Contributions of MNCs to poverty alleviation through CSR programs: Odisha Perspective

Purpose – This study investigates how Multinational Corporations' (MNCs') CSR initiatives have helped to reduce poverty in Odisha, India. Design/methodology/approach – By means of consulting annual reports, websites, and publications for quantitative information, we have used a mixed-method approach to gather qualitative data by interviewing Managers of five MNCs. The average contribution of sample MNCs to CSR programmes is only 0.52% of profits after taxes, with ranges from 2.96 to 0.15%. Findings – Results showed that MNCs' Corporate Social Responsibility (CSR) programmes had little impact on reducing poverty in Odisha District. According to the report, the majority of MNCs do not give as much as they are able to and do not have a policy of giving a decent amount of their profits to CSR with a focus on reducing poverty. Research limitations/implications – The current study is limited to studying the contributions of MNCs in poverty alleviation by means of only their CSR programs and also the current study is confined to the state of Odisha. Thus, leaving future scope for budding researchers to identify the impact of such programs with respect to other regions and also other activities that may contribute in poverty alleviation. Practical implications - According to the study, in order to accelerate the movement towards poverty alleviation for a joyful, prosperous, and forward-thinking Odisha, the Government of Odisha must act sincerely and purposefully to encourage MNCs to actively participate in CSR programmes that prove to be beneficial for reducing poverty by providing the necessary policy framework and motivating supports. Originality/value – This paper is one of a few attempts to identify the contributions of MNCs in poverty alleviation specifically in the state of Odisha in India, by means of CSR programs in particular

PLACKETT-BURMAN DESIGN AS A TOOL FOR SCREENING AND PROCESS OPTIMIZATION OF RIVASTIGMINE-LOADED LIPID NANOCARRIERS

Objective: Plackett–Burman experimental design is used to identify the most important factors early in the experimentation phase when complete knowledge about the system is usually unavailable. The objective of this study was to screen out the most important factors affecting the size and entrapment efficiency of rivastigmine hydrogen tartrate (RHT) nanostructured lipid carriers (NLCs). Methods: The RHT-loaded NLC was prepared by the modified solvent emulsification-diffusion method. The independent variables selected for Plackett–Burman design were drug: lipid ratio, solid lipid/liquid lipid (S/L) ratio, concentration Ryoto sugar ester (%w/v), the concentration of poloxamer 188 (%w/v), sonication time (min), sonication amplitude, and stirring time (h). Results: The R2 value for the particle size equation was 86.16%. p value was (<0.05) 0.048 in case of sonication time. In case of entrapment efficiency, the R2 value was 87.12%. The p value (p<0.05) for S/L ratio and the Ryoto sugar (% w/v) was 0.028 and 0.042, respectively. Conclusion: It can be concluded that sonication time has a significant effect on particle size, whereas S/L ratio and Ryoto sugar ester concentration have a significant effect on entrapment efficiency

A Toxicological Study on Seed Extracts of Asparagus Racemosus Linn (Ethanolic and Water) in Experimental Animals

The study investigated the in-vivo oral acute toxicity and in-vitro cytotoxicity (Neutral red assay (NRU) by Aqueous and Ethanol extract of Asparagus racemosus Linn seed. In this in-vivo study, water extract was found to be more toxic to zebrafish. The medium lethal concentration (LD50) values of water and ethanol extract were 1070.8 mg/L and 1822.4 mg/L respectively for 96 hours of exposure. The correlation coefficient of water and ethanol extract were 0.972 and 0.9829 for linear regression curves between extract concentration and death percentage. In the study also 50% to 100 % mortality was observed with 968mg/L and 2129.6 mg/L water extract, whereas only 28.57% and 57.14% mortality were observed for ethanol extract in the same concentration range. The lower concentrations, such as 90.90 mg/L, 200mg/L and 440 mg/L having no mortality, were considered safe for zebrafish.  In in vitro study (NRU assay) on the SH-SY5Y cell line, the same trend was observed where water extract was found to be more toxic to the cell line. The results indicate that 43% of cell death was caused by Ethanolic extract at 500 μg/ml concentration. Hence, the IC50 value was found to be 562.1 μg/ml, whereas approximately 52% cell inhibition was recorded at only 100 μg/mL of aqueous extract. Hence, the IC50 value was found to be 114.7 μg/ml for aqueous extract. Both studies showed that the ethanol extract was less toxic, hence more effective compared to the water extract

Performance Analysis of Disaster Management Using WSN Technology

AbstractIn this research paper we propose a model of Wireless Sensor Networksused for pre-detection of disasters. Here we have discussed the basic architecture of WSNs and how these can be used in disaster management. The major reasons for mass destruction are Earthquake and Tsunami. Millions of lives are lost owing to these. Disaster, be it natural or man-made has a catastrophic impact on lives, money and infrastructure. We do not have a sensitive system yet which provides pre detection of these calamities. Therefore we need to take serious measures to ensure our safety from these disasters. WSNs are a new technology which can be helpful in these situations. The paper also throws light on the future scope of the topic. The information derived can be stored and used for future reference to predict climate of the area at a particular time period

A Conceptual Model for Building the Relationship Between Augmented Reality, Experiential Marketing & Brand Equity

Purpose:   This study aims to build a conceptual model based on the S-O-R (Stimulus-Organism-Response) framework to understand how Augmented Reality influences brand equity. The proposed model is intended to look into the influence of AR attributes like interactivity, vividness, modality, novelty, and media richness on consumers’ experiential values and brand equity in e-commerce.   Theoretical framework: This study developed the conceptual model by following the Stimulus-Organism-Response (S-O-R) Model  (Mehrabian & Russell, 1974).   Design/methodology/approach: To advance the conceptual and managerial understanding of AR as an experiential marketing tool, this study followed the systematic literature review approach to build the integrated conceptual model.   Findings: Results of the study has developed a conceptual model for studying the application of AR technology as an experiential marketing tool on customer purchasing experience and brand equity in the e-commerce setting.   Research, Practical & Social implications: This study contributes towards the available literature by studying the AR and its various dimensions for building brand equity. This study will not only assist the e-commerce firms in their decision-making process about adopting this technology but also the marketers to develop the effective marketing strategies for consumer experience of AR uses on e-commerce platforms.   Originality/value: This study introduces modality of AR as a media characteristic in its interface in creating seamless user experience in the proposed model

A Comprehensive Review on Anti-Cancer Properties of Amaranthus viridis

Amaranthus Viridis L. belongs to the Family (Amaranthaceae) commonly known as “Chowlai” which a common name. A. Viridis contains several compounds like Quercetin, Kaempferol, Hydroxycinnamic acids (HCs) (coumaric acid, ferulic acid, sinapic acid, caffeic acid, chlorogenic acid, rosmarinic acid), Syringic acid (SA), Rutin, Vitexin, Vanillic acid, etc . In search of new activities and chemical entities, phytochemical screening of the extract from leaves of A. Viridis L. indicates the presence of biologically active constituents saponins, tannins, phenols, flavonoids, alkaloids, cardiac glycoside, steroids, and triterpenoids. Quercetin is the aglycone form of several other flavonoid glycosides, Kaempferol (3,4′,5,7- tetrahydroxyflavone) is a natural flavonol, a type of flavonoid, Syringic acid (SA) is a phenolic compound of natural origin. Syringic acid (SA) is a phenolic compound which obtained from natural origin. SA is an excellent compound to be used as a therapeutic agent in various diseases (diabetes, CVDs, cancer, cerebral ischemia, neuro and liver damage) and possesses anti-oxidant, antimicrobial, anti-inflammatory, and antiendotoxic activities. Vitexin (apigenin-8-C-glucoside) has also shows the wide range of pharmacological effects, including but not limited to anti-oxidant, anti-cancer, anti-inflammatory, and neuroprotective effects. Vanillic acid shows the anti-cancer activity

XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages

Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XALIGNV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area