Query: CFD Analysis of Convective Heat Transfer Coefficient on External Surfaces of Buildings

Relevant passages:
Application of CFD in building performance simulation for the outdoor environment : an overview <=SEP=> This paper provides an overview of the application of CFD in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) wind-driven rain on building facades, (3) convective heat transfer coefficients at exterior building surfaces, and (4) air pollutant dispersion around buildings. For each topic, its background, the need for CFD, an overview of some past CFD studies, a discussion about accuracy and some perspectives for practical application are provided. The paper indicates that for all four topics, CFD offers considerable advantages compared to wind tunnel modelling or (semi-)empirical formulae because it can provide detailed whole-flow field data under fully controlled conditions and without similarity constraints. The main limitations are the deficiencies of steady RANS modelling, the increased complexity and computational expense of LES and the requirement of systematic and time-consuming CFD solution verification and validation studies.

Numerical Study of Urban Canyon Microclimate Related to Geometrical Parameters <=SEP=> In this study a microclimate analysis on a particular urban configuration: the—street canyon—has been carried out. The analysis, conducted by performing numerical simulations using the finite volumes commercial code ANSYS-Fluent, shows the flow field in an urban environment, taking into account three different aspect ratios (H/W). This analysis can be helpful in the study on urban microclimate and on the heat exchanges with the buildings. Fluid-dynamic fields on vertical planes within the canyon, have been evaluated. The results show the importance of the geometrical configuration, in relation to the ratio between the height (H) of the buildings and the width (W) of the road. This is a very important subject from the point of view of “Smart Cities”, considering the urban canyon as a subsystem of a larger one (the city), which is affected by climate changes.

BEST PRACTICE GUIDELINE FOR THE CFD SIMULATION OF FLOWS IN THE URBAN ENVIRONMENT <=SEP=> Legal notice by the COST Office Neither the COST Office nor any person acting on its behalf is responsible for the use which might be made of the information contained in the present publication. The COST Office is not responsible for the external web sites referred to in the present publication. No permission to reproduce or utilize the contents of this book by any means is necessary, other than in the case of images, diagrams or other material from other copyright holders. In such cases permission of the copyright holders is required. This book may be cited as: Title of the book and Action Number

Urban Physics : Effect of the micro-climate on comfort , health and energy demand <=SEP=> Y-NC-ND license. Abstract The global trend towards urbanisation explains the growing interest in the study of the modification of the urban climate due to the heat island effect and global warming, and its impact on energy use of buildings. Also urban comfort, health and durability, referring respectively to pedestrian wind/ thermal comfort, pollutant dispersion and wind-driven rain are of interest. Urban Physics is a wellestablished discipline, incorporating relevant branches of physics, environmental chemistry, aerodynamics, meteorology and statistics. Therefore, Urban Physics is well positioned to provide keycontributions to the current urban problems and challenges. The present paper addresses the role of Urban Physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain. Furthermore, the three major research methods applied in Urban Physics, namely field experiments, wind tunnel experiments and numerical simulations are discussed. Case studies illustrate the current challenges and the relevant contributions of Urban Physics. & 2012. Higher Education Press Limited Company. Production and hosting by Elsevier B.V. Open access under CC BY-NC-ND license.

Verification , Validation , and Predictive Capability in Computational Engineering and Physics <=SEP=> Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, i.e., experimental data, is the issue.

Practical path planning and path following for a non-holonomic mobile robot based on visual servoing <=SEP=> In this paper, a practical real-time path planning and robot navigation algorithm for a non-holonomic indoor mobile robot based on visual servoing is implemented. The proposed algorithm is divided into three parts; the first part uses Multi-Stencils Fast Marching (MSFM) as a path planning method. But the generated path results from fast marching methods when used directly, not guaranteed to be safe and smooth. Subsequently, the robot can touch corners, walls and obstacles. The proposed algorithm uses image processing methods to solve this problem. The second part estimates the position and orientation of the robot, from the visual information, to follow the desired path with avoiding obstacles. The third part proposes a decentralized PD-like Fuzzy Logic Controller (FLC) to keep up the robot on the desired path. Experimental results show that the developed design is valid to estimate shortest-path by avoiding obstacles and able to guide the robot to follow the path in real-time.

Exploiting Passive Dynamics with Variable Stiffness Actuation in Robot Brachiation <=SEP=> This paper explores a passive control strategy with variable stiffness actuation for swing movements. We consider brachiation as an example of a highly dynamic task which requires exploitation of gravity in an efficient manner for successful task execution. First, we present our passive control strateg y considering a pendulum with variable stiffness actuation. Then, we formulate the problem based an optimal control framework with temporal optimization in order to simultaneously find an appropriate stiffness profile and movement duration such that the resultant movement will be able to exploit the passive dynamics of the robot. Finally, numerical evaluations on a twolink brachiating robot with a variable stiffness actuator (VSA) model are provided to demonstrate the effectiveness of our approach under different task requirements, modelling errors and switching in the robot dynamics. In addition, we discuss the issue of task description in terms of the choice of cost function for successful task execution in optimal control.

Analysis of Statistical Question Classification for Fact-Based Questions <=SEP=> Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.

The Ease of Doing Business Index as a tool for Investment location decisions <=SEP=> The Ease of Doing Business Index (EDBI) uses 41 variables to compare the business environment of different countries. It is widely used by policy makers, researchers and multinational companies. This paper aims to assess EDBI’s consistency and validity in representing the business environment by using factor analysis. It is found that the EDBI presents a limited consistency and descriptive power of a country’s business environment. The consequence of these findings is that multinational firms should handle carefully the EDBI in their investment decisions. GEE The Ease of Doing Business Index as a tool for Investment location decisions – João Zambujal-Oliveira, Ricardo Pinheiro-Alves

UTAssistant: A Web Platform Supporting Usability Testing in Italian Public Administrations <=SEP=> Even if the benefits of the usability testing are remarkable, it is scarcely adopted in the software development process. To foster its adoption, this paper presents a Web platform, UTAssistant, that supports people, also without skills in Human-Computer Interaction (HCI), in evaluating Web site usability.

A Game Theoretic Framework for Incentives in P2P Systems <=SEP=> Peer-To-Peer (P2P) networks are self-organizing, distributed systems, with no centralized authority or infrastructure. Because of the voluntary participation, the availability of resources in a P2P system can be highly variable and unpredictable. In this paper, we use ideas from Game Theory to study the interaction of strategic and rational peers, and propose a differential service-based incentive scheme to improve the system’s performance.

Fast and Robust Joint Models for Biomedical Event Extraction <=SEP=> Extracting biomedical events from literature has attracted much recent attention. The bestperforming systems so far have been pipelines of simple subtask-specific local classifiers. A natural drawback of such approaches are cascading errors introduced in early stages of the pipeline. We present three joint models of increasing complexity designed to overcome this problem. The first model performs joint trigger and argument extraction, and lends itself to a simple, efficient and exact inference algorithm. The second model captures correlations between events, while the third model ensures consistency between arguments of the same event. Inference in these models is kept tractable through dual decomposition. The first two models outperform the previous best joint approaches and are very competitive with respect to the current state-of-theart. The third model yields the best results reported so far on the BioNLP 2009 shared task, the BioNLP 2011 Genia task and the BioNLP 2011 Infectious Diseases task.

The Stratosphere platform for big data analytics <=SEP=> We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.

How to improve knowledge transfer strategies and practices in education ? Answers from a systematic literature review <=SEP=> Building on the systematic review methodology, this paper aims to examine the knowledge transfer process in education and its main determinants in this specific context. Our findings suggest that linkage agents are central actors in the knowledge transfer process. Their intervention is critical to help adapt the knowledge produced by researchers and make it easier to adopt and use by practitioners. Moreover, the effectiveness of this process hinges on several factors that were broken down into three major categories: determinants related to transferredknowledge attributes, those related to the actors involved in the process, and determinants related to transfer mechanisms.

Laryngeal Tumor Detection and Classification in Endoscopic Video <=SEP=> The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is then performed based on a statistical analysis of the blood vessels' characteristics, such as thickness, tortuosity, and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this study into clinical practice. Furthermore, to our best knowledge, this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.

Generalized Simulated Annealing <=SEP=> We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and can be quicker than both. Key-words: Simulated annealing; Nonconvex optimization; Gradient descent; Generalized Statistical Mechanics.

Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement <=SEP=> Cross-validation is a mainstay for measuring performance and progress in machine learning. There are subtle differences in how exactly to compute accuracy, F-measure and Area Under the ROC Curve (AUC) in cross-validation studies. However, these details are not discussed in the literature, and incompatible methods are used by various papers and software packages. This leads to inconsistency across the research literature. Anomalies in performance calculations for particular folds and situations go undiscovered when they are buried in aggregated results over many folds and datasets, without ever a person looking at the intermediate performance measurements. This research note clarifies and illustrates the differences, and it provides guidance for how best to measure classification performance under cross-validation. In particular, there are several divergent methods used for computing F-measure, which is often recommended as a performance measure under class imbalance, e.g., for text classification domains and in one-vs.-all reductions of datasets having many classes. We show by experiment that all but one of these computation methods leads to biased measurements, especially under high class imbalance. This paper is of particular interest to those designing machine learning software libraries and researchers focused on high class imbalance.

An Effective High Threating Alarm Mining Method for Cloud Security Management <=SEP=> Security equipment such as intrusion prevention system is an important supplementary for security management. They reduce the difficulty of network management by giving alarms corresponding to different attacks instead of raw traffic packet inspection. But there are many false alarms due to their running mechanism, which greatly reduces its usability. In this paper, we develop a hierarchical framework to mine high threating alarms from the massive alarm logs, and aim to provide fundamental and useful information for administrators to design efficient management policy. First, the alarms are divided into two parts based on their attributes, the first part mainly includes several kinds of famous attacks which are critical for security management, we proposed a similar alarm mining method based on Choquet integral to cluster and rank the frequently occurred attacks. The rest alarms constitute the second part, which are caused by the potential threats attacks, also include many false alarms. To reduce the effect of false alarms and rank the potential threats, we employ the frequent pattern mining algorithm to mine correlation rules and then filter false alarms. Following, we proposed a self-adapting threat degree calculation method to qualify the threat degree of these alarms after filtering. To verity the methods developed, an experimental platform is constructed in the campus network of Xi’an Jiaotong University. Experimental results based on the data collected verify the efficiency of the developed methods. For the first kind of alarms, the similar alarms mining accuracy is higher than 97% and the alarms are ranked with different processing urgencies. For the rest alarms, the proposed methods have filtering accuracy above 80% and can rank the potential threats. Based on the ranking results, administrators can deal with the high threats with their limited time and energy, in turn, keep the network under control.

Application of Data Mining Classification Algorithms for Breast Cancer Diagnosis <=SEP=> Breast cancer is one of the diseases that represent a large number of incidence and mortality in the world. Data mining classifications techniques will be effective tools for classifying data of cancer to facilitate decision-making.
 The objective of this paper is to compare the performance of different machine learning algorithms in the diagnosis of breast cancer, to define exactly if this type of cancer is a benign or malignant tumor.
 Six machine learning algorithms were evaluated in this research Bayes Network (BN), Support Vector Machine (SVM), k-nearest neighbors algorithm (Knn), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression. The simulation of the algorithms is done using the WEKA tool (The Waikato Environment for Knowledge Analysis) on the Wisconsin breast cancer dataset available in UCI machine learning repository.

An Unsupervised Clustering Tool for Unstructured Data <=SEP=> We present an unsupervised clustering tool, Principal Direction Divisive Partitioning, which is a scal-able and versatile top-down method applicable to any set of data that can be represented as numerical vectors. A description of the basic method, a summary of the main application areas where this has been used, and some recent results on the selection of signiicant words as well as the process of updating clusters as new data arrives is discussed.

Basic studies on wet adhesion system for wall climbing robots <=SEP=> This paper reports a vacuum-based wet adhesion system for wall climbing robots. In this adhesion system, a suction cup adheres on a wet surface. The problems addressed are an adherability on a rough surface, which is comes from the seal action of a liquid, and low friction between suction cup and adhered rough and smooth surfaces which is comes form lubricating action of a liquid. Generally, it is difficult that a vacuumed suction cup adheres on rough surface such concrete plate and hardly slidable. In this paper, the adhesion force and friction when a suction cup adheres on smooth glass plate and concrete plate are measured and compared wet condition with dry condition. The experiment result showed that a viscosity is important at the sealing performance of adhesion on rough surface. The silicon oil of a viscosity of 5000cSt allows a suction cup to adhere on concrete surface. In this condition it comes up to the adhesion force when a suction cup adheres on smooth glass with dry condition.

DIFusion: Fast Skip-Scan with Zero Space Overhead <=SEP=> Scan is a crucial operation in main-memory column-stores. It scans a column and returns a result bit vector indicating which records satisfy a filter predicate. ByteSlice is an in-memory data layout that chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of ByteSlice is padded with 0's, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0's), forming our new layout coined DIFusion (Data Index Fusion). DIFusion enables skip-scan, a new fast scan that inherits the early-stopping capability from ByteSlice and at the same time possesses the data-skipping ability of index with zero space overhead. Empirical results show that skip-scan on DIFusion outperforms scan on ByteSlice.

Selection from alphabetic and numeric menu trees using a touch screen: breadth, depth, and width <=SEP=> Goal items were selected by a series of touch-menu choices among sequentially subdivided ranges of integers or alphabetically ordered words. The number of alternatives at each step, <italic>b</italic>, was varied, and, inversely, the size of the target area for the touch. Mean response time for each screen was well described by <italic>T</italic> = <italic>k</italic>+<italic>c</italic>log<italic>b</italic>, in agreement with the Hick-Hyman and Fitts' laws for decision and movement components in series. It is shown that this function favors breadth over depth in menus, whereas others might not. Speculations are offered as to when various functions could be expected.

FILA: Fine-grained indoor localization <=SEP=> Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.

Detection of covert attacks on cyber-physical systems by extending the system dynamics with an auxiliary system <=SEP=> Securing cyber-physical systems is vital for our modern society since they are widely used in critical infrastructure like power grid control and water distribution. One of the most sophisticated attacks on these systems is the covert attack, where an attacker changes the system inputs and disguises his influence on the system outputs by changing them accordingly. In this paper an approach to detect such an attack by extending the original system with a switched auxiliary system is proposed. Furthermore, a detection system using a switched Luenberger observer is presented. The effectiveness of the proposed method is illustrated by a simulation example.

Mapping CMMI Project Management Process Areas to SCRUM Practices <=SEP=> Over the past years, the capability maturity model (CMM) and capability maturity model integration (CMMI) have been broadly used for assessing organizational maturity and process capability throughout the world. However, the rapid pace of change in information technology has caused increasing frustration to the heavyweight plans, specifications, and other documentation imposed by contractual inertia and maturity model compliance criteria. In light of that, agile methodologies have been adopted to tackle this challenge. The aim of our paper is to present mapping between CMMI and one of these methodologies, Scrum. It shows how Scrum addresses the Project Management Process Areas of CMMI. This is useful for organizations that have their plan-driven process based on the CMMI model and are planning to improve its processes toward agility or to help organizations to define a new project management framework based on both CMMI and Scrum practices.

Matchmaker: constructing constrained texture maps <=SEP=> Texture mapping enhances the visual realism of 3D models by adding fine details. To achieve the best results, it is often necessary to force a correspondence between some of the details of the texture and the features of the model.The most common method for mapping texture onto 3D meshes is to use a planar parameterization of the mesh. This, however, does not reflect any special correspondence between the mesh geometry and the texture. The Matchmaker algorithm presented here forces user-defined feature correspondence for planar parameterization of meshes. This is achieved by adding positional constraints to the planar parameterization. Matchmaker allows users to introduce scores of constraints while maintaining a valid one-to-one mapping between the embedding and the 3D surface. Matchmaker's constraint mechanism can be used for other applications requiring parameterization besides texture mapping, such as morphing and remeshing.Matchmaker begins with an unconstrained planar embedding of the 3D mesh generated by conventional methods. It moves the constrained vertices to the required positions by matching a triangulation of these positions to a triangulation of the planar mesh formed by paths between constrained vertices. The matching triangulations are used to generate a new parameterization that satisfies the constraints while minimizing the deviation from the original 3D geometry.

Translating evidence-based decision making into practice: EBDM concepts and finding the evidence. <=SEP=> This is the first of 2 articles that focuses on strategies that can be used to integrate an evidence-based decision making [EBDM] approach into practice. The articles will focus on EBDM methodology and enhancing skills, including how to find valid evidence to answer clinical questions, critically appraise the evidence found and determine if it applies. In addition, online resources will be identified to supplement information presented in each article. The purpose of this article is to define evidence-based decision making and discuss skills necessary for practitioners to efficiently adopt EBDM. It will provide a guide for finding evidence to answer a clinical question using PubMed's specialized searching tools under Clinical Queries.

A First Step towards Eye State Prediction Using EEG <=SEP=> In this paper, we investigate how the eye state (open or closed) can be predicted by measuring brain waves with an EEG. To this end, we recorded a corpus containing the activation strength of the fourteen electrodes of a commercial EEG headset as well as the manually annotated eye state corresponding to the recorded data. We tested 42 different machine learning algorithms on their performance to predict the eye state after training with the corpus. The best-performing classifier, KStar, produced a classification error rate of only 2.7% which is a 94% relative reduction over the majority vote of 44.9% classification error.

Differences in energy expenditure during high-speed versus standard-speed yoga: A randomized sequence crossover trial. <=SEP=> OBJECTIVES
To compare energy expenditure and volume of oxygen consumption and carbon dioxide production during a high-speed yoga and a standard-speed yoga program.


DESIGN
Randomized repeated measures controlled trial.


SETTING
A laboratory of neuromuscular research and active aging.


INTERVENTIONS
Sun-Salutation B was performed, for eight minutes, at a high speed versus and a standard-speed separately while oxygen consumption was recorded. Caloric expenditure was calculated using volume of oxygen consumption and carbon dioxide production.


MAIN OUTCOME MEASURES
Difference in energy expenditure (kcal) of HSY and SSY.


RESULTS
Significant differences were observed in energy expenditure between yoga speeds with high-speed yoga producing significantly higher energy expenditure than standard-speed yoga (MD=18.55, SE=1.86, p<0.01). Significant differences were also seen between high-speed and standard-speed yoga for volume of oxygen consumed and carbon dioxide produced.


CONCLUSIONS
High-speed yoga results in a significantly greater caloric expenditure than standard-speed yoga. High-speed yoga may be an effective alternative program for those targeting cardiometabolic markers.