Miracle-dz/UniFAR-FTUs huggingface dataset:
Características Antropométricas y la Ingesta Dietética de los Jugadores de la División de Honor y División de Honor B de un Club de Rugby Español - Ciencias del Ejercicio

espanolLos objetivos del estudio son asociar las caracteristicas antropometricas dependiendo de la posicion de juego y la categoria; asi mismo comparar su ingesta dietetica con los requerimientos de los jugadores de un club de rugby espanol. Cincuenta y tres jugadores de Rugby Union de la Division de Honor (28) y la Division de Honor B (25) se sometieron a mediciones antropometricas para estimar masa magra, grasa y muscular. Tambien se realizo un recordatorio de 24 horas de la ingesta dietetica, todas las mediciones se tomaron una vez. Se identifico que los jugadores de la Division de Honor tuvieron valores significativamente mas altos para la edad, masa corporal, muscular (kg) y magra, esto se debe a las adaptaciones normales relacionadas con la maduracion y desarrollo. En cuanto a la posicion, encontramos un efecto significativo en la talla, masa corporal, IMC, pliegues cutaneos, grasa corporal (%), masa muscular (%), la masa muscular (kg) y la masa corporal magra, siendo los delanteros mas altos, mas pesados y con mayores cantidades de musculo y grasa corporal, debido a las exigencias fisicas de cada posicion de juego. La ingesta de energia y carbohidratos no cumplen con las recomendaciones dieteticas; empero, la ingesta de proteinas estuvo por encima de los valores recomendados. Estos hallazgos pueden proporcionar datos de referencia para futuras investigaciones con jugadores de Rugby Union por categoria y posicion, asi como para entrenadores, entrenadores fisicos y nutricionistas sobre la composicion corporal y la ingesta dietetica de los jugadores. EnglishThis study aimed to associate the anthropometrical characteristics of the playing position and category level and to compare their dietary intake with the requirements of a Spanish Rugby Club. Altogether, 53 Rugby Union players of the Honor Division (28) and Honor Division B (25) underwent measurements of standard to estimate lean mass, fat mass, and muscle mass. A 24-hour recall of the dietary intake was also performed, all measurements were taken once. Findings identified Honor Division players had significantly higher values on age body mass, muscle mass (kg) and lean body mass, due to normal adaptations related to maturation and development. Regarding the position we found a significant effect on height, body mass, BMI, sum of skinfolds, body fat (%), muscle mass (%), muscle mass (kg) and lean body mass was found, being Forwards taller, heavier and with larger amounts of muscle and body fat due to the physical demands of each playing position. The energy and carbohydrates intake fail to fulfill the dietary recommendation; however, protein intake was above the recommended values. These findings may provide reference data for future research with Rugby Union players by level and position, as well as for trainers, physical trainers, and nutritionists on the body composition and dietary intake of the players.
Research Developments and Directions in Speech Recognition and Understanding, Part 1

To advance research, it is important to identify promising future research directions, especially those that have not been adequately pursued or funded in the past. The working group producing this article was charged to elicit from the human language technology (HLT) community a set of well-considered directions or rich areas for future research that could lead to major paradigm shifts in the field of automatic speech recognition (ASR) and understanding. ASR has been an area of great interest and activity to the signal processing and HLT communities over the past several decades. As a first step, this group reviewed major developments in the field and the circumstances that led to their success and then focused on areas it deemed especially fertile for future research. Part 1 of this article will focus on historically significant developments in the ASR area, including several major research efforts that were guided by different funding agencies, and suggest general areas in which to focus research. Part 2 (to appear in the next issue) will explore in more detail several new avenues holding promise for substantial improvements in ASR performance. These entail cross-disciplinary research and specific approaches to address three-to-five-year grand challenges aimed at stimulating advanced research by dealing with realistic tasks of broad interest.
Application of Pi-Sigma Neural Networks and Ridge Polynomial Neural Networks to Financial Time Series Prediction

This chapter discusses the use of two artificial Higher Order Neural Networks (HONNs) models; the PiSigma Neural Networks and the Ridge Polynomial Neural Networks, in financial time series forecasting. The networks were used to forecast the upcoming trends of three noisy financial signals; the exchange rate between the US Dollar and the Euro, the exchange rate between the Japanese Yen and the Euro, and the United States 10-year government bond. In particular, we systematically investigate a method of pre-processing the signals in order to reduce the trends in them. The performance of the networks is benchmarked against the performance of Multilayer Perceptrons. From the simulation results, the predictions clearly demonstrated that HONNs models, particularly Ridge Polynomial Neural Networks generate higher profit returns with fast convergence, therefore show considerable promise as a decision making tool. It is hoped that individual investor could benefit from the use of this forecasting tool.
Multi-Hypothesis Parsing of Tabular Data in Comma-Separated Values (CSV) Files

htmlabstractTabular data on the web comes in various formats and shapes. Preparing data for data analysis and integration requires manual steps which go beyond simple parsing of the data. The preparation includes steps like correct configuration of the parser, removing of meaningless rows, casting of data types and reshaping of the table structure. The goal of this thesis is the development of a robust and modular system which is able to automatically transform messy CSV data sources into a tidy tabular data structure. The highly diverse corpus of CSV files from the UK open data hub will serve as a basis for the evaluation of the system.
Communicating science to motivate action: A case study of birds and climate change

Climate change is an existential threat to biodiversity and people, but building political will for action on climate change remains elusive. Research on climate change communications to increase effectiveness at motivating action include these recommendations: pairing risk and opportunity in message framing, localizing information, and appealing to a sense of place. Here, we present a case study on mobilizing bird enthusiasts to take action on climate change. The science communicated found that over two-thirds of North American birds are moderately or highly vulnerable to climate change under a 3.0°C warming scenario. Of these climate-vulnerable species, 76% would have reduced vulnerability if warming were stabilized at 1.5°C. These findings were summarized and communicated with the public in an award-winning interactive website, the “Birds and Climate Visualizer,” that allowed individuals to look up any location by postal ZIP code and learn what birds would be impacted by climate. Also included was a Climate Action Handbook providing sample personal actions. The communications rollout earned 2.5 billion media impressions, the visualizer has been used at least 42,000 times, and handbook circulated to 250,000 households. More than 33,000 people read headlines from the report or explored the visualizer and then completed an online action in support of clean energy policies. The message frame combined with the visualizer and handbook as tools to deliver the frame have contributed to climate legislative wins in four states. These early successes suggest that message and visual online tools that localize and personalize a message are effective to motivate action.
Transcriptome and 16S rRNA analysis revealed the response of largemouth bass (Micropterus salmoides) to Rhabdovirus infection

To better understand the response of largemouth bass (Micropterus salmoides) to Micropterus salmoides rhabdovirus (MSRV) infection, we investigated the intestinal bacterial flora and transcriptome profile of fish at 72 hours post-infection (hpi). Total of 1574 differentially expressed genes (DEGs) were identified in largemouth bass spleen following MSRV infection, including 573 upregulated and 1001 downregulated genes. KEGG and GO enrichment analysis revealed that upregulated genes were enriched in certain antiviral related signaling pathway, including NOD-like receptor (NLR), RIG-I like receptors (RLR) and regulation of the interferon (IFN)-γ-mediated signaling pathway, whereas some immune-related DEGs enriched in focal adhesion (FA) and ECM-receptor interaction(ECM-RI) were downregulated, as well as genes associated with metabolic processes, such as peroxisome proliferator-activated receptors (PPAR), adipocytokine signaling pathway, Glycerolipid and Retinol metabolism. Furthermore, the principal component analysis (PCA) and phylogenetic analysis revealed that MSRV infection significantly affected the microbiota of largemouth bass intestine; the LEfSe analysis showed that relative abundances of Streptococcus were significantly increased, while the content of Akkermansia, Enterococcus and Lactobacillus were remarkably decreased in the fish intestine following MSRV infection. Additionally, a high correlation was determined between the expressions of interferon-related upregulated genes and the relative abundance of Streptococcus by redundancy analysis (RDA). These results collectively illustrated that intestinal microbiota composition might be associated with the immune-related gene expression in largemouth bass in response to MSRV infection.
Process Algebra for Parallel and Distributed Processing

Exploring state-of-the-art applications, Process Algebra for Parallel and Distributed Processing shows how one formal method of reasoningprocess algebrahas become a powerful tool for solving design and implementation challenges of concurrent systems. Parallel Programming Divided into three parts, the book begins by parallelizing an algorithm for the Cell Broadband Engine processor of Sony, Toshiba, and IBM. It also develops a runtime environment that can be ported to different parallel platforms and describes the formal model of action systems. Distributed Systems The next part presents a process algebra (mCRL2) that targets distributed applications, looks at how to turn prose descriptions into unambiguous specifications, extends pi-calculus to create a service-oriented mobility abstract machine, and introduces the Channel Ambient Machine for mobile applications. Embedded Systems The final section combines state-based Z with the event-based process algebra CSP in a formal methodology called Circus. It also develops a pair of process algebras (PARS) to address the problem of scheduling in real-time embedded systems and emphasizes the reuse of concurrent artifacts across different hardware platforms. Focusing on the contributors own research work, this volume highlights the recent growth in this burgeoning industry. It proposes solutions to the problems of designing and implementing todays multicore processor architectures, clusters, and grids.
Energy Optimal Scheduling Based on Smart Grid Technology Load Control Thermostat

In view of the present situation of large-scale centralized development of new energy sources, the necessity of new energy sources developing and the characteristics of the new energy sources are analyzed. The traditional scheduling technology support system has been unable to cope with large-scale new energy grid connected power grid operation to bring challenges. We introduces a based on multi time scale of new energy coordination optimization scheduling method, including years/ monthly new energy power plan, recently and days rolling optimal scheduling based on the ongoing promotion application of smart grid dispatching control system, the platform design and development of new energy scheduling technology support system. Weighted factor queuing algorithm allows users to choose according to their own preferences and needs of the load device response, to ensure that the control effect, but also take into account the user's comfort and fairness.
Performance Analysis of Video Transmission Using Sequential Distortion Minimization Method for Digital Video Broadcasting Terrestrial

This paper presents about the transmission of Digital Video Broadcasting system with streaming video resolution 640x480 on different IQ rate and modulation. In the video transmission, distortion often occurs, so the received video has bad quality. Key frames selection algorithm is flexibel on a change of video, but on these methods, the temporal information of a video sequence is omitted. To minimize distortion between the original video and received video, we aimed at adding methodology using sequential distortion minimization algorithm. Its aim was to create a new video, better than original video without significant loss of content between the original video and received video, fixed sequentially. The reliability of video transmission was observed based on a constellation diagram, with the best result on IQ rate 2 Mhz and modulation 8 QAM. The best video transmission was also investigated using SEDIM (Sequential Distortion Minimization Method) and without SEDIM. The experimental result showed that the PSNR (Peak Signal to Noise Ratio) average of video transmission using SEDIM was an increase from 19,855 dB to 48,386 dB and SSIM (Structural Similarity) average increase 10,49%. The experimental results and comparison of proposed method obtained a good performance. USRP board was used as RF front-end on 2,2 GHz.
Planar Processing Primer

Thank you for downloading planar processing primer. Maybe you have knowledge that, people have search numerous times for their favorite readings like this planar processing primer, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their desktop computer. planar processing primer is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the planar processing primer is universally compatible with any devices to read.
Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements

Conversational recommender system (CRS) helps customers get products fitted their needs by repeated interaction mechanisms. When customers want to buy products having many and high tech features (e.g., cars, smartphones, notebook, etc.), most users are not familiar with product technical features. The more natural way to elicit customers’ needs is by asking what they really want to use with the product they want (we call as product functional requirements). In this paper, we analyze four factors, e.g., perceived usefulness, perceived ease of use, trust and perceived enjoyment associated to user’s intention to adopt the interaction model (in CRS) based on product functional requirements. Result of experiment using technology acceptance model (TAM) indicates that, for users who aren’t familiar with technical features, perceives usefulness is a main factor influencing users’ adoption. Meanwhile, perceived enjoyment plays a role on user’s intention to adopt this interaction model, for users who are familiar with technical features of product.
Multiple Level Sets Segmentation for High Resolution Remote Sensing Imagery

The paper presents an unsupervised segmentation model based on multiple level sets evolution for high resolution satellite imagery.The model can remove ambiguities occurring to multiple levels sets methods.In the numerical implementation,a novel re-initialization technique for level set functions is suggested to accelerate curves evolution steadily.Experiments show that our method can alleviate "pepper and salt effect" compared with pixel-based method,and to lessen over-and under-segmentation compared to object-based methods.
e-Discovery Team at TREC 2015 Total Recall Track

The 2015 TREC Total Recall Track provided instant relevance feedback in thirty prejudged topics searching three different datasets. The e-Discovery Team of three attorneys specializing in legal search participated in all thirty topics using Kroll Ontrack’s search and review software, eDiscovery.com Review (EDR). They employed a hybrid approach to continuous active learning that uses both manual and automatic searches. A variety of manual search methods were used to find training documents, including high probability ranked documents and keywords, an ad hoc process the Team calls multimodal. In the one topic (109) requiring legal analysis the Team’s approach was significantly more effective than all other participants, including the fully automated approaches that otherwise attained comparable scores. In all topics the Team’s hybrid multimodal method consistently attained the highest F1 values at the time of Reasonable Call, equivalent to a stop point. In all topics the Team’s multimodal human machine approach also found relevant documents more quickly and with greater precision than the fully automated or other methods.
2-V, 1-GHz CMOS inductorless LNAs with 2-3dB NF

Two 1-GHz single-stage single-ended low noise amplifiers (LNAs), requiring one external inductor and matched to 50-Ohm at both the input and output, have been designed using a 0.5-/spl mu/m CMOS technology. Both LNAs employ a cascode topology with a resistive pull-up load and with no on-chip inductor. The second topology uses an ac-coupled active-inductor load to provide more selectivity and reject the image channel. The LNAs have a gain greater than 10 dB at 1 GHz with a noise figure lower than 3 dB at a power dissipation of 17 mW from a 2 V supply. The inductor-less LNA has an IIP3 of -3.8 dBm compared to the active-inductor LNAs IIP3 of -21 dBm and their reverse isolation is higher than 45 dB.
Adaptive Perspectives on Human-Technology Interaction: Methods and Models for Cognitive Engineering and Human-Computer Interaction (Human-Technology Interaction)

In everyday life, and particularly in the modern workplace, information technology and automation increasingly mediate, augment, and sometimes even interfere with how humans interact with their environment. How to understand and support cognition in human-technology interaction is both a practically and socially relevant problem. The chapters in this volume frame this problem in adaptive terms: How are behavior and cognition adapted, or perhaps ill-adapted, to the demands and opportunities of an environment where interaction is mediated by tools and technology? The authors draw heavily on the work of Egon Brunswik, a pioneer in ecological and cognitive psychology, as well as on modern refinements and extensions of Brunswikian ideas, including Hammond's Social Judgment Theory, Gigerenzer's Ecological Rationality and Anderson's Rational Analysis. Inspired by Brunswik's view of cognition as "coming to terms" with the "casual texture" of the external world, the chapters in this volume provide quantitative and computational models and measures for studying how people come to terms with an increasingly technological ecology, and provide insights for supporting cognition and performance through design, training, and other interventions. The methods, models, and measures presented in this book provide timely and important resources for addressing problems in the rapidly growing field of human-technology interaction. The book will be of interest to researchers, students, and practitioners in human factors, cognitive engineering, human-computer interaction, judgment and decision making, and cognitive science.Please visit this website for additional materials:http://www.humanfactors. illinois.edu/resources/OtherResourses/LensModel.aspx
Efficient Hierarchical Clustering Routing in Wireless Sensor Networks

The routing protocol allows routing data from source to destination through intermediate nodes up to the sink. In the hierarchical routing based on the clustering, every cluster head node forms its cluster with their neighbor nodes.The cluster heads are responsible of their clusters. In fact they coordinate between nodes in the cluster for intra-cluster communication and route data to the other clusters for inter-cluster communication. In this article we propose a centralized and periodic protocol named Efficient Clustering Routing Protocol (ECRP). Our protocol is based on a modified version of CLARANS algorithm; it takes into account the location and the remaining energy of the sensor nodes to divide the sensed area to disjoint and balanced clusters.The simulation results show that the proposed protocol is more efficient than the existing hierarchical protocols in terms of packet delivery ratio, and energy consumption.
Online Bayesian learning in dynamic models: an illustrative introduction to particle methods

This chapter reviews the main advances, over the last two decades, in the particle filter (PF) literature for dynamic models. We focus the discussion around the bootstrap filter (BF) and the auxiliary particle filter (APF), as these are the basis for most of the contributions in the literature. Both filters are then extended to accommodate sequential parameter learning, an area that has gained renewed attention over the last couple of years. The chapter is mainly intended for those researchers and practitioners with little or no practical experience with PF and are looking for a hands-on approach to the subject. With that in mind, we implement and compare the discussed particle filters in two well known contexts: the AR(1) plus noise model and the stochastic volatility model with AR(1) dynamics, or simply SV-AR(1) model. The AR(1) plus noise model is used as a benchmark since all sequential distributions are available in closed-form when parameters are kept fixed. The SV-AR(1) provides an illustration of the ability of PF to deal with traditionally challenging non-linear models.
Full Design of Dependable Third Party Services

This document describes the designs of a generic distributed certification authority and of a trusted party for optimistic fair exchange that are based on fault-tolerant middleware for service replication. It also discusses other uses of the replication middleware for implementing trusted services. It may serve as a blueprint for building generic trusted third-party services that use the state-machine replication approach.
Feedback Source Modality Effects in Game-Based Training

In this paper we explore available tools for integrating intelligent tutoring communications in game-based learning platforms and examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research is the open-source Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools to author, deliver, and evaluate intelligent tutoring technologies. Influenced by social cognitive theory and cognitive load theory based research, the resulting experiment looks at varying approaches for using an Embodied Pedagogical Agent (EPA) defined as a tutor in a game-based training environment. Treatments were authored to examine tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding. A condition effectiveness evaluation is presented examining treatment effects on performance, reported mental demand, and perceived usefulness of feedback. Outcomes support the use of an EPA present in GIFT’s TUI when compared to effectiveness scores on audio prompts alone and the game embedded EPA.