Search results

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.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

Purpose – The purpose of this study was to determine the efficacy value of national street lighting on energy conservation and carbon dioxide (CO₂) emission reduction.

Design/Methodology/Approach – The methods used are the measurement of electrical parameters (low voltage network), the national road illumination level with SON lamp specification, 400 W, 180 W, and 110 Lumen/W, the simulation of energy conservation calculation, and the CO₂ emission reduction obtained by utilizing panel solar cells as a source of energy and LED lights for illumination.

Finding – The results show the efficacy of a 100-W light bulb at an altitude of 8 m for the following specification of light bulbs: LED, 130 Lumens/W, SON, 110 Lumen/W, and MBF, 53 Lumen/W gives the illumination level respectively 13,913 Lux, 11,773 Lux, and 5,672 Lux. By replacing the 180 W SON lamp with an LED, 100 W, of energy conservation by 3.171 GW h is obtained, which is equivalent to a CO₂ emission reduction of 3.641 kTon CO₂.

Originality/Value – This study is a continuation of a study of energy conservation with the utilization of solar cells as an electrical power source for an LED bulb that replaces low-voltage networks as a power source for the bulb type SON.

This chapter has been seminal work of Dr K.S.S. Iyer, which has taken time to develop, for over the last 56 years to be presented here. The method in advance predictive analytics has developed, from his several other applications, in predictive modeling by using the stochastic point process technique. In the chapter on advance predictive analytics, Dr Iyer is collecting his approaches and generalizing it in this chapter. In this chapter, two of the techniques of stochastic point process known as Product Density and Random point process used in modelling problems in High energy particles and cancer, are redefined to suit problems currently in demand in IoT and customer equity in marketing (Iyer, Patil, & Chetlapalli, 2014b). This formulation arises from these techniques being used in different fields like energy requirement in Internet of Things (IoT) devices, growth of cancer cells, cosmic rays’ study, to customer equity and many more approaches.

Chapter 3 examines the attributes of an external stimulus, which the brain collects and models to construct a sensation. An important aspect of this process is the sensory system's filtering capacity, which removes extraneous and irrelevant information from the modeled information. The response mechanisms of all five senses are discussed to establish the practice of viewing the discipline (psychophysics) from multiple perspectives (senses). The differences in multiple perspectives on the same data is compiled into a model of the attributes to which the brain attends to engage with a sensation.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.