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This paper aims to present the mixing and decay characteristics of Mach 0.8 elliptic jet manipulated by air tabs, through numerical simulation.

The Air Tabs are secondary jets injected through two 1-mm diameter tubes, which are placed at the exit plane of the elliptic primary nozzle. Two air tab configurations are considered for the present study; in the first case, they are placed along the major axis, and in the second case along the minor axis of the elliptic nozzle exit. The air tab Mach numbers considered are 0.8, 0.9 and 1.0. The numerical simulations have been performed in ANSYS-CFX software using the k-e turbulence model.

The jet centerline (X) total pressure decay reveals that air tabs placed along the minor axis promote jet mixing more efficiently than those placed along the major axis. It is also observed that the air tabs along the minor axis provoke the jet to split into two parts thus resulting in faster spread in the perpendicular direction (Z). When compared against the uncontrolled jet, air tabs at Mach 1.0 placed along the minor axis produce 80% reduction in the potential core, whereas that along the major axis brings down the axis-switching location to 0.95 Deq from 3.7 Deq.

In aerospace applications, enhancing the mixing between fuel and air would result in better combustion efficiency, faster exhaust jet mixing with ambient, which in turn result in reduced jet acoustics as well as infrared signature, thereby promotes environmental sustainability.

To the best of the authors’ knowledge, there has not been a thorough analysis of the effects of air tabs on the mixing and decay characteristics of elliptic Mach 0.8 jet.

The purpose of this study is to evaluate the methods employed for classifying and quantifying the potential of squeezing in tunnels. Along with the empirical and semi‐empirical approaches presently available in order to anticipate the potential of squeezing tunnel problems, the squeezing potential of Karaj water transfer tunnel and North West Tunnel Convey (NWTC) tunnels (Lot 2), located in Iran, are evaluated and presented. Those two case studies have an interesting geology profile and parameters to identify and then evaluate the squeezing potential.

In recent years, there has been an increasing interest in the tunnel construction. This paper describes the squeezing behavior of poor rock mass associated with deformability and strength properties. In Karaj water transfer tunnel, there are eight lithological rock types; and NWTC tunnel (Lot2) has 21 Lithological rock types. The parameters for rock classification, such as rock quality designation (RQD), rock mass rating (RMR), modified RMR, Q‐system, geological strength index (GSI), rock mass index (RMi), and rock structure rating (RSR) are evaluated and presented here. The parameters mentioned above are the input parameters for squeezing study in Karaj and NWTC tunnels. According to different methods of squeezing evaluation of tunnel presented in tables, the results of two case studies are presented in this paper.

One of the more significant findings to emerge from this study showed that about 3 km of the second part of NWTC tunnel, and 2 km of the Karaj tunnel have high squeezing potential. This research deals with not only an overview of the methods used for the identifying and quantifying of squeezing along with the empirical and semi‐empirical approaches presently available in order to anticipate the potential of squeezing tunnel problem, but also the case studies of NWTC and Karaj tunnels to evaluate and compare the potential of squeezing by different methods. These two tunnel case studies have high potential of squeezing therefore the lining of those two tunnels must be strong enough to overcome this issue.

This study is a precise and concise comparison of the evaluation of tunnels under squeezing rock condition. The present study confirms the previous findings and contributes additional evidence that suggests that there are many studies conducted using empirical and analytical methods to determine the squeezing phenomenon in tunnels. This paper responds to the various questions like, what is the squeezing phenomenon. How can we quantify the potential of squeezing in weak rock? What are the different approaches to the understanding of squeezing phenomenon?

Multilevel inverter (MLI) is a prevailing sensible alternative to two-level inverters that offer a high-quality output voltage waveform, wherein the multiple input direct current (DC) levels are established by using isolated DC sources, batteries and renewable energy sources. The purpose of this paper is to develop MLI to offer lower total harmonic distortion (THD), higher output voltage levels and reduced switching components for high power applications.

In this paper, a new tapped sources stack succored modified HX bridge MLI (TSSSMHXBMLI) topology is proposed which includes two modules, such as tapped sources stack (TSS) and modified HX bridge inverter, which perform their function in a single stage. Also, this paper outlines the formulaic implementation of the multicarrier/sub-harmonic pulse width modulation (MCPWM/SHPWM) in a Xilinx Spartan3E-500 field programmable gate array (FPGA) is suitable for the developed MLI.

The feasibility of the suggested topology is well proved by both simulation and experiment results.

This paper examines a new topology of TSSSMHXBMLI with a view to minimize total count of switching components against basic MLI topologies. The operating sequence of the suggested TSSSMHXBMLI topology is verified with the simulation study followed by an experimental investigation.

The simulation and experimental results of suggested MLI topology reveals to obtain lower THD, higher output voltage levels and reduced switching components for high power applications.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

The purpose of this paper is to investigate the relationship between corporate social responsibility (CSR) report readability and earnings management (EM). Additionally, the authors examine the association between CSR spending and CSR report readability to understand the clarity in disclosure when the firms’ investment in CSR is higher.

The sample for this study consists of 156 publicly traded firms in India spanning 2016–2021. The authors use the Fog Index to measure the readability of CSR reports and absolute discretionary accruals and absolute accrual EM as the proxy for EM. The empirical models are tested using a two-stage system GMM dynamic panel data model.

The authors find a negative association between the Fog Index and EM, indicating that the readability of CSR reports decreases when EM increases in a firm. Managers use complex words to mislead the stakeholders when there are more EM practices. The authors’ additional analysis shows a positive association between CSR spending and CSR report readability, indicating that the firm spends more reports on the initiatives more precisely, avoiding words with more syllables.

This study offers novel evidence by examining the readability of CSR reports published by Indian firms. Given the mandatory requirement of CSR in India, the readability of disclosures is associated with EM. The authors’ unique focus on CSR report readability provides a new direction for future researchers as they can explore the influence of regulatory mandates on CSR reporting practices.

For the first time in a decade, a new form of pneumonia virus, coronavirus, COVID-19, appeared in Wuhan, China. To date, it has affected millions of people, killed thousands and resulted in thousands of deaths around the world. To stop the spread of this virus, isolate the infected people. Computed tomography (CT) imaging is very accurate in revealing the details of the lungs and allows oncologists to detect COVID. However, the analysis of CT scans, which can include hundreds of images, may cause delays in hospitals. The use of artificial intelligence (AI) in radiology could help to COVID-19-positive cancer in this manner is the main purpose of the work.

CT scans are a medical imaging procedure that gives a three-dimensional (3D) representation of the lungs for clinical purposes. The volumetric 3D data sets can be regarded as axial, coronal and transverse data sets. By using AI, we can diagnose the virus presence.

The paper discusses the use of an AI for COVID-19, and CT classification issue and vaccination details of COVID-19 have been detailed in this paper.

Originality of the work is, all the data can be collected genuinely and did research work doneown methodology.

Digital technologies emerged as innovative avenues for launching new products, advertising brands, increasing customer awareness and thus leaving a remarkable impact on the online marketplace. The present study analyzed the effects of crucial antecedents of AR interactive technology on customers' behavior toward AR-based e-commerce websites.

Convenience sampling was used to collect primary data from 357 iGen respondents aged 16–22 years; residing in New Delhi and the NCR region of India and examined using the structural equation modeling technique.

Results revealed that technology anxiety and virtuality significantly influence customers' attitudes and behavioral intentions toward AR-based e-commerce websites. However, interactivity and innovativeness remain non-significant. Additionally, non-significant moderating effects were identified for the moderators, i.e. trust and need for touch. At the same time, gender has a significant moderating effect only for the association between technology anxiety and attitude toward AR-based e-commerce websites.

The study summarizes numerous theoretical and managerial implications for AR-based website designers and policymakers, followed by the crucial limitations and directions for future research.

The present research provides a significant understanding of the e-commerce industry by providing valuable insights about young iGen consumers' perceptions of AR-based e-commerce websites.

This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature.

This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations.

The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service.

The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties.

The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly.

The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains.

Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

The strict lockdown during COVID-19 has suspended construction, procurement and logistics in construction industry and affected the entire Supply Chain Management (SCM). The COVID-19 has been universally observed in all sectors, but a limited study has been done in construction industry and also the ways to mitigate it. Hence, the current study aims to identify the impact of COVID-19 in Construction Supply Chain (CSC) and to analyse the influence of factors on Supply Chain (SC) performance during uncertainty in the construction industry.

From previous studies, the factors enhancing the SCM performance are taken. The research follows a qualitative in-depth interview to identify the factors and quantitative study to collect data using a questionnaire. Samples for the study were chosen using purposing sampling techniques. The collected data were analysed using “Statistical Package for Social Science” (SPSS V 20).

Effect of COVID-19 is more on medium sized builders and developers. A positive impact is found between SC agility, SC resilience and information technology (IT) capabilities on SC performance.

Firstly, the study adopted cross-sectional data to test the hypothesis. The study results on the combined effect of the factors SC agility, SC resilience and IT capability during uncertainty were found to have a positive influence. The longitudinal study would enrich the understanding of the research. Secondly, the study was focussed on the perspective of the dynamic capability view. Further studies can adopt a multi-method approach to develop a theoretical framework using in-depth study and empirical validation. It will help in a better understanding of the current situation and young discipline in SC. Thirdly, the study has considered only Kerala construction companies; hence, the data were less. More data with better results will be obtained if the study has included construction companies from other states. Fourthly, the study can be extended to understand the interdependent effects of SC agility, SC resilience and information technology capability on performance. Presently, the interdependent effect in the study is limited.

The study helps the construction professionals to understand the preventive measures needed for the industry during any uncertainty. Also, the study is expected to help the construction managers and leaders with better performance in SCM and other business activities by adopting IT connectivity and being agile in all situations. Moreover, the study also helps policymakers take active solutions by providing awareness and incentives to the construction industry professionals. The literature and the factors will also help the academicians in further research progress.

The research is important for construction professionals to comprehend the impact of COVID-19 and to analyse the influence of factors such as SC agility, SC resilience and IT capabilities on improving the SC performance. This research can assist private and public construction firms to enhance SC performance during times of uncertainty. The result is likely to help construction companies with better SC performance and helps academicians' further progress in research.

The purpose of this paper is to investigate how three core elements of critical management studies (CMSs), “de-naturalisation”, “reflexivity” and “(non)-performative intent”, can help expand the current debate in the supply chain management (SCM) field.

The authors used a systematic literature review to select 103 articles published in 12 high-ranking journals in the SCM field based on the Academic Journal Guide of the Chartered Association of Business Schools.

The findings of this study suggest that SCM studies can be narrowed down into four major CMSs themes: “power”, “ethics and environmental issues”, “diversity” and “working conditions”, but even these themes are still under-discussed and undertheorized in SCM. The literature the authors reviewed is more concerned with explaining these phenomena than questioning them and proposing new agendas. This paper, therefore, will discuss how these three core elements of CMS can help transform the “hidden” issues of SCM, which it will do by illustrating it in the context of buyer–supplier relationships and lean manufacturing.

This research will encourage SCM scholars who are interested in conducting more critical studies and teaching the harmful effects of global supply chains.

This paper highlights that a combination of SCM and CMS approaches is important when we decide to adopt a more critical “constructive” view of supply chain challenges and engage practical and critical views, respectively, to generate knowledge that not only increases (corporate) performance but also highlights social needs and values.