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The purpose of this study is to assess the quality of honey from different agro-climatic zones of Himachal Pradesh in terms of physicochemical characteristics and mineral status.

Three honey-producing locations were selected within each agro-climatic zone, honey sampled from four separate apiaries within each location and analyzed for physicochemical characteristics and mineral status using standard methodologies. The data were analyzed using one-way analysis of variance with one-way classification, after appropriate transformation through online OP-STAT software and MS Excel. The correlation coefficient (r) was also calculated. Principal component analysis was done using XL-STAT software.

The honey of Zone 4 had highest fructose (36.62%), F:G ratio (1.55), acidity (46.07 meq/kg), vitamin C (25.04 mg/100 g) and diastase (19.22 DN), whereas the pollen density (76,666.67 pollen grains per 10 g), pH (5.94), sucrose (6.94%), hydroxy methyl furfuraldehyde (70.20 mg/kg), amino acid (103.83 mg/100 g), phenols (77.39 mg/100 g), Ca (81.04 mg/kg) and K (354.17 mg/kg) were highest for Zone 2. Highest electrical conductivity (0.24 mS/cm), moisture (16.50 %), glucose (34.20%) and P content (62.93 mg/kg) were recorded for Zone 1. Correlation studies indicated a significant positive correlation between pH and EC; EC and moisture; colour and pollen density. Examining the graphical distribution of the honey samples, a natural separation between honeys of four different agro-climatic zones was obtained.

The impact of geographical/agro-climatic variations in physicochemical characteristics of honey has not been worked out under the present scenario in Himachal Pradesh.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

The purpose of this paper is to elucidate the methodology of total interpretive structural modeling (TISM) in order to provide interpretation for direct as well as significant transitive linkages in a directed graph.

This study begins by unfolding the concepts and advantages of TISM. The step-by-step methodology of TISM is exemplified by employing it to analyze the mutual dependence among inhibitors of smartphone manufacturing ecosystem development (SMED). Cross-impact matrix multiplication applied to the classification analysis is also performed to graphically represent these inhibitors based on their driving power and dependence.

This study highlights the significance of TISM over conventional interpretive structural modeling (ISM). The inhibitors of SMED are explored by reviewing existing literature and obtaining experts’ opinions. TISM is employed to classify these inhibitors in order to devise a five-level hierarchical structure based on their driving power and dependence.

This study facilitates decision makers to take required actions to mitigate these inhibitors. Inhibitors (with strong driving power), which occupy the bottom level in the TISM hierarchy, require more attention from top management and effective monitoring of these inhibitors can assist in achieving the organizations’ goals.

By unfolding the benefits of TISM over ISM, this study is an endeavor to develop insights toward utilization of TISM for modeling inhibitors of SMED. This paper elaborates step-by-step procedure to perform TISM and hence makes it simple for researchers to understand its concepts. To the best of the authors’ knowledge, this is the first study that analyzes the inhibitors of SMED by utilizing TISM approach.

As the today's world is leading toward the digital dependency and after the world pandemic of COVID-19, the dependency of students and the university is completely through a digital medium, in context with that the higher education according to the demand of the generation is leading towards digital transformation. The digital transformation in the sector of education is the road map for the sustainable management and development of education. The digital transformation is the new pillar of education in which the students are mostly reliable. The digitalization in the field of education will lead to simple and clarified as well as multiple way for acquiring the knowledge. As the integration of the new model of education system is applied and implemented throughout the globe, the digital medium plays a significant role for the smooth and the systemic development of the model. In this chapter, the pathway for the development of the well-stable and well-developed strategies is considered in which the integration of the essential requirements, proper guidance, and advantages of the model is dependent for the transformation to digital medium of the higher education that will be leading to the development of the management and the education system. The foundation of that transformation model is detailed in the paper for the digitalization of higher education.

The primary objective of the present study was to develop an appropriate scale for evaluating LMX by investigating how individuals personally perceive and encounter distinct relationships (both high quality and low quality) with their supervisors, with a specific focus on the Indian context.

The scale was administered on a sample of 290 middle-level managers from two large manufacturing organizations located in North India.

The factors identified as important for the construct of leader-member exchange were affect, loyalty, and contribution.

The internal consistency reliability of the LMX contribution dimension is very low. Future researchers should add a few additional items to increase the reliability of the contribution scale of LMX scale so that it fulfills adequate criteria of reliability. Further, the supervisor–subordinate relationship from both supervisor and subordinate perspectives should also be examined.

This study has made significant advancements in the field of LMX. The findings will also be utilized by the authorities of the organization in focusing future training for its managers.

The findings of this research will help not only advancement in the field of LMX but will also help the manager using LMX to influence subordinates to have better knowledge on which factors to focus on to get better results.

Overall, the results of the current study provide evidence for the sound reliability and validity of the leader-member exchange scale with employees of Indian manufacturing organizations, supporting its use with these populations. Further, this scale is suitable not only in Indian culture but also in the Western cultural context, as the results corroborate the findings of Western scholars, indicating a fair level of cross-cultural validity. However, future research should also address the cross-validation of the factor structure of LMX on other samples and occupations.

The implementation of value management (VM) principles in stealth construction projects is explored comprehensively in the chapter. It elucidated how VM positively influences various facets of construction, including environmental protection, health and safety, project delivery duration, economy, and aesthetics. Applying VM techniques, such as proactive risk management, resource optimisation, and stakeholder collaboration, is essential for achieving project objectives while ensuring sustainability, efficiency, and stakeholder satisfaction. Furthermore, the chapter emphasises VM’s benefits, challenges, and relevance across all stages of the construction lifecycle, from pre-construction planning to post-construction evaluation, underscoring its integral role in driving continuous improvement and innovation in the construction industry. Overall, the discourse emphasises the importance of VM in optimising outcomes and maximising value in stealth construction projects.

This study explores coopetition opportunities between e-tailers and brick-and-mortar (BM) retailers and provides a conceptual framework. These opportunities may be triggered by events such as social distancing causing crises (SDCC).

A grounded theory based approach was used wherein 119 news articles and 48 academic papers are the main sources of data to analyse the real-world responses. A typical qualitative methodology, including open and axial coding, was used. To further analyse the insights obtained, six in-depth interviews were conducted.

Non-customer-interfacing-based coopetition, such as small BM stores serving as e-marketplace sellers and customer-interfacing-based coopetition, such as large BM stores serving as showrooms, are some potential coopetition opportunities.

The majority of the available studies dwell more on offline retailers developing online channels. This study investigates the opposite situation and conceptualises a new understanding of how e-tailers and BM retailers can work together more harmoniously. This study can be used as a springboard by academicians for future research on a larger scale. Five research propositions are offered that can guide hypothesis generation. Development of case studies and consulting services for the industry are the other research opportunities.

Social distancing as a measure may vanish from the world with time; however, social distancing's implications are still pertinent given that new diseases, including new variants of pandemic potential, could continue to emerge. The study puts forward propositions based on theoretical dimensions and second-order themes derived from first-order categories. These propositions are about the drivers of coopetition and the opportunities with both large and small BM stores that e-tailers can leverage during a crisis, given that launching e-tailers' own BM stores demands large investments. This study has social and economic implications too.

This study investigates coopetition, an important trend but lacking adequate research. Whilst only few studies examine coopetition from a crises' perspective, this study investigates develops a new understanding of coopetition opportunities between e-tailers and BM retailers. This study adds to the scarce literature how such opportunities may be triggered by events such as SDCC.

Implementing artificial intelligence (AI) in healthcare organizations involves the entire organization. This groundbreaking technology is becoming central to achieve the goals of the new healthcare through the ongoing commitment to sustainability despite the severe lack of resources. Decision-makers in healthcare need knowledge and skills to prepare for the changes in many professional activities in the years ahead. Furthermore, chief medical officers and clinical leaders need to act on the opportunities that AI can bring, starting from its integration into the reality of healthcare settings while working with those responsible for managing and implementing AI in compliance with current legislation in Europe and the United States. Finally, stakeholders need to know how to leverage AI capabilities and how to recognize its limitations and its opportunities in administrative applications (admin AI) to optimize day-to-day operations and clinical applications (non-admin AI). In this view, clinical leaders and health care decision-makers may appreciate AI as a new way to provide sustainable social and healthcare services.

Over the past decade, India has emerged as one of the major exporters of agricultural products in the international market. Although agriculture in India accounts for about 50% of the economy's employment, its contribution as share to India's gross domestic product is significantly low. India primarily has emphasized on the production of food grains since the government policies promote not only exports but also food security and sustenance of rural and vulnerable sections of the economy. In recent times, India has witnessed a sharp increase in the productivity of food grains, but the underlying factors are of grave concern since issues such as suboptimal production, underutilization of resources and inability to adopt advanced technologies remain unacknowledged. The present study delves into various aspects of the production of food grains across 30 Indian states and emphasizes upon measuring the efficiency of food grain production across the 30 states on the basis of the non-parametric technique of Data Envelopment Analysis (DEA). The evaluation also considers economies of scale. The results highlight the fact that the Indian states are about 21% inefficient in terms of food grain production with the average efficiency score being 0.79. The methodology adopted for this study incorporates crucial factors such as usage of land area, usage of fertilizers and allocation of bank credit to carry the analysis forward. The present study has also aimed at providing certain policy recommendations for the policymakers in this regard so that the states can sustainably improve their efficiency in terms of the production of food grains.

This paper aims to optimize the quantum of aonla pulp that could be mixed with guava pulp to make a nutritional rich fruit bar. The developed fruit bar will not only help in the improvement of processing value of both Guava and underused but highly nutritional Aonla but also serve the purpose of improvement in nutritional status of consumers.

Response surface methodology (RSM) using Box–Behnken design was used with the process variables as aonla and guava pulp ratio, PR (30:70, 40:60, 50:50); pectin concentration, PC (0, 0.15, 0.30%); and drying temperature, DT (50, 60, 70°C) for optimization of process conditions. The prepared mixed fruit leather was evaluated for physico-chemical, textural and sensory properties such as titratable acidity (TA), ascorbic acid content (AA), L value (lightness), cutting force (CF), taste and overall acceptability (OAA).

Second-order regression models fitted for TA, AA, L value (lightness), CF, taste and OAA were highly significant (P = 0.01) with the coefficient of determination (R² = 0.85). The TA and AA of mixed fruit bar increased whereas L value, CF, taste and OAA decreased with increasing level of aonla pulp in the blend formulation. The optimum process conditions for mixed aonla-guava bar with desirable characteristics were 40:60 (PR), 0.02% (PC) and 56°C (DT). The corresponding optimum values of TA, AA, L value, CF, taste and OAA were 1.00%, 164 mg/100 g, 50, 5066 g, 7.83 and 7.92, respectively. The design formulation and data analysis using RSM validated the optimum solution.

This paper demonstrates that optimum blending of aonla and guava pulp has improved the overall nutritional characteristics and acceptability of the final product. This will not only help in reducing the associated post-harvest losses but also encourage the cultivators/local processing industries by stabilizing the price during glut sea.