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The main purpose of this study is to explore different aspects of the Green Lean Six Sigma (GLSS) approach, application status and potential benefits from a comprehensive review of the literature and provide an avenue for future research work. This study also provides a conceptual framework for GLSS.

To do a systematic analysis of the literature, a systematic literature review methodology has been used in this research work. From the reputed databases, 140 articles were identified to explore hidden aspects of GLSS. Exploration of articles in different continents, year-wise, approach-wise and journal-wise was also done to find the execution status of GLSS.

This study depicts that GLSS implementation is increasing year by year, and it leads to considerable improvement in all dimensions of sustainability. Enablers, barriers, tools and potential benefits that foster the execution of GLSS in industrial organizations are also identified based on a systematic review of the literature.

The study’s uniqueness lies in that, to the best of the authors’ knowledge, this study is the first of its kind that depicts the execution status of GLSS, and its different facets, explores different available frameworks and provides avenues for potential research in this area for potential researchers and practitioners.

The study aims to analyze the relationship between teachers’ information and communication technology (ICT) efficacy and the ICT environment in teaching-learning. For this, the current research attempts to explore and understand the role of ICT factors in higher education and develop a framework for future researchers to gain a substantial understanding of the teachers' ICT efficacy and ICT environment.

Teachers’ ICT efficacy has been analyzed in three domains, i.e. technological, content and pedagogical. The ICT environment is measured on training aspects, ICT tools and administrative support. The researcher adopted purposive sampling as a part of the non-probability sampling technique. The questionnaire was circulated among the experts through e-mail to collect the required response sheets. The experts are working in different academic institutes in India, primarily from premier institutes of the country. It covers all the regions of the country, and 22 experts are taken in the research study for collecting the data. The study uses the decision-making trial and evaluation laboratory method to explore the link among identified factors and criteria through the expert’s opinion method to achieve the set objectives within the Indian context.

The results indicate that the content efficacy of a teacher is pivotal to providing sound teaching-learning using digital tools and techniques. The developed model, measuring the cause-effect relationship based on the role of ICT efficacy of teachers in delivering teaching, will enable academic organizations to frame policies and strategies that will focus on enhancing teachers' competencies towards self and organizational growth.

The present research is one of the pioneering works that investigates the factors of ICT in higher education.

The purpose of this study is to review the different facets associated with Industry 5.0 (I5.0) and propose a conceptual framework to boost the applicability of this novel technological cum social aspects within industrial organizations for improved organizational sustainability.

This research work adopted a bibliometric analysis that encapsulates a quantitative set of tools for bibliometric and bibliographic information. This study uses the database of Scopus to acquire data related to different facets of I5.0. The study implies a different spectrum of terms to reach the final corpus of 91 articles related to I5.0. Furthermore, a conceptual define, measure, analyze, improve and control (DMAIC)-based framework based on different literature findings is proposed and validated based on the input of experts from different parts of the world.

The results indicate that I5.0 is still in its infancy. The wider applicability of I5.0 demands comprehensive theoretical knowledge of different facets of this new paradigm and the development of a framework to adopt it on a larger scale. Organizations that are in the race to adopt I5.0 face major challenges related to the digitization of processes along with well-defined cyber-physical systems and the lack of a dedicated framework to execute I5.0. Furthermore, the result also suggests that manufacturing industries are more ready to adopt I5.0 practices as compared to service industries, which can be attributed to well-defined technological measures available in manufacturing settings.

To the best of the authors’ knowledge, this is one of the first studies that explore different know-how and challenges and provides a holistic view of I5.0 by providing a systematic adoption framework.

The main aim of this study is to review different aspects of Industry 5.0 (I5.0) along with Kaizen measures to foster this novel aspect of industrial sustainability. The study makes a comprehensive study to explore the implementation status of I5.0 in industries, key technologies, adoption level in different nations and barriers to I5.0 adoption together with mitigation actions.

To do a systematic study of the literature, the authors have used preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology to extract articles related to the field of the study.

It has been found that academic literature on the I5.0 is continuously growing as the wheel of time is running. Most of the studies on I5.0 are conceptual-based, and manufacturing and medical industries are the flag bearer in the adoption of this novel aspect. Further, due to I5.0's infancy, many organizations face difficulty to adopt the same due to financial burden, resistive nature, a well-designed standard for cyber-physical systems (CPS) and an effective mechanism for human–robot collaboration. Further studies also provide avenues for future research in terms of the identification of collaborative mechanisms between machines and wells, the establishment of different standards for comparison and the development of I5.0-enabled models for different industrial domains.

The study is the first of its kind that reviews different facets of I5.0in conjunction with Kaizen's measures and application areas and provides avenues for future research to improve an organization's environmental and social sustainability.

This research aims to examine the macro determinants that significantly affect financial development in the Middle East and North Africa (MENA) region, which could be used furtherly to play a major role in economic sustainability since one of the major driving forces for economic development is the financial development.

The significant determinants of financial development should be efficiently used by the MENA region countries for creating huge financial sector development and innovation, stimulating economic development in turn and leading to the completion of the cycle of development and sustainability. To achieve this study's objective, the researcher employed a quantitative method to develop an econometric model.

This model consisted of two Panel EGLS Cross-Section Random Effects Models (REMs) in which Domestic credit to the private sector as a percentage of GDP (?PCGDP?_it) and stock market capitalization ratio (?SMC?_it) were taken as the dependent variables. In addition, the independent variables included the corruption perception index, financial freedom (FF), political stability (PS) and trade openness (TO). The researcher extracted the data for the analysis from different databases including the World Bank, the Organization for Economic Cooperation and Development and the International Monetary Fund. Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

This paper aims to capture real-time images of tourists during their visitation. This effort is to clarify a debate among scholars that there is a lack of current effort to genuinely represent an accurate image of the tourist experience during their visit. Previous studies on destination image focused on measuring and successfully capturing the tourists' perceived image using the perspective of “before and after” visitation.

The paper applies volunteer-employed photography and questionnaire methods to capture real-time tourist images. The paper was conducted in Kuala Lumpur, involving 384 international tourists. The data are analysed by supplemental photo analysis, was categorised into manifest and latent content.

The paper provides empirical insights into the changes in tourists' image when visiting an urban destination. The insights suggest that a city's image during visitation continuously changes based on the tourists' movement and preferences.

The findings of this paper are critical in assisting tourism agencies and authorities in portraying an accurate image to achieve greater tourism satisfaction.

This paper contributes to the interpretation and portrayal of the real-time image of Kuala Lumpur based on the manifest and latent content of the photos taken.

The institutional conditions of primary care provision remain understudied in low- and middle-income countries. This study analyzes how primary care doctors cope with medical uncertainty in municipal clinics in urban India. As street-level bureaucrats, the municipal doctors occupy two roles simultaneously: medical professional and state agent. They operate under conditions that characterize health systems in low-resource contexts globally: inadequate state investment, weak regulation and low societal trust. The study investigates how, in these conditions, the doctors respond to clinical risk, specifically related to noncommunicable diseases (NCDs).

The analysis draws on year-long ethnographic fieldwork in Pune (2013–14), a city of three million, including 30 semi-structured interviews with municipal doctors.

Interpreting their municipal mandate to exclude NCDs and reasoning their medical expertise as insufficient to treat NCDs, the doctors routinely referred NCD cases. They expressed concerns about violence from patients, negative media attention and unsupportive municipal authorities should anything go wrong clinically.

The study contextualizes street-level service-delivery in weak institutional conditions. Whereas street-level workers may commonly standardize practices to reduce workload, here the doctors routinized NCD care to avoid the sociopolitical consequences of clinical uncertainty. Modalities of the welfare state and medical care in India – manifest in weak municipal capacity and healthcare regulation – appear to compel restraint in service-delivery. The analysis highlights how norms and social relations may shape primary care provision and quality.

The purpose of this study was to evaluate the extract of Moringa stenopetala seed oil, by organic solvents (methanol and hexane), for its efficacy against microbial activity on cotton fabrics. The selected microbes for the study were two types of bacteria which are Gram-positive (S. aureus) and Gram-negative (E. coli).

Two types of bacteria, Gram-positive (S. aureus) and Gram-negative (E. coli) were used. The extract was applied on fabrics at a concentration of 5, 10 and 15 g/L using the pad-dry-cure method and antibacterial activities verified by the bacterial-growth reduction method. The treated fabrics were evaluated for antimicrobial activity against the bacteria before and after 15 washing cycles. The extract was examined for molecular structural change using fourier transform infrared spectroscopy (FTIR) and physical properties of the fabric; tensile strength, elongation, air permeability, stiffness and wettability were evaluated.

Results showed treated fabrics reduces the growth of Gram-positive (S. aureus) and Gram-negative (E. coli) bacteria from 77.6%–100% before wash and 45.8%–85.2% after wash for both extract concentrations. Comparing extracts, hexane extract reduces all bacteria growth than methanol extract for both extract concentrations while S. aureus was more susceptible to antimicrobial agents than E. coli at a lower concentration. As result, the tensile strength and air permeability were relatively lower than untreated ones without affecting the comfort properties of the fabric.

This study indicates that the Moringa stenopetala seed oil extract has a strong antimicrobial activity.