Online interventions to help college students to improve the degree of integration of their argumentative synthesis

Writing an argumentative synthesis is a common but demanding task, consequently undergraduates require some instruction. The objective of this study was to test the effectiveness of two interventions on integrative argumentation: one of them was focused on the product features of argumentative texts; and the other one on the processes involved in the written argumentation. Sixty-six undergraduate students participated voluntarily. As an academic task, they were asked to write a pre-test synthesis after reading two sources which presented contradictory positions about an educational issue, then to read two new texts about a different but equivalent issue, and write a post-test synthesis following one of two types of instructional virtual environments. The instructions, implemented in Moodle, presented similar tools, employing videos, graphic organizers, and exercises. The first condition (n = 33) focused on the linguistic features while the second (n = 33), focused on the process, including explicit instruction and a script with critical questions to guide the reading and writing processes. In this study we have also analyzed how the students in the process condition answered some of the critical questions. The results show that the level of integration of the written products improved in both conditions, although this improvement was more pronounced in the process intervention. Nonetheless, the products that achieved medium and maximum integration were still limited. Despite the lack of a relationship between how students answered the critical questions and the level of integration in their post-test, the case analysis highlights certain educational implications and further research.

Application of machine learning to predict the thermal power plant process condition

This paper deals with the development of an algorithm for predicting thermal power plant process variables. The input data are described, and the data cleaning algorithm is presented along with the Python frameworks used. The employed machine learning model is discussed, and the results are presented.

Influence of assembly procesS condition of A gantry crane on the corrosion resistance of welds

The article presents an analysis of the manufacturing process of gantry crane elements in the unit production regime. Gantry cranes are one of the most important heavy-duty mechanical components designed for lifting and loading heavy and bulky loads. In the analyzed case, the assembly of large-size elements in the form of a welding process was carried out outside. Therefore, there was a problem with the corrosion resistance of the elements produced in this way. In this article, special attention is paid to the performance of welded joints of such large elements and their disadvantages related to the conditions of the assembly process. These joints were made on the basis of typical, low-carbon structural steel (grade S355J2, steel with the number 1.0562) and Nickel alloy (Alloy 59, X2CrNiMoCuN22-5-3, steel with the number 2.4605) using the metal electrode. The weldability of investigated joints was assessed. The aim of the article is to analyze the influence of assembly conditions and subsequent corrosion protection of joints on their corrosion resistance under operating conditions. Appropriate tests have been carried out for this purpose. The tests showed the negative influence of external conditions on the corrosion resistance of the entire gantry crane structure.

Strategies to Overcome Interlanguage Fossilization by Using Flipped Learning in Pandemic Era

Learning foreign language means learn all aspects of the language, such as language features, sociolinguistics, psycholinguistics, grammar, structure and culture. The different of these aspects from target language and native language will create different form output both of them, that known as the interlanguage. Interlanguage fossilization is common in the process of foreign language learning, which causes foreign language learners not make progress any more. Based on teaching learning process condition, researcher applied the strategies to overcome interlanguage fossilization by using flipped learning in pandemic era. This research focuses on grammatical errors. This study belongs to quantitative method. The study conducted in the pandemic era when the teaching learning process of the students in Padang applied in two shifts both face to face learning and online learning. The research conducted in 3 weeks of learning process. each week consists of two meetings by applying flipped learning Approach. The result of this study showed that there was positive effect of using strategies in flipped learning to overcome their fossilization of target language. The result indicates that almost all of the learners’ grammatical errors could be eliminated step by step and need more process and times.

Predicting the Difficulty Score of Spinal Anaesthesia

Background: Spinal anaesthesia is the commonest regional anaesthesia conducted for several surgical procedures. Objectives: This study aims to predict the difficulty score of spinal anaesthesia to scale back the complications and ultimately improve anaesthesia quality. Materials and Methods: Patients undergoing various surgeries involving spinal anaesthesia were taken in this study and several parameters like demographic details, body mass index, spinous process condition were recorded pre operatively to see how they influenced the difficulty of performing spinal anesthesia on them. Results: Out of the 101 patients enrolled in this study, 53 underwent an easy SA by the first attempt in the first space. It was moderate in 36 and difficult in 12 patients. Conclusion: Considering the examination of patients with respect to BMI, lumbar spinous process status and deformities, radiological signs of lumbar vertebrae can be helpful in predicting how difficult the SA procedure is going to be.

Effect of FW conditions on mechanical and microstructural characteristic of AA6061/SiC/Graphite hybrid composites joint by empirical relationship

Friction welded AA6061 matrix hybrid composite joints were investigated to understand the process effect on the metallurgical properties with the aid of empirical relationships. SiC of 10 % with the standard particle size of 25 µm and 5 % graphite with the particle size of 30 µm were added into the AA6061 matrix. The investigation has 20 sets of experiments as per the matrix designed. Each process condition, namely rotation speed (N), upset load (F), and upset time (T), as well as their impact on joint properties, were investigated individually, with the estimated tensile strength correlated to their corresponding metallographic properties. The observation of this study concludes that the disparity in grain size is mainly affected by the availability of heat sources and the plasticized material during the friction stage and it is highly influenced by rotation speed. Furthermore, the lower level parameters produce the defective joint while the higher-level parameters are attributed to ejecting the extensive amount of hot material from the joint interface. Finer reduction in grain size of 1.5 µm and ample plasticized material consolidation at the optimized welding conditions of 1600 rpm rotation speed, 3.5 kN upset load, and 4 s upset time were attributed to achieving the maximum tensile strength of 167 MPa.

Introduction of high tensile strain into Ge-on-Insulator structures by oxidation and annealing at high temperature

It is demonstrated in this work that a high temperature thermal process including oxidation and N2 annealing at 850 oC can provide tensile strain of ~0.58 % at maximum into Ge-on-Insulator (GOI) structures without any special patterning or external stressors. The different impacts of oxidation and annealing on tensile strain generation, surface roughness and crystal qualities in the GOI structures fabricated by Ge condensation and wafer bonding are systematically examined. Tensile strain of 0.47 % is achieved without severe thermal damages under the optimal thermal process condition, which indicates the high potential of the present method for improving the performance of GOI n-channel MOSFETs. The influence of thermal expansion mismatch between Ge and SiO2 are suggested as a possible physical origin of high amount of tensile strain into GOI structures.

Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory

Purpose This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development. Design/methodology/approach Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied. Findings The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%. Originality/value An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

The Effect of Vacuum Deep Frying Technology and Raphanus sativus on the Quality of Surimi Cubes

This study uses a response surface methodology to optimize the vacuum deep frying process of surimi cubes. The effects of vacuum deep frying temperature, frying time, and thickness on the hardness and color difference of surimi cubes with Raphanus sativus were studied. Further, the manuscript explored the quality changes of surimi cubes under different frying processes (vacuum deep frying, atmospheric deep frying, and shallow frying). Moreover, the Chinese Min-Cantonese cuisine-Raphanus sativus was utilized as auxiliary raw material to change the hardness and reduce the oil content. The optimal parameters of response surface methodology determined were: vacuum deep frying temperature 130 °C, frying time 900 s, and thickness 0.75 cm. Additionally, under this process condition, the hardness of the surimi chunks was 2015 ± 48.17 g, and the color difference was 23.27 ± 1.86. Surimi cubes without Raphanus sativus have superior elasticity and low hardness, and surimi cubes with Raphanus sativus have little color difference and high chewability. After the vacuum deep frying process, there was a high protein content and superior crispness. Shallow frying and adding Raphanus sativus effectively reduced the product’s oil content. Therefore, Raphanus sativus is suitable as a potential nutritional supplement to broaden its application in fried surimi foods.

Material Modeling of PMMA Film for Hot Embossing Process

This study provides an analysis of the hot embossing process with poly methyl methacrylate (PMMA) film. The hot embossing process engraves a fine pattern on a flexible film using a stamp, applied heat and pressure. As the quality of the embossing pattern varies according to various process variables, the mechanism of making the embossed shape is complicated and difficult to analyze. Therefore, analysis takes much time and cost because it usually has to perform a lot of experiments to find an appropriate process condition. In this paper, the hot embossing process was analyzed using a computational analysis method to quickly find the optimal process. To do this, we analyzed the embossing phenomenon using the finite element method (FEM) and arbitrary Lagrangian–Eulerian (ALE) re-mesh technique. For this purpose, we developed a constitutive model considering the strain, strain rate, temperature-dependent stress and softening of the flexible film. Work hardening, strain softening, and temperature-softening behavior of PMMA materials were well described by the proposed method. The developed constitutive model were applied in the embossing analysis via user-subroutine. This proposed method allowed a precise analysis of the phenomenon of film change during the hot embossing process.