considering subjective questionnaires distributed in offices, which could include physical workspace assessment. It excluded studies limited to: (a) laboratory experiments;(b) isolated cognitive tests;(c) office layouts other than OPO;(d) systematic reviews;and (e) mathematical models. Sixty studies were identified and the screening process resulted in 11 selected for inclusion, which indicated irrelevant speech, chatting, and telephone ringing as the main noise sources causing productivity loss, stress, and low comfort rates due to distraction and lack of privacy. To overcome these impacts, researchers suggested the use of sound-absorbing surfaces, separated zones for different tasks and headphones, although their effectiveness relies on human behaviour and economic feasibility. Thus, the evidence indicates that noise is a recurrent issue in OPOs, it demonstrates the importance of appropriate acoustic performance of the workspace and the necessity of new studies regarding OPO workers' perception of noise and their health, particularly after the COVID-19 new safety guidelines. © The Author(s) 2023.

Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification (QIDEXAI-CDC) model for HIoT systems. The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems. The QIDEXAI-CDC model primarily uses bilateral filtering (BF) as a preprocessing tool to eradicate the noise. In addition, RetinaNet is applied for the generation of useful feature vectors from radiological images. For COVID-19 detection and classification, quantum-inspired differential evolution (QIDE) with kernel extreme learning machine (KELM) model is utilized. The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model. In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model, a wide range of simulations was carried out. Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches. © 2023 Authors. All rights reserved.

Digital communities for older adults deploying human-centric technologies for super-smart society

Purpose In today's super-smart society, it is expected that everyone can easily use new devices, functions and services, and benefit from advanced Information Technology (Japan Cabinet Office,n.d.). However, the information gap among Older Adults in the Internet society is becoming more pronounced as face-to-face communication is severely restricted in the COVID-19 pandemic. One of the reasons why it is difficult for older adults to access information utilizing Information Technology is because it is too complicated for them to create a mental model. A mental model is an "image of behavior" in a person's mind, such as "this will happen if that happens”. People's behavior is greatly influenced by their mental models. When using digital devices, users need to create a mental model not only of the inputs and outputs of the device in front of them, but also of the applications that run inside the device and the data on the Internet that lies beyond it. Fear and anxiety caused by the inability to create a mental model will act as a disincentive to the use of digital devices. Therefore it is naturally assumed that those older adults can utilize smart devices as a means to access to the Internet and to be connected with others if an appropriate mental model can be created. Ambient Computing (Loi, 2019) as a part of human-centric technology may simplify a mental model and solve the problem. Method We conducted survey interviews with older adults to investigate how the difficulty of creating a mental model affected the use of digital devices (Nameki et al., 2022). The results showed that family and community support is effective in facilitating older adults to create mental models and become accustomed to the use digital devices. However, it is sometimes difficult to provide continuous support to older adults, and how to ensure continuity is a challenge to solve the digital divide among older adults. Our case studies conducted with several older adults confirmed that simplifying their mental model can promote digital device usage and encourage their participation into the digital community. We propose human-centric technology, specifically Ambient Computing, to help older adults use digital devices and realize digital community where older adults can utilize information technologies without stress, access necessary information, and communicate with each other. If local community activities are digitized and expanded into a virtual space, the way older adults interact with their local community will change dramatically. Result and discussion Older Adults who have difficulty using digital devices due to a lack of mental model can use Information Technology and participate in the digital community by creating proper mental model with help of human-centric technology. Within the digital community, they will not only be able to reduce restrictions due to declining physical capabilities but also receive continuous support and have opportunities to join community activities themselves. For example, older adults who are unable to go out have been on the receiving end of nursing care and related supports. However, in a virtual community, they can become providers of support by utilizing their knowledge. They can share their knowledge and experiences in specialized fields they have cultivated during their working years, impart their know-how in various industries, train young engineers, and so on. We are convinced that the realization and activation of digital communities utilizing human-centric technology will be the cornerstone for realizing a truly super-smart society. Older adults can improve their quality of life by utilizing their own abilities as providers of support. In addition, it is expected that these findings will be diffused and feedback through the community, motivating the elderly as service providers and forming an ecosystem that leads to better services. © 2022, Gerontechnology. All Rights Reserved.

Variational deep embedding-based active learning for the diagnosis of pneumonia.

Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia.

Remote Teaching-Learning in Higher Education: Pedagogical Perspectives of Service Learning

How do we transition service-learning from high touch to high-tech? While Waldner et al. (2012) were trying to answer this question in the Web 2.0 era, humankind is facing the unprecedented crisis caused by COVID-19. As it is evident that we cannot return to the world as it was before, hence our common humanity necessitates solidarity. (UNESCO 2020). The crisis has hinted at the need for reconnection between ‘humanity and planet’ hence human-centric learning teaching is the need of the hour. Conventional higher education (HE) institutions undergo forced transition to technology engaging Remote Teaching-Learning (RTL) through a virtual platform on an emergency basis. Most of the teachers are facing challenges on how to make remote learning relatable to the everyday life of the learners and how to inculcate the sense of humanity in RTL. Hence the service-learning is facing two-fold challenges—first, how to design engagement of the student with the community to ensure experiential learning at a time when people are mostly remaining separated from conventional social interaction, and second, how teachers can effectively explore constructionist pedagogy through the virtual communication mode in such RTL. Along with technology, pedagogy has emerged as the most instrumental factor in self-direction, collaboration, resilience, and learning with confidence (Panda 2020). A new pedagogical discourse is evolving, highlighting the symbiotic journey between teachers and learners (Young 2020) which is pertinent in service-learning too. In this exploratory research, semiformal telephonic interviews, online written responses through Google form, and online Focused Group Discussions (FGDs) were adopted to collect relevant information, and subsequently data analysis and interpretation were done by triangulation. The work explored and documented the experience of the faculties of higher education regarding emerging trends of techno-pedagogical innovations which could be instrumental for service learning in RTL format. © 2022 by Nova Science Publishers, Inc.

PowerQoPE: A Personal Quality of Internet Protection and Experience Configurator

Security configuration remains obscure for many Internet users, especially those with limited computing skills. This obscurity exposes such users to various Internet attacks. Recently, there has been an increase in cyberattacks targeted at individuals due to the remote workforce imposed by the COVID 19 pandemic. These attacks have exposed the inefficiencies of the non-human-centric implementation of Internet security mechanisms and protocols. Security research usually positions users as the weakest link in the security ecosystem, making system and protocol developers exclude the users in the development process. This stereotypical approach has negatively affected users’ security uptake. Mostly, security systems are not comprehensible for an average user, negatively affecting performance and Quality of Experience. This causes the users to shun using security mechanisms. Building on human-centric cybersecurity research, we present a tool that aids in configuring Internet Quality of protection and Experience (referred to as PowerQoPE in this paper). We describe its architecture and design methodology and finally present evaluation results. Preliminary evaluation results show that user-centric and data-driven approaches in the design of Internet security systems improves users’ Quality of Experience. The controlled experiment results show that users are not really stupid;they know what they want and that given proper security configuration platforms with proper framing of components and information, they can make optimal security decisions. © 2022, IFIP International Federation for Information Processing.

Effect of Personality Traits and Stressor Inducers on Users’ Cognitive Load During Interactions with VR Environments

In this paper, HCI-based design criteria focusing on managing the cognitive load of users during their interaction with Virtual Reality (VR) based training environments are presented. The design criteria explored in the paper help lay a foundation for the creation of Human Centric VR environments to train users in two healthcare domains. The first domain is orthopedic surgery, and the second domain is related to the Covid-19 pandemic. The HCI-based perspective presented in the paper investigates criteria such as personality traits and stress inducers and their impact on cognitive load. The paper delineates the implementation of the VR based environments and a set of attributes that guide and influence the content of the environments. Testing and assessment strategy is described and results are also included which provide insights into the impact of such HCI-based criteria on participants’ acquisition of skills and knowledge during interactions with the VR environments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lessons Learnt From a Greenfield Hangar-Based 1,000-Bedded Temporary Hospital in India.

During the COVID-19 pandemic, the total number of hospital beds in the National Capital Region (NCR) of Delhi was 54,321 (roughly 300 beds per one lakh population), which was inadequate for the patients. Therefore, the Indian government initiated the construction of a 1,000-bedded greenfield hangar-based hospital to bridge the healthcare gap. As a result, Intensive Care Unit (ICU) beds in the facility augmented the COVID-19 care ICU beds in the city by 11%. The authors were involved in the planning, developing, and initiating the functioning of 1,000-bedded Dedicated COVID-19 Hospital (DCH). The hospital was conceptualized, built, and operationalized in 12 days only. Lessons learned from this experience would be of benefit should similar situations arise in future. Coordinating structural designing early with the entire project team-from facility administrators and medical practitioners to architects, consultants, and contractors-can result in a structure that better matches the facility's long-term needs and often saves construction time and costs. This article enumerates various challenges faced and the way they were addressed. This hangar-based hospital can be rapidly constructed and deployed on a massive scale. While structural integrity is essential, the planning team was particularly aware of the patient-centric modality of healthcare. Many modifications were carried out in the structure based on patient inputs. Informal discussions with discharged patients and relatives revealed that the human-centric approach was the mainstay of the therapy.

Toward human-centric urban infrastructure: Text mining for social media data to identify the public perception of COVID-19 policy in transportation hubs.

The COVID-19 pandemic has made transportation hubs vulnerable to public health risks. In response, policies using nonpharmaceutical interventions have been implemented, changing the way individuals interact within these facilities. However, the impact of building design and operation on policy efficacy is not fully discovered, making it critical to investigate how these policies are perceived and complied in different building spaces. Therefore, we investigate the spatial drivers of user perceptions and policy compliance in airports. Using text mining, we analyze 103,428 Google Maps reviews of 64 major hub airports in the US to identify representative topics of passenger concerns in airports (i.e., Staff, Shop, Space, and Service). Our results show that passengers express having positive experiences with Staff and Shop, but neutral or negative experiences with Service and Space, which indicates how building design has impacted policy compliance and the vulnerability of health crises. Furthermore, we discuss the actual review comments with respect to 1) spatial design and planning, 2) gate assignment and operation, 3) airport service policy, and 4) building maintenance, which will construct the foundational knowledge to improve the resilience of transportation hubs to future health crises.

Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence.

BACKGROUND: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. OBJECTIVE: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. METHODS: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. RESULTS: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. CONCLUSIONS: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises.