响应面法优化马血中凝血酶原激活条件的研究

以凝血酶比活力为响应值,在单因素实验的基础上,筛选出激活时间、激活温度、Ca2+浓度三个对激活影响较大的因素,响应面法进行优化,并验证优化方案。结果表明,凝血酶原激活工艺的优化参数为:激活时间为2.8h,温度为32℃,Ca+2浓度为0.11mol/L。在此条件下,凝血酶比活力为51.16U/mg。      

Potential Inhibitory Effect of Vitamins Against COVID-19

Coronavirus disease 2019 (COVID-19) is a current pandemic that has affected more than 195 countries worldwide. In this severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, when treatment strategies are not yet clear and vaccines are not available, vitamins are an excellent choice to protect against this viral infection. The rationale behind this study was to examine the inhibitory effect of vitamins B, C, and D against the main protease of SARSCoV-2 and angiotensin-converting enzyme 2 (ACE2), which have critical rolesin the immune system. Molecular docking, performed by using MOE-Dock of the Chemical Computing Group, was used to understand the mechanism. The vitamins all docked within the active sites of the M^pro (PDB ID:6LU7) and ACE2 receptor proteins (PDB ID:6VW1). Vitamins B and C delivered maximum energy scores against both targets, while vitamin D displayed a binding energy score of −7.9532 kcal/mol for M^pro and −7.9297 for ACE2. The efficiency of all three vitamins is higher than the binding energy score of chloroquine (−6.889 kcal/mol), which is now under clinical trials. The use of vitamins is beneficial, being immune system restorative, and they also act as anti-COVID agents. Although the potential beneficial effects of vitamin B and C are revealed through docking studies, further clinical trials are required for the validation of these results.     

An Unsteady Oscillatory Flow of Generalized Casson Fluid with Heat and Mass Transfer: A Comparative Fractional Model

It is of high interest to study laminar flow with mass and heat transfer phenomena that occur in a viscoelastic fluid taken over a vertical plate due to its importance in many technological processes and its increased industrial applications. Because of its wide range of applications, this study aims at evaluating the solutions corresponding to Casson fluids’ oscillating flow using fractional-derivatives. As it has a combined mass-heat transfer effect, we considered the fluid flow upon an oscillatory infinite vertical-plate. Furthermore, we used two new fractional approaches of fractional derivatives, named AB (Atangana–Baleanu) and CF (Caputo–Fabrizio), on dimensionless governing equations and then we compared their results. The Laplace transformation technique is used to get the most accurate solutions of oscillating motion of any generalized Casson fluid because of the Cosine oscillation passed over the infinite vertical-plate. We obtained and analyzed the distribution of concentration, expressions for the velocity-field and the temperature graphically, using various parameters of interest. We also analyzed the Nusselt number and the skin friction due to their important engineering usage.     

Deep Trajectory Classification Model for Congestion Detection in Human Crowds

In high-density gatherings, crowd disasters frequently occur despite all the safety measures. Timely detection of congestion in human crowds using automated analysis of video footage can prevent crowd disasters. Recent work on the prevention of crowd disasters has been based on manual analysis of video footage. Some methods also measure crowd congestion by estimating crowd density. However, crowd density alone cannot provide reliable information about congestion. This paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian trajectories. The proposed framework divided the input video into several temporal segments. We then extracted dense trajectories from each temporal segment and converted these into a spatio-temporal image without losing information. A classification model based on convolutional neural networks was then trained using spatio-temporal images. Next, we generated a score map by encoding each point trajectory with its respective class score. After this, we obtained the congested regions by employing the non-maximum suppression method on the score map. Finally, we demonstrated the proposed framework’s effectiveness by performing a series of experiments on challenging video sequences.     

Influence of Sintering Temperatures on the Phase Structure and Magnetic Properties of Spark Plasma Sintered SmCo5 Magnets

Journal of Superconductivity and Novel Magnetism - SmCo5 magnets were prepared by spark plasma sintering (SPS) technique. The phase constitutions, magnetic properties, and microstructures of...     

A Study on Thermal Response of Nanoparticles in External Magnetic Field

Journal of Superconductivity and Novel Magnetism - Hyperthermia has become an important area of research for cancer treatment throughout the world. Choosing appropriate magnetic nanoparticles for...     

Pristine and Janus monolayers of vanadium dichalcogenides: potential materials for overall water splitting and solar energy conversion

Journal of Materials Science - Emerging two-dimensional (2D) transition metal dichalcogenides with admirable optoelectronic and electrochemical properties can be used in many practical applications...     

Evolutionary game of end-of-life vehicle recycling groups under government regulation

Clean Technologies and Environmental Policy - This paper studies the competition between the illegal recycling group and the legitimate recycling group in the recycling market of end-of-life...     

Performance Evaluation of Virtualization Methodologies to Facilitate NFV Deployment

The development of the Next-Generation Wireless Network (NGWN) is becoming a reality. To conduct specialized processes more, rapid network deployment has become essential. Methodologies like Network Function Virtualization (NFV), Software-Defined Networks (SDN), and cloud computing will be crucial in addressing various challenges that 5G networks will face, particularly adaptability, scalability, and reliability. The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms, including hypervisors, clouds, and containers, which will serve as a guide to dealing with the stimulating environment of 5G. This is particularly crucial when implementing network operations at the edge of 5G networks, where limited resources and prompt user responses are mandatory. Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’ latency and network throughput at the expense of higher virtualized processor use. In contrast to public clouds, where a set of rules is created to allow only the appropriate traffic, security is still a problem with containers.     

Energy optimization in a smart community grid system using genetic algorithm

A smart community grid is an electrical network, which connects several producers, consumers, and prosumers to share energy in an intelligent and secure way. The main challenges of smart community grid are demand response, demand bidding, dynamic electricity tariffs, demand-side management, and prosumers handling. The current state-of-the-art smart grid decision making is focused on consumers and producers behavior while the aim of this research is to achieve prosumer's different goals in an optimized and intelligent way. A genetic algorithm (GA)-based solution is proposed to share energy in an optimized way without affecting the prosumers' preferences. Six prosumers smart community grids data sets are used to validate the performance of the proposed system. The results show that the proposed method significantly improves the loss of energy sharing without compromising the user's preferences.