Through a systematic review of publications in reputed peer-reviewed journals, this paper investigates the role of blockchain technology in sustainable supply chain management. It uses the What, Who, Where, When, How, and Why (5W+1H) pattern to formulate research objectives and questions. The review considers publications since 2015, and it includes 187 papers published in 2017, 2018, 2019, and the early part of 2020, since no significant publications were found in the year 2015 or 2016 on this subject. It proposes a reusable classification framework—emerging technology literature classification level (ETLCL) framework—based on grounded theory and the technology readiness level for conducting literature reviews in various focus areas of an emerging technology. Subsequently, the study uses ETLCL to classify the literature on our focus area. The results show traceability and transparency as the key benefits of applying blockchain technology. They also indicate a heightened interest in blockchain-based information systems for sustainable supply chain management starting since 2017. This paper offers invaluable insights for managers and leaders who envision sustainability as an essential component of their business. The findings demonstrate the disruptive power and role of blockchain-based information systems. Given the relative novelty of the topic and its scattered literature, the paper helps practitioners examining its various aspects by directing them to the right information sources.

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn’t appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.

Some new 5-methoxy/ethoxy-2,3-[2'-(3''-chloro-2''-oxo-4''-substituted-aryl-1''-azetidinyl)-1',3',4'-thiadiazino]indoles 13-20 and 5-methoxy/ethoxy-2,3-[2'-(2''-substituted-aryl-4''-oxo-1'',3''-thiazolidin-3''-yl)-1',3',4'-thiadiazino]indoles 21-28 have been synthesized from 5-methoxy/ethoxy-2,3-[2'-(substituted-benzylidinylimino)-1',3',4'-thiadiazino]indoles 5-12. These newly synthesized compounds were characterized by elemental and spectral analysis. Further, compounds 5-28 of the present series have been screened for their antibacterial and antifungal activities. Both minimal inhibitory concentration (MIC) and inhibition zones were determined in order to monitor the efficacy of the synthesized compounds. Compounds 14 and 16 were found to be the most potent members of the present series, they showed maximal antibacterial and antifungal properties much better than the standard drugs.

This article studies the adoption of blockchain technology in Indian Micro, Small, and Medium Enterprises (MSME) in the context of innovations in supply chain management (SCM) using blockchain technology. Besides finance, SCM is one of the main areas where disruptive innovations based on blockchain technology are going to be deployed. Blockchain technology's unique proposition lies in the attributes of trust, transparency, traceability, immutability, and decentralization. MSME's form the backbone of the Indian economy. This article provides a socio-technical factors-based analysis of the adoption of blockchain technology in Indian MSME, particularly in a SCM context. Sustainability is an important variable that is expected to moderate the relationship between socio-technical factors and large-scale adoption. The relationships are tested via a survey of professionals in MSMEs in India who are both familiar with blockchain technology and sustainable SCM. This study shall offer a deeper understanding of the application of socio-technical systems theory for the adoption of blockchain technologies by MSMEs in India.

Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.

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Background: Indian mustard (Brassica juncea) is an important rabi crop raised under rainfed areas of Haryana. Optimum time of sowing plays an important role in augmenting the productivity of Indian mustard. There is a need to find out the optimum time of sowing of Indian mustard to overcome the yield gap under present scenario of climatic conditions. Different cultivars may respond differently to different sowing time. The current study aimed to study the effect of date of sowing on growth, seed yield and economics of Indian mustard varieties under rainfed conditions. Methods: In this field experiments were conducted during rabi 2018-19 and 2019-20 at Dryland Agriculture Research Farm, CCS Haryana Agricultural University, Hisar. The experiment comprising of 3 dates of sowing (16 October, 23 October and 30 October) and 4 varieties of Indian mustard (RH 30, RH 406, RH 725 and RH 761) was laid out in split plot design with three replications. Result: Crop sown on 23th October produced significantly taller plants over 30th October sowing. However, yield attributes viz., number of siliquae per plant, number of seeds per siliqua and test weight was successively decreased with delayed sowing of the crop. Seed yield was highest (2499 kg/ha) when sown on 16th October as compared to 23th October and 30th October sowing. Highest net returns (₹ 92674/-) and BC ratio (5.23) was recorded in 16th October sowing over other dates of sowing. Among varieties, RH 725 produced significantly higher seed (2583 kg/ha) and stover yield (7415 kg/ha) over RH 30, RH 406 and RH 761. Variety RH 725 also recorded higher net returns of ₹ 95940/- and B:C ratio of 5.38 compared to other varieties.