E-Voting in India Using Blockchain and It’s Modus Operandi

2019 ◽  
Vol 16 (9) ◽  
pp. 3774-3777
Author(s):  
Kailash Kumar ◽  
Avinash Sharma
Keyword(s):  
Peer To Peer ◽  
Basic Requirement ◽  
Modus Operandi ◽  
Proposed Model ◽  
Wide Range ◽  
Long Time ◽  
Voting System ◽  
Potential Benefits

Creating an environment to fulfill the basic requirement of E-Voting is indeed a challenge for long time. Blockchain, distributed peer to peer technology has wide range of applications from financial to non-financial sectors. It can be utilized in E-Voting system as well. Electronic casting a ballot or e-casting a ballot has been in existence since seventies. The E-Voting system and its requirements are identified in this paper along with some limitations of the Blockchain in E-Voting system. The blockchain has obvious advantages of increased efficiency, increased productivity and less prone to error over paper based voting system. This paper presents the potential benefits of using the blockchain which can effectively be used in e-casting the votes. The proposed model is based on the fundamental requirements of e-casting to the verification of voters. The limitations of the blockchain with regards to E-Voting is also presented in this paper.

A novel mathematical model including the wetness parameter as a variable for prevention of pressure ulcers

2021 ◽  
pp. 095441192110485
Author(s):  
Fadime Öğülmüş Demircan ◽  
İbrahim Yücedağ ◽  
Metin Toz
Keyword(s):  
Mathematical Model ◽  
Pressure Ulcer ◽  
Pressure Ulcers ◽  
Ulcer Formation ◽  
Hospital Capacity ◽  
Proposed Model ◽  
Wide Range ◽  
Temperature And Humidity ◽  
Long Time ◽  
External Conditions

Pressure ulcers are injuries caused by external conditions such as pressure, friction, shear, and humidity resulting from staying in the same position for a long time in bedridden patients. It is a serious problem worldwide when assessed in terms of hospital capacity, nursing staff employment and treatment costs. In this study, we developed a novel mathematical model based on one of our previous models to prevent pressure ulcers or delay injuries. The proposed model uses a human thermal model that includes skin temperature, hypothalamus temperature, regional perspiration coefficient, and unconsciously loss of water amount. Moreover, in our model, we defined a variable wetness parameter in addition to the parameters, pressure, temperature, and humidity. The proposed model is mathematically defined in detail and tested for a wide range of parameters to show the model’s effectiveness in determining the pressure ulcer formation risk. The model is also compared with a model from the literature that based on only the general parameters, pressure, temperature, and humidity. The obtained results showed that the model determines the risk of the occurrence of the pressure ulcer more precisely than the compared one.

The Problem of Identifying and Modeling the Relationship between Monetary Factor and Inflation in the Russian Economy

2008 ◽  
pp. 61-76
Author(s):  
A. Porshakov ◽  
A. Ponomarenko
Keyword(s):  
Money Supply ◽  
Money Demand ◽  
Russian Economy ◽  
Long Run ◽  
Complex Approach ◽  
Wide Range ◽  
Long Time ◽  
The Relationship ◽  
Supply Side Factors

The role of monetary factor in generating inflationary processes in Russia has stimulated various debates in social and scientific circles for a relatively long time. The authors show that identification of the specificity of relationship between money and inflation requires a complex approach based on statistical modeling and involving a wide range of indicators relevant for the price changes in the economy. As a result a model of inflation for Russia implying the decomposition of inflation dynamics into demand-side and supply-side factors is suggested. The main conclusion drawn is that during the recent years the volume of inflationary pressures in the Russian economy has been determined by the deviation of money supply from money demand, rather than by money supply alone. At the same time, monetary factor has a long-run spread over time impact on inflation.

Comperehencive review of Tankana

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Dr. Jyotsna Sankpal ◽  
Dr. Jyotsna Takalikar
Keyword(s):  
Heart Disease ◽  
Medical Science ◽  
Menstrual Disorders ◽  
Therapeutic Applications ◽  
Indian Medical ◽  
Wide Range ◽  
Long Time

Rasa Shastra and Bhaishajya Kalpana is branch of the ancient Indian medical science based on herbs and herbo-mineral preparation. Tankana has been described under Uparasa Tankana, which is one among the Kshara Trayas has been used since very long time in Ayurveda. It has a wide range of therapeutic applications, including diseases like Varna (ulcers), Shvasa (asthma), Kasa (cough), Hrudya (beneficial to heart disease), Streepushpajanana (menstrual disorders) etc. It is used in the form of compound formulations like Parpati, Kupipakwa, Khalvee Rasayana, Churna, Vati, Lepa etc. In this paper Tankana Shodhana procedure, different synonyms, dose, Anupana, indications and different formulations containing Tankana Bhasma has been discussed.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

scholarly journals A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

2014 ◽  
Vol 22 (1) ◽  
pp. 159-188 ◽  
Author(s):  
Mikdam Turkey ◽  
Riccardo Poli
Keyword(s):  
Population Dynamics ◽  
Dynamic Behaviour ◽  
Fitness Landscape ◽  
Population Based ◽  
Levels Of Abstraction ◽  
Collective Dynamic ◽  
Model Studies ◽  
Proposed Model ◽  
Wide Range ◽  
Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

scholarly journals Physical Properties of Thermoplastic Starch Derived from Natural Resources and Its Blends: A Review

Polymers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1396
Author(s):  
Z. N. Diyana ◽  
R. Jumaidin ◽  
Mohd Zulkefli Selamat ◽  
Ihwan Ghazali ◽  
Norliza Julmohammad ◽  
...  
Keyword(s):  
Thermoplastic Starch ◽  
Natural Fibre ◽  
Polymer Materials ◽  
Secondary Pollution ◽  
Wide Range ◽  
Potential Applications ◽  
Long Time ◽  
To Come ◽  
Main Component ◽  
Significant Attention

Thermoplastic starch composites have attracted significant attention due to the rise of environmental pollutions induced by the use of synthetic petroleum-based polymer materials. The degradation of traditional plastics requires an unusually long time, which may lead to high cost and secondary pollution. To solve these difficulties, more petroleum-based plastics should be substituted with sustainable bio-based plastics. Renewable and natural materials that are abundant in nature are potential candidates for a wide range of polymers, which can be used to replace their synthetic counterparts. This paper focuses on some aspects of biopolymers and their classes, providing a description of starch as a main component of biopolymers, composites, and potential applications of thermoplastics starch-based in packaging application. Currently, biopolymer composites blended with other components have exhibited several enhanced qualities. The same behavior is also observed when natural fibre is incorporated with biopolymers. However, it should be noted that the degree of compatibility between starch and other biopolymers extensively varies depending on the specific biopolymer. Although their efficacy is yet to reach the level of their fossil fuel counterparts, biopolymers have made a distinguishing mark, which will continue to inspire the creation of novel substances for many years to come.

scholarly journals Ceramic Cracks Segmentation with Deep Learning

2021 ◽  
Vol 11 (13) ◽  
pp. 6017
Author(s):  
Gerivan Santos Junior ◽  
Janderson Ferreira ◽  
Cristian Millán-Arias ◽  
Ramiro Daniel ◽  
Alberto Casado Junior ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ceramic Plate ◽  
Ceramic Tiles ◽  
Entire Area ◽  
Manual Inspection ◽  
Optical Inspection ◽  
Proposed Model ◽  
Processing Step ◽  
Long Time ◽  
Automated Optical Inspection

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

Prediction of High Viscosity Liquid/Gas Two-Phase Slug Length in Horizontal and Slightly Inclined Pipelines

2021 ◽  
Author(s):  
Omar Shaaban ◽  
Eissa Al-Safran
Keyword(s):  
Numerical Optimization ◽  
Flow Behavior ◽  
Mechanistic Model ◽  
High Viscosity ◽  
Oil Well ◽  
Liquid Slug ◽  
Two Phase ◽  
Proposed Model ◽  
Wide Range ◽  
Flow Closure

Abstract The production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.

Investment in the agricultural sector as a way to solve food security problems in developing countries: the example of investment activity of the US agribusiness

2021 ◽  
Author(s):  
Elena Stepanovna Ustinovich ◽  
Tatyana Petrovna Boldyreva
Keyword(s):  
Developing Countries ◽  
Large Scale ◽  
Agricultural Sector ◽  
International Investment ◽  
Investment Activity ◽  
Agricultural Investment ◽  
Wide Range ◽  
The Us ◽  
The World ◽  
Potential Benefits

It is clear to everyone that investment in the agricultural sector in developing countries is one of the most effective ways to reduce poverty and hunger in the world. Agricultural investment can generate a wide range of development opportunities. However, these benefi ts cannot be expected to arise automatically. Some forms of large-scale investment pose significant risks to investor states. It should be noted, however, that, despite discussions about the potential benefits and risks of international investment, there is still no evidence of negative actual consequences for the countries receiving investments. This article examines the issues of investment activity in relation to developing countries using the example of US agribusiness entities.

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