Narsee Monjee Institute of Management Studies

The separation of decision-making and risk-bearing function in a dispersed ownership structure offers a possibility for the opportunistic conduct of managers (Shleifer & Vishny, 1997). Hence, it necessitates a proactive governing mechanism for the protection of shareholders as well as stakeholders’ interest in an organization (Xie et al., 2002). The pivotal objective of the paper is to analyze the effectiveness of corporate governance (CG) in reducing earnings management practices in listed Indian firms. The sample of 270 listed Indian firms in the National Stock Exchange of India (NSE) throughout 9 years from 2007–2008 to 2015–2016 was analyzed using the SmartPLS. From the major findings of statistical analysis using path coefficients, it has been observed governance through ownership and board committees (audit, compensation risk committees) is statistically insignificant in controlling earnings management (Biswas et al., 2022). In addition, the results revealed that board structure, activities, characteristics, and environmental, social, and governance (ESG) disclosures have a significant negative impact on discretionary accruals measured through the modified Jones model.

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival.

This article invites the reader to reflect on the practice and teaching of sociology through reflexivity in India in a new emerging space of liberal arts in private universities. These spaces can be considered as the fringe of sociology teaching. I argue that students in private universities grapple with a ‘crisis of relatedness’ regarding sociological discourse, and the debates they study leave them with different questions. I suggest that the understanding of social facts and issues is different and distant from those studying in public universities. The different lived experiences produce different sociological imaginations with the engagement of the same sociological texts. Teaching sociology in liberal arts spaces could mark the emergence of a generation of sociologists in India who have their training rooted in private universities. This new location of sociology students asks us to revisit the ongoing debate of skill-based sociology versus critical sociology that generates new questions for reflexivity and social location of both practitioner and student of sociology.

Essential trace metals like zinc (Zn), iron (Fe), and copper (Cu) play an important physiological role in the metabolomics and healthy functioning of body organs, including the brain. However, abnormal accumulation of trace metals in the brain and dyshomeostasis in the different regions of the brain have emerged as contributing factors in neuronal degeneration, Aβ aggregation, and Tau formation. The link between these essential trace metal ions and the risk of AD has been widely studied, although the conclusions have been ambiguous. Despite the absence of evidence for any clinical benefit, therapeutic chelation is still hypothesized to be a therapeutic option for AD. Furthermore, the parameters like bioavailability, ability to cross the BBB, and chelation specificity must be taken into consideration while selecting a suitable chelation therapy. The data in this review summarizes that the primary intervention in AD is brain metal homeostasis along with brain metal scavenging. This review evaluates the impact of different trace metals (Cu, Zn, Fe) on normal brain functioning and their association with neurodegeneration in AD. Also, it investigates the therapeutic potential of metal chelators in the management of AD. An extensive literature search was carried out on the "Web of Science, PubMed, Science Direct, and Google Scholar" to investigate the effect of trace elements in neurological impairment and the role of metal chelators in AD. In addition, the current review highlights the advantages and limitations of chelation therapies and the difficulties involved in developing selective metal chelation therapy in AD patients.

Understanding and establishing a link between the physicochemical characteristics of nanoparticles (NPs) and their biological interactions poses to be a great challenge in the field of nanotherapeutics. Recent analytical advancements concerning bio-nanointerfaces have accelerated the quest to comprehend the fate of nanocarrier systems in vivo. Scientists have discovered that protein corona, an adsorbed layer of biomolecules on the surface of NPs takes a leading part in interacting with cells and in the cellular uptake process, thereby determining the in vivo behaviour of NPs. Another useful method to assess the in vivo fate of NPs is by performing dissolution testing. This forms the basis for in vitro in vivo correlation (IVIVC), relating in vitro dissolution of NPs and their in vivo properties. Scientists are continuously directing their efforts towards establishing IVIVC for different nanocarrier systems while concurrently gaining insights into protein corona. This review primarily summarizes the importance of protein corona and its interaction with nanoparticles. It also gives an insight into the factors affecting the interaction and various in vitro dissolution media used for varied nanocarrier systems. The article concludes with a discussion of the limitations of IVIVC modelling and its position from a regulatory perspective.

Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Regarding all performance metrics, the proposed approach outperforms both conventional and cutting-edge methods.

Unlabelled: The existing research on fresh food supply chains (FFSC) sustainability consisting of fur fundamental pillars, namely green (G), resilient (R), agile (A), and sustainability (S) (hereafter GRAS), is explored sparsely and needs thorough investigation. Further, conceptualization and mutual interactions among GRAS enablers that can help perpetuate sustainable supply chains (SSC) still need to be addressed. This study proposes a methodological framework to evaluate the SCS from the perspective of GRAS enablers with an application for the Indian FFSC. A mixed-method sequential approach was used with interviews followed by integrated fuzzy interpretive structural modelling-decision-making trial and evaluation laboratory (FISM-DEMATEL) techniques. The study recognizes twenty supply chain sustainability (SCS) enablers through an extensive literature review and discussions with the expert group. The research discloses that the firms' 'organization culture' acts as the most powerful driver in achieving sustainability in FFSC, followed by the firms' 'environmental certification program' and 'financial strength.' This investigation helps the managers/policymakers of the Indian FFSC to ascertain and comprehend the most significant SCS enablers to achieve sustainability in the supply chain (SC). The causation of SCS enablers supports the managers in systematically focusing on the most significant enablers and working towards their successful implementation. According to our knowledge, this is the first scholarly work that establishes hierarchies and interrelationships among GRAS enablers, thereby providing a holistic picture to decision-makers while adapting such practices. Supplementary information: The online version contains supplementary material available at 10.1007/s10479-023-05176-x.

As the world's population ages, the prevalence of age‐related neurological disorders such as Alzheimer's disease (AD) is increasing. There is currently no treatment for Alzheimer's disease, and the few approved medications have a low success rate in lowering symptoms. As a result, several attempts are underway worldwide to identify new targets for the therapy of Alzheimer's disease. In pre‐clinical studies of Alzheimer's disease, it was recently found that inhibition of angiotensin‐converting enzyme (ACE) and blocking of the angiotensin II receptors reduce symptoms of neurodegeneration, Aβ plaque development, and tau hyperphosphorylation. Angiotensin II type I (AT1) blockers, such as telmisartan, candesartan, valsartan, and others, have a wide safety margin and are commonly used to treat hypertension. Renal and cardiovascular failure are reduced due to their vascular protective actions. Inhibition of AT1 receptors in the brain has a neuroprotective impact in humans, reducing the risk of stroke, increasing cognition, and slowing the progression of Alzheimer's disease. The review focuses on the mechanisms via which AT1 blockers may act beneficially in Alzheimer's disease. Although their effect is evident in pre‐clinical studies, clinical trials, on the other hand, are in short supply to validate the strategy. More dose‐response experiments with possible AT1 blockers and brain‐targeted administration will be needed in the future.

In the 21 st century, the world is completely dependent on technology. Technology is used in every domain. One such domain is the marketing and sales domain. After the Covid pandemic, the reliance on internet for convenience has increased substantially. This is where E-commerce companies and their online advertisements come into picture. The U.S. online advertising had increased from $8.1 billion in 2000 to $21.2 billion in 2007 and from 3.2 percent of all advertising to 8.8 percent over that time period. Considering how important online ads have become for revenue increment in different companies, it has become a domain that requires analysis. This study aims to analyse the online ad sales revenue generated by Meta and Google using the Analysis of Variance (ANOVA) method. It will also analyse how different groups of customers respond to online ads and draw a significant inference based on this.

Dementia is a collective term for various physiological conditions related to abnormal functions of the brain like a deficit of memory, thinking, behavior, and cognitive ability, and difficulty in performing routine activities. Severity in the pathophysiological factors leads to various neurodegenerative diseases (comes under the umbrella term “dementia”) including Alzheimer’s disease, vascular dementia, Lewy body associated dementia, frontotemporal dementia, Parkinson’s disease, viral infection associated neurological manifestation, and mixed dementia. With more than 50 million patients worldwide, dementia is considered one of the most widespread neurological disorders with no potential therapy. Very limited drugs are available for the treatment of different types of dementia which only exerts symptomatic effects. The complex anatomy of the brain and the presence of various protective mechanisms create major constraints in dementia therapy. Thus, various novel carrier systems have been explored to target the bioactives to the brain across blood–brain barrier (BBB). Liposome is one of the most promising novel delivery systems for brain targeting therapeutics. The phospholipid bilayer allows better permeation across the BBB and amphiphilic nature supported the encapsulation of both lipophilic and hydrophilic moieties. Alongside, it is also found suitable for the brain targeting of large molecules like protein and peptides that have wide application in dementia therapy. This review chapter highlighted different types of dementia, application of stealth liposome, surface-modified liposomes, and direct nose-to-brain delivery of liposome along with its application for delivery of protein/peptide and gene therapy.

Quercetin is a natural flavonoid with well-established anti-proliferative activities against a variety of cancers. Telomerase inhibitor MST-312 also exhibits anti-proliferative effect on various cancer cells independent of its effect on telomere shortening. However, due to their low absorption and toxicity at higher doses, their clinical development is limited. In the present study, we examine the synergistic potential of their combination in cancer cells, which may result in a decrease in the therapeutic dosage of these compounds. We report that MST-312 and quercetin exhibit strong synergism in ovarian cancer cells with combination index range from 0.2 to 0.7. Co-treatment with MST-312 and quercetin upregulates the DNA damage and augments apoptosis when compared to treatment with either compound alone or a vehicle. We also examined the effect of these compounds on the proliferation of normal ovarian surface epithelial cells (OSEs). MST-312 has a cytoprotective impact in OSEs at lower dosages, but is inhibitory at higher doses. Quercetin did not affect the OSEs proliferation at low concentrations while at higher concentrations it is inhibitory. Notably, combination of MST-312 and quercetin had no discernible impact on OSEs. These observations have significant implications for future efforts towards maximizing efficacy in cancer therapeutics as this co-treatment specifically affects cancer cells and reduces the effective dosage of both the compounds.

The advent of the information age has led tothe availability of overwhelming choices of products to userswhich create need of various Recommendation Systems (RS). Recommendation System belongs to the methods of InformationRetrieval, Data Mining and Machine Learning algorithms. Multifaceted Recommendation System Engine (MF-RISE) helpthe users to get personalized recommendations, helps users to select correct product from wide range of products using userfeedback, ratings and reviews provided by users. In real-world scenarios, recommenders have many non-functionalrequirements of technical nature and must handle huge amountdata. Evaluation of Multifaceted Recommendation System Enginemust take these issues into account in order to be producemaximum useful recommendations. The many researchers have proposed a wide range of recommendationsystems algorithms. This study investigates there are threepopular existing types of recommendation systems algorithms, Collaborative Filtering (CF), Content-Based Filtering (CB), andHybrid Recommendation System. The MovieLens dataset with its3 variants was utilized for the purpose of this study. The studiedevaluation methods consider both quantitative and qualitativeaspects of algorithm with many evaluation parameters like meansquared error (MSE), root mean squared error (RMSE), Test Timeand Fit Time are calculated for each recommender algorithmimplementation. The study identifies the gaps and challengesfaced by every recommender algorithm. This study will alsohelp researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.

Separation of decision-making and risk-bearing functions in dispersed ownership structures offer scope for opportunistic behaviour of managers. Hence necessitate proactive governing mechanism for ratification and monitoring of decisions from the initiation and implementation of the decisions for protection of shareholders as well as stake holders interest. The motive of this paper is to evaluate whether risky behaviour provide conducive environment for earning manipulation and analyze the moderating effect of corporate governance (CG) and ownership attributes in reducing earnings management practices in high risk conditions. A structural Partial least square (PLS) equations model was developed to analyze the relationship and CG constructs moderating role was evaluated. Based on a sample of 270 listed Indian firms in NSE during the period of 9 years from 2007–2008 to 2015–2016 using smart-PLS, it was detected that market risk, financing and investing risk manifold opportunities of managers to maximise their own benefits by manipulating the information in their discretion (discretionary accruals measured through modified Jones Model). CG mechanism constructs like audit, risk and compensation committees, board activities, board characteristics, transparency have been found significant in moderating the relationship between risk and earning management while board composition and ownership structure have failed to restrain the opportunistic behaviour of managers in favourable risky environment contradicting acceptance of efficient monitoring hypothesis and resource based view in India corporate setting.

The promising technology of IoT in the fourth industrial revolution connects everything to the Internet in the digital world of the current era. Though the gigantic connectivity among things through the blend of multifarious technologies offers potential opportunities, it also increases the overhead to optimise the energy consumption in IoT networks. Energy optimisation has become the major concern in the IoT realm because of the continuous sensing of the constrained sensor nodes and data transmission to longer distances. The state‐of‐the‐art studies do not take into account the management of duty‐cycling and the process of efficient route discovery hand in hand. Also, the prevalent studies focus on the management of the sensor node's duty cycling (DC) solely instead of adaptive DC scheduling of the communication unit of the sensor node that consumes more energy during transmission. Therefore, keeping in view the existing tribulations regarding energy consumption, the authors attempt to devise an energy‐efficient routing approach using On‐Demand Duty Cycling and Ant‐Colony optimisation (DC‐ACO) for IoT. The ACO‐based routing approach is applicable in IoT networks because the ants’ environment is conceptualised as a distributed set of interconnected graph nodes. The proposed approach poses the empirical notion to manage the energy consumption of the IoT network by considering the key performance indicators (KPI) like energy consumption, packet delivery rate, average residual energy, mobility factor, distance, throughput, and network lifespan to accomplish the tangible outputs. The proposed approach is modelled using Data flow Diagrams (DFDs) and algorithms supported by results. Experimental results show significant improvement in relative throughput, network lifetime, and energy efficiency by 45%, 78%, and 68%, respectively, after simulating for successive iterations.

This paper examines and portrays the impact of direct and indirect tax revenue on economic growth in India. Numerous studies were conducted earlier to study taxation from various perspectives, but very few have discussed the impact of taxation on economic growth. Various tax revenue reports from 1990 to 2021 were collected to conduct the present research. An ARDL inbound and outbound tests were applied to continue the investigation further. The study identifies tax variables that have a positive relationship with economic performance. Finally, an in-depth analysis was conducted between variables with the help of ARDL long-run trend analysis. The outcome reveals that direct and indirect tax growth rates positively impact GDP growth. It was suggested that government should focus on corporate tax policies, and more incentives for upcoming and existing entrepreneurs must be provided. The present study is limited only to tax and GDP revenue; it does not focus on economic or corporate policies. However, this research forms a base for further study in taxation, which will support designing policies for economic growth. It is known that the Indian government has introduced nation-one tax policy and implemented GST. Hence, further study can be conducted on the dynamics of GST and its impact on GDP or revenue growth rates.

Cancer is a highly lethal disease, and its incidence has rapidly increased worldwide over the past few decades. Although chemotherapeutics and surgery are widely used in clinical settings, they are often insufficient to provide the cure for cancer patients. Hence, more effective treatment options are highly needed. Although licorice has been used as a medicinal herb since ancient times, the knowledge about molecular mechanisms behind its diverse bioactivities is still rather new. In this review article, different anticancer properties (antiproliferative, antiangiogenic, antimetastatic, antioxidant, and anti-inflammatory effects) of various bioactive constituents of licorice (Glycyrrhiza glabra L.) are thoroughly described. Multiple licorice constituents have been shown to bind to and inhibit the activities of various cellular targets, including B-cell lymphoma 2, cyclin-dependent kinase 2, phosphatidylinositol 3-kinase, c-Jun N-terminal kinases, mammalian target of rapamycin, nuclear factor-κB, signal transducer and activator of transcription 3, vascular endothelial growth factor, and matrix metalloproteinase-3, resulting in reduced carcinogenesis in several in vitro and in vivo models with no evident toxicity. Emerging evidence is bringing forth licorice as an anticancer agent as well as bottlenecks in its potential clinical application. It is expected that overcoming toxicity-related obstacles by using novel nanotechnological methods might importantly facilitate the use of anticancer properties of licorice-derived phytochemicals in the future. Therefore, anticancer studies with licorice components must be continued. Overall, licorice could be a natural alternative to the present medication for eradicating new emergent illnesses while having just minor side effects.

Advancements in the field of educational technology have made Massive Open Online Courses (MOOCs) ubiquitous. MOOCs are accessible from anywhere, anytime and without any entry–exit criteria. The initial acceptance of MOOCs has been studied in the extant research, but limited studies have delved into understanding the factors impacting post-adoption usage behaviour. The aim of this research is to investigate the factors influencing MOOCs’ satisfaction and continuance intention using an integrated model derived from the Information System Success and Expectancy Confirmation Model. The study has proposed MOOC Satisfaction Continuance Model using existing scales and empirically tested it with a cross-sectional research design. Structured questionnaire was employed for collecting primary data from 513 respondents using a convenience sample. A partial least square structural equation modelling technique was employed using SMART PLS for testing the hypothesized relationships of the model. The study’s findings demonstrate that perceived usefulness, hedonic motivation, information quality and system quality positively impact the satisfaction of MOOCs. Moreover, perceived usefulness, hedonic motivation and satisfaction significantly influence the continued use intention. The study’s findings will be beneficial to MOOC platform providers, universities and facilitators in understanding and designing effective learning systems.

This research is aimed to discover how people’s buying habits changed during the COVID-19 epidemic and what variables drove consumption expenditure in India. Additionally, the study wanted to establish what factors influenced consumption expenditure in India. Consumption expenditure was shown to have declined all the way through the pandemic in the research that was conducted, which was based on one hundred survey data samples that were obtained in 2021 and 2022. When compared to levels preceding the outbreak, the amount of money spent on housing, food, and drinks did not significantly change. On the other hand, throughout the course of the previous several years, fewer dollars have been spent on things like clothes, entertainment, and education. It was shown that age, the number of members in a family, and the income of the household all had substantial influence on changes in spending. During the course of the epidemic, residents found that making purchases online became an essential supplementary method of buying. It is anticipated that this tendency will continue even after the virus has been contained. In order to provide a concise summary of the suggestions in light of the results, we have made two points. First and foremost, young people who are not married are the primary group that is responsible for the recovery of consumer spending in a variety of industries, including but not limited to fashion, leisure, education, and public transportation. In the meantime, the Government needs to enact legislation that will enhance the general quality of the products and services that are accessible for consumers to purchase online.

Electronic Dance Music, otherwise referred to as EDM, is an umbrella term for an array of musical genres that arose during the 1970s, primarily produced using electronic instruments and computer programmes. Over the years, Electronic Dance Music has branched into various sub-genres because of its spread throughout the world, making it difficult to distinguish these sub-genres from each other since they often sound very similar. We propose a method to make the identification and classification of these sub-genres easier using the metadata of the songs from Spotify. We chose five of the most prominent sub-genres of EDM and extracted the metadata of approximately 34,500 songs, which was reduced to a total of 15,000 songs, equally distributed for the five sub-genres after data cleaning. We trained the metadata on multiple machine learning algorithms such as logistic regression, K-nearest neighbours, random forests and more. We obtain accuracy scores for classification ranging between 0.833 and 0.913.