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This paper presents experiment results that examine the validity of extracting blade vibration signature from the shaft torsional vibration signals. A special test rig was designed and manufactured for this objective. A set of strain gages were bonded to the shaft and to the blades to measure the shaft twisting and blade bending deformations respectively. A controlled frequency exciter excited the blade vibration. The shaft torsional and blade bending vibration signals were simultaneously recorded and presented in the time and frequency domains. The blade bending vibration frequencies appeared dominantly in the shaft torsional vibration signals for all blade vibration frequencies up to 100Hz. For frequencies higher than 100Hz, less sensitivity of the torsional vibration to blade vibration was observed.

The purpose of this paper is to present an imperceptible and robust watermarking algorithm with high embedding capacity for digital images based on discrete wavelet transform (DWT) domain.

First, the watermark image is scrambled using chaotic sequence and mapped to avoid the block effect after embedding watermark into the host image. Then, the scrambled watermark is inserted in LH₂ and HL₂ sub‐bands of the DWT of the host image to provide a good tradeoff between the transparency and the robustness of watermarks.

This paper presents experimental results and compares the results to other methods. It can be seen from the comparison that this method can obtain a better performance in many cases.

One of the main differences of this technique, compared to other wavelet watermarking techniques, is in the selection of the wavelet coefficients of the host image. When performing second level of the DWT, most methods in the current literature select the approximation sub‐band (LL₂) to insert the watermark. The technique presented in this paper decomposes the image using DWT twice, and then obtains the significant coefficients (LH₂ and HL₂ sub‐bands) of the host image to insert the watermark.

This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the student’s intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned.

The target population of this research – the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for two months, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships.

Usage has a negative impact on a student’s creativity, but increased usage of ChatGPT encourages a student’s adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision.

This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. The findings of the study provide implications that help to create effective digital education strategies for stakeholders.

This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort.

The methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers.

The study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process.

There are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques.

The authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.

This study aims to delve into the perspectives of educators on integrating ChatGPT, an AI language model into management education. In the current research, educators were asked to talk as widely as possible about the perceived benefits, limitations of ChatGPT in management education and strategies to improve ChatGPT for management education. Also, shedding light on what motivates or inhibits them to use ChatGPT in management education in the Indian context.

Interpretative phenomenological analysis commonly uses purposive sampling. In this research, the purpose is to delve into educators’ perspectives on ChatGPT in management education. The data was collected from the universities offering management education in Uttarakhand, India. The final sample size for the study was constrained to 57 educators, reflecting the point of theoretical saturation in data collection.

The present study involved educators discussing the various advantages of using ChatGPT in the context of management education. When educators were interviewed, their responses were categorized into nine distinct sub-themes related to the benefits of ChatGPT in management education. Similarly, when educators were asked to provide their insights on the limitations of using ChatGPT in management education, their responses were grouped into six sub-themes that emerged during the interviews. Furthermore, in the process of interviewing educators about potential strategies to enhance ChatGPT for management education, their feedback was organized into seven sub-themes, reflecting the various approaches suggested by the educators.

In the qualitative study, perceptions and experiences of educators at a certain period are captured. It would be necessary to conduct longitudinal research to comprehend how perceptions and experiences might change over time. The study’s exclusive focus on management education may not adequately reflect the experiences and viewpoints of educators in another discipline. The findings may not be generalizable and applicable to other educational disciplines.

The research has helped in identifying the strengths and limitations of ChatGPT as perceived by educators for management education. Understanding educators’ perceptions and experiences with ChatGPT provided valuable insight into how the tool is being used in real-world educational settings. These insights can guide higher education institutions, policymakers and ChatGPT service providers in refining and improving the ChatGPT tool to better align with the specific needs of management educators.

Amid the rising interest in ChatGPT’s educational applications, a research gap exists in exploring educators’ perspectives on AI tools like ChatGPT. While some studies have addressed its role in fields like medical, engineering, legal education and natural sciences, the context of management education remains underexplored. This study focuses on educators’ experiences with ChatGPT in transforming management education, aiming to reveal its benefits, limitations and factors influencing adoption. As research in this area is limited, educators’ insights can guide higher education institutions, ChatGPT providers and policymakers in effectively implementing ChatGPT in Indian management education.

A distinction must be drawn between a dismissal on the one hand, and on the other a repudiation of a contract of employment as a result of a breach of a fundamental term of that contract. When such a repudiation has been accepted by the innocent party then a termination of employment takes place. Such termination does not constitute dismissal (see London v. James Laidlaw & Sons Ltd (1974) IRLR 136 and Gannon v. J. C. Firth (1976) IRLR 415 EAT).

This qualitative work aims to explore the university students’ attitude toward advantages, drawbacks and prospects of ChatGPT.

This paper applies well accepted Colaizzi’s phenomenological descriptive method of enquiry and content analysis method to reveal the ChatGPT user experience of students in the higher education level.

The study’s findings indicate that ChatGPT enhances the quality of learning and facilitates faster learning among university students. However, despite numerous positive outcomes, it is noted that ChatGPT may diminish students' creativity by swiftly addressing their critical queries. Over time, students may experience a decline in patience and critical thinking skills as they excessively rely on ChatGPT, potentially leading to ethical misconduct.

This paper primarily explores the advantages and drawbacks of using ChatGPT in the university context of Bangladesh. The present study creates a platform for future research in this domain with comprehensive study design. The study results alert the policy makers to improve upcoming version of ChatGPT with convenient user experience and academicians as this paper unleash several positive as well as negative consequences of using this AI-enabled chatbot.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.

The proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.

The findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.

The study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.

The study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.

Argan nutshell wood (ANW) has been used in this study as an agricultural solid waste to remove Congo red (CR) from an aqueous solution in single and mixture binary in the presence of methylene blue (MB) or crystal violet (CV).

The ANW was characterized by Fourier transform infrared and scanning electron microscope analysis. The effect of ANW dose (8–40 gL⁻¹), contact time (0–180 min), pH of the solution (4–11) and CR dye concentration (100–500 mgL⁻¹) on CR adsorption was studied in batch mode and evaluated by kinetic and isotherm models in a single system. In the binary system, the CR removal was studied from a CR + MB and CR + CV mixture with different percentages of dyes, ranging from 0% to 100%.

The pseudo-second-order and the Langmuir models could best describe the CR sorption onto ANW in a single system. In addition, in the case of the binary system, there is the appearance of a synergistic phenomenon between the CR and the other cationic dyes and the CR adsorption capacity increased until 12.24 mg g-1 and 12.06 mg g-1 in the presence of the MB and CV in the mixture, respectively.

This study demonstrated that ANW prepared can be suggested as an excellent potential adsorbent to remove dyes from wastewaters from single and mixture systems.

This study is original.