Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbohydrate supplementation attenuates IMP accumulation in human muscle during prolonged exercise.

The effect of carbohydrate (CHO) ingestion on metabolic responses to exercise has been investigated. Subjects cycled at approximately 70% of maximal oxygen uptake to fatigue [135 +/- 17 (+/- SE) min] on the first occasion (control, CON) and at the same work load and duration on the second occasion but with addition of ingestion of CHO during the exercise. Biopsies were taken from the quadriceps femoris muscle before and after exercise. The sum of the hexose monophosphates (HMP), as well as lactate and alanine, in muscle was higher after CHO exercise (P less than or equal to 0.05, P less than or equal to 0.05, and P less than or equal to 0.01, respectively). Acetylcarnitine increased during exercise but was not significantly different between treatments after exercise (CON, 6.6 +/- 1.7; CHO, 10.0 +/- 1.2 mmol/kg dry wt; P = NS). The sum of the tricarboxylic acid cycle intermediates (TCAI; citrate + malate + fumarate) was increased during exercise and was higher after CHO exercise (2.34 +/- 0.32 vs. 1.68 +/- 0.17 mmol/kg dry wt; P less than or equal to 0.05). IMP was less than 0.1 mmol/kg dry wt at rest and increased to 0.77 +/- 0.26 (CON) and 0.29 +/- 0.11 mmol/kg dry wt (CHO) (P less than or equal to 0.05) during exercise. It was recently found that during prolonged exercise there is initially a rapid and large expansion of TCAI and glycogenolytic intermediates in human muscle followed by a continuous decline in TCAI and glycogenolytic intermediates [K. Sahlin, A. Katz, and S. Broberg. Am. J. Physiol. 259 (Cell Physiol. 28): C834-C841, 1990]. (ABSTRACT TRUNCATED AT 250 WORDS)
Forecasting of retail sales data using differential evolution

The paper aims to develop an efficient forecasting model using differential evolution (DE) based learning rule. The structure chosen is an adaptive linear combiner whose weights are trained using DE. The prediction performance of the resulting model is evaluated by feeding features of retail sales data for different months' ahead prediction. These results are compared with those obtained by GA based approach. The comparison demonstrates improved prediction of sales data by the proposed DE method.
Analysis of the wind speed distribution over real terrain topography using one-and three-dimensional models

Wind energy is one of the most developing branch of the power industry. Consequently, great e orts are now focused on increasing its e ciency and reliability. The leading challenge of the wind energy development is the question of the energy production predictability and the wind resource assessment [1], [3], [4]. Moreover wind turbines could have serious impact on the electric grid and on the quality of delivered power. This motivates us to investigate how di erent atmospheric conditions could in uence the mean wind velocity and its variation in the neighbourhood of wind turbines.
Towards risk based prevention of grooming attacks

The increasing incidents of children sexual exploitation through cyberspace demand for proper protection with technological defense mechanisms. This paper aims to present and evaluate methods and tools that are appropriate towards the prevention of child sexual abuse through Internet based communications. Attacking categories and strategies that predators follow are analyzed and modeled. Moreover, a comparative review of existing risk modeling methods, which is based on a set of proposed criteria, is presented. This comparison results in the conclusion that only two of the reviewed risk modeling methods can be adapted on the intended grooming attack detection system: Bayesian and Markovian. The proposed approach is concluded with a discussion on particular methods and tools for accurate attack probability calculation.
Modification of advanced boundary discriminative noise detection algorithm

Aim at random-valued impulse noise detection in two boundaries, advanced boundary discriminative noise detection(ABDND) used global histogram to obtain noise boundary and got good detection results. However, the rate of false detection increases a lot for ABDND when the range of noise boundary is broadened. Modification of ABDND (MABDND) is proposed based on ABDND in this paper which includes two stages. Firstly, it uses the global histogram to obtain the noise boundary as the same as ABDND. Secondly, the statistic of part histogram is employed to find out pixels of false detection in the first stage, and marks them as uncorrupt pixels. The merit of MABDND is to use the verified technique in the second stage to rectify many pixels of false detection in the first stage so keep a low rate both for miss detection and false detection. Image Lena and Peppers are used for simulations, and the experimental results show the performance of MABDND is better than that of ABDND, especially, when the range of random-valued is wide.
Issues in architectural modeling and evolution in the know-it-all case study

The Know-It-All Project is investigating methodologies for the development, application, and evolution of frameworks. A concrete framework for database management systems is being developed as a case study for the methodology research. The methodology revolves around a set of models for the domain, the functionality, the architecture, the design, and the code. These models reflect the common and variable features of the domain. There are several issues with respect to architecture that we have encountered and are exploring. These include: (1) the choice of models for the architecture; (2) the design of the architecture and its evaluation; (3) the evolution of the architecture by extending the concept of refactoring from source code to architecture; and (4) the modeling of variation in architectures across the product line. The point of the paper is to report on the status of architectural modeling in our case study and to highlight issues still to be resolved.
Annex: Radon - Rapid Discovery of Topological Relations

Datasets containing geo-spatial resources are increasingly being represented according to the Linked Data principles. Several time-efficient approaches for discovering links between RDF resources have been developed over the last years. However, the time-efficient discovery of topological relations between geospatial resources has been paid little attention to. We address this research gap by presenting Radon, a novel approach for the rapid computation of topological relations between geo-spatial resources. Our approach uses a sparse tiling index in combination with minimum bounding boxes to reduce the computation time of topological relations. Our evaluation of Radon's runtime on 45 datasets and in more than 800 experiments shows that it outperforms the state of the art by up to 3 orders of magnitude while maintaining an F-measure of 100%. Moreover, our experiments suggest that Radon scales up well when implemented in parallel.
Re-engineering simultaneous internet sessions process-separated browsers

Analysis of contemporary web browser sessions for forensic purposes has one major challenge - that of distinguishing Internet events and sessions across multiple tabs. While some information is contained inside the log files, identifying the coherency and concurrency is necessary to generate "specificity of attribution". In this work, we focus on isolating multiple simultaneous browser sessions using network and browser-related metadata on browsers that deploy a separate process for each browser session, which we term process-separated browser implementation. In a previous work, it was shown that network artifacts can be associated with browser artifacts to relate network stream with browser sessions on multi-threaded browser implementations. However, in process-separated browser implementations, where each session has its own network stream, the ability to track all network streams via a single process is not available. Therefore, there is a need to associate each network stream with its corresponding process for reconstruction. In this paper, we propose an algorithm to reconstruct multiple simultaneous browser sessions on browser applications that use a separate process for each browser session. We achieve this by developing a representation for the information associated with a browser session. Further, we define two relationships, viz., 'stream coherency' and 'session concurrency' based on the associations discovered among the network and browser artifacts. Finally, we develop an algorithm called "Samhita" to identify number of simultaneous browser sessions that are deployed and associate them with the respective processes and network streams. We take the reader through specially designed experiments to elicit browser-session intelligence and the process to separate out the tabbed sessions using the timing information present in the browser context and session context.
Fast ray tracing for microcellular and indoor environments

We present a very efficient algorithm which calculates deterministically the path losses, delay profiles and other characteristics of a mobile channel. The algorithm can be used both for indoor and outdoor environments. We apply the technique of tracing rays and compute reflections, transmissions and diffractions with the help of geometrical optics, heuristical approximations and the uniform theory of diffraction (UTD).
Combining QTL and microarray data : the current state of play

In general, microarray experiments do not take into account other sources of information. For instance, molecular marker or phenotype data may also be recorded. Several articles have suggested that more insight might be gained by combining such data with microarray data. This article provides a review of current trends in combining such data as well as results of recent studies. Schadt et al (2003) coined the term ’eQTL’ or expression QTL for studies where gene expression is considered to be a trait and QTL analysis is conducted. To efficiently combine such data it would seem either that simple but fast tests like single marker intersection tests or some form of dimension reduction, such as PCA or PLS, is required. In the latter approach, by reducing the space of gene expression levels, more realistic QTL models than are currently employed for eQTL studies may be adopted. If the latter approach is adopted, simulation studies indicate that the benefit of combining molecular and gene expression data increases as the number of cDNA levels affecting the trait and the size of the QTL effect decreases and when genes are co–expressed. Also, it is clear from several eQTL studies that considering a single gene at a time may be inappropriate due to epistatic interactions.
Transcription factors in Candida albicans - environmental control of morphogenesis.

Awareness of Candida albicans as a major human health threat has risen during recent years. Although infections by C. albicans can be relatively mild and superficial, systemic mycoses often occur in immunocompromised patients, or even as a consequence of long-term therapy with broad-spectrum antibiotics or of chemotherapy (reviewed by Odds, 1988). Effective antifungal agents which are free of side-effects are urgently needed. There is hope that recently developed techniques of manipulating C. albicans and the sequencing of its genome will lead to a thorough understanding of the virulence and biology of this fungal pathogen, thus offering the possibility of a knowledge-based approach to novel antifungal agents.
Real-Time Self-Organizing Autonomous Clustering for Mobile Ad Hoc Networks

In order to realize hierarchical routings that should be executed in large-scaled mobile ad hoc networks that vary their topologies considerably with time, we have developed autonomous clustering with high scalability and adaptability. In autonomous clustering, an entire network is divided into disjoint subnetworks (called clusters), and they are maintained by periodically sending messages within the clusters. This paper proposes real-time self-organizing autonomous clustering, in which interval and power for transmitting such messages are dynamically changed depending on variation of mobility and density of the clusters. Since their mobility and density can be estimated by local information obtained in the clusters and according to their respective change the clusters are self-organized in real-time way, the proposed real-time self-organizing autonomous clustering can contribute to enhancement of its scalability and adaptability.
HIV-1 Associated Dementia: Clinical Features and Pathogenesis

Abstract HIV-1 infection is characterized by multiple neurological syndromes occuring at all stages of infection. HIV-1-associated dementia, however, is the most devastating CNS consequence of AIDS because of its poor prognosis and functional impairment. A clinical triad of progressive cognitive decline, motor dysfunction, and behavioural abnormalities typifies this subcortical dementia which eventually affects 15 to 20% of AIDS patients. Neuroimaging, CSF studies and neuropsychological testing are frequently required in diagnosing HIV-associated dementia, to exclude other conditions including psychiatric illnesses, opportunistic diseases and systemic disorders. The pathogenesis of HIV dementia is uncertain and there is evidence that multiple mechanisms of neurological injury occur. These mechanisms include: the role of neurovirulent strains of HIV; the potential neurotoxicity of HIV gp120, nitric oxide and quinolinic acid; immunologically mediated CNS injury through the action of cytokines and arachidonic acid metabolites; and altered blood-brain barrier permeability. A collective approach involving clinical studies, in vitro assays and animal models will provide greater insight into the pathogenesis and the rational development of therapy for HIV dementia.
Revisiting a Formal Framework for Modeling Aspects in the Design Phase

Software architecture description languages (ADLs) and formal methods have been successfully used together during the design phase allowing the semantic validation of systems early in the development process. Recently, a lot of aspect-oriented ADLs (AO ADLs) have been proposed however, most of them, neglects the modeling of aspect-oriented (AO) concepts using formal specification. In this paper we present AOZCL, an aspect-oriented extension to a formal framework (ZCL) with a built-in ADL. AOZCL supports the formal modeling of aspect-oriented architectures. Besides the linguistic issues, we want to carry out more rigorous analysis of the architecture and their crosscutting concerns. To analyze the components' behavior, ZCL uses state machines. We also present the states machines of components considering aspects to realize the behavioral implications of crosscutting concerns. We use a case study to illustrate the use of AOZCL as an ADL.
Reducing the Test Effort of Variability-Rich Systems by Using Feature Interaction Knowledge and Variability-Aware Source Code Analysis

To keep up with the growing demand for customized software solutions that are tailored to specific customer requirements, techniques like Software Product Line Engineering (SPLE) or the more ad-hoc clone-and-own (where engineers do not build each product from anew, but instead maximize the reuse of the available assets in building product families) have been devised. However testing such highly variable software systems has proven challenging. To improve this, the goal of this doctoral research plan is to propose an automated approach to reduce the effort for testing, while improving its effectiveness. The proposed approach aims to detect code parts that have to be considered when devising a test suite by employing source code analysis and test execution monitoring, which we argue have not been used to their full potential in this context.
The experience of self in the world: The personal and social contexts of identity change after brain injury

The consequences of brain injury are well documented and include a range of dif®culties across emotional, cognitive, physical, and social domains. In the social domain, loss of opportunity, support, or capacity to manage the demands of situations in the workplace, community, with family and friends is common. Research is beginning to focus on changes in both social and personal identity following brain injury, and theoretical frameworks are being drawn upon that allow consideration of the relationship between biological, psychological, and social factors. Social identity is de®ned by social roles, and group memberships and also provides an important means through which we form and maintain our sense of self (see Jetten, Haslam, & Haslam, this volume).
Serotonin Receptor Activation Inhibits Sodium Current and Dendritic Excitability in Prefrontal Cortex via a Protein Kinase C-Dependent Mechanism

The serotonin (5-HT) innervation of the prefrontal cortex (PFC) exerts a powerful modulatory influence on neuronal activity in this cortical region, although the mechanisms through which 5-HT modulates cellular activity are unclear. Voltage-dependent Na+channels are one potential target of 5-HT receptor signaling that have wide-ranging effects on activity. Molecular and electrophysiological studies were used to test this potential linkage. Single cell RT-PCR profiling revealed that the vast majority of pyramidal neurons expressed detectable levels of 5-HT2a and/or 5-HT2c receptor mRNA with half of the cells expressing both mRNAs. Whole-cell voltage-clamp recordings of dissociated pyramidal neurons showed that 5-HT2a/c receptor activation reduced rapidly inactivating Na+ currents by reducing maximal current amplitude and shifting fast inactivation voltage dependence. These effects were mediated by Gq activation of phospholipase C, leading to activation of protein kinase C (PKC). 5-HT2a/c receptor stimulation also reduced the amplitude of persistent Na+ current without altering its activation voltage dependence. This modulation was also mediated by PKC. Although 5-HT2a,c receptor activation did not affect somatic action potentials of layer V pyramidal neurons in PFC slices, it did reduce the amplitude of action potentials backpropagating into the apical dendrite. These findings show that 5-HT2a,creceptor activation reduces dendritic excitability and may negatively modulate activity-dependent dendritic synaptic plasticity.
Classification of Parkinson's disease based on Multilayer Perceptrons Neural Network

Parkinson's disease (PD) is the second commonest late life neurodegenerative disease after Alzheimer's disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep structures of the brain called the basal ganglia and substantia nigra. Therefore, accurate prediction of PD need to be done in order to assist medical or bio-informatics practitioners for initial diagnose of PD based on variety of test results. This paper described the analysis conducted based on two training algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) of Multilayer Perceptrons (MLPs) Neural Network in diagnosing PD. The dataset information of this project has been taken form the Parkinson Disease Data Set. Results attained confirmed that the LM performed well with accuracy rate of 92.95% while SCG obtained 78.21% accuracy.
Situation Detection Based on Knowledge Fusion Patterns

With the increasing use of sensors and actuators in technical systems and knowledge-intensive services the need for processing the information captured by these sensors and “making sense” out of it increases. Knowledge fusion is supposed to contribute to this field since it aims at integrating knowledge from different sources. Development of knowledge fusion solutions is a complex task which can be compared to systems and software development. As in other development areas there is a need for efficient development processes which can be supported by reusing solution parts, such as patterns or components. The paper brings together experiences from knowledge fusion subsystem development and from design of knowledge fusion patterns. The main contributions of this paper are (1) a real-world application scenario presenting typical requirements to knowledge fusion systems, (2) application of knowledge fusion patterns from context-based decision support to situation recognition, (3) recommendations from this application case.
New public management and the rise of public sector performance audit

Purpose In the context of global new public management reform trends and the associated phenomenon of performance auditing (PA), the purpose of this paper is to explore the rise of performance audit in Australia and examines its focus across audit jurisdictions and the role key stakeholders play in driving its practice. Design/methodology/approach The study adopts a multi-jurisdictional analysis of PA in Australia to explore its scale and focus, drawing on the theoretical tools of Goffman. Documentary analysis and interview methods are employed. Findings Performance audit growth has continued but not always consistently over time and across audit jurisdictions. Despite auditor discourse concerning backstage performance audit intentions being strongly focussed on evaluating programme outcomes, published front stage reports retain a strong control focus. While this appears to reflect Auditors-General (AGs) reluctance to critique government policy, nonetheless there are signs of direct and indirectly recursive relationships emerging between AGs and parliamentarians, the media and the public. Research limitations/implications PA merits renewed researcher attention as it is now an established process but with ongoing variability in focus and stakeholder influence. Social implications As an audit technology now well-embedded in the public sector accountability setting, it offers potential insights into matters of local, state and national importance for parliament and the public, but exhibits variable underlying drivers, agendas and styles of presentation that have the capacity to enhance or detract from the public interest. Originality/value Performance audit emerges as a complex practice deployed as a mask by auditors in managing their relationship with key stakeholders.
Location of fault on UPFC based transmission lines using sparse S-Transform

This paper presents a new fault location algorithm for a transmission line operating with a Unified Power Flow Controller (UPFC). The proposed algorithm is based on a new matrix version of the conventional S-Transform, Sparse S-Transform (SST), using selected frequency components to increase the speed of the computation significantly. A variety of faults are initiated before and after the location of UPFC on the transmission line using MATLAB/SIMULINK platform. The location of fault is estimated using SST with significant accuracies. The computational speed of the SST is almost 30 times faster than the conventional discrete S-Transform which makes it a suitable candidate for real-time application in digital relaying.