Preparation and characterization of in1−xGaxAsyP1−y epilayers by liquid-phase epitaxy

In_1–x Ga _x As _y P_1–y epilayers with three different solid compositions of ln_0.73Ga_0.27As_0.60P_1.40, In_0.59Ga_0.41As_0.87P_0.13 and ln_0.53Ga_0.47As were grown on (1 0 0) InP substrate at 623° C by the step cooling technique of liquid-phase epitaxy. From the optical transmission measurements, the corresponding wavelengths of the InGaAsP epilayers were 1.30, 1.55 and 1.69 m, respectively, which are in good agreement with those obtained from the calculations using Vegard's law. The full widths at half maximum of the photoluminescent spectra at 14 K of these layers were as low as 18.6, 22.5 and 7.9meV, respectively. The electron mobility of the InGaAsP epilayers is a function of the solid composition with the ln_0.53Ga_0.47As epilayer having the highest electron mobility. The mobility and concentration of this layer are 8,873cm²V^–1 sec^–1, 9.7×10¹⁵cm^–3 and 22,900 cm²V^–1 sec^–1, 8.5×10¹⁵cm^–3 at 300 and 77 K, respectively. The compensation ratio is between 2 and 5.     

Energy harvesting assisted cognitive radio: random location-based transceivers scheme and performance analysis

We consider spectrum-sharing scenario where coexist two communication networks including primary network and secondary network using the same spectrum. While the primary network includes directional multi-transceivers, the secondary network consists of relaying-based transceiver forwarding signals by energy harvesting assisted relay node. In cognitive radio, signals transmitted from secondary network are sufficiently small so that all of primary network receivers have signal to noise ratio (SNR) greater than a given threshold. In contrast, the transmitted signals from primary network cause increasing noise which is difficult to demodulate at secondary network nodes and hence it leads to the peak power constraint. In this paper, we focus on the influence of random location of transceivers at primary network using decode-and-forward protocol. Specifically, we derive closed-form outage probability expression of the secondary network under random location of transceivers and peak power constraint of primary network. This investigation shows the relationship between the fraction of energy harvesting time \(\alpha \) of time switching-based relaying protocol on outage probability of secondary network and throughput. In addition, we analyse the influence of the number of primary network transceivers as well as primary network’s SNR threshold on secondary network. Furthermore, the trade-off between increasing energy harvesting and rate was investigated under the effect of energy conversion efficiency. The accuracy of the expressions is validated via Monte-Carlo simulations. Numerical results highlight the trade-offs associated with the various energy harvesting time allocations as a function of outage performance.     

An integrated numerical and experimental framework for modeling of CTB and GD1b ganglioside binding kinetics

A mathematical model is developed and validated for a multistep binding process between cholera toxin subunit B (CTB) and GD1b receptors that precedes cholera infection. To study the dynamics of the complex CTB‐GD1b binding mechanisms, cooperative binding effect and GD1b receptor aggregation in the host cell membrane are considered. More reliable parameters for the CTB‐GD1b binding kinetics are estimated by quantitatively calibrating the proposed multistep binding model against the experimental measurements obtained from the novel nanocube‐based biosensor. Specifically, a numerical scheme that includes the sensitivity analysis, parameter estimation and dynamic optimization is implemented for the model calibration. Through this scheme, identifiable model parameters are determined. After those selected parameters are estimated, the calibrated model and the experimental measurements were in reasonable agreement for different CTB and GD1b concentrations, which shows a promising approach for identification of the kinetics of CTB binding to the host cell membrane. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3882–3893, 2018     

A study on flow fields and performance of water wheel turbine using experimental and numerical analyses

In this paper, an analysis of the performance and flow fields of water wheel turbines for tidal energy extraction is carried out using experimental and numerical methods. The purpose of this work is to develop a water turbine suitable for sites, where fast and shallow surface flows are available, such as rivers or tidal currents. For both methods, the water wheel turbine is tested over a range of tip speed ratios with a differing number of rotor blades, ranging between three and twelve. The results indicate that the numerical simulation shows agreement with the experiment in most cases. Also, the water wheel turbine operates effectively at a range of small tip-speed ratios, where the highest turbine efficiency is produced. Under the same working conditions, the turbines using between six and nine blades generate a greater efficiency and cause lesser reverse flows than others when submerged in water. In contrast, the 3-bladed turbine is the least efficient design as it produces the lowest amount of energy and causes intense vibrations and noises. These noises are a result of a collision between the incoming flow of the channel and the wheel blades during the experimentation, especially at high load conditions. By adding more blades, the torque generated is improved considerably; however, the upstream and downstream depths of the turbine, in this case, are also elevated significantly. Furthermore, in the inlet region, the 3-bladed and 6-bladed turbines have a smaller shock loss and a lower resistance to the main flow from the inlet than the others. Meanwhile, it is found that the flow in the outlet region on the turbines with between nine and twelve blades is in the opposite direction to the wheel’s rotation, significantly obstructing the main flow from the inlet.     

Silica nanoparticles induce neurodegeneration-like changes in behavior,neuropathology, and affect synapse through MAPK activation

Background

Silica nanoparticles (SiO₂-NPs) are naturally enriched and broadly utilized in the manufacturing industry. While previous studies have demonstrated toxicity in neuronal cell lines after SiO₂-NPs exposure, the role of SiO₂-NPs in neurodegeneration is largely unknown. Here, we evaluated the effects of SiO₂-NPs-exposure on behavior, neuropathology, and synapse in young adult mice and primary cortical neuron cultures.

Results

Male C57BL/6 N mice (3 months old) were exposed to either vehicle (sterile PBS) or fluorescein isothiocyanate (FITC)-tagged SiO₂-NPs (NP) using intranasal instillation. Behavioral tests were performed after 1 and 2 months of exposure. We observed decreased social activity at both time points as well as anxiety and cognitive impairment after 2 months in the NP-exposed mice. NP deposition was primarily detected in the medial prefrontal cortex and the hippocampus. Neurodegeneration-like pathological changes, including reduced Nissl staining, increased tau phosphorylation, and neuroinflammation, were also present in the brains of NP-exposed mice. Furthermore, we observed NP-induced impairment in exocytosis along with decreased synapsin I and increased synaptophysin expression in the synaptosome fractions isolated from the frontal cortex as well as primary neuronal cultures. Extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) were also activated in the frontal cortex of NP-exposed mice. Moreover, inhibition of ERK activation prevented NP-mediated changes in exocytosis in cultured neurons, highlighting a key role in the changes induced by NP exposure.

Conclusions

Intranasal instillation of SiO₂-NPs results in mood dysfunction and cognitive impairment in young adult mice and causes neurodegeneration-like pathology and synaptic changes via ERK activation.

     

Evaluation of design alternatives in collaborative development and production of modular products

In the globally competitive business environment, collaborative product development has become an important strategy for enterprises to reduce risks and enhance their competitiveness. The planning phase becomes more significant and complicated when utilizing external resources to determine a product solution to achieve customer satisfaction and business goals. The decision process for assessing design alternatives depends on the tradeoffs between quality, time, and cost. In this research, we propose a framework for collaborative product development and production of modular products. It aims at linking customer requirements, generating design alternatives, and then evaluating and selecting these choices to determine the optimum solution. They are considered from both design aspects and manufacturing concerns. Product strategy is discussed as the most important factor in the evaluation process. Further, a practical application of a system-on-a-chip product planning process was carried out to demonstrate the completeness and benefit of our proposed framework.     

Investigation of the Effect of Various Production Lubricants on Aluminum Alloy Strip Rolling Characteristics

Metallurgist - Results are provided for an experimental study of the effect of various production lubricants on deformation and force parameters during cold rolling of strips made of aluminum...     

Neural networks for classifying surface defects on automotive valve stem seals

Quality improvement systems, as opposed to quality control systems, generally require feedback information on the nature and extent of defects being encountered so as to take appropriate remedial actions. This paper discusses the use of neural networks to classify surface defects on automotive valve stem seals. The neural networks are to be incorporated in an automated visual inspection machine forming part of an overall quality improvement system. Three types of neural networks are considered: the adaptive logic network, the backpropagation multi-layer perceptron (BMLP) and the Kohonen feature map. The BMLP has the best classification accuracy (90%). When different BMLP modules are combined, each to classify a range of defect sizes, the accuracy increases due to “synergy” between the individual modules.     

Control of the freezing interface morphology in solidification processes in the presence of natural convection

This paper presents a methodology for the solution of an inverse solidification design problem in the presence of natural convection. In particular, the boundary heat flux q₀ in the fixed mold wall, δΩ₀, is calculated such that a desired freezing front velocity and shape are obtained. As the front velocity together with the flux history q_ms on the solid side of the freezing front play a determinant role in the obtained cast structure, the potential applications of the proposed methods to the control of casting processes are enormous. The proposed technique consists of first solving a direct natural convection problem of the liquid phase in an a priori known shrinking cavity, Ω_L(t), before solving an ill-posed inverse design conduction problem in the solid phase in an a priori known growing region, Ω_S(t). The direct convection problem is used to evaluate the flux q_ml in the liquid side of the freezing front. A front tracking deforming finite element technique is employed. The flux q_ml can be used together with the Stefan condition to provide the freezing interface flux q_ms in the solid side of the front. As such, two boundary conditions (flux q_ms and freezing temperature θ_m) are especified along the (known) freezing interface δΩ_I(t). The developed design technique uses the adjoint method to calculate in L₂ the derivative of the cost functional, ∥θ_m – θ( x , t; q₀)∥, that expresses the square error between the calculated temperature θ( x , t; q₀) in the solid phase along δΩ_I(t) and the given melting temperature. The minimization of this cost functional is performed by the conjugate gradient method via the solutions of the direct, sensitivity and adjoint problems. A front tracking finite element technique is employed in this inverse analysis. Finally, an example is presented for the solidification of a superheated incompressible liquid aluminium, where the effects of natural convection in the moving interface shape are controlled with a proper adjustment of the cooling boundary conditions.