Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Green Exercise on Blood Pressure, Heart Rate and Mood State in Primary School Children

The aim of this study was exploratory and sought to examine the effect on blood pressure (BP), heart rate (HR) and mood state responses in primary school children of moderate intensity cycling whilst viewing a green environment compared to exercise alone. Following ethics approval and parental informed consent, 14 children (seven boys, seven girls, Mean age ± SD = 10 ± 1 years) undertook two, 15 min bouts of cycling at a moderate exercise intensity in a counterbalanced order. In one bout they cycled whilst viewing a film of cycling in a forest setting. In the other condition participants cycled with no visual stimulus. Pre-, immediately post-exercise and 15 min post-exercise, BP, HR and Mood state were assessed. Analysis of variance, indicated significant condition X time interaction for SBP (p = 0.04). Bonferroni post-hoc pairwise comparisons indicated that systolic blood pressure (SBP) 15 min post exercise was significantly lower following green exercise compared to the control condition (p = 0.01). There were no significant differences in diastolic blood pressure (DBP) (all p > 0.05). HR immediately post exercise was significantly higher than HR pre exercise irrespective of green exercise or control condition (p = 0.001). Mood scores for fatigue were significantly higher and scores for vigor lower 15 min post exercise irrespective of green exercise or control condition (both p = 0.0001). Gender was not significant in any analyses (p > 0.05). Thus, the present study identifies an augmented post exercise hypotensive effect for children following green exercise compared to exercise alone.
LWiSSy: A domain specific language to model wireless sensor and actuators network systems

Wireless sensor and actuator network systems (WSAN) are built on a broad range of sensor platforms and low level programming languages. Such systems are potentially useful for a myriad of applications, from different domains. Thus, developers need to deal with details of different platforms and programming abstractions of sensor operational systems, and they also need to have specific knowledge on the distinct domains. In this context, we propose LWiSSy, a domain specific language (DSL) to model WSAN systems whose main goals are to allow (i) domain experts to directly contribute in the development of WSANs without having knowledge on low level sensor platforms, and (ii) network experts to program sensor nodes to meet application needs without having specific knowledge on the application domain. We describe how to develop WSAN systems using LWiSSy and analyze the impact of its usage through an experiment.
Classiication of Sleep Apnea Events by Means of Radial Basis Function Networks

Abst ract-Sleep apneas, cessations of breathing during sleep, which lead to shortened life expectation (adults) and indicate SIDS-risks (premature born babies) must be diagnosed reliably. Therefore a monitoring system is established and attempts are made for automatic scoring to reduce clinical eeorts. This paper describes the detection and classiication of sleep apnea events by means of radial basis function networks (RBFN). A monitoring device, based on electric eld plethysmography, which detects respiratory and cardiac activity is presented. In addition, heart rate and blood oxygen saturation are recorded. Pre-processing is performed by several feature extraction algorithms including time dependent Fourier transform, cepstrum transform , linear predictive coding, linear predictive coding cepstrum (LPCC) and wavelet transform. RBFNs are trained with learning vector quan-tization (LVQ) and self organizing map (SOM). To achieve high classiication rates, optimization of number and spread of neurons are performed. Best results with classiication rates of 64% 3:4% (adults) and 62:6% 6:8% (babies) is obtained using LPCC and data reduction through principle components analysis (PCA). The net consisted of 20 hidden neurons and is trained with LVQ. All results are derived using 10-fold cross validation.
The Repurchase Agreement Refined: Gcf Repo

One of the largest and most important of the money markets is the market for repurchase agreements. In a repurchase agreement, a borrower of money effectively agrees to provide securities as collateral to the lender to mitigate credit risk. GCF Repo is a recent innovation in this market that reduces transaction costs, enhances liquidity, and facilitates the efficient use of collateral.
COMPETITIVE INTERFIRM DYNAMICS WITHIN AN INDUSTRIAL MARKET SYSTEM

Scholars concerned with economic processes in industrial economies have long been concerned with the dynamics of firms' adaptation to new circumstances. This article develops a conceptual framework within which industrial dynamics and competitive interfirm interactions can be analyzed in terms of adaptive responses to changes in the complex system of which the firms form part-rather than in terms of anomalies and exceptions, such as the ''market failures'' and ''externalities''favored in mainstream microeconomics. In order to do so, a conceptual framework is introduced, dubbed an Industrial Market System (IMS), in which firms play a central role as actors, and where their resources, routines and relations with each other generate complex structures such as networks, consortia, and development blocks which mediate and shape the strategic responses made by firms. dynamism of the ''new economy'' and the startup firms it generates. There have also been parallel developments such as the rising importance of corporate spinoffs (as counter-trend to mergers and acquisitions) whereby firms seek greater focus and entrepreneurial initiative through divesting parts of their operations as viable businesses.
UNITA: A Reference Model of University IT Architecture

Information technology is one of the most critical tools in higher education, and higher education becomes increasingly dependent on its IT capabilities. With the rapid development of information technologies, university's IT architecture have been changing accordingly. This article presents an improved reference model of university IT architecture named UNITA. UNITA is a general, comprehensive and extensible model, it consists of six layers, including hardware infrastructure, data and information resource, software system, user service, cyber security and IT governance. Compared with the existing model, the improvements of UNITA are: (a) both "service" and "IT governance" are listed separately as a layer in the model, (b) UNITA model reflects the "cloud-pipe-device" structure of the hardware, (c) UNITA model emphasizes the importance of data in higher education institute, (d) UNITA model can be applied to other enterprise industries and can be easily extended to adopt the emerging new technologies.
Relationship of Teacher Training and School Characteristics to Middle School State Assessment Results

Purpose:To examine the relationship between the South Carolina middle school physical education assessment results and the school characteristics. Additionally, the relationship between teacher training attendance and student achievement were determined. Method:Student performance on four physical education indicators in 63 middle schools (and 116 teachers) were reported to the South Carolina Physical Education Assessment Program. Multiple regression analyses were conducted to determine the relationships between school characteristics as predictors of the performance indicator. ANOVAs were conducted to determine the relationship to teacher training and the performance indicators. Results:Statewide averages of student performance indicated that slightly over 50% of middle school students were rated as competent in all physical education indicators except health-related fitness (31.2%). The variability was high among all indicators. The correlations between the poverty index and the physical education indic.
Outage probability and power allocation for MIMO AF two-way relaying over Rician fading

In this paper, we analyze the outage performance of MIMO AF two-way relaying system over Rician fading channels. Specially, all nodes in the system are mounted with multiple antennas, and a joint transmit/receive antenna selection scheme is employed. First, we derive an approximated outage probability expression, which matches the simulated outage probability quite well from medium to high SNR region. For the purpose of revealing the system diversity order, the asymptotic outage probability expression is also derived. To minimize the outage probability, we then proposed a power allocation scheme by using the Newton's method. Finally, Monte Carlo simulation results are presented to verify the analytical results. The simulation results also prove that the proposed power allocation scheme outperforms equal power allocation scheme.
Comparison of algebraic reconstruction techniques and maximum likelihood-expectation maximization tomographic algorithms for reconstruction of Gaussian plume

The maximum likelihood-expectation maximization (ML-EM) and algebraic reconstruction techniques (ART) algorithm are two different iterative algorithms commonly used in the optical remote sensing tomography techniques. In this paper, the two algorithm are compared and analyzed on some evaluation parameters of reconstruction quality with the Gaussian plume model at C level of atmospheric stability as the simulation of gas diffusion. The experimental results show that in aspect of smoothness, peak shape and tailing peak position of reconstructed concentration distribution, ML-EM algorithm performs better. The ML-EM algorithm convergence, in terms of MSE, is much more rapid than that of ART algorithm. While in terms of PE, it becomes deteriorated compared to that of ART algorithm at slightly higher iterative numbers. This study is valuable in the search for optical remote sensing tomographic problems with limited projection data and fan-beam geometry.
Tracking a depth camera: Parameter exploration for fast ICP

The increasing number of ICP variants leads to an explosion of algorithms and parameters. This renders difficult the selection of the appropriate combination for a given application. In this paper, we propose a state-of-the-art, modular, and efficient implementation of an ICP library. We took advantage of the recent availability of fast depth cameras to demonstrate one application example: a 3D pose tracker running at 30 Hz. For this application, we show the modularity of our ICP library by optimizing the use of lean and simple descriptors in order to ease the matching of 3D point clouds. This tracker is then evaluated using datasets recorded along a ground truth of millimeter accuracy. We provide both source code and datasets to the community in order to accelerate further comparisons in this field.
Design of a large scale multi-cluster wireless sensor network

In this paper we propose a new approach to designing a large scale wireless sensor network (WSN) with a cluster-tree topology. Sensors in each cluster communicate directly with their associated cluster head (CH) through contention-based transmissions, and communications between CHs use contention free transmissions. The objective is to balance the energy consumptions of all the CHs, while providing satisfactory throughput. Given the network coverage and throughput requirement, we find the total number of levels of the cluster tree, number and coverage area of the clusters in each level, and amounts of time for contention-based and contention free transmissions at each CH. Our results show some interesting observations about traffic load distribution among the clusters and the CH timeline allocations.
Online Resource Management for Data Center with Energy Capping

The past few years have been witnessing a surging demand for cloud computing services, resulting in a huge carbon footprint and making energy cost one of the top operational costs of data centers. Meanwhile, as sustainable computing has become increasingly important, data centers are constantly pressured to cap the long-term usage of their energy produced from carbon-intensive sources (a.k.a., “brown” energy). In this paper, we study energy budgeting and propose a novel online resource management algorithm, called ORM, to control the number of active servers for delaysensitive workloads in a data center for minimizing the operational cost while satisfying the energy capping constraint. We rigorously prove that ORM achieves a close-tominimum operational cost compared to the optimal offline algorithm with future information, while bounding the potential violation of energy capping, in an almost arbitrarily random environment. We also perform a trace-based simulation study to complement the analysis and validate the effectiveness of ORM.
G1 cyclins block the Ime1 pathway to make mitosis and meiosis incompatible in budding yeast

Diploid yeast cells switch from mitosis to meiosis when starved of essential nutrients. While G1 cyclins play a key role in initiating the mitotic cell cycle, entry into meiosis depends on Ime1, a transcriptional activator regulated by both nutritional and cell‐type signals. We show here that G1 cyclins downregulate IME1 transcription and prevent the accumulation of the Ime1 protein within the nucleus, which results in repression of early‐meiotic gene expression. As G1‐cyclin deficient cells do not require nutrient starvation to undergo meiosis, G1 cyclin would exert its role by transmitting essential nutritional signals to Ime1 function. The existence of a negative cross‐talk mechanism between mitosis and meiosis may help explain why these two developmental options are incompatible in budding yeast.
Analysing Issues for Applying E-learning to the Subject of Electricity in Higher Education in Turkey

Effective integration of e-learning in Higher Education Institutions (HEIs) is influenced by many factors with the learning domain and socio-political context being two of the critical ones. The overarching goal of our research is to investigate how to implement e-learning in HEIs in the field of electricity in Turkey. A survey has been conducted to measure teachers' readiness for elearning in these HEIs. It consisted of two phases: (i) a web-based questionnaire with closeended and open-ended questions; (ii) semi-structured online interviews. This paper reports our findings of analysing the qualitative data that address four main aspects: current educational issues in those HEIs; perceptions of academic staff regarding e-learning; advantages and disadvantages of e-learning as a solution for the issues; strategies to implement e-learning. Overall, our results show that e-learning will bring an innovation into the HEIs and it should be integrated with the blended learning approach.
On the Number of Many-to-Many Alignments of N Sequences

We count the number of alignments of $N \ge 1$ sequences when match-up types are from a specified set $S\subseteq \mathbb{N}^N$. Equivalently, we count the number of nonnegative integer matrices whose rows sum to a given fixed vector and each of whose columns lie in $S$. We provide a new asymptotic formula for the case $S=\{(s_1,\ldots,s_N) \:|\: 1\le s_i\le 2\}$.
A time- and message-optimal distributed algorithm for minimum spanning trees

This paper presents a randomized (Las Vegas) distributed algorithm that constructs a minimum spanning tree (MST) in weighted networks with optimal (up to polylogarithmic factors) time and message complexity. This algorithm runs in Õ(D + √n) time and exchanges Õ(m) messages (both with high probability), where n is the number of nodes of the network, D is the diameter, and m is the number of edges. This is the first distributed MST algorithm that matches simultaneously the time lower bound of Ω(D + √n) [Elkin, SIAM J. Comput. 2006] and the message lower bound of Ω(m) [Kutten et al., J. ACM 2015], which both apply to randomized Monte Carlo algorithms. The prior time and message lower bounds are derived using two completely different graph constructions; the existing lower bound construction that shows one lower bound does not work for the other. To complement our algorithm, we present a new lower bound graph construction for which any distributed MST algorithm requires both Ω(D + √n) rounds and Ω(m) messages.
2016 Ieee International Conference on Big Data (big Data) Improving Clustering Efficiency by Simhash-based K-means Algorithm for Big Data Analytics

—K-Means algorithm is one of the most popular methods for flat clustering, but it's time-consuming in similarity calculation for big data, which causes lower performance in practice. Previous studies proposed improvements for finding better initial centroids to facilitate effective assignment of the data points to suitable clusters with reduced time complexity. However, in vector space representation, as the data volume increases, the dimension of vector space becomes higher which takes more time in similarity calculation. In this paper, we propose a SimHash-based K-Means clustering algorithm that used locality-sensitive hashing and dimensionality reduction to improve the efficiency in big data analytics. The experimental results showed that our proposed method greatly reduces the processing time of K-Means clustering without significantly affecting the effectiveness. Further investigation is needed to verify the performance for data in larger scale.
Distribution and Numbers of Bowhead Whales (Balaena mysticetus) in Northwestern Hudson Bay in August 1995

There is interest among the Inuit of Nunavut in renewing subsistence hunting of bowhead whales. Managing a limited harvest while allowing for stock recovery from commercial whaling requires some estimate of stock numbers. The large geographic range of bowhead whales in the eastern Canadian Arctic precludes cost-effective estimation of total stock size; however, estimates of summer aggregation sizes can be derived from sampling of summering areas. Although these numbers do not represent total stock size, they do provide indicators of the minimum number of bowheads known to be present and may be useful as indices for monitoring stock recovery. A visual aerial survey conducted in northwestern Hudson Bay resulted in an estimate of 75 ± 27.5 (95% Confidence Interval = 17-133) bowhead whales. This estimate is conservative because it was not corrected for submerged whales or for whales that were at the surface but not seen by observers. Most sightings of whales were made in Repulse Bay and Frozen Strait.
Collective Threshold Model Based on Utility and Psychological Theories

In this paper the authors investigate critical phase transition characteristic of collective action by considering the mechanisms of both rational utility and psychological threshold based on the Granovetter 1978's threshold model. Numeric simulation is used to observe the collective dynamics with consideration of both spatial factor and social network friendship density. The authors observe that activation threshold model with both utility and psychological thresholds included shows more stable in phase transition than that in the classic model. The authors also find that spatial factor and friendship network density have trivial impact on final equilibrium of collective behavior.
Exploring political intolerance in a post-apartheid generation of South Africans : the role of intergroup threat and negative intergroup emotion

Background and aim: There are several variables that are associated with the development of political intolerance in society. This study uses the intergroup emotions theory as a framework to investigate whether intergroup threat (more specifically perceived racial threat) and negative intergroup emotions precipitate political intolerance in a well educated, postapartheid generation of South Africans. It also examines whether outgroup appraisals (perceptions of outgroup strength) and degree of ingroup identification have a role to play in this hypothesised relationship. This study extends Gibson and Gouws‟ (2003) work on threat and intolerance as well as Kuklinski, Riggle, Ottati, Schwarz and Wyer‟s (1991) work on the influence of emotion on people‟s tolerance judgements. Method: Participants were randomly assigned to two experimental groups. The independent variable, racial threat, was manipulated such that one group experienced more threat (increased threat group) than the other group (reduced threat group). Following this, the effect of this manipulation on the rest of the study variables was examined using questionnaires that were completed by the participants. The increased threat group comprised 68 participants and the reduced threat group, 55. All these were South African, psychology students from the University of Cape Town (UCT) and were of different racial groups. Before the main study commenced, pilot studies were used to select and develop experimental stimuli for the manipulation of racial threat as well as to develop and refine scales for the different study variables. The pilot sample comprised 164 students from UCT. Thus the entire study sample comprised 287 participants Results and conclusion: In line with the hypotheses of the study, t-tests revealed that the increased threat group expressed significantly more anger, t (121) = 4.98, p <. 01, d=. 91; fear, t (121) = 4.14, p < . 01, d = . 75; and political intolerance, t (121) = 1.74, p = . 04, d= . 32 than the reduced threat group. No significant differences were found regarding outgroup appraisal U ni ve rs ity o f C ap e To w n 4 and racial identification. Similarly, linear regression analyses indicated that perceived racial threat is positively related to political intolerance, R 2 = . 05, F (1, 121) = 6.83, p = . 01 and that this relationship is mediated by negative intergroup emotion. Outgroup appraisal and racial identification did not significantly influence this relationship. These findings strongly suggest that perceived racial threat and negative intergroup emotions are potent triggers of political
Is Modulation of Oxidative Stress an Answer? The State of the Art of Redox Therapeutic Actions in Neurodegenerative Diseases

The central nervous system is particularly sensitive to oxidative stress due to many reasons, including its high oxygen consumption even under basal conditions, high production of reactive oxygen and nitrogen species from specific neurochemical reactions, and the increased deposition of metal ions in the brain with aging. For this reason, along with inflammation, oxidative stress seems to be one of the main inducers of neurodegeneration, causing excitotoxicity, neuronal loss, and axonal damage, ultimately being now considered a key element in the onset and progression of several neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, and hereditary spastic paraplegia. Thus, the present paper reviews the role of oxidative stress and of its mechanistic insights underlying the pathogenesis of these neurodegenerative diseases, with particular focus on current studies on its modulation as a potential and promising therapeutic strategy.