The influence of auditory feedback on speed choice, violations and comfort in a driving simulation game

Two experiments are reported which explore the relationships between auditory feedback (engine noise), speed choice, driving violations and driver comfort. Participants played a driving simulation game with different levels of auditory feedback in the form of engine noise. In Experiment 1, a between-subjects design revealed that no noise and low levels of engine noise (65 dB(A)) resulted in participants driving at faster speeds than in the medium (75 dB(A)) and high (85 dB(A)) levels of engine noise conditions. The low noise feedback conditions were also associated with decreases in driver comfort. Experiment 2 also demonstrated that low levels of engine noise feedback (no feedback and 70 dB(A)) were associated with increases in driving speed, and driving violations relative to higher levels of feedback (75 dB(A) and 80 dB(A)). Implications exist for current car manufacturing trends which emphasise a growing increase in noise insulation for the driver. © 2011 Elsevier Ltd. All rights reserved

Ralph W. Steen Library 2015 – 2016 Quantitative Comparative Statistical Analysis

For academic libraries at public state universities, the challenge continues on how to evaluate performance, measure progress, and find meaningful ways to demonstrate their worth. The challenge is to find meaningful ways to demonstrate how library programs and services contribute to learning outcomes and student success. This report presents a comparative analysis of Ralph W. Steen Library and ten peer institutions to highlight areas of excellence and under-performance that may warrant further attention

Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing

In the modern world of digitalization, data growth, aggregation and sharing have escalated drastically. Users share huge amounts of data due to the widespread adoption of Internet-of-things (IoT) and cloud-based smart devices. Such data could have confidential attributes about various individuals. Therefore, privacy preservation has become an important concern. Many privacy-preserving data publication models have been proposed to ensure data sharing without privacy disclosures. However, publishing high-dimensional data with sufficient privacy is still a challenging task and very little focus has been given to propound optimal privacy solutions for high-dimensional data. In this paper, we propose a novel privacy-preserving model to anonymize high-dimensional data (prone to various privacy attacks including probabilistic, skewness, and gender-specific). Our proposed model is a combination of l-diversity along with constrained slicing and vertical division. The proposed model can protect the above-stated attacks with minimal information loss. The extensive experiments on real-world datasets advocate the outperformance of our proposed model among its counterparts

Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility

A prospective and observational study on complications of type 2 diabetes mellitus in correlation with body mass index

Background: The aim of this study is to observe the prevalence of complications of diabetes mellitus (Type 2) among patients and to minimize their occurrence through patient education. The study helps to assess the clinical data of patients with diabetes mellitus (Type 2) along with the analysis of patterns, frequencies and predictive factors of microvascular, macrovascular complications and to educate and minimize the complications of diabetes mellitus among patients.Methods: Prospective and observational study was conducted among the type 2 diabetes mellitus patients at Sree Diabetes Clinic for a period of 6 months. The patients were interviewed using the patient data collection form which included demographic details, chief complaints and different diagnostic tools to detect type of complications. Both micro and macrovascular complications were evaluated.Results: A total of 150 type 2 diabetic cases were collected. Out of these 38(25.33%) patients were having BMI <25, and 112(74.67%) were having BMI ≥25 (overweight and obese). Out of 150 diabetic cases collected, a total of 131 diabetic complications were found. Out of these, 64(42.6%) were neuropathy, 3(2%) were nephropathy, 20(13.3%) were retinopathy and 17(11.3%) were having cardiovascular complications. Out of 112 patients with BMI ≥25, 60(54%) were found to have diabetic complications and out of 38 patients with BMI <25, 18(47%) were found to have diabetic complications. 90 out of 150 patients had a history of hypertension and 17 out of 150 patients had an abnormal lipid level.Conclusions: In this study, author found that obesity is a major risk factor for the development of diabetes mellitus and its complications

A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Re-defining the Empirical ZZ Ceti Instability Strip

We use the new ZZ Ceti stars (hydrogen atmosphere white dwarf variables; DAVs) discovered within the Sloan Digital Sky Survey (Mukadam et al. 2004) to re-define the empirical ZZ Ceti instability strip. This is the first time since the discovery of white dwarf variables in 1968 that we have a homogeneous set of spectra acquired using the same instrument on the same telescope, and with consistent data reductions, for a statistically significant sample of ZZ Ceti stars. The homogeneity of the spectra reduces the scatter in the spectroscopic temperatures and we find a narrow instability strip of width ~950K, from 10850--11800K. We question the purity of the DAV instability strip as we find several non-variables within. We present our best fit for the red edge and our constraint for the blue edge of the instability strip, determined using a statistical approach.Comment: 14 pages, 5 pages, ApJ paper, accepte

Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning

Federated Learning (FL) is a machine learning (ML) strategy that is performed in a decentralized environment. The training is performed locally by the client on the global model shared by the server. Federated learning has recently been used as a service (FLaaS) to provide a collaborative training environment to independent third-party applications. However, the widespread adoption in distributed settings of FL has opened venues for a number of security attacks. A number of studies have been performed to prevent multiple FL attacks. However, sophisticated attacks, such as label-flipping attacks, have received little or no attention. From the said perspective, this research is focused on providing a defense mechanism for the aforesaid attack. The proposed approach is based on Type-based Cohorts (TC) with Kernel Principal Component Analysis (KPCA) to detect and defend against label-flipping attacks. Moreover, to improve the performance of the network, we will deploy Multi-path Service Routing (MSR) for edge nodes to work effectively. The KPCA will be used to secure the network from attacks. The proposed mechanism will provide an effective and secure FL system. The proposed approach is evaluated with respect to the following measures: execution time, memory consumption, information loss, accuracy, service request violations, and the request’s waiting time

Ecofriendly Dyes: Extraction, Characterization and Potential Applications

Indians were thought to be forerunners in the technique of natural dyeing. Although indigenous knowledge systems have been practiced for many years, the usage of natural dyes has declined over generations owing to a lack of documentation and accurate understanding of the extraction and dyeing processes. As a result, natural dyes aren't commercially viable. Currently, all ecologically hazardous synthetic chemical dyes are utilized to colour textile fabrics. They are nonbiodegradable, carcinogenic, and cause water contamination and waste disposal issues. Natural colours provide a viable answer to these issues. Natural dyes are used to colour textiles, meals, medicines, and cosmetics. Dyes are also used in small amounts to colour paper, leather, shoe polish, wood, cane, candles, and other materials. Historically, dyes were obtained only from natural sources. Natural dyes, on the other hand, suffer from the inherent limitations of uniform application and dye standardization, since dyes obtained from comparable plants or natural sources are impacted and exposed to the vagaries of climate, soil, cultivation practices, and so on. As a result, standardization procedures play a critical and essential role for natural dyes to be properly commercialized and compete with synthetic dyes. This study is all about natural dyes, their extraction, characterization, applications, and their uses