Finite temperature phase transition in the two-dimensional Coulomb glass at low disorders

We present numerical evidence using Monte Carlo simulations of finite-temperature phase transition in two dimensional Coulomb Glass lattice model with random site energies at half-filling. For the disorder strengths ($W$) studied in this paper, we find the existence of charge-ordered phase (COP) below the critical temperature ($T_{c}(W)$). Also, the probability distribution of staggered magnetization calculated at each W shows a two-peak structure at their respective critical temperature. Thus the phase transition from fluid to COP as a function of temperature is second order for all $W$. We find no evidence of a spin glass phase between a fluid and the COP. Further, we have used a finite-size scaling analysis to calculate the critical exponents. The critical exponents at zero disorder are different from the one found at finite disorders, which indicates that the disorder is a relevant parameter here. The critical exponent for correlation length of $\nu$ increases and $T_{c}$ decreases with increasing disorder. Similar behaviour for $\nu$ was seen in the work of Overlin et al for three dimensional Coulomb Glass model with a positional disorder. Our study also shows that other critical exponents are also a function of the disorder.Comment: 7 pages, 20 figure

Sentiment Analysis of Twitter Data Using Naive Bayes Algorithm

Sentiment analysis the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral. Now a days the growth of social websites, blogging services and electronic media con-tributes huge amount of user give messages such as customer reviews, comments and opinions. Sentiment Analysis is an important term referred to collect information in a source by using NLP, computational linguistics and text analysis and to make decision by subjective information extracting and analyzing opinion, identifying positive and negative reviews measuring how positively and negatively an entity (public ,organization, product) is involved. Sentiment analysis is the area of study to analyze people’s reviews, emotion, attitudes and emotion from written languages. We concentrate on field of different opinion classification techniques, performed on any data set. Now a days most popular approaches are Bag of words and feature extraction used by researchers to deal with sentiment analysis i.e used by politician, news groups, manufactures organization, movies, products etc

Analysis of Twitter Data Using Deep Learning Approach: LSTM

Sentiment analysis the procedure of computationally identifying and categorizing evaluations expressed in a chunk of text, especially with a view to decide whether the writer’s mind-set toward a selected subject matter, product, etc. is high-quality, poor, or impartial[1]. Now a days the growth of social websites, running a blog offerings and electronic media con-tributes big amount of consumer supply messages which includes customer reviews, remarks and evaluations. Sentiment evaluation is an important term cited gather facts in a source with the aid of the usage of NLP, computational[2] linguistics and text analysis and to make decision through subjective information extracting and analyzing opinion, figuring out advantageous and bad opinions measuring how definitely and negatively an entity (public ,organization, product) is concerned. in the beyond decade , researcher have performed the sentiment analysis using device getting to know techniques which include guide vector gadget, naive bayes , maximum entropy method etc. Sentient analysis on social media textual content received lot of recognition because it includes pointers and pointers. lately deep gaining knowledge of methods like long short-term memory (LSTM) and convolution neural network (CNN) have gained recognition by means of displaying promising effects for speech and photograph processing, obligations in NLP through learning functions wealthy deep illustration from the facts robotically

Research Paper on Software Cost Estimation Using Fuzzy Logic

Software cost estimation is one of the biggest challenges in these days due to tremendous completion. You have to bid so close so that you can get the consignment if your cost estimation is too low are too high in that cases organization has to suffer that why it becomes very crucial to get consignment. One of the important issues in software project management is accurate and reliable estimation of software time, cost, and manpower, especially in the early phase of software development. Software attributes usually have properties of uncertainty and vagueness when they are measured by human judgment. A software cost estimation model incorporates fuzzy logic can overcome the uncertainty and vagueness of software attributes. However, determination of the suitable fuzzy rule sets for fuzzy inference system plays an important role in coming up with accurate and reliable software estimates. The objective of our research was to examine the application of applying fuzzy logic in software cost estimation that can perform more accurate result. In fuzzy logic there are various membership function for example Gaussian, triangular, trapezoidal and many more. Out of these by hit and trial method we find triangular membership function (MF) yields least MRE and MMRE and this MRE must be less than 25%. In our research this value came around 15% which is very fair enough to estimate. Cost can be found out using the equation if payment is known Cost = Effort * (Payment Month). Therefore the effort needed for a particular software project using fuzzy logic is estimated. In our research NASA (93) data set used to calculate fuzzy logic COCOMO II. From this table size of code and actual effort has been taken. In end after comparing the result we found that our proposed technique is far superior to base work

The effect of screening on the relaxation dynamics in the Coulomb glass

This paper examines the relaxation dynamics of a two-dimensional Coulomb glass lattice model with high disorders. The study aims to investigate the effects of disorder and Coulomb interactions on glassy dynamics by computing the eigenvalue distribution of the linear dynamical matrix using mean-field approximations. The findings highlight the significance of the single-particle density of states (DOS) as the main controlling parameter affecting the relaxation at intermediate and long times. For the model with unscreened Coulomb interactions, our results indicate that the depletion of the DOS near the Fermi level leads to logarithmic decay at intermediate times. As the relaxation progresses to longer times, a power-law decay emerges, with the exponent approaching zero as the disorder strength increases, suggesting the manifestation of logarithmic decay at high disorders. The effects of screening of interactions on the dynamics are also studied at various screening and disorder strengths. The findings reveal that screening leads to the filling of the gap in the density of states, causing deviation from logarithmic decay at intermediate disorders. Moreover, in the strong disorder regime, the relaxation dynamics are dominated by disorder, and even with screened Coulomb interactions, the electronic relaxation remains similar to the unscreened case. The time at which crossover to exponential decay occurs increases with increasing disorder and interaction strength.Comment: 10 pages, 7 figure

Phase Ordering Kinetics of the Asymmetric Coulomb Glass Model

We present results for phase ordering kinetics in the {\it Coulomb glass} (CG) model, which describes electrons on a lattice with unscreened Coulombic repulsion. The filling factor is denoted by $K \in [0,1]$. For a square lattice with $K=0.5$ (symmetric CG), the ground state is a checkerboard with alternating electrons and holes. In this paper, we focus on the asymmetric CG where $K \lesssim 0.5$, i.e., the ground state is checkerboard-like with excess holes distributed uniformly. There is no explicit quenched disorder in our system, though the Coulombic interaction gives rise to frustration. We find that the evolution morphology is in the same dynamical universality class as the ordering ferromagnet. Further, the domain growth law is slightly slower than the {\it Lifshitz-Cahn-Allen} law, $L(t) \sim t^{1/2}$, i.e., the growth exponent is underestimated. We speculate that this could be a signature of logarithmic growth in the asymptotic regime.Comment: 21 pages, 6 figure

A Study on Needle Sticks Injury among Nursing Staff of a Tertiary Care Hospital of Haryana

Background: Needle stick injury is one of the most common forms of occupational hazards in a hospital setting. HIV, HBV and HCV are the three most common infections that can be propagated through this route. Nursing staffs of the hospital are the most vulnerable group to this form of occupational hazards. Methods: The study was carried out in Pt. B. D. Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana. The hospital employs almost 350 female nurses at various levels. 172 nurses were selected by judgmental sampling to calculate the prevalence of needle stick injury in the past five years and also the awareness of the staff. Results: The prevalence of needle stick injury was about 63%. The most common mode was during IV access/ IM injections followed by recapping of needle. The heavy load of patients was the main reason attributed by about 60% of the participants having needle stick injury followed by wrong technique and own carelessness. The use of gloves during IV access and other procedures was seen in only 63% participants.Conclusion: The present study showed the prevalence of needle stick injury to be quite high. Proper training of health care providers regarding techniques to prevent needle stick injuries is the need of the hou

Real-Time Face Mask Detection using Deep Learning

The outbreak of COVID-19 has taught everyone the importance of face masks in their lives. SARS-COV-2(Severe Acute Respiratory Syndrome) is a communicable virus that is transmitted from a person while speaking, sneezing in the form of respiratory droplets. It spreads by touching an infected surface or by being in contact with an infected person. Healthcare officials from the World Health Organization and local authorities are propelling people to wear face masks as it is one of the comprehensive strategies to overcome the transmission. Amid the advancement of technology, deep learning and computer vision have proved to be an effective way in recognition through image processing. This system is a real-time application to detect people if they are wearing a mask or are without a mask. It has been trained with the dataset that contains around 4000 images using 224x224 as width and height of the image and have achieved an accuracy rate of 98%. In this research, this model has been trained and compiled with 2 CNN for differentiating accuracy to choose the best for this type of model.It can be put into action in public areas such as airports, railways, schools, offices, etc. to check if COVID-19 guidelines are being adhered to or not