Impact of contraception and IVF hormones on metabolic, endocrine, and inflammatory status

Assisted reproductive technologies (ART) represent commonly utilized management strategies for infertility with multifactorial causes (including genetically predisposed diseases). Amongst ART, in vitro fertilization (IVF) is the most popular. IVF treatment may predispose the mother to increased risks and complications during pregnancy, and there may be adverse fetal outcomes. Hormonal therapies, including oral contraceptives, may impair glucose and lipid metabolism, and promote insulin resistance and inflammation. IVF treatment involves administration of reproductive hormones, similar in composition but in much higher doses than those used for oral contraception. The provision of IVF reproductive hormones to mice associates with glucose intolerance. In addition, the physiological and hormonal changes of pregnancy can trigger an inflammatory response, and metabolic and endocrine changes. There is controversy regarding the potential effects of IVF hormonal therapies in the promotion of diabetogenic and inflammatory states, additional to those that occur during pregnancy, and which may therefore predispose women with IVF-conceived pregnancies to adverse obstetric outcomes compared with women with spontaneously conceived pregnancies. This review summarizes the limited published evidence regarding the effect of IVF-based fertility therapies on glucose homeostasis, insulin resistance, cardio-metabolic profile, and markers of inflammation

Optimization of Wastewater Treatment Plant Design using Process Dynamic Simulation: A Case Study from Kurdistan, Iraq

Satisfactory effluent characteristics are indispensable to evaluate the performance of any wastewater treatment plant (WWTP) design. Dynamic simulation software has a great role in pursuing this objective, in which an efficient and cost-effective design is constantly performed. In this study, a dynamic simulator sewage treatment operation analysis over time (STOAT) has been used under certain influent conditions to optimize design possibilities for modifying an existing primary WWTP College of Engineering Wastewater Treatment Plant (COEWWTP) at Erbil, Kurdistan, Iraq. The optimization was established on the basis of total suspended solids (TSS) and biochemical oxygen demand (BOD) characteristics in the effluent. Two alternative design schemes were proposed; trickling biofilter and aeration basin. In the dynamic simulation for the investigated design schemes, the predicted effluent profile showed that each of the existing and trickling biofilter processes has failed to correspond to the valid effluent limitation, whereas predicted results of the aeration basin exhibited an effluent profile that meets TSS and BOD allowable limits. Different simulation models have been implemented by STOAT to simulate treatment processes in studied design approaches: ASAL 1 model; BOD model; BOD semi-dynamic model; and SSED 1 model. This study offers an additional understanding of WWTP design and facilitates the application of dynamic simulators as tools for wastewater treatment development in Kurdistan

Numerical Simulation of a High Mach Number Jet Flow

The recent efforts to develop accurate numerical schemes for transition and turbulent flows are motivated, among other factors, by the need for accurate prediction of flow noise. The success of developing high speed civil transport plane (HSCT) is contingent upon our understanding and suppression of the jet exhaust noise. The radiated sound can be directly obtained by solving the full (time-dependent) compressible Navier-Stokes equations. However, this requires computational storage that is beyond currently available machines. This difficulty can be overcome by limiting the solution domain to the near field where the jet is nonlinear and then use acoustic analogy (e.g., Lighthill) to relate the far-field noise to the near-field sources. The later requires obtaining the time-dependent flow field. The other difficulty in aeroacoustics computations is that at high Reynolds numbers the turbulent flow has a large range of scales. Direct numerical simulations (DNS) cannot obtain all the scales of motion at high Reynolds number of technological interest. However, it is believed that the large scale structure is more efficient than the small-scale structure in radiating noise. Thus, one can model the small scales and calculate the acoustically active scales. The large scale structure in the noise-producing initial region of the jet can be viewed as a wavelike nature, the net radiated sound is the net cancellation after integration over space. As such, aeroacoustics computations are highly sensitive to errors in computing the sound sources. It is therefore essential to use a high-order numerical scheme to predict the flow field. The present paper presents the first step in a ongoing effort to predict jet noise. The emphasis here is in accurate prediction of the unsteady flow field. We solve the full time-dependent Navier-Stokes equations by a high order finite difference method. Time accurate spatial simulations of both plane and axisymmetric jet are presented. Jet Mach numbers of 1.5 and 2.1 are considered. Reynolds number in the simulations was about a million. Our numerical model is based on the 2-4 scheme by Gottlieb & Turkel. Bayliss et al. applied the 2-4 scheme in boundary layer computations. This scheme was also used by Ragab and Sheen to study the nonlinear development of supersonic instability waves in a mixing layer. In this study, we present two dimensional direct simulation results for both plane and axisymmetric jets. These results are compared with linear theory predictions. These computations were made for near nozzle exit region and velocity in spanwise/azimuthal direction was assumed to be zero

The Effects of Blades Number, Blade Thickness, Blade Tip Angle, and Twist Angle on the Performance of the Rotor Wind Turbines

 تعتبر محركات الرياح واحدة من المصادر المهمة في توليد الطاقة الكهربائية. ذلك لان تعتبر من مصادر الطاقة المتجددة. كما انها في مقدمة المصادر الصديقة للبيئة.هذا البحث يتناول كيفية زيادة كفاءة هذه المحركات من خلال تسليط الضوء على العوامل الموثرة الرئيسية في في سلوك هذه المحركات. كما يتضمن البحث تقييم عن مدى اعتمادية معامل القدرة لهذه المحركات على معدل سرعة الرياح الخاص بامكان تنصيب هذه المحركات. يستخدم هذا البحث ايضا احد برامج المحاكات لدراسة سلوك المحركات مع تغيير عدد الريش وكذلك زوايها. The paper gives an experimental study on the performance on the wind turbine rotors with several numbers of blades as well as twist angle. The main goal of this study is to demonstrate the effect blades number, tip angles as well as the twist angle of the blades on the power coefficient (Cp) of the rotor. Moreover, this result represents a simple evaluation about the range of depending power coefficient on the average wind speed. Also, this paper studies the performance of wind turbines which are tested by carrying out 2-dimensional dynamic using ANSYS-Fluent

An assessment of artificial damping models for aeroacoustic calculations

We present a study of the effect of artificial dissipation models on nonlinear wave computations using a few high order schemes. Our motivation is to assess the effectiveness of artificial dissipation models for their suitability for aeroacoustic computations. We solve three model problems in one dimension using the Euler equations. Initial conditions are chosen to generate nonlinear waves in the computational domain. We examine various dissipation models in central difference schemes such as the Dispersion Relation Preserving (DRP) scheme and the standard fourth and sixth order schemes. We also make a similar study with the fourth order MacCormack scheme due to Gottieb and Turkel

Breathers on quantized superfluid vortices

We consider the propagation of breathers along a quantized superfluid vortex. Using the correspondence between the local induction approximation (LIA) and the nonlinear Schrödinger equation, we identify a set of initial conditions corresponding to breather solutions of vortex motion governed by the LIA. These initial conditions, which give rise to a long-wavelength modulational instability, result in the emergence of large amplitude perturbations that are localized in both space and time. The emergent structures on the vortex filament are analogous to loop solitons but arise from the dual action of bending and twisting of the vortex. Although the breather solutions we study are exact solutions of the LIA equations, we demonstrate through full numerical simulations that their key emergent attributes carry over to vortex dynamics governed by the Biot-Savart law and to quantized vortices described by the Gross-Pitaevskii equation. The breather excitations can lead to self-reconnections, a mechanism that can play an important role within the crossover range of scales in superfluid turbulence. Moreover, the observation of breather solutions on vortices in a field model suggests that these solutions are expected to arise in a wide range of other physical contexts from classical vortices to cosmological strings

Mathematical modelling of solid tumour growth: a Dynamical Density Functional Theory-based model

We present a theoretical framework based on an extension of Dynamical Density Functional Theory (DDFT) to describe the structure and dynamics of cells in living tissues and tumours. DDFT is a microscopic statistical mechanical theory for the time evolution of the density distribution of interacting many-particle systems. The theory accounts for cell pair-interactions, different cell types, phenotypes and cell birth and death processes (including cell division), in order to provide a biophysically consistent description of processes bridging across the scales, including the description of the tissue structure down to the level of the individual cells. Analysis of the model is presented for a single species and a two-species cases, the latter describing competition between a cancerous and healthy cells. In suitable parameter regimes, model results are consistent with biological observations. Of particular note, divergent tumour growth behaviour, mirroring metastatic and benign growth characteristics, are shown to be dependent on the cell pair-interaction parameters

Detection of (FecB) Gene Polymorphism in Local Sheep Breed at Different Area of Iraq

The rate of ovulation has largely influenced by both genetic and environmental factors. Currently, "the Booroola FecB gene" considered as a guide to improve the twin productivity in sheep flocks meanwhile retain the important other features desired in the flock. This study intended to detect the mutation in FecB gene using PCR-RFLP screening method by using specific primers designed to introduce a point mutation in PCR product of FecB gene. A thirty, local sheep breed from the middle and south part of Iraq were used in this study. FecB gene was detected by forced digestion of PCR products using Eco471 (AvaII) digestion enzyme. The results of this study revealed the lack of (190bp band wild type) gene mutation in all samples of the study that is meant local Iraqi sheep breed are non-carrier for polymorphism genetic factor. However, future study is highly recommended with a large number of "local sheep" for better understanding of this feature

Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process

Manufacturing cost for machining components is affected by the available machining parameters which include the selection of appropriate cutting material, cutting tools, and machining data of cutting speed, feed, and depth of cut. Computerized machining data systems have been classified into two general types, the mathematical model and the database model. The database model is based on the collection and storage of a large quantity of data from laboratory experiments and workshop experience, which can then simply retrieve recommended cutting speeds and feed. The most widely used source of such data is the Machining Data Handbook (MDH) published by Metcut Research Association, (1980). Although the handbook approach is often a logical and effective solution to the requirement of machining data, but it has limitations. The applications of computational intelligence in manufacturing, in particular, play a leading role in the technological development of intelligent manufacturing systems. In this study an intelligent learning system was developed to automate the collection of the machining data used by the skilled machinist. The Machine Learning Method is utilized for this task, which gives the computer the ability to learn. Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. ANN is a computer-based simulation of the living nervous system which works quite differently from conventional programming. The design network is trained by presenting several target machining data that the network must learn according to a learning rule (algorithm). In designing the network, a combination of back propagation or generalized delta learning rule with sigmoid transfer function has been used. The machining data available in MDH was used to train the designed network. One cutting material (medium carbide steel) with its complete set of cutting tools (High Speed Steel, Brazed Uncoated Carbide, Indexable Uncoated Carbide, and Coated Carbide) discretized into 243 data sets was used in one training session for the designed network. Building knowledge within the network was measured by calculating the total percentage of error between target machining data and the outputs from the network during the training process. The process of building the machining data knowledge (training) was successfully achieved. A Comparison between the learned target machining data and data from MDH shows a low percentage of error. An Intelligent Learning System for the turning process was developed. Visual C++ object-oriented programming language was used to build the Intelligent Learning System for Turning. Live data can be fed into the system from indirect way (Keyboard, Internet) or directly from machine to computer. The developed system may open the door for automating the collection of machining data for all manufacturing processe