Volume 10 | Issue 6 |

 Abstract

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Trapezitis, Stretching, strengthening, Range of motion, Neck Disability

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MAKE IN INDIA, SWADESHI, AYURVEDA.

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Life style disorder is the conditions result as a change in living and behavioral pattern of human. Due to increase in sedentary life style pattern, job requirement, competitive life and fast move to achieve success are the cause of all these diseases that includes hypertension, obesity, diabetes, cardiovascular diseases, mental disorders, polycystic ovarian diseases (PCOD), etc. Lack of exercised or over exercises, over conscious towards health, eating low fats-high proteins , high carbohydrates foods, unnecessarily used of minerals and vitamins, unhealthy diet pattern, improper sleep pattern, smoking, used of tobacco, used alcohol, used of Narcotic drugs, stress, mental un-stability etc. are also supportive reasons for life style disorders. But Ayurveda provides good solution with proper dietary management, lifestyle advises, detoxification and bio-purification procedures called Panchkarma, medicaments and rejuvenation therapies to such a global burden.

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Lifestyle Disorders, Dincharya, Ayurveda healthy habits.

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Electric vehicles are an important option for reducing emission of greenhouse gases. Electric vehicles Not only reduce the dependency on fossil fuel but also diminish the impact of ozone depleting Substances and promote large scale renewable deployment. Despite comprehensive research on the Attributes and characteristics of electric vehicles and the nature of their charging infrastructure, electric Vehicle production and network modeling continues to evolve and be constrained. The paper provides an overview of the studies of electric vehicle, hybrid electric vehicle, plug-in-hybrid electric vehicle and battery electric vehicle penetration rate into the market and discusses their different modeling approach and optimization techniques.

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electric vehicle to grid optimization technique CO2 reduction.

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Heavy oil comprises of 75% of petroleum resources in the world. Only a small fraction of these resources have been produced due to crucial and expensive techniques than those of the conventional oils. Due to the decline in the production of easy oil, industries are striving hard for utilizing unconventional oil resources which consists of mainly heavy oils, extra heavy oils and bitumen.These resources consists of viscous oils so their use requires extra efforts to ensure the viability of the oil recovery from the reservoir and its subsequent transportation to production wells and to ports and refineries. For satisfying the demand of the market the oil companies need to improve the efficiency of upstream, midstream and downstream. The paper mentions the challenges faced by the oil companies during production and transportation. Different technologies are reviewed and the benefits and constraints of each technology are highlighted with the view that this will provide direction for improvement and development of novel technologies for heavy oil transportation via pipelines. The main properties of heavy crude oils like high viscosity, density, etc as well as traditional and emergent methods for their recovery and transportation are discussed. The challenges to achieve viable recovery and transportation of unconventional oils are compared for the different alternatives purpose. The work specifically focuses on the heavy oils, its properties, efficient methods of recovery and transportation. If the challenges mentioned in the review are handled properly than there is a significant potential for the upcoming innovative projects to become economically flexible

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Heavy oil, bitumen, transportation

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IoT, ARDUINO UNO, SENSORS, ESP8266 WIFI.

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We know the Carbon nanotubes (CNTs) are nanostructures derived from rolled graphene planes and possess various interesting chemical and physical properties. Carbon nanotubes (CNTs) are allotropes of carbon with a nanostructure that can have a length-to-diameter ratio greater than 1,000,000. These cylindrical carbon molecules have novel properties that make them potentially useful in many applications in nanotechnology. Their unique surface area, stiffness, strength and resilience have led to much excitement in the field of pharmacy. CNTs can be conjugated with various biological molecules including drugs, proteins and nucleic acid to afford bio-functionalities. CNTs exist as single (SWNTs), and multi-walled (MWNTs) structures. They present several interesting properties, such as high aspect-ratio, ultra-light weight, strength, high thermal conductivity and electronic properties ranging from metallic to semiconducting. The CNTs are valuable in the field of drug delivery, blood cancer, breast cancer, brain cancer, liver cancer, cervical cancer, gene therapy, immune therapy, biomedical imaging, biosensors and tissue engineering.

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Carbon nanotubes, Single and multiple walled nanotubes, Nano medicines

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Recent studies have highlighted that insider threats are more destructive than external network threats. This project mainly focuses on the user behavior to detect the insider attack within the organization. Based on the above considerations, we have come up with some solutions where we focus on the behavior of individual user, User activities and analysis on the access rights and usage to check the outcome as whether they are Normal user with no harm or Abnormal (Malicious). Feature Engineering is the process where we select the fixed set of procedures to identify behavior of the employee effectively. The implementation is done by applying various machine learning and deep learning algorithms to get high classification accuracy of the model. The trained data is feed to the Model engine to gain the experience about the user activities and test data is used to find the accuracy of the model and defining the behavior of the user a normal or malicious. The data used is the CMU CERT synthetic insider threat dataset version r5.2 Our unique approach produces comparatively good accuracy of 100%.

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Insider threat, deep learning, machine learning, user behavior, and information security.

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Pleurotus sajor-caju was cultivated on six agro wastes viz., ground nut straw, soybean straw, gram straw, wheat straw, sugarcane trash and sorghum stalk to determine the suitability of these agro wastes on yield, total carbohydrate, protein, fat, crude fibre and ash content. Soybean straw showed significantly highest yield (752.64 gm/kg and 75.26 % B.E.) with maximum protein content (24.95 %). Significantly maximum moisture and crude fibre content of Pleurotus was recorded on sugarcane trash i.e. 88.59 % and 7.09 %, respectively. Maximum total carbohydrate (57.72 %) was recorded on wheat straw, while maximum fat (2.95 %) and ash content (7.13 %) of Pleurotus in mushrooms cultivated on groundnut straw.

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Pleurotus sajor-caju, agro wastes, yield, B.E., fruiting bodies

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This research focuses on the fabrication of LM13 aluminum alloy-based Hybrid Aluminum Matrix Composites (HAMC) and the use of Artificial Neural Networks(ANN) to predict their physical properties. The objective of this study is to calculate the hardness and density of Hybrid Aluminum matrix composites (HAMC). The estimations are made using the ANN's Levenberg-Marquardt method. The stir casting technique is used to manufacture the HAMC with Graphite (Gr) and Boron carbide (B4C) reinforcement into the LM13 aluminum alloy. The L9 orthogonal array was used to see how process variables like Graphite (Gr) reinforcement, Boron carbide (B4C) reinforcement, stirring speed, and stirring time affect the outcomes. The material's Brinell hardness number (BHN) and density were explored as output characteristics. The prediction performance of the ANN was evaluated using the determination coefficient (R2) and mean square error (MSE). The average R2 and MSE are 0.9944 and 0.003963, respectively, indicate that the ANN effectively fitted the experimental response variable values. The findings are also supported by the correlation coefficient (R) of 0.99721.

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LM13 Aluminum Alloy, Hybrid composite, Stir Casting, Physical Characteristics, Artificial neural network

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