IoT based Driver Drowsiness and Pothole Detection Alert System

One of the common in progressing countries is the maintenance of roads. Well maintained roads contribute a major portion to the country’s economy. Identification of pavement distress such as potholes and humps not only help drivers to avoid accidents or vehicle damages, but also helps authorities to maintain roads. This paper discusses various pothole detection methods that have been developed and proposes a simple and cost-effective solution to identify the potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Not only Potholes and humps are the main cause of accidents other than over speeding and drowsiness of driver includes the issue of accidents. Drowsy state may be caused by lack of sleep, medication, tiredness, drugs or driving continuously for long period of time. So, here is the solution for detecting the potholes and humps and to alert the driver from drowsiness while driving. In this paper, the system is structured to detect potholes and to alert the drowsy driver by using the ultrasonic sensor, eyeblink sensor and IR sensor and microcontroller. Ultrasonic sensor senses the humps, IR sensor senses the potholes and eye blink sensor the blinking of eye and this sensing signals fed into the Arduino to alert the driver by buzzer sound

Biology and management of mealybug, Paracoccus marginatus Williams and Granara de Willink on Jatropha curcas L.

Jatropha cultivation is gaining importance as potential source of biofuel. Recently Paracoccus marginatus has been found to cause serious damage on Jatropha. Studies on the biology and management of P. marginatus at GKVK, Bangalore revealed that the females had three nymphal instars without any pupal stage, while the male had three nymphal instars besides, pre-pupal and pupal stages. The total nymphal period for female ranged from 14 to 21 days, (mean- 17.32±1.6 days) while for male the range was 16 to 23 days, (mean- 18.9±1.3 days). Bisexual and parthenogenetic modes of reproduction were observed. The fecundity of the female mealybug ranged from 248 to 967, with an average of 618.9±19 eggs. Evaluation of insecticides revealed that during first spray and second spray, mean per cent reduction of mealy bug population was highest in profenophos 0.05% (68.05 and 79.35) followed by buprofezin 0.025% (63.61 and 72.69). Least per cent reduction of mealy bug was observed in the NSKE 5% (17.94 and 25.77) treatment

Fabrication, Mechanical and Wear Properties of Aluminum (Al6061)-Silicon Carbide-Graphite Hybrid Metal Matrix Composites

In recent times, the use of aluminum alloy-based Hybrid Metal Matrix Composites (HMMCs) is being increased in aerospace and automotive applications. HMMCs compensate for the low desirable properties of each filler used. However, the mechanical properties of HMMCs are not well understood. In particular, microstructural investigations and wear optimization studies of HMMCs are not clear. Therefore, further studies are required. The present study is aimed at fabricating and mechanical and wear characterizing and microstructure investigating of Silicon Carbide (SiC) and Graphite (Gr) added in Aluminum (Al) alloy Al6061 HMMCs. The addition of SiC particles was in the range from 0 to 9 weight percentage (wt.%) in steps of 3, along with the addition of 1 wt.% Gr in powder form. The presence of alloying elements in the Al6061 alloy was identified using the Energy Dispersive X-Ray Analysis (EDX). The dispersion of SiC and Gr particles in the alloy was investigated using metallurgical microscope and Scanning Electron Microscopy (SEM). The gain in strength can be attributed to the growth in dislocation density. The nature of fracture was quasi-cleavage. The microstructure examination reveals the uniform dispersion of the reinforcement. Density, hardness, and Ultimate Tensile Strength values observed to be increased with increased contents of SiC reinforcement. Besides, wear studies were performed in dry sliding conditions. Optimization studies were performed to investigate the effect of parameters that affecting the wear. The sliding wear resistance was noticed to be improved concerning higher amounts of reinforcement leading to a decrease in delamination and adhesive wear. The predicted values for the wear rate have also been compared with the experimental results and good correlation is obtained

Assessment of social media usage and its influence among dental faculty members in Davangere city – A cross sectional survey

Background: Rapid increases in dental knowledge and associated technologies, a growing integration of evidence-based practice into the curriculum, and shifting faculty roles (from lecturers and content experts to facilitators) have profoundly altered dental education. Aim: The aim of this study is to assess the usage of social media and its influence on clinical practice among dental faculty members of dental colleges in Davangere city. Materials and Methods: Descriptive cross-sectional survey was carried out among the convenience sample of 88 dental faculty members of two dental colleges in Davangere city. An investigator-designed questionnaire comprising 15 close-ended questions related to the usage and various aspects of the influence of social media was used as a tool in the present survey. Ethical clearance was obtained from Institutional Review Board of Ethics committee of college. Descriptive statistics were generated regarding frequencies and percentages. Results: Among the participants, 64.8% reported the use of social media in their profession. Around 62.5% used social media to exchange opinions and views regarding cases with colleagues. According to 39.8% of participants, it can potentially improve the quality of care delivered to patients. Conclusion: In the present study, dental faculty used social media for many reasons, but mainly to serve public and other dental professionals and to communicate with other dental professionals on social media. Since social media in dental education is still in its infancy, research should be undertaken to determine the optimal ways for incorporating these technologies into both traditional and e-learning courses

Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach

This research illustrates the technical efficiency of the pan-India paddy cultivation status obtained through a stochastic frontier approach. The results suggest that the mean technical efficiency varies from 0.64 in Gujarat to 0.95 in Odisha. Inputs like human labor, mechanical labor, fertilizer, irrigation and insecticide were found to determine the yield in paddy cultivation across India (except for Chhattisgarh). Inefficiency in the paddy production in Punjab, Bihar, West Bengal, Andhra Pradesh, Tamil Nadu, Kerala, Assam, Gujarat and Odisha in 2016–2017 was caused by technical inefficiency due to poor input management, as suggested by the significant σ2U and σ2v values of the stochastic frontier model. In addition, most of the farm groups in the study operated in the high-efficiency group (80–90% technical efficiency). No specific pattern of input use can be visualized through descriptive measures to give any specific policy implication. Thus, machine learning algorithms based on the input parameters were tested on the data in order to predict the farmers’ efficiency class for individual states. The highest mean accuracy of 0.80 for the models of all of the states was achieved in random forest models. Among the various states of India, the best random forest prediction model based on accuracy was fitted to the input data of Bihar (0.91), followed by Uttar Pradesh (0.89), Andhra Pradesh (0.88), Assam (0.88) and West Bengal (0.86). Thus, the study provides a technique for the classification and prediction of a farmer’s efficiency group from the levels of input use in paddy cultivation for each state in the study. The study uses the DES input dataset to classify and predict the efficiency group of the farmer, as other machine learning models in agriculture have used mostly satellite, spectral imaging and soil property data to detect disease, weeds and crops