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After completion of the case study, the students will be able to discuss topics such as new venture creation and opportunity sensing, knowledge sharing and employee bonding and the use of social networks in business growth.

This case study focuses on the entrepreneurial journey of Mr Nikhil Methiya, the owner of Dronelab Technology Private Limited, which provides surveying, inspection, agriculture, surveillance and research and development services using drone technologies. This case highlights how Methiya used his minimal resources to grew his business, diversified his activities and developed a sound company profile and work culture to provide the best services to clients. This case also discusses the role of social networks in business growth and expansion, the use of effectuation theory in forming new businesses and the importance of conducting a SWOT analysis to understand a firm’s internal and external environments. Furthermore, this case touches upon the challenges and opportunities of the drone industry in India. It leaves readers in a dilemma should Methiya plan to expand his business to Europe and Africa in the upcoming years. This case study is suitable for postgraduate management students specializing in entrepreneurship and can serve as a valuable resource for the Venture Creation Program’s start-up strategy and execution. The case study’s pedagogy involves discussion-based learning.

This case study can be used in management for an entrepreneurship specialty course. It is ideal for postgraduate students and has a moderate level of difficulty.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

It is critical for small and medium-scale enterprises (SMEs) to review and monitor sustainability performance indicators across three dimensions: economic, environmental and social to attain long-term competitiveness. SMEs lack a holistic perspective on sustainability; they are frequently hindered from contemplating environmentally favorable investments beyond what is legally needed. The purpose of this paper is to present a joint Decision-making trial and Evaluation Laboratory (DEMATEL) and NK methodology for developing a process model for introducing and implementing sustainable competitiveness practices for SMEs.

This study addresses the issue by adopting a sustainable competitiveness practices framework and applying a novel method that integrates DEMATEL and NK model for evaluating and developing the implementation path model for Indian manufacturing SMEs.

This paper also demonstrates that not only the relational practice itself but also the order in which the relational practices are implemented can be related to performance. According to the authors' preliminary findings, organizations in this study should first implement a social dimension, which includes sustainable leadership, knowledge sharing, etc., then an economic dimension like quality, sustainable innovations, etc., and finally environmental dimensions like green marketing, solid waste reduction, etc., with their management for competitiveness.

These findings offer some preliminary information as well as advice for managers and policymakers looking to integrate sustainable efforts.

This study asserts that not only the interdependent practice but also the sequence of implementation is important and can relate to the performance. The path result shows that the organization develops first sustainable design and product development (economic), sustainable leadership (social) and solid waste reduction (environmental) practices.

There is no such study that develops a process model for introducing and implementing sustainable competitiveness of SMEs which assesses and analyzes the interdependencies across relational behaviors, to the best of authors’ knowledge. The novelty of this work lies in integrating DEMATEL-NK model approach.

The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes essential in this scenario to realize the global net-zero goals. The purpose of the proposed study is to evaluate the impact of the widespread adoption of such guidelines in a building community in the context of mixed-mode buildings.

This study decentralizes the theme of improving the energy efficiency of the national building stock in parcels by proposing a community-based hybrid bottom-up modelling approach using urban building energy modelling (UBEM) techniques to analyze the effectiveness of the community-wide implementation of energy conservation guidelines.

In this study, the UBEM is developed and validated for the 14-building residential community in Mumbai, India, adopting the framework. Employing Energy Conservation Building Code (ECBC) compliance on the UBEM shows an energy use reduction potential of up to 15%. The results also reveal that ECBC compliance is more advantageous considering the effects of climate change.

In developing countries where the availability of existing building stock information is minimal, the proposed study formulates a holistic framework for developing a detailed UBEM for the residential building stock from scratch. A unique method of assessing the actual cooling load of the developed UBEM is presented. A thorough sensitivity analysis approach to investigate the effect of cooling space fraction on the energy consumption of the building stock is presented, which would assist in choosing the appropriate retrofit strategies. The proposed study's outcomes can significantly transform the formulation and validation of appropriate energy policies.

The study explores and builds theories in Customer Perceived Values (CPVs) that drive counterfeit buying intention, using a Grounded Theory Approach (GTA) in an emerging market, India.

Counterfeit studies have either resorted to a survey approach or modelling approach in investigating various aspects and dimensions. This study, among a few, attempted a GTA in building theory on CPVs. Based on the observations and recorded responses that emerged through several Focus Group Discussions (FGDs); conducted in two metropolitan cities (India), newer insights into this illicit phenomenon of “Counterfeiting” were derived.

Adding to the counterfeit literature, the study presents a comprehensive view of the CPVs. Findings reveal economic, socio-normative, pleasure-based, euphemistic, acquisition-centrality, self-regulating, situational and sustainable consumption values that influence counterfeit attitudes and in turn impact counterfeit buying intentions. Although Economic Values (ECV) have been the primary motivation for counterfeit purchase, complex and newer values that emerged through this research study bears significance.

As a single point of reference, this study will provide impetus to scholars and academicians in expanding the counterfeit research domain. While aiding policymakers and marketers in further understanding this illicit practice, it will also guide brand managers in strategizing their offerings and reaching out to the masses with strong brand aesthetic values.

Based on a systematic literature review using the 4 Ws framework, this study is one of the few attempts that has adopted a GTA to explore and develop theories on CPVs in counterfeit research.

This chapter is an exploratory study of women’s football in Goa during two defining periods in its history: 1975–1991 and 2017–present. Anchoring the analysis within the intersection of sport, gender, and decolonisation, the chapter aims to address the peripheralisation of women’s sport in academic work on sport in India. Examining the evolution of women’s football in Goa and the multifarious factors that stilted its advancement, this research demonstrates how the systemic challenges that have historically plagued women’s sport continue to hamper its progress. The chapter argues that the professionalisation of women’s sport is indispensable to unlocking its potential and doing justice to the players and other stakeholders who continue to pursue it despite manifold challenges.

This study aims to comprehensively address the challenge of delineating traffic scenarios in video footage captured by an embedded camera within an autonomous vehicle.

This methodology involves systematically elucidating the traffic context by leveraging data from the object recognition subsystem embedded in vehicular road infrastructure. A knowledge base containing production rules and logical inference mechanism was developed. These components enable real-time procedures for describing traffic situations.

The production rule system focuses on semantically modeling entities that are categorized as traffic lights and road signs. The effectiveness of the methodology was tested experimentally using diverse image datasets representing various meteorological conditions. A thorough analysis of the results was conducted, which opens avenues for future research.

Originality lies in the potential integration of the developed methodology into an autonomous vehicle’s control system, working alongside other procedures that analyze the current situation. These applications extend to driver assistance systems, harmonized with augmented reality technology, and enhance human decision-making processes.

This paper aims to investigate the influence of “BroncoCut-X” (copper core-ZnCu50 coating) electrode on the machining of Ti-3Al-2.5V in view of its extensive use in aerospace and medical applications. The machining parameters are selected as Spark-off Time (ST_off), Spark-on Time (ST_on), Wire-speed (S_w), Wire-Tension (WT) and Servo-Voltage (S_v) to explore the machining outcomes. The response characteristics are measured in terms of material removal rate (MRR), average kerf width (KW) and average-surface roughness (SA).

Taguchi’s approach is used to design the experiment. The “AC Progress V2 high precision CNC-WEDM” is used to conduct the experiments with ϕ 0.25 mm diameter wire electrode. The machining performance characteristics are examined using main effect plots and analysis of variance. The grey-relation analysis and fuzzy interference system techniques have been developed to combine (called grey-fuzzy reasoning grade) the experimental response while Rao-Algorithm is used to calculate the optimal performance.

The hybrid optimization result is obtained as ST_off = 50µs, ST_on = 105µs, S_w = 7 m/min, WT = 12N and S_v=20V. Additionally, the result is compared with the firefly algorithm and improved gray-wolf optimizer to check the efficacy of the intended approach. The confirmatory test has been further conducted to verify optimization results and recorded 8.14% overall machinability enhancement. Moreover, the scanning electron microscopy analysis further demonstrated effectiveness in the WEDMed surface with a maximum 4.32 µm recast layer.

The adopted methodology helped to attain the highest machinability level. To the best of the authors’ knowledge, this work is the first investigation within the considered parametric range and adopted optimization technique for Ti-3Al-2.5V using the wire-electro discharge machining.

This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.

The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.

The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.

The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.