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In the management world, leadership is a quality associated with business leaders, social entrepreneurs and political figures. Doctors are rarely considered as possessing or requiring leadership skills. With doctors, one thinks of skill and knowledge, but for some strange reason, leadership is hardly associated with doctors. This paper aims to highlight the leadership aspects unique to doctors. This study highlights why leadership training is imperative for doctors, outlines current status of leadership training for doctors in India and sets out proposals for effective leadership building.

Methodology is based on a two-pronged explanatory approach – the first is review of current literature in the context of leadership training of doctors, and the second is review of circumstances unique to the line of work undertaken by doctors that shed light on the need for leadership.

This paper highlights the imperative need for leadership training for doctors in India. It recommends leadership training on a continuous basis in their career life cycle as with the other professions. It also calls for involvement of all stakeholders in the medical community to foster leadership training – medical educational institutions, hospitals, medical councils and members of the medical fraternity.

Akin to leadership training programs conducted for IT and management professionals, this paper recommends that similar programs be conducted for doctors.

There are very few studies conducted in the Indian context on leadership training needs for doctors. This paper explains the importance of leadership training for doctors and suggests ways it can be implemented throughout the medical education life cycle of a doctor’s career.

The main goal of the physical education (PE) environment is that each individual trained should achieve self-fulfillment with the large group of students involved with their own efforts. Deep learning is applying transferrable knowledge in new situations to help the students master in tough circumstances. In PE training, injuries occur when working together as a team. Safety measures are taken immediately as an emergency response to reduce the potential risk in students by providing first aid. To provide safety measures for the injured student immediately, the environment is monitored in real-time using a GPS.

Theory of Humanities Education (ToHE) infers that it has less collection of theories and a wide range of applications than the state-of-the-art systems. ToHE allows students to think creatively and play a vital role in one’s health which is a critical aspect in PE. The ToHE theory focuses on two main concepts, i.e. by using a methodological approach to analyse and deep learning to solve the problem. PE motivates college students to follow a healthy and active lifestyle.

The proposed system is deployed in real time for monitoring the student’s performance and provides an emergency response with an accuracy rate of 90%.

The deep learning offers solutions to the injuries by using the deep convolutional neural network to provide interpretability of the consequence by training it with various injuries that occur in the playground and inappropriate use of sports equipment. A case study provided in this paper outlines an emergency response scenario to an injured student in sports training.

The application of rinse cycle softener after the household laundry process has become more common in recent times. This study aims to understand the effect of repeated rinse cycle softener treatment on the mechanical and frictional properties of the cotton fabric.

Cotton-woven fabric is treated with commercial rinse cycle softener repeatedly for 15 times. After treatment, the fabric was evaluated for the changes in mechanical properties using the Kawabata evaluation system.

The results of this study revealed that the softener treatment reduces the tensile properties (41.25%) and increases the overall extensibility of the fabric up to 20.89%. The shear (34.57%) and bending rigidity of the treated fabric are reduced considerably than the untreated fabric (58.02%). The increment in the fabric softness and fluffiness was confirmed with the increment in the compression and the difference between the initial and final thickness at maximum pressure. Statistical significance (p < 0.05) is noted only in the case of bending and surface friction properties (dynamic friction).

The usage of rinse cycle softeners in the household laundry has a significant influence on the comfort characteristics of the cotton-woven fabric. Repeated usage of rinse cycle softener increased the fabric softness and fluffiness of the fabric and also reduced the tensile properties significantly.

The authors wanted to find out the most important mechanisms for encouraging innovative behavior in the Indian manufacturing sector.

The researchers collected data from Indian manufacturing organizations. They distributed questionnaires and received 288 complete ones. Items measured critical concepts. For OJ one example was “I have been fairly rewarded for the effort I put forth”. For KS, one sample was, “When I have learned something new, I tell my colleagues about it” and, “When they have learned something new, my colleagues tell me about it”. Meanwhile, IB was measured using items such as “I generate original solutions for problems”.

It highlighted the pivotal role of OJ in bolstering employees’ IB. When companies treat employees fairly, it encourages positive social interactions that lead to perceptions of supportiveness and trustworthiness. Employees reciprocate these sentiments with positive behavior. The study also showed the positive predictive influence of KS on IB. Finally, the results showed that the relationship between OJ and IB is complex, but KS is a pivotal mediator. Promotion of OJ, KS and IM is “vital” to spark innovation.

The authors felt their most important finding was to highlight the critical role of the underlying mechanism of KS, which is where individuals exchange implicit and explicit knowledge to create new knowledge. In addition, previous researchers have looked at the role of organizational justice in encouraging innovative behavior, but evidence from non-Western countries is scarce.

This paper aims to focus on the stochastic analysis of composite rotor blades with matrix cracking in forward flight condition.

The effect of matrix cracking and uncertainties are introduced to the aeroelastic analysis through the cross-sectional stiffness properties obtained using thin-walled beam formulation, which is based on a mixed force and a displacement method. Forward flight analysis is carried out using an aeroelastic analysis methodology developed for composite rotor blades based on the finite element method in space and time. The effects of matrix cracking are introduced through the changes in the extension, extension-bending and bending matrices of composites, whereas the effect of uncertainties are introduced through the stochastic properties obtained from previous experimental and analytical studies.

The stochastic behavior of helicopter hub loads, blade root forces and blade tip responses are obtained for different crack densities. Further, assuming the behavior of progressive damage in same beam is measurable as compared to its undamaged state, the stochastic behaviors of delta values of various measurements are studied. From the stochastic analysis of forward flight behavior of composite rotor blades at various matrix cracking levels, it is observed that the histograms of these behaviors get mixed due to uncertainties. This analysis brings out the parameters which can be used for effective prediction of matrix cracking level under various uncertainties.

The behavior is useful for the development of a realistic online matrix crack prediction system.

Instead of introducing the white noise in the simulated data for testing the robustness of damage prediction algorithm, a systematic approach is developed to model uncertainties along with damage in forward flight simulation.

To improve the problems as the heavy burden of sewage treatment and environmental pollution caused by the traditional sodium hydrosulfite reduction dyeing of indigo, this study aims to carry out the direct electrochemical reduction dyeing for indigo with the eco-friendly Cu(II)/sodium borohydride reduction system under normal temperature and pressure conditions.

The electrochemical behavior of Cu(II)/sodium borohydride reduction system was investigated by cyclic voltammetry. And, the dyeing performance of the Cu(II)/sodium borohydride reduction system was developed by optimizing the concentration of copper sulfate in the anode electrolyte, applied voltage and reduction time via single-factor and orthogonal integrated analysis.

The dyeing performance of the Cu(II)/sodium borohydride reduction system is superior to that of the traditional reduction dyeing with sodium hydrosulfite. In the case of the optimized condition, the soaping fastness and dry/wet rubbing fastness of the dyed fabric in the two reduction dyeing processes were basically comparable, the K/S value of electrocatalytic reduction of indigo by Cu(II)/NaBH4 is 11.81, which is higher than that obtained by traditional sodium hydrosulfite reduction dyeing of indigo.

The innovative electrocatalytic reduction system applied herein uses sodium borohydride as the hydrogen source combined with Cu(II) complex as the catalyst, which can serve as a medium for electron transfer and active the dye molecule to make it easier to be reduced. The electrochemical dyeing strategy presented here provides a new idea to improve the reduction dyeing performance of indigo by sodium borohydride.

Corporate social responsibility (CSR) is viewed as a differentiating strategy that wins over stakeholders’ confidence. Due to the potential strategic and positive effects on businesses, the study of CSR and its relationship to competitiveness has gained relevance. While studies have examined the impact of CSR activities on firm competitiveness, the findings so far remain contradictory. Further research on the underlying processes/mechanisms that explain how CSR contributes to competitiveness remains scarce. Accordingly, this study aims to look into the link between CSR and competitiveness with a focus on Asian business and management studies.

By using a bibliometric approach, this paper aims to provide a review of the state-of-the-art research on the linkage between CSR and competitiveness in Asian context. The sample for this research included all 538 studies from the period of 2001–2023 in the Scopus database. A bibliometric study included both co-occurrence and co-citation analysis.

The study’s findings made significant contributions by identifying seven distinct clusters of co-occurrences. Using co-citation, three journals-based co-citation clusters and another three authors-based co-citation clusters are identified. The findings show how processes/mechanisms such as – accountability, multi-stakeholder dialogue/engagement, resource generation, emphasizing sustainable development goals and emerging markets, redefining strategy, cultivating value/vision and CSR leadership – are increasing in importance.

Overall, the authors argue that CSR-led competitiveness is indeed one of the key drivers for improved sustainability performance of a firm.

Based on findings, a conceptual framework has been proposed highlighting different processes and mechanisms that influence the CSR-led competitiveness – outcomes relationship.

Energy-efficiency leads to productivity gains as it can lower operating and maintenance costs, increase production yields per unit of manufacturing input and improve staff accountability. Implementation of energy-efficient technologies amongst industries, the factors influencing them and the barriers to their adoption have been the subject of several studies during the past three to four decades. Though energy-use behaviours of individuals or households are sufficiently explored, industrial energy conservation behaviour is scarcely studied. This study identifies the relationship between the different behavioural elements to open up a door for behaviourally informed intervention research.

Total interpretive structural modelling technique was used to determine the relationship between different elements of the behaviour of energy managers. Expert responses were collected to understand the relationship between the behavioural elements, through telephone interviews.

The study identified the relationship between the behavioural elements and found imperfect evaluation as the key element with the highest driving power to influence other elements.

The authors postulate that a behaviourally informed intervention strategy that looks into the elements with high driving power such as imperfect evaluation, lack of focus on energy-saving measures and the lack of sharing energy-saving objectives can lead to: an increase in the adoption of energy efficiency measures and thereby a reduction in the energy efficiency gap; greater productivity gains and reduced greenhouse gas (GHG) emissions; Preparation of M&V protocol that incorporates behavioural, organisational and informational barriers.

Various policy level interventions and regulatory measures in the energy field which did not address the behavioural barriers are found unsuccessful in narrowing the energy-efficiency gap, reducing the GHG gas emissions and global warming. Understanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement.

Understanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement. This study argues that through the systematic analysis of the imperfect evaluation of energy audit recommendations, it is possible to increase the adoption of energy efficiency measures that can lead to greater productivity gains and reduced GHG emissions.

In the realm of energy behaviour studies, very little research has been done to understand industrial energy behaviour (IEB) that influences the willingness to adopt (WTA) energy-efficient measures. Most of the studies on energy behaviour were focused on the residential and commercial sectors where the behaviour under investigation was under volitional control, that is, where people believe that they can execute the behaviour whenever they are willing to do so. The purpose of this paper is to examine the factors influencing the industry’s intentions and behaviour that leads to enhanced adoption of energy efficiency measures recommended through energy audits. In particular, this paper aims to extend the existing behaviour intention models using the total interpretive structural modelling (TISM) method and expert feedback to develop an IEB model

TISM technique was used to determine the relationship between different elements of the behaviour. Responses were collected from experts in the field of energy efficiency to understand the relationship between identified factors, their driving power and dependency.

The results show that values, socialisation and leadership of individuals are the key driving factors in deciding the individual energy behaviour. WTA energy-saving measures recommended by an energy auditor are found to be highly dependent on the organisational policies such as energy policy, delegation of power to energy manager and life cycle cost evaluation in purchase policy.

This study has a few limitations that warrant consideration in future research. First, the data came from a small sample of energy experts based on a convenience sample of Indian experts. This limits the generalizability of the results. Individual and organizational behaviour analysed in this study looked into a few select characteristics, derived from the literature review and expert feedback, which may pose questions about the standard for behaviours in different industries.

Reasons for non-adoption of energy audit recommendations are rarely shared by the industries and the analysis of individual and organisational behaviour through structured questionnaire and surveys have serious limitations. Under this circumstance, collecting expert feedback and using the TISM method to build an IEB model can help to build strategies to enhance the adoption of energy-efficient measures.

Various policy level interventions and regulatory measures in the energy field, adopted across the globe, are found unsuccessful in narrowing the energy-efficiency gap, reducing the greenhouse gas (GHG) emissions and global warming. Understanding the key driving factors can help develop effective intervention strategies to improve energy efficiency and reduce GHG emissions.

The industry energy behaviour model with driving, linking and dependent factors and factor hierarchy is a novel contribution to the theory of organisational behaviour. The model takes into consideration both the individual and organisational factors where the decision-making is not strictly under volitional control. Understanding the key driving factor of behaviour can help design an effective intervention strategy that addresses the barriers to energy efficiency improvement. The results imply that it is important to carry out post energy audit studies to understand the implementation rate of recommendations and also the individual and organisational factors that influence the WTA energy-saving measures.

Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.

This paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.

The performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.

This paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.