Growth and biochemical changes in mammary glands of ewes from 1 to 18 months of age

Growth and development of the mammary gland of ewe lambs was characterized by changes of various biochemical constituents. Mammary glands were removed from 49 ewe lambs ranging between 1 and 18 mo of age, weighed before and after trimming, and samples of the trimmed glands were fat extracted and dried. The dried fat-free tissues were weighed, ground, and analyzed for nucleic acids, nitrogen, sodium, potassium, and chloride contents. Fresh samples were used for glycogen determination. Trimmed weight of mammary glands increased rapidly after the 9th mo of age. The pattern was similar for nucleic acid content of mammary glands. Dry matter and fat contents of mammary glands increased up to the 9th mo, fat decreased thereafter, and dry matter remained constant. Protein content was low up to the 9th mo and increased thereafter. Sodium and glycogen content in mammary glands increased, but potassium decreased linearly from 1 to 18 mo of age. Allometric growth of mammary tissue started at about 3 mo of age, before onset of puberty at 8 mo of age, but intense mammary metabolic activity started only after the ewe attained puberty.     

Fault diagnosis in power transformers using multi-class logical analysis of data

This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of condition based maintenance (CBM), historical data containing condition indices and the state of the machine are the inputs to LAD. After training and testing phases, LAD generates patterns that characterize the faulty states according to the type of fault, and differentiate between these states and the normal state. These patterns are found by solving a mixed 0–1 integer linear programming problem. They are then used to detect and to identify a future unknown state of equipment. The diagnostic technique has already been tested on several known machine learning datasets. The results proved that the performance of this technique is comparable to other conventional approaches, such as neural network and support vector machine, with the added advantage of the clear interpretability of the generated patterns, which are rules characterizing the faults’ types. To demonstrate its merit in fault diagnosis, the technique is used in the detection and identification of faults in power transformers using dissolved gas analysis data. The paper reaches the conclusion that the multi-class LAD based fault detection and identification is a promising diagnostic approach in CBM.     

A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems

This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their operational constraints, such as transmission losses, valve-point effects, multi-fuel options, prohibited operating zones, ramp rate limits and spinning reserve. These practical operation constraints which can usually be found at the same time in realistic power system operations make the economic load dispatch problem a nonsmooth optimization problem having complex and nonconvex features with heavy equality and inequality constraints.The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. To improve the global optimization property of DE, the PSO procedure is integrated as additional mutation operator.The effectiveness of the proposed algorithm (termed DEPSO) is demonstrated by solving four kinds of ELD problems with nonsmooth and nonconvex solution spaces. The comparative results with some of the most recently published methods confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions for practical ELD problems.     

Design for Six Sigma through collaborative multiobjective optimization

This paper presents a global methodology for designing product for Six Sigma. First, we combine a feasibility-modeling technique with an interactive multiobjective algorithm taking into account the decision maker’s preferences (IMOP) to generate several Pareto-optimal solutions that maintain a probability of constraint satisfaction. These solutions are called reliable Pareto-optimal solutions.The solutions found by the algorithm fulfill as much as possible the decision makers’ requirements. Second, we develop a procedure for choosing a solution for implementation from among the reliable Pareto-optimal solutions generated by the algorithm. This procedure is based on the robust design and philosophy of Six Sigma. Finally, the critical characteristics are identified to help the managers develop the manufacturing system and its related control plans in order to achieve quality products. The proposed methodology is applied to vehicle crash-worthiness design optimization for side impact with structural weight and front door velocity under side impact as objectives.     

Candidate architectures for emerging IoV: a survey and comparative study

Intelligent Transportation System (ITS) is observing significant evolution in terms of technology and investment worldwide. This has given birth to the new concept of Internet of vehicles (IoV) as one of the leading applications of the Internet of Things. IoV aims to offer a better sharing of information and communication between vehicles, enabling higher cooperation for common interests. IoV is increasingly attracting the interest of a significant body of research. The e ort was mostly focused on solving various problems encountered in traditional VANETs, such as lack of coordination between vehicles, insufficient information, scalability, etc. Rapidly, IoV observed, particularly interesting advances taking advantage of exponential growth in communication and data analysis technologies. This includes cloud and/or fog computing, large data analytics, machine learning, and artificial intelligence. In this paper, we make a survey of the existing and recently proposed architecture solutions for IoV systems. Moreover, we define a list of criteria, features, and properties associated to the various architectures in order of making critical and insightful comparisons and assessments. Finally, we outline the key future research perspectives on the topic and define the key technical aspects that will help drive the future of IoV architectures.

     

On Microstructure and Microhardness of Isothermally Aged UNS S32760 and the Effect on Toughness and Corrosion Behavior

This paper investigates the microstructure and secondary phase precipitations obtained in UNS S32760 super duplex stainless steel and their effect on impact toughness and corrosion resistance. The heat treatment included first solution annealing at 1150 °C followed by water quenching, then isothermal heating at different temperatures from 350 to 950 °C for different times, ranging from less than 1 min to 600 min, followed by water quenching again. Microscopic investigation, microhardness tests, and x-ray diffraction (XRD) analysis were used to identify the microstructure and secondary phase precipitations formed by heat treatment. The study indicates a fair correlation between the microscopic observations and microhardness results, while XRD analysis defined the phase’s chemistry and confirmed the microscopic and hardness results. In addition to the austenite (γ) and ferrite (α) phases of the duplex structure, secondary phases of (σ, χ, and chromium nitrides) are observed at a high temperature range, while (?) and (aged ?) are observed at a lower temperature range. It is concluded that the microhardness test can be used to identify the phases appearing in the microstructure, which results in fair prediction for the TTT diagram and σ-phase range. The variation of toughness and corrosion resistance by heat treatment differs depending on the secondary phase formation.     

A local approach to creep fracture by slow crack growth in an MDPE: Damage modelling

Slow crack growth (SCG) behaviour has been investigated under creep conditions in a medium density ethylene–butene copolymer (MDPE) on both axisymmetrical Full Notched Creep Tensile (FNCT) and Double Edge Notched Tensile (DENT) samples tested at 60 °C. An attempt is made to predict the long-term failure of a component under creep loading conditions, using an incremental damage law. The experimental creep damage observations were compared to the creep stress–strain distributions calculated by finite element method. Such comparison can provide a damage evolution law as a function of the maximum principal stress and the creep strain. The failure criterion is expressed in terms of a critical creep damage over a critical distance. This model is applied to creep crack growth on the FNCT and DENT samples.