Pd(II)/Pd(0) Anchored on Magnetic Organic–Inorganic Hybrid Mesoporous Silica Nanoparticles: A Nanocatalyst for Suzuki–Miyaura and Heck–Mizoroki Coupling Reactions

Journal of Inorganic and Organometallic Polymers and Materials - 3-Chloropropyltrimethoxysilane (CPTMS) was grafted on the surface of silica coated Fe3O4 core (Fe3O4@MCM-41) and then condensed with...     

Densification improvement of spark plasma sintered TiB2-based composites with micron-, submicron- and nano-sized SiC particulates

Taguchi design of experiments methodology was used to determine the most influential spark plasma sintering (SPS) parameters on densification of TiB₂–SiC ceramic composites. In this case, four processing factors (SPS temperature, soaking time, applied external pressure and SiC particle size) at three levels were examined in order to acquire the optimum conditions. The statistical analysis identified the sintering temperature as the most effective factor influencing the relative density of TiB₂–SiC ceramics. A relative density of 99.5% was achieved at the optimal SPS conditions; i.e. temperature of 1800?°C, soaking time of 15?min and pressure of 30?MPa by adding 200-nm SiC particulates to the TiB₂ matrix. The experimental measurements and predicted values for the relative density of composite fabricated at the optimum SPS conditions and reinforced with the proper SiC particle size were almost similar. The mechanisms of sintering and densification of spark plasma sintered TiB₂–SiC composites were discussed in details.     

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,...     

Observer-based reliable control for Lipschitz nonlinear networked control systems with quadratic protocol

International Journal of Control, Automation and Systems - In this paper, observer-based reliable control problem for nonlinear networked control systems (NCSs) with quadratic protocol (QP) in...     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.     

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)....     

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.