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Analyses of teaching and learning in higher education are increasingly being based on a distinction between surface and deep learning. This distinction is helpful for investigating approaches used by teachers as well as student preferences for teaching and learning. Surface learning places an emphasis on memorizing facts and information as well as the relatively passive reproduction of content. In contrast, deep learning involves an intention to understand, the critical assessment of content and relating new information to past knowledge in meaningful ways. There has been an assumption that in the U.A.E. there is an orientation to surface learning in schools and higher education. To examine this assumption, an adaptation of questionnaires used with Western students (the Approaches to Study Skills Inventory for Students) was used with a small sample of ZU students. There are limitations in the use of this procedure and difficulties in interpreting the results. However, the results suggest that ZU students show strong beliefs and preference for deep learning approaches in addition to surface learning approaches. This finding is consistent with evidence obtained from student responses to assessment tasks, where there was evidence of deep learning. It was concluded that learning outcomes for ZU students could be enhanced by employing deep learning approaches to teaching and learning.

This study aims to computational numerical simulations to clarify and explore the influences of periodic cellular lattice (PCL) morphological parameters – such as lattice structure topology (simple cubic, body-centered cubic, z-reinforced body-centered cubic [BCCZ], face-centered cubic and z-reinforced face-centered cubic [FCCZ] lattice structures) and porosity value ( ) – on the thermal-hydraulic characteristics of the novel trussed fin-and-elliptical tube heat exchanger (FETHX), which has led to a deeper understanding of the superior heat transfer enhancement ability of the PCL structure.

A three-dimensional computational fluid dynamics (CFD) model is proposed in this paper to provide better understanding of the fluid flow and heat transfer behavior of the PCL structures in the trussed FETHXs associated with different structure topologies and high-porosities. The flow governing equations of the trussed FETHX are solved by the CFD software ANSYS CFX® and use the Menter SST turbulence model to accurately predict flow characteristics in the fluid flow region.

The thermal-hydraulic performance benchmarks analysis – such as field synergy performance and performance evaluation criteria – conducted during this research successfully identified demonstrates that if the high porosity of all PCL structures decrease to 92%, the best thermal-hydraulic performance is provided. Overall, according to the obtained outcomes, the trussed FETHX with the advantages of using BCCZ lattice structure at 92% porosity presents good thermal-hydraulic performance enhancement among all the investigated PCL structures.

To the best of the authors’ knowledge, this paper is one of the first in the literature that provides thorough thermal-hydraulic characteristics of a novel trussed FETHX with high-porosity PCL structures.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

This study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness (2–4 mm), the conventional nondestructive testing method of weld quality is difficult to implement.

In order to solve this problem, the ultrasonic creeping wave detection technology was proposed. The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks. The detection technology was used to test the actual welded test blocks, and compared with the results of X-ray test and destructive test (tensile test) to verify the accuracy of the ultrasonic creeping wave test results.

It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects. However, due to special detection method and protection, the detection speed is slow, which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body. It can be used as an auxiliary detection method for a small number of sampling inspection. The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more, the results of creeping wave detection correspond well with the actual incomplete penetration defects.

The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints. It is recommended to use the echo amplitude of the 10 mm × 0.2 mm × 0.5 mm notch as the criterion for weld qualification.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

Porous silicon (Si) was fabricated by using three different wet etching methods, namely, direct current photo-assisted electrochemical (DCPEC), alternating CPEC (ACPEC) and two-step ACPEC etching. This study aims to investigate the structural properties of porous structures formed by using these etching methods and to identify which etching method works best.

Si n(100) was used to fabricate porous Si using three different etching methods (DCPEC, ACPEC and two-step ACPEC). All the samples were etched with the same current density and etching duration. The samples were etched by using hydrofluoric acid-based electrolytes under the illumination of an incandescent lamp.

Field emission scanning electron microscopy (FESEM) images showed that porous Si etched using the two-step ACPEC method has a higher porosity and density than porous Si etched using DCPEC and ACPEC. The atomic force microscopy results supported the FESEM results showing that porous Si etched using the two-step ACPEC method has the highest surface roughness relative to the samples produced using the other two methods. High resolution X-ray diffraction revealed that porous Si produced through two-step ACPEC has the highest peak intensity out of the three porous Si samples suggesting an improvement in pore uniformity with a better crystalline quality.

Two-step ACPEC method is a fairly new etching method and many of its fundamental properties are yet to be established. This work presents a comparison of the effect of these three different etching methods on the structural properties of Si. The results obtained indicated that the two-step ACPEC method produced an etched sample with a higher porosity, pore density, surface roughness, improvement in uniformity of pores and better crystalline quality than the other etching methods.

The purpose of this paper is to test whether self-verification striving serves as an individual difference antecedent of emotional labor and explore whether various emotional labor tactics acted as mediating mechanisms through which self-verification striving relates to employee outcomes.

The sample used in this paper consisted of supervisor–subordinate dyads working in six hotels in South Korea and used multi-level analyses and the Monte Carlo method to test the research hypotheses presented in this paper.

Self-verification striving was positively and directly related to job performance as well as two out of three forms of emotional labor (i.e. the expression of naturally felt emotions and deep acting). Self-verification striving also indirectly related to job satisfaction through the expression of naturally felt emotions and indirectly related to job performance through deep acting.

The findings of this paper suggest that organizations should consider self-verification striving as an employment selection criterion and provide training programs to help their customer service employees engage in appropriate types of emotional labor.

This paper is the first to explore the underlying mechanisms through which self-verification striving relates to employee outcomes. It also empirically bolsters the notion that expressing naturally felt emotions is an important means of authentic self-expression that positively contributes to job satisfaction. Further, the authors found that self-verification striving positively relates to job performance partially through deep acting. Moreover, they have shown that self-verification striving, as an individual differences variable, is an antecedent of different types of emotional labor.

The purpose of this paper is to investigate the relationship between preferred choice of school subject and student information behaviour (IB).

Mixed methods were employed. In all, 152 students, teachers and librarians participated in interviews or focus groups. In total, 1,375 students, key stage 3 (11-14 years) to postgraduate, responded to a questionnaire. The research population was drawn from eight schools, two further education colleges and three universities. Insights from the literature review and the qualitative research phase led to a hypothesis which was investigated using the questionnaire: that students studying hard subjects are less likely to engage in deep IB than students studying soft subjects.

Results support the hypothesis that preferences for subjects at school affect choice of university degree. The hypothesis that a preference for hard or soft subjects affects IB is supported by results of an analysis in which like or dislike of maths/ICT is correlated with responses to the survey. Interviewees’ comments led to the proposal that academic subjects can be classified according to whether a subject helps students to acquire a “tool of the Mind” or to apply such a tool. A model suggesting how IB may differ depending on whether intellectual tools are being acquired or applied is proposed.

The “inner logic” of certain subjects and their pedagogies appears closely linked to IB. This should be considered when developing teaching programmes.

The findings offer a new perspective on subject classification and its association with IB, and a new model of the association between IB and tool acquisition or application is proposed, incorporating the perspectives of both teacher and student.