Prevalence of Vitamin A deficiency among school going children of Jasra block of Allahabad, India

The present study was conducted to find out the prevalence of vitamin A deficiency(VAD) among school going children of district Allahabad in year 2015 to assess the nutritional status of selected school going children (aged 6-12 years). The six months study was based on school going children in four selected village in Jasra block of Allahabad district.A structured Performa was used to collect the information. Out of the 105 children examined, 2 (1.90%) had clinical signs of night blindness. The overall prevalence of VAD was found to be 10.47%. Most of them exhibited dull and lusterless appearance of conjunctiva, non-had bitot’s spot, any corneal xerosis, corneal scare and keratomalacia. The prevalence of VAD was higher in girls rather than in boys. To overcome this problem of VAD persisting in community, nutrition education regarding regular intake of plant food rich in carotene such as green leafy vegetables, yellow fruits, carrots and animal foods containing retinol like fish liver oil, fortified food like vana- spati, margarine should be strengthened

Biochemical Evaluation of SRH Analogs as Potential LuxS Inhibitors

Quorum sensing in bacteria is a process of cell-to-cell communication facilitated by small molecules called autoinducers. Interspecies quorum sensing is facilitated by the autoinducer AI-2. The enzyme LuxS catalyzes the formation of AI-2 from S-ribosyl homocysteine (SRH) in a wide variety of bacterial species. Inhibition of LuxS would therefore inhibit interspecies quorum sensing. The goal of this project is to establish biochemical assays for the evaluation of small molecules as potential LuxS inhibitors. The first assay is a conventional colorimetric assay that utilizes Ellman’s reagent to quantify the homocysteine byproduct of DPD production by LuxS. For this assay purified enzyme (LuxS), a negative control (LuxS C84A), and the substrate (SRH) are required. His-tagged LuxS and LuxS C84A have been purified from overexpression cultures of Escherichia coli cells freshly transformed with a vector harboring the appropriate gene. Chemically-synthesized and quantified SRH was also acquired after extensive efforts of our fellow organic chemists. Ellman’s assay was then performed to determine the activity of house-purified LuxS. This assay would be optimized in future to be performed in 96 well plate to avoid excess consumption of enzyme and substrate. In addition, a fluorescence proximity assay is envisioned for the evaluation of a subset of LuxS inhibitors that function by preventing protein dimerization. This assay requires that purified LuxS be conjugated with an appropriate fluorophore, likely via a cysteine residue. An appropriate LuxS variant i.e. LuxS Y71C to which a single fluorophore would attach was acquired after extensive troubleshooting. An appropriate fluorophore would be attached to this LuxS variant for determination of potential dimerization inhibition

An Analytical Review on Plant Leaf Disease Classification in Agriculture Area

In this procedure, we suggest that plant disease detection systems use imaging technology to automatically recognise the signs of a plant's ailments on its leaves and stem, assisting in the cultivation of healthy plants in a farm. These devices keep an eye on the plant's leaves and stem, and any alteration seen in its distinguishing characteristics will be automatically detected and also reported to the user. The disease detection techniques that are currently used in plants are evaluated in this work. Plant disease research focuses on a specific plant's visually discernible patterns. In order to successfully cultivate crops, it is determined that identifying plants, leaves, and stems as well as determining the presence of pests, illnesses, or their proportion are all extremely important. The method that many farmers use to find and identify plant diseases is simple observation with the unaided eye. On vast farms, it is less practical and necessitates constant observation. Additionally, non-native illnesses are unknown to the farmers. The primary goals of this research are to pinpoint the area that is afflicted, identify the disease that is present, and enhance classification performance by increasing segmentation accuracy

Artificial Intelligence in Legal System: Pros, Cons and Challenges

Artificial intelligence (AI) implies a system that behaves like a human. The use of Artificial Intelligence started with the concept of robotics which is now part of almost every electronic device in the form of Google Assistant, Cortana, etc. Artificial Intelligence systems have various real-world applications such as customer services, speech recognition, automated stock trading, computer visions, etc. Artificial Intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms that seek to create expert systems that make predictions or classifications based on input data. With the global invasion of Artificial Intelligence, the concept so be brought closer to the legal system in India for deep learning information management to control the legal system in the country. Therefore, this paper strategically presents a study that evaluates the use of Artificial Intelligence in the legal system of the country. The paper also focuses on the advantages of Artificial Intelligence for the legal system

Towards Energy-Efficient Data Dissemination in Vehicular Ad-Hoc Networks (VANETs): A Cross-Layer Design

Due to their capacity to permit communication between automobiles and infrastructure components, Vehicular Ad-Hoc Networks (VANETs) are growing in popularity. Intelligent transportation systems (ITS) have seen the emergence of Vehicular Ad-Hoc Networks (VANETs) as a promising technology, but effective data distribution is still a problem. Conventional methods of data sharing require a lot of energy and are inappropriate for VANETs with limited resources. The efficient transmission of data between vehicles, which consumes a lot of energy, is one of the major difficulties in VANETs. We suggest a cross-layer design strategy in this study to increase the energy efficiency of data distribution in VANETs. To maximize network performance, the design incorporates the capabilities of many layers of the protocol stack. We implement several energy-saving techniques, such as cooperative communication, power control, priority-based message scheduling, and sleep mode. The simulation findings demonstrate that the suggested design strategy can greatly lower energy usage and enhance VANETs' sustainability

Biology: Inquiry-Based Learning Assignment

Students perform a literature review on a biology topic, choose 2-3 papers published in scientific journals to discuss in their assignment, provide a final opinion on the topic, and some voluntarily present on their selected topic

Data Mining for Fraud Detection: An Overview of Techniques and Applications

The process of data mining involves extracting knowledge and insights from vast amounts of data. It can be done through the use of various computational and statistical techniques to identify anomalous, correlational, and pattern patterns. On the other hand, fraud detection is a process that involves identifying and preventing activities that are fraudulent. This process can be carried out through the use of various technologies and techniques, such as artificial intelligence and data mining. It aims to minimize the financial losses caused by these types of activities and ensure that the company follows proper regulatory and legal requirements. The ability to identify fraud prior to it happening is very important for organizations to prevent it from happening. This paper looks into the various aspects of data mining and how it can be utilized for fraud detection. This paper discusses the various aspects of fraud detection and how it can be utilized for organizations to prevent it from happening. We then talk about the various techniques that are used for this process, such as clustering, unsupervised learning, and neural networks. In addition, we talk about the various data preprocessing techniques that are used in the detection of fraud. These include data normalization, feature selection, and extraction. Data visualization is also important in interpreting and understanding the results of mining analyses. The paper then covers the various fraud detection applications of data mining. These include healthcare fraud, credit card fraud, financial statement fraud, and insurance fraud. We provide examples of how these techniques have been utilized to identify fraudulent activities. The paper then discusses the limitations of mining data for fraud detection, as well as the need for an integrated approach that combines various techniques, such as human intervention and audit trails. This paper provides an extensive overview of the various aspects of this field and highlights the significance of this technology in the fight against financial crime

Reconciling service levels by customers on a non-constrained production line for raw material and finished goods inventory levels

Supply chains are often under constant pressure to offer high service level to its customers by efficiently managing inventory levels despite the variability in demand, lead time etc. A national cat litter company, DEF Corporation, is trying to evaluate the optimal levels of inventory, both cycle stock and safety stock on a non-constrained production line which is constrained by the limited storage space for raw material. The research aims to develop an Economic Order Quantity (EOQ) model for DEF Corporation and to determine the amount of finished goods inventory essential to compensate for the limited amount of raw material inventory that could be stocked due to limited storage. The study would also calculate safety stocks to deal with demand uncertainties. The results would provide a model for calculating optimum levels of inventory that would manage demand variability. The study would be beneficial for DEF as it would avoid lost sales due to stock outs