Applied Science and Biotechnology Journal for Advanced Research

A Review Study on Insecure Food Habits and its Impact on Health & Healthcare

2024-05-15T07:37:21+00:00

Health is primary matter for every individual, to large extent it has to be a primary matter. Healthcare is immensely required elements for human being. It has the potential of scaling up towards business avenues. Health is such an issue that gets attention of States and Central government primarily to the extent that the local authorities are also involved. Healthcare Entrepreneurship has always been the massive support to the Economic growth. Healthcare Entrepreneurship wasn’t potentially the preferred choice however the advent of the Pharmacy Courses and the avenue of online operations in Pharmacy sector gave a good boost to the Healthcare sector. Food habits eventually have an impact on the health and insecure food habit destroys the physical & mental health of human being. Insecure food habits is characterized by limited access to nutritious and safe food, have emerged as a pressing public health concern worldwide.

This review study comprehensively examines the intricate relationship between insecure food habits and their profound impact on health and healthcare systems. Drawing upon secondary data investigations, this study explores the multifaceted repercussions of food insecurity on various dimensions of health, encompassing nutritional deficiencies, chronic diseases, mental health disorders, and overall well-being. Through a systematic analysis of existing literature, this review underscores the urgent need for targeted interventions and policy measures to alleviate food insecurity and mitigate its adverse health outcomes. This study provides valuable insights to inform evidence-based strategies aimed at promoting health equity and enhancing the resilience of healthcare systems in the face of food insecurity challenges.

A Study on Deep Learning Architectures and Dimensionality Reduction Techniques on Gene Expression Data

2024-05-15T07:47:32+00:00

Genomics, driven by the evolution of high-throughput sequencing and microarray technologies, has become one of the key inventions of cracking the secrets of complex biological systems. The deep learning architecture not only provides with a powerful tool to derive the hidden insights from the huge amount of genomic data, but also enables to mine meaningful information. In this study, we will examine the application of deep learning methods in the analysis of genomics data, specifically on dimensionality reduction and predictive modeling for binary phenotypes. We focus on the problems with the existing strategies, spot the avenues for the further research, and provide you with a glimpse of the dramatic influence of deep learning on genomics. In this study, we delve into the application of deep learning methods in the analysis of genomic data, with a specific focus on two crucial aspects: dimensionality reduction and predictive modeling for binary phenotypes. Dimensionality reduction techniques are essential for tackling the high-dimensional nature of genomic data, where thousands or even millions of features (e.g., gene expressions, genetic variants) are measured for each sample. Deep learning models can effectively capture the complex relationships and patterns within this high-dimensional space, enabling the extraction of lower-dimensional representations that preserve the most salient information. Throughout this study, we critically examine the existing strategies and approaches in the field of genomics, identifying their limitations and highlighting the avenues for further research. We explore how deep learning can address these challenges and provide a glimpse into the dramatic influence this technology is poised to have on the field of genomics.

Development and Characterization of Pili (Canarium ovatum Engl.) Wine

2024-04-02T06:21:25+00:00

This study aimed to estimate the potential of pili (Canarium ovatum L.) pomace as substrate for the production of fruit wine. The pili fruit wine was characterized in terms of physico-chemical characteristics (pH, TSS and alcohol content) and consumer acceptability level (appearance, taste, aroma, mouthfeel and overall acceptability). It was produced using 5%, 10% and 15% pili pomace as Treatment 1, 2 and 3, respectively. Results showed increase in alcohol content and TSS with increase in concentration of pili pomace while there is decrease in pH as concentration of pili pomace is increased. It was also observed that there is a gradual decrease in total soluble solids and pH and a gradual increase in alcohol content as fermentation time proceeded. Thirty (30) sensory panelists rated pili fruit wine as highly acceptable as commercial wine. Results of the consumer acceptability survey of the pili wine obtained an average rating of 7.71 in overall acceptability which can be interpreted as high liking for the product. Except for appearance, consumer acceptability results of pili fruit wine did not show any significant differences (at 0.05 significance level) in terms of taste, aroma, mouthfeel and overall acceptability when compared to commercial wine.

Nutrition Education Based Millets Consumption Analysis of Children in India

2024-06-12T05:46:39+00:00

Hidden hunger and MNDs are predominant epidemic among children, particularly in developing nations like India. A sample of 400 parents & respective preschool children (3 - 6 years) were investigated. Knowledge assessment of parents shows, during pre-test, 23.3% parents had good knowledge, 63.2% had average knowledge and 13.5% had poor knowledge, which improved after intervention, and during post-test, 68% parents had good knowledge, 22.3% had average knowledge and 9.8% had poor knowledge. Results of millets consumption of children shows that, during pre-intervention, consumption of millets was 8.8% on daily basis, 9.3% on alternately basis, 7.5% on weekly basis, 6.3% on monthly basis, 6.8% on occasionally basis and 61.5% never use to consume millets. After administration of educational intervention on parents for 6 months, the consumption of millets improved and during post-test the millets consumption was 14.5% on daily basis, 24% on alternately basis, 22.8% on weekly basis, 15.3% on monthly basis, 5.3% on occasionally basis and 18.3% continued to never consume millets.

Understanding Insulin Mechanisms, Economic Implications, and Future Prospects

2024-06-16T06:24:17+00:00

Diabetes, a persistent metabolic challenge affecting various organs,are of three primary types—Type 1, Type 2, and Gestational—stemming from intricate interplays of genetics and environment. On a global scale, 537 million adults grapple with diabetes, with India experiencing a growing burden. The vital role of insulin in glucose regulation involves a complex biosynthesis process. Economic hurdles, compounded by soaring insulin prices, call for policy interventions to ensure accessible healthcare. Diverse insulin types cater to distinct patient needs, while biosimilars, like the FDA-approved Semglee, offer affordability. Economic analyses underscore the advantages of biosimilars, highlighting the dynamic landscape of diabetes management and treatment costs.

Graph Neural Network Recommendation System for Football Formation

2024-06-16T06:10:25+00:00

In usual, the ﬂow of a football game have different phase, and change from one to another, and the coach is due to observe them, understand and solve the tasks in the game by using appropriate structural strategies.
Therefore, it is a critical issues for a coach to decide what kind of structural strategies have been effective for their own team. Therefore, we propose 3 different views to help to the coach to make decisions. First of views, we formulate the passing ball path as a network (passing net- work. More speciﬁc, we utilize clustering coeffcient to determine the relations between players. It turnouts that core player will have a strong cluster ability. And our propose network focus not only on local network, but global passing relations.-Final of views, we propose a novel reinforcement learning based Graph-to- Graph framework to decide structure of team. We formulate the positions of players as a graph, and we use the current graph as input, while our deigns return award will effect the structure of team by change the positions step by step. In experiment, we simulate the result of our team versus 3 different level team.

Neural Radiance Fields Convert 2D to 3D Texture

2024-06-21T06:58:36+00:00

The objective of our project is to capture pictures or videos by surrounding a circle of objects, such as chairs, tables, cars, and more.[1]Utilizing advanced 3D reconstruction technology, we aim to generate 3D models of these captured objects. Post reconstruction, these 3D models can be edited through an intuitive interface, enabling users to apply different textures and make other modifications. This project has significant applications in various domains such as home decoration, vehicle customization, and beyond. For the 3D reconstruction in this project, we employed Nvidia's latest ngp-instant method, which leverages hash encoding for 3D graphics reconstruction. This technique offers a faster inference speed compared to traditional NeRF (Neural Radiance Fields). Following the 3D reconstruction, we apply volume rendering to visualize the 3D models. To facilitate user editability[2], we integrated an editable interface inspired by StyleGAN, utilizing a texture loss function to transform the 3D model into a customizable texture. This combination of technologies allows for a seamless and efficient process in creating and editing 3D models from 2D images.

Financial Fraud Detection in Listed Companies Using Deep Learning and Textual Emotion Analysis

2024-07-04T05:06:23+00:00

Financial fraud of listed companies refers to the bad faithless behaviour that improperly distorts accounting information, which hurts the company's management, economic development and social interests. At present, the existing research mainly focuses on financial digital data, while the exploration of text information and deep learning algorithms is relatively small. Therefore, this paper proposes a financial fraud identification method for listed companies based on deep learning and integrated text-emotional features. Firstly, the financial index is preprocessed, and then the Bi-LSTM model is used to extract the emotional features of the stock review text. Subsequently, a residual-cross-convolutional (RCC) parallel network structure is used to identify financial fraud. The network simultaneously uses a Residual network, Cross network, Convolutional network and long short-term memory network to extract the characteristics of financial fraud in a parallel way. It obtains the final recognition result through batch standardisation and a full connection layer.