Discrimination Prevention with Privacy Preservation in Data Mining

Discrimination is a crucial issue while considering the legality and morality of data mining. People never like to be distinguished on basis of their caste, gender, nationality, etc. specifically while making decisions with consideration of this attributes, like offering them a job, property, loan, and so forth. Also when a data is released for analysis, there are chances that personal data may be misused for different reasons. Therefore, many companies and institutions are trying to bridge the gap between mining and sharing user data so that their services are improved and user privacy is ensured. We are trying to adapt an approach which will publish the discrimination free data such that probability of learning sensitive value of individual will be reduced. Concept of Slicing is used for preserving privacy. Slicing is better approach of privacy publishing in comparison with other approaches like generalization and bucketization, as it preserves correlation between attributes and it prevents attribute disclosure. The input to the system is discriminated dataset and output will be the sliced data that helps to preserve privacy

Functionalization of carbon nanotubes using phenosafranin

The functionalization of carbon nanotubes by using phenosafranin was discussed. The self-assembly of phenosafranin (PSF) to multiwalled carbon nanotube (MWNT) was shown by using spectroscopic analysis and atomic force microscopy (AFM) phase imaging studies. It was observed that the shift in absorption spectra was associated with charge transfer of valence electrons from PSF to electron accepting sites on the MWNT. The Raman-active disorder modes were used to fingerprint PSF attachment to MWNT via defect states. A molecular topographic visual confirmation of PSF attached to the MWNT was obtained by using AFM phase imaging

Acute Decline in Renal Function, Inflammation, and Cardiovascular Risk after an Acute Coronary Syndrome

Background and objectives: Chronic kidney disease is associated with a higher risk of cardiovascular outcomes. The prognostic significance of worsening renal function has also been shown in various cohorts of cardiac disease; however, the predictors of worsening renal function and the contribution of inflammation remains to be established. Design, setting, participants, & measurements: Worsening renal function was defined as a 25% or more decrease in estimated GFR (eGFR) over a 1-mo period in patients after a non-ST or ST elevation acute coronary syndromes participating in the Aggrastat-to-Zocor Trial; this occurred in 5% of the 3795 participants. Results: A baseline C-reactive protein (CRP) in the fourth quartile was a significant predictor of developing worsening renal function (odds ratio, 2.48; 95% confidence interval, 1.49, 4.14). After adjusting for baseline CRP and eGFR, worsening renal function remained a strong multivariate predictor for the combined cardiovascular composite of CV death, recurrent myocardial infarction (MI), heart failure or stroke (hazard ratio, 1.6; 95% confidence interval, 1.1, 2.3). Conclusions: Patients with an early decline in renal function after an acute coronary syndrome are at a significant increased risk for recurrent cardiovascular events. CRP is an independent predictor for subsequent decline in renal function and reinforces the idea that inflammation may be related to the pathophysiology of progressive renal disease

A method for the reconstruction of unknown non-monotonic growth functions in the chemostat

We propose an adaptive control law that allows one to identify unstable steady states of the open-loop system in the single-species chemostat model without the knowledge of the growth function. We then show how one can use this control law to trace out (reconstruct) the whole graph of the growth function. The process of tracing out the graph can be performed either continuously or step-wise. We present and compare both approaches. Even in the case of two species in competition, which is not directly accessible with our approach due to lack of controllability, feedback control improves identifiability of the non-dominant growth rate.Comment: expansion of ideas from proceedings paper (17 pages, 8 figures), proceedings paper is version v

Quantum Interference Effects in Electronic Transport through Nanotube Contacts

Quantum interference has dramatic effects on electronic transport through nanotube contacts. In optimal configuration the intertube conductance can approach that of a perfect nanotube ($4e^2/h$). The maximum conductance increases rapidly with the contact length up to 10 nm, beyond which it exhibits long wavelength oscillations. This is attributed to the resonant cavity-like interference phenomena in the contact region. For two concentric nanotubes symmetry breaking reduces the maximum intertube conductance from $4e^2/h$ to $2e^2/h$. The phenomena discussed here can serve as a foundation for building nanotube electronic circuits and high speed nanoscale electromechanical devices

A Review on Utilization of Light Weight Fly Ash Cenosphere as Filler in both Polymer and Alloy-Based Composites

 Fly Ash Cenospheres (FACs) are obtained from the coal power plants in the form of hollow spherical particles by burning the coal. FAC was started to use in early 1980-1985 as lightweight filler material in producing composites of cementitious and at present many researchers are focusing on use of FAC as filler in polymer and metals. In this paper, the systematic review on research activities and application of FAC in manufacturing light weight products are done. The FAC influence on the mechanical and physical properties of incorporated polymer and alloy based composites were summarized. Prospects of future for its use were also suggested and summarized in this paper

Performance of Groundnut Markets in Karnataka, India: An Economic Analysis

Spatial market integration refers to a situation in which the price of the commodity in spatially separated markets moves together and price signals and information are transmitted smoothly. The present study aimed to evaluate the spatial price linkages between groundnut markets in Karnataka using monthly price data of groundnut (2002- 2022). The required secondary data were collected from published reports of APMC’s, Krishimaratavahini etc. For the study top ten groundnut markets such as Yadgir, Laxmeshwar, Raichur, Ballari, Challakere, Hubbali, Gadag, Mundargi, Chitradurga and Kottur were selected based on groundnut arrivals. The study uses Augmented Dickey-Fuller, Johansen co-integration, Granger causality test and Vector error correction model (VECM) to fulfil the objectives of the study. The study revealed that all the price series of the groundnut market were found to be non-stationery at level and stationery at first difference except Kottur market. There is a bidirectional causation relationship in price transmission among all chosen groundnut markets except Yadgir-Raichur, Ballari-Raichur, Challakere-Raichur, Gadag-Raichur, Mundargi-Raichur, Kottur-Raichur, Challakere-Ballari where prices influenced unidirectional. Five out of the ten markets were cointegrated at a significance level of 5 per cent indicating that the chosen groundnut markets had long-run equilibrium relationships. The results revealed that the trace statistics values of groundnut markets viz, 361.49, 267.84, 196.54, 146.72 and 101.41 were found to be higher than compared to critical values indicating the existence of five co-integrating equations between selected groundnut markets. The error correction term of groundnut markets indicated that the Error Correction terms of Yadgir, Raichur, Ballari and Kottur markets were found to be significant and negative depicting that the change in the price of groundnut in Yadgir, Raichur, Ballari and Kottur market have influenced and have an impact on the price of other groundnut markets in the long run period

Advanced Dual-Optimized Neural Network Model with Integrated CSO and OBD for Precise Classification and Prediction of North Indian Light Classical Music Genres

Introduction: categorizing North Indian Light Classical Music genres presents a considerable challenge due to their intricate nature. This research introduces a Dual Optimized Neural Network (DONN) model designed to achieve elevated levels of accuracy and efficiency, thereby enhancing the understanding of these music genres. Creating a network of artificial neural with accurate classification and prediction of given genres is the primary objective. This is achieved through the integration of Cat Swarm Optimization (CSO) for enhanced adaptability and Optimal Brain Damage (OBD) for effective network pruning. Methods: the DONN model employs CSO to investigate the solution space effectively while using OBD to minimize unnecessary network connections, thereby improving both computational efficiency and generalization capabilities. The methodology involves modelling the network using a dataset of North Indian Light Classical Music, optimizing the search process with CSO, and applying OBD for network pruning. Results: the DONN model demonstrated a remarkable 98 % accuracy in classifying eleven distinct genres, outperforming previous methods, and highlighting superior classification accuracy and resilience. Compared to earlier research work and Swarm Optimization like Bat and Ant Colony, and Particle Swarm Algorithm, this model shows higher accuracy and efficiency. The fusion of CSO and OBD significantly enhances performance, improving generalization and reducing computational complexity. Conclusions: overall, the DONN model, optimized with CSO and OBD, significantly advances the classification and prediction of North Indian Light Classical Music genres. This research offers a robust and reliable tool for music classification, contributing to a deeper understanding and appreciation of these genres.

Structure and properties of C\u3csub\u3e60\u3c/sub\u3e@SWNT

Our recent achievement of high-yield C60@SWNT synthesis facilitates characterization by various techniques, including selected area electron diffraction (SAD) and Raman spectroscopy. The obtained SAD patterns show that interior C60 molecules sit on a simple 1-D lattice having a parameter of 1.00 nm. Simulated SAD patterns and real-space measurements both support this determination and do not indicate a lattice with a more complex basis, e.g. a dimer basis. Empty and bulk-filled SWNTs (22%, 56%, and 90% yields), each subjected to identical processing steps, were examined by room temperature Raman spectroscopy. Systematic differences are seen between the spectra of filled and unfilled SWNTs, particularly with respect to the G- and RBM-bands of the nanotubes. We present a possible explanation for this behavior

Reproducible synthesis of C\u3csub\u3e60\u3c/sub\u3e@SWNT in 90% yields

In previous works, we have shown our discovery of C60@SWNT and first described the general mechanism of filling, which involves the vapor phase transport of C60 molecules to openings in the SWNTs\u27 walls. Here, we discuss the high-yield synthesis of C60@SWNT by refinements to our method. Yields are measured by a calibrated weight uptake technique, a methodology that is not subject to many of the potential pitfalls inherent to other techniques that have been applied. At certain processing conditions, yields exceeding 90% were obtained and corroborated by transmission electron microscopy. From our data, we determine the parameters most important for creating endohedral SWNT supramolecular assemblies by the vapor phase method. Our results pave the way for successful single-tube measurements and for high-yield filling with non-fullerenes