bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role Of Social Studies Teachers In Developing Students' Disciplined Learning Attitudes At Madrasah Aliyah Laboratory Of The State Islamic University Of North Sumatra Medan
Authors: Harahap Dinda Tazkiyah , Endayani Henni , Rachman Fatur
Published: August 2024

This study examines the role of social studies teachers in fostering students' learning discipline attitudes at Madrasah Aliyah Laboratorium UINSU Medan. The methodology of this study uses a qualitative research approach with a descriptive method. Data were collected through observation, interviews, and documentation. The subjects of the study consisted of 4 social studies teachers, 5 students of class X MA Laboratorium UINSU Medan and WKM in the student affairs field. The results of the study showed the role of social studies teachers who can foster students' learning discipline attitudes, namely social studies teachers have a role as mentors, social studies teachers have a role as motivators, social studies teachers have a role as demonstrator, social studies teachers have a role as facilitators, social studies teachers play a role as learning managers. In conclusion, the role of social studies teachers is very much needed in fostering students' learning discipline attitudes, therefore it is important for social studies teachers to carry out their roles well so that all students at Madrasah Aliyah Laboratorium UINSU Medan, especially class X students, can apply disciplined attitudes in learning when at school.
Kesalahan Berbahasa Arab Perspektif Howard Douglas Brown: Studi Kasus Mahasiswa PTDU Malang
Authors: Ritonga Apri Wardana , Desrani Ayu , Desrani Ayu , Ernawati Ernawati
Published: August 2024

This study aims to analyze students’ Arabic language errors from Howard Douglas Brown’s perspective, identifying three errors, namely interlingual transfer, intralingual transfer, and context of learning. An interpretive paradigm and qualitative approach were used in this study using a case study method at Daarul Ukhuwwah College (PTDU), Malang. Data was collected through observation, interviews and documentation, then analyzed using qualitative analysis and data triangulation. The results of the study show that students often make mistakes in Arabic grammar, vocabulary and pronunciation according to Brown’s theory of interlingual transfer, intralingual transfer and context of learning. Students’ perceptions of language errors show a basic understanding of the second language learning process, where errors are considered as a step in understanding and using Arabic. The implications of this study include expanding understanding of the needs of Islamic boarding school students in learning Arabic as well as adapting the curriculum and teaching methods to increase the effectiveness of Arabic language learning in Islamic boarding schools.
Az autentikusság nyomában
Authors: Szák Orsolya
Published: August 2024

Írásomban a turisztikai autentikusság kérdéskörével foglalkozom. A témára hivatalosan az 1960-as években kezdtek el figyelmet fordítani a szakemberek, miután Daniel Boorstin publikálta a pszeudoeseményekkel kapcsolatos munkáját. A könyv az utazás koncepciójának megváltozását, az új szemléletek ismertetését és a turisták „becsapását” érzékelteti egy újszerű megközelítésben.
Boorstin teóriáit Dean MacCannell gondolta tovább. Véleménye szerint a helyiek és a turisták elhatárolódnak egymástól, ugyanis az adott településen élők nem kívánják a látogatókkal megosztani mindennapjaikat, vagyis a valóság egy részét tudatosan elzárják a kívülállóktól.
A turisztikai autentikusság Boorstin és MacCannell publikációitól indul. Ennek a kezdeti (talán visszafogott) időszaka ugyanakkor nem sokkal később átvált posztmodern megközelítésbe.
Ning Wang mindenképp az újszerű gondolatokat pártolja. Írásában három különféle típusú autentikusságot sorol fel: az objektív, a konstruktív (másnéven szimbolikus), és az egzisztenciális autentikusságot. Az első a Boorstin és MacCannell-féle hagyományos megközelítés, vagyis a valóság meg- és eljátszásáról szól. A második az előbb említett szöges ellentéte, a harmadik pedig ironikus módon az inautentikusság kérdéskörét is magában foglalja.
Wang vizsgálatai ugyanakkor nem nyújtanak átfogó magyarázatot az autentikusság kérdésének vizsgálatában. Ezt a hiányt a cikkben az utolsó meghatározó szereplő, Tim Edensor próbálja meg pótolni. Ugyanakkor, Edensor kissé eltávolodik a szó szoros értelmében vett autentikusságtól és igyekszik a színpadiasság kérdéskörében gondolkodni.
A turizmus színpadiasságát kategorizálja, az emberek és az események kapcsolatát elemzi, a mindennapi turizmusban résztvevőket tanulmányozza, az úgynevezett performanszokat vizsgálja. Edensor tanulmányából kihagyja az autentikus szó használatát, mégis egyértelmű az olvasója számára, hogy mi érdekli és mivel foglalkozik a szerző.
A cikkben felsorolt írások elengedhetetlennek bizonyulnak a turizmus autentikusságának kutatásában – még abban az esetben is, ha nem eredményeznek egyértelmű végkifejletet. Illetve, ahogy Ning Wang mondta, az autentikusság még számos kutatási lehetőséget kínál.
Cannabis and Alcohol: Will Cannabis Users Use More Alcohol and How Legalization of Cannabis Affect This Trend?
Authors: Zhang Yawen
Published: August 2024

Health and Social risks related to marijuana and alcohol are a serious problem in the United States. This research tried to introduce OLS regression in order to figure out the correlation between use of alcohol and cannabis, and the trend before and after legalization of cannabis, which is a factor that has not been researched in previous literature of alcohol, while a lot of controls were added to the model. It finds out that there is higher likelihood for marijuana users to co-use alcohol, but people tend to consume less alcohol if marijuana is legal. This may be because of the booster effect of cannabis on alcohol. These results might be a sign for future policymakers to implement more demand-side policies to reduce potential cannabis and alcohol addiction risks, as supply-side policies such as legalization might work in the opposite direction. For example, higher taxation on cannabis and alcohol might help.
Unveiling AI's Accent in Spoken English Education
Authors: Jiang Xinjie
Published: August 2024

Viewed against the backdrop of the technological landscape, artificial intelligence has become the shining point of the 21st century. In recent years, AI has been pervasively applied into various realms in modern society, among which the incorporation into spoken English education has become one of the most heated topics. To navigate the application of AI in spoken English learning, this paper evaluates the effectiveness based on the Interaction Hypothesis proposed by Michael Long. To be specific, this research focuses on the comprehensive application of AI in the realm of spoken English acquisition, underscoring why AI language models have gained in popularity in innumerable apps concerning language learning and how AI serves as a double-edged sword in spoken English education. By illustrating the language acquisition process, a strong connection between the Interaction Hypothesis and AI-based spoken English learning is constructed and clarified. Through all-round evaluation and analysis, the article concludes that despite challenges, AI has not only acted as a knowledgeable mentor for individual learners,but also provided an effective driving force for the innovation and improvement of spoken English education. In the future, more advancements concerning human-like interactions and immersive learning environment are likely to emerge.
Global Media Integration: Sino-US Co-productions in the Age of Globalization
Authors: Chen Yiru
Published: August 2024

In recent years, one of the hottest topics in film development has been co-production. It is an effective channel for connecting the non-Western film market with the international film market, as well as a new option for international film companies such as Hollywood in the age of media globalisation. Current media globalization has significantly weakened the hegemonic position of cultural empires, and Hollywood, as the most typical Western cultural hegemonic collective, has actively sought co-production with other countries to maintain its international reputation status. Regardless of whether American hegemony persists, this essay argues that co-productions provide new opportunities and growing opportunities for the global media industry. This study will begin with a definition of globalisation and Sino-US production, followed by a discussion of the challenges and benefits of Sino-US cooperation. Following that, using The Great Wall and Iron Man 3 as case studies, discuss the global impact of Sino-US media cooperation.
Exploring the Multiverse: Understanding Its Implications in Philosophy
Authors: Li Yihui
Published: August 2024

Although numerous theories supporting the concept of a multiverse have been proposed, observational proof remains elusive. The introduction of a multiverse would fundamentally transform the understanding of humanity's position in the world and the universe. This paper investigates the existence of a multiverse and its ramifications within the domains of philosophy and religion. It challenges conventional notions of knowledge and its interpretation within a scientific framework. While direct observation may never be attainable, compelling factors and existing evidence, such as the fine-tuning problem and inflationary cosmology, strongly indicate the plausibility of such an existence. Consequently, this paper builds upon established scientific theories and examines the reshaping of epistemology, ethics, and the philosophy of religion, advocating for novel approaches to defining knowledge and expanding the boundaries of traditional scientific inquiry.
Olson's Theory of Collective Action and Online Mass Incidents
Authors: Liu Yuqing
Published: August 2024

This study adopts a case analysis approach, examining the cause of online mass incidents through the fire incident at Zhonglan Apartment Complex of Communication University of China. Ultimately, the author concludes that from the perspective of group size, the primary reason for the occurrence of online mass incidents lies in the disparity between costs and benefits. When costs significantly outweigh benefits, groups are less likely to take action; conversely, when costs are substantially lower than benefits, the opposite occurs. Based on this conclusion, public authorities can devise tailored response measures for online mass incidents involving different group sizes.
Can Pain Be Understood by Words? A Literature Review of Pain-Language Association
Authors: Jin Yanjie
Published: August 2024

This review systematically explores the complicated interplay between pain and language, including nociceptive pain and empathic pain. While previous study focusing on physiological aspects of pain, this paper addressed how linguistics influence pain communication and perception. The finding reveals that language can influence bother subjective pain and the pain when we observe others. It is notable that the use of specific pain descriptors and metaphors significantly impacts individuals' pain perception and linguistic context can influence peoples nociceptive pain experience. For empathic pain, there are similar brain activations when communicating with people who are suffering from pain. Besides, there is evidence that communication and interactions can benefit painful patients and reduce pain. To conclude, this paper empathizes the vital role language plays in individuals own pain experience and feeling others pain. Future studies should aim to have standardized tools and frameworks for studying the linguistic aspect of pain, especially in the social inter-brain context, which is vital for understanding and communicating pain. Addressing the linguistic dimension of pain can help with pain assessment and management, leading to better patient care.
The "Female-to-Male" Phenomenon in Short Videos: A Feminist Media Analysis
Authors: Wang Yuqing
Published: August 2024

Feminist media analysis is a set of critical analytical tools used to understand how different kinds of media contribute to the molding of gender and identity, which has played a crucial part in the development of feminism and the media-driven society. This paper conducts a feminist media analysis to investigate the phenomenon of "female-to-male" presentations in TikTok short videos. This paper aims to challenge some traditional notions of "performing gender," as conceptualized by digital media theories, which disrupt established performance standards and promote broader acceptance of diverse practices. Focusing on TikTok users Dayao Tongxue, Chen Lijun, Lapmoby, and Northeast Rain Sister, this research analyzes the content and influence of those influencers to understand how their performances challenge conventional norms and cultivate inclusiveness. The research concludes by examining the impact of social media on the formation of gender identity, specifically in the transition from language-sex to the identification as male or female.
Study on the Cultural Impact of Local Materials in Regional Architectural Design
Authors: Sun Lingwen
Published: August 2024

As a result of globalization, architectural design has also integrated into the trend of becoming unified. However, regional architecture has become something that opposes unification, allowing each region to maintain its unique characteristics while also protecting the environment. This research focuses on the cultural meaning of natural materials utilization in regional architectural design, allocating the specifics for how the local materials influence both ecological and cultural importance. Positive results arrived at by case studies of architects who used local materials in architecture confirm the role of local architecture as a mediator between cultural heritage and sustainable development. Utilizing literature analysis as a mode of study to critically examine the historical background, technological advancement, and socio-economic condition helps the stimulation of regional architecture. The research elaborates on the importance of using regional materials as a way to develop architectural practices that reflect cultural identity as well as to ensure the ecology of the area. Finally, the paper gives a brief discussion on the future development direction and trends in the regional cultural aspects of architecture design.
Multiqubit quantum dense coding
Authors: Zhao Ruoling
Published: August 2024

Multipartite quantum-information transmission has been a key task for practice purposes and applications in the past decades. In this paper, we present a novel protocol for the multiqubit super dense coding, by using the Greenberger-Horne-Zeilinger (GHZ) states. Our protocol involves measurements doable in the current technique of labs, and thus is possibly realizable and may be extendable to more complicated cases for many systems. In quantum physics, the way the object is measured will affect the code, and the information wont work like puzzles as the condition in general physics which means that, when the divided message is put together, the complete information will be obtained. It requires more complex work to get useful information, like the cooperation of code accepters. To compare with the way in general physics, it is more safe in quantum physics.
Correlation between fishing yield and microplastic levels across nations
Authors: Wang Zilong
Published: August 2024

This research looks into the association between fishing and microplastic contamination across countries, with a particular focus on how the growth of microplastic levels can impact fishery production. Through the use of a mixed-effect model we were able to look through data from 100 nations during the period 1990-2021, where fishery yield data was extracted from The World Bank and microplastic concentration data was obtained from the National Centers for Environmental Information; population data served as a control variable in our analysis. We found that there is indeed a significant negative correlation between levels of microplastics and fishing yield: an increase by 1 piece of microplastic per cubic meter leads to decrease in fishing yield by anything between 65 and 100 metric tonnes (95% confidence interval). This relationship held true for about 93% of all coastal countries studied. To accommodate for differences amongst nations, we introduced random intercepts and slopes in our mixed-effect model which helped capture variations specific to each country while still identifying an overarching pattern. The research we are doing is on the connection between fish catches and microplastic pollution which takes place in the different countries of the world, where we focus more on how high microplastic levels influence fishery production. Having made use of a mixed-effect model, we have been able to look at data that represents 100 nations within the period of time between 1990 and 2021; The World Bank provided us with fishery yield data while microplastic concentration data came from the National Centers for Environmental Information. In addition to these variables, population data was used as a control variable. The summary of our analysis points towards a significant inverse relationship noted between microplastic levels and fishing yield: an increase by one piece of plastic results in a decline by somewhere between 65 to 100 metric tonnes (95% confidence interval). This generalization held true for about 93% coastal countries considered under this study. To capture specific variations among nations but also identify an overall trend line while dealing with inter-country variability, random intercepts and slope components were included as part of our mixed-effect model methodology.
An Empirical Analysis of the Digital Economy Enabling NQP
Authors: Ke Li
Published: August 2024

The 2024 Government Work Report of China explicitly emphasizes the need to "vigorously promote the construction of a modern industrial system and accelerate the development of new quality productivity (NQP)," underscoring the critical importance of NQP in the current era. This paper assesses the development of the provincial digital economy across four dimensions: digital infrastructure, digital industrialisation, industrial digitalisation, and digital governance. Simultaneously, the development of provincial NQP is measured through three dimensions: technological innovation, industrial innovation, and factor innovation. Using the Kernel density estimation method, this study analyzes the dynamic evolution of NQP development in China from multiple perspectives. Additionally, a double fixed-effects model is employed to empirically examine the role of the digital economy in empowering NQP, and a random forest approach is applied to investigate the "non-linear spillover effect" of the digital economy on NQP development. The findings of this study are as follows: () The digital economy significantly advances NQP, primarily by promoting technological innovation, optimizing employment structures, and enhancing factor synergy, with digital industrialization being a key driver of this progress. () The digital economy exhibits a non-linear spillover effect on NQP development.
Quantitative Strategies Based on FinGPTs Analysis of the Federal Reserves Attitude
Authors: Chen Sirui
Published: August 2024

This paper explores the impact of the Federal Reserve (Fed) on financial markets, specifically through the sentiment scoring of Fed meeting minutes by the AI analysis model FinGPT and its correlation with market performance. The article first introduces the method of sentiment analysis using FinGPT and conducts an in-depth analysis of the meeting minutes from 1990 to 2020 using this method. Furthermore, key market data, such as the S&P 500 Index, real estate, and financial sector indices, are used to assess the actual impact of the Feds attitude on market dynamics. Through quantitative trading and backtesting analysis, this study verifies the performance and effectiveness of a single-factor strategy in different markets. The results show that while the Feds sentiment scores have high predictive validity and market adaptability in the financial sector, their performance in the real estate market and the broader stock market index is relatively weaker. The findings indicate that the market applicability of the strategy is significantly influenced by market type and economic environment. Future strategy optimization needs to consider these factors to enhance adaptability and efficiency across different markets.
The Comparison of Feature Selection Methods and Feature Combinations Based on the Stock Prediction
Authors: Shen Xiaowan
Published: August 2024

Predicting short-term stock prices is a significant and challenging research area due to market volatility. Machine learning (ML) uses algorithms to learn patterns from data, improving prediction accuracy over time. Feature selection (FS) methods enhance model accuracy and efficiency. Evaluating and selecting the best FS methods and feature combinations is essential for improving prediction performance. This paper evaluates three feature selection (FS) methods by scoring technical indicators and using three models with 30 different indicator combinations to predict outcomes. Error rates are used to measure accuracy. The analysis reveals that incorporating all features yields the lowest average error rate in this paper, and Williams R is attached to the greatest importance in this paper. Among the FS methods, Mutual Information (MI) and Random Forest (RF) outperform the correlation coefficient. Future work can focus on two main areas: considering more indicators and combinations, and exploring additional feature selection (FS) methods to identify the best one.
Analyzing the Strategy of Digital Transformation to Improve Company Valuation
Authors: Yan Ruitong
Published: August 2024

Competition among firms has intensified due to technological advancements and societal progress. As a result, several companies are suggesting implementing digital transformation as a means to enhance the value of their enterprise and obtain a competitive edge. The objective of this study was to examine the influence of digital transformation on the financial worth of a firm. This research employs Midea as a case study to do a comprehensive analysis that combines the theories of cost-benefit and business model innovation. The analysis and observation are based on the data provided by the firm. This study not only broadens the research trajectory for researchers to investigate the enhancement of business valuation, but it also provides a point of reference for associated firms to improve their performance. This study not only broadens the research trajectory for academics to investigate the enhancement of business valuation, but also provides a point of reference for associated firms to improve their performance.
A Study of Chinese Stock Price Prediction Based on LSTM and Time Series Linear Regression Model
Authors: Xiao Shuting
Published: August 2024

With the advancement of technology and the increasing popularity of machine learning, new opportunities have emerged across various industries. In the financial sector, an increasing number of individuals are attempting to utilize machine learning to enhance the accuracy of stock price predictions. This paper will also endeavor to apply the LSTM model for predicting the stock prices of companies listed on the main boards of the Chinese stock market, while simultaneously comparing it with traditional time series linear regression model. Against the highly complex backdrop of the stock market, this paper aims to explore whether machine learning can surpass traditional models in achieving superior predictive results. However, in this research, the result did not show that LSTM overperform significantly linear regression model due to completely different economic and political background and limited parameters used. In order to improve the accuracy and stability of LSTM, it might be considered to incorporate additional influencing factors and combining with other models for market prediction.
Solution to the problem of setting up medical points and road maintenance in mountainous areas based on optimization
Authors: Li Jingxuan , Ye Shuangkai , Li Xingyao
Published: August 2024

Assume there are 100 villages in a mountainous area. To improve the medical standards of the local residents, three medical points need to be established in several villages, and the roads need to be repaired. This paper modifies the Floyd algorithm to derive all the shortest paths between nodes. Based on the Prim algorithm, a minimum spanning tree model is constructed to find the road maintenance method with the lowest cost; a nested greedy algorithm is used to construct an optimization model under multiple variables, effectively solving the problem of setting up medical points and road maintenance in mountainous areas.
Evaluating the efficacy of machine learning in calibrating low-cost sensors
Authors: Wang Zhihan
Published: August 2024

Ambient air quality monitoring requires low-cost environmental sensor devices that are affordable and feasible for large-scale implementation. However, issues such as sensor drift, environmental sensitivity, and inter-sensor variability affect data accuracy and cannot be adequately addressed by traditional calibration methods. This paper summarizes the use of machine learning techniques for calibrating low-cost sensors. The literature review shows that machine learning models like Random Forest, Support Vector Regression, and Neural Networks significantly improve sensor accuracy and reliability. For instance, Random Forest models reduced the root mean squared error by 30% for PM2.5 measurements, while Neural Networks achieved an R value of 0.997 for methane sensors. Integrating machine learning with IoT and mobile technologies enhances real-time monitoring and spatial resolution. Identified gaps include the quality of training datasets, managing environmental variability, and improving model transferability across different contexts. Addressing these gaps through advanced models and real-time calibration methodologies will further enhance sensor performance, ensuring more precise and reliable environmental data.
Research on movie recommendation algorithms based on machine learning
Authors: Wang Kai
Published: August 2024

In recent years, the number of movies released worldwide has grown exponentially. Due to the large number of movies, it is difficult for users to find movies that match their preferences. Therefore, with the development of Internet technology, it has become an important research direction how to filter out the movies that users are interested in from the massive movie data. This paper mainly focuses on the film recommendation algorithms based on machine learning, including the traditional collaborative filtering algorithm, rating-based sorting recommendation algorithm and content-based recommendation algorithm. By conducting a detailed analysis of the principles, advantages and disadvantages, as well as application scenarios of these recommendation algorithms, this paper aims to identify methods that best fit current movie recommendation systems. The objective is to improve the real-time and personalized recommendation, while providing users with better film recommendation services.
ADHD fMRI classification through CBAM-Autoencoder framework
Authors: Hong Shiwei
Published: August 2024

This work presents a novel method for leveraging resting-state functional magnetic resonance imaging (fMRI) data to accurately detect Attention Deficit Hyperactivity Disorder (ADHD). The proposed method integrates the Convolutional Block Attention Module (CBAM) with a lightweight Autoencoder network to effectively extract and highlight salient features within fMRI scans. By leveraging attention mechanisms, the model focuses on important local details while filtering out irrelevant information, thereby enhancing diagnostic precision. Extensive experimentation on the ADHD-200 dataset showcases the efficacy of the proposed approach, demonstrating its ability to improve classification performance significantly. Specifically, the method achieved an average accuracy of 91.7% across the NYU, 93.8% across the KKI, 86.4% across the NI, 89.1% across the PU, and 83.5% across the PU_1 datasets. This research underscores the potential of attention-based deep learning techniques in advancing ADHD diagnosis using neuroimaging data.
A comprehensive review of models for vehicle detection based on computer vision analysis in autonomous vehicle
Authors: Mo Yunxuan
Published: August 2024

The application of computer vision analysis technology based on traditional image analysis and machine learning techniques in the field of vehicle detection is the focus of this paper. This paper fills the gap in previous research and provides a comprehensive overview and comparison of vehicle detection models based on computer vision analysis. This paper first briefly outlines the goals of vehicle recognition, evaluation indicators of models, and widely used datasets; then, it summarizes vehicle detection models based on traditional image processing techniques and machine learning techniques. Finally, the advantages and disadvantages of various models and sensors are discussed, and potential future development directions are proposed.
Enhanced 3D object detection for autonomous driving: A spatial-temporal alignment approach in Bird's Eye View scenarios
Authors: Wang Ruoxi
Published: August 2024

This paper presents a novel 3D object detection algorithm designed for Bird's Eye View (BEV) scenarios, which significantly improves detection capabilities by integrating spatial and temporal features. The core of our approach is the spatial-temporal alignment module that efficiently processes information across different time steps and spatial locations, enhancing the precision and robustness of object detection. We employ a temporal self-attention mechanism to capture the motion information of objects over time, allowing the model to correlate features across various time steps for identifying and tracking moving objects. Additionally, a spatial cross-attention mechanism is utilized to focus on spatial features within regions of interest, promoting interactions between features extracted from camera views and BEV queries. Our method also implements temporal feature integration and multi-scale feature fusion to enhance detection stability and accuracy for fast-moving objects and to capture multi-scale context information, respectively. The model employs an enriched feature set post alignment for 3D bounding box prediction, ascertaining the position, dimensions, and orientation of objects. We conducted experiments on two public datasets for autonomous driving nuScenes and Waymo Open Dataset, demonstrating that our method outperforms previous BEVFormer and other state-of-the-art methods in terms of detection accuracy and robustness. The paper concludes with potential future directions for optimizing the BEVFormer model's performance and exploring its application in broader scenarios and tasks.
Citrus recognition in orchard scene based on modified HSV-morphology method
Authors: Yan Jinyuan , Liang Kaiqi , Liu Chenxing , Gao Ming
Published: August 2024

The orchard has always been an important scene for citrus pickers, and the existence of factors such as leaf occlusion and color similarity always lead to the difficulty of robot recognition. Based on a citrus image dataset collected from actual orchard scenes, we extract image features and build a mathematical model to identify and count the number of oranges in each image, and show the distribution of apples in the entire dataset. Firstly, this paper establishes a model based on HSV method, focusing on recognizing yellow citrus. Secondly, with the further analysis of the data set, it is found that the cyan citrus exists. Therefore, this paper defines the empirical HSV range of two kinds of citrus at the same time and introduces contour processing to optimize the model. In order to accurately distinguish the immature citrus from the leaves with similar colors and the citrus occluded by leaves, this paper uses more detailed color threshold setting and morphological operation, and makes the eccentricity of the contour less than 0.85. At the same time, considering the size and overlap of citrus, morphological operations such as dilation and erosion and contour detection techniques are applied to further improve the recognition ability of the model. Finally, the experimental results show that the average accuracy of the established model reaches 92.1%, which has a good recognition effect on citrus.
Label-refined EEG emotion classification based on intelligent media communication
Authors: Fu Zitian , Sun Qiao
Published: August 2024

With the development of cognitive computing and intelligent media communication, recognizing the users emotion using electroencephalography (EEG) has garnered increasing attention. However, building a cross-subject emotion recognition model with good generalization performance is really difficult. To achieve remarkable performance, a label-refined domain adversarial neural network is designed and EEG samples are subdivided into 6 groups according to the differences in users' brain activities under different movie stimuli. Based on the experiment of SEED and SEED-IV datasets, the classification accuracy of our method reaches 93.354.14% and 84.086.01%, achieving the state-of-the-art performance. Experiments show that using different movie stimuli to refine labels can achieve efficient cross-subject emotion recognition.
Research on the tri-block copolymer-directed self-assembly synthesis of bicontinuous structures for electrical energy storage
Authors: Wu Zexiong
Published: August 2024

The burgeoning demand for more efficient energy storage systems necessitates advancements in electrode material technologies. Bicontinuous structures, synthesized using tri-block copolymers, present a promising avenue due to their unique properties that can enhance battery performance. This study aims to explore the potential of tri-block copolymer-directed self-assembly in synthesizing bicontinuous structures and to assess their application as innovative electrode materials in electrical energy storage systems. It was found that the synthesized bicontinuous structures demonstrate superior ionic pathways, resulting in enhanced ion transport and improved electrochemical performance compared to traditional electrode materials. The copolymers contribute to the formation of a highly ordered mesoporous architecture, leading to increased surface area and more active sites for electrochemical reactions. Furthermore, these structures exhibit significant stability during cycling, attributed to their ability to accommodate volume expansions and distribute mechanical stress effectively. Future research should focus on the extension of the synthesis process and the further optimization of the electrochemical properties in order to make the transition from laboratory studies to practical applications.
Innovative methods for integrating translation memory and CAT tools: Enhancing intelligent support in human translation processes
Authors: Zhang Jiazhen , Zhou Lin , Bai Wei
Published: August 2024

This paper explores the innovative integration of Translation Memory (TM) and Computer-Assisted Translation (CAT) tools to enhance translation efficiency, consistency, and quality in multinational organizations. By adopting a user-centric interface design, modular system architecture, and cloud-based deployment, the integrated system addresses diverse translation needs while ensuring data security and privacy. Key features include real-time translation suggestions powered by machine learning algorithms, seamless access to TM databases, and real-time collaboration tools. The paper discusses implementation strategies, challenges, and solutions, highlighting the importance of user training and continuous improvement. A case study demonstrates significant improvements in translation speed, accuracy, and user satisfaction, underscoring the potential of advanced translation technologies to transform translation workflows. The findings provide valuable insights into best practices for successful implementation and optimization of TM and CAT tools in complex, large-scale environments.
Factors Influencing Class Satisfaction in Online and Offline Blended Classes - Focusing on Digital Competence, Interactions between Teachers and Learners, and Interactions between Team-members
Authors: Kim Eun Joo
Published: August 2024

The class environment in 2022 is based on full face-to-face classes in accordance with the easing of social distancing, but many instructors and learners are conducting a mixture of non-face-to-face and face-to-face classes. In the post-COVID-19 class environment, a plan to effectively apply online and offline blended classes is needed. This study analyzes how college students' digital competence, teacher-learner interaction, and team-member interaction affect class satisfaction in online and offline blended classes. The research subjects of the study were students of four-year university E located in Gyeonggi-do. Data were collected through an online survey method between June and July 2022. The analysis method was frequency analysis and descriptive statistical analysis. In addition, correlation analysis was conducted to confirm the validity of variables and to confirm multicollinearity. Moreover, factor factor analysis was conducted to confirm the validity of the measurement tool developed in this study. Finally, multiple regression analysis was conducted to verify the influence of college students' digital competence, teacher-learner interaction, and team-member interaction on class satisfaction of online and offline blended classes. As a result of the study, it was found that interaction factors between teacher-learners and team-members, excluding digital competency, affect class satisfaction in online and offline blended classes. These results suggest that to improve class satisfaction in operating online and offline blended classes in the post-COVID-19 era, it is necessary to come up with teaching strategies that can enhance interaction between teacher-learners and team-members.
Empowering Teachers' Learning into Action to Enhance Active Learning in the Classroom
Authors: Pondongnok Chalermchai , Phrakhrusutheejariyawattana Phrakhrusutheejariyawattana
Published: August 2024

This research is a Research and Development (R&D) study aimed at developing educational innovation entitled "Online Self-Training Program to Empower Teachers' Learning into Action to Enhance Active Learning in the Classroom", and implemented in schools. The online self-training program consists of 2 projects 1) a Development project for teacher learning which includes 7 self-training modules, and 2) the implementation of an online self-training program project which includes 1 self-training module to be used as a teaching guideline. The research consists of 4 steps. The final step was an experimental study. The results of the first project found that 12 participating teachers achieved post-test scores that met the standard of 90/90. The average post-test score was statistically significantly higher than the average pre-test score. Additionally, results from the second project found that, according to students’ perception scores, the average post-test score was statistically significantly higher than the average pre-test score among 500 students. The results confirm that the "Online Self-Training Program to Empower Teachers' Learning into Action to Enhance Active Learning in the Classroom," is an effective educational innovation for teachers. Therefore, it can be disseminated for the benefit of teachers in schools on a broader scale in the future. Additionally, results from the second project found that, according to students' perception scores, the average post-test score was statistically significantly higher than the average pre-test score among 500 students. The results confirm that the "Online Self-Training Program to Empower Teachers' Learning into Action to Enhance Active Learning in the Classroom" is an effective education innovation for teachers. Therefore, it can be disseminated for the benefit of teachers in schools on a broader scale in the future.
GERÇEKTEN SANALA: 1 MWP GÜNEŞ SANTRALİNİN PVSYST SİMÜLASYON PROGRAMIYLA PERFORMANS ANALİZİ
Authors: Şahin Zafer Ramazan
Published: August 2024

Bu çalışma, Göksun Belediyesi’ne ait 1179 MWp kurulu güçteki güneş santralinin PVsyst programıyla simülasyonunu içermekte olup, elde edilen sonuçlar gerçek üretim verileri ile karşılaştırılmıştır. Analiz, termal kayıp, kablolama kaybı, gölgeleme kaybı, uyumsuzluk kaybı, tozlanma ve karlanma kaybı, panel kaybı, invertör kaybı gibi faktörleri kapsayarak, sistemin performansındaki kayıplar detaylı bir şekilde değerlendirilmiştir. 2023 senesi için santral çalışmasından alınan gerçek zamanlı veriler santralin toplamda 1893005 kW elektrik enerjisi ürettiğini göstermiştir. PVsyst programı ile yapılan simülasyoda ise aynı süre zarfında santralin 1896851 kW enerji üretebileceği tahmin edilmiştir. İki set veri arasında sadece 3846 kW yani %0,203’lük bir fark tespit edilmiştir. Simülasyon değeri ile gerçek değer arasında neredeyse ihmal edilebilecek bu az fark PVsyst programının santral çalışmasını yüksek bir doğrulukla modelleyerek simülasyonunu yapabildiğini göstermektedir.
AVRUPA BLOK ZİNCİRİ HİZMETLERİ ALTYAPISININ (EBSI) SÜREÇ ANALİZİ MODELİ İLE İNCELENMESİ
Authors: Aktaş Derya , Tan Evrim , Arap İbrahim
Published: August 2024

ÖZET
Kamu sektöründe blok zincirine ilişkin projelerin yaşama geçme ya da uygulama aşamasında sorunlar olduğu gözlemlenmektedir. Bu çalışmada Avrupa’da devlet çapında en önemli ve daha önce örneği bulunmayan bir proje olan Avrupa Blok Zinciri Hizmet Altyapısı (EBSI) ele alınmıştır. EBSI, Avrupa Birliği üye ülkeleri ile Norveç ve Lihtenşyan’ın dahil olduğu blok zinciri tabanlı sınır ötesi kamu yönetimi hizmet altyapısı kurmayı amaçlayan bir projedir. EBSI sürecini kamu politikası analizi perspektifinden ele alarak hangi süreçlerde tıkanma olduğu, bu tıkanmaların nedenleri ve sorun alanlarına yönelik üretilen cevapları tespit etmek amaçlanmıştır. Bu amaç doğrultusunda nitel yöntem kullanılmış olup, doküman incelemesi ve saha gözlemlerinden yararlanılarak EBSI süreç analizi modeli ile incelenmiştir. EBSI teknik altyapısı ile proje uygulamalarını destekleyecek kapasiteye gelmiş olmakla beraber uygulama aşamasına geçilmemiştir. EBSI’nin uygulamaya geçmesinin önündeki temel zorluklar yönetişim yapısı, yasal/düzenleyici çerçeveler, kurumsal hazırlıklar, EBSI aktörler arası birlikte çalışabilirlik (işbirliği) ve uygulama araçlarının geliştirilme sürecinin devam etmesinden kaynaklanmaktadır. EBSI politika geliştirme sürecinde karşılaşılan zorluklar yeni bir kurumsal yapının, Europeum EDIC (Avrupa Dijital Altyapı Konsorsiyumu) doğmasına neden olmuş ve bazı hukuksal düzenlemeleri beraberinde getirmiştir
Unraveling Epistemic Trust
Authors:   , Knapen Saskia Renate Yvonne
Published: August 2024

This thesis presents the results of our attempt to unravel the, at the start of this project, still mainly theoretical concept of epistemic trust (ET). ET refers to the capacity to consider conveyed knowledge as trustworthy, relevant to the self, and generalizable to other contexts. Early negative childhood experiences may not only lead to attachment insecurity and impaired mentalizing but may also dispose an individual to adopt a rigid and pervasive hypervigilant position toward information coming from others, resulting in high levels of epistemic mistrust (EM). This disposition of EM is believed to increase the risk of developing psychopathology and might explain the profound rigidity and the ‘hard to reach’ character of patients with severe psychopathology. Although the concept of ET is essentially transdiagnostic, a more intrinsic relationship between epistemic mistrust and the development of personality disorders (PDs), more specifically Borderline PDs (BPD), is assumed.
The general aim of this thesis was to clarify the concept of ET and to make ET assessable by developing and validating a clinically feasible measurement instrument to generate empirical support for the basic theoretical assumptions about epistemic trust and childhood adversity, attachment, mentalizing, and personality pathology.
Understanding Intersectionalities in Psychosocial Well-Being & Justice — Transformational Processes in the Interface between Child Mental Health & Law
Authors:   , Ramaswamy Sheila
Published: August 2024

The thesis aims to provide an understanding of intersectionalities in psychosocial well-being and justice, including methods and processes for transforming children’s interface with the law. It combines a strong rights perspective with rigorous scientific evidence, to demonstrate how child-centred approaches may be applied to serve the needs of children interacting with mental health and judicial systems. While the findings, as they pertain to problem-solving and systems transformation, are generalizable to other vulnerable populations interacting with various types of systems, they are also contextualized to the unique socio-cultural challenges found in the traditional societies (and systems) in low-to-middle-income countries--where systems, especially those catering to marginalized and disempowered groups, such as children, tend to be at a relatively nascent stage. Herein lies the importance of this thesis, and the contribution it has made to the child law systems in India-- a country that is grappling with implementing child protection policies and laws against a backdrop of problems characterized by a considerable population of children suffering from adversity and maltreatment, (child) mental health and protection services that lack adequate knowledge and skills to assist these children, and a judicial system that is not designed or equipped to deal with vulnerable witnesses such as children.
In addition to its scientific contribution, the practical relevance of the thesis is evident in the plethora of methods it has developed for the use of systems, to interact with children, in comport with the tenets of child rights and mental health; furthermore, the acceptance of these methods are exemplified by the Indian Supreme Court’s direction to use them in child protection practice, such to assist children in conflict with law, through processes of juvenile transfer.
Options for a Liability Framework for Commercial Low-Earth Orbit Destinations
Authors: Santana Adriana ‘Ana’
Published: August 2024

Commercial Low-Earth Orbit destinations (CLDs) are the next milestone in the development of commercial space transportation, but the legal responsibilities of spaceflight providers, personnel, and spaceflight participants (SFPs) on board are not easily discernible. This article describes the purpose and goals of CLDs; assesses current regulations enforced against private companies’ activities in space; and proposes different liability frameworks for application to CLD activity. Development of a comprehensive liability regime is crucial to promoting innovation in commercial space, while also protecting the safety interests of all personnel and participants on board.
Research on Optimization Strategies of Curriculum System in the New Quality Teaching Reform of Intelligent Control Technology Specialty
Authors: Bu Lingtao , Yang Dingcheng
Published: August 2024

This research focuses on the optimization strategies of the curriculum system of the intelligent control technology major under the background of the new quality teaching reform. Through a comprehensive and in-depth analysis of the numerous problems existing in the current curriculum system, and in close combination with the latest demands of industry development and cutting-edge educational and teaching theories, a series of highly targeted and practically operable optimization strategies are proposed. The fundamental purpose is to significantly improve the teaching quality of the intelligent control technology major and spare no effort to cultivate high-quality professionals who can fully adapt to the requirements of the times.