Generalized 3D high cycle fatigue criteria for multiscale bridging‐based progressive damage analysis of multilayer composite parts under random loads and material deterioration

Two frameworks are employed to develop two distinct categories of multiaxial high cycle fatigue life assessment models for composite components experiencing general and random loading conditions. In this regard, the decay in the material properties with cycles is also taken into account. It is obvious that in multilayer components, the fatigue failure is a progressive process that may be accompanied by gradual or sudden changes in the material properties and, consequently, the resulting stresses. In addition to using the traditional progressive damage analyses, a new concept is proposed for tracing of the localized fatigue failures more accurately. It is postulated that generally, the stress components have distinct frequencies, phase shifts, and mean values that all vary with time in a random manner. The proposed fatigue criteria, especially, the equivalent‐stress–based ones, are capable of predicting various fatigue failure modes, such as the fibre breakage, matrix cracking, and interfacial debonding. A special and comprehensive fatigue failure tracking and cycle counting algorithms that are capable of handling the mentioned general peculiarities are proposed. The proposed HCF criteria and the relevant fatigue life assessment algorithm are then implemented on a composite multilayer mono‐leaf spring of a realistic vehicle under a random field‐measured loading condition, as a typical component, and the results are compared and the experimental results conducted by the authors, for accuracy investigations. The considered stochastic road inputs have been chosen on the basis of the consumption times and field measurements.     

Experimental and numerical study of isotropic circular plates' response to underwater explosive loading,created by conic shock tube

Today, to reduce the cost and increase the safety, test devices like conic shock tube have been widely used to investigate the underwater explosion phenomenon and its impact on structures. A shock tube is designed, manufactured and utilized in the mechanic of explosion laboratory of mechanic faculty of K.N. Toosi University of Technology to study the effect of isotropic metal plates' material in the present study. The source which creates shock in the utilized shock tube is an explosive material and the positive point is that in such a tube a high pressure is produced with a tiny explosive charge. In order to investigate the effect of the material and the geometry of the utilized metal plate, three materials are considered with two different thicknesses in the experimental tests. The behavior of the plate can be measured if the amount of the pressure produced by the explosive charge and the amount of plate's transformation is specified. To present a semi‐experimental equation of the behavior of the plate which is under the explosion loading with the water interface in the experimental tests, numerical simulation is performed with LS‐Dyna software. At the end, by combining the experimental and simulation results, the effect of the material and the thickness changes is studied specifically and an explosive charge is provided by adding weight parameter to anticipate the transformation of these metal plates.     

Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

In this study, diagnosis of hepatitis disease, which is a very common and important disease, is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM) and simulated annealing (SA). Simulated annealing is a stochastic method currently in wide use for difficult optimization problems. Intensively explored support vector machine due to its several unique advantages is successfully verified as a predicting method in recent years. We take the dataset used in our study from the UCI machine learning database. The classification accuracy is obtained via 10-fold cross validation. The obtained classification accuracy of our method is 96.25% and it is very promising with regard to the other classification methods in the literature for this problem.     

Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques

There is growing demand on datacenters to serve more clients with reasonable response times, demanding more hardware resources, and higher energy consumption. Energy-aware datacenters have thus been amongst the forerunners to deploy virtualization technology to multiplex their physical machines (PMs) to as many virtual machines (VMs) as possible in order to utilize their hardware resources more effectively and save power. The achievement of this objective strongly depends on how smart VMs are consolidated. In this paper, we show that blind consolidation of VMs not only does not reduce the power consumption of datacenters but it can lead to energy wastage. We present four models, namely the target system model, the application model, the energy model, and the migration model, to identify the performance interferences between processor and disk utilizations and the costs of migrating VMs. We also present a consolidation fitness metric to evaluate the merit of consolidating a number of known VMs on a PM based on the processing and storage workloads of VMs. We then propose an energy-aware scheduling algorithm using a set of objective functions in terms of this consolidation fitness metric and presented power and migration models. The proposed scheduling algorithm assigns a set of VMs to a set of PMs in a way to minimize the total power consumption of PMs in the whole datacenter. Empirical results show nearly 24.9% power savings and nearly 1.2% performance degradation when the proposed scheduling algorithm is used compared to when other scheduling algorithms are used.     

Synthesis and characterization of PVAm/SBA-15 as a novel organic–inorganic hybrid basic catalyst

Composite polyvinyl amine/SBA-15 (PVAm/SBA-15) in various amounts of SBA-15 were prepared and characterized. The physical and chemical properties of PVAm/SBA-15 were investigated using FT-IR, XRD, BET, SEM and TGA techniques. The catalytic performance of each material was determined for the Knoevenagel condensation reaction between carbonyl compounds and ethyl cyanoacetate in the presence of ethanol as solvent. The effects of reaction temperature, solvent and the amounts of catalyst as well as recyclability of the catalyst were investigated. The catalyst used for this synthetically useful transformation showed a considerable degree of reusability besides being very active.     

Local Insulin-Derived Amyloidosis Model Confronted with Silymarin: Histological Insights and Gene Expression of MMP,TNF-α, and IL-6

Amyloidosis is a heterogeneous group of protein deposition diseases associated with the presence of amyloid fibrils in tissues. Analogs of insulin that are used for treating diabetic patients (including regular insulin) can form amyloid fibrils, both in vitro and in vivo as reported in patients. The main purpose of this study was the induction of localized insulin-generated amyloidosis and the observation of silymarin effects on this process. In order to obtain amyloid structures, regular insulin was incubated at 37 °C for 24 h. Congo red absorbance and transmission electron microscopy images validated the formation of amyloid fibrils. Those fibrils were then injected subcutaneously into rats once per day for 6, 12 or 18 consecutive days in the presence or absence of silymarin, and caused development of firm waxy masses. These masses were excised and stained with Hematoxylin and Eosin, Congo red and Thioflavin S. Histological examination showed adipose cells and connective tissue in which amyloid deposition was visible. Amyloids decreased in the presence of silymarin, and the same effect was observed when silymarin was added to normal insulin and injected subsequently. Furthermore, plasma concentrations of MMP2, TNF-α, and IL-6 inflammatory factors were measured, and their gene expression was locally assessed in the masses by immunohistochemistry. All three factors increased in the amyloidosis state, while silymarin had an attenuating effect on their plasma levels and gene expression. In conclusion, we believe that silymarin could be effective in counteracting insulin-generated local amyloidosis.     

Toughening of epoxy resin systems using core–shell rubber particles: a literature review

Journal of Materials Science - Excellent thermal and mechanical properties and high chemical resistance with low shrinkage of epoxy resins open a wide window of various industrial applications,...     

Pricing and ordering decisions in a supply chain with imperfect quality items and inspection under buyback of defective items

In this article, the economic production and inventory model in a three-layer supply chain including one distributor, one manufacturer and one retailer for a single-product and general demand functions under three scenarios is developed. We assume that during the production process, both healthy and defective items are generated. As the first scenario, we develop the first model, in which the defective items are not reworked and all considered as scrape, while in the second model, we assume that the defective items are reworked and are sold as perfect item. In the second scenario, we assume that defective item can be sold with lower price than the selling price. Moreover, raw materials with imperfect quality are sent back from a distributor to outside supplier under a lower price. Determining the order quantity of the distributor and the selling prices of the distributor and the manufacturer as well as the retailer was the goal of this article such that the total profit of each member is maximised. In order to solve the models, the Stackelberg approach is employed between the members, and the concavity of the profit functions is proved using several theorems. Then, closed form solutions are derived for the decision variables and a solution algorithm is proposed to determine the optimal solutions. Finally, a numerical example is presented to illustrate the applicability of the model.     

Simulation of structural features on mechanochemical synthesis of Al2O3–TiB2 nanocomposite by optimized artificial neural network

In this study, structural features of alumina–titanium diboride nanocomposite (Al₂O₃–TiB₂) were simulated from the mixture of titanium dioxide, boric acid and pure aluminum as raw materials via mechanochemical process using the optimized artificial neural network. The phase transformation and structural evolutions during the mechanochemical process were characterized using X-ray powder diffractometry (XRD). For better understanding the refining crystallite size and amorphization phenomena during the milling, XRD data were modeled and simulated by artificial neural network (ANN). An ANN consisting of three layers of neurons was trained using a back-propagation learning rule. Also, the ANN was optimized by Taguchi method. Additionally, the crystallite size, interplanar distance, amorphization degree and lattice strain were compared for the simulated values and experimental results.