Classical and Quantum Mechanics of Anyons

We review aspects of classical and quantum mechanics of many anyons confined in an oscillator potential. The quantum mechanics of many anyons is complicated due to the occurrence of multivalued wavefunctions. Nevertheless there exists, for arbitrary number of anyons, a subset of exact solutions which may be interpreted as the breathing modes or equivalently collective modes of the full system. Choosing the three-anyon system as an example, we also discuss the anatomy of the so called ``missing'' states which are in fact known numerically and are set apart from the known exact states by their nonlinear dependence on the statistical parameter in the spectrum. Though classically the equations of motion remains unchanged in the presence of the statistical interaction, the system is non-integrable because the configuration space is now multiply connected. In fact we show that even though the number of constants of motion is the same as the number of degrees of freedom the system is in general not integrable via action-angle variables. This is probably the first known example of a many body pseudo-integrable system. We discuss the classification of the orbits and the symmetry reduction due to the interaction. We also sketch the application of periodic orbit theory (POT) to many anyon systems and show the presence of eigenvalues that are potentially non-linear as a function of the statistical parameter. Finally we perform the semiclassical analysis of the ground state by minimizing the Hamiltonian with fixed angular momentum and further minimization over the quantized values of the angular momentum.Comment: 44 pages, one figure, eps file. References update

Development of Digital Repository and Retrieval System for Rose Germplasm Management

Live repository of rose consisting of different genotypes and species of roses available across the globe has been established at ICAR-IIHR. All these genotypes have been characterized for 60 morphological characters for description of these varieties. Along with the live repository of plants, efforts have been made to develop digital repository of all these genotypes. The digital repository consists of description of characters, quantitative measurement for selected important characters and images for all the descriptors. A web-enabled interface has been developed for the selective retrieval of accessions with desired characters, and also for retrieval of all the information for the selected genotype. The information system will be useful across the germplasm collection centers, for the breeders and other end users by enabling them to select the appropriate germplasm andavoid duplicates

Social Media Based Algorithmic Clinical Decision Support Learning from Behavioral Predispositions

Behavioral disorders are disabilities characterized by an individual’s mood, thinking, and social interactions. The commonality of behavioral disorders amongst the United States population has increased in the last few years, with an estimated 50% of all Americans diagnosed with a behavioral disorder at some point in their lifetime. AttentionDeficit/Hyperactivity Disorder is one such behavioral disorder that is a severe public health concern because of its high prevalence, incurable nature, significant impact on domestic life, and peer relationships. Symptomatically, in theory, ADHD is characterized by inattention, hyperactivity, and impulsivity. Access to providers who can offer diagnosis and treat the disorder varies by location. The ever-increasing use of social media can be effectively employed in the diagnosis and treatment of the disorder. Study of behavior and in extension, the study of individuals with behavioral disorders is made easier through the uninhibited setting in which posts are created on social media platforms. Outside the United States, diagnosis rates of the disorder are low, as it is mainly considered to be an American disorder. This impression was reinforced by the perception that the disorder is caused by social and cultural factors common to American society. However, in reality, the disorder can as quickly affect people of different races and cultures worldwide, but recognition of the disorder in the medical community has been slow. This may be due to its adverse impact on an individual, their families, and society. This dissertation focuses on providing clinicians with a clinical decision support system to overcome the societal stigma associated with the disorder and to ensure the accurate and efficient diagnosis of individuals with the disorder. The results provided in this dissertation assist in the diagnosis of individuals with Attention Deficit Hyperactivity Disorder. Data for individuals with the disorder is collected through posts of self-reported diagnoses on Twitter using the Twitter API. Previous research has proved that there are differences in behavior before and after the diagnosis of the disorder. To capitalize on this, symptomatic differences of the disease before and after diagnosis are discovered and evaluated. The symptoms of the disorder, namely, inattention, hyperactivity, and impulsivity, are quantified using measures of sentiment and semantics. A separate group of users without the disorder, the control group, are collected for validation. The analysis poses a three-class classification problem, with the classes being pre-diagnosed, postdiagnosed, and control groups. Decision trees are used to force all possible outcomes in the semantic and sentiment differences in the three classes of users to create a clear delineation. Behavioral disorders diagnosed by a clinician are based on identifying whether a patient deviates from an identified normal. This is evaluated by answering a set list of questions that quantify behavior. To achieve the same without manual intervention, ease in interpretability - decision trees are chosen. Classification using a decision tree is on a tweetlevel and a user-level. Four cases are used both analyses: pre-diagnosed vs. post-diagnosed group, pre-diagnosed vs. control group, post-diagnosed vs. control group, and prediagnosed vs. post-diagnosed vs. control group. The analysis on a user-level provides a higher degree of accuracy, with 93% accuracy for the case post-diagnosed vs. control group. The accuracy of the cases identifies the number of people who can be correctly classified into their respective groups. Low accuracy for the tweet-level results fortifies the opinion that the sparsity of information in tweet level analysis is a disadvantage. This is overcome by analyzing on a user level. The accuracy of the classifier can be further improved upon by the addition of features such as age and gender. The addition of these features may also be useful in predicting time to remission and peak of the disorder in future studies

Clinical study of placenta previa and its effect on maternal health and fetal outcome

Background: When the placenta is implanted partially or completely in the lower uterine segment it is called placenta previa. The objective of the study was to determine the incidence, obstetric risk factors, obstetric management, maternal mortality and morbidity, perinatal outcome in women presenting with placenta previa.Methods: Total 106 pregnant women with placenta previa were analyzed between January to December 2015. After applying the inclusion and exclusion criteria these women were analyzed with respect to their age, parity, gestational age and clinical features at presentation, history of warning bleeding, duration  of hospitalization, need for blood transfusion, period of gestation at delivery, route of delivery and ICU admissions. For the newborn APGAR score, birth weight, need for NICU admission, still birth rate, neonatal mortality rate are noted down.Results: In this study 0.64% of the deliveries were complicated with placenta previa among them 23.6% women were above 30 years of age and 80.2% were multigravidas. 60.4% had major degree placenta previa, 36.8% had prior cesarean deliveries, 7.5% had prior abortion, 39.7% preterm deliveries. 85.8% cases delivered by cesarean delivery, 12.7% cases had postpartum haemorrhage and 4.7% had adherent placenta. There were 86.8% ICU admissions, 3.8% cases of acute kidney injury in present series.Conclusions: Advancing maternal age, multiparity, prior cesarean section, and prior abortions are independent risk factors for placenta previa. Placenta praevia remains a risk factor for adverse maternal and perinatal outcome. The detection of placenta previa should encourage a careful evaluation with timely delivery to reduce the associated maternal and perinatal complications

A new method for secure kNN algorithm to guarantee the security of the outsourced data maintaining its searchability

Distributed computing is a promising IT framework that can make a ton out of IT resources in a profitable and versatile manner. Continuously different associations plan to move their close by information the board systems to the cloud and store and manage their item information on cloud servers. A going with challenge is the way to guarantee the security of the monetarily mystery information while keeping up the capacity to look through the information. In this paper, a security protecting information search plan is suggested that can support both the identifier-based and include based item look. Specifically, two novel rundown trees are created and encoded that can be looked without knowing the plaintext information

Biometric Authentication using Nonparametric Methods

The physiological and behavioral trait is employed to develop biometric authentication systems. The proposed work deals with the authentication of iris and signature based on minimum variance criteria. The iris patterns are preprocessed based on area of the connected components. The segmented image used for authentication consists of the region with large variations in the gray level values. The image region is split into quadtree components. The components with minimum variance are determined from the training samples. Hu moments are applied on the components. The summation of moment values corresponding to minimum variance components are provided as input vector to k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU database consisting of 45 subjects. The number of subjects with zero False Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance Rate [FAR] was 45. This paper addresses the computational load reduction in off-line signature verification based on minimal features using k-means, fuzzy k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and FAR of 10% was achieved using k-nn classifier. The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. The system aims to provide simple, fast and robust system using less number of features when compared to state of art works.Comment: 20 page

Occurrence of linezolid induced thrombocytopenia and its association with the risk factors: a review article

Linezolid is the oxazolidinone group of antibiotic with wide range of activity against the gram positive bacteria including methicillin resistant staphylococcus aureus and penicillin resistant pneumococci and vancomycin resistant enterococci. Patients who are on linezolid were reported to have reversible myelosuppression especially thrombocytopenia and anaemia. Since there are less number of studies regarding the occurrence of thrombocytopenia and the risk factors associated with it, this study was undertaken to evaluate the occurrence of linezolid induced thrombocytopenia and its association with risk factors. It was a systematic review with synthesis of available literature in English language. Articles were retrieved using search terms included “linezolid”, “and”, “or”, “thrombocytopenia” from Clinical key and PubMed, published during 2000 - 2017. Out of 16 studies retrieved, only 7 studies were analysed based on inclusion and exclusion criteria; of them, 3 were found to be prospective and retrospective cohort each and only one was retrospective cross-sectional study. The occurrence of linezolid induced thrombocytopenia range from 18-50% with normal renal function and 57% of incidence associated with renal insufficiency patients. The risk factors were found to be dose of linezolid >18-27mg/kg, body weight of subjects <55kg, creatinine clearance <88.39 to 60ml/min/1.73m2 and baseline platelet count <200*103/mm3, serum albumin concentration, serum creatinine, concomitant caspofungin therapy and duration of linezolid therapy

Feature Extraction in Music information retrival using Machine Learning Algorithms

Music classification is essential for faster Music record recovery. Separating the ideal arrangement of highlights and selecting the best investigation technique are critical for obtaining the best results from sound grouping. The extraction of sound elements could be viewed as an exceptional case of information sound information being transformed into sound instances. Music division and order can provide a rich dataset for the analysis of sight and sound substances. Because of the great dimensionality of sound highlights as well as the variable length of sound fragments, Music layout is dependent on the overpowering computation. By focusing on rhythmic aspects of different songs, this article provides an introduction of some of the possibilities for computing music similarity. Almost every MIR toolkit includes a method for extracting the beats per minute (BPM) and consequently the tempo of each music. The simplest method of computing very low-level rhythmic similarities is to sort and compare songs solely by their tempo There are undoubtedly far better and more precise solutions.  work discusses some of the most promising ways for computing rhythm similarities in a Big Data framework usaing machine Learning algorithms