Genetic parameters for tunisian holsteins using a test-day random regression model

Genetic parameters of milk, fat, and protein yields were estimated in the first 3 lactations for registered Tunisian Holsteins. Data included 140,187; 97,404; and 62,221 test-day production records collected on 22,538; 15,257; and 9,722 first-, second-, and third-parity cows, respectively. Records were of cows calving from 1992 to 2004 in 96 herds. (Co)variance components were estimated by Bayesian methods and a 3-trait-3-lactation random regression model. Gibbs sampling was used to obtain posterior distributions. The model included herd × test date, age × season of calving × stage of lactation [classes of 25 days in milk (DIM)], production sector × stage of lactation (classes of 5 DIM) as fixed effects, and random regression coefficients for additive genetic, permanent environmental, and herd-year of calving effects, which were defined as modified constant, linear, and quadratic Legendre coefficients. Heritability estimates for 305-d milk, fat and protein yields were moderate (0.12 to 0.18) and in the same range of parameters estimated in management systems with low to medium production levels. Heritabilities of test-day milk and protein yields for selected DIM were higher in the middle than at the beginning or the end of lactation. Inversely, heritabilities of fat yield were high at the peripheries of lactation. Genetic correlations among 305-d yield traits ranged from 0.50 to 0.86. The largest genetic correlation was observed between the first and second lactation, potentially due to the limited expression of genetic potential of superior cows in later lactations. Results suggested a lack of adaptation under the local management and climatic conditions. Results should be useful to implement a BLUP evaluation for the Tunisian cow population; however, results also indicated that further research focused on data quality might be needed.     

A new approach for cost allocation and reactive power pricing in a deregulated environment

Power industry has been facing restructuring problems during the past decade. Appropriate management of reactive power is very essential for supporting power system security. Reactive power has dominant effects on real energy transfer. Furthermore, it can support the secure operation of the system as an ancillary service. However, most researches have been focused on active power as the main good transacted in electricity markets. On the other hand, while reactive power production cost is highly dependent on real power output, it is mainly confined to local consumption. As a result, to avoid market power and to maintain the secure operation of the system, a fair cost allocation method seems to be very essential. Appropriate pricing of reactive power as an ancillary service has been a challenging problem during the past decade. However, most methods proposed so far for reactive power pricing are essentially based on empirical approximations. In this paper, a new method for reactive power cost allocation is proposed. The method is based on calculation of the accurate cost which will be imposed on generators due to supporting reactive power. The proposed method is fair, accurate and realistic and it can be formulated very easily. Furthermore, a new approach based on tracing algorithm is proposed for pricing of reactive power which considers the cost of both active and reactive losses allocated to each generator. Application of the proposed method on IEEE 9-bus standard network confirms its validity and effectiveness.     

Effect of Adding Transition Metals to Copper on the Dehydrogenation Reaction of Ethanol

The present work aims to investigate the effect adding Ag, Co, Ni, Cd and Pt to copper on ethanol dehydrogenation. The catalysts synthesized by deposition–precipitation method were characterized using various physicochemical methods such as N₂ adsorption–desorption, TPR, SEM–EDX, XRD, XPS and TGA–DSC-MS. Catalytic evaluation results revealed that the predominant product of the reaction was acetaldehyde. Monometallic copper or mixed with Cd, Ag or Co show good catalytic performances. Adding nickel to copper improves the process conversion but reduces acetaldehyde selectivity, giving rise to methane in produced hydrogen. Pt-Cu/SiO₂ catalyst guides the reaction towards diethyl ether. Time on stream tests performed during 12 h at 260 °C, showed that adding Cd to Cu enhances its stability by over 30% of conversion, this is explained by the reduction of copper crystallites sintering, which makes Cd-Cu/SiO₂ a promising catalyst for the production of acetaldehyde by ethanol dehydrogenation.

Graphic Abstract

     

Voltage security constrained active and reactive power pricing considering reactive market power

Reactive power support and voltage stability are considered to be very essential for preserving system security. This paper proposes a new market-based approach for voltage security constrained active and reactive power pricing. The problem is modeled as a multi-objective OPF in which the social welfare and the distance to voltage collapse point are maximized at same time. An important feature of the proposed approach is using the reactive market power index, Herfindahl–Hirschman Index (HHI), to assign optimal weighting factors of the multi-objective function. In addition, in this method not only the reactive power is considered but typical price is also provided based on real costs. The results show that the proposed method allows market operators and participants to preserve the level of security and social welfare within acceptable range by controlling the weighting factors and monitoring the HHI with regard to reactive market power. Using the proposed method and considering reactive power market, a suitable range of weighting factor can be determined ensuring the optimal bidding as well as satisfying the voltage security of the system.     

DPV: a taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection

Multimedia Tools and Applications - Deep learning (DL) is a type of machine learning capable of processing large quantities of data to provide analytic results based on a particular...     

Enhancing the prediction of type 2 diabetes mellitus using sparse balanced SVM

Multimedia Tools and Applications - The natural population-based prediction of type 2 diabetes is costly since it needs a high number of resources. Even though much research has used machine...     

Location detection and disambiguation from twitter messages

A remarkable amount of Twitter messages are generated every second. Detecting the location entities mentioned in these messages is useful in text mining applications. Therefore, techniques for extracting the location entities from the Twitter textual content are needed. In this work, we approach this task in a similar manner to the Named Entity Recognition (NER) task, but we focus only on locations, while NER systems detect names of persons, organizations, locations, and sometimes more (e.g., dates, times). But, unlike NER systems, we address a deeper task: classifying the detected locations into names of cities, provinces/states, and countries in order to map them into physical locations. We approach the task in a novel way, consisting in two stages. In the first stage, we train Conditional Random Fields (CRF) models that are able to detect the locations mentioned in the messages. We train three classifiers: one for cities, one for provinces/states, and one for countries, with various sets of features. Since a dataset annotated with this kind of information was not available, we collected and annotated our own dataset to use for training and testing. In the second stage, we resolve the remaining ambiguities, namely, cases when there exists more than one place with the same name. We proposed a set of heuristics able to choose the correct physical location in these cases. Our two-stage model will allow a social media monitoring system to visualize the places mentioned in Twitter messages on a map of the world or to compute statistics about locations. This kind of information can be of interest to business or marketing applications.     

Energy efficient teaching-learning-based optimization for the discrete routing problem in wireless sensor networks

Wireless sensor networks (WSNs) are composed of sensor nodes, having limited energy resources and low processing capability. Accordingly, major challenges are involved in WSNs Routing. Thus, in many use cases, routing is considered as an NP-hard optimization problem. Many routing protocols are based on metaheuristics, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Despite the fact that metaheuristics have provided elegant solutions, they still suffer from complexity concerns and difficulty of parameter tuning. In this paper, we propose a new routing approach based on Teaching Learning Based Optimization (TLBO) which is a recent and robust method, consisting on two essential phases: Teacher and Learner. As TLBO was proposed for continuous optimization problems, this work presents the first use of TLBO for the discrete problem of WSN routing. The approach is well founded theoretically as well as detailed algorithmically. Experimental results show that our approach allows obtaining lower energy consumption which leads to a better WSN lifetime. Our method is also compared to some typical routing methods; PSO approach, advanced ACO approach, Improved Harmony based approach (IHSBEER) and Ad-hoc On-demand Distance Vector (AODV) routing protocol, to illustrate TLBO’s routing efficiency.     

Simultaneously achievement of high recoverable energy density and efficiency in sodium niobate-based ceramics

Journal of Materials Science: Materials in Electronics - Single lead-free Na0.73Bi0.09(Nb1???xTax)O3 (x?=?0, 0.10, 0.20, 0.30, and 0.40) ceramic phases were processed...