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Drawing on the extant literature that suggests marketing imperfections are an opportunity to create and establish a foundation for sustainable entrepreneurial prospects and sustainability, this study aims to delineate a marketing mix strategy for Indian medicinal and aromatic plant (MAP) resources to optimize the benefits derived from their existing MAP business and address its marketing imperfections.

A case study research design was used to investigate the MAP sector, and 37 in-depth interviews were conducted to collect the primary data. Given the study’s exploratory nature, an inductive approach was used for data analysis, and conventional qualitative content analysis was performed to analyze the data.

The findings reveal that a marketing mix strategy is relevant for linking MAP businesses to the country’s sustainable livelihood options, entrepreneurial prospects, resource management and the economy and for improving the sector’s global competitive position.

To the best of the author’s knowledge, this study is the first to explore and present a marketing mix strategy for Indian MAP resources. Thus, it extends the marketing and entrepreneurship literature regarding natural resource businesses to advance sustainable entrepreneurial prospects and sustainability. The study concludes by offering strategic clues for implementing the marketing mix strategy in the Indian MAP sector and businesses.

The main purpose of the paper is timing analysis of mixed critical applications on the multicore system to identify an efficient task scheduling mechanism to achieve three main objectives improving schedulability, achieving reliability and minimizing the number of cores used. The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

The paper opted for a simulation-based study. The simulation of mixed critical applications, like air traffic control systems and synthetic workloads, is carried out using a litmus-real time testbed on an Ubuntu machine. The heuristic algorithms for task allocation based on utilization factors and task criticalities are proposed for partitioned approaches with multiple objectives.

Both partitioned earliest deadline first (EDF) with the utilization-based heuristic and EDF-virtual deadline (VD) with a criticality-based heuristic for allocation works well, as it schedules the air traffic system with a 98% success ratio (SR) using only three processor cores with transient faults being handled by the active backup of the tasks. With synthetic task loads, the proposed criticality-based heuristic works well with EDF-VD, as the SR is 94%. The validation of the proposed heuristic is done with a global and partitioned approach of scheduling, considering active backups to make the system reliable. There is an improvement in SR by 11% as compared to the global approach and a 17% improvement in comparison with the partitioned fixed-priority approach with only three processor cores being used.

The simulations of mixed critical tasks are carried out on a real-time kernel based on Linux and are generalizable in Linux-based environments.

The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

This paper fulfills an identified need to have multi-objective task scheduling in a mixed critical system. The timing analysis helps to identify performance risks and assess alternative architectures used to achieve reliability in terms of transient faults.

Mergers and acquisitions (M&As) are important strategic actions undertaken by firms to access resources and markets. However, firms face substantial challenges in M&As during deal completion. While prior literature reviews synthesize the studies on the post-merger consequences of M&As, the literature on deal completion is largely fragmented. In this paper, the authors synthesize prior literature on deal completion into the antecedents and consequences framework and map various studies across the international business and management, finance and accounting literature at the macro-, meso- and micro-levels.

The authors adopt a content analysis-based methodology to conduct the review. First, the authors identify existing literature on deal completion based on keyword searches. Next, the authors propose a framework that integrates the extant literature from a multi-theoretic perspective across four broad themes: concepts, antecedents, implications and moderators. In this study, the authors consider not only empirical but also conceptual papers to strengthen the theoretical foundations of M&A literature. Finally, after synthesizing various studies, the authors highlight a future research agenda on deal completion.

Based on the review, this study provides important avenues for future research on M&A deal completion.

This study theoretically integrates multi-disciplinary and multi-country research on acquisition completion.

Businesses are increasingly vulnerable and exposed to physical climate change risks, which can cascade through local, national and international supply chains. Currently, few methodologies can capture how physical risks impact businesses via the supply chains, yet outside the business literature, methodologies such as sustainability assessments can assess cascading impacts.

Adopting a scoping review framework by Arksey and O'Malley (2005) and the PRISMA extension for scoping reviews (PRISMA-ScR), this paper reviews 27 articles that assess climate risk in supply chains.

The literature on supply chain risks of climate change using quantitative techniques is limited. Our review confirms that no research adopts sustainability assessment methods to assess climate risk at a business-level.

Alongside the need to quantify physical risks to businesses is the growing awareness that climate change impacts traverse global supply chains. We review the state of the literature on methodological approaches and identify the opportunities for researchers to use sustainability assessment methods to assess climate risk in the supply chains of an individual business.

The natural control, feedback, stimuli and protection of these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation system was created that integrated natural interaction assisted by electroencephalogram (EEG), which enabled the movements in the virtual environment and real wheelchair. For blind wheelchair operator patients, this paper involved of expounding the proper methodology. For educating the value of life and independence of blind wheelchair users, outcomes have proven that virtual reality (VR) with EEG signals has that potential.

Individuals face numerous challenges with many disorders, particularly when multiple dysfunctions are diagnosed and especially for visually effected wheelchair users. This scenario, in reality, creates in a degree of incapacity on the part of the wheelchair user in terms of performing simple activities. Based on their specific medical needs, confined patients are treated in a modified method. Independent navigation is secured for individuals with vision and motor disabilities. There is a necessity for communication which justifies the use of VR in this navigation situation. For the effective integration of locomotion besides, it must be under natural guidance. EEG, which uses random brain impulses, has made significant progress in the field of health. The custom of an automated audio announcement system modified to have the help of VR and EEG for the training of locomotion and individualized interaction of wheelchair users with visual disability is demonstrated in this study through an experiment. Enabling the patients who were otherwise deemed incapacitated to participate in social activities, as the aim was to have efficient connections.

To protect their life straightaway and to report all these disputes, the military system should have high speed, more precise portable prototype device for nursing the soldier health, recognition of solider location and report about health sharing system to the concerned system. Field programmable gate array (FPGA)-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals, the soldier’s health is observed on systematic bases. By emerging Verilog hardware description language (HDL) programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t the whole work is approved in a Vivado Design Suite. Classification of different abnormalities and cloud storage of EEG along with the type of abnormalities, artifact elimination, abnormalities identification based on feature extraction, exist in the segment of suggested architecture. Irregularity circumstances are noticed through developed prototype system and alert the physically challenged (PHC) individual via an audio announcement. An actual method for eradicating motion artifacts from EEG signals that have anomalies in the PHC person’s brain has been established, and the established system is a portable device that can deliver differences in brain signal variation intensity. Primarily the EEG signals can be taken and the undesirable artifact can be detached, later structures can be mined by discrete wavelet transform these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as multirate support vector machine classifiers when the features have been extracted using a combination of hidden Markov model (HMM) and Gaussian mixture model (GMM). Intended for capable declaration about action taken by a blind person, these result signals are protected in storage devices and conveyed to the controller. Pretending daily motion schedules allows the pretentious EEG signals to be caught. Aimed at the validation of planned system, the database can be used and continued with numerous recorded signals of EEG. The projected strategy executes better in terms of re-storing theta, delta, alpha and beta complexes of the original EEG with less alteration and a higher signal to noise ratio (SNR) value of the EEG signal, which illustrates in the quantitative analysis. The projected method used Verilog HDL and MATLAB software for both formation and authorization of results to yield improved results. Since from the achieved results, it is initiated that 32% enhancement in SNR, 14% in mean squared error (MSE) and 65% enhancement in recognition of anomalies, hence design is effectively certified and proved for standard EEG signals data sets on FPGA.

The proposed system can be used in military applications as it is high speed and excellent precise in terms of identification of abnormality, the developed system is portable and very precise. FPGA-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals the soldier health is observed in systematic bases. The proposed system is developed using Verilog HDL programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t and synthesised using in Vivado Design Suite software tool.

Despite the significance of the construction industry to the nation's economic growth, there is empirical evidence that the sector is lagging behind other industries in terms of productivity growth. The need for improvements inspired the industry's stakeholders to consider using emerging technologies that support the enhancement. This research aims to report augmented reality applications essential for contractors' productivity improvement.

This study systematically reviewed academic journals. The selection of journal articles entailed searching Scopus and Web of Science databases. Relevant articles for reviews were identified and screened. Content analysis was used to classify key applications into six categories. The research results were limited to journal articles published between 2010 and 2021.

Augmented reality can improve construction productivity through its applications in assembly, training and education, monitoring and controlling, interdisciplinary function, health and safety and design information.

The research provides a direction for contractors on key augmented reality applications they can leverage to improve their organisations' productivity.

This study investigates the relationships between knowledge management capabilities (KMC), supply chain agility (SCA) and disruption mitigation performance (DMP) in the U.S. fashion retail industry (FRI) during turbulent times, such as a pandemic.

An online survey was used to collect 320 responses from U.S. fashion retail professionals. Structural equation modeling was used for analysis.

Among the two KMCs, knowledge infrastructure capabilities act as enabling factors for knowledge process capabilities (KPC) in U.S. fashion retail settings. The KPC were found to be positively associated with SCA, and SCA was positively associated with both pre- and post-DMP of U.S. fashion retailers.

This study adds to the literature on KMC, SCA and DMP from the FRI context and illustrates the impact of effective organizational knowledge management for supply chain (SC) disruption mitigation through agility in a volatile market.

The results inform fashion retail companies on how to transform their organizational dimensions through effective management of knowledge, i.e. digital escalation and innovation, to establish an agile and sustainable SC to mitigate future market disruptions.

Little is known about the extent to which management and engineering publications dedicated to the study of quality management topics make use of mixed methods, what types of studies have been conducted and how effective mixed methods have been. The aim of the current paper is to analyse how mixed methods have been used in quality management research.

To address this purpose, a bibliometric analysis was conducted of papers using mixed methods designs to investigate quality management issues and published in the SCOPUS database. CiteSpace software was used to assist in the categorisation and mapping process.

Ninety articles were identified and analysed. The results show that mixed methods are mainly used either to compare different perspectives drawn from quantitative and qualitative data or to develop better measurement instruments. Sequential mixes occur more often than concurrent approaches. Moreover, there is a link between the purpose of the study and the approaches followed to combine qualitative and quantitative methods. Yet, the contribution of the use of mixed methods to achieving the aims of the study is not easy to assess as the purposes of using mixed methods are often not clearly stated.

As one of the first papers to examine how qualitative and quantitative methods are being combined in quality management research, this study is expected to contribute to the literature by providing some insights into how mixed methods can be more effectively used in this field.

This paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.

The authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.

With these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.

This study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.