bluuebunny/crossref_metadata_2025 huggingface dataset:
An investigation into deep learning for the analysis of medical images
Authors: Hu Chaohao
Published: March 2024

In recent years, deep learning has emerged as a pivotal paradigm in the analysis of medical images, with convolutional networks serving as a cornerstone of this advancement. This paper delves into a comprehensive exploration of the fundamental principles underpinning deep learning and its applications within the domain of medical image analysis. Through a meticulous review of many contemporary contributions, this study synthesizes the latest developments in the field, emphasizing tasks like image classification, object detection, segmentation, and registration. The inquiry spans diverse medical disciplines, encompassing neurology, retinal imaging, pulmonary studies, digital pathology, breast and cardiac evaluations, and musculoskeletal analyses. As a culmination, the paper not only assesses the present state-of-the-art achievements but also critically discusses persistent challenges and illuminates promising avenues for future research endeavors.
Federated learning-based YOLOv8 for face detection
Authors: Peng Ruijia
Published: March 2024

Recognizing the paramount importance of face detection in the realm of computer vision, there is an urgent need to address the vital concern of protecting individuals' privacy. Face detection inherently involves the handling of extremely sensitive personal information. To tackle this challenge, this study puts forth a proposal to incorporate Federated Learning into the face detection model. The objective is to maintain data localization and enhance security throughout the experiments by harnessing the decentralized nature of collaborative learning. The experimental procedure for Federated learning in face recognition models encompasses several key steps: device selection, global model initialization, model distribution to devices, local training, local model updates, model aggregation, global model updates, and multiple iterations. This methodology enables the collective training of models by dispersed devices, hence enhancing recognition performance, all the while ensuring the preservation of user data privacy. In addition, it is imperative to integrate Federated learning with YOLOv8 in order to establish a distributed target detection system. This method entails numerous devices engaging in local YOLOv8 model training, hence safeguarding data privacy and minimising data transmission. The empirical findings indicate that the use of joint learning in the face detection model leads to successful identification of the face model. In the future, there will be a consideration of novel federated learning algorithms with the aim of enhancing privacy.
The efficient application analysis of FPGA in automotive intelligent control
Authors: Liu Dingwei
Published: March 2024

In recent times, the automotive industry has witnessed a remarkable transformation with the rapid development of automotive intelligent control systems. This evolution has shifted consumer expectations from cars being mere modes of transportation to multifunctional lifestyle assistants. A pivotal player in this transformative journey is Field-Programmable Gate Arrays (FPGAs), which have made significant contributions by delivering high-performance and efficiency enhancements across various facets of automotive intelligent control. This article delves into the diverse applications of FPGA technology within the realm of automotive intelligent control, classifying them into three distinct categories. Firstly, it explores FPGA applications in autonomous driving image processing, highlighting their role in enabling real-time image analysis and recognition, a critical component of self-driving vehicles. Secondly, the paper examines FPGA applications in automotive control function implementation, showcasing how FPGAs facilitate the efficient execution of complex control algorithms and decision-making processes in modern automobiles. Lastly, it investigates FPGA applications in automotive electronic design, emphasizing their role in enhancing the overall reliability and performance of electronic systems in vehicles.
Subscribing prediction of term deposit based on decision tree, random forest and support vector machine
Authors: Chen Yifan
Published: March 2024

To classify customers and predict their behaviour based on some of their features and what they do before is most people want to do. This study finds a data set about bank customers from Kaggle and use three different classification models to classify customers and predict whether they will subscribe a term deposit based on some of their features. The three classification models are decision tree model, random forest model and support vector machine model. Firstly, using these models to get the feature importance and accuracy rate to evaluate the result. In addition, this study changes the parameters about these models and find how can get better result. Based on these models to process data set and getting the result, this paper also compared their results and find advantages and disadvantages of three models. Finally, this paper also discusses how to improve the models so that they can get the better result and solve some other problems.
Overview of potential customer prediction for products based on machine learning
Authors: Wang Qihang
Published: March 2024

With the development of the Internet, e-commerce, as a new way of online shopping, has provided great convenience to people's lives. Due to the complex potential relationship between consumers and products, it is difficult to recommend products according to consumers' needs, which increases the difficulty of online shopping. Therefore, how to predict the potential consumers of commodities has attracted the wide attention of researchers. Fortunately, machine learning-based approaches have made text and images that can model complex underlying relationships between data. Therefore, researchers have introduced machine learning into the product potential consumer prediction field and achieved good results. This paper first introduces the relevant data set and the product potential users' forecast evaluation index. Then, it summarizes the relevant product potential consumer prediction methods based on machine learning. Finally, the paper summarizes the whole article and looks forward to future research methods.
A comparative study of the deep learning based model and the conventional machine learning based models in human activity recognition
Authors: Wang Shaoyang
Published: March 2024

Human activity recognition (HAR) has been widely studied as a research field in human behavior analysis due to its huge potential in various application domains such as health care and behavioral science. Recently, deep learning (DL) based methods have also been successfully applied to predict various human activities. This research aims at building different Python-based models to perform HAR using smartphones and calculating and comparing the accuracy of the models to select the optimal one. Four models were built to classify and predict human activities: Deep Convolutional Neural Network (DCNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The results of the experiments in this paper show that the Deep Convolutional Neural Network achieves an average recognition accuracy rate of 95.49%, exceeding the other three models. The underlying reason may be that Deep Convolutional Neural Network is based on a more advanced algorithm deep learning technique.
The impact of Q-learning parameters on robot path planning problems in different complex environment
Authors: Wang Zihan
Published: March 2024

With the development of reinforcement learning algorithms, its efficient problem-solving model and the universality of the method have been favoured by many scholars. More and more robot path planning problems are solved using reinforcement learning methods. This article focuses on the Q-learning algorithm, uses MATLAB to study the impact of Q-learning parameters on robot path planning problems in different complex environments, and tries to find the optimal solution to the parameters. Research has found that environmental complexity has a significant impact on the speed at which robots solve path planning problems, and the optimal solutions to problem parameters in different environments require detailed analysis of specific problems.
Analysis of the text matching problem
Authors: Hu Dingyuan
Published: March 2024

As a matter of fact, with the rapid development of computation ability as well as machine learning scenarios, the natural language model had widely use in society in recent years. To be specific, lots of software and applications have been developed, such as Chat-GPT, machine translation and text generation. Among various applications, text matching constitutes a foundational challenge within the realm of natural language processing (NLP). With this in mind, this study delineates the advancements encapsulated by contemporary models pivotal to text matching methodologies, i.e., LSTM, Transformer as well as BERT. At the same time, this study will make a comprehensive demonstration of them, including the principle and the related works. In the meantime, this study will also provide 10 well-known dataset of Text Matching problem. Finally, this research presents analysis of the limitation for this task as well as gives the future prospect for text matching at the same time.
Analysis the improvements of YOLOv5 algorithms: NRT-YOLO, MR-YOLO and YPH-YOLOv5
Authors: Tao Wenlei
Published: March 2024

Computer Vision (CV) is a fundamental aspect of artificial intelligence, with applications spanning multiple domains. The YOLO (You Only Look Once) algorithm has significantly contributed to real-time object recognition in CV. This paper explores the evolution of the YOLO algorithm, focusing on the improvements brought by three specialized variants: NRT-YOLO, MR-YOLO, and TPH-YOLOv5. NRT-YOLO addresses the challenge by introducing the C3NRT module, enhancing precision while maintaining low complexity. MR-YOLO optimizes YOLOv5 for industrial quality control, improving speed and accuracy. TPH-YOLOv5 enhances object detection in drone-captured images by introducing additional prediction heads and transformer modules. Despite these advancements, YOLO algorithms have limitations, including difficulty in detecting small objects and issues in complex scenes. Nevertheless, the research sheds light on the continuous evolution of YOLO algorithms, offering insights into real-time object detection, with applications in manufacturing, healthcare, transportation, and more. The significance of this research lies in showcasing the adaptability and potential of the YOLO framework. As YOLO continues to evolve, it promises to revolutionize various industries and applications, providing robust, real-time object detection solutions.
AI applications in video games and future expectations
Authors: Tian Xinhe
Published: March 2024

In recent years, artificial intelligence (AI) techniques have been extensively applied in various industries, including the gaming industry. The integration of different AI models into games has brought about a new era of game development, optimization, and overall gaming experience. This paper presents an overview of several AI algorithms that have been utilized in games, discussing their applications and providing predictions for the future of AI in gaming. It outlines how these algorithms have been incorporated into games to improve various aspects of gameplay. The application part of the paper focuses on game content optimization, specifically from the perspective of enhancing player experience. It examines areas such as player interaction and in-game elements, explaining how AI algorithms have been used to optimize these aspects and create a more immersive gaming experience. Furthermore, the future expectations section revolves around further advancements in optimizing the gaming experience and incorporating AI into the game design and development stages. It also discusses potential improvements in hardware that can better support AI integration in games. Finally, the article concludes with a summary and expectations for the future development of AI in gaming, highlighting the potential benefits and advancements that can be achieved through the continued use of AI algorithms.
Comparative study on personalized open-domain generative dialogue systems
Authors: Gu Yang
Published: March 2024

Developing personalized dialogue systems is a monumental challenge in the rapidly evolving domain of Natural Language Processing (NLP). These systems are engineered to facilitate more natural, intuitive, and user-friendly interactions between humans and machines. Their theoretical significance extends to real-world applications, making them an invaluable asset in today's digital age. Over recent years, this area has garnered immense attention from researchers and academics, becoming a hotbed for innovation and study. This paper aims to contribute to this burgeoning field by conducting an in-depth comparative analysis of the current leading personalized language models. Specifically, this paper will scrutinize models grounded in deep learning algorithms and those that employ reinforcement learning techniques. This research objective is to dissect each approach's unique advantages and limitations. By doing so, the paper hopes to identify actionable avenues for improvement and optimization. Furthermore, the paper will offer a forward-looking perspective, outlining potential advancements and innovations that could shape the future landscape of personalized dialogue systems in Natural Language Processing.
Research on emotional analysis and integration of multimodal data report
Authors: Jin Xiaodi
Published: March 2024

In recent years, emotional analysis has remained a hot topic, permeating people's daily lives and playing an indispensable role in various environments. Researchers can improve products and services and forecast user behavior using sentiment analysis to understand consumers' emotional inclinations and reactions. This paper's comprehensive review of emotional analysis is conducted through various research data. This study integrates emotional analysis and multimodal data research, elaborating on emotional analysis's characteristics and pros and cons from aspects such as facial expressions, voice information, and textual data. This paper points out the current applications of emotional analysis and recent research advancements, selecting various features and methods for emotion tendency classification, and utilizing existing technological tools for analysis. Lastly, this work combines multimodal data with various studies to make emotional analysis more widely and aptly applied in everyday life. There are greater opportunities for sentiment analysis in various domains thanks to the advancements in multimodal sentiment analysis research and modal fusion technologies.
Comparison of stock price prediction models for linear models, random forest and LSTM
Authors: Lu Zenan
Published: March 2024

With the rapid development of financial markets, accurate stock price prediction is significant to investors and financial institutions. Many researchers proposed stock price prediction models, including linear models, random forests, and LSTMs. However, few studies have comprehensively compared the three models. This study aims to fill this gap by analysing the forecasting effectiveness of different models through empirical studies. This research is to explore the application of linear models, random forests, and LSTM models in predicting stock prices and analyse and compare the principles, advantages and disadvantages, and the scope of application of these three models. According to the analysis, they all have their scope of application and limitations in different situations. In practical application, the appropriate model can be chosen for prediction and analysis according to the specific data sets and research purpose. Meanwhile, it is also possible to try to integrate and improve different models to get better prediction results. In addition, the influence of data quality and completeness, feature selection and extraction from the prediction results should be noted to improve the prediction accuracy and stability of the model. In conclusion, this thesis provides some references and lessons for related studies and practical applications by analysing and comparing the applications of LSTM, linear models, and random forests in predicting stock prices.
Application of cloud computing platform in industrial big data processing
Authors: Yao Ziyan
Published: March 2024

With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big data processing and explores its potential impact on improving data processing efficiency, security, and cost-effectiveness. The article first reviews the basic principles and key characteristics of cloud computing technology, and then analyzes the characteristics and processing requirements of industrial big data. In particular, this study focuses on the application of cloud computing in real-time data processing, predictive maintenance, and optimization, and demonstrates its practical effects through case studies. At the same time, this article also discusses the main challenges encountered during the implementation process, such as data security, privacy protection, performance and scalability issues, and proposes corresponding solution strategies. Finally, this article looks forward to the future trends of the integration of cloud computing and industrial big data, as well as the application prospects of emerging technologies such as artificial intelligence and machine learning in this field. The results of this study not only provide practical guidance for cloud computing applications in the industry, but also provide a basis for further research in academia.
A fast convergence bidirectional a star path planning algorithm
Authors: Luo Yixin
Published: March 2024

A* and bidirectional A* have been classical pathfinding algorithms for long with many derivatives specially designed for their exclusive situations. To solve the problem of the bidirectional A* algorithm being inefficient in some situations, this paper gives a kind of optimized algorithm with a new dynamic parameter in the heuristic function and a 25% increase in efficiency. By modifying the weights, the proportion between distance and efficiency can be well balanced. This paper uses some typical problems for simulation and paints the scatter diagram of different weights to figure out the relationship between the weight and the balance. In the end, the author gives suggestions on optimizing the coefficient due to the rule, which stands for gradually promoting the weight of the dynamic parameter until the path doesnt keep optimal, for various situations and how to further modify the heuristic function for better performance.
Synthetic rain image generation via CycleGAN
Authors: Yang Kaiqi , Lu Yanfeng
Published: March 2024

In the domain of autonomous driving and computer vision, the formidable challenge of adverse weather conditions, particularly rainy weather, profoundly impacts image quality and visibility. Rain streaks pose a significant impediment to accurate object detection, especially concerning pedestrians and vehicles on the road. While existing solutions have focused on training rain removal models on paired images, acquiring such data with congruent backgrounds has been a substantial hurdle. Three distinct images, each featuring a diverse background, were deliberately chosen for the rain removal experiment. The deliberate selection of these varied backgrounds allowed us to rigorously assess the efficacy of our rain removal method across diverse environmental contexts. The compelling results obtained from this experiment affirm the method's ability to effectively mitigate rain line across a spectrum of backgrounds, thus establishing its robustness and versatility in real-world scenarios. The resulting images are then employed to train our rain removal model, marking a significant advancement in our endeavor. Our innovative approach stands poised to revolutionize rain removal techniques, aligning synthetic and authentic rain images more closely in real-world scenarios.
The application of reinforcement learning algorithms in intelligent games
Authors: Bi Herui
Published: March 2024

The ongoing advancement of computer hardware has spurred the need for high-quality gaming experiences. The pressing need for intelligent games, the proven efficacy of reinforcement learning in various fields, the advancement of deep reinforcement learning technology, and the desire for customized gaming experiences collectively propel the ongoing exploration and implementation of reinforcement learning in intelligent games.This study use algorithms sourced from the literature review to examine multiple facets of reinforcement learning, including the modeling of game settings, reward design, the utilization of deep learning approaches, and adversarial learning. The utilization of reinforcement learning in intelligent games holds immense importance. Initially, it enhances the gaming encounter and renders intelligent games more demanding and tailored. Furthermore, the game gains complexity and intrigue by the implementation of intelligent adversaries capable of assimilating and adjusting to the player's tactics. This not only fosters the advancement of AI technology through research and development, but also broadens the utilization of reinforcement learning in other domains, like autonomous driving and smart manufacturing. Furthermore, tailored encounters, cutting-edge game design, and utilization in schooling are likewise significant contributions, rendering reinforcement learning a pivotal element in propelling technological advancement and augmenting user experience.
Work procedure action recognition based on skeleton and video features
Authors: Cai Yu-Chen , Zhang Zhi-Cong
Published: March 2024

In the field of industrial engineering, traditional methods for analyzing manufacturing process actions have limitations such as time-consuming, labor-intensive, and experience-dependent. To address these challenges in action analysis, we propose an intelligent action recognition method based on both skeleton and video features, aiming to replace manual decomposition of action elements. The MediaPipe framework is used for human posture estimation to obtain the skeleton sequence, and the CNN-GRU model is constructed action recognition based on skeleton features. For hand movements involving the use of industrial gloves, an enhanced TimeSformer video understanding model is introduced for action recognition based on video features. This improvement incorporates uniform attention and external attention mechanisms, resulting in enhanced model performance. The final experimental validation for the self-constructed process action dataset shows that the online detection speed of the skeleton action recognition model reaches 25 FPS, and the accuracy of the end-to-end video action recognition model is improved by 10.5 percentage points compared to the base model.
Research Progress and Trends of Pigai.org Automated Writing Evaluation System in English Writing: A Systematic Bibliometric Analysis (2011-2023)
Authors: Xiao Dunjiao , Mohamad Maslawati , Li Wenyi
Published: March 2024

This study presents a systematic literature review on the application of the Pigai.org automated writing evaluation system in English writing in China. It aims to explore the research progress and trends in English writing during the period (2011-2023). Journal papers in this study were extracted from the two main databases: CNKI and EBSCOhost, dating from January 1, 2011, to June 22, 2023. The PRISMA 2020 guidelines and the bibliometric analysis technique design were adopted to conduct an in-depth analysis of the journal articles on the Pigai.org automatic writing evaluation system. Additionally, a comparative study on Performance Analysis, Science Mapping and Network Analysis on the number of published articles, authors, institutions, h-index(h) papers, and keywords were identified. Besides that, the thematic analysis of the keywords revealed the research focuses of Pigai.org in English writing education. Thus, the findings culminated the research progress and trends in China and the world by discussing the implications of the results and indicated promising directions for future research.
A Study on the Lexicographic Approaches Utilized by English Language Scholars from Saudi Arabia: Emphasizing the Use of Dictionaries
Authors: Shariq Mohammad , Al-Hattami Abdulghani
Published: March 2024

This research investigates the lexicographic strategies employed by Saudi English language scholars, focusing on their utilization of dictionaries. The study also assesses the effectiveness of bidirectional Arabic-English dictionaries, particularly for Saudi Arabian students learning English as a foreign language. This study utilized (SPSS versiоn 21.0) tо analyze data cоllected оn dictiоnary use strategies amоng Saudi students. The Likert scale was emplоyed tо encоde respоnses, and Crоnbach's cоefficient alpha analysis ensured internal cоnsistency. Results indicated that strategic dictiоnary use amоng Saudi students falls within the medium range, with a mean scоre оf 2.87. Nоtably, participants shоwed a higher inclinatiоn tоward emplоying lооk-up strategies (average = 3.69) and dictiоnary selectiоn strategies (average = 3.51), while repоrting less frequent utilizatiоn оf awareness strategies (average = 2.01) and lemmatizatiоn strategies (mean = 2.10). As far as the efficacy, usage, attitudes, and impacts of bi-directional online translation tools are concerned, participants unanimously rated these highly (average= 4.7) for saving time, in cases where the word(s) was unknown (average= 4.5), and in recording the new word in the mental lexicon (average= 4.2), but as confusing (average= 4.8) when used for complex word structure translation. In conclusion, Saudi Language and Translation program students demоnstrated mоderate scоres acrоss the fоur categоries оf dictiоnary use strategies, with a higher inclinatiоn tоward lооk-up and dictiоnary selectiоn strategies and lesser emphasis оn awareness and lemmatizatiоn strategies.
The Impact of Virtual Learning on Students’ Engagement in the Saudi EFL Context: A Connectivism-Theoretic Perspective
Authors: Khafaga Ayman , Maneh Al-Johani Hanan
Published: March 2024

Drawing on the connectivism learning theory (CLT), this paper probes the extent to which virtual learning (VL) influences students’ engagement (SE) in the Saudi EFL context. More specifically, this paper tests the hypothesis of whether or not VL improves SE in terms of eight learning variables: class attendance, class participation, the acquisition of the four language skills, learning anxiety, learning motivation, learning self-efficacy, willingness to communicate, and learning autonomy. This paper adopts a mixed-method approach, represented by both quantitative and qualitative methods of analysis and constituting two methodological instruments: a questionnaire and an interview. The sample consists of 256 EFL majors who are studying English at Prince Sattam bin Abdulaziz University, Saudi Arabia, and 14 EFL teachers who are teaching English at the same university. Three main findings are reported in this study: first, there is a positive attitudinal perception by the participants concerning the impact of VL on SE; second, VL increases students’ attendance, enhances their class participation, learning motivation, learning self-efficacy, learning autonomy, and willingness to communicate, and it decreases students’ learning anxiety; and third, SE is influenced by VL in terms of both productive and receptive language skills; positively, with regard to speaking and listening; and negatively, in terms of reading and writing. Pedagogically, the results of this paper necessitate reconsidering the efficiency with which new technologies are used in learning and teaching, and it is further anticipated to offer promising potential for the possibility of whether or not to completely depend on virtual learning in the near future.
Bilateral Flexion-Type Supracondylar Humerus Fracture
Authors: Uslu Muhammed , Solmaz Mahsum , Daşcı Mustafa Fatih , Beytemür Ozan
Published: March 2024

Supracondylar humerus fractures are common in the pediatric population, but flexion fractures are very rare in this population. The need for open reduction in these fractures is high and can be challenging for orthopaedic surgeons. In this article, we report a 9-year-old patient with bilateral flexion-type humeral fracture treated with closed reduction, which, to our knowledge, is the first report in the literature. We concluded that the first step in the treatment of flexion-type supracondylar fractures should be closed reduction with the help of an experienced assistant surgeon and that successful results can be obtained even in bilateral flexion-type fractures with appropriate treatment and follow-up.
Manual diagnosis of thermal skin burns. The first report
Authors: Stepanova Marina , Egorova Irina , Zinov'ev Evgeniy , Barsukova Irina , Novoseltsev Svyatoslav , Narushak Irina
Published: March 2024

The aim of the study is to assess the possibility of studying the depth and area of high-temperature skin lesions in burns by manual methods. Materials and methods: 99 patients with II-III degree thermal skin burns with a lesion area of up to 10% of the body surface were examined at admission to hospital as well as in dynamics. Clinical (palpation diagnostics), instrumental (laser Doppler fluorometry), analytical, and statistical methods of research were used. The study result is the substantiation of the manual (palpation) diagnosis method for determining the true depth and area of the burn wound, the condition of the underlying tissues, the involvement of which in the burn process in the projection of the burn wound affects the course and prognosis of II-III degree thermal burns in patients and their quality of life in the future. The novelty, informativeness, and efficiency of the manual method of diagnosis are shown.
A clinical case of curing spinal canal stenosis with reduction in the size of a herniated intervertebral disc by manual therapy methods, which was considered in the concepts of fundamental physiology
Authors: Krivo Yuri , Deryabin Ivan , Machnev Yuri , Vorobyevsky Igor , Suslov Sergey
Published: March 2024

A clinical case of cure of spinal canal stenosis and reduction in the size of a herniated intervertebral canal by manual therapy methods and involvement of the patient in therapeutic activities through physical exercises is considered through the prism of neurophysiological knowledge about reality, the principles of national Russian and the preceding Soviet medicine.

There is an analysis of the ideas, which are typically used in clinical practice by neurologists and neurosurgeons for analysis, synthesis, and justification of treatment prescription according to their profile and the limited use by them of knowledge of fundamental physiology about the real structure of the human body and its functions in a specific clinical case.

Attention is focused on the scientific nature of the methods of manual therapy, applied for diagnosis and treatment, of the national Russian and Soviet schools of medicine, including the Czechoslovak school of manual medicine, which differs significantly from traditional medical chiropractic with different metaphysical justification in the chiropractic schools of American osteopathy and chiropractic, applied kinesiology, and also in Indian Ayurveda, Japanese shiatsu, reiki, mutual aid techniques of martial arts of Indochina, traditional Chinese medicine.

The clinical analysis uses scientific knowledge of fundamental physiology, neurophysiology, systemic psychophysiology, cognitive science about the activity of functional systems, which is significant for the patient’s recovery, including ensembles of neurons of central generators of the ordered activity, sensitivity regulation systems, medial and lateral movement control systems, and neuromuscular complexes of motor units. The normalization of their activity by manual therapy methods leads the patient to clinical recovery and an active life of high quality.
Prediction model for diabetes mellitus using machine learning algorithms for enhanced diagnosis and prognosis in healthcare
Authors: Sarkar Prosanjeet Jyotirmay , Chanagala Satyanarayana , Chandra George Chellin Jeya , Ruby Usha , Manda Kavitha
Published: March 2024

Diabetes mellitus (DM) affects the hormone insulin, which causes improper glucose metabolism and raises the body’s blood sugar levels. With 4.2 million fatalities in 2019, DM is one of the top 10 global causes of mortality. Early detection of DM will aid in its treatment and avert complications. There must be a quick and simple technique to diagnose it. Such diseases can be managed, and human lives can be saved with early diagnosis. Smart prediction techniques like machine learning (ML) have produced encouraging outcomes in predictive classifications. There has been a lot of interest in ML-based decision-support platforms for the prediction of chronic illnesses to provide improved diagnosis and prognosis help to medical professionals and the general population. By building predictive models using diagnostic medical datasets gathered from DM patients, ML algorithms efficiently extract knowledge that helps predict diabetic individuals. The association between DM and a healthy lifestyle is used in the model. In this study, the NHANES (National Health and Nutrition Examination Survey) data set is utilized, along with five ML methods such as Artificial Neural Networks (ANN), CATBoost, XGBoost, XGBoost-histogram, and Light GBM to predict DM. The results of the experiment demonstrate that the XGB-h model outperformed other ML methods regarding area under the receiver operating characteristic curve (AUC-ROC) and accuracy. The most effective XGB-h framework can be used in a mobile app and a website to rapidly forecast DM. Real-time prediction using details delivered by the model at runtime can be developed as a whole bundle as a product. Clinicians can quickly determine who is likely to get diabetes using the proposed strategy, which will facilitate prompt intervention and caring.
Methods and Applications of Investment Decisions in the Catering Industry
Authors: Yan Chaoheng , Tatyana Grigoryevna Sviridova , Liang Xinxin
Published: March 2024

With economic globalization and consumption upgrading, investment activities in the catering industry have become more frequent and complex. This study aims to explore effective methods for investment decision-making in the catering industry and empirically analyze their application with a view to improving investment efficiency and accuracy. Through the method of financial analysis difference calculation, we comprehensively analyze the investment payback period, risk assessment and expected returns. Model analysis reveals the performance and applicability of different investment methods in actual operations. The results show that combining the unique industry characteristics of catering and adopting a customized comprehensive decision-making model can significantly improve the success rate of investment decisions. Therefore, good risk assessment and decision-making methods can bring clearer investment directions and higher economic benefits to investors in the catering industry.
Tailoring endoscopic third ventriculostomy for diverse 3rd ventricular floor landscapes
Authors:   Senior Consultant Neurosurgeon, Anu Institute of Neuro and Cardiac Sciences, Vijayawada, India, Teegala Ramesh
Published: March 2024

Endoscopic third ventriculostomy (ETV) offers an effective minimally invasive solution for obstructive hydrocephalus. This video chapter provides insights into patient selection, surgical technique, and post-operative care for ETV. The chapter outlines ETV’s evolution and indications, emphasizing meticulous patient selection based on radiological parameters and clinical evaluation. Pre-operative planning ensures procedural precision, with detailed guidance on optimal head positioning and burr hole placement. Under general anesthesia, the ETV technique involves precise ventricular access and stoma creation using channeled neuroendoscopes. Post[1]operative management focuses on vigilant monitoring and outcome assessment. ETV demonstrates success rates ranging from 70 to 90%, with proper patient selection and pre-operative evaluation associated with better outcomes. This chapter serves as a valuable resource for neurosurgeons navigating hydrocephalus, showcasing ETV’s efficacy in select cases.
An analysis of the use of sound in Iwai Shunji's film ‘Love Letter’
Authors: Wei Yuxin
Published: March 2024

Iwai Shunji, a Japanese filmmaker, is responsible for directing the film "Love Letter," which is considered an outstanding example of a pure love picture from East Asia. The film is lauded for its sincere and uncomplicated approach to the narrative, its new and breathtaking camera images, and its delicate and gentle emotional expressions. The sensitive depiction of feelings from the viewpoint of the female protagonist and the aesthetics of materialism demonstrate the unadulterated emotions of East Asian love in a manner that is both new and uncluttered. The concept of "love without knowing where it comes from," which is sometimes believed to be the epitome of a crush, is shown as the emotional output of the characters in this movie. This article will investigate the qualities and benefits of the use of sound in Love Letter, directed by Iwai Shunji, in terms of the relevance of the study, the introduction to the film's plot, and the use of sound. Specifically, the article will focus on the characteristics and advantages of the use of sound.
Investigation and Research on the Market Demand for the Development of Light Food Stalls in College Canteens
Authors: Wu Xinxu , Yao Jingwen , Zhao Hui , Su Licheng , Chen Yue , Zhou Zejiong
Published: March 2024

Through a questionnaire survey, this question in-depth understanding of the views and needs of college students in Longzihu District of Bengbu on light food stalls, so as to clarify their preferences and intentions for dietary choices. This paper will collect and descriptively analyze the perceptions and expectations of college students on light food stalls in college canteens, further explore the market demand and development advantages of LIGHTFOOD customized light food stalls in college canteens, and provide suggestions and references for the sustainable development of the light food market in college canteens.
Research on Credit Risk Measurement and Management of Commercial Banks in China
Authors: Li Wenyang
Published: March 2024

This comprehensive study explores the sophisticated landscape of credit risk measurement and management within the commercial banking sector of China, a critical component in maintaining the resilience and stability of its burgeoning financial system. As China's economy continues to integrate with the global market, its banking sector faces escalating credit risk challenges, necessitating advanced and effective management strategies. This paper critically examines the existing methodologies for measuring credit risk, including both traditional approaches and modern innovations that leverage big data and artificial intelligence, assessing their applicability and efficiency in the Chinese context. Moreover, we delve into the multifaceted strategies employed by Chinese commercial banks to manage credit risk, focusing on the integration of regulatory frameworks, technological advancements, and best practices in risk management. Through a methodical review of literature and case studies of prominent Chinese banks, this study identifies key trends, challenges, and opportunities in credit risk management. We evaluate the effectiveness of current practices and propose a set of strategic recommendations aimed at enhancing the robustness of credit risk management systems. These include adopting a more holistic approach to risk assessment, leveraging technology to predict and mitigate risk, and fostering a culture of risk awareness and compliance.
Analysis on the legal problems of data exit
Authors: Zhang Zhongsen
Published: March 2024

Faced with the exponential growth of cross-border data flow, it is too weak to supervise through existing laws, regulations and rules, and the regulatory rules for cross-border data flow should be further refined and improved. This paper summarizes the existing legal problems by analyzing the current status of data exit legal supervision in our country. Based on China's national conditions and respecting the objective laws of the development of the digital economy, targeted countermeasures and suggestions are proposed from many aspects, a scientific and coordinated regulatory system for cross-border data flow is built, a dynamic balance between development and security is sought, and cross-border data governance is adapted to the growth needs of the digital economy, so as to ultimately achieve a good law and good governance for cross-border data supervision.
Isabel’s Growth and Metamorphosis: The Spatial Narrative in The Portrait of a Lady
Authors: Liu Chenrong , Xing Yuyan
Published: March 2024

As one of Henry James’ best-selling novels, The Portrait of a Lady is also considered one of his most successful works. According to “The Third Space” proposed by Edward Suja, Isabel’s movements in the novel is not only the background of the article but also influences the characterization of her. The author reveals the conflict between European and American cultures through the depiction of Isabel’s choice of marriage, reflecting James’ desire to pursue cross-cultural conflict, and the third space in which Isabel is situated also demonstrates her growth from a naive girl to a mature woman, and ultimately realizes her self-identity.
Fragmentation and Self-Deception: Study of the Human-Machine Body from the Posthuman Perspective-The Case of Kazuo Ishiguro’s Klara and the SunP model; quantitative evaluation; expert interviews; indicator system
Authors: Yu Jiali
Published: March 2024

In the context of the unstoppable trend of artificial intelligence, science and technology have become the theme of the times. Will the rapid development of modern technology, such as biotechnology and artificial intelligence, dehumanize us? Can a machine have human consciousness? In his novel Klara and the Sun, Kazuo Ishiguro criticizes the arrogance of technological rationality and the arrogance of anthropocentrism from the perspective of a “non-human” robot. The relationship between humans and machines has become a problem that humans need to re-examine. With the help of post-humanism, this paper aims to explore the physical changes and behavioral actions of robots and humans in the novel to reveal the “split” between man and machine and the “self-deception” of humans in the novel, so as to finally trigger thinking about how humans and machines can coexist harmoniously at the juncture between humans and posthumans, and provide reference for the future society between humans and non-humans.
Experimental study on compressive properties of mixed fiber concrete after fire
Authors: Chen Xiaoqiang , Wang Zechang
Published: March 2024

In recent years, the application of high performance self-compacting concrete with high strength, high durability and high workability has become more and more widespread, and has gradually become the main building material. However, high-performance self-compacting concrete increases brittleness while increasing strength, and at the same time, explosion resistance and fire resistance are significantly reduced. With the development of cities, natural gas and intelligent electrical equipment are becoming more and more popular, but people's awareness of fire safety has not been improved, making the incidence of fire in buildings on the rise. In recent years, the development of hybrid fiber self-compacting concrete (HFRSCC), which combines the high workability of self-compacting concrete (SCC) and the high toughness and high crack-resistant properties of fiber concrete (FRC), giving full play to the fiber's anti-cracking, reinforcing and toughening effect, and after the fire and high temperature effect, the components of the use of hybrid fiber self-compacting concrete to maintain a certain degree of residual load-bearing capacity. The introduction of fibers for reinforcement and toughening, to improve the mechanical properties of concrete materials, blast resistance and fire resistance performance points to a direction.
SAÚDE FUNCIONAL DE MULHERES ADULTAS JOVENS: EXISTEM RELAÇÕES ENTRE ALTERAÇÕES DO ARCO PLANTAR E PRESENÇA DE SINDROME CRUZADA INFERIOR?
Authors: Lucca Danielli de Pont de , Longen Daniela Vitorassi , Longen Willians Cassiano
Published: March 2024

Introdução: A Síndrome Cruzada Inferior (SCI) é uma condição caracterizada por diferentes padrões de fraqueza muscular e rigidez que conectam os lados dorsal e ventral do corpo, a mesma pode surgir como resultado de diversas situações. O pé é um elemento importante para a estrutura corporal, principalmente para o sistema postural, nesse contexto, alterações podais podem ser responsáveis por causar desequilíbrios posturais, assim como também os pés se ajustam aos desequilíbrios de estruturas suprajacentes a eles. O objetivo deste estudo foi analisar possível relação entre a SCI e alterações plantares em mulheres jovens. Métodos: Foi aplicado um Questionário Sociodemográfico, realizada avaliação físico-postural da coluna vertebral, testes de força muscular e encurtamento dos músculos envolvidos na caracterização da SCI, por fim feito a análise plantar através de um podoscópio em 74 estudantes universitárias do sexo feminino. Resultados: Das voluntárias avaliadas, 52,9% possuem alterações plantares. Notou-se que 23% apresentaram padrão postural típico de SCI. Houve um alto índice de fraqueza dos músculos abdominais e glúteo máximo relacionadas com SCI. Discussão: A presença de alterações plantares foi encontrada tanto em voluntárias com SCI quanto em voluntárias sem a Síndrome, permitindo considerar que a presença de determinada alteração, não se mostrou associada à presença da SCI. Conclusão: A maior parte da amostra apresentou alterações do arco plantar. Tais alterações não apresentaram relação significativa com a SCI. A presença de fraqueza muscular e alterações do arco plantar encontradas são dois fatores que embora tenham se mostrado independentes, denunciam tendências não restritas à SCI mas que merecem atenção.