An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

Supramolecular π–π assembly of a neutral [Cu(salen)] complex via the templating effect of an ionic inorganic complex Na2[Cu(mnt)2] forming a framework type material having well-defined channels

A classical ionic inorganic complex Na₂[Cu(mnt)₂] (mnt²⁻ = maleonitriledithiolate = 1,2-dicyanoethylenedithiolate), that acts as a template in assembling neutral [Cu(salen)] (salen = bis(salicylidene)ethylenediamine) complexes forming a framework type arrangement, is accommodated in the channel formed in the crystal structure of a new type of host–guest compound [Cu(salen)]₄ · Na₂[Cu(mnt)₂] (1). The non-covalent supramolecular interactions among [Cu(salen)] complexes and between [Cu(salen)] and [Cu(mnt)₂]²⁻ complexes in the crystal lattice of 1 result in weak antiferromagnetic coupling.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

Flux measurements for a DD neutron generator using neutron activation analysis

The fast and maximum thermal neutron fluxes from the DD-109 neutron generator at the University of Sharjah were experimentally measured by the activation technique using different neutron reactions. The thermal and fast neutron fluxes were found to be 2.960 × 10~6 and6.186 × 10~7 n/cm~2 s, respectively. This was done to verify the modeling results for the optimum moderator thickness needed to maximize the thermal neutron flux. The optimum moderator thickness was found to be between 3.5 and4 cm. The present data were compared with the detailed MCNP model-based calculation performed in earlier work to simulate the generator.     

The Structure And Properties of a Thermo‐mechanically Processed Low Carbon High Strength Steel

Research efforts were given towards development of low carbon high strength steels since recent past. The present study deals with the development of a low carbon high strength steel alloyed with Mn, Ni, Mo, Cu and microalloyed with Ti and Nb. The steel was subjected to three stage controlled rolling operation followed by accelerated cooling. The structure and properties of the steel at various processing conditions were evaluated. Microstructural observation reveals predominantly lath martensite along with twinned martensite structure at all processing conditions. High strength values at higher finish rolling temperatures have been obtained due to fine martensitic structure along with tiny precipitates of microalloying carbide and carbonitride. The strength value increases marginally at lower finishing temperature due to comparatively finer lath size of martensite and increased precipitation density of carbides, carbonitrides along with Cu particles. The variation in impact toughness properties at different finish rolling temperatures is found to be negligible at ambient and subambient temperatures. The formation of stable and large TiN/TiCN particles during casting have impaired the impact toughness values at ambient and at ‐40°C temperatures.     

Twinning-induced sluggish evolution of texture during recrystallization in AISI 316L stainless steel after cold rolling

This paper deals with the evolution of texture in AISI 316L austenitic stainless steel during annealing after 95 pct cold rolling. After 95 pct cold rolling, the texture is mainly of the brass type {110}〈112〉, along with a scatter toward the S orientation {123}〈634〉 and Goss orientation {011}〈100〉. Weak evidence of Cu component is observed at this high deformation level. During annealing, recovery is observed before any detectable recrystallization. Recrystallization proceeds through nucleation of subgrain by twinning within the deformed matrix and, later, preferential growth of those to consume the deformed matrix. After recrystallization, the overall texture intensity was weak; however, there are some discernible texture components. There was no existence of the brass component at this stage. Major components are centered on Goss orientation and Cu component {112}〈111〉 as well as the BR component {236}〈385〉. Also, a few orientations come up after recrystallization (i.e., {142}〈2−11〉 and {012}〈221〉). With increase in annealing temperature, the textural evolution shows emergence of weak texture with another new component, {197}〈211〉. The evolution of texture was correlated with the deformation texture through twin chain reaction.     

Neural network optimized with evolutionary computing technique for solving the 2-dimensional Bratu problem

In this paper, a stochastic technique is developed to solve 2-dimensional Bratu equations using feed-forward artificial neural networks, optimized with genetic and interior-point algorithms. The 2-dimensional equations are first transformed into a 1-dimensional boundary value problem, and a mathematical model of the transformed equation is then formulated with neural networks using an unsupervised error. Network weights are optimized to minimize the error. Evolutionary computing based on genetic algorithms is used as a tool for global search, integrated with an interior-point method for rapid local convergence. The methodology is applied to solve three cases of boundary value problems for the Bratu equations. The accuracy, convergence and effectiveness of the scheme is validated for a large number of simulations. Comparison of results is made with the exact solution derived using MATHEMATICA, and is found to be in good agreement.     

Development of controlled strength-loss resorbable beta-tricalcium phosphate bioceramic structures

Controlling the strength-loss rate during biodegradation is a bottleneck in developing viable resorbable ceramic implants. Resorbable beta-tricalcium phosphate (β-TCP) bioceramic is known for its excellent biocompatibility. However, it exhibits poor sinterability and poor flexural strength. Here, we improved sintering behavior and biaxial flexural strength of β-TCP bioceramic without altering its biocompatibility by introducing multi-oxide sintering additives, in small quantities. These additives could also tailor the rate of resorption and hardness deterioration of β-TCP. A range of additives were prepared and introduced into β-TCP powder. Resultant powders were uniaxially pressed and sintered at 1250 °C, in air. Considerable improvement in densification (up to 33%) and biaxial flexural strength (up to 43%) were achieved. X-ray powder diffraction (XRD) analysis confirmed that the additives didn't alter the phase purity. In vitro cytotoxicity and biocompatibility analyses were performed using a prostate cancer cell-line. Results showed that the doped and pure β-TCP structures were non-toxic and biocompatible.