Evaluation of TBM performance prediction models and sensitivity analysis of input parameters

Bulletin of Engineering Geology and the Environment - In recent years, anumber of models and formulas have been proposed to estimate the penetration rate of hard rock tunnel boring machines (TBMs)....     

Multicriterion optimal electric drive vehicle selection based on lifecycle emission and lifecycle cost

This research paper examines the optimal choice among conventional gasoline vehicles, hybrid electric vehicles (HEVs), plug‐in HEVs (PHEV), and full‐battery EVs taking into account the different characteristics of these vehicles, such as cost, emissions per mile, and vehicle miles to be traveled between refueling and acceleration time. The existing challenges for wide‐spread deployment of EVs are availability of charging infrastructure, higher cost, long time for charging, and lower travel millage compared with conventional vehicles. Statistical data are considered for determining the spatially varying average daily vehicle miles traveled (VMT) across the United States, which, together with charging behavior, can influence the optimal choice among EV with different travel ranges. Two alternative cases for charging are examined: (1) home‐only charging and (2) home plus work charging. The motivation of this work is to select the optimal EV among their types when lifecycle cost and lifecycle emission are considered. The optimization model seeks to minimize total lifecycle cost and emissions for each level of VMT per day. It is found that when lifecycle cost is the sole objective, HEV is usually the best choice, especially for higher VMT levels. When lifecycle greenhouse gas emission is the sole objective, PHEV1 (PHEV with 1 charging station) is the optimal solution over a wide range of VMTs. The outcome of this provides a roadmap for the selection of EVs based on their annual VMT to reduce both lifecycle emission and lifecycle cost.     

Synthesis,Corrosion and Bioactivity Evaluation of Gelatin/Silicon and Magnesium Co-Doped Fluorapatite Nanocomposite Coating Applied on AZ31 Mg Alloy

In this study, a nano-composite composed of gelatin as the matrix and Si-Mg-FA nano-particles as an additive was deposited on the AZ31 Mg alloy via dip coating method. In addition, a coating composed of MgO, MgSiO₃ and Mg₂SiO₄ phases was applied on the AZ31 Mg alloy by anodizing process. It was found that the Nano-composite coating with a uniform distribution of nano-particles within the gelatin matrix with the thickness of about 9 µm was dense, crack-free and uniform whereas the surface of anodized layer was relatively coarse due to the presence of flaws and micro-cracks. The surface morphology, EDS analysis and FTIR results revealed the ability of nano-composite coated specimen to form the bone-like apatite. Due to the presence of aforementioned phases and special surface features, the anodized specimen possessed higher and lower corrosion resistance than uncoated and nano-composite coated specimens, respectively. The passive coating resistances (R_CT) of nano-composite, anodized specimen and uncoated samples were 2164, 1449 and 1024 Ω cm², respectively.     

Synthesis and Characterization of Functionally Gradient Ni-ZrO<Subscript>2</Subscript> Composite Coating

For the first time, functionally ZrO₂ content graded Ni-ZrO₂ composite coating has been successfully co-electrodeposited from a bath with gradually increasing stirring rate. For this, different composite coatings were electroplated in the same bath with different stirring rates to find the optimum stirring rate in which the maximum content with uniform distribution of ZrO₂ particles in the coating can be achieved. To produce ZrO₂ content graded Ni-ZrO₂ composite coating, the stirring rate was continuously increased from 0 to optimum value. By increasing of ZrO₂ particles content, the microhardness increases from interface towards the surface of the coating. The results of wear resistance measurements, Electrochemical impedance spectroscopy and potentiodynamic polarization test revealed that wear and corrosion resistances of functionally graded Ni-ZrO₂ (FGNZ) is higher than that of ordinary Ni-ZrO₂ (ONZ) composite coating. This result has been attributed to lower mechanical mismatch between coating and substrate in the functionally graded composite coating with respect to the uniformly distributed one.     

The application of internet of things in healthcare: a systematic literature review and classification

Universal Access in the Information Society - The Internet of Things (IoT) is an ecosystem that integrates physical objects, software and hardware to interact with each other. Aging of population,...     

Applying multiple decomposition methods and optimization techniques for achieving optimal cost in mixed materials heat exchanger networks

The optimization of the total annual cost in heat exchanger networks has been one of the overarching goals when synthesizing these networks. Several methodologies and techniques have been developed to achieve optimal costs in mixed material heat exchanger networks. This paper demonstrates the application of two decomposition methodologies (total decomposition and partial decomposition) for typical cost rules. The objective function was defined as the optimization and minimization of the total annual cost in mixed materials heat exchanger network. Three optimization algorithms, hybrid genetic‐particle swarm optimization (GA‐PSO), shuffled frog leaping algorithm (SFLA) techniques, and ant colony optimization (ACO), were used to further optimize the total cost in mixed materials heat exchanger network. The results indicate that the total annual cost in partial decomposition method was smaller than that in full integration method and total decomposition method. The reduction of the total annual cost was about 27% for GA‐PSO algorithm, 24% for SFLA and 10% for ACO relative to the results reported in this work. In partial decomposition method, at least one mixed material of heat exchanger was used to reduce the hot and cold utility for decreasing the total annual cost. Partial decomposition method resulted in the highest reduction of the total annual cost compared with other methods. Percentage of difference of the total annual cost were 0.36%, 1.92%, and 5.05% for full integration, total decomposition, and partial decomposition methods, respectively, in comparison with the previous studies. Results have been compared with the results of other studies to demonstrate the accuracy of the applied algorithms.     

Modeling the heat of vaporization of petroleum fractions and pure hydrocarbons

The present contribution was aimed to estimate the vaporization enthalpy of petroleum fractions and pure hydrocarbons by using the combination of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) called GA-ANFIS. This tool can approximate the vaporization enthalpy as a function of the specific gravity, molecular weight, and boiling point temperature with high accuracy based on 122 data gathered from the previously published literature. Furthermore, results from the proposed model have been compared with different correlations and its acceptable predictive ability against other correlations was proved in order to the estimation of the vaporization enthalpy. The percentage of absolute relative deviation and R-squared (R²) was 1.64% and 0.9967%, respectively. This tool is simple to use and can be of considerable help for petroleum engineers to have an accurate estimation of vaporization enthalpy of hydrocarbon fractions of pure hydrocarbons.     

A New Algorithm for the Elimination of Common Subexpressions in Hardware Implementation of Digital Filters by Using Genetic Programming

A new algorithm based on Genetic Programming (GP) for the problem of optimization of Multiple constant Multiplication (MCM) by Common Subexpression Elimination(CSE) is developed. This method is used for hardware optimization for DSP systems. A solution based on Genetic Programming is shown in this paper. The performance of the technique is demonstrated in one- and multi-dimensional digital filters with constant coefficients.     

Harnessing Normal and Engineered Mesenchymal Stem Cells Derived Exosomes for Cancer Therapy: Opportunity and Challenges

There remains a vital necessity for new therapeutic approaches to combat metastatic cancers, which cause globally over 8 million deaths per year. Mesenchymal stem cells (MSCs) display aptitude as new therapeutic choices for cancer treatment. Exosomes, the most important mediator of MSCs, regulate tumor progression. The potential of harnessing exosomes from MSCs (MSCs-Exo) in cancer therapy is now being documented. MSCs-Exo can promote tumor progression by affecting tumor growth, metastasis, immunity, angiogenesis, and drug resistance. However, contradictory evidence has suggested that MSCs-Exo suppress tumors through several mechanisms. Therefore, the exact association between MSCs-Exo and tumors remains controversial. Accordingly, the applications of MSCs-Exo as novel drug delivery systems and standalone therapeutics are being extensively explored. In addition, engineering MSCs-Exo for targeting tumor cells has opened a new avenue for improving the efficiency of antitumor therapy. However, effective implementation in the clinical trials will need the establishment of standards for MSCs-Exo isolation and characterization as well as loading and engineering methods. The studies outlined in this review highlight the pivotal roles of MSCs-Exo in tumor progression and the promising potential of MSCs-Exo as therapeutic drug delivery vehicles for cancer treatment.