Thermal QCD Sum Rules for sigma(600) Meson

In the present work, the temperature dependence of the scalar mesons parameters is investigated in the framework of thermal QCD sum rules. We calculate sigma-pole and the non-resonant two-pion continuum contributions to the spectral density. Taking into account additional operators appearing at finite temperature, the thermal QCD sum rules are derived. The temperature dependence of the shifts in the mass and leptonic decay constant of scalar sigma(600) meson is calculated.Comment: 10 pages, 2 figure

Weather and Photoperiod Indices of Autumn and Winter Dabbling Duck Abundance in the Mississippi and Atlantic Flyways of North America

Climate change may influence autumn and winter distributions of dabbling ducks throughout the Atlantic and Mississippi Flyways of North America. To determine how weather and photoperiod influenced autumn-winter abundances of dabbling ducks at staging areas in eastern North America, I modeled weather and photoperiod variables with rate of change in relative abundance of various dabbling duck species over space and time. Latitude was incorporated into models to determine if changes in duck abundance in relation to weather severity were influenced by locale. Changes in abundance were best described by weather models incorporating temperature and snowfall variables for all species except blue-winged teal (Anas discors), which was best explained by photoperiod. Latitude was present in all top models for all study species. My findings aid wildlife management efforts in predicting potential changes in the non-breeding distribution of ducks resulting from climate change

Pengenalan Ucapan Kata Sebagai Pengendali Gerakan Robot Lengan Secara Real-Time dengan Metode Linear Predictive Coding – Neuro Fuzzy

Sejak beberapa dekade terakhir ini, peran robot dalam industri maupun kehidupan sehari-hari semakin meningkat. Hampir tidak ada cabang industri teknologi tinggi yang tidak dibantu robot. Dalam kehidupan sehari-hari, berbagai bentuk robot diciptakan untuk membantu atau memudahkan aktivitas manusia. Namun seiring dengan tingkat kebutuhan manusia terhadap robot, tingkat resiko kesulitan manusia dalam menggunakan teknologi tersebut semakin tinggi.  Hal ini ditunjukkan dengan banyaknya kecelakaan akibat tidak adanya teknologi yang memudahkan manusia dalam berinteraksi dengan robot secara interaktif. Pada umumnya robot-robot tersebut dikendalikan melalui input keyboard dari Personal Computer (PC) atau remote control analog, dan bukan melalui suara ucapan. Oleh karena itu perlu dirancang suatu robot yang bergerak sesuai perintah suara ucapan. Jika suara ucapan digunakan untuk mengendalikan suatu robot, maka sistem yang dipakai harus berjalan secara realtime sehingga robot dapat dikendalikan secara interaktif. Pada tugas akhir ini akan dikembangkan sebuah suatu perangkat lunak sistem pengenalan suara menggunakan metode Linear Predictive Coding (LPC)  dan Neuro-Fuzzy. Perangkat lunak tersebut akan digunakan untuk mengendalikan robot lengan yang terhubung pada kabel serial RS-232 suatu PC melalui komunikasi serial. Dalam penelitian ini diharapkan dengan menerapkan metode Linear Predictive Coding (LPC)  dan Neuro-Fuzzy pada sistem pengenalan suara dapat digunakan untuk mengidentifikasi perintah suara dengan tingkat keberhasilan yang tinggi sehingga dapat digunakan sebagai pengendali robot yang handal. Berdasarkan dari hasil pengujian yang dilakukan pengenalan jaringan untuk data baru lebih rendah terhadap data latihan. Prosentase pengenalan suara dari dalam database sebesar 100 %, dan prosentase untuk pengenalan suara dari luar database 12,5%

OPTIMIZATION OF A SELECTION SCHEME FOR MILK COMPOSITION AND YIELD IN MILKING EWES : Example of the Lacaune Breed

Management of the genetic improvement of milking ewes depends: on the obvious fact that they are both dairy animals and sheep. This paper deals with the Lacaune breed situation in France. It paints out the way to build the selection scheme,on two particular aspects:the need to rationalize and simplify milk recording both for milk composition and milk yield, and the concurrent use of AI and natural mating, within the scope of a pyramidal management of the population. We sum up the main results of studies on these different aspects in this paper. In the course of the last twenty years,phenotypic and genetic improvement for the nucleus and base flocks agrees with these theorical studies

CRITERIA FOR EVALUATING A SELECTION SCRENE: SOME PROPOSALS

It is possible to describe a realized selection by means of an indicator, f(x): probability for an individual of value x to be selected. Various models for this indicator are proposed, in the univariate case, and on the assumption that individuals are ranked on a linear index of measured variables. Estimators are defined on the basis of these models for rating traits controlled during selection. A numeric example with ewes is given

Study on consumers’ attitudes towards Terms and Conditions (T&Cs): Final Report

This report was produced under the EU Consumer Programme (2014-2020) in the frame of a service contract with the Consumers, Health, Agriculture and Food Executive Agency (Chafea) acting under the mandate from the European Commission.Previous research has shown that when buying products and services online, the vast majority of consumers accept Terms and Conditions (T&Cs) without even reading them. The current research examined effects of interventions aimed at making consumers aware of the quality of such T&Cs. This was done by 1) shortening and simplifying the T&Cs and 2) adding a quality cue to an online store, such as the presence of a logo of a national consumer organisation accompanied by the statement “these terms and conditions are fair”. The main study consisted of three experiments and was conducted in 12 Member States with 1000 respondents in each Member State. In each experiment, consumers visited an online store and went through all the steps of an ordering process. One of these steps was accepting the T&Cs. Key findings are that shortening and simplifying the terms and conditions results in improved readership of the T&Cs, a slightly better understanding of the T&Cs, and a more positive attitude towards the T&Cs. Moreover, adding a quality cue to an online store increases trust and purchase intentions. Which quality cue is trusted the most depends on what type of online store consumers are visiting. For domestic online stores, a quality cue by a national consumer organisation is trusted most; for foreign online stores, a quality cue by a European consumer organisation is trusted most. The patterns were similar across Member States

A Hybrid Approach Support Vector Machine (SVM) – Neuro Fuzzy For Fast Data Classification

In recent decade, support vector machine (SVM) was a machine learning method that widely used in several application domains. It was due to SVM has a good performance for solving data classification problems, particularly in non-linear case. Nevertheless, several studies indicated that SVM still has some inadequacies, especially the high time complexity in testing phase that is caused by increasing the number of support vector for high dimensional data. To address this problem, we propose a hybrid approach SVM – Neuro Fuzzy (SVMNF), which neuro fuzzy here is used to avoid influence of support vector in testing phase of SVM. Moreover, our approach is also equipped with a feature selection that can reduce data attributes in testing phase, so that it can improve the effectiveness of time computation. Based on our evaluation in real benchmark datasets, our approach outperformed SVM in testing phase for solving data classification problems without significantly affecting the accuracy of SVM

A Hybrid Approach Support Vector Machine (SVM) – Neuro Fuzzy for Fast Data Classification

In recent decade, support vector machine (SVM) was a machine learning method that widely used in several application domains. It was due to SVM has a good performance for solving data classification problems, particularly in non-linear case. Nevertheless, several studies indicated that SVM still has some inadequacies, especially the high time complexity in testing phase that is caused by increasing the number of support vector for high dimensional data. To address this problem, we propose a hybrid approach SVM – Neuro Fuzzy (SVMNF), which neuro fuzzy here is used to avoid influence of support vector in testing phase of SVM. Moreover, our approach is also equipped with a feature selection that can reduce data attributes in testing phase, so that it can improve the effectiveness of time computation. Based on our evaluation in real benchmark datasets, our approach outperformed SVM in testing phase for solving data classification problems without significantly affecting the accuracy of SVM