Volume 9 | Issue 8 |

 Abstract

Animals are important and should be cared for in human life. They should be provided with appropriate care in a proper, suitable and safe environment. A system is needed to remotely track and monitor animals. New technologies evolve over the years. As major enabling technology for tracking worldwide stuff, RFID (Radio Frequency Identifier) has developed. As a power supplier, RFID tags need battery to run the microchip circuitry, and transmit signal to the RFID reader. This is the system we develop that helps users understand the behaviour of the animal for research/study purposes who are not all allowed to track and study these animals.

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Spodoptera litura is a global and major pest, which attacks a good range of cultivated plants including wheat, pulses, oilseeds, fiber crops, vegetables and decorative plants. This pest can grow and survive on several plant species, belonging to approximately 37 families in India and it feeds on at least 157 plants species. There is a need for a more imaginative and holistic approach to research directed towards the management of this pest. In the present study we analyzed the impact of different tomato varieties on adult emergence, sex ratio and longevity of Spodoptera litura. In the present experiment we used Arka Abha, Arka Saurabh, Pant Bahar, Ratna and Pusa Gaurav tomato varieties as artificial diet for the pest. Results indicated that adult emergence of Spodoptera litura was minimum (69.33%) on variety Arka Saurabh and maximum (85.64%) on variety Pant Bahar. It is recorded from the data that the male longevity is maximum in Pant Bahar (30.33 days) and minimum in Arka Saurabh (23.92 days). It is amply documented that maximum female longevity recorded in Pant Bahar (38.65%) and minimum female longevity recorded in Arka Saurabh (31.65%). It is apparent from the data that the minimum sex ratio observed in Arka Saurabh (1:1.23) and maximum sex ratio recorded in Pant Bahar (1:1.93).

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Arka Abha, Arka Saurabh, Pant Bahar, Ratna, Pusa Gaurav, Spodoptera litura

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Aim The aim of the present research was to study the Knowledge, Attitude and Belief of Gynaecologist, Practicing Physiotherapist and Other Medical Practitioners of Jalgaon district about exercises during pregnancy. Introduction Pregnancy is the period of dynamic physiological changes for mother. Exercise plays an important role for the development of the baby and mother. Recent study shows that exercise during pregnancy is safe for both mother and the foetus5.Thoughbenefits of exercises during pregnancy are already proven, many pregnant females are still afraid of doing exercises because of inadequate guidance that is being provided to them during their pregnancy period. Hence the survey was carried out to study the knowledge, attitude and belief of Gynaecologist, Medical practitioners, Physiotherapist and Other health care professionals of Jalgaon district about exercises during pregnancy. Material and Method 165 Gynaecologist,Medical practitioners, practicing Physiotherapist and Other health professionals were included in this study as per the convenient sampling method. A self-made questionnaire was distributed among the willing participants. Data was analysed statistically & results were found out. Results Demographic data was analysed. Then all the questions of the questionnaire were divided into 3 broad components (knowledge, attitude & belief) & they were assessed for the responses. Statistical analysis was done for all the questions of the questionnaire. Conclusion This study concludes that the medical practitioners, gynecologists, physiotherapist and other health care professionals of Jalgaon district are apprised of the benefits of exercise during pregnancy. They also believe in suggesting the exercise program for their pregnant patients. But there is a dearth in the attitude towards recommending the patients to physiotherapist for specific exercise prescription for every patient.

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Attitude, Belief, Pregnancy, Benefits of exercises, Medical practitioners

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Seaweed is the marine algae that grows in oceans, rivers and lakes. This plant consists of vitamins, minerals, amino acids and proteins. The present study characterized and analyzed the petroleum ether of Eucheuma Spinosum. The extract was subjected to various phytochemical, antimicrobial and anti inflammatory and to determine its cosmetic potential. Due to its vast variety of bioagents it possess antimicrobial and antioxidant properties which have found their way into cosmetic product formulation.

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Phytochemical, Antimicrobial, Cosmetic potential and bioagents

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The key issues in water level research are the complex underground waters in reservoirs and the prediction of water level volatility patterns. Previous studies have relied on past data to forecast the future trend of a reservoir's water level, rarely taking into account co-movement across reservoirs in the same region. In this project, we attempt to integrate a complicated underground water technique with artificial intelligence to forecast water level trends in related reservoirs for forecasting. For multivariate reservoir time series, we first suggest a new pattern subterranean water construction approach. It has been discovered that properties of underground water morphology, such as average heat, humidity, depth, and evaporation can all be used to spot periods of rapid change in the reservoir. To anticipate a reservoir's next-day volatility patterns, K-Means (K-Means), Nave Bayes (NB), and support vector machine (SVM) algorithms use the architecture feature variables for each combination symbolic pattern as input variables. The findings show that for the two methods, border and search approaches may be utilized for discover the best models. Forecasting the dammed water level and managing the dam in the reservoir are two of these tasks that are critical in evaluating structural concerns in dams, water supply, and available resources in many situations. the state of the water, biodiversity conservation, and navigation management are the issues that this initiative is addressing. The challenge of predicting blocked water levels in reservoirs can be solved by evaluating a variety of predictive variables (input data).

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Dam water level; K-Means; Naive Bayes; SVM Algorithms.

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Virtual Learning Environment (VLE), Web Based Learning, Student Performance Prediction, k-NN, Naive Bayes, Logistic Regression, Liner Regression, Support Vector Machines (SVM), Decision Trees, Random Forest,

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Fuzzy logic controller is a multiple valued logic in which input space is mapped with output space with help of rules. In this study, semi-active suspension is developed using Fuzzy Logic. The proposed system is simulated using MATLAB/Simulink. A 4 degree of freedom half car model is formulated. Two road inputs are used to simulate the system. A Fuzzy Logic controller is designed to vary the damping coefficient of the system in real time. Body acceleration, tyre load variation and suspension deflection are assessed in the simulation. The results simulated with fuzzy logic controller are compared with the passive suspension. According to the comparison, it is noted that semi active system gives better results than the conventional system.

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Half car Model, Semi-active Suspension, Fuzzy Logic Controller, Mathematical Model

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The world is reworking in a digital era. However, the field of medicine was quite repulsive to technology. Recently, the advent of newer technologies like machine learning has catalyzed its adoption into healthcare. The blending of technology and medicine is facilitating a wealth of innovation that continues to improve lives. With the realm of possibility, machine learning is discovering various trends in a dataset and it is globally practiced in various medical conditions to predict the results, diagnose, analyze, treat, and recover. Machine Learning is aiding a lot to fight the battle against Covid-19. For instance, a face scanner that uses ML is used to detect whether a person has a fever or not. Similarly, the data from wearable technology like Apple Watch and Fit bit can be used to detect the changes in resting heart rate patterns which help in detecting corona virus. According to a study by the Hindustan Times, the number of cases is rapidly increasing. Careful risk assessments should identify hotspots and clusters, and continued efforts should be made to further strengthen capacities to respond, especially at sub-national levels. The core public health measures for the Covid-19 response remain, rapidly detect, test, isolate, treat, and trace all contacts. The work presented in this paper represents the system that predicts the number of corona virus cases in the upcoming days as well as the possibility of the infection in a particular person based on the symptoms. The work focuses on Linear Regression and SVM models for predicting the curve of active cases. SVM is least affected by noisy data, and it is not prone to over fitting. To diagnose a person our application has a certain question that needs to be answered. Based on this, the KNN model provides the maximum likelihood result of a person being infected or not. Tracking and monitoring in the course of such pandemic help us to be prepared.

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Covid-19 Symptoms, Plasma Recommendation, SVM, KNN, Linear Regression Algorithms.

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Lactobacillus in Amla drinks with the aim of formulation of probiotic drink and to analyse the nutritive value and self life of the product.Lactobacillus was capable of growing 15 percentage Amla drink at natural pH 30 degree Celsius . Microbial population pH and organic acid metabolism were measured during the fermentation period and the viability of all strains was also determined during the storage 4 degree Celsius with 4 weeks.The result indicated that Lactobacillus increased the pH at the initial stage .Amla drink could be stored at refrigerated condition for 30 days.Amla juice proved to be a suitable media for production of a fermented probiotic drink.

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Lactobacillus,Amla, fermentation, probiotic, microbial activity .

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Recent years have witnessed the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft, and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand and to improve service responsiveness and quality. Despite this, on the drivers' side, dynamic pricing creates a new problem: how to seek passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. We develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability to quantitatively explain the desired relationship. Finally, we evaluate our model by predicting drivers' revenue.

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Taxi Revenue Prediction, Forecasting, SVM, KNN, CNN Algorithm

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