How Seafood Wholesale Markets Matter for Urban Food Security: Evidence from Chennai, India.

Urban sites gather poverty in particular locations and often require bulk food system approaches for addressing prevalent food security and nutrition needs. The food systems that service them are, however, characterized by perishability and large irregularities in supply. Seafood is currently recognized as contributing in a major way to food security and nutrition, and it is to assessing the role of wholesale markets in meeting the needs of the urban poor that this paper is directed. It zooms in on the city of Chennai, India, where an estimated 40% of the population is considered poor and marine fish plays a crucial role in diets. Building on one-and-a-half years of field research in the pre-COVID-19 period, the paper analyses the performance of one of the city's largest fish wholesale markets, Vanagaram, in relation to the four commonly recognized pillars of food security. Results demonstrate how urban food systems function as major suppliers of fish (and other food items) to thousands of low- and middle-income households. Most importantly, this case study demonstrates the crucial role that is played by wholesale markets in merging low-price fish supplies from different geographic regions and thereby ensuring food security of poorer inhabitants.

Dans les zones urbaines, la pauvreté se concentre dans des sites spécifiques qui nécessitent souvent des systèmes alimentaires qui s'appuient sur la vente en gros pour répondre aux besoins en matière de sécurité alimentaire et d'apport nutritionnel. Les systèmes alimentaires qui les desservent sont cependant caractérisés par la périssabilité des aliments et de grandes irrégularités dans l'approvisionnement. Actuellement, il est reconnu que les produits de la mer contribuent de façon significative à la sécurité alimentaire et aux apports nutritionnels. Cet article cherche à évaluer le rôle du marché de la vente en gros dans la satisfaction des besoins des personnes en situation de pauvreté dans les villes. Il se concentre sur la ville de Chennai, en Inde, où environ 40% de la population est considérée comme étant en situation de pauvreté et où les poissons de mer jouent un rôle crucial dans l'alimentation. Cet article s'appuie sur des recherches menées sur le terrain pendant un an et demi avant la COVID-19, et analyze la performance de l'un des plus grands marchés de vente en gros de poisson de la ville, Vanagaram, par rapport aux quatre piliers communément reconnus de la sécurité alimentaire. Les résultats montrent la façon dont les systèmes alimentaires urbains fonctionnent comme d'importants fournisseurs de poisson (et d'autres produits alimentaires) pour des milliers de ménages à revenu faible ou moyen. Plus important encore, cette étude de cas démontre le rôle crucial que jouent les marchés de vente en gros pour faire fusionner l'approvisionnement en poisson à bas prix issu de différentes zones géographiques et pour garantir la sécurité alimentaire des habitant·e·s les plus pauvres.

Corporate financial management decisions during COVID-19

Financial decisions such as investment and financing will have to be made in accordance with the environmental conditions prevailing in a particular time. This chapter will focus on the corporate financial decisions during the current pandemic situation. In financing decisions, cost of capital is the determining factor, and accordingly, financing modes are preferred by the organizations. Financial decisions made in a difficult time are resulting in negative cash profits in most of the cases. With the ongoing COVID-19 crisis, the stock markets suffered a severe shock resulting in negative returns, and the stock market crash risk has become higher than normal. COVID-19 has made the financing and investment markets more challenging. Financing decisions are the hardest decisions that companies could make during COVID-19 times as the cost of capital might skyrocketed and the companies themselves would find their profits declining significantly. Corporates must now be thinking seriously about alternative sources of funding for their business requirements. © 2023, IGI Global.

Social Network Marketing and its Influence on Online Purchase of Grocery Products during Covid-19 Pandemic: Mediating Role of Brand Awareness

Digital technologies in the present day have witnessed phenomenal growth in the field of marketing and advertising of products. The online shopping is a relatively growing phenomenon, which is attracting a lot of attention in the present Covid-19 pandemic period. The influence of social networking sites (SNS) is being so widely discussed around the world and a number of studies have been on the use of SNS among consumers in India. Many grocery shoppers are concerned about exposing themselves to Covid-19 by going to stores and have chosen to buy groceries online by either stepping up the online delivery service or pickup services, or by trying for the first time.Several studies have primarily discussed how businesses communicate with consumers, but the gap in the study is whether social media can actually change consumers' knowledge of brands.The aim of this study is to analyze factors that influence the customer in adapting to social networks in order to increase brand awareness and intention to purchase food products online. The paper also examines the mediating role of brand awareness to the relationship of SNS and intention to purchase. © 2023 Academy of Taiwan Information Systems Research. All rights reserved.

Light weight recommendation system for social networking analysis using a hybrid BERT-SVM classifier algorithm

Social media platforms, such as Twitter, Instagram, and Facebook, have facilitated mass communication and connection. Due to the development as well as the advancement of social platforms, the spreading of fake news has increased. Many studies have been performed for detecting fake news with machine learning algorithms;but these existing methods had several difficulties, such as rapid propagation, access method and insignificant selection of features, and low accuracy of the text classification. Therefore, to overcome these issues, this paper proposed a hybrid Bidirectional Encoder Representations from Transformers — Support Vector Machine (BERT-SVM) model with a recommendation system that used to predict whether the information is fake or real. The proposed model consists of three phases: preprocessing, feature selection and classification. The dataset is gathered from Twitter social media related to COVID-19 real-time data. Preprocessing stage comprises Splitting, Stop word removal, Lemmatization and Spell correction. Term Frequency Inverse Document Frequency (TF-IDF) converter is utilized to extract the features and convert text to binary vectors. A hybrid BERT-SVM classification model is used to predict the data. Finally, the predicted data is compared with the preprocessed data. The proposed model is implemented in MATLAB software with several performance metrics carried out, and these parameters attained better performance: accuracy is 98 %, the error is 2 %, precision is 99 %, specificity is 99 %, and sensitivity is 98 %. Therefore the better effectiveness of the proposed model than existing approaches is shown. The proposed social networking analysis model provides effective fake news prediction that can be used to identify the Twitter comments, either real or fake. © Kiruthika N.S., Thailambal G., 2022.

Associations between digital amnesia, sleep disorders and somatic symptoms among youth

Digitalization has accelerated and improved the accuracy of information processing. It has changed society into a connected world. The spread of COVID-19 changed individuals' daily routines drastically over the past two years, especially among youth due to the temporary closure of colleges and universities. The usage of digital devices by youth has increased as a result of online learning platforms, leading to digital reliance and a negative impact on their sleep quality. Therefore, this study aimed to investigate (1) the association between digital amnesia, sleep disorders and somatic symptoms, (2) the difference in digital amnesia based on the demographic categories, (3) the effect of digital amnesia and sleep disorders on somatic symptoms. A cross-sectional study was conducted on 326 young people aged 18 to 25 years. Data was collected from participants using the Digital Amnesia Scale, Sleep Disorders Symptom Checklist (SDS-CL-17), and Somatic Symptom Disorder-B Criteria Scale (SSD-12). There was a significant association between digital amnesia, sleep disorders, and somatic symptoms among youth. Youth differed significantly in their digital amnesia based on their demographic categories such as gender and family type. Somatic symptoms among youth were significantly impacted by digital amnesia and sleep disorders. Youth could overcome their digital amnesia by utilizing digital devices effectively. Youth could also be sensitized about adverse effects of digital amnesia. Taking a digital break could help them improve their quality of sleep and reduce somatic symptoms. © 2022 Babeș-Bolyai University. All rights reserved.

How Seafood Wholesale Markets Matter for Urban Food Security: Evidence from Chennai, India

Urban sites gather poverty in particular locations and often require bulk food system approaches for addressing prevalent food security and nutrition needs. The food systems that service them are, however, characterized by perishability and large irregularities in supply. Seafood is currently recognized as contributing in a major way to food security and nutrition, and it is to assessing the role of wholesale markets in meeting the needs of the urban poor that this paper is directed. It zooms in on the city of Chennai, India, where an estimated 40% of the population is considered poor and marine fish plays a crucial role in diets. Building on one-and-a-half years of field research in the pre-COVID-19 period, the paper analyses the performance of one of the city’s largest fish wholesale markets, Vanagaram, in relation to the four commonly recognized pillars of food security. Results demonstrate how urban food systems function as major suppliers of fish (and other food items) to thousands of low- and middle-income households. Most importantly, this case study demonstrates the crucial role that is played by wholesale markets in merging low-price fish supplies from different geographic regions and thereby ensuring food security of poorer inhabitants.

Vaccines, repurposed drugs and alternative biomedicines for the management and prevention of covid-19

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) popularly called as COVID-19, is a pandemic having affected >200 countries. Globally, quarantine measures have been implemented to slow down the spread of the virus. Yet, the available vaccines and drugs for treating COVID-19 are still in design and developmental stage, requiring clinical validation. This review is focused on the progress in the development of medicines against SARS-CoV-2. As an alternative approach, both conventional and traditional biomedicines are also reported to be in practice, to treat the SARS-CoV-2 infected patients. Considering the therapeutic values of the folk medicines, this review focuses on the usage of high value added products from plants, against COVID-19 in managing the symptoms like fever, cough, cold, sore throat, respiratory disorders and kidney dysfunctions enlisting a few used since time immemorial. It is ardently hoped that scientific intervention of such traditional plants can be integrated to harmonise with modern medicine, to ensure its dosage and safety in augmenting disease management.

Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19.

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several (∼100's) new therapeutic agents that outperform Food and Drug Administration (FDA) (∼1000's) and non-FDA molecules (∼million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality.

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies.

The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. Here, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host-virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors (2χn, topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.