Search results

Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.

This paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.

The R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.

The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

This study aims at investigating the impact of the perceived importance of critical cultural readiness factors (CRFs) is on perceived importance of Lean Six Sigma (LSS) technical critical success factors (CSFs) in UK manufacturing sector.

A survey questionnaire through a multiple embedded case study was conducted. The study involves surveying people in the manufacturing firms followed by non-parametric Kruskal–Wallis test to study the relationships.

It was found that the people's perception towards impact of CRFs on technical CSFs of LSS projects is different depending upon each CRF, demographic factors and technical CSFs. This means that particular CRFs need to be prioritised to address LSS technical CSFs.

The study fills the research gap in investigating the perception of people towards inter-relationship of cultural or soft CSFs of LSS and technical or hard CSFs of LSS in manufacturing firms. Nevertheless, the authors suggest further multi-case study analysis covering different manufacturing fields as future studies.

The study is crucial for managers financially to be ready to invest on a successful LSS project and it helps them to diagnose the cultural causes of failure in a more timely way and effectively.

This is a preliminary study focussing on analysing inter-relationship between perceived importance of soft readiness factors and perceived importance of implementing success factors as a missing jigsaw in the current literature.

The purpose of this paper is to examine the market integration and direction of causality of wholesale and retail prices for the chickpea legume in major chickpea markets in India.

In this paper, the authors employ the Johansen co-integration test, Granger causality test, vector autoregression (VAR), and vector error correction model (VECM) to examine the integration of markets. The authors use monthly wholesale and retail price data of the chickpea crop from select markets in India spanning January 2003–December 2020.

The results of this study strongly confirm the co-integration and interdependency of the selected chickpea markets in India. However, the speed of adjustment of prices in the wholesale market is weakest in Bikaner, followed by Daryapur and Narsinghpur; it is relatively moderate in Gulbarga. In contrast, the speed of adjustment is negative for Bhopal and Delhi, weak for Nasik, and moderate for retail market prices in Bangalore. The results of the causality test show that the Narsinghpur, Daryapur, and Gulbarga markets are the most influential, with bidirectional relations in the case of wholesale market prices. Meanwhile, the Bangalore market is the most connected and effective retail market among the selected retail markets. It has bidirectional price transmission with two other markets, i.e. Bhopal and Nasik.

This paper calls for forthcoming studies to investigate the impact of external and internal factors, such as market infrastructure; government policy regarding self-reliant production; product physical characteristics; and rate of utilization indicating market integration. They should also focus on strengthening information technology for the regular flow of market information to help farmers increase their incomes.

Very few studies have explored market efficiency and direction of causality using both linear and nonlinear techniques for wholesale and retail prices of chickpea in India.

Lean Six Sigma (LSS) is receiving a tremendous attention as a modern process of streaming to improve the organizational ability and customer satisfaction. A successful LSS implementation is influenced by various factors and the execution of all the influencing factors simultaneously is a very difficult task for any organization. From the perspective of limitation of resources, this paper aims to present a basic issue in an LSS implementation of clustering complex and impacting factors into groups to achieve them in a stepwise manner. This paper aims to present a fundamental issue of clustering the complex and impacting factors of an LSS implementation into groups to achieve them stepwise.

A total of 40 relevant influencing factors toward an LSS implementation have been identified from the extensive literature review and duly validated with experts’ opinions. Integrated fuzzy set theory and decision-making trial and evaluation laboratory (DEMATEL) approach are demonstrated to explore the causal relationships among influencing factors of the LSS implementation. An empirical case analysis of an Indian manufacturing organization is carried out to illustrate the utilization of the proposed model.

The proposed framework effectively finds out the significance of each influencing factor of an LSS implementation and clustered into cause–effect groups. As per the results of the empirical case analysis, ten critical success factors (CSFs) of the LSS implementation are evaluated for the successful LSS implementation. Top management pays more attention to achieve them and implement them in a phase-wise approach under the limitations of accessible resources.

The presented framework provides an effective, precise and systematic decision support tool for recognizing CSFs of the LSS implementation. The organization, decision-makers, industrial practitioners and academic researchers may be able to comprehend the cause–effect relationship of the influencing factors of the LSS implementation. The exploratory nature and the single case study are two major limitations of this analysis. The developed model is heavily dependent on the experts’ opinions; hence, any bias in judgment will influence the final result.

This analysis is the first of its kind of effort, according to the best of the authors’ knowledge, to classify the influencing factors of LSS implementation into the cause–effect cluster. The outcomes of this analysis make the complexity of a problem easier in handling and assisting the decision-making.

This study aims to investigate the antecedents of social interaction among Indian retail investors and fund managers to understand how these factors influence investment decisions. By identifying and examining these antecedents, the study aims to shed light on the social dynamics that shape investment behavior in the Indian financial market.

The researchers have mainly adopted an interpretive strategy for the present study. Qualitative data elicited through semistructured interviews with six retail investors and two fund managers were subjected to qualitative thematic analysis.

Our research found several factors that make Indian retail investors and fund managers connect and make financial decisions. Peers can improve a person’s investing performance through social facilitation, and discussing investment suggestions and lessons learned can affect a group’s investment behavior. Social norms also influenced investors’ financial decisions, demonstrating compliance. Investor closeness increased information sharing. Finally, the fear of missing out (FOMO), a psychological phenomenon where people fear missing out on rewarding experiences, encouraged social engagement as investors sought appealing prospects.

The researchers interviewed eight carefully selected interviewees across the divide between retail investors and fund managers. Adopting other grouping criteria, conducting a focus group discussion with more respondents or adopting a mixed-methods approach may increase our understanding of the investment decision behaviors of Indian retail investors and fund managers.

The findings have far-reaching consequences, from deepening our knowledge of investors’ motivations and actions to directing individual savers, informing the development of financial literacy initiatives, influencing fund management practices and inspiring additional research in this study area.

This research, including retail investors and fund managers, significantly contributes to the literature on investment decisions and behavioral finance, particularly in the context of Indian investors and managers. This study’s unique perspective and comprehensive approach make it a valuable addition to the field, sparking interest and further exploration among academics, practitioners and investors alike.

Industries have been the most significant contributor to carbon emissions since the beginning of the Industrial Revolution. The transition to Industry 5.0 (I5.0) marks a pivotal moment in the industrial revolution, which aims to reconcile productivity with environmental responsibility. As concerns about the decline of environmental quality increase and the demand for sustainable industrial methods intensifies, experts recognize the shift toward the I5.0 transition as a crucial turning point.

This review study explores the convergence of green technological advancements with the evolving landscape of I5.0, thereby presenting a roadmap toward carbon neutrality. Through an extensive analysis of literature spanning from 2012 to 2024, sourced from the Scopus database, the research study unravels the transformative potential of green technological innovations, artificial intelligence, green supply chain management and the metaverse.

The findings underscore the urgent imperative of integrating green technologies into the fabric of I5.0, highlighting the opportunities and challenges inherent in this endeavor. Furthermore, the study provides insights tailored for policymakers, regulators, researchers and environmental stakeholders, fostering informed decision-making toward a carbon-neutral future.

This review serves as a call to action, urging collective efforts to harness innovation for the betterment of industry and the environment.

This research lies in the domain of Vedic mathematics, and it explores the application of the related Vedic sutras in different branches of mathematics, science, education, and engineering across Asia and Europe.

The study embraced a qualitative research design followed by a systematic literature review (SLR) approach which describes the significance of Vedic mathematics. The study made use of purposive sampling through which the data were collected from 102 articles by using inclusion and exclusion criteria. It includes publication years, the types of research methods, and uses of Vedic mathematics sutras in different branches of knowledge stated by the researchers. Its goal is to offer a more thorough explanation and an evaluation of how the inquiry affected the conclusions. The articles examined in this review included all the journal articles and doctoral theses from the databases of Google Scholar, Science Direct, Scopus, and Sodhganga which were published during the period 2010–2022.

The research found that the application of the sutras of Vedic mathematics has been increasing immensely in India. The researchers in this area are fond of qualitative research methods. This research has shown that sutras of Vedic mathematics especially “Urdhvatiryakbhyam” and “Nikhilam Navatascaramana Dasastah” have been frequently used in mathematics and engineering in technical higher education. The impact of other sutras has been quite useful, which augments that in many disciplines where the applications of Vedic mathematics are prevalent, it can be functional. The study concludes by reprising the result, its limitations, and the use of Vedic mathematics as a sustainable source of knowledge.

Vedic mathematics is an area where a lot of potential applications are created in science, mathematics, engineering, and education. Even with the latest technological advancements like learning analytics, artificial intelligence has its connection with this branch of learning, which is the greatest treasure of the Indian knowledge system. The research in this area is not reported in any databases or any standard format so researchers find it difficult to locate and study this broad conceptual domain.

It will help the reader and other academic stakeholders to widen their view on the new and innovative techniques of Vedic mathematics. It is advised that additional studies would look at and evaluate papers published after this time so that readers may get a wider view of the concept of Vedic mathematics.

It will help society to know the essence of Vedic mathematics that how useful it is. Vedic mathematics helps learners to learn in a very factual and accurate manner especially while dealing with mathematical calculations. It will enhance the problem-solving skills among learners. It will be beneficial for all types of learners which will help them to become better individuals for a nation.

The paper enriches understanding of the potential applications of different sutras from Vedic literature in different fields of knowledge. The outcome of the research encourages educationists and policymakers to include Vedic mathematics in the curriculum to foster quantitative reasoning and problem-solving at varied levels of learning.

The aim of this study is to investigate the humidity-sensing of polyaniline–zinc oxide (PANI–ZnO) nanocomposites. Humidity sensor has wide applications in drug industries, food industries and domestic purpose to regulate the humidity level.

PANI–ZnO composites were prepared by in situ polymerization method, and further humidity response was tested by using a two-probe sensor setup.

PANI-ZnO composites surface were modified by using camphor sulphonic acid. DC conductivity is due to the hopping of polorans. Thermal coefficient value varies from 1.7 to 2.3. The 30 weight per cent composite shows high sensitivity among other composites.

These composites can be used only at room temperature or moderate temperature, i.e. below 280°C.

The composites are prepared in tetrapod shape that has a large surface area and more stability. Therefore, these materials would be the replacement for conventional materials.

These sensors have many applications in food and drug preservation, domestic purposes, etc.

This work is original, and not being considered for publication elsewhere. In this work, the charge transport properties were evaluated based on the resistivity change when samples were exposed to humidity.