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.     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.     

Thermodynamic and kinetic studies for the adsorption of Hg(II) by nano-TiO2 from aqueous solution

Titanium dioxide nanocrystals were employed, for the first time, for the sorption of Hg(II) ions from aqueous solutions. The effects of varying parameters such as pH, temperature, initial metal concentration, and contact time on the adsorption process were examined. Adsorption equilibrium was established in 420 min and the maximum adsorption of Hg(II) on the TiO₂ was observed to occur at pH 8.0. The adsorption data correlated with Freundlich, Langmuir, Dubinin–Radushkevich (D–R), and Temkin isotherms. The Freundlich isotherm showed the best fit to the equilibrium data. The Pseudo-first order and pseudo-second-order kinetic models were studied to analyze the kinetic data. A second-order kinetic model fit the data with the (k₂ = 2.8126 × 10^?3 g mg^?1min^?1, 303 K). The intraparticle diffusion models were applied to ascertain the rate-controlling step. The thermodynamic parameters (ΔG°, ΔH°, and ΔS°) were calculated which showed an endothermic adsorption process. The equilibrium parameter (R_L) indicated that TiO₂ nanocrystals are useful for Hg(II) removal from aqueous solutions.     

Interaction of α-Cyano-4-hydroxycinnamic Acid Drug with Inorganic BN Nanocluster: A Density Functional Study

We inspected the possibility of using a synthesized boron nitride nanocluster (B₁₂N₁₂) as a potential chemical sensor for anticancer α-cyano-4-hydroxycinnamic acid (CHC) drug by performing density functional theory calculations. It was found that CHC drug is mainly adsorbed via its acidic group on the BN nanocluster with adsorption energy about ??23.7 kcal/mol. In view of the high decrease of HOMO–LUMO energy gap (E_g) of the BN nanocluster after the adsorption process, it is expected that this process induces a significant increase in its electrical conductivity. Thus, the BN nanocluster is suggested as a potential sensor for CHC drug detection. We predicted that the BN cluster benefits from a short recovery time (~?22.7 s) and high sensitivity (55.2% decrease in the E_g). Also, it is interesting that the BN nanocluster can be used as a sensor in the pristine form without needing to the functionalization, doping, decoration, etc. Finally, we showed that by increasing the percentage of Hartree–Fock exchange of the functional, the adsorption energy, and sensitivity are increased and decreased, respectively. Also, the sensitivity of the BN nanocluster is predicted to decrease in water solution.     

Corrosion behavior of Zr41.2Ti13.8Ni10Cu12.5Be22.5 bulk metallic glass in various aqueous solutions

In this study electrochemical corrosion behavior of Zr_41.2Ti_13.8Ni₁₀Cu_12.5Be_22.5 bulk metallic glass (BMG) in various aqueous solutions (3.5% NaCl, 1 N HNO₃, H₂SO₄ and HCl) in order to compare with 316L stainless steel were thoroughly investigated. Scanning electron microscopy (SEM) was used to evaluate the influence of corrosion on the surface topography in immersion specimens to examine where the corrosion pits initiated. Corrosion rate of Zr_41.2Ti_13.8Ni₁₀Cu_12.5Be_22.5 BMG in 3.5% NaCl solution was ∼0.6 mpy and its excellent corrosion resistance can be concluded. Polarization and SEM results also vouched a remarkable corrosion resistance of Zr_41.2Ti_13.8Ni₁₀Cu_12.5Be_22.5 BMG in 3.5% NaCl aqueous solution in comparison with 316L stainless steel.     

Thermoeconomic optimization of a shell and tube condenser using both genetic algorithm and particle swarm

This paper presents a thermoeconomic optimization of a shell and tube condenser, based on two new optimization methods, namely genetic and particle swarm (PS) algorithms. The procedure is selected to find the optimal total cost including investment and operation cost of the condenser. Initial cost includes condenser surface area and operational cost includes pump output power to overcome the pressure loss. Design parameters are tube number, number of tube pass, inlet and outlet tube diameters, tube pitch ratio and tube arrangements (30, 45, 60 and 90°). Therefore, shell diameter should be selected less than 7 m, tube length should be less than 15 m, and ratio of diameter to tube length should be in the range of 1/12 to 1/3. In addition, it is found that GA provides better results for computer CPU running time, compared to PS algorithm. Finally, a sensitivity analysis of design parameters at the optimal point is conducted. Results show that an increase in the tube number leads to decrease in the objective function first then it leads to a considerable increment in objective function.     

Dairy Cattle Breeding Simulation Program: A simulation program to teach animal breeding principles and practices

A dairy cattle breeding simulation program (DCBSP v.4.9) has been developed to teach undergraduate and graduate students animal breeding principles associated with selection for multiple traits in dairy cattle. The current version of the program was written in FORTRAN 90, and a web-based interface was developed for the students to interact with the program in the teaching environment. This software simulates a population of dairy cattle herds and artificial insemination bulls through several generations by integrating students’ decisions about mating, culling, and selection of new heifers and bulls based on a multivariate animal mixed model evaluation and marker-assisted selection. All simulation parameters (e.g., number of herds and cows per herd, variance components, effect of genetic markers) can be defined by the administrator of the program in relation to the animal breeding course. During each running period, the program simulates the composition of each herd during a virtual year, generating new calves and new productive records and performing a genetic evaluation for all productive traits. A herd-specific productive summary of all demographic, productive, and genetic data is provided to the students at the end of each simulation period. After several running periods, the genetic trend can be evaluated, providing a realistic experience for the development of animal breeding skills that will be relevant to students with a basic knowledge of animal breeding. Earlier versions of this program have been used at several universities where it has proven to be a very useful teaching tool to illustrate the theoretical basis of animal breeding in livestock.     

Flow over trapezoidal side weir

In this paper, the hydraulic characteristics of a sharp crested trapezoidal side weir have been experimentally and theoretically investigated. It was found that the DeMarchi coefficient of discharge for a sharp crested trapezoidal side weir in subcritical flow is related to the main channel Froude number, the side slope of weir, ratio of weir height to upstream depth of flow and ratio of weir length to upstream depth of flow. Suitable equations for the discharge coefficient are also obtained.