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The aim of this chapter is to consider the socially responsible aspects generally hidden from researchers by considering examples in the fields of archaeology and restoration of cultural and historical places and objects. This chapter is written as a result of practical efforts taken by the first author in the field of restoration through laboratory research and represents findings from experience. The main finding of the research is that the reality of social responsibility has been lost in its defining and needs to be actually observed in practice. This chapter has been written according to research undertaken in Iran as a historical country. More general applications can be developed. The chapter uses the field of archaeology and restoration to take into consideration social responsibility and shows the more general implications of the approach taken. In doing so, it shows the relevance of social responsibility to all walks of life. This chapter is the result of years of practice in the field of restoration and sustainability, and the combined approach taken is unique.

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

Purpose – This chapter provides an overview of the policy implications of neuroscience and argues that research initiatives, individual use, and aggregate social consequences of unfolding knowledge about the brain and the accompanying applications require particularly close scrutiny because of the centrality of the brain to human behavior and thoughts.

Design/methodology/approach – The chapter summarizes the technological context of interventions in the brain and discusses their policy implications. It then examines research findings, principally from neuroimaging studies, that relate to decision making and emotions and looks at their potential impact on frameworks of political decision making.

Findings – Research on brain structure and functioning raises difficult policy issues and necessitates a reevaluation of our assumptions concerning the policy process, itself.

Practical implications – Given the inevitability of expanded strategies for exploration and therapy of the brain and the concerns they raise, it is important that these issues surrounding their application be clarified and debated before such techniques fall into routine use.

Originality/value – The chapter provides original analysis of the policy ramifications of interventions in the brain and neuroscience in general and makes some observations about the brain and society.

This chapter presents a sociological analysis of the work involved in producing neuroimaging scans used in clinical practice. Drawing on fieldwork in magnetic resonance imaging (MRI) units in hospitals and free-standing imaging centers; in-depth interviews with technologists, radiologists, and neurologists; and reviews of relevant medical literatures, this analysis demonstrates how assembly line techniques structure neuroimaging work. Neuroimages (after being ordered by the referring clinician) are created in an image production line where scans of brains, breasts, livers, and other body parts are all produced: although some facilities may focus on one area of the body, most create an array of scans. Following MRI scans as they are produced demonstrates how medical work emphasizes repetition, specialization, and efficiency – key features of mass production. On the medical assembly line, the organization of work aims to transform patients into objects – ones that multiply as scans are created and circulated. Neurologists, radiologists, and technologists are positioned as skilled workers who manage the flow of bodies and the production of knowledge with the aim of producing health or, at the very least, knowledge of illness. Patients are also actors who actively impact the imaging production process. Previous scholarship has shown that diagnostic work involves a distributed form of expertise; one that involves patients, other medical professionals, machines, and neurologists. This chapter demonstrates that the deployment and synchronization of this expertise is a form of labor, involving distinct professions, professional hierarchies, and reimbursement systems. Working conditions are central to the production of MRI scans as knowledge and contribute to the social shaping of neuroimaging techniques.

In this chapter, the problem of facial palsy has been addressed. Facial palsy is a term used for disruption of facial muscles and could result in temporary or permanent damage of the facial nerve. Patients suffering from facial palsy have issues in doing normal day-to-day activities like eating, drinking, talking, and face psychosocial distress because of their physical appearance. To diagnose and treat facial palsy, the first step is to determine the level of facial paralysis that has affected the patient. This is the most important and challenging step. The research done here proposes how quantitative technology can be used to automate the process of diagnosing the degree of facial paralysis in a fast and efficient way.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.