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This work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.

In this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.

For the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.

Epilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Strict carbon-cap policy is one of the basic policies proposed by the regulatory bodies to reduce the anthropogenic greenhouse gas emission. The purpose of this paper is to examine whether it is beneficial for a company to invest in green technology or not under the strict carbon-cap policy and for that a two echelon supply chain model is developed. This paper gives insight about judicious decision about investment on green technology.

Mathematical modeling approach has been adopted to understand the effect of investment on green technology. All the cost and emissions parameters have been derived and the total cost (TC) and total emission equations have been formulated mathematically. Two constrained mixed-integer nonlinear programming (MINLP) problems have been formulated and solved considering with or without green investment. Further, supply chain cost is optimized without carbon constraint to understand the effect of carbon constraint.

The investment in green technology can reduce the total supply chain cost. The study reveals that handling different parameters optimally can reduce both cost and emissions.

This paper tries to assess the effectiveness of green investment on technology under strict carbon-cap policy on a supply chain and, thereby, added value to the existing work. It examines the role played by various parameters under strict carbon-cap policy to draw insights, which will be beneficial for the academic community and managers.

In the wake of the extraordinary financial scandals that both preceded and followed the September–October Financial Crises of 2008, discussions about the executive virtues of honesty and integrity are no longer academic or esoteric, but critically urgent and challenging. As representatives of the corporation, its products and services, corporate executives in general, and production, accounting, finance, and marketing executives in particular, must be the frontline public relations and goodwill ambassadors for their firms, products, and services. As academicians of business education, we must also analyze these corporate wrongdoings as objectively and ethically as possible. What is wrong must be declared and condemned as wrong, what is right must be affirmed and acknowledged as right. We owe it to our students, our profession, our stakeholders, and to the business world. Contemporary American philosopher Alasdair MacIntyre (1981) proposes the issue of morality in a threefold question: Who am I? Who ought I to become? How ought I to get there? The answer to every question refers to the virtues, especially to corporate executive virtues. This chapter explores corporate executive virtues, especially the classical cardinal virtues of prudence, temperance, fortitude, and justice as defining and enhancing corporate executive life.

The purpose of this paper is to perform the computational fluid dynamics (CFD) simulation with experimental validation to investigate the particle segregation effect in abrupt and smooth shapes circulating fluidized bed (CFB) risers.

The experimental investigations were carried out in lab-scale CFB systems and the CFD simulations were performed by using commercial software BARRACUDA. Special attention was paid to investigate the gas-particle flow behavior at the top of the riser with three different superficial velocities, namely, 4, 6 and 7.7 m/s. Here, a CFD-based noble simulation approach called multi-phase particle in cell (MP-PIC) was used to investigate the effect of traditional drag models (Wen-Yu, Ergun, Wen-Yu-Ergun and Richardson-Davidson-Harrison) on particle flow characteristics in CFB riser.

Findings from the experimentations revealed that the increase in gas velocity leads to decrease the mixing index inside the riser. Moreover, the solid holdup found more in abrupt riser than smooth riser at the constant gas velocity. Despite the more experimental investigations, the findings with CFD simulations revealed that the MP-PIC approach, which was combined with different drag models could be more effective for the practical (industrial) design of CFB riser. Well agreement was found between the simulation and experimental outputs. The simulation work was compared with experimental data, which shows the good agreement (<4%).

The experimental and simulation study performed in this research study constitutes an easy-to-use with different drag coefficient. The proposed MP-PIC model is more effective for large particles fluidized bed, which can be helpful for further research on industrial gas-particle fluidized bed reactors. This study is expected to give throughout the analysis of CFB hydrodynamics with further exploration of overall fluidization.

Corruption in India reached a crescendo between 2011 and 2013, with the exposure of the 2G Spectrum scandal and the “Coalgate” report fiasco at the top of all recent events. The largest working democracy is under the scanner. As the third largest economy in Asia, a nuclear power, and an information technology powerhouse, India has a lot to clean up. Current experience shows the failure of the top investigative agencies and the lack of political will to tackle corruption. The spate of high-level corruption scandals has also led to a popular movement in 2011, which also fizzled out, including the newly introduced “Anti-Corruption, Grievance Redressal and Whistleblower Protection Act, 2011.” This chapter examines the several issues involved.

This paper aims to examine the traditional business communities and family businesses of India, their emergence and sustained growth.

The authors analyze the role of business communities in family businesses of India and identify business communities that have still sustained and marked a global presence.

Business communities such as Marwaris have the knack for business activities and are leaders of family businesses in India today, who have sustained their past success and continue to create new histories. Other traditional business communities such as Parsis, Sindhis, Chettiars and Gujarati banias have not been able to sustain much. Possible reasons were switching to white-collar jobs, taking up diplomacy and other professions, inter caste marriages, international migration in search of business and Indian government policies.

This study provides a useful source of information for academics, policy-makers and economists.

Traditional business communities populate the list of family businesses that have marked their global presence. This paper identifies various factors that are responsible for the growth and sustainability of these business communities.

The study clarifies the role of business communities in domestic economic development.

The paper explored traditional business communities of India and assessed their role in family businesses of India that currently mark a global presence.

The objective of the present study is to identify and analyze a set of critical success factors (CSFs) for ice-cream industry [cold chain management (CCM)] that helps in increasing the efficacy, quality, performance and growth of the supply chain organization.

A questionnaire survey with companies in ice-cream sector and a panel study with experts were conducted to identify and validate CSFs and their associated sub-factors. Eight CSFs identified from the cold chain domain vetted for the ice-cream industry and then prioritized by using one of the most well-known decision-making frameworks, Decision-Making Trial and Evaluation Laboratory. The general verdicts of the modelling and its application to the real-world case have been tested through an ice-cream company supply chain.

The result shows that the significant CSFs accountable for the growth of the ice-cream industry are the infrastructure and capacity building, consistent product improvement and operational efficiencies of the value chain. Subsequently, it was identified that the use of IT and related technologies and improved processes for operations also play a considerable role in the performance of ice-cream industry.

The study successfully outlines the effective CCM practices for critical issues. The proposed methodology and factor modelling case demonstration might be useful in analyzing the logistic chains of products such as fruits, drugs and meat.

The meritorious identification of critical areas and executing mitigation plans bring notable benefits to the firms such as improved operational efficiencies, improved time to market performance and product innovation, which bring additional benefits to the producers.

To assess the state of supply chain management (SCM) research in India and to understand the research trends and methodologies used. The present study also aims to create a taxonomy of the subject areas researched in India.

The present study employed the systematic literature review methodology. Literature from 395 peer journal papers in 67 leading journals over a 20-year period (2000–2020 Quarter-1) was comprehensively reviewed and assessed.

SCM research in India started around the year 2000. The quantum of research was low (single digit) until 2010. There has been steady growth over the last decade, and over 50% of the total papers up until now has been published in the last four years. The present study created a three-tiered taxonomy of the subject areas and classified the papers as per it. The first tier (level-1) has seven categories (SCM strategy, network design, SCM processes and integration, IT systems, skills, performance measurement and others). A perusal of the newly created taxonomy revealed that, except for a few areas under level-1 categories (such as SCM processes and SCM strategy), the other level-1 categories have not seen much research. Similarly, there is little or no research in a large number of level-2 categories (such as outsourcing strategy, channel strategy, demand management, demand fulfillment, customer relationship management, integrated supply chain planning, new product development, returns, supply chain orientation, performance monitoring, performance improvement, SCM adoption process, SCM implementation issues and quantified benefits of SCM). Methodologically, the rigor of SCM research in India needs improvement.

A comprehensive taxonomy of SCM subject areas researched in India at three cascading levels was created for the first time in the present study. The taxonomy will help provide researchers with a clear understanding of the structure of the subject areas and help in identifying areas where research has been carried out and the subject areas where gaps exist for future research to proceed. The present study also provides an overview of the methodological rigor of SCM research in India and points out some of the limitations that researchers should avoid in future studies.