Miracle-dz/UniFAR-FTUs huggingface dataset:
Ischemic preconditioning: Potential impact on exercise performance and underlying mechanisms

Originally, in clinical settings, ischemic preconditioning (IPC) has been used to delay cardiac cell injury and protect against myocardial and vascular damage. Furthermore, as this manipulation is relatively easy and noninvasive, previous studies have examined how IPC may have beneficial effects on exercise performance. However, because of various factors, such as different populations, exercise modes and intensities, and IPC protocols, not enough evidence is available to achieve a consensus on the impact of IPC on exercise performance, e.g., time to failure during exercise, time trial performance, and peak power. Existing evidence suggests that IPC seems not to impair exercise performance, though one study found an impair-ment. However, about half of the previous studies showed beneficial effects of IPC on exercise performance. Similarly, the physiological responses from IPC are varied. It is still possible that various factors, such as exercise mode and intensity, heterogeneous population and IPC protocol may affect exercise performance. Previous studies showed that effective blood flow via an increase in nitric oxide and the improvement of metabolic efficiency might be candidate factors that can explain the effect of IPC on exercise performance, although no direct evidence has been obtained. This review aims to identify potential sources of variation in these effects on exercise performance with IPC.
Tolerance to the organic nitrates: new ideas, new mechanisms, continued mystery.

Nitroglycerin (NTG) and other organic nitrates continue to be important drugs that are often used in cardiovascular medicine. All organic nitrates undergo denitrification, releasing nitric oxide (NO) through a biotransformation process that remains poorly understood to date. NO activates the enzyme soluble guanylyl cyclase in smooth muscle cells, causing increased levels of the second messenger cGMP. Finally, cGMP activates a cGMP-dependent protein kinase. This enzyme mediates vasorelaxation through inhibitory phosphorylation of different proteins involved in the regulation of intracellular Ca2+ levels. See p 2338 All of the organic nitrates have potent short-term vascular, hemodynamic, and clinical effects. A critical limitation in their clinical application is the development of tolerance, whereby their initial effects are rapidly lost during sustained administration. Although there seems to be little doubt that NO, or some NO adjunct, mediates the pharmacodynamic effect of nitrates, the mechanism(s) underlying the development of tolerance remain an important and vexing problem for cardiovascular pharmacology. The phenomenon of nitrate tolerance has been recognized for more than a century and has been intensely investigated for 3 decades. Despite these efforts, a unifying hypothesis concerning the mechanism of tolerance continues to be elusive. Initial investigations focused on abnormalities in biotransformation and denitrification of organic nitrates. Almost 30 years ago, Needleman and Johnson1 proposed that tolerance might result from impaired nitrate metabolism caused by the reduced bioavailability of sulfhydryl groups. Although sulfhydryl depletion is no longer thought to play a causal role in tolerance,2 abnormalities in nitrate biotransformation remain the subject of active investigation.3 Importantly, investigations in this area are severely limited by our poor understanding of the biotransformation pathway and by significant analytical problems in quantifying NO and/or NO adjunct production rates. Approximately 10 years ago, research in the field of nitrate tolerance shifted toward the description of systemic, …
Multi-objective Optimization of Technical Stock Market Indicators using GAs

financial researches showed that technical indicators are useful tools for stock prediction. Technical indicators are used to generate trading signals (buy/sell) signals. The main problem of an indicator usage is to determine its appropriate parameters. In this paper a new GA based technique for optimizing the parameters of a collection of technical indicators over two objective functions Sharpe ratio and annual profit is proposed. The technique handles four indicators DEMAC (Double Exponential Moving Average Crossovers), RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and MARSI (Moving Average RSI) indicators. The technique was tested on 30 years of historical data of DJIA (Dow Jones Industrial Average) stock index. Results showed that the optimized parameters obtained by the proposed technique improved the profits obtained by the indicators with their typical parameters, the Buy and Hold strategy and the random strategy.
How to Write a Bachelor Thesis in (APA) Style

Bachelor students at the Faculty of Psychology in Bergen are required to write their thesis according to the guidelines of scientific writing by the American Psychological Association (APA). These guidelines provide help with structuring the thesis, describing research methods, reporting findings, referencing, and using a clear scientific writing style. Despite its relevance, teaching the APA guidelines has been considered somewhat boring by both students and teaching staff – largely due to the technicality of the subject. The course project was to re-design the APA course such that students would become more involved. This was achieved, for instance, by exercises in which students had to discover the APA citation rules themselves, rather than teaching them in a lecture style. Subsequent students’ evaluations showed overall high satisfaction with all measured aspects. Possible improvements are discussed.
On the parametrization of solutions of the Yang--Baxter equations

We study all five-, six-, and one eight-vertex type two-state solutions of the Yang-Baxter equations in the form $A_{12} B_{13} C_{23} = C_{23} B_{13} A_{12}$, and analyze the interplay of the `gauge' and `inversion' symmetries of these solution. Starting with algebraic solutions, whose parameters have no specific interpretation, and then using these symmetries we can construct a parametrization where we can identify global, color and spectral parameters. We show in particular how the distribution of these parameters may be changed by a change of gauge.
Fast computation of query relaxations for knowledge-based recommenders

“No matching product found” is an undesirable message for the user of an online product finder application or interactive recommender system. Query Relaxation is an approach to recovery from such retrieval failures and works by eliminating individual parts of the original query in order to find products that satisfy as many of the user's constraints as possible. In this paper, new techniques for the fast computation of “user-optimal” query relaxations are proposed. We show how the number of costly catalog queries can be minimized with the help of a query pre-processing approach, how we can compute relaxations that contain at least n items in the recommendation, and finally, how a recent conflict-detection algorithm can be applied for fast determination of preferred conflicts in interactive recovery scenarios.
An Experimental Study of the Genetic Algorithm Convergence

Genetic algorithm is a well-known heuristic search algorithm, typically used to generate valuable solutions to optimization and search problems. The most important operation in a genetic algorithm is crossover, as it has the greatest effect on its convergence rate. Therefore, in order to achieve the most optimal results in a reasonable time, one has to decide on the crossover type, as well as make a selection of a crossover point. In order to explore the effect of the crossover point selection methods on the convergence rate, we conducted experiments based on different crossover point selection criteria, whereby the results indicate the high importance of controlling the randomization of the crossover point selection range.
A real-time traffic classification approach

Real-Time traffic classification is a fundamental task for many network management decisions: by timely identifying the applications that generate traffic on a specific network link, network managers can optimize the utilization of their networks; better Quality-of-Service (QoS) can be offered to connected clients while preventing the saturation of many network resources. In addition, the timely identification of malicious traffic, or of traffic presenting anomalous patterns, can be also achieved for assuring the protection of the connected hosts and network resources. However, achieving such ability is not an easy task. The inherent complexity of current Internet applications and services together with the existence of several privacy and legal restrictions prevent the analysis of the contents of the packets, thus preventing an accurate and timely traffic classification. In this paper, we address this issue by analyzing captured Internet traffic over several classification windows, until an accurate identification decision is achieved. The use of metrics such as the packet Inter-Arrival Time and the packet length will allow us to reduce the width of the classification windows, therefore achieving real-time identification. A multi-scale decomposition is then performed for evaluating the existing frequency components and a frequency spectrum profile is obtained. This profile allows an association, using several probabilistic approaches, of the traffic with the corresponding application. The obtained results show that the proposed approach can accurately and timely identify the traffic generated by the most important Internet applications, as well as identify traffic presenting illicit patterns.
Acute influenza virus-associated encephalitis and encephalopathy in adults: a challenging diagnosis

Background: Acute influenza-associated encephalopathy/encephalitis (IAE) in adults is a rare but well-known complication of influenza virus infection. The diagnosis is difficult to make due to the absence of distinctive clinical symptoms and validated diagnostic criteria. We present an illustrative case and a case review on acute IAE in adults. Methods: We performed a Medline search of the English literature using the terms influenz*, encephal* and adult, and constructed a database of detailed descriptions of patients with influenza virus infection with influenza-like symptoms at the onset of neurological symptoms. Results: A total of 44 patients were included. Confusion and seizures were the most prevalent neurological symptoms, present in 12 (27 %) and 10 (23 %) patients, respectively. Magnetic resonance imaging (MRI) was performed in 21 patients and anomalies were found in 13 (62 %), with lesions located throughout the brain. Influenza virus RNA was detected in cerebrospinal fluid (CSF) in 5 (16 %) of 32 patients. Eight (18 %) of the forty-four patients died. The benefits of antiviral and immunomodulatory therapy have not been well studied. Discussion: Our results show that many different neurological symptoms can be present in patients with acute onset IAE. Therefore, the diagnosis should be considered in patients with fever and neurological symptoms, especially during the influenza season. Laboratory diagnosis consists of demonstration of influenza virus RNA in brain tissue, CSF or respiratory samples, and demonstration of intrathecal antibody production against influenza virus. The presence of brain lesions in MRI and influenza virus in CSF appear to be of prognostic value.
High-speed Obstacle Detection and Avoidance Using a Model of the Locust Brain

The animal kingdom abounds with creatures that rely on vision to judge distances to various objects in their field of view. Even simple creatures such as insects, that cannot perform stereopsis, use vision on a routine basis. Designers of mobile robots, however, have tended to eschew cameras in favour of active range sensing devices. In this paper, we present an efficient solution that uses monocular vision to do a sort of range sensing. It works by implementing a computational model of the locust’s Lobula Giant Movement Detector (LGMD), a visual interneuron tuned to pick up objects moving on a direct collision course. The system uses a particle filter to make sufficiently accurate estimates of times-to-impact while processing input images at 30 frames per second.
PdaDriver: A Handheld System for Remote Driving

Abstract PdaDriverisaPersonalDigitalAssistant(PDA)system for vehicle teleoperation. It is designed tobeeasy-to-deploy,tominimizetheneedfortraining,andtoenableeﬀectiveremotedrivingthroughmulti-plecontrolmodes.ThispaperpresentsthemotivationforPdaDriver,itscurrentdesign,andrecentoutdoortestswithamobilerobot. Keywords: vehicleteleoperation,remotedriving,per-sonaldigitalassistant,handhelduserinterface 1 Introduction For some remote driving applications, installingcontrol stations with multiple displays and high-bandwidthcommunicationisinfeasibleorprohibitive.For other applications, the vehicle is driven by op-erators for whom extensive training is impractical.In these situations, we need a driving system thatrequires minimal infrastructure and that does nottightlycoupleperformancetooperatorexperience[1]. Tosatisfythisneed,wehavedeveloped PdaDriver ,aPersonalDigitalAssistant(PDA)systemforremotedriving. PdaDriver uses multiple control modes tomakevehicleteleoperationfastandeﬃcient. Wede-signed PdaDriver to minimize the need for training,toenablerapidcommandgeneration,andtoimprovesituationalawareness. WeﬁrstdevelopedPdaDriverin2000toteleoper-ate a military training robot[1]. This version ran ona Casio Cassiopeia and used a wired, serial link. In2001, we extended PdaDriver to support “collabora-tivecontrol”,asystemmodelinwhichtherobotasksquestionstothehumantoobtainassistancewithcog-nitivetasks[2]. Weusedthissecondversiontostudycollaborativenavigationandexploration[2,3]. During 2002, we modiﬁed PdaDriver to run on aCompaqiPAQPocketPCwithwirelessethernet(Fig-ure 1, left). We did this for several reasons: theiPAQdisplayworkswellindirectsunlight(importantfor ﬁeld use); the ethernet link provides high band-widthandlowlatency;andtheiPAQhasgreaterbat-tery life than the Casio. In addition, we integratedFigure 1:
A Novel Approach to Hardware/Software Partitioning for Reconfigurable Embedded Systems

Hardware/software partition is a crucial point in the design of a reconfigurable embedded system. Reconfigurable computing is a promising approach to overcome the traditional trade-off between flexibility and performance in the design of computer architectures which adapt their hardware to each application to achieve a high performance of dedicated hardware. In this paper, some hardware and software partitioning algorithms were analyzed and summarized first, then a innovative algorithm for task partition and scheduling is proposed based on new features of reconfigurable hardware such as dynamic reconfiguration and the delay of reconfiguration. In the proposed algorithm, a large-scale application is decomposed into multiple sub-tasks of suitable granularity and each sub-task has constraint relationship with each other. And a directed acyclic graph (DAG) which presents the relationship between tasks was drawn according to the execution order of tasks. Then the specific application presented in the DAG is mapped to the hardware and software platform by a strategy called GATS which combine the Genetic Algorithm and the Tabu Search algorithm together. The shortest time of assignment and task execution order can be found by the priority-based scheduling method. The experimental results show that the method is of high performance and can effectively mapping the application task to the reconfigurable system.
An Optimal Color Mapping Strategy Based on Energy Minimization for Time-Varying Data

Color mapping plays a critical role in visualization of time-varying data and also sets a challenge for researchers due to the consistency of mapping and great changes in time-varying data. In order to solve this problem and generate feature-prominent animation, we present a two phase optimization technique using bilateral filtering and global energy functions. In the first phase, for each time step, we use a weighted mapping function which combines linear mapping and feature histogram. In the second phase, an optimization function taking global color mapping and minimum color difference into consideration is designed. So users can clearly distinguish between data in variable time steps and easily understand the corresponding relationships between different structures. The experiments' results show that our method can achieve high quality visualization for both static and time-varying data.
KVLight: A Lightweight Key-Value Store for Distributed Access in Cloud

Key-value stores (KVS) are finding use in Big Data applications as the store offers a flexible data model, scalability in number of distributed nodes, and high availability. In a cloud environment, a distributed KVS is often deployed over the local file system of the nodes in a cluster of virtual machines (VMs). Parallel file system (PFS) offers an alternate approach to disk storage, however a distributed key value store running over a parallel file system can experience overheads due to its unawareness of the PFS. Additionally, distributed KVS requires persistent running services which is not cost effective under the pay-as-you-go model of cloud computing because resources have to be held even under periods of no workload. We propose KVLight, a lightweight KVS that runs over PFS. It is lightweight in the sense that it shifts the responsibility of reliable data storage to the PFS and focuses on performance. Specifically, KVLight is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not currently designed for. Furthermore, it allows on-demand access without running persistent services in front of the file system. Empirical results show that KVLight outperforms Cassandra and Voldemort, two state-of-the-art KVSs, under both synthetic and realistic workloads.
EFFECT OF WORKPLACE INCIVILITY ON COWORKER HELPING: THE MEDIATING ROLE OF HURT FEELINGS

Workplace incivility is an issue of growing interest among scholars. Previous studies have shown that workplace incivility has detrimental effects on various individual outcomes, including helping behavior among employees. However, studies testing the proposition that workplace incivility has an adverse effect on helping behavior are limited. Therefore, underpinned by negative norm of reciprocity, theory of affective events and theory of reasoned action, this study examined (a) the effect of workplace incivility on coworker helping and (b) the mediating role of hurt feelings on the relationship between workplace incivility and coworker helping. Data were collected from a sample of 149 employees working in Malaysia using judgmental and snowball sampling. The initial analysis revealed that the data were significantly skewed. As a result, the Partial Least Squares path modeling (PLS-SEM) was employed to analyze the data. The results showed that workplace incivility significantly predicted coworker helping and hurt feelings. The findings also indicate that hurt feelings partially mediated the relationship between workplace incivility and coworker helping. Practically, the findings of this study revealed the importance of managing workplace incivility and negative emotions in encouraging helping among coworkers in organizations.
A Reappraisal of the Solar Photospheric C/O Ratio

An accurate determination of photospheric solar abundances requires detailed modeling of the solar granulation and accounting for departures from local thermodynamical equilibrium (LTE). We argue that the forbidden C I line at 8727 Å is largely immune to departures from LTE and can be realistically modeled using LTE radiative transfer in a time-dependent three-dimensional simulation of solar surface convection. We analyze the [C I] line in the solar flux spectrum to derive the abundance log ϵ(C) = 8.39 ± 0.04 dex. Combining this result with our parallel analysis of [O I] λ6300, we find C/O = 0.50 ± 0.07, in agreement with the ratios measured in the solar corona from gamma-ray spectroscopy and solar energetic particles.
A semi-naive Bayes model to forecast the probability distribution of excess returns in the U.S. stock market

In this paper, a semi-naive Bayes method to forecast the distribution of future excess returns of stocks in a multi-dimensional variable space is presented. Unlike regression models, the proposed model takes into account both the nonlinearity and the interaction between the variables. The conditional probability distributions of excess returns for the largest 1500 U.S. stocks are numerically estimated using only historical price and volume data. The probability distributions are then used to calculate the expected returns as a function of 20 variables. The model predictions are tested with a market neutral portfolio comprised of 21 long and 21 short stocks with an average turnover of one month. An average annual return of 31.8% with a Sharpe ratio of 2.34 was obtained over a 20-year time period, from 1987 to 2006.