Simulation Analysis of the Heat Transfer Performance of an N-type Microchannel Heat Exchanger

A microchannel heat exchanger with a triangular wave and symmetrical triangular wave structure was proposed in this paper. In addition, a new N-type microchannel heat exchanger was developed to balance the heat transfer performance and pressure drop. The relationship between different configurations of the N structure of the microchannel and the heat transfer performance was analyzed. The results showed that, at a high inlet flow rate, the symmetrical triangular wave microchannel had the best heat transfer performance, followed by the triangular wave microchannel and the straight channel. At the same flow rate, the degree of disturbance of the fluid was highest in the symmetrical N-structure microchannel, and an excellent heat transfer effect was observed.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Lecture capture with real‐time rearrangement of visual elements: impact on student performance

The primary goal of this study is to create and test a lecture‐capture system that can rearrange visual elements while recording is still taking place, in such a way that student performance can be positively influenced. The system we have devised is capable of integrating and rearranging multimedia sources, including learning content, the instructor and students' images, into lecture videos that are embedded in a website for students to review after school. The present study employed a two‐group experimental design, with 153 participants (145 females and 8 males) making up an experimental group in which lecture courses were recorded using the new lecture‐capture system, and 149 participants (140 females and 9 males) forming a control group whose lectures were recorded by traditional means. All participants were in the freshman college and studying Introduction to Computer and Information Science in one of six classes, and were randomly assigned to one of the two groups. The participants' midterm examination and final examination scores were collected as indicators of their academic performance, with their mathematics entrance scores used as a pre‐test. The findings obtained from analysis of covariance (ANCOVA) suggest that appropriate rearrangement of visual elements in lecture videos can significantly impact students' learning performance.     

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

Models for solar radiation prediction based on different measurement sites

The modeling of solar radiation for forecasting its availability is a key tool for managing photovoltaic (PV) plants and, hence, is of primary importance for energy production in a smart grid scenario. However, the variability of the weather phenomena is an unavoidable obstacle in the prediction of the energy produced by the solar radiation conversion. The use of the data collected in the past can be useful to capture the daily and seasonal variability, while measurement of the recent past can be exploited to provide a short term prediction. It is well known that a good measurement of the solar radiation requires not only a high class radiometer, but also a correct management of the instrument. In order to reduce the cost related to the management of the monitoring apparatus, a solution could be to evaluate the PV plant performance using data collected by public weather station installed near the plant. In this paper, two experiments are conducted. In the first, the plausibility of the short term prediction of the solar radiation, based on data collected in the near past on the same site is investigated. In the second experiment, the same prediction is operated using data collected by a public weather station located at ten kilometers from the solar plant. Several prediction techniques belonging from both computational intelligence and statistical fields have been challenged in this task. In particular, Support Vector Machine for Regression, Extreme Learning Machine and Autoregressive models have been used and compared with the persistence and the k-NN predictors. The prediction accuracy achieved in the two experimental conditions are then compared and the results are discussed.     

Assessment of spray quality from an external mix nozzle and its impact on SQL grinding performance

Spray quality is the critical factor which decides the efficacy of Small Quantity Lubrication (SQL) technology in a high specific energy involved machining process like grinding. Yet, the understanding about spray quality, the actual process mechanics and its effect on machining performance is inadequate. The present work is an attempt to establish a correlation between the spray input variables, quality of the spray and machining performance of SQL grinding through modelling and experiments. Using computational fluid dynamic techniques, the variation of droplet size, droplet velocity, number of droplets and heat transfer coefficient have been analysed at different input parameters and the computed trends have been verified and validated. CFD modelling of spray indicates that it is possible to produce aerosol medium with high heat dissipation ability at moderately high air pressure and low flow rate. It also shows that any increase in atomising air pressure favourably leads to notable increase in wetting area and also results in substantial enhancement in heat dissipation ability. Reduction of residual stress is thus remarkably good. On the other hand, grinding fluid flow rate, if increased, offers significantly better lubricity and reduces the grinding force which also reduces tensile residual stress. Short spell grinding test results are found to be in good agreement with CFD results.     

Geopolymer concrete-filled pultruded composite beams – Concrete mix design and application

The limited research on the geopolymer concrete mix design for targeting a specific strength is identified an obstacle for their effective design and wide use. In this paper, a mix design procedure has been proposed for fly-ash based geopolymer concrete and its use as infill hybrid composite beam is investigated. Then, the structural performance of geopolymer concrete filled hybrid composite beam is investigated to determine their possible application in civil infrastructure. Firstly, a detailed procedure of mix design for fly-ash based geopolymer concrete is presented. Secondly, three hybrid beams filled with geopolymer concrete were prepared and tested in a four-point bending setup to evaluate their flexural modulus and modulus of rupture. Numerical and analytical evaluation of the behaviour of hybrid beam were performed and results showed a good agreement with the experimental investigation. Thirdly, the suitability of the beam for a composite railway sleeper is evaluated and compared with existing timber and composite sleepers. Finally, the beams’ performance in a ballast railway track is analysed using Strand7 finite element simulation software and the results showed that the new concept of using geopolymer concrete as infill to pultruded composite section satisfied the stiffness and strength requirements for a railway sleeper.     

Effect of in-app components,medium, and screen size of electronic textbooks on reading performance,behavior, and perception

Although many app-based textbooks are available for students, reading have not been thoroughly outlined. This study aimed to understand how changes from paper to electronic textbooks have affected the academic reading task, investigate student users’ perceptions of in-app components and screen sizes, and identify issues affecting in-app components and task requirements. A mixed factorial design experiment was employed. Results showed that there were no significant changes in comprehension and time spent reading between print text and the iPad. Yet, student highlighting, notetaking, and reading behavior and perception significantly changed based on condition. In addition, students struggled to use in-app components and found them frustrating especially when accounting for sentence splitting. The findings presented can assist in understanding the changes in student reading behavior, which can be used to improve interface design of future e-textbooks.