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This work proposes classification of two-class motor imagery electroencephalogram signals using different automated machine learning algorithms. Here data are decomposed into various frequency bands identified by wavelet transform and will span the range of 0–30 Hz.

Statistical measures will be applied to these frequency bands to identify features that will subsequently be used to train the classifiers. Further, the assessment parameters such as SNR, mean, SD and entropy are calculated to analyze the performance of the proposed work.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

This paper aims to suggest the preferred mode of financing for major sub-sectors of infrastructure: roads, seaports, telecommunication and energy by examining which mode of infrastructure financing – public, private or public–private partnership (PPP) – has the maximum positive impact on the overall GDP of India. The same exercise was carried out for the overall infrastructure sector by integrating data from all the four sub-sectors.

The structural vector autoregressive approach was used with the period of analysis taken from 1995 to 2014. The stationary properties of the variables were checked by the Phillips–Perron unit root.

The PPP mode of financing was found to make the maximum positive impact on the GDP of India. Considering the four sub-sectors individually, it was concluded that the private mode of financing in roads, energy and telecom sectors has the maximum positive impact on the GDP, while the PPP gives optimal benefit to the seaports sector.

Results will aid the Indian Government and policymakers to efficiently design and develop their economic policies accordingly.

The study is novel in a sense that it helps to address the lack of research into the area of infrastructure financing in India.

Aluminium metal matrix composites are used in automotive and aerospace industries because of their high performance and weight reduction benefits. The current investigation aims to focus on the development of the stir cast aluminium-boron carbide composites with enhanced mechanical and tribological properties.

The aluminium-boron carbide composites are produced by stir casting process. Aluminium alloy A356 is chosen as the matrix material and three sets of composites are produced with different weight fractions of boron carbide particles. Higher particle size (63 µm) of boron carbide is chosen as the reinforcement material. Aluminium-boron carbide composites are tested for mechanical and tribological properties. The effect of process parameters like load, speed and percentage of reinforcement on the wear rate are studied using the pin-on-disc method. The interaction of the process parameters with the wear rate is analysed by DesignExpert software using RSM methodology and desirability analysis. The coded levels for parameters for independent variables used in the experimental design are arranged according to the central composite design. The worn surface of the pin is examined using a scanning electron microscope. The phases and reaction products of the composites are identified by X-ray diffraction (XRD) analysis.

Aluminium-boron carbide composites reveal better mechanical properties compared to monolithic aluminium alloys. Mechanical properties improved with the addition of strontium-based master alloy Al10Sr. The ultimate tensile strength, hardness and compressive strength increase with an increase in the reinforcement content. The wettability of the boron carbide particles in the matrix improved with the addition of potassium flurotitanate to the melt. Uniform dispersion of particles into the alloy during melting is facilitated by the addition of magnesium. Wear resistance is optimal at 8 per cent of boron carbide with a load 20 N and sliding speed of 348 RPM. The wear rate is optimized by the numerical optimization method using desirability analysis. The amount of wear is less in Al-B₄C composites when compared to unreinforced aluminium alloy. The wear rate increases with an increase in load and decreases with the sliding speed. The wear resistance increases with an increase in the weight fraction of the boron carbide particles.

The produced Al-B₄C composites can retain properties at high temperature. It is used in nuclear and automotive products owing its high specific strength and stiffness. The main applications are neutron absorbers, armour plates, high-performance bicycles, brake pads, sand blasting nozzles and pump seals.

Al/B₄C composites have good potential in the development of wear-resistant products.

The purpose of this study is, First, to leverage the limitation of annotated data and to identify the cognitive level of learning objectives efficiently, this study adopts transfer learning by using word2vec and a bidirectional gated recurrent units (GRU) that can fully take into account the context and improves the classification of the model. This study adds a layer based on attention mechanism (AM), which captures the context vector and gives keywords higher weight for text classification. Second, this study explains the authors’ model’s results with local interpretable model-agnostic explanations (LIME).

Bloom's taxonomy levels of cognition are commonly used as a reference standard for identifying e-learning contents. Many action verbs in Bloom's taxonomy, however, overlap at different levels of the hierarchy, causing uncertainty regarding the cognitive level expected. Some studies have looked into the cognitive classification of e-learning content but none has looked into learning objectives. On the other hand, most of these research papers just adopt classical machine learning algorithms. The main constraint of this study is the availability of annotated learning objectives data sets. This study managed to build a data set of 2,400 learning objectives, but this size remains limited.

This study’s experiments show that the proposed model achieves highest scores of accuracy: 90.62%, F1-score and loss. The proposed model succeeds in classifying learning objectives, which contain ambiguous verb from the Bloom’s taxonomy action verbs, while the same model without the attention layer fails. This study’s LIME explainer aids in visualizing the most essential features of the text, which contributes to justifying the final classification.

In this study, the main objective is to propose a model that outperforms the baseline models for learning objectives classification based on the six cognitive levels of Bloom's taxonomy. In this sense, this study builds the bidirectional GRU (BiGRU)-attention model based on the combination of the BiGRU algorithm with the AM. This study feeds the architecture with word2vec embeddings. To prove the effectiveness of the proposed model, this study compares it with four classical machine learning algorithms that are widely used for the cognitive classification of text: Bayes naive, logistic regression, support vector machine and K-nearest neighbors and with GRU. The main constraint related to this study is the absence of annotated data; there is no annotated learning objective data set based on Bloom’s taxonomy's cognitive levels. To overcome this problem, this study seemed to have no choice but to build the data set.

Olanzapine is widely prescribed in the treatment of schizophrenia and various psychiatric illnesses. Schizophrenia patients have been reported to eat a diet that contain higher in fat and lower in fiber. High dietary fat intake can predispose to the development of metabolic abnormalities and exacerbate hepatic changes. The aim of the paper is to investigate the effect of olanzapine and high fat diet on blood glucose, lipid profile and the liver in rats.

Twenty-four healthy male Sprague Dawley rats were divided into following groups: group I was given normal diet, group II was given high fat diet, group III was given high fat diet and olanzapine (5 mg/kg/day intraperitoneally twice daily) and group IV was given normal diet and olanzapine (at same dose). After 30 days, the blood samples were collected to assess levels of blood glucose and total lipid profile. Also, liver specimens were processed for histological study by using light microscope.

Group III showed significant increase in weight, blood glucose (p < 0.05), total cholesterol (p < 0.05), low-density lipoprotein-cholesterol (p < 0.05) and decrease in high-density lipoprotein-cholesterol (p < 0.05) when compared to group II. While group III revealed several histological changes including, dilatation and congestion of central veins and blood sinusoids as well some hepatocytes appeared damaged and were replaced by inflammatory cellular infiltrate.

These results suggest that olanzapine and high fat diet greatly increased the blood glucose, total cholesterol, LDL-C and considerable decreased HDL-C as well as mild inflammatory changes

The purpose of this paper is to propose the application of a decision-making tool for performance evaluation of Indian Railway zones. It basically seeks to analyze the effects of various evaluation criteria on the performance of Indian Railways using a combined multi-criteria decision-making approach which employs decision-making trial and evaluation laboratory (DEMATEL) and “VIse Kriterijumska Optimizacija kompromisno Resenje” (VIKOR) methods.

The performance of 16 Indian Railway zones is first evaluated using DEMATEL method which addresses the inter-relationships between different criteria with the aid of a relationship structure. The VIKOR method which is a compromise ranking approach is then adopted to rank those candidate railway zones. Pareto analysis is also carried out to identify the benchmark railway zones for the under/poor performers so as to improve their operational excellence.

A numerical example from Indian Railways is illustrated and solved for better understanding of the integrated decision-making tool in which the relevant information for the considered railway zones with respect to different evaluation criteria are collected from various websites and Indian Railways annual statistical report. Western and North-Eastern zones, respectively, take the first and the last positions in the derived ranking list. The relevance of selecting different performance indices/evaluation criteria is also discussed.

The application of this integrated methodology would serve as a systematic approach for measurement of the aggregate operational performance of Indian Railway zones so as to gain valuable academic and practical insights. It is also expected to provide an insightful guidance to the railway administrators in taking valuable strategic decisions in promoting the service of Indian Railways.

The integrated DEMATEL-VIKOR method is conceptually simple and easily comprehensible which can consider numerous attributes simultaneously. This paper enables the readers to gain some valuable inputs from a managerial perspective for Indian Railways to formulate strategies for its zones to foster better performance.

The context of the case presents an account of how corporate social responsibility (CSR) initiated by Lawrence & Mayo (L&M), a company dealing in optical frames for 140 years, helped to build brand equity, image and identity, creating a strategic advantage against competition. The case had a deep-rooted theoretical association with a theory such as the triple bottom line theory (three Ps: profit, people and planet) on CSR. The case helps to understand and clarify the role of CSR in brand equity. It also gives an insight into the value and culture of L&M, and its impact on various stakeholders, namely, employees and customers.

This case is related to the CSR orientation of L&M and its impact on brand equity. As a brand, L&M is over 140 years old and has a dynamic and trending optics market in India. There is a dilemma in the company around the impact of CSR on brand equity, customer engagement and company goodwill. This case focuses on maintaining and improving brand equity, identity and image through CSR initiatives.

Undergraduate and postgraduate students, essential for students focusing on Marketing and CSR disciplines.

Teaching notes are available for educators only.

CSS 8: Marketing.

This study aims to understand the phenomenon of organisational anthropomorphism, a psychological process in which employees attribute personality characteristics to organisations. Anthropomorphism, a psychological concept, after making its mark in consumer behavioural studies, is emerging into the domain of organisational behaviour. This less deliberated concept is explored through the lens of psychological cues and motives leading to the identification of its managerial antecedents of organisational citizenship behaviour (OCB) and corporate social responsibility (CSR) and their confluence into organisational culture. Further, the relationship between organisational culture and organisational anthropomorphism is established through literature review with a number of propositions and a framework.

This paper explores the available literature on organisational anthropomorphism and the constructs of organisational culture by employing literature review methodology. On the basis of selected research studies sourced from high ranked journals from Web of Science, Scopus and journal homepages, domain-based and theory-based reviews were performed to comprehend the concept of organisational anthropomorphism.

This study identifies OCB and CSR as the antecedents of organisational anthropomorphism through the psychological cues and motives, comes out with a number of propositions and recommends a framework based on the same.

This paper helps managers study employee behaviours and observe the organisation’s connections with society. Moreover, this study benefits organisations to brand themselves better amongst employees and external stakeholders.

This paper helps managers study employees’ citizenship behaviours through anthropomorphic cues exhibited by employees and improve organisation-employee association. It also suggests organisations to brand themselves using anthropomorphic social traits to stage itself as a socially responsible entity among external stakeholders. The empirical validation of proposed framework through quantitative and qualitative methods is proposed to be the future scope of the study.