A Study on Performance Diagnosis of College English Courses in General Education after Core Competency-Based Reform －The Case of I University

This study conducted a basic learning performance diagnosis for each College English course falling under the purview of basic general education. In line with the five core competencies of I University, the area of foreign language in basic general education focused on communication competency. This study explored the reactions of students taking College English courses to examine whether each course matched the core competency focused on communication, especially the basic learning competence for English communication skills and experiences acquired through the knowledge provided and what changes were needed in the curriculum to improve students’ English communication competence. Through a survey, educational improvements recognized by students who participated in College English I (reading), College English II (listening), and College English III (speaking) classes were compared and analyzed. The performance diagnosis for the study aimed to measure learning performance obtained through each course and utilize it as a basic mean for improving curricula for College English courses. For performance diagnosis, the Learning Performance Diagnosis Tool developed by the Korea National Institute for General Education’s General Education Standards in 2016 was selected. Diagnosis questions from the areas of “basic learning competency” and “knowledge and experience” were selected, and each question was measured using a 5-point Likert scale. The performance diagnosis survey involved 1,366 of 2,063 students (66.2%) in all three courses. Based on the analysis result, students reported that their English communication competency had improved in each course. This result could be interpreted as indicating that the core competency pursed in the area of foreign language at I University and the direction of education pursued in College English courses were appropriate. However, slightly different from basic learning competence area, the performance indicators relating to class activities questions in knowledge and experience area were found to be lower than that of other questions. During the semester, online and offline classes had been conducted depending on the COVID-19 situation, and it can be assumed that this situation resulted in many inconveniences in terms of utilizing effective class activities where students could interact with the professor and classmates. It also seemed urgent to develop effective non-face-to-face or online class activities to prepare for the COVID-19 pandemic era.

Sportspecific performance diagnosis in ski mountaineering — comparison of a sportspecific cardiopulmonary exercise test on a treadmill versus outdoors and at altitude, a pilot study

Background: Ski mountaineering is a competitive sport that has gained popularity during the last years. As most competitions are held in altitudes between 1500 m and 3500 m, a considerable amount of training occurs at various hypobaric hypoxia degrees. It was establishing a sport-specific cardiopulmonary exercise protocol using standard ski mountaineering equipment on a treadmill. This study investigated altitude’s effects on a self-regulated incremental exercise field test at 3100 m with this protocol. Methods: Six athletes were tested (24.2 ± 4.2 years) from the German Ski Mountaineering National Team with a portable telemetric cardiopulmonary exercise test equipment. First, an incremental indoor step test with skis on a treadmill (altitude 310 m) and four days later outdoor on glacier snow (3085 m) after three days of acclimatization. All athletes were exposed to repetitive intermittent hypoxia during the weeks before the test. Standard cardiopulmonary exercise parameters were recorded while individual training zones were defined according to ventilatory thresholds. Results: In highly trained athletes, mean V̇ O2peak (72/ml kg KG/min) was reduced by 25% or 9% per 1000 m altitude gain and by 18% and 23% at the first and second ventilatory thresholds, respectively. Mean maximum heart rate and the heart rate at the ventilatory thresholds were reduced at altitude compared to sea-level, as was the O2 pulse. Conclusion: Due to distinctive individual reactions to hypoxia, cold, etc., an individual and sport-specific field performance analysis, representing the daily training environment, is highly useful in world-class athletes for precise training control. Our self-regulated cardiopulmonary field protocol could well prove to serve in such a way.

Energy performance diagnosis for the residential building façade in Algeria

As it is clear, worldwide buildings are the largest consumer of the final energy consumption. In Algeria, it has been reported that 33% of the overall energy consumption was attributed to buildings. This is due to the design and constructional techniques of the residential buildings, which do not address the local climatic condition. To assess this situation, the study is focused on analyzing the existing residential buildings in Algeria, in terms of energy, thermal, daylight, and indoor air quality performance, using a dynamic simulation software. Typical building design in a hot and dry climate was selected. The results revealed that the existing residential buildings do not comply with the energy-efficient design standards. It was concluded that further strategies should be applied in this sector, in terms of building design, materials, and façade configuration.

Smart Fault Diagnostics using Convolutional Neural Network and Adam Stochastic Optimization

Navigation, aviation and several other fields of engineering extensively make use of rotating machinery. The stability and safety of the equipment as well as the personnel are affected by this machinery. Use of deep learning as the basis of intelligent fault diagnosis schemes has and investigation of other relevant fault diagnosis schemes has a large scope for development. Thorough exploration needs to be performed in deep neural network (DNN) based schemes as shallow layer network structure based fault diagnosis schemes that are currently available has several considerable limitations. The nonlinear problems may be processed during intelligent fault diagnosis using deep convolutional neural network, which is a special structure DNN. The convolutional neural network (CNN) based scheme is emphasized in this paper. The principle and basic structure of the model are introduced. In rotating machinery, the fault diagnosis schemes using CNN are analyzed and summarized. Various CNN schemes, the potential mechanisms and performance diagnosis are analyzed. A novel smart fault diagnosis strategy is proposed while highlighting the potential aspects of existing schemes and reviewing the challenges.

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Bioinformatics and computational biology have significantly contributed to the generation of vast and important knowledge that can lead to great improvements and advancements in biology and its related fields. Over the past three decades, a wide range of tools and methods have been developed and proposed to enhance performance, diagnosis, and throughput while maintaining feasibility and convenience for users. Here, we propose a new user-friendly comprehensive tool called VIRMOTIF to analyze DNA sequences. VIRMOTIF brings different tools together as one package so that users can perform their analysis as a whole and in one place. VIRMOTIF is able to complete different tasks, including computing the number or probability of motifs appearing in DNA sequences, visualizing data using the matplotlib and heatmap libraries, and clustering data using four different methods, namely K-means, PCA, Mean Shift, and ClusterMap. VIRMOTIF is the only tool with the ability to analyze genomic motifs based on their frequency and representation (D-ratio) in a virus genome.