Reviewerly/exHarmony huggingface dataset:
A Study on the Factors Affecting Career Choice of Entrants to Specialized High School in Fisheries and Maritime

The purpose of this study is to find out the factors that influence the motivation of students in specialized high schools in the fisheries and Maritime sectors. The conclusion of this study is summarized as follows. First, students at specialized high schools in the fisheries and Maritime sectors tend to be self-directed in admission, and their parents have a great influence. Second, students of specialized high schools in the fisheries and Maritime sectors are thinking positively about the future of the fisheries and Maritime industries along with specialized high schools in the fisheries and shipping sectors. Third, students at specialized high schools in the fisheries and Maritime sectors differed in the factors affecting career choice depending on their location. Fourth, students at specialized high schools in the fisheries and Maritime sectors differed in the factors affecting career choice depending on the department. The policy suggestions based on the above research are as follows. First, specialized high schools in the fisheries and Maritime sectors should seek discriminatory ways to secure new students according to school and department. Second, efforts should be made to positive expectations for the fisheries and shipping sectors of students at specialized high schools in the fisheries and Maritime sectors.
A Qualitative Study of Chinese International Students in Korea on Their Perception and Understanding of Ocean Literacy

Ocean literacy, a central idea in marine education, concentrates on enhancing individual and societal awareness, knowledge, and conduct towards the marine environment. Its significance lies in its contribution towards global marine conservation and sustainable development. Based on grounded theory and qualitative research methods, with open-ended interviews, this study investigates the perceptions and comprehension of Chinese overseas students in South Korea on the concept of China
The Effect of Distance Learning Types and Circumstances of High School Students on the Academic Achievement Level

The purpose of this study is to analyze how the types and circumstances of remote classes recognized by high school students affected students" academic achievement. The result of this study is as follows: first, the types and circumstances of distance learning was found to be more helpful for Korean, math, and English subjects, the lower the achievement level. Second, it is appeared that the types of distance learning have affected more on academic achievement compared to the distance learning circumstances. Third, the impact of distance learning types on each achievement level for each subject was derived differently. Fourth, in the distance learning circumstances, there were differences in statistical significance by achievement level and subject, but the direction of positive and negative influence of each variable was derived to be the same. Based on these results, implications were presented.
Exploring the Teaching and Learning Method of University Classics reading education

The purpose of this study is to explore the teaching and learning methods of classic readings in university education in order to redefine the status of classical literature as an exploration of life values and to propose a meaningful classical literature reading method. To this end, the study was conducted by conducting in-depth interviews among qualitative research methods with research participants who are teaching in the field of classical reading education. Through these interviews, 14 semantics units, 6 semantics categories, and 3 essential themes were derived, and educational approaches were explored based on the findings. In order to connect modern learners with classical literature, it is necessary to consider students
Analysis of Educational Needs of Learning Competencies of High-Achieving Students in Non-Metropolitan University : Focusing on the A University

In non-metropolitan universities, the focus is primarily on managing academically low-achieving students to prevent dropouts, with insufficient attention given to supporting learning activities for academically high-achieving students. Therefore, the purpose of this study is to analyze the learning competencies considered important by academically high-achieving students, investigate the discrepancies in their performance levels, and provide insights for the development of a learning support program. For this purpose, research tools were developed, and t-tests were conducted to analyze the differences in importance and performance across eight items related to four learning competencies. The priority of educational needs was assessed using the Borich’s Needs Assessment and The Locus for Focus Model. The research findings revealed significant differences in the importance and performance of all eight items related to learning competencies, with one item identified as the top priority in the educational needs analysis, and four items as secondary priorities. In summary, high-achieving students showed a preference for individual learning but also recognized the significance of cooperative learning. Furthermore, they actively utilized note-taking as an effective cognitive strategy to restructure learning content and summarize key concepts. Therefore, this note-taking strategy proves valuable for academic achievement and can be recommended to low-achieving students. Additionally, high-achieving students expressed the need for mentorship to support the successful execution of their learning plans and receive feedback on their learning activities. They also sought learning activities that would enhance their ability to present logical arguments. These learning activities will contribute to improving the academic achievement of academically high-achieving students.
Parallel EM algorithm on Hadoop for large-scale hidden Markov model parameter estimation

This paper aims to propose a parallel EM algorithm based on Hadoop for parameter estimation of large-scale hidden Markov models (HMM). HMM is a commonly used statistical model. However, since the parameter estimation of HMM involves the storage and processing of large-scale data sets, traditional serial algorithms have certain limitations in efficiency. This paper introduces the Hadoop parallel computing framework, divides the task into multiple subtasks through the MapReduce programming model and assigns them to different machines for parallel computing, which improves the efficiency and scalability of parameter estimation. The results show that using parallel EM algorithm for large-scale hidden Markov model parameter estimation on Hadoop is feasible and effective.
Application of “3S” Technology in Land Resource Management

Land resource management is one of the most important ways to deal with the relationship between land resource and human being to improve land use efficiency and gain more benefits. “3S” technology refers to remote sensing (RS), global positioning system (GPS) and geological information system (GIS). “3S” technology is widely used in different fields generally as an excellent method and it has advantages to exploit in land resource management. Now as the population grows, the conflict between land resource and mankind extrudes with global resource shortage. The effort using “3S” cannot be ignored. This article explains the definition and ability of “3S” technology and analyze the application of “3S” in the Investigation of land resource, land environment dynamic monitoring, land evaluation, land consolidation and land planning. The author concludes the utilization of “3S” technology in land resource management and looks forward its development in the future from different perspectives. Informatization, intelligent and integration are three directions “3S” will make progress. This article takes some examples to illustrate how to help “3S” technology step forward in the future. Through these ways, the optimized “3S” technology has ability to overcome disadvantages and build up new use of the way. So the efficiency of land resource management will be improved obviously to achieve the sustainable use goal of land resource.
Obstacle Detection and Avoidance Using Ultrasonic Sensors in Autonomous Robots

Autonomous robots have recently gained traction in a variety of industries due to their efficiency and possible cost savings. However, assuring the safety and dependability of these robots is critical, especially in terms of obstacle detection and avoidance. Ultrasonic sensors, which are known for their low cost, user-friendliness, and dependability, have emerged as a popular and practical solution to this problem. In this paper, the author offer a framework for using numerous ultrasonic sensors in autonomous robots to detect and avoid obstacles.In our approach, real-time sensor data processing is used to correctly identify and quantify the distances to obstacles, which subsequently guides the robot's path adjustment using obstacle avoidance algorithms. The findings show that our proposed framework for multiple ultrasonic sensor-based range and obstacle avoidance is capable of identifying and avoiding obstacles in a variety of situations. Furthermore, this study conducted a comparison analysis between our method and standard single ultrasonic sensor-based obstacle identification and avoidance approaches, indicating that our approach is more accurate, reliable, and robust.This method ensures safe and dependable navigation in a variety of applications, including mobile robots, autonomous vehicles, and drones. Overall, our research advances the development of low-cost, highly dependable autonomous robots for obstacle recognition and avoidance, allowing them to navigate difficult settings.
Systematic Analysis and Application Prospect of Decision Tree

Decision making is common practice for everyone. One must make multiple decisions to move on during his/her lifetime. People are always eager to make the optimum decisions so that they could save energy and step in a right path. Although people try to avoid inferior options that comes with risk and danger, things happen from time to time. In this study, a powerful tool, decision tree, will be introduced to address this problem. With assistance of decision tree, one will make better choices more validly and more efficiently. Some concepts and algorithms of decision tree will be also included to understand examples from the application part. The purpose of this study is to introduce the concept of decision tree, analyze the advantages of decision tree and discuss its future. Although decision tree is widely utilized in many aspects in society, it still has shortcomings like overfitting and underfitting. Fortunately, there are methods such as pruning and random forest to solve these problems. The future of decision tree will be promising.
Application of Geographic Information System in Assessing Rooftop Photovoltaic Potential

Achieving net-zero carbon emissions is a fundamental way to slow global warming, and dealing with massive carbon emissions from the construction industry is a priority. These days, with the growth of the clean energy industry, the market for photovoltaic (PV) power generation using solar energy is booming. In order to estimate PV power potential on a large scale, geographic information systems (GIS) have been applied in case studies in selected areas; however, there is a lack of overview of the comprehensive application of GIS technology in this subject. In this paper, the application of GIS in estimating PV potential is thoroughly analyzed from three perspectives: data source, application, and limitation. In terms of data source, this paper classifies the data into low, medium, and high precision, and the merits and demerits of each class are analyzed. The paper divides the application into two parts: calculating the received solar energy and the available roof area. The former analyses two available plug-ins in GIS software and a calculation formula that combines with GIS. The latter includes a variety of methods for calculating the total roof area, shading effects, vegetation coverage, and tilt &amp; Azimuth. Although using GIS to estimate PV potential is well-developed, there are still limitations regarding accuracy, speed, and data accessibility. This paper hopes to contribute to the estimation of PV power potential to reach a higher level of accuracy and maturity in order to promote more projects and constructions with large-scale PV power generation systems. Indeed, the future of PV power generation is promising and will be a significant step towards net-zero carbon dioxide emissions for all humankind.
An Stable Intelligence Medical Trolley Path Planning System Based on Improved Breadth First Search Algorithm

Intelligence medical trolley path planning system is a comprehensive intelligent system that integrates many functions such as environmental perception, decision planning and motion control. Due to the characteristics of low speed, simple operating conditions, and priority information, it is unmanned one of the first scenes that can be achieved, with high research value. Although Intelligence Medical Trolley System has been widely developed and developed, many problems still need to be resolved.For example, the search path is not the optimal path resulting in reduced efficiency, or the algorithm is not stable enough, causing the trolley to deviate from the running path.The Path Planning algorithm is the core content of the robot. This article focuses on the improvement of path planning algorithm and determine the feasibility of the algorithm through simulation experiments and optimize. The feasibility of the algorithm is confirmed and optimized by simulation experiments. Finally, through the infrared sensor and the physical sign combine the reality with the algorithm results, and verifies the path planning algorithm with the mobile robot entity. This design uses c++ as the development language, and then uses Arduino as the development platform, lets the mobile robot move according to the path which produced by the algorithm. The experimental results show that the system based on Breath First Search (BFS) algorithm is feasible and can be applied to the working environment of hospital.
Research On Intelligent Engineering Management System Based on Rfid

The radio frequency electron label technology is one kind of automatic diagnosis technology, its through the wireless radio frequency way carries on the non-contact two-way data communication, utilizes the wireless radio frequency way to the record media to carry on the renewal, thus achieves the recognition target and the exchange of the data purpose. The radio frequency electron label technology's appearance, causes the article to scan when may to the supply chain in the product logistics information to carry on the sharing and the real-time tracking, fully combines the modern information technology, establishes the supply chain and the seller between the information interaction, objectively improves the supply chain and the logistics management standardization. In addition, based on the radio frequency electron label technology and establishes the wisdom project management system through carries on the data interaction, realizes the long-distance range reads, the non-contact recognition, the rapid readout, the simultaneous processing many labels the data and so on the operation, may help the enterprise operator to do well the material recognition work, satisfies the enterprise practical demand practically.
Ecological Risk Assessment of Crude Oil Impacted Farmland Soils: A Case Study of Ohaji/Egbema in Niger Delta, Nigeria

The study assessed the impact of crude oil on farmland soils in Ohaji Egbema. Farmlands used for this study are farms that were indirectly impacted through overland flow and acid rain. A total of 42 farmland soil samples were used for this study. The farmland soil samples were collected at a depth range of 0 - 10 cm using a hand trowel through random sampling. The samples were air dried for 72 hours before laboratory analysis for arsenic (As), nickel (Ni), chromium (Cr), cadmium (Cd) and lead (Pb). The results obtained show that heavy metals in farmland soils at different locations in Ohaji/Egbema varied in both concentration and trend. The observed trends at different locations are: Cd&gt;As&gt;Ni&gt;Cr&gt;Pb for Mmahu, Abaezi, Abacheke and Awarra while for Assa and Obitti, it is Cd&gt;Ni&gt;As&gt;Cr&gt;Pb. To establish the extent the farmland soils were polluted, contamination factor (CF), degree of contamination (Cd) and geoaccumulation index (Igeo) were applied. Results of the Cf showed that Ni, Cr and Pb had low contamination, As had moderate contamination while Cd had high contamination in the farmland soils with a range of 155.15 - 206.87 and Cd of 1242.72. The Igeo for Cd ranged from 31.13 to 41.52, an indication of extreme contamination. The associated potential ecological risks were assessed using potential contamination index (PCI), ecological risk factor (Eri) and ecological risk index (RI). It was found that Cd had PCI ranging from 155.22 - 198.51, Eri range of 4654.44 - 6206.11 and RI of 37311.56. This indicates that the farmland soils of Ohaji/Egbema are highly contaminated with Cd and exposed to severe ecological risk.
Identifying professional photographers through image quality and aesthetics in <scp>Flickr</scp>

Abstract In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users’ interaction which can be used to perform a data‐driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. It incorporates multimodal data, combining information from various sources such as user profiles, photo metadata, and crowdsourced features. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self‐reported occupation labels and several feature representations out of the user, photo and crowdsourced sets. We also examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture. Finally, we depicted which characteristics differentiate professional photographers from non‐professionals. As far as we know, the results presented in this work represent an important novelty for identifying expertise in the domain of photography, which researchers from various domains can utilise for related applications.
Provably secure lightweight certificateless lattice‐based authenticated key exchange scheme for <scp>IIoT</scp>

Summary Through the Industrial Internet of Things (IIoT), the manufacturing enterprises have significantly enhanced the production efficiency. The transmission of data in IIoT over public channels has brought about information leakage issues. Therefore, it is crucial to agree on a session key among the participants. In recent years, numerous authenticated key exchange (AKE) schemes have been designed. However, those AKE schemes which adopt the certificateless approach to tackle certificate management and key escrow may remain vulnerable to quantum attacks, and other AKE schemes which are based on lattice hard problem so as to achieve the post quantum security may incur key management issues. To simultaneously address the aforementioned challenges, we propose a lightweight certificateless lattice‐based authenticated key exchange (C2LAKE) scheme. Upon the hardness assumptions of the inhomogeneous small integer solution (ISIS) and computational bilateral inhomogeneous small integer solution (CBi‐ISIS) problems, the C2LAKE scheme has been demonstrated to be secure in the eCK model and the random oracle model (ROM). Better trade‐off among security and functionality features, and communication and computation costs make the C2LAKE scheme suitable and applicable in the IIoT environment.
Optimization of Bridge Crane Main Beam Dimensions Based on Sparrow Search Algorithm and Finite Element Analysis

With the development of information technology, mathematical statistics methods have been widely used in the optimization of mechanical components. This study optimizes the main beam of a bridge crane based on the sparrow search algorithm and finite element analysis. The size of the main beam of a bridge crane has a significant impact on its structural strength and stability, making the optimization of this problem practically significant. Firstly, different sizes of main beams are simulated using finite element analysis under the most critical working conditions to obtain their structural stress and deformation. Then, the Sparrow Search Algorithm is introduced as the optimization algorithm to obtain the optimal size of the main beam through iterative solving. Finally, the effectiveness and superiority of the Sparrow Search Algorithm in the optimization of the main beam of a bridge crane are validated by comparing it with traditional optimization algorithms. Experimental results demonstrate that the optimization of the main beam of a bridge crane based on the Sparrow Search Algorithm and finite element analysis can effectively improve the structural performance of the main beam, offering certain engineering application value.
Design and Application of New Support and Power System of Residential Building Machine

In recent years, with the transformation and upgrading of national development and the acceleration of urbanization, the construction mode of high energy consumption, high pollution and high labor intensity in the construction industry can no longer meet the development needs. The State Council and the Ministry of Housing and Urban-Rural Development have successfully issued policies and initiatives to improve industrialization and intelligent construction. In 2021, the Ministry of Housing and Urban-Rural Development issued a building height limit order, and it has become a trend that aerial building machines have begun to diversify into ordinary high-rise residences. The "14th Five-Year Plan" construction industry development plan issued by the Ministry of Housing and Urban-Rural Development proposed accelerating the coordinated development of intelligent construction and new building industrialization and promoting digital design, intelligent production and intelligent construction. In response to the national policy guidance and leading the development of the industry, solve the inherent problems in the construction industry, learn from the application concept of super high-rise heavy-duty construction equipment integration, create a "factory-like" construction environment for ordinary high-rise through lightweight, standardization and intelligent and other innovative technologies, and explore a construction integration platform suitable for high-rise residences, called "high-rise building industrialization construction lightweight construction operation integration platform" (referred to as "building machine"). Based on the first generation of building construction machines, continuous improvement and replacement, the supporting power system has dramatically improved in support capacity and lightweight. The effective combination of a support column, hydraulic cylinder, and pump station has been realized, which is more convenient and faster, and the construction is more efficient.