bluuebunny/crossref_metadata_2025 huggingface dataset:
Model performance comparison and evaluation of bank fraud prevention in simulated datasets
Authors: Yang Zhilu
Published: March 2024

The significance of robust fraud detection systems in the banking sector has grown imperative due to the growing prevalence of online transactions. However, the datasets in these particular areas exhibit a greater abundance and diversity. The large amount and variety of data in these domains necessitate the utilization of synthetic sales data, which is derived from real data, as an innovative approach for studying fraud prevention. This study initially derives importance scores for various features through the utilization of random forests. Subsequently, four features that exhibit the highest correlation with fraudulent transactions are selected for further investigation. The training and prediction processes for both random forests and decision tree models are then performed. The study compared the performance of random forests and decision tree models in fraud monitoring using four features. The results indicate that random forests outperform decision trees in terms of accuracy, recall, precision, and F1 scores, with improvements of 0.68%, 0.62%, 0.68%, and 0.65% respectively. These findings provide a comprehensive analysis of the performance comparison between random forests and decision tree models in the context of fraud monitoring.
A millimeter-wave MIMO antenna applied to the n260 frequency band
Authors: Nan Jingchang , Wang Yixin , Zheng Lu , Zhao Longyou
Published: March 2024

In order to effectively alleviate the current situation of low-frequency spectrum congestion and meet people's demand for high-speed communication, a four element multiple input multiple output (MIMO) millimeter wave antenna was designed for the n260 frequency band (37-40GHz), with an overall size of 10mm12mm 0.508mm. This antenna is based on a dipole broadband millimeter wave unit antenna and adopts a coplanar waveguide feeding method. The four element antenna is placed orthogonally to improve the isolation of the antenna. The operating bandwidth is 36.76-41.73GHz, fully covering the n260 frequency band of 5G, with a relative bandwidth of 12.7%. The simulation results show that the impedance bandwidth of each port of the antenna covers this frequency band, and the isolation degree between each antenna unit port is less than -15dB. The envelope correlation coefficient (ECC) is less than 0.05, and has good radiation characteristics, which meets the performance requirements of MIMO antenna and can ensure the normal operation of the antenna.
Predictive analytics with music: Advancing tree-based models for song rating prediction
Authors: Xu Jiaxuan
Published: March 2024

This paper presents an in-depth exploration of predictive analytics applied to a dataset of 19,485 songs from the Kaggle Predictive Analysis Competition (PAC), with the objective of forecasting song ratings based on auditory features. The study employs a range of tree-based models, including regression tree, bagging, and random forest, and confronts various data preprocessing challenges, particularly in handling missing data and incorporating genre as a significant predictive feature. Through the creation of dummy variables for genre classification and careful model selection, the research demonstrates an enhanced approach to predictive accuracy. The effectiveness of these models is rigorously evaluated using test Root Mean Square Error (RMSE), providing valuable insights into their predictive performance. This paper contributes to the field of music analytics by offering a comprehensive analysis of tree-based predictive models and their application in the nuanced task of song rating prediction, highlighting the importance of methodological refinement in predictive analytics.
Research and recommendations on user immersion in Virtual Reality (VR) technology based on data mining
Authors: Peng Xinrui
Published: March 2024

This paper describes the applications and challenges of virtual reality (VR) technology, with a focus on how to improve users' sense of experience in VR environments. In order to better understand users' sense of experience in VR environments, this paper uses a variety of methods for data analysis. The findings show that the older and longer the duration, the more immersive the user is and the higher the immersion with Oculus Rift. 30-36 year old males, 32-35 year old females, and 34-40 year olds experienced higher immersion, while 34-44 year olds had the lowest motion sickness but the best experience. Men with HTC Vive experience higher immersion, while women and others with Oculus Rift experience higher immersion. According to the study, the HTC Vive is best for users under the age of 30, the Playstation VR is best for users aged 30-50, and the Oculus Rift is best for users over 50. In terms of duration, the Oculus Rift provides the best experience for users under 10 minutes and over 40 minutes, while the HTC Vive is better in the 10-40 minute duration. Male users are most immersed at 23-31 minutes, female users are most immersed at 23-31 minutes, and other users are most immersed at 26-33 minutes. Motion sickness was lowest, but the best duration of the experience was 31 minutes.HTC Vive users felt higher immersion at 25-32 minutes, Playstation VR users at 21-28 minutes, and Oculus Rift users at 25-34 minutes. These conclusions provide an important reference basis for improving users' experience in the VR environment. At the same time, this paper puts forward targeted recommendations based on the conclusions drawn from the statistical analysis of the data, which can largely help users to improve the sense of VR experience and bring users an immersive virtual reality experience.
Shortest Hamiltonian circuit algorithm for complex environments
Authors: Wang Yike
Published: March 2024

Simply taking into account the static path length is insufficient to compute the shortest Hamiltonian circuit in complicated situations. To incorporate the influence of many factors on the shortest Hamiltonian circuit, this paper introduces a multi-objective fuzzy comprehensive evaluation model and quantitatively assesses the fuzzy factors. To better correctly reflect each influencing factor's importance in the problem-solving process, this paper also builds a hierarchical model to decide the weights assigned to each one. Finally, this paper adopts the state compression dynamic programming algorithm to solve the dynamic shortest Hamiltonian loop. In this study, an example analysis is also performed to confirm the algorithm's efficacy. Compared with the traditional algorithm, the algorithm designed in this paper comprehensively considers the influence of several factors when calculating the shortest Hamiltonian loop, and can more comprehensively evaluate the advantages and disadvantages of the path.
Generative adversarial networks: Core principles, cutting-edge models, broad applications, and contemporary challenges
Authors: Ye Xiangzhong
Published: March 2024

Generative adversarial networks stand out as one of the most notable innovations in the field of artificial intelligence. Often lauded for their capacity to emulate specific data distributions, their primary function is to discern the underlying characteristics of these distributions and subsequently generate data that mirrors them. In the realm of computer vision, GANs have showcased remarkable prowess by producing high-quality, realistic content. This capability has not only bolstered their reputation but also expanded their applicability across a multitude of tasks. However, the ascendancy of GANs isnt without its set of challenges. Training them can often be a delicate balancing act, as they require careful tuning to ensure stability. Issues like mode collapse, where the generator produces limited varieties of outputs, or training instabilities are not uncommon. Nonetheless, the inherent scalability and versatility of GANs continue to captivate researchers, making them a hotspot for innovation. As we delve deeper into the AI epoch, the potential of GANs remains vast, presenting both unprecedented opportunities and challenges.
Stock price forecast model for CATL based on BP neural network regression
Authors: Dai Meng
Published: March 2024

With the introduction of the "Dual Carbon" policy and the increasing environmental awareness among residents, the new energy vehicle industry is experiencing positive growth momentum. New energy vehicles use non-traditional energy sources as their power supply, effectively reducing carbon emissions, enhancing energy efficiency, and contributing to the improvement of China's existing energy landscape, thus supporting environmental protection and the early realization of "carbon peak" and "carbon neutrality" goals. Contemporary Amperex Technology Co., Ltd. (CATL), a prominent and competitive player in China's emerging clean energy industry, focuses on researching, developing, manufacturing, and marketing power battery and energy storage systems specifically designed for new energy vehicles. Moreover, in recent years, machine learning and deep learning have gained wide application in various domains, including stock price prediction and financial investment. This paper constructs a stock price prediction model for CATL based on a BP neural network regression, considering factors related to traditional energy, carbon trading, environmental aspects, and industry-specific factors.
A comparative study of machine learning-based regression models for supply chain management
Authors: Lu Xiangning
Published: March 2024

The rise of machine learning technology has opened up unprecedented opportunities for the retail industry. Machine learning, as an essential branch of artificial intelligence, enables computers to improve their performance through continuous learning and experience. It has demonstrated its ability to handle large-scale data and complex problems effectively. In retail, machine learning predictions and methods can also lead to significant breakthroughs in supply chain management, helping businesses identify better ways to maintain economic stability and growth, which are crucial for improving people's living standards, eliminating poverty, promoting social stability, driving technological progress, and reducing inequality. This is achieved through different algorithmic regression methods, which can predict future trends and consumer behavior with high accuracy. Machine learning algorithms can analyze vast amounts of data to identify patterns and trends and make accurate predictions about future demand, product inventory levels, and other important factors that drive business success in the retail industry.
Systematic analysis of FPGA-based hardware accelerators for convolutional neural networks
Authors: Zhang Fangrong
Published: March 2024

In the modern era, machine learning stands as a pivotal component of artificial intelligence, exerting a profound impact on various domains. This article delineates a methodology for designing and applying Field Programmable Gate Array (FPGA) based hardware accelerators for convolutional neural networks (CNNs). Initially, this paper introduces CNNs, a subset of deep learning techniques, and underscore their pivotal role in artificial intelligence, spanning domains such as image recognition, speech processing, and natural language understanding. Subsequently, we delve into the intricacies of FPGA, an adaptable logic device characterized by high integration and versatility, elucidating our approach to creating a hardware accelerator tailored for CNNs on the FPGA platform. To enhance computational efficiency, we employ technical strategies like dual cache structures, loop unrolling, and loop tiling for accelerating the convolutional layers. Finally, through empirical experiments employing YOLOv2, and validate the efficacy and superiority of our designed hardware accelerator model. This paper anticipates that in the forthcoming years, the methodology and research into FPGA-based CNN hardware accelerators will yield even more substantial contributions, propelling the advancement and widespread adoption of deep learning technology.
Applications of 5G technology in flexible electronics
Authors: Ren Pengcheng
Published: March 2024

As an emerging field in the industry, flexible electronics not only integrates technologies in fields such as electronic circuits, materials, and flat displays, but also spans industries such as semiconductors, materials, chemicals, and printed circuit boards. Its application importance in various fields such as information, energy, healthcare, and manufacturing are increasingly prominent. The main topic of this article is the implementation of 5G technology in the flexible electronics industry. To begin with, this article presents the traits of flexible electronics. Secondly, it stated the development of 5G and introduced its characteristics. The result shows that 5G is not only an air interface technology with higher rates, larger bandwidth, and stronger capabilities, but also an intelligent network for user experience and business applications. Then, combined with 5G and flexible electronics, they are applied in three aspects: mobile antennas, intelligent equipment, and remote medicine. Finally, the current problems and future prospects in the fields of flexible electronics and 5G were summarized. Currently, it faces challenges such as signal interference and power consumption due to the unique characteristics of flexible materials. And in the future, 5G can potentially enhance the performance of flexible electronics, leading to the creation of new products and services in various industries.
Exploring the development and application of LSTM variants
Authors: Chen Nuo
Published: March 2024

Long Short-Term Memory (LSTM) is receiving increasing attention as the development of deep learning technology. The gate structure of LSTM enhances long-term memory, forming its superior capacity to complete tasks that challenge traditional RNN. However, considering the wide variety of applications, a comprehensive understanding of the development and application of the model, which is vital for future research, is comparatively lacking. Therefore, this paper is produced with the hope of offering an overview of the development of LSTM. It shows the process of development from RNN to LSTM and explains the aim and necessity of LSTMs birth. After that it introduces the structure of LSTM, analyses its advantages over RNN, and discusses the application of some popular LSTM variants, such as peephole LSTM, bidirectional LSTM, and GRU. Hopefully, this work can provide a more profound knowledge of LSTM's benefits and potential, identifying worthwhile avenues or fields of future research.
Application of machine learning in lung cancer prediction
Authors: Yang Dingjingmu
Published: March 2024

Lung cancer is a life-threatening disease that is mainly caused by long-term smoking, and genetic reasons. This disease is terribly difficult to treat, but the survival rate can be largely increased by an early diagnosis. However, most people with lung cancer are not detected until the late stage. In recent years, many researchers have developed effective pre-diagnosis methods based on machine learning techniques. Machine learning technique enables the computer to learn from data and perform tasks. This review paper lists machine learning models that can be applied to lung cancer probability prediction. The models are trained by datasets of three types of backgrounds: genetic data, clinical data, and histological data. Each model uses different machine learning algorithms, and all of the models perform excellent ability in predicting. This paper suggests that machine learning models can be applied in screening for lung cancer.
Machine learning and deep learning-based sentiment analysis of IMDB user reviews
Authors: Xia Shengjie
Published: March 2024

Movie reviews have always been a popular and enduring subject of interest among researchers. Sentiment analysis plays a significant role in this domain. The utilization of machine learning and natural language processing techniques can provide valuable insights into the emotional responses of audiences towards movies, as well as facilitate the appraisal of their reputation and market potential. This is achieved through the analysis of sentiment expressed in movie reviews. Furthermore, this approach is highly valuable in various application domains such as data mining, web mining, and social media analysis. This paper aims to conduct a comparative analysis by utilizing typical models based on machine learning and neural networks, along with the integration of natural language processing techniques. The IMDB database, which contains 50,000 reviews, will be used, and data preprocessing will be performed before applying these models. By comparing the accuracy of each model, insights regarding movie reviews can be derived.
The ubiquitous influence of image recognition in daily life
Authors: Gao Xuanye
Published: March 2024

Image recognition technology has emerged due to the quick advancement of computer science. Using image recognition technology, a computer can identify and categorize diverse items in an image according to their functions. Artificial intelligence, image processing, and image recognition technology are all combined in this technology. The human eye and image recognition have a similar fundamental operating principle. The best outcomes can be achieved by taking out a few crucial elements from the image and contrasting them with the data that has been captured. This study describes how image recognition technology is used widely in modern life. It can improve people's lives if picture recognition is quick and precise. However, there are still many issues with picture recognition technology, such as security issues with gathering private information and loss of objectivity owing to algorithmic bias. Image recognition technology will become increasingly crucial in the connected world due to the ongoing development and enhancement of computer power. Image recognition technology will push what is technically possible in visual perception and artificial intelligence.
Comparative review of advanced seatbelt detection: Infrared sensors vs. image-based systems
Authors: Lu Haizhou , Pan Hengyue , Sun Zijun
Published: March 2024

The seatbelt, often heralded as the first line of defense in vehicular safety, plays an indispensable role in mitigating the risks associated with traffic incidents. Consequently, the push towards enhancing transportation safety has led to the emergence of sophisticated seatbelt detection systems. In this study, we undertake a comprehensive examination of four cutting-edge detection methodologies: RF (Radio Frequency), Infrared Marker Vision, Convolutional Neural Networks (CNN), and the You Only Look Once (YOLO) approach. Each method is dissected to understand its efficacy in determining if a passenger has properly donned their seatbelt. Beyond just immediate detection, this paper also casts a vision towards the horizon of seatbelt detection advancements. We explore how such detection systems might seamlessly integrate with advanced vehicle safety infrastructures, and postulate on their pivotal role in the burgeoning domain of autonomous vehicles. As self-driving cars become an imminent reality, the importance of reliable seatbelt detection mechanisms will only magnify. Thus, through this paper, we not only shed light on current methodologies but also endeavor to chart the trajectory of future innovations in the realm of seatbelt safety detection.
Research on a community life route guidance system based on virtual reality technology
Authors: Zheng Qianmu
Published: March 2024

Virtual reality technology holds significant potential for applications in the field of urban visualization, although it is primarily utilized for showcasing architectural forms or aiding in planning and design processes. To enhance the integration of urban visualization technology into the everyday lives of ordinary residents and expand the user base beyond planners, this paper introduces the Community Life Route Guidance System, leveraging the presentation of economic livelihood to afford residents interactive experiences within authentic living scenarios. By incorporating and aligning the location and information of Points of Interest (POIs) within the urban model of a single community and its surrounding neighborhoods, it generates, calculates, and filters 'life routes' comprising four categories of commercial nodes. The objective is to serve new residents in the community by providing life guidance or to offer prospective homebuyers a reference point for the convenience of community commercial facilities. The specific technical pathway encompasses: constructing an environmental model, processing and associating POI data, life path identification based on the Dijkstra algorithm, and path visualization with user interaction.
Analysis of implementation for big data techniques in consumer behavior
Authors: Liu Shengchen
Published: March 2024

Consumer behavior can refer to both the purchasing behavior of consumers towards goods and the decision-making behavior of consumers when choosing goods, the research in this field involves psychology, marketing, and other disciplines. As a popular data mining and processing technology, big data technology has powerful information analysis capabilities. Therefore, with this in mind, applying big data technology to consumer behavior research can greatly improve the efficiency. This study focuses on the implementation for big data techniques in consumer behavior, including application scenarios, application methods, as well as application efficiency. The AIDMA, AISAS, and SOR models are suitable for using big data analysis technology. Big data technology helps enterprises influence external factors in consumer behavior factors, thereby influencing consumer decision-making behavior through internal perception factors. Overall, these results aim to collect and summarize relevant knowledge about consumer behavior and big data for future scholars to investigate and explore the implementation schemes.
Application of big data analysis in water pollution monitoring
Authors: Zhou Chen
Published: March 2024

Contemporarily, the quick development of an abundant amount of big data analysis technologies has brought great convenience to individuals' everyday existence. In terms of environmental protection, especially water pollution monitoring, this technological progress is particularly critical. As the global demand for clean water resources grows and global industrialization intensifies pollution of the water environment, the adoption of advanced data analysis technologies has become critical. Among the vast array of machine learning architectures, three particularly stand out due to their significance and widespread adoption: the artificial neural network (ANN), which serves as a foundational pillar in understanding complex data patterns; the multi-layer perceptron neural network (MLPNN), a sophisticated evolution that allows for deeper computations and learning; and the adaptive neuro-fuzzy inference system (ANFIS), which brilliantly combines neural and fuzzy logic principles for intricate problem solving. These models not only have high accuracy due to their wide application, but they still have their own limitations. This article aims to introduce the methods, basic principles, and application scenarios of these models. In addition, this article also compares the advantages and limitations of these machine learning models, thereby providing some new ideas for future improvements and innovations in model algorithms, application scenarios, and integration.
Psychological AI: A critical analysis of capabilities, limitations, and ramifications
Authors: Wang Bingyao
Published: March 2024

As artificial intelligence (AI) continues to advance, machine learning and natural language processing (NLP) offer new possibilities in various areas of psychology, such as therapy, diagnostics, treatment planning, demographic profiling, sentiment analysis, and consumer psychology. However, AI research in psychology is still lacking. This study aims to explore the specific applications of AI in the field of psychology. Firstly, the article delves into the efficacy and cost-effectiveness of chatbots in delivering cognitive behavioral therapy (CBT). Next, the article explores the current state of AI in detecting mental health disorders, highlighting the use of digital footprints as a diagnostic tool. Furthermore, the research investigates how AI can assist medical professionals in developing focused treatment plans. It analyzes studies that aim to optimize treatment strategies using AI technologies. The article also examines the influence of demographic characteristics on our understanding and application of psychology. Lastly, the article touches on the use of AI in analyzing sentiment to understand and predict consumer behavior. Ethical implications of using AI-driven psychological assessments are also discussed. It concludes with a summary of key findings and offers a glimpse into potential future developments in this field.
Analyzing film and drama reviews: Distinguishing trolls from genuine audience feedback based on the BERT model
Authors: He Jiabei
Published: March 2024

With the expanding influence of the Internet, an increasing number of individuals rely on viewer reviews to make informed decisions about whether to watch a movie or TV series. However, the prevalence of manipulated or "navy" reviews, employed by companies to boost their products' reputation, has created a significant challenge. While numerous studies have dissected film and drama reviews, a notable gap exists in discerning genuine audience feedback from deceptive ones. This article's research focus is on evaluating the model's capacity to effectively differentiate between authentic audience comments and navy reviews and delving into the complexities encountered when the model assesses comments, as well as highlighting the disparities between model-generated judgments and human assessments. This article first collects a large amount of different types of comment data, annotates these data, and then uses these data to train and fine tune the BERT model. Finally, the results are obtained and analyzed to determine the reasons. This article found that the accuracy rate of the model's judgment comments is around 71.08%, which is more accurate and stable. However, there are still some issues when judging comments with emojis and emoticons, and certain data is needed to support the judgment of comments for different movies or dramas. There are also certain issues with the dataset, as the data is manually annotated, and the annotation of the dataset itself may also be influenced by the annotator, which may lead to inaccurate judgments.
Trust management systems in Wireless Sensor Networks
Authors: Yue Xinyu
Published: March 2024

The article begins by offering a comprehensive review of the current research landscape concerning trust management mechanisms. It elucidates the foundational concepts behind trust management mechanisms, subsequently detailing various attack models and the inherent vulnerabilities they exploit. A significant portion of the discussion delves into the primary computational methodologies employed in trust management. These encompass a range of techniques such as Bayesian statistics, subjective logic, fuzzy logic, D-s evidence theory, entropy theory, cloud theory, hierarchical analysis, fog computing, and machine learning. From this foundational understanding, the piece transitions to outline the challenges poised to shape the evolution of trust management mechanisms. This section not only emphasizes the hurdles currently faced by researchers and practitioners but also attempts to forecast the potential obstacles of the future. In culmination, the article encapsulates both the current state of research and the anticipated directions that promise to steer the trajectory of trust management mechanisms in forthcoming years. This holistic perspective aims to provide readers with a clear roadmap of the field's progression, emphasizing both its achievements and the milestones yet to be attained.
House price prediction using machine learning
Authors: Li Chenxi
Published: March 2024

The role of the real estate industry in economic development and social progress reflects the economic well-being of individuals and regions. With the increase of people's income level, the demand for housing is also increasing. Therefore, making a more accurate house price forecast will help people make the most correct strategy to buy a house when they need it. This study focuses on house price prediction in King County, Washington, a diverse real estate market. Leveraging machine learning models such as linear regression, random forest, neural networks and XGBoost, these supervised learning models are used to delve into house price forecasting. This research includes random forest and XGBoost, are implemented using Scikit-Learn tools. Besides, the Feedforward Neural Network is introduced with the drop out layer in order to reduce the occurrence of model fitting situations. The findings reveal that XGBoost achieves the highest accuracy, making it well-suited for precise price predictions. Additionally, the research identifies grade, sqft_living, and latitude as the three most influential features significantly affecting house prices within the dataset.
Comparison and analysis of two methods for improving the accuracy of OpenNMT in literary and vernacular Chinese translation
Authors: Li Yiwen
Published: March 2024

As machine translation advances, challenges persist in achieving high accuracy when translating between Literary and Vernacular Chinese. Literary Chinese is known for its concise writing style, often characterized by monosyllabic words. In contrast, Vernacular Chinese incorporates more polysyllabic words. This study utilizes the OpenNMT system to address these challenges and employs different Classical Chinese word segmentation tools to train 2-layer Long Short-Term Memory and Transformer models. These models are then compared and analyzed to measure the improvement in precision. The research findings reveal that adopting a character-level-based word segmentation method for Classical Chinese, coupled with training the Transformer model using OpenNMT, significantly enhances precision. This outcome validates the current observation that existing Classical Chinese word segmentation methods lack sufficient accuracy, consequently impacting the quality of translations between Literary and Vernacular Chinese. By exploring and investigating these approaches, this study contributes to advancing machine translation techniques for improving accuracy in rendering Literary and Vernacular Chinese translations.
Application of Monte Carlo Tree Search algorithm in Go playing
Authors: Jiang Wanyi
Published: March 2024

The reputation of Monte Carlo Tree Search (MCTS) algorithm is built upon its ability to handle a large number of options and make optimal decisions and detailed plans in situations with limited information. It was initially applied in the game of Go, a traditional and complex combinatorial strategy game, which severely challenged the efficiency of traditional brute-force algorithms that relied on simulating every node and branch in the search tree. The success of AlphaGo, developed by Google, represents a significant advancement in artificial intelligence technology, utilizing deep learning networks and MCTS techniques. In this article, we analyze the limitations of traditional brute-force approaches in Go playing and compare them with the application of MCTS, which overcomes these limitations through the iterative process of four steps: selection, expansion, simulation, and back-propagation. The discussion covers the application of MCTS in the game of Go. Besides, we evaluate the advantages and shortcomings of the MCTS algorithm. The article concludes with a summary and prospects for future research.
Emotion recognition based on ResNet and transfer learning
Authors: Liu Dongwei
Published: March 2024

In the fields of psychology and artificial intelligence, emotion recognition is a crucial area of research. As an expression of body language, emotion recognition has a profound impact on our life and interpersonal relationships while existing models have the phenomenon of exploding or disappearing gradient indices due to the lack of information. To alleviate the aforementioned limitations, this paper further introduces the idea of residual jumping and transfer learning to conduct further research and exploration for the emotion recognition. Specifically, based on the RAF-DB dataset, the self-created model Awei and the pre-trained VGG model are combined. Numerous tests show that the suggested approach is effective. By reducing the amount of information lost during the convolution process, the residual idea can help with the issue of an exploding or vanishing gradient index brought on by an excessively long backpropagation. This will increase the correctness of the model. The pre-trained VGG model's addition improves the Awei model's parameter modification's efficacy and accuracy.
RRT algorithm learning and optimization
Authors: Yao Jiajun
Published: March 2024

LWith the increasing maturity of RRT algorithm, more and more fields are starting to use this algorithm. For example, in path planning problems, this algorithm has been well applied because it has good performance and real-time performance. The RRT algorithm is a path planning algorithm based on tree structure. It continuously explores unknown regions, finds feasible paths, and ultimately connects the starting point and target point by randomly expanding the nodes of the tree. The RRT algorithm has good fast exploration ability and low computational complexity, making it suitable for path planning problems in various environments. This article focuses on studying the parameters in various RRT algorithms. Through analysis and comparison, more reasonable parameters were ultimately found. This article also involves optimizing and improving the RRT algorithm using the RRT * algorithm. The research in this article can further understand the application scenarios of the RRT algorithm. It is expected that this algorithm will be better applied in the field of autonomous driving in the future.
The Contemporary Transmission Methods and Education of Chinese Jinghe Opera to Address Current Challenges in Hubei Province
Authors: Fu Xiaocao , Choatchamrat Sarawut
Published: March 2024

The objective of this qualitative research study is to analyze the contemporary transmission methods and educational approaches employed in preserving and revitalizing Chinese Jinghe opera to address current challenges in Hubei Province. The research site, Jingzhou City in Hubei Province, serves as the backdrop for exploring this multifaceted cultural heritage. Nine key informants, including scholars, practitioners, and community members, provide diverse perspectives on Jinghe Opera and its transmission. Semi-structured interviews serve as the basis for data collection, and thematic analysis follows to reveal recurring themes and patterns. The study reveals the historical significance of Jinghe Opera, its evolution, and the critical roles of supporting institutions, such as the Intangible Cultural Heritage Protection Center and Yangtze University. Challenges facing Jinghe Opera, such as a shortage of original works and an aging transmission workforce, are discussed. Proposed solutions include fostering creativity, modernizing education, and establishing institutional support. This research contributes to the broader discourse on cultural heritage preservation and demonstrates the resilience of traditional art forms in a changing cultural landscape.
Return and reintegration in Uganda
Authors: Angida Jesca ,   International Labour Organization,
Published: March 2024

This brief summarizes the ILO FAIRWAY programme support and highlights some of the lessons learned, in the context of the reintegration landscape in Uganda. It ends with recommendations for future interventions, for improved social and economic reintegration outcomes for migrant workers.
Nutritional status and dietary characteristics of pregnant women with gestational diabetes mellitus at National hospital of Endocrinology in 2023
Authors: Nguyen Huong Giang , Nguyen Thi Ha Thu , Hoang Trong Tuan , Le Thu Trang
Published: March 2024

Objective: To describe the nutritional status and dietary characteristics of pregnant women with gestational diabetes mellitus at the National Hospital of Endocrinology in 2023.
Method: A cross-sectional descriptive study combined with a retrospective review of 185 pregnant women was conducted from March to August 2023.
Results: Women aged 30-34 years accounted for the highest proportion at 38.4%. Before pregnancy, the rates of overweight, obesity, and normal weight were 21.1%, 13%, and 61.6%, respectively. During pregnancy, the average weight gain was 0.55 ± 0.3 kg/week, with 53% of women gaining excess weight. The average total energy intake was 1427.2 ± 319 kcal/day, with an average Protein : Lipid : Glucid ratio of 21.2 : 27.5 : 51.2%. The average values for some nutrients were as follow: fiber 10.9 ± 5.3 g, calcium 722.2 ± 307.7 mg, iron 13.2 ± 7.8 mg, vitamin D 1.58 ± 2.05 µg, folate 296.71 ± 136.91 µg, vitamin C 149.64 ± 78.27 mg, vitamin B1 1.39 ± 0.45 mg.
Conclusion: The prevalence of overweight and obesity before pregnancy among women with gestational diabetes mellitus was relatively high, and the rate of weight gain during pregnancy was not reasonable. In addition, the dietary intake was unbalanced and failed to meet recommended requirements.
GÜNCEL YARGITAY KARARLARI IŞIĞINDA REKABET İHLALİNDEN KAYNAKLANAN TAZMİNAT DAVALARINDA ZAMANAŞIMI SORUNU
Authors: Gündoğdu Gökmen
Published: March 2024

Mal veya hizmet piyasalarında rekabeti ihlal eden bazı eylemler 4054 sayılı Rekabetin Korunması Hakkında Kanun (RKHK) m.4, 6 ve 7 hükümleri çerçevesinde yasaklanmıştır. RKHK m.57 hükmü ise m.4 ve 6’da düzenlenen rekabet ihlallerini sayarak bu ihlallerin hukuki sorumluluk doğurduğunu belirtmektedir. RKHK m.58 hükmünde tazminat davası düzenlenmiş ancak bu davaların tabi olacağı zamanaşımı konusuna yer verilmemiştir. Öğretide hâkim görüş ve Yargıtay, isabetli olarak bunun haksız fiil sorumluluğu olduğunu savunmaktadır. Bu durumda zamanaşımı konusunda 6098 sayılı Türk Borçlar Kanunu (TBK) m.72 hükmünün uygulanması gerekir. Bu ise zararı ve faili öğrenme tarihinden itibaren iki yıllık normal ve rekabet ihlali tarihinden itibaren on yıllık azami zamanaşımı süresinin uygulanması demektir. TBK m.72/1 son cümlede, eylemin ceza kanunlarına göre cezayı gerektiren bir fiil olması ve bu fiil için ceza kanunlarında daha uzun bir zamanaşımı süresi öngörülmesi halinde, bu sürenin hukuk davası için de uygulanacağı belirtilmektedir.
RKHK m.4 ve 6’nın ihlali, RKHK m.16/3 uyarınca esasa ilişkin idari para cezasını gerektirmektedir. RKHK uyarınca verilecek idari para cezaları ise 5326 sayılı Kabahatler Kanunu (KK) m.2 ve m.16/1’de “kabahat” olarak nitelendirilmiş ve KK m.20/3’te sekiz yıllık zamanaşımına tabi tutulmuştur. Sekiz yıllık bu zamanaşımı süresinin TBK m.72/1 son cümle atfı sebebiyle RKHK m.57’ye dayalı açılan tazminat davalarında da uygulanması gerekir. Bu sürenin başlangıç anı ise, hâkim öğretinin savunduğu ve Yargıtay’ın uyguladığının aksine, zararın ve failin öğrenildiği tarih değil rekabet ihlalinin gerçekleştiği veya devam eden eylemlerin son bulduğu tarihtir.
Bununla birlikte, rekabet ihlaline dayalı tazminat davalarında zamanaşımı konusunda mutlaka özel bir düzenleme getirilmeli ve 2014/104EU sayılı Direktif m.10 hükmü esas alınarak en az beş yıllık bir zamanaşımı süresi tanınmalıdır. Zararın ve tazminat sorumlusunu öğrenildiği tarihte başlayacak olan bu zamanaşımı süresinin, Rekabet Kurulu’nun bu eylem için bir soruşturma açması halinde duracağı düzenlenmelidir.
Rancang Bangun Sistem Informasi Manajemen Aset Berbasis Web untuk Menghitung Penyusutan Fiskal
Authors: Nasrul Nasrul , Saptono Henry , Wibowo Edi , Amalia Amalia
Published: March 2024

Pengelolaan aset yang efektif merupakan komponen penting dalam menjaga stabilitas keuangan dan operasional suatu lembaga. Di STT Terpadu Nurul Fikri, pengelolaan aset masih mengandalkan proses manual, yang mengakibatkan kurang efisiensi dan meningkatkan risiko terhadap kesalahan. Salah satu aspek yang krusial dalam pengelolaan aset adalah perhitungan penyusutan fiskal, sebuah proses yang mempertimbangkan pengurangan nilai aset selama masa pemakaian untuk keperluan fiskal atau perpajakan. Meskipun esensial, proses perhitungan penyusutan fiskal di STT Terpadu Nurul Fikri masih bergantung pada penggunaan Spreadsheet Excel secara manual. Pendekatan ini rentan terhadap kesalahan, sulit dilacak, dan memerlukan waktu yang cukup lama. Penelitian ini bertujuan untuk merancang dan mengembangkan sebuah sistem informasi manajemen aset berbasis web menggunakan Framework Laravel, dengan metode pengembangan perangkat lunak Extreme Programming (XP), yang memberikan fleksibilitas bagi pengembang untuk menyesuaikan diri dengan perubahan kebutuhan dan memastikan kualitas produk yang dihasilkan. Kesimpulan dari penelitian ini adalah dengan menggunakan pendekatan XP dan memanfaatkan Framework Laravel, pengembangan sistem ini diharapkan dapat memberikan solusi yang efektif dalam mengatasi tantangan yang dihadapi dalam pengelolaan aset, sehingga mempermudah pihak manajemen STT Terpadu Nurul Fikri untuk mengelola asetnya dan memudahkan dalam melakukan perhitungan penyusutan fiskal asetnya.
Penerapan ISO 9001:2015 untuk Peningkatan Layanan Website BMKG.GO.ID dalam Mendukung Penyebaran Informasi Bencana Gempa Bumi
Authors: Dwi Hastanto Andriana , Rustianto April
Published: March 2024

ISO 9001:2015 merupakan sebuah standar atau pedoman bertaraf internasional di bidang Sistem Manajemen Mutu (SMM). Penerapan SMM tidak terbatas pada industri tertentu dan dapat disesuaikan dengan sifat, jenis dan ukuran organisasi. Penelitian ini merupakan sebuah kajian akademis dari implementasi ISO 9001:2015 terhadap pelayanan yang diberikan oleh situs www.bmkg.go.id. Khususnya dalam upaya mendukung diseminasi informasi gempa bumi. Variabel penelitian ini adalah persepsi kepuasan pengguna yang dikumpulkan melalui survei oleh PUSJARKOM BMKG pada tanggal 15 Maret 2022. Terdiri dari empat pertanyaan atau instrumen penelitian, jawaban untuk setiap pertanyaan adalah dalam bentuk narasi persepsi yang kemudian dilakukan pemberian nilai atau bobot berdasarkan Skala Likert. PUSJARKOM BMKG menggunakan teknik modifikasi Skala Likert dengan menghilangkan alternatif netral, agar jawaban responden jelas. Dengan jumlah responden sebanyak 753 orang, berhasil didapat untuk nilai kepuasan layanan sebesar 94,5%, nilai kesesuaian layanan sebesar 98,4%, nilai kecepatan layanan sebesar 95,4%, dan nilai kepercayaan layanan sebesar 77,2%. Hasil dari kuesioner ini, diuji validitas dan reliabilitasnya secara statistik dengan bantuan perangkat lunak Python. Hasil validitas untuk setiap instrumen tersebut adalah sah, dan hasil reliabilitas untuk variabel penelitian dinyatakan andal berdasarkan nilai Cronbach’s Alpha 0,652.
Chat Bot
Authors: Dilipkumar Tushar
Published: March 2024

Chatbots are becoming increasingly important as gateways to digital services and information used in areas such as customer service, healthcare, education, and workplace support. However, there is limited knowledge about the impact of chatbots on individual, collective, and societal levels. Moreover, there are still many challenges that must be overcome before chatbots can realize their full potential. Correspondingly, chatbots have become an important research area in recent years. To support the advancement of knowledge in this emerging research area, we propose a research agenda in the form of future directions and issues to be addressed through chatbot research. This proposal culminates years of discussion in the CONVERSATIONS workshop series on chatbot research. After thorough exploratory analysis among workshop participants, we explore future directions around six topics of interest: (a) users and their impact; (b) user experience and design; (c) Frameworks and Platforms, (d) Collaborative Chatbots, (e) ) Democratization of Chatbots, and (e) Ethics and Privacy. For each of these topics, we provide an overview of the state-of-the-art, discuss key research challenges, and suggest promising directions for future research. The six themes are detailed from a five-year perspective and should be considered as elements of an interdisciplinary research program co-developed by passionate researchers in the field.
DENETİM RAPORLARINDAKİ DENETÇİ̇ GÖRÜŞLERİNİN OKUNABİLİRLİĞİ ÜZERİNE BİR İNCELEME: BORSA İSTANBUL YAKIN İZLEME PAZARI ÖRNEĞİ
Authors: Alataş Ahmet
Published: March 2024

İşletmeler tarafından belirli dönemler itibariyle açıklanan finansal tablolar ve denetim raporları, yatırımcıların alacakları ekonomik kararlarda kritik öneme sahiptir. Denetim raporundaki denetçi görüşü ve açıklamaları, bilgi kullanıcılarının dikkatini çekmektedir. Bu çalışmanın amacı, olumlu harici görüşe sahip olan firmaların denetim görüşü gerekçe paragrafının okunabilirlik seviyesini değerlendirmektir. Araştırma kapsamında, Borsa İstanbul (BİST) Yakın İzleme Pazarında faaliyet gösteren 13 işletmenin 2018-2022 yılları arasında yayımlanan 65 adet denetim raporu incelenmiştir. Olumlu görüş dışında denetim görüşüne sahip olan 50 denetim raporu çalışma kapsamına alınmıştır. Çalışmanın yöntemi olarak Ateşman okunabilirlik endeksi ile Kruskal Wallis H ve Mann Whitney U istatistiksel testleri uygulanmıştır. Uygulama sonuçlarına göre, BİST Yakın İzleme Pazarında işlem gören çeşitli sektörlerdeki işletmelerin olumlu harici denetim görüşü alan denetim raporlarındaki görüşün gerekçe paragraflarının okunabilirliği, %60 oranında '‘Zor’’ ve ‘‘Çok Zor’’ seviyede, %40 oranında ‘‘Orta Güçlük’’ seviyesinde yazılmıştır. Ayrıca, alt sektörler arasında yapılan karşılaştırmada denetim görüşünü destekleyen paragrafların okunabilirlik düzeyi en zor olan alt sektörün Perakende Ticaret olduğu tespit edilmiştir. Denetçinin eğitim düzeyi, unvanı ve alt sektörüyle gerekçe paragrafların okunabilirlik seviyesi arasında anlamlı bir ilişki belirlenmemiştir. Bu bulgular, görüşün dayanağı paragraflarının yüksek oranda düşük okunabilirlik seviyesinde yazıldığını ortaya koymaktadır.
Learning to Hear Students’ Voices: Teachers’ Experiences on Student Mentoring
Authors: Turgut Sema , Taşçı Gülşah
Published: March 2024

In recent years, mentoring practices have become increasingly common in different disciplines. One of these disciplines is education. In this connection, mentoring at the macro level contributes to the education system, while mentoring at the micro level reduces school dropout rates, increases academic success, supports students in their career journeys and protects them against any problems and unhealthy habits. In this context, the objective of this study is to provide an in-depth examination of teachers’ student mentoring experiences in the school context. To this end, phenomenological design, one of the qualitative research methods, was used in this study and the study was carried out with face-to-face interviews. The maximum variation sampling method, one of the purposeful sampling methods, was used to select the participants. A total of 15 teachers selected from different branches formed the study group of the study. The data obtained from the study were transcribed and the thematic analysis method was used to determine the emerging themes and, in this way, a total of five themes were determined: identification, the role of the mentor, the types of mentoring used by the mentor, the mentoring strategies used by the mentor and the problems encountered during the student mentoring process.