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The novel COVID-19 has emerged as a severe threat to global health globally, affecting over 210 countries and regions. The profound dilemma interrupted global trade and social activities and enormously influenced daily lives through social distance and confinements. The outbreak of COVID-19 has exacerbated human misery due to the crippling of economies globally. The effects are substantial on health, economy, environment, and society. Nearly every country is trying to prevent the transmission of this communicable disease. Remedial policies include testing and treating patients, isolating suspects through contact tracking, banning public gatherings, and asserting a complete or partial shutdown. In this context, the present paper's core objective is to investigate the impact of the COVID-19 pandemic on the environment and energy market, society, economy, and global protective measures taken to reduce COVID-19 transmission. The study's main contribution is revealed lessons to provide insights for business and the efficacy of governments’ initiative globally. Finally, this paper describes future actions for governments, leaders, energy providers, and all stakeholders in response to the global pandemic crisis.

PurposeThis paper examines the mediating role of manufacturing strategies in the relationship between competitive strategies and firm performance.Design/methodology/approachThis study gathered 250 responses from firms in a developing country's key manufacturing sectors, including mechanical, electronics, automotive, textile and food. First, descriptive statistics were applied to fix outliers like respondent biases, missing values and normality issues. Second, exploratory factors analysis (EFA) ensured data adequacy and homogeneity through Kaiser–Meyer–Olkin (KMO) and Bartlett tests. Finally, confirmatory factors analysis (CFA) was used to identify the interactions (direct, indirect and total effects) between latent variables representing manufacturing strategies (quality, cost, delivery and flexibility), competitive strategies (cost-leadership and differentiation) and firms' performance (sales growth and profitability). In total, two structural equation modelling (SEM) models (SEM-I, SEM-II) were created to test the hypotheses.FindingsOf the 40 items identified by the literature review, four were outliers, and three could not satisfy the EFA criteria (eigenvalue >1). Only 33 items could therefore reach CFA. SEM–I and SEM-II study results found no direct relationship between competitive strategies and firm performance (−0.03 = β = 0.08; p > 0.05). However, the findings revealed that cost-leadership could be an appropriate strategic choice and improved firms' performance if the quality and delivery are focussed (0.20 = β = 0.87; p < 0.001). While competitive strategies impact manufacturing strategies positively, the latter is only a mediator between the cost-leadership strategy and the firms' performance.Originality/valueThis research shows that the cost-leadership approach currently seems viable; however, flexibility and cost requirements were not satisfied due to infeasible product differentiation. These results will be beneficial to executives interested in investing in India's industries.

PurposeThis paper aims to explore the last-mile (LM) challenges faced by on-demand food delivery (ODFD) riders during the coronavirus pandemic. This study contributes to the literature on the less-explored domain of ODFD services.Design/methodology/approachA grounded theory methodology is used. Riders working for multiple ODFD firms in various urban and semi-urban areas of India were interviewed. Open, axial and selective coding of interview transcripts was done.FindingsA grounded model is developed consisting of riders' challenges represented broadly under four core categories: Operational, Customer-related, Organisational and Technological issues. The study indicates that while some of the challenges are inherent to the ODFD supply chain, these have been visibly exposed and intensified by COVID-19, while other challenges are specific to the pandemic.Research limitations/implicationsThe model is a qualitative proposition representing LM delivery issues in ODFD services faced by the riders in India's urban and semi-urban areas during the COVID-19 pandemic. Other countries may face similar problems, but further studies are necessary to confirm or refute the findings.Practical implicationsODFD companies must address the riders' issues to better adapt to the current and future disruptions and improve riders' quality of work–life to achieve operational excellence.Originality/valueThis study builds on the extant ODFD literature by focusing on one of its less addressed aspects: the working conditions of the riders. This work is conducted amid the COVID-19 pandemic in the context of a developing country and aims to study the challenges in ODFD operations.

The past decade has seen episodes of increasingly severe air pollution across much of the highly populated Indo-Gangetic Plain (IGP), particularly during the post-monsoon season when crop residue burning (CRB) is most prevalent. Recent studies have suggested that a major, possibly dominant contributor to this air quality decline is that northwest (NW) Indian rice residue burning has shifted later into the post-monsoon season, as an unintended consequence of a 2009 groundwater preservation policy that delayed the sowing of irrigated rice paddy. Here we combine air quality modelling of fine particulate matter (PM2.5) over IGP cities, with meteorology, fire and smoke emissions data to directly test this hypothesis. Our analysis of satellite-derived agricultural fires shows that an approximate 10 d shift in the timing of NW India post-monsoon residue burning occurred since the introduction of the 2009 groundwater preservation policy. For the air quality crisis of 2016, we found that NW Indian CRB timing shifts made a small contribution to worsening air quality (3% over Delhi) during the post-monsoon season. However, if the same agricultural fires were further delayed, air quality in the CRB source region (i.e. Ludhiana) and for Delhi could have deteriorated by 30% and 4.4%, respectively. Simulations for other years highlight strong inter-annual variabilities in the impact of these timing shifts, with the magnitude and even direction of PM2.5 concentration changes strongly dependent on specific meteorological conditions. Overall we find post-monsoon IGP air quality to be far more sensitive to meteorology and the amount of residue burned in the fields of NW India than to the timing shifts in residue burning. Our study calls for immediate actions to provide farmers affordable and sustainable alternatives to residue burning to hasten its effective prohibition, which is paramount to reducing the intensity of post-monsoon IGP air pollution episodes.

The agri-food sector is an endless source of expansion for nourishing a vast population, but there is a considerable need to develop high-standard procedures through intelligent and innovative technologies, such as artificial intelligence (AI) and big data. This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. Logistics, supply chain, marketing, and production patterns are covered along with food sub-sector applications for artificial intelligence techniques. It is found that utilization of AI techniques and the intelligent optimization algorithm also leads to significant process and production management. Thus, digital technologies are a boon for the food industry, where AI and big data have enabled us to achieve optimum results in realtime.

Food-based components represent major sources of functional bioactive compounds. Milk is a rich source of multiple bioactive peptides that not only help to fulfill consumers ‘nutritional requirements but also play a significant role in preventing several health disorders. Understanding the chemical composition of milk and its products is critical for producing consistent and high-quality dairy products and functional dairy ingredients. Over the last two decades, peptides have gained significant attention by scientific evidence for its beneficial health impacts besides their established nutrient value. Increasing awareness of essential milk proteins has facilitated the development of novel milk protein products that are progressively required for nutritional benefits. The need to better understand the beneficial effects of milk-protein derived peptides has, therefore, led to the development of analytical approaches for the isolation, separation and identification of bioactive peptides in complex dairy products. Continuous emphasis is on the biological function and nutritional characteristics of milk constituents using several powerful techniques, namely omics, model cell lines, gut microbiome analysis and imaging techniques. This review briefly describes the state-of-the-art approach of peptidomics and lipidomics profiling approaches for the identification and detection of milk-derived bioactive peptides while taking into account recent progress in their analysis and emphasizing the difficulty of analysis of these functional and endogenous peptides.

The primary objectives of modern agriculture includes the environmental sustainability, low production costs, improved plants’ resilience to various biotic and abiotic stresses, and high sowing seed value. Delayed and inconsistent field emergence poses a significant threat in the production of agri-crop, especially during drought and adverse weather conditions. To open new routes of nutrients’ acquisition and revolutionizing the adapted solutions, stewardship plans will be needed to address these questions. One approach is the identification of plant based bioactive molecules capable of altering plant metabolism pathways which may enhance plant performance in a brief period of time and in a cost-effective manner. A biostimulant is a plant material, microorganism, or any other organic compound that not only improves the nutritional aspects, vitality, general health but also enhances the seed quality performance. They may be effectively utilized in both horticultural and cereal crops. The biologically active substances in biostimulant biopreparations are protein hydrolysates (PHs), seaweed extracts, fulvic acids, humic acids, nitrogenous compounds, beneficial bacterial, and fungal agents. In this review, the state of the art and future prospects for biostimulant seedlings are reported and discussed. Biostimulants have been gaining interest as they stimulate crop physiology and biochemistry such as the ratio of leaf photosynthetic pigments (carotenoids and chlorophyll), enhanced antioxidant potential, tremendous root growth, improved nutrient use efficiency (NUE), and reduced fertilizers consumption. Thus, all these properties make the biostimulants fit for internal market operations. Furthermore, a special consideration has been given to the application of biostimulants in intensive agricultural systems that minimize the fertilizers’ usage without affecting quality and yield along with the limits imposed by European Union (EU) regulations.

Biomedical waste management is an essential aspect of human and environmental safety. The healthcare industries and the unfortunate pandemic have increased the generation of biomedical waste. If biomedical waste is not managed safely, it poses human health and ecological risks. Hence, the study aims to appraise the scenario of biomedical waste management in India and to identify its effect on human health and the environment. The study used a systematic approach to review all the rules and regulations related to biomedical waste management issued by the Government of India from time to time. Further, the study explored the strengths and weaknesses of the current BMW management rules using the SWOT analysis model. All recent and relevant literature was critically examined using scoping review approaches to better understand the health and environmental risks associated with poor biomedical waste management to propose the best practices and future direction. It was found that needle stick injury is a major hazard to human health during segregation. Poor segregation practices can lead to the mixing of biomedical waste with municipal solid waste. Hence, there is a need for proper training about the current biomedical waste rules with a specific focus on biomedical waste segregation at the time of generation. Each process involved in biomedical waste management can adversely impact the environment and human health if not managed well. The impact and gaps of poor biomedical waste management from generation to disposal have been identified. The study recommends routine awareness programs and capacity building for proper biomedical waste management and to minimize the associated environmental and human health risks. These risks could be minimized further through implementing scientific and systematic approaches in biomedical waste treatment and management, including regulatory compliance.

Background: The global dairy market is experiencing a massive transition as dairy farming has recently undergone modernization. As a result, the dairy industry needs to improve its operational efficiencies by implementing effective optimization techniques. Conventional and emerging optimization techniques have already gained momentum in the dairy industry. This study's objective was to explore the optimization techniques developed for or implemented in the dairy supply chain (DSC) and to investigate how these techniques can improve the DSC. Methods: A systematic review approach based on PRISMA guidelines were adopted to conduct this review. The authors used descriptive statistics for statistical analysis. Results: Modernization has led the dairy industry to improve its operational efficiencies by implementing the most effective optimization techniques. Researchers have used mathematical modeling-based methods and are shifting to artificial intelligence (AI) and machine learning (ML) -based approaches in the DSC. The mathematical modeling-based techniques remain dominant (56% of articles), but AI and ML-based techniques are gaining traction (used in around 44% of articles). Conclusions: The review findings show insight into the benefits and implications of optimization techniques in the DSC. This research shows how optimization techniques are associated with every phase of the DSC and how new technologies have affected the supply chain.

The agri-food sector contributes significantly to economic and social advancements globally despite numerous challenges such as food safety and security, demand and supply gaps, product quality, traceability, etc. Digital technologies offer effective and sustainable ways to these challenges through reduced human interference and improved data-accuracy. Innovations led by digital transformations in the agri-food supply chains (AFSCs) are the main aim of ‘Agri-Food 4.0’. This brings significant transformations in the agri-food sector by reducing food wastage, real-time product monitoring, reducing scalability issues, etc. This paper presents a systematic review of the innovations in the agri-food for digital technologies such as internet-of-things, artificial intelligence, big data, RFID, robotics, block-chain technology, etc. The employment of these technologies from the ‘farm to fork’ along AFSC emphasizes a review of 159 articles solicited from different sources. This paper also highlights digitization in developing smart, sensible, and sustainable agri-food supply chain systems.