nils-herrmann/clean_scirepeval huggingface dataset:
CFD Simulation of Turbulent Flows in Proto X 3 Bioenergy Micro Gas Turbine Combustor using STD k e and RNG k e Model for Green Building Application

This paper presents a numerical analysis of gas flow in the annular combustion chamber of a Proto X 3 Bioenergy micro gas turbine for green building applications. The computational fluids dynamics (CFD) simulation was conducted in two dimensions, turbulent flow and gas phase combustion, with the goal of comparing the effects of different models in real conditions. Two different turbulence models, standard (STD) k e and renormalization group (RNG) k e, were applied for simulations. The fuel used was biogas produced from animal waste. Fuel consumption was assumed to be 100 kJ/s for simulations. The results of the simulations were analyzed and compared for reference. The temperature and the mass fraction of CH4, H2, O2, and CO2 distributions gave almost the same results for both models; therefore, both models (STD k e and RNG k e) could be used to represent the combustion process phenomenon without many significant differences.
Investigation of Inverse Turbulent Prandtl Number with Four RNG k e Turbulence Models on Compressor Discharge Pipe of Bioenergy Micro Gas Turbine

Inverse Turbulent Prandtl number (a) is an important parameter in RNG k e turbulence models since it affects the ratio of molecular viscosity and turbulent viscosity. In curved pipe, this highly affects the model prediction to a large range eddy scale flow. According to Yakhot Orzag, the a range from 1 1.3929 has not been investigated in detail in curved pipe flow (Yakhot Orszag, 1986) and specific Re. This paper varied inverse turbulent Prandtl number a to 1 1.3 in RNG k e turbulence model on cylindrical curved pipe in order to obtain the optimum value of a to predict unfully developed flow in the curve with curve ratio R/D of 1.607. Analysis was conducted numericaly with inlet specified Re of 40900 which was generated from the experiment at a 1, 1.1, 1.2, 1.3. Wall surface roughness is not considered in this paper. With assumption that thermal diffusivity is always dominant to turbulent viscosity, higher Inverse turbulent Prandtl number represent domination of turbulent viscosity to molecular viscosity of the flow and predict to have more interaction between large scale eddy to small scale eddy as well. The results show the use of a 1.3 has increased the turbulent kinetic energy by 7% and the turbulent dissipation by 5% compared to general inverse turbulent Prandtl number of 1. The value difference shows that the use of higher a on RNG turbulence model described more interaction between eddies in secondary and swirling flow at pipe curve at Re 40900.
Learning distributed word representation with multi contextual mixed embedding
Venue: Knowl. Based Syst.

Learning distributed word representations has been a popular method for various natural language processing applications such as word analogy and similarity, document classification and sentiment analysis. However, most existing word embedding models only exploit a shallow slide window as the context to predict the target word. Because the semantic of each word is also influenced by its global context, as the distributional models usually induced the word representations from the global co occurrence matrix, the window based models are insufficient to capture semantic knowledge. In this paper, we propose a novel hybrid model called mixed word embedding (MWE) based on the well known word2vec toolbox. Specifically, the proposed MWE model combines the two variants of word2vec, i.e. SKIP GRAM and CBOW, in a seamless way via sharing a common encoding structure, which is able to capture the syntax information of words more accurately. Furthermore, it incorporates a global text vector into the CBOW variant so as to capture more semantic information. Our MWE preserves the same time complexity as the SKIP GRAM. To evaluate our MWE model efficiently and adaptively, we study our model on linguistic and application perspectives with both English and Chinese dataset. For linguistics, we conduct empirical studies on word analogies and similarities. The learned latent representations on both document classification and sentiment analysis are considered for application point of view of this work. The experimental results show that our MWE model is very competitive in all tasks as compared with the state of the art word embedding models such as CBOW, SKIP GRAM, and GloVe.
Text Big Data Analytics: Exploring API opportunity: Internet as global storage how to get the situation awareness from dark data
Venue: 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT)

Text Big Data Analytics Study <Third Wave> is described. Morphological matrix of several Keywords Phrases was collected from Internet's open textual resources using API. The results are analyzed from the point of view that global Internet's audience forms <people to IT> system through that we can study the three levels of behaviour of Global society. Level 1 countries' activity on the Internet. Level 2 technological evolution in countries. Level 3 countries' reaction to destructive stress factors. Cluster analysis was used for investigation of Level 1. Simon Wardley's Value Chain Map was used for investigation of Level 2. And diagrams with percentage ratio of different sets of Keywords Phrases were used for investigation of Level 3. Article is focused on regions of Eurasia and North Africa. The Study findings are following: among post Soviet countries Russia shows the highest activity on Internet and has advantages of economic development, as well as Georgia; Azerbaijan among some countries from Eurasia and North Africa shows the lowest risk to become fragile country under Global warming.
Association Between Cannabis and Psychosis: Epidemiologic Evidence
Venue: Biological Psychiatry

Associations between cannabis use and psychotic outcomes are consistently reported, but establishing causality from observational designs can be problematic. We review the evidence from longitudinal studies that have examined this relationship and discuss the epidemiologic evidence for and against interpreting the findings as causal. We also review the evidence identifying groups at particularly high risk of developing psychosis from using cannabis. Overall, evidence from epidemiologic studies provides strong enough evidence to warrant a public health message that cannabis use can increase the risk of psychotic disorders. However, further studies are required to determine the magnitude of this effect, to determine the effect of different strains of cannabis on risk, and to identify high risk groups particularly susceptible to the effects of cannabis on psychosis. We also discuss complementary epidemiologic methods that can help address these questions.
Cannabis and Psychosis: a Critical Overview of the Relationship
Venue: Current Psychiatry Reports

Interest in the relationship between cannabis use and psychosis has increased dramatically in recent years, in part because of concerns related to the growing availability of cannabis and potential risks to health and human functioning. There now exists a plethora of scientific articles addressing this issue, but few provide a clear verdict about the causal nature of the cannabis psychosis association. Here, we review recent research reports on cannabis and psychosis, giving particular attention to how each report provides evidence relating to two hypotheses: (1) cannabis as a contributing cause and (2) shared vulnerability. Two primary kinds of data are brought to bear on this issue: studies done with schizophrenic patients and studies of first episode psychosis. Evidence reviewed here suggests that cannabis does not in itself cause a psychosis disorder. Rather, the evidence leads us to conclude that both early use and heavy use of cannabis are more likely in individuals with a vulnerability to psychosis. The role of early and heavy cannabis use as a prodromal sign merits further examination, along with a variety of other problem behaviors (e.g. early or heavy use of cigarettes or alcohol and poor school performance) Future research studies that focus exclusively on the cannabis psychosis association will therefore be of little value in our quest to better understand psychosis and how and why it occurs.
Fluctuation phenomena in chaotic Dirac quantum dots: Artificial atoms on graphene flakes

We develop the stub model for the Dirac Quantum Dot, an electron confining device on a grapheme surface. Analytical results for the average conductance and the correlation functions are obtained and found in agreement with those found previously using semiclassical calculation. Comparison with available data are presented. The results reported here demonstrate the applicability of Random Matrix Theory in the case of Dirac electrons confined in a stadium.
Universal Spin Hall Conductance Fluctuations in Chaotic Dirac Quantum Dots

We present complete analytical and numerical results that demonstrate the anomalous universal fluctuations of the spin Hall conductance in chiral materials such as graphene and topological insulators. We investigated both the corresponding fluctuations, the Universal Fractionated and the Universal Quantized, and also the open channels orbital number crossover between the two regimes. In particular, we show that the Wigner Dyson symmetries does not properly describe such conductances and the preponderant role of the chiral classes on the Dirac quantum dots. The results are analytical and solve outstanding issues.
Time reversal symmetry breaking and decoherence in chaotic Dirac billiards

In this work, we perform a statistical study on Dirac Billiards in the extreme quantum limit (a single open channel on the leads) Our numerical analysis uses a large ensemble of random matrices and demonstrates the preponderant role of dephasing mechanisms in such chaotic billiards. Physical implementations of these billiards range from quantum dots of graphene to topological insulators structures. We show, in particular, that the role of finite crossover fields between the universal symmetries quickly leaves the conductance to the asymptotic limit of unitary ensembles. Furthermore, we show that the dephasing mechanisms strikingly lead Dirac billiards from the extreme quantum regime to the semiclassical Gaussian regime.
Optimal Control of Combined Wind and Hydrogen System Based on Genetic Algorithm
Venue: 2016 International Conference on Smart City and Systems Engineering (ICSCSE)

To deal with increasing demand for clean and efficient energy in the residential sector, distributed energy resources including wind energy, fuel cell, etc. have been paid more and more attention. This paper presents an economical optimal energy management strategy for a grid tied wind fuel cell hybrid power supply system. The hybrid system meets the electrical load demand and sells the excess energy to the grid. The objective is to minimize energy cost and make full use of fuel cell system, taking into account the time of use electricity tariff. The optimal control problem is solved using genetic algorithm and test it with real wind condition. The power to/from the inverter, electrolyzer hydrogen power and fuel cell power are the control variables while the hydrogen in the storage tank are the state variables. The performance of the proposed control strategy is tested by simulating different operating scenarios, with or without fuel cell system feed in or rather export to the grid. The results clearly demonstrate that the new method is able to optimize the operating costs and provide optimally schedules of the combined wind and hydrogen system accurately.
Recent Advances in Energy Efficient Routing Protocols for Wireless Sensor Networks: A Review
Venue: IEEE Access

Due to a battery constraint in wireless sensor networks (WSNs) prolonging their lifetime is important. Energy efficient routing techniques for WSNs play a great role in doing so. In this paper, we articulate this problem and classify current routing protocols for WSNs into two categories according to their orientation toward either homogeneous or heterogeneous WSNs. They are further classified into static and mobile ones. We give an overview of these protocols in each category by summarizing their characteristics, limitations, and applications. Finally, some open issues in energy efficient routing protocol design for WSNs are indicated.
An improved harmony search based energy efficient routing algorithm for wireless sensor networks
Venue: Appl. Soft Comput.

new encoding of harmony memory for routing in WSNs has been proposed.A new generation method of a new harmony for routing in WSNs has been proposed.The dynamic adaptation is introduced for the parameter HMCR to improve the performance of the proposed routing algorithm.An effective local search strategy is proposed to improve the convergence speed and the accuracy of the proposed routing algorithm.An energy efficient objective function model is proposed. Wireless sensor networks (WSNs) is one of the most important technologies in this century. As sensor nodes have limited energy resources, designing energy efficient routing algorithms for WSNs has become the research focus. And because WSNs routing for maximizing the network lifetime is a NP hard problem, many researchers try to optimize it with meta heuristics. However, due to the uncertain variable number and strong constraints of WSNs routing problem, most meta heuristics are inappropriate in designing routing algorithms for WSNs. This paper proposes an Improved Harmony Search Based Energy Efficient Routing Algorithm (IHSBEER) for WSNs, which is based on harmony search (HS) algorithm (a meta heuristic) To address the WSNs routing problem with HS algorithm, several key improvements have been put forward: First of all, the encoding of harmony memory has been improved based on the characteristics of routing in WSNs. Secondly, the improvisation of a new harmony has also been improved. We have introduced dynamic adaptation for the parameter HMCR to avoid the prematurity in early generations and strengthen its local search ability in late generations. Meanwhile, the adjustment process of HS algorithm has been discarded to make the proposed routing algorithm containing less parameters. Thirdly, an effective local search strategy is proposed to enhance the local search ability, so as to improve the convergence speed and the accuracy of routing algorithm. In addition, an objective function model that considers both the energy consumption and the length of path is developed. The detailed descriptions and performance test results of the proposed approach are included. The experimental results clearly show the advantages of the proposed routing algorithm for WSNs.
La valoracion que los graduados tienen sobre su formacion universitaria desde su situacion laboral actual

En este articulo analizamos la valoracion de los titulados universitarios sobre la formacion recibida y su utilidad en la ocupacion actual. El objetivo es analizar si existen diferencias por el nivel formativo de origen y el sexo de los estudiantes. A su vez, examinamos si las caracteristicas de la situacion laboral intervienen en estas relaciones afectando diferencialmente segun las caracteristicas sociodemograficas. Los resultados muestran la existencia de diferencias valorativas entre los jovenes segun su origen social y sexo, siendo las mujeres y los jovenes de clase trabajadora los que valoran mejor su formacion recibida. Finalmente, con la introduccion de la situacion laboral como variable de control constatamos que, en algunos casos, estas diferencias valorativas son debidas al contexto laboral actual de los graduados mas que a diferencias sociodemograficas.
Interactive Courseware Quranic Verses on the Integration of Naqli and Aqli Knowledge

This research is an interactive courseware called "Quranic Verses on The Integration of Naqli and Aqli Knowledge" This courseware is developed to facilitate users especially among students to fully understand about sciences knowledge in Quran and will expose the miracle of the Quran with interactive ways. The targets of this courseware are for all ages that learn about sciences knowledge in the Quran especially students who are learning about this knowledge, lecturers who are teaching their students on this knowledge, scientist, researcher and others. This courseware consists of Quranic verses and their meanings. This courseware provided 15 categories of knowledge and its meaning in Quran from Surah alBaqarah to Surah al Naas. Moreover, this courseware also combines the notes and the verses of Quran that are in audio and interactive images forms which is the most effective tool to attract the user to use this courseware. This courseware is developed by using Adobe Flash Player Cs6 as the main platform and some other software is used to support in developing this project. "Interactive Courseware: Quranic Verses on The Integration of Naqli and Aqli Knowledge" is a courseware that is easy to learn and interactive. We hope that with the development of this courseware, it can assist all people especially the Muslims who would like to learn about this knowledge in understanding the verses of Quran and the science knowledge in Quran in easier way as well as it could be as one of teaching tool for lecturers to teach their students on this knowledge. We also hope with this interactive courseware to be applied, it will expose the miracle of the Quran to the world and introduce the science knowledge that has been in the Quran since its existence.  Interactive courseware, Integration, Quran, Naqli, Aqli.
From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways
Venue: Molecular Psychiatry

The human body hosts an enormous abundance and diversity of microbes, which perform a range of essential and beneficial functions. Our appreciation of the importance of these microbial communities to many aspects of human physiology has grown dramatically in recent years. We know, for example, that animals raised in a germ free environment exhibit substantially altered immune and metabolic function, while the disruption of commensal microbiota in humans is associated with the development of a growing number of diseases. Evidence is now emerging that, through interactions with the gut brain axis, the bidirectional communication system between the central nervous system and the gastrointestinal tract, the gut microbiome can also influence neural development, cognition and behaviour, with recent evidence that changes in behaviour alter gut microbiota composition, while modifications of the microbiome can induce depressive like behaviours. Although an association between enteropathy and certain psychiatric conditions has long been recognized, it now appears that gut microbes represent direct mediators of psychopathology. Here, we examine roles of gut microbiome in shaping brain development and neurological function, and the mechanisms by which it can contribute to mental illness. Further, we discuss how the insight provided by this new and exciting field of research can inform care and provide a basis for the design of novel, microbiota targeted, therapies.
The Myth Of Mental Illness Foundations Of A Theory Of Personal Conduct

Thank you very much for reading the myth of mental illness foundations of a theory of personal conduct. As you may know, people have look hundreds times for their chosen books like this the myth of mental illness foundations of a theory of personal conduct, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious virus inside their laptop.
Voice Recognition Using k Nearest Neighbor and Double Distance Method
Venue: 2016 International Conference on Industrial Engineering, Management Science and Application (ICIMSA)

Voice recognition process is started with voice feature extraction using Mel Frequency Cepstrum Coefficient (MFCC) The purpose of the MFCC method is to get the signal feature that correlate to the human voice. The converted signal from analog to digital is needed in the MFCC method. The digital signal has a time domain and it make the analysis harder. So, the domain time is converted to time domain for make the analysis more accurate. Furthermore, after get the feature, the recognition step is using k Nearest Neighbor (kNN) method with k number is one. Euclidean Distance is used to get the similarity of the data training and data testing. The previous research shows that kNN has a high accuracy if use normal data, but it has lower accurate when using outlier data. Base on this problem, this research develop a new method to handle the outlier data using kNN and double distance measurement. The double distance method is note each distance of each data to the center of the kNN data. The calculation of the distance is used on recognition step. The accuracy of the method is tested on experiment. The experiment is using 11 subjects as data training and data testing. Each voice of subject is recorded three times. The result of the experiment with kNN method with one data center is 84.85% and the experiment result using double distance measurement is 96.97% The result shows that the double distance method increase the accuracy of voice recognition.
Registries of Domain Relevant Semantic Reference Models Help Bootstrap Interoperability in Domains with Fragmented Data Resources
Venue: SWAT4LS

The specialist field of rare diseases must connect its vast array of globally distributed disease and patient registries to maximise their value. Unfortunately, many registries are "boutique" with few or no staff with formal informatics training. At a series of Bring Your Own Data workshops, we helped registry owners transform their data into formally structured triple stores following the Linked Data principles and demonstrated the potential of data linkage. We documented several useful approaches that we believe could be followed independently by other registry owners worldwide, including: that the transformation to Linked Data could be considered as passing through layers of increasing semantic complexity; that only a subset of ontologies are relevant at each layer; and that certain data transformation processes could be modelled as an "archetype" and presented to registry staff to fill in with their data. We propose that formally capturing these ontological layers and archetypes, and registering them as a reference and teaching resource will facilitate the wider community of non expert data owners self direct their own data transformations.
An Examination of the Effectiveness of Child Endangerment Laws in Preventing Child Fatalities in Alcohol Involved Motor Vehicle Crashes.
Venue: Journal of studies on alcohol and drugs

The aim of this study was to assess the impact of U.S. child endangerment laws on the prevalence of child passengers fatally injured in motor vehicle crashes in which the adult driver was drinking.  We used data from the 2002 2012 Fatality Analysis Reporting System. We conducted both bivariate and multivariate analyses using Heckman selection models.  After adjusting for several cofactors, including driver demographics and blood alcohol concentration, child seat positioning, and seat belt laws, we found that passing a DUI child endangerment law may have no impact at all on the likelihood of finding impaired drivers among those driving with children.  There are a number of reasons why DUI child endangerment laws have not been effective in saving the lives of young passengers who are driven by adult drinking drivers. These reasons include lack of publicity and education, as well as issues related to enforcement. Potential solutions are suggested that include examining sanctions and strengthening of DUI child endangerment laws.