The Effect of Using Plastic Strips and Sheets on the Properties of Slurry Infiltrated Fiber Concrete

Slurry Infiltrated Fiber Concrete (SIFCON) is a high-strong material that is regarded as a unique type of high fiber content concrete. This paper aims to study the influence of the use of plastic strips and plastic sheets in the SIFCON slurry. Three sets (normal SIFCON as control, SIFCON with plastic strips, and SIFCON with plastic sheet), in a 1:1.08 cement-sand ratio by weight has been used with water to cement ratio (w/c) by weight equal to 0.3, and superplasticizer equal to 1% by weight. In addition, 6% by volume crimped steel fibers with an aspect ratio of 60 were applied and 1.34% by volume plastic was used, in strips of 5×1cm for both prism and cube samples and in sheets of 25×5cm and 7×7cm for prism and cube samples respectively. The compressive and flexural strength tests studies were conducted on typical cubes of 10×10×10cm and prisms of 40×7×7cm respectively to find out the way the plastic affects the SIFCON properties. The results indicate that the models with plastic sheets placed in SIFCON slurry give the highest compressive and flexural strength whereas the models with plastic strips gave the lowest. The difference percentages in compressive and flexural strength were -27.3, 8, -3.8 and 66.6% for all sets respectively when compared to the control set (using no plastic).

A novel PSII photosynthetic control is activated in anoxic cultures of green algae

Photosynthetic green algae face an ever-changing environment of fluctuating light as well as unstable oxygen levels, which via the production of free radicals constantly challenges the integrity of the photosynthetic complexes. To face such challenges, a complex photosynthetic control network monitors and tightly control the membrane redox potential. Here, we show that not only that the photosynthetic control set the rate limiting step of photosynthetic linear electron flow, but also, upon its ultimate dissipation, it triggers intrinsic alternations in the activity of the photosynthetic complexes. These changes have a grave and prolonged effect on the activity of photosystem II, leading to a massive 3-fold decrease in its electron output. We came into this conclusion via studying a variety of green algae species and applying advance mass-spectrometry and diverse spectroscopic techniques. Our results shed new light on the mechanism of photosynthetic regulation and provide new target for improving photosynthesis.

A New Modulated Finite Control Set-Model Predictive Control of Quasi-Z-Source Inverter for PMSM Drives

In this paper, a new modulated finite control set-model predictive control (FCS-MPC) methodology is proposed for a quasi-Z-source inverter (qZSI). The application of the qZSI in this paper is to drive the permanent magnet synchronous machine (PMSM). The proposed methodology calculates the optimal duration time (ODT) for the candidate vector from the switching patterns of the inverter after it is selected from the FCS-MPC algorithm. The control objective of the FCS-MPC are the three-phase currents of PMSM, when the motor speed is below or equal to the base speed. While at a speed beyond the based speed, the inductor current and capacitor voltage of the qZS network are added as control objectives. For each candidate optimal vector, the optimal time, which is a part of the sampling interval, is determined based on minimizing the ripples of the control objectives using a quadratic cost function. Then, the optimal vector is applied only to the inverter switches during the calculated ODT at the start of the sampling interval, while the zero vector is applied during the remaining part of the sampling interval. To reduce the calculation burden, the zero-state is excluded from the possible states of the inverter, and the sub-cost function definition is used for the inductor current regulation. The proposed modulated FCS-MPC is compared with the unmodulated FCS-MPC at the same parameters to handle a fair comparison. The simulation results based on the MATLAB/Simulink© software shows the superiority of the proposed algorithm compared to the unmodulated FCS-MPC in terms of a lower ripple in the inductor current and capacitor voltage, and a lower THD for the PMSM currents.

Predictive Model of Adaptive Cruise Control Speed to Enhance Engine Operating Conditions

This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP’s indicates that the predictive model outperforms constant ACCSSP.