Reviewerly/exHarmony huggingface dataset:
A Study of High School Students' Reasoning from Conditional Propositions in Real-life Context

Objectives The purpose of this study was to analyze what kind of reasoning high school students make in conditional propositions in real-life contexts and the reasons for making such reasoning using selection tasks.&#x0D; Methods To address the research question, 334 students from high schools located in Gyeongsang Nam-do were selected and they participated in the survey of reasoning from conditional propositions in a real-life context. Their response data were classified according to criteria, and the results were analyzed.&#x0D; Results As a result, the number of participants who made correct inferences about the ‘card reasoning problem’, ‘COVID-19 checkup problem’, was analyzed to be 7.78% and 9.28%. When these response were considered together with the reasons for reasoning, it was confirmed that only a small number of participant (3.29%, 2.99%) made inferences considering the falsifiability.&#x0D; Conclusions Base on the result, it was proposed to use real-life context conditional propositions tasks as pedagogical materials that help recognize mathematical usefulness and enhance logical thinking. If you balance the reasoning tasks of the mathematical context with the real-life context, It will be an opportunity to learn reasoning that considers not only the inclusion of the truth set but also the falsification.
Development of Youth Entrepreneurship Education Program Based on OECD Learning Compass 2030

Objectives This study aims to develop an entrepreneurship education program for teenagers that can be applied to career classes or free semester system, taking into account the direction of future education and the competencies students should cultivate presented by the OECD Learning Compass 2030.&#x0D; Methods For program planning and design, first, the theoretical basis for program development was prepared by considering previous studies on OECD learning compass 2030, entrepreneurship, entrepreneurship education, Design thinking, Lean Startup, business model, learner-centered education, and self-regulated learning. Second, implications were derived by analyzing cases focusing on the contents of the existing youth education program. Third, based on this, the direction, purpose, process, main contents, and effectiveness verification method of the educational program were presented.&#x0D; Results The direction of the program for entrepreneurship education suitable for teenagers was derived, and the program's purpose and program processes such as ‘discover’, ‘imagine’, ‘act’, and ‘reflect’ were presented, and the program's main contents and effectiveness evaluation plan were discussed in detail.&#x0D; Conclusions This study developed a youth entrepreneurship program that can be applied in terms of career education in the school field. It is believed to be in line with the 2022 revised curriculum, which emphasizes competency development because it is based on the OECD Learning Compass 2030, which overcomes the limitations of the existing youth entrepreneurship education program focused on startups, emphasizes entrepreneurship in various contexts. However, since this study has limitations that focus on planning and design, it is required to secure the validity of the program through field application and evaluation.
The Effects of University Students’ Online Class Structure Factors and Process Factors on Outcomes factors

Objectives The purposes of this study were to examine the differential effects of college students' online class structure factors and process factors on on outcome factors(online class satisfaction, learning outcomes).&#x0D; Methods For the above purpose, the data of D university collected through 2021 Korea-National Survey of Student Engagement(K-NSSE) was used. Structure factors and process factors were independent variables, and the effects on outcome factors(online class satisfaction, learning outcomes) were analyzed using multiple regression models.&#x0D; Results Structural and process factors explained 55% of the total change in online class satisfaction. In the case of the effect of structural factors and process factors on learning outcomes, 40% of the total change in knowledge and skills related to major and job, 19% of the total change in media competency, 33% of the total change in critical and analytic thinking skills, and total change in problem-solving competency.&#x0D; Conclusions Based on the results of the study, the improvement directions were discussed and further studies were suggested. And this study was to provide empirical information to improve the quality of online classes in universities in the future by exploring factors that affect online class satisfaction and learning outcomes.
Comparative analysis of classification results between science teachers and machine learning models about classifying state of matter

Objectives This study compares and analyzes state classification results of matter between developed machine learning model and research participants who are elementary and middle school science teachers, identifies the cause of the situation in which classification results are inconsistent. And based on the results, we tried to find educational implications that can help learn state classification of matter.&#x0D; Methods For 31 elementary and middle school science teachers enrolled in the Graduate School of Education at the College of Education located in the central region, matter classification activities were performed and a decision tree algorithm was applied to the machine learning model. And the effectiveness of the program was confirmed through model performance evaluation such as accuracy, F1-score, precision, recall.&#x0D; Results The classification accuracy of developed machine learning model for classifying state of matter was 0.820, the F1-score was 0.820, the precision was 0.826 and the recall was 0.820. In addition, the degree of discrepancy between the classification results of science teachers and the classification results of the decision tree algorithm was larger in heterogeneous mixtures than in pure matters or homogeneous mixtures. This discrepancy was analyzed as a phenomenon that occurs because science teachers do not consistently apply the classification criteria from the macroscopic and microscopic perspectives or do have the concept that a specific matter is a specific state in advance when classifying the state of matter. Based on the results of these studies, the cause of confusion revealed in the process of classifying the state of matter pointed out in previous studies was found.&#x0D; Conclusions Based on the research results, it was possible to find the cause of confusion revealed in the process of classifying the state of matter of students pointed out in previous studies, and since machine learning can be an effective tool for diagnosing learning conditions, it is suggested that teacher training is needed to utilize it.
Analysis of Articles on HRM in the Korean Journal of Organization &amp; Human Resource Management from 1980 to May 2022

This study attempts to analyze the articles published in the Korean Journal of Organization &amp; Human Resource Management from 1980 to May 2022, and to suggest for the future research directions. The total number of published articles in the journal is 297. This paper suggests the main characteristics of articles and research trends depending on themes and phases. More specifically, the main themes can be classified into general human resource management/ strategic human resource management, recruitment/selection and turnover practices, employee evaluation practices, compensation practices, human resource development, global human resource management &amp; development, job, and etc. Moreover, the phases can be divided into 1980-1990s (the Asian financial crisis), 2000s (the glabal financial crisis), and post-2010s (global hyper-competition). In addition to descriptive statistics, this study tried to analyze the research trends by utilizing untypical text mining with Python. Furturemore, this study suggests the global research trends by using SciVal of Elviser. Based on these findings, in the discussion part, this study suggests future research directions in the human resource management field.
Multi-Class classification of prostate cancer MRI images based on UCLA score using RegNetY-320 model

Prostate cancer (PCa) is the most frequent non-skin cancer in males, and it represents a significant global healthcare problem. Another challenge is the development of an accurate classification model for the detection of PCa. As a result, developing an accurate classification model for PCa is extremely important and has potentially significant clinical implications. These models have the potential to boost the benefits of treatment while also increasing the likelihood of a patient's survival. The PCa is classified in this research based on the T2w magnetic resonance images (MRI), which are collected from the prostate dataset. In this work, a deep learning-based approach RegNetY-320 model is proposed to classify and detect the PCa. The RegNet model is a pre-trained deep transfer learning architecture, which is most commonly used for the image classification process. The proposed model is optimized using various optimizers, including Adam, AdaMax, SGD, RMSprop, Ftrl, and Nadam, and the optimized models are termed model-01 to model-06. For the evaluation, a multiclass classification system was employed to classify 845 patient records from a mpMRI dataset with a unique "UCLA" score of the ROI. Accuracy, precision, sensitivity, specificity, and the F1 score are all calculated based on the classification for the purpose of performance analysis. Finally, in accordance with the findings, the performances of the various proposed models are evaluated in order to determine their validity. When compared among proposed models, the proposed model RegNetY-320_Model-03 optimized using the RMSprop optimizer has obtained greater performance with 95.70 percent accuracy, which is the highest accuracy performance obtained in this research.
Synthesis and characterization of N-acyl alkanolamide surfactant from fatty acids and alcohol amines using sodium methoxide catalyst

Surfactant is a substance added to the liquid that improves the spread's properties by weakening the liquid's surface voltage. Surfactants have a molecular structure consisting of hydrophilic groups and hydrophobic groups. Surfactant needs in Indonesia continue to increase along with the development of industries in Indonesia. This study will be synthesized by the N-acyl surfactant alkanolamide using one type of alcohol amine, namely diethanolamine, through a reaction of amidation between fatty acids and using organic solvents tert-amyl alcohol. The catalyst used is a chemical catalyst, namely sodium methoxide. Furthermore, the purpose of this research is to determine the effect of reaction variables and determine the characteristics of the N-acyl alkanolamide surfactant in the form of acidic numbers and spreading numbers. This study showed for lauric acid, palmitate acid, and the best stearate acid at 90 oC reaction temperatures and 2-hour reaction times, while the best succinic acid at 100 oC and a reaction time of 2 hours. The relationship between reaction rate and time is directly proportional; the longer the reaction time, the higher the rate. The conversion relationship with reaction time is directly proportional, where conversion will increase with the increase in reaction time. However, this does not happen to all fatty acids. Due to the increase in reaction time, it will not provide significant results on surfactant products. The relation between the reaction temperature and the conversion acquisition at the time obtained decreased along with the increase in reaction temperatures. The acid numbers in the best conditions in a row are 40.67 for N-palmitoyl diethanolamine, 47.41 for N-lauroyl diethanolamine, 63.11 for N-succinyl diethanolamine, and 22.44 for N-stearoyl diethanolamine. The saponification number in the best conditions in a row is 36.47 for N-palmitoyl diethanolamine, 23.84 for N-lauroyl diethanolamine, 57.50 for N-succinoyl diethanolamine, and 7.01 for N-stearoyl diethanolamine.
Соёлын утгын зэрэгцүүлсэн судалгааны асуудалд

Language shows the cultural characters of a nation and therefore any language of a nation carries the most sophisticated part of culture itself. Language manifests the features of the nation, including national cultual lexicon and national cultural connotation which have close relevance to its national culture. Although National cultural lexicon has national cultural connotation which is the embodiment of the national culture, they are still categorized as language units and national cultural semantics is still semantics. In intercultural communication, words which carry national cultural connotation always become an obstacle in acquisition. People from different cultures should pay much attention to the national cultural connotation of the target language, otherwise misunderstanding is hard to be voided. Hence, when a language learner or culture transmitter is in communication with someone from a totally different background, he should have some knowledge of the national cultural semantics in that language and compare them in details. The better understanding of this, the more successful the communication will be.
A Study on the Satisfaction Factors Analysis of On-board Training for Cadet in Shipping Company

Cadets who boarded the ship for the first time may feel high stress due to unfamiliar environment, burden of work, unfair treatment, etc. On the other hand, they may feel satisfaction through the satisfaction of expectations and sense of achievement experienced in the practice process. The difference of stress and satisfaction of cadets who experienced a ship for the first time will have a significant impact on future boarding decisions. Therefore, it is necessary to reduce stress and increase satisfaction during the practice period for successful practice of cadets. The purpose of this study is to analyze the factors influencing the satisfaction that the cadet feel during the on-board training by measuring the satisfaction. The participants of this study were 129 cadets from Mokpo Maritime University who completed the on-board training in shipping company and the collected data were analyzed by SPSS Statistics 28. In this study, satisfaction measurement, general characteristic analysis for participants, satisfaction difference analysis, correlation analysis, multiple regression analysis, and binomial logistic regression analysis were performed. As a result of this study, the overall average satisfaction degree was 3.27±0.06. in the order of environment, education, consciousness, these were analyzed as above-average satisfaction degree. and satisfaction analysis according to the general characteristics showed statistically significant results only in gender and major satisfaction. correlation analysis showed statistically significant results as a whole, and in particular, change mind to positive and group life factors had a relatively high correlation. finally, it was analyzed that change mind to positive and group life factors affect overall satisfaction through multiple regression analysis and binomial logistic regression analysis. Therefore, in improving the satisfaction of cadets, it is necessary to develop complex improvements that can improve the satisfaction of all such as consciousness, education and environment.
3D-Distortion Based Rate Distortion Optimization for Video-Based Point Cloud Compression

The state-of-the-art video-based point cloud compression(V-PCC) has a high efficiency of compressing 3D point cloud by projecting points onto 2D images.These images are then padded and compressed by High-Efficiency Video Coding(HEVC).Pixels in padded 2D images are classified into three groups including origin pixels, padded pixels and unoccupied pixels.Origin pixels are generated from projection of 3D point cloud.Padded pixels and unoccupied pixels are generated by copying values from origin pixels during image padding.For padded pixels, they are reconstructed to 3D space during geometry reconstruction as well as origin pixels.For unoccupied pixels, they are not reconstructed.The rate distortion optimization(RDO) used in HEVC is mainly aimed at keeping the balance between video distortion and video bitrates.However, traditional RDO is unreliable for padded pixels and unoccupied pixels, which leads to significant waste of bits in geometry reconstruction.In this paper, we propose a new RDO scheme which takes 3D-Distortion into account instead of traditional video distortion for padded pixels and unoccupied pixels.Firstly, these pixels are classified based on the occupancy map.Secondly, different strategies are applied to these pixels to calculate their 3D-Distortions.Finally, the obtained 3D-Distortions replace the sum square error(SSE) during the full RDO process in intra prediction and inter prediction.The proposed method is applied to geometry frames.Experimental results show that the proposed algorithm achieves an average of 31.41% and 6.14% bitrate saving for D1 metric in Random Access setting and All Intra setting on geometry videos compared with V-PCC anchor.
A Study on the Mediating Effect of Career Decision-making Self-efficacy in the Eelationship between Educational Service Quality and Major Flow in Fisheries and Maritime High School

The purpose of this study is to study the mediating effect of career decision-making self-efficacy in the relationship between educational service quality and major flow perceived by students of fisheries and maritime High School to help students have satisfaction and pride in their major. In addition, it aims to help students select students by creating a tangible and intangible environment through educational service quality, a business element, for fisheries and maritime High Schools, which are struggling with student recruitment due to the decrease in the school-age population. The study was conducted on 253 students enrolled in fisheries and maritime High School. The results of this study are summarized as follows. First, there were no differences in educational service quality, major flow, and career decision-making self-efficacy according to gender, but there were significant differences in type and reliability among the sub-variables of educational service quality depending on the grade. Second, the relationship between educational service quality and major flow perceived by fisheries and maritime high school students shows a significant positive correlation. Third, the relationship between educational service quality and career decision-making self-efficacy perceived by fisheries and maritime high school students shows a significant positive correlation. Fourth, career decision-making self-efficacy has a mediating effect in the relationship between educational service quality and major flow perceived by fisheries and maritime high school students.
A Quantitative Approach to Minimize Energy Consumption in Cloud Data Centres using VM Consolidation Algorithm

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources.The evolution of cloud has taken place in the development of data centers and numerous servers across the globe.But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides.It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration.But the performance may get affected in case of harsh consolidation of VMs.So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption.This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers.The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure.The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm.PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers.Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns.Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request.The proposed algorithms' efficiency was validated through different simulations of extensive nature.The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA.On the basis of the project algorithms, the energy consumption got
Machine learning tests for data mining in engineering

Searching the intelligent aspects and identifying the rules in the huge amount of data automatically created nowadays is an appropriate method to explore and understand a world that is becoming more complex. Fortunately, the means of investigations are synchronous with the actual level of technological development. One might remark that, computer science being a rapid development field, it became an inspiring environment for other domains of science. The paper presents data mining techniques, original implementations of the linear/quadratic regression and of the K-means clustering methods and two machine learning models using the “Microsoft Machine Learning Studio (classic)” software. Some of the conclusions are: the data input process is simple and flexible, making it possible to access a wide range of data sources; there is an important library of algorithms that can be easily included in the experiment; data control may be thoroughly done using various methods, including visualizations; the machine learning application offers reliable results; the output consists of several types of reusable ‘objects’: trained models, data in various formats, web services. The paper is an important stage on the path of a trust development process which is intended to finish by considering machine learning instruments as common instruments in engineering.
Thermal number influence on the quantum teleportation of a thermal Gaussian state.

Implementing of high efficiency quantum teleportation protocols is in the key task for development of accessible and secure quantum communication. For a successful quantum teleportation two conditions should be satisfied: fidelity of teleportation greater then 1/2 for pure states and the existence of the entanglement between resource state modes during all teleportation process. Quantum teleportation using Gaussian states is a perspective direction because they can be easily implemented and manipulated using laser radiation. The simplest case for the continuous variable quantum teleportation represent teleportation of a pure state. However the thermal noise cannot be eliminated from the system, and the thermal component should be taken into account. In this work we put in evidence how the increase of average thermal photon number influence the fidelity of teleportation of a Gaussian state. We use Lindblad master equation and covariance matrix formalism to describe the temporal evolution of studied system and to put in evidence dependence of the fidelity of teleportation on the environment conditions and resource state parameters. As well we calculated the entanglement between the resource state modes to check if all conditions for a successful teleportation are satisfied.