Miracle-dz/UniFAR-FTUs huggingface dataset:
Is admission hyperglycaemia in non-diabetic patients with acute myocardial infarction a surrogate for previously undiagnosed abnormal glucose tolerance?

AIMS To investigate whether admission hyperglycaemia in non-diabetic patients with acute myocardial infarction (AMI) is a surrogate for previously undiagnosed abnormal glucose tolerance. METHODS AND RESULTS Two hundred non-diabetic patients with AMI were divided into three groups: 81 patients with admission glucose < 7.8 mmol/L (group 1), 83 patients with admission glucose > or = 7.8 mmol/L and < 11.1 mmol/L (group 2), and 36 patients with admission glucose > or = 11.1 mmol/L (group 3). Abnormal glucose tolerance, diabetes, or impaired glucose tolerance (IGT) was diagnosed by oral glucose tolerance test (OGTT). OGTT identified diabetes in 53 patients (27%) and IGT in 78 patients (39%). When the fasting glucose criteria were applied, however, only 14 patients (7%) were diagnosed as having diabetes. The prevalence of abnormal glucose tolerance was similar among the three groups: 67% in group 1, 63% in group 2, and 69% in group 3 (P = 0.74). The relation of fasting glucose (r2 = 0.50, P < 0.001) and HbA1c (r2 = 0.34, P < 0.001) to 2-h post-load glucose was significant, but the relation of admission glucose to 2-h post-load glucose was not significant (r2 = 0.02, P = 0.08). Multivariable analysis showed that fasting glucose and HbA1c were independent predictors of abnormal glucose tolerance, but admission glucose was not. CONCLUSION Admission hyperglycaemia in non-diabetic patients with AMI does not represent previously undiagnosed abnormal glucose tolerance. Fasting glucose and HbA1c, rather than admission glucose, may be useful to predict abnormal glucose tolerance. However, these parameters lacked sensitivity. OGTT should be considered in all non-diabetic patients with AMI.
An Enhanced Fear Appeal Rhetorical Framework: Leveraging Threats to the Human Asset Through Sanctioning Rhetoric

Fear appeals, which are used widely in information security campaigns, have become common tools in motivating individual compliance with information security policies and procedures. However, empirical assessments of the effectiveness of fear appeals have yielded mixed results, leading IS security scholars and practitioners to question the validity of the conventional fear appeal framework and the manner in which fear appeal behavioral modeling theories, such as protection motivation theory (PMT), have been applied to the study of information security phenomena. We contend that the conventional fear appeal rhetorical framework is inadequate when used in the context of information security threat warnings and that its primary behavioral modeling theory, PMT, has been misspecified in the extant information security research. Based on these arguments, we propose an enhanced fear appeal rhetorical framework that leverages sanctioning rhetoric as a secondary vector of threats to the human asset, thereby adding the dimension of personal relevance, which is critically absent from previous fear appeal frameworks and PMT-grounded security studies. Following a hypothetical scenario research approach involving the employees of a Finnish city government, we validate the efficacy of the enhanced fear appeal framework and determine that informal sanction rhetoric effectively enhances conventional fear appeals, thus providing a significant positive influence on compliance intentions.
A comparative exploration of FreeBSD bug lifetimes

In this paper, we explore the viability of mining the basic data provided in bug repositories to predict bug lifetimes. We follow the method of Lucas D. Panjer as described in his paper, Predicting Eclipse Bug Lifetimes. However, in place of Eclipse data, the FreeBSD bug repository is used. We compare the predictive accuracy of five different classification algorithms applied to the two data sets. In addition, we propose future work on whether there is a more informative way of classifying bugs than is considered by current bug tracking systems.
Protecting revenue in the distribution industry: a new approach with the revenue assurance and audit process

This paper presents the main aspects regarding nontechnical losses-NTL, including energy theft, frauds, process and IT flaws, data inconsistencies and bad debt and an innovative approach to protect revenue. The proposed new approach is the implementation of a formal RAAP-revenue assurance and audit process-that organizes, integrates, prioritizes and optimizes all initiatives to reduce NTL. This process guarantees that NTL will be detected, analyzed and fixed, and that all started initiatives will be followed up until termination, then feeding back the process to improve other new initiatives, so reassuring the effectiveness. This formal revenue assurance process is supported by software, fully documented and auditable, and integrated with other internal processes, as field inspection, commercial, customer support, auditing, etc. The revenue assurance and audit process (RAAP) described in this paper allows the distribution company to face NTLs with a formally-organized process using automated tools, and to be ready to execute revenue management according to ISO 9000 standards best practices. A case study with field results will be also presented.
Real-time Voice Transmissions over the Internet Table of Contents

This research identi es the problems encountered in transmitting voice over the Internet and proposes approaches to solve these problems. The current Internet is not very suitable for transmitting real-time data because its underlying protocols and switches were only engineered to transmit non-real time data. The problems posed by voice over the Internet are studied by conducting a series of experiments. These problems caused by high loss, large delay, and jitter will seriously a ect the transmission quality. A good design thus needs to combine di erent components that solve these problems together. Silence removal and compression is used to reduce bandwidth usage. Jitter bu ers are used to smooth the burstiness in the received stream caused by the network. To conceal loss, we investigate existing methods and propose two new nonredundant reconstruction algorithms based on a simple but e ective average reconstruction scheme. One is to apply adaptive ltering on top of average reconstruction in order to explore the signal trend and to obtain better estimations. E ects of important lter parameters, such as lter length and adaptation step size, are studied. Another new method, called transformation-based reconstruction method, is developed to handle low reconstruction quality caused by some rapidly changing parts of signals. Its basic idea is to let the sender transform the input voice stream, based on the reconstruction method used at the receiver, before sending the data packets. Consequently, the receiver is able to recover much better from losses of packets than it would without any knowledge of what the signals should be. This method can improve reconstruction quality without signi cant computational cost and can be easily extended to di erent interleaving factors and di erent interpolation-based reconstruction algorithms.
Electronic nose detects heart failure from exhaled breath

Electronic noses (enoses) were a applied for diagnosing various diseases when analyzing gases from skin surface. The objective of this study was to prove if enoses are suitable for an early detection and monitoring of heart failure (HF) in a patient's breath. The exhaled breath was collected from 13 patients with decompensated HF (DHF), 16 patients of compensated HF (CHF) and 13 controls without HF (CON). Principal component analysis in combination with discriminant function analysis (DA) was used to discriminate between the patient groups. The breath samples were separated between DHF and CHF with an accuracy of 91% and between CON and CHF with 97%. The results suggest that an easy to use, inexpensive and non-invasive working enose could be applied for diagnosing and monitoring of HF patients based on the analysis of exhaled breath.
Interaction between growth arrest-DNA damage protein 34 and Src kinase Lyn negatively regulates genotoxic apoptosis

Genotoxic stresses activate intracellular signaling molecules, which lead to growth arrest, DNA repair, and/or apoptosis. Among these molecules are the growth arrest and DNA damage protein 34 (GADD34) and the Src-related protein tyrosine kinase Lyn. Here, we report that these two proteins physically and functionally interact to regulate DNA damage-induced apoptosis. Multiple isolates of GADD34 and the related murine protein MyD116 were identified as binding partners of Lyn in a yeast two-hybrid screen. The specific interaction was confirmed by in vitro association of GADD34 with glutathione S-transferase fusion proteins containing the Src Homology 3 (SH3) domain of Lyn, as well as coimmunoprecipitation of GADD34 and Lyn from mammalian cells. GADD34 was tyrosine-phosphorylated in vivo in a Lyn-dependent manner. Lyn efficiently phosphorylated affinity-purified GADD34 in vitro. Lyn negatively regulated the proapoptotic function of GADD34 in a kinase-dependent manner. Expression of wild-type, but not kinase-inactive, Lyn weakened promotion of apoptosis by GADD34 following treatment with methyl-methanesulfonate or ionizing radiation in HEK293 and HeLa cells. In contrast, pretreatment of cells with the Src-specific tyrosine kinase inhibitor PP1 strengthened promotion of apoptosis by GADD34. We propose that Lyn regulates the proapoptotic function of GADD34 by binding and phosphorylating it.
Blind Deconvolution Problems : A Literature Review and Practitioner ’ s Guide

Blind deconvolution is the process of deconvolving a known acquired signal with an unknown point spread function (PSF) or channel encoding, by jointly estimating the system’s input as well as the system’s PSF. Models for this problem often present as the convolution or product of the underlying signal and the system’s PSF. The acquired signal is bi-linear in these two unknown signals (i.e. linear in one if the other is constant), which results in an inverse problem that is non-convex. In addition, the number of observations to uniquely reconstruct the signal and the system’s PSF is often unattainable as the number of degrees of freedom grows as the product of the size of the signal with the size of the PSF, thus the problem is often underdetermined. While the task seems daunting, there has been a plethora of work over the past twenty years with the aim of performing this task with exact recovery, similar to that of sparse vector recovery in compressed sensing and of low rank matrix recovery in matrix completion. There are a variety of works in the area of exact recovery to pose convex and non-convex problem alike as well as theory regarding under what conditions this is possible. We shall see that advances in these fields compliment the blind deconvolution problem as it can be posed as a linear inverse problem with rank constraint and relaxed to a convex program. In this literature review the point of view as a practitioner in the areas of signal processing and computational imaging (or computational sensing) is taken. This review is laid out as follows: section 2 goes over into several applications and importance, section 3 reviews the technique of lifting as well as several convex and non-convex problem formulations, section 4 explains some of the theoretical guarantees that accompany convex and non-convex problem formulations, and finally section 5 discusses a broader picture regarding computation and memory for large scale problems.
Statistical Multiresolution Dantzig Estimation in Imaging: Fundamental Concepts and Algorithmic Framework

In this paper we are concerned with fully automatic and locally adaptive estimation of functions in a "signal + noise"-model where the regression function may additionally be blurred by a linear operator, e.g. by a convolution. To this end, we introduce a general class of statistical multiresolution estimators and develop an algorithmic framework for computing those. By this we mean estimators that are defined as solutions of convex optimization problems with supremum-type constraints. We employ a combination of the alternating direction method of multipliers with Dykstra's algorithm for computing orthogonal projections onto intersections of convex sets and prove numerical convergence. The capability of the proposed method is illustrated by various examples from imaging and signal detection.
ATGen: automatic test data generation using constraint logic programming and symbolic execution †

The verification and validation of software through dynamic testing is an area of software engineering where progress towards automation has been slow. In particular the automatic design and generation of test data remains, by and large, a manual activity. This is despite the high promises that the symbolic execution technique engendered when it was first proposed as a method for automatic test data generation. This paper presents an automatic test data generator based on constraint logic programming and symbolic execution. After reviewing the symbolic execution technique, approaches for the resolution of the technical difficulties that have so far prevented symbolic execution from reaching its full potential in the area of automatic test data generation are presented. ATGen, an automatic test data generator, based on symbolic execution and that uses constraint logic programming, is then discussed. Copyright © 2001 John Wiley & Sons, Ltd.
TweetMogaz: a news portal of tweets

Twitter is currently one of the largest social hubs for users to spread and discuss news. For most of the top news stories happening, there are corresponding discussions on social media. In this demonstration TweetMogaz is presented, which is a platform for microblog search and filtering. It creates a real-time comprehensive report about what people discuss and share around news happening in certain regions. TweetMogaz reports the most popular tweets, jokes, videos, images, and news articles that people share about top news stories. Moreover, it allows users to search for specific topics. A scalable automatic technique for microblog filtering is used to obtain relevant tweets to a certain news category in a region. TweetMogaz.com demonstrates the effectiveness of our filtering technique for reporting public response toward news in different Arabic regions including Egypt and Syria in real-time.
Arabic handwritten texts clusterization based on Feature Relation Graph (FRG)

Text clustering is known as the problem of grouping texts so that all texts within a group share a similar measure (similar author, similar genre, etc.) This task became very important for the last two decades because of increased number of text documents in digital form which needs to be organized and processed. We investigate a new metric based on the Feature Relation Graph (FRG) for Arabic handwritten texts clustering. This metric has proved to be effective for the text independent Persian writer identification. We have used it to solve more general problem of texts clustering. Pattern based features are extracted from handwritten texts using Gabor and XGabor filters. The extracted features are represented for each cluster by using the FRG. We apply several clustering algorithms in a space of FRGs. Numerical experiments to demonstrate effectiveness of proposed metric and compare effectiveness of different algorithms are provided.
A conflict based SAW method for Constraint Satisfaction Problems

Evolutionary algorithms have employed the SAW (Stepwise Adaptation ofWeights) method in order to solve CSPs (Constraint Satisfaction Problems). This method originated in hill-climbing algorithms used to solve instances of 3-SAT by adapting a weight for each clause. Originally, adaptation of weights for solving CSPs was done by assigning a weight for each variable or each constraint. Here we investigate a SAW method which assigns a weight for each conflict. Two simple stochastic CSP solvers are presented. For both we show that constraint based SAW and conflict based SAW perform equally on easy CSP samples, but the conflict based SAW outperforms the constraint based SAW when applied to hard CSPs. Moreover, the best of the two suggested algorithms in its conflict based SAWversion performs better than the best known evolutionary algorithm for CSPs that uses weight adaptation, and even better than the best known evolutionary algorithm for CSPs in general.
Intraoperative localization of recurrent medullary carcinoma of the thyroid using Tc-99m HYNIC-TATE and a surgical gamma probe.

Preoperative octreotide scintigraphy imaging has opened up a new era to approach recurrent medullary thyroid carcinoma (MTC). We describe the case of a 53-year-old woman who was diagnosed with recurrent MTC and elevated serum calcitonin level 2000 pg/mL. Preoperatively, Tc-99m HYNIC-TATE scintigraphy was used in identifying 2 focal abnormalities in the right upper cervical and left upper mediastinal regions, and a hand-held gamma probe was used intraoperatively to identify the hot areas. Six months after intraoperative dissection, serum calcitonin level and Tc-99m HYNICTATE scintigraphy were found normal. This case demonstrates the proper localization for recurrent MTC using Tc-99m HYNIC-TATE and a surgical gamma probe.
An Evaluation of Extended vs. Weighted Least Squares for Parameter Estimation in Physiological Modeling

Weighted least squares (WLS) is the technique of choice for parameter estimation from noisy data in physiological modeling. WLS can be derived from maximum likelihood theory, provided that the measurement error variance is known and independent of the model parameters and the weights are calculated as the inverse of the measurement error variance. However, using measured values in lieu of predicted values to quantify the measurement error variance is approximately valid only when the noise in the data is relatively low. This practice may thus introduce sampling variation in the resulting estimates, as weights can be seriously mis-specified. To avoid this, extended least squares (ELS) has been used, especially in pharmacokinetics. ELS uses an augmented objective function where the measurement error variance depends explicitly on the model parameters. Although it is more complex, ELS accounts for the Gaussian maximum likelihood statistical model of the data better than WLS, yet its usage is not as widespread. The use of ELS in high data noise situations will result in more accurate parameter estimates than WLS (when the underlying model is correct). To support this claim, we have undertaken a simulation study using four different models with varying amounts of noise in the data and further assuming that the measurement error standard deviation is proportional to the model prediction. We also motivate this in terms of maximum likelihood and comment on the practical consequences of using WLS and ELS as well as give practical guidelines for choosing one method over the other.
Performance analysis of a multi-hop IEEE 802.15.4g OFDM system in multi-PHY layer network

This paper presents the performance analysis of an IEEE 802.15. 4g multi-hop orthogonal frequency division multiplexing (OFDM) system in a network with multiple physical (PHY) layer design. Identified usage models for the IEEE 802.15. 4g standard are low energy wireless sensor network and smart grid/utility network applications. For that, a total of three PHY layer designs under a unified medium access control (MAC) are specified to address different market segments. The multi-PHY layer architecture has an advantage of covering a wide range of pervasive use case scenarios, at the expense of inter-PHY interference (IPI) among respective PHY layers. This paper focuses on the OFDM PHY that promises high data rate and superior propagation performance. To enable optimized operation in the targeted applications, a multi-hop MAC layer design is specified to extend the operating range of the network, while low energy features are designed to increase energy efficiency of the system. Additionally, an IPI mitigation technique is also developed to combat IPI generated from the interfering network. As a result, it is found that the average throughput of a multi-hop OFDM network is 8% lower than that of a single-hop OFDM network, but with larger achievable operating range. Secondly, with the proposed IPI mitigation technique, no degradation among devices with different PHY layer designs is observed to take place. Thirdly, with the energy-efficient non-beacon-enabled mode, devices are consuming less power, at the expense of immunity reduction against interferer.
Pain and the Neuromatrix in the Brain

The neuromatrix theory of pain proposes that pain is a multidimensional experience produced by characteristic “neurosignature” patterns of nerve impulses generated by a widely distributed neural network—the “body-self neuromatrix”—in the brain. These neurosignature patterns may be triggered by sensory inputs, but they may also be generated independently of them. Acute pains evoked by brief noxious inputs have been meticulously investigated by neuroscientists, and their sensory transmission mechanisms are generally well understood. In contrast, chronic pain syndromes, which are often characterized by severe pain associated with little or no discernible injury or pathology, remain a mystery. Furthermore, chronic psychological or physical stress is often associated with chronic pain, but the relationship is poorly understood. The neuromatrix theory of pain provides a new conceptual framework to examine these problems. It proposes that the output patterns of the body-self neuromatrix activate perceptual, homeostatic, and behavioral programs after injury, pathology, or chronic stress. Pain, then, is produced by the output of a widely distributed neural network in the brain rather than directly by sensory input evoked by injury, inflammation, or other pathology. The neuromatrix, which is genetically determined and modified by sensory experience, is the primary mechanism that generates the neural pattern that produces pain. Its output pattern is determined by multiple influences, of which the somatic sensory input is only a part, that converge on the neuromatrix. Dr. Melzack is Professor Emeritus, Department of Psychology, McGill University. Direct correspondence and requests for reprints to him at Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, Quebec, Canada H3A 1B1; 514-398-6084 phone; 514-398-4896 fax; rmelzack@ego.psych.mcgill.ca.
A novel adaptive intrusion detection system based on data mining

A data mining based adaptive intrusion detection model (DMAIDM) is presented in this paper. The DMAIDM applies a fast heuristic clustering algorithm for mixed data (FHCAM) to distinguish intrusions from legal behaviors efficiently and an attribute-constrained based fuzzy mining algorithm (ACFMA) to construct intrusion pattern-database automatically. Verification tests are carried out by using the 10% subset of KDD Cup 1999 data set, the average detection rate is 71.67% and the average false detection rate is 0.92%. And the detection rate increases from 65.25% (the second subset) to 85.7% (the ninth subset) adaptively. The experimental results reveal that the DMAIDM is successful in terms of not only accuracy but also efficiency in networks intrusion detection.