Parametric study of CFST column element with and without shear studs under axial compression
The compressive performance of the concrete-filled steel tubular (CFST) members is investigated in this study. Tests are carried out on the specimens under axial compression and bending. The parameter of the experimental work is the steel relative amount of the cross section, and the hollow tubular members are designed for comparison. In recent times, engineers have increasingly utilized composite members of concrete-filled steel tubes (CFSTs) in contemporary projects such as buildings and bridges. Concrete Filled Steel Structures (CFST) offers wide benefits like high strength, ductility, energy absorption with the combined benefits of steels and concrete. It also reduces the time consumption in constructing since it doesn’t require shuttering works hence they are frequently used. Moreover, the CFST members are more economical and allow for rapid construction and cost savings by eliminating formwork and workmanship. Concrete filled steel tube is gaining supplementary popularity now days in construction area. Concrete filled steel tube is component with good performance resulting from the confinement effect of steel with concrete and design versatility need. This Paper present a review the performance of CFST comparing the models with & without shear studs differentiating position of shear studs. The composite actions of steel and concrete to occur there need a strong bond between steel and concrete interface. In the present study, it mainly focuses on design load carrying capacity of CFST using Euro code and Indian code &compares it with the analytical & experimental result. Analysis of CFST column using the Finite element method (ABAQUS) software and the experimental study is done on the selected case under concentric loading condition.
Published by: Bhushan Bhaskar Patil, Dr. Ashok Kasnale
Author: Bhushan Bhaskar Patil
Paper ID: V5I4-1194
Paper Status: published
Published: July 15, 2019
Evaluation of properties of paver block using dismantaled concrete for medium traffic
Concrete paver blocks are versatile, attractive, cost-effective and functional, blocks for the construction of pavement, if paver blocks are correctly manufactured and laid then paver blocks, required very less maintenance. In India as per Indian standard paver blocks can be used for roadways. Paver blocks are divided into different traffic categories by Indian standard i.e. very heavy traffic, non-traffic, Light-traffic, medium traffic, and heavy traffic. Wastes of the demolished building are generally used in landfills, these waste contain waste of bricks and concrete. These demolished building wastes which are also known as Demolish Concrete Aggregates are increasing gradually; Many engineers are working on these wastes to make it effectively useable. One of the best uses of this waste is to use it as a coarse aggregate in concrete. In this project coarse aggregate is replaced by Demolish Concrete Aggregate which contains wastes of concrete from demolished i.e. wastes from a beam, column, and slab up to 100% at an interval of 10% in M 40 concrete of paver blocks for medium traffic. Demolish Concrete Aggregate which passes from 10mm IS sieve and retained on 4.75mm IS sieve was used. For this project test like compressive strength and the flexural test was performed on paver blocks and to evaluate the workability of fresh concrete slump cone test was performed. The study indicated that compressive strength, flexural strength, and workability of concrete is required but aggregate enough to be used as paver blocks for medium traffic.
Published by: Prashant Udeniyan, Abhay Kumar Jha, Barun Kumar
Author: Prashant Udeniyan
Paper ID: V5I4-1182
Paper Status: published
Published: July 13, 2019
Securing image document using RSA
Abstract: It is difficult to store and secure all information or data in paper format which also creates a problem for a proper search, storing and durability of information. Increase in new smart technologies and ease of living has developed various ways to share small and vital information digitally with the use of various applications and devices. But as the ways of sharing information and technologies are increasing, the risk of misusing the information is also increasing. Protection and security of information is a necessary feature of such applications, software or devices which handle such information. In the case of images, the security systems still lack to provide security. As the increase in digitization, the documents such as identity proof, educational qualification, and various certificates are uploaded online on various government or private website and mobile applications to verify proper identification or eligibility of documents related to that person. Such uploads of documents or proofs are done in image format. But sharing of this information may lead to a serious loss or misuse of that information. This paper deals with securing image with the use of OCR and RSA algorithm for securing the information The image would be converted into text file and the generated text file would be encrypted and send through the network to the destination and again reverse or decryption process will be followed on the other side and text file will be again generated to its image form.
Published by: Omkar Prakash Dalvi
Author: Omkar Prakash Dalvi
Paper ID: V5I4-1179
Paper Status: published
Published: July 12, 2019
Solid Dispersion: Different methods of enhancing solubility and classification of solid dispersion
Solubility is one of the most significant parameters that affects the absorption and bioavailability of the drugs. Amongst the newly developed drugs 40% possesses low aqueous solubility, so it becomes a great challenge to enhance the solubility of such drugs in order to enhance the bioavailability. Solid dispersion is one of the solubility enhancing methods to enhance their bioavailability. The current article highlights the study of various methods of enhancing solubility and solid dispersion’s advantages over them.
Published by: Sandeep Verma, Inder kumar, Amit chaudhary
Author: Sandeep Verma
Paper ID: V5I4-1175
Paper Status: published
Published: July 12, 2019
Early prediction of Alzheimer’s Disease based on neuroimaging and deep learning: Review
Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection. Compared to traditional classifiers based on R-fMRI time series data. The proposed work is also able to classify the different types of Alzheimer’s disease as well as a particular stage of the disease. Finally, we will compare our deep learning approach accuracy with existing systems. In this paper, we proposed a system using deep learning with brain network and clinical relevant text information to make an early diagnosis of Alzheimer’s Disease (AD). The clinical relevant text information includes age, gender and ApoE gene of the subject. The brain network is constructed by computing the functional connectivity of brain regions using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection.
Published by: Souparnika Padaki Patil, Dr. Anant M. Bagade
Author: Souparnika Padaki Patil
Paper ID: V5I4-1174
Paper Status: published
Published: July 12, 2019
An investigation on anti-depressant activity of fresh fruit juice of Malus domestica in experimental animal models
Objective: To evaluate the anti-depressant effect of acute and chronic administration of fresh fruit juice of Malus domestica in experimental animal models. Methods: Anti-depressant activity of fresh fruit juice of Malus domestica was investigated in experimental animal models. Two doses 0.5ml and 1ml of FFJMD (oral route) was subjected for the evaluation as acute (1day) and chronic treatment (10days). Imipramine (10mg/kg oral) was used as standard in all the models of animals and parameters estimated includes estimation of biochemical parameter (mono amino oxidase). Results: Both the lower (0.5ml) and higher dose (1ml) of Malus domestica fresh fruit juice showed dose dependent significant decrease in depression. In acute and chronic forced swim test as well as acute tail suspension test, duration of immobility was significantly reduced in the FFJMD 1 ml and 0.5 ml treated group but the effectiveness was found more in FFJMD 1 ml. In hole board test there is increase in activity with FFJMD 0.5 ml and 1 ml treated groups and increase in biochemical parameter such as mono amino oxidase when compared with depressive control. The antidepressant activity of 1 ml was comparable to that of Imipramine 10 mg/kg. Conclusion:The present study suggests that fresh fruit juice of Malus domestica has antidepressant activity in both the doses but more beneficial effect was found in chronic administration at 1 ml. It would be advisable to encourage consumption of Malus domestica extract in patients with depression because of its nutritional and functional properties.
Published by: Avrin Romitha Lobo
Author: Avrin Romitha Lobo
Paper ID: V5I4-1171
Paper Status: published
Published: July 12, 2019
