bluuebunny/crossref_metadata_2025 huggingface dataset:
The investigation of artificial intelligence-based applications in music education
Authors: Wang Ge
Published: February 2024

The field of music education has garnered significant attention due to the burgeoning advancements in artificial intelligence technology. Currently, there are many works on applying Artificial Intelligence (AI) technology to the field of music education, and this review aims to comprehensively summarize the application of AI methods in multiple subfields of music education. In this paper, according to the focus of AI technology, the areas of music education to which it has been applied are divided into four modules, namely personalized learning, automatic assessment, composition assistance and interactive teaching. Firstly, representative AI methods applied to each of these four subfields are introduced, including but not limited to: the principle of the AI method, the improvement brought by the application. The text then describes the current status of the application of these methods in their domains, discusses the limitations of the application, and makes brief conjectures on how to break through these limitations. The research in this review paper is important for the development of AI technology applied to the field of music education. At the same time, this review also provides directions and suggestions for future research to promote the further development of AI in the field of music education.
The comprehensive investigation of the role related to artificial intelligence in education
Authors: Zhou Bowen
Published: February 2024

This systematic literature review extensively explores the prospects of Artificial Intelligence (AI) applications in the realm of learning from 2019 to 2023. It delves into key areas such as intelligent tutoring systems and online learning platforms, while meticulously examining their underlying technologies. The coverage includes methods for integrating AI into student models, as well as algorithms for enhancing adaptive learning and improving the precision of personalized learning content recommendations. The paper also discusses challenges and prospects, including the potential of AI to reshape the teaching profession and privacy concerns. The review underscores the ongoing necessity of striking a balance between human involvement and automation, emphasizing that AI serves as an auxiliary tool rather than a replacement in education. Additionally, the study investigates the integration of AI in online learning, emphasizing the need for user-friendly interfaces and robust data protection policies. Lastly, the research highlights the importance of interdisciplinary collaboration and ethical considerations, envisioning a future where AI and education seamlessly harmonize.
A review of continuum robot
Authors: Ji Kenneth , Fang Yuyu
Published: February 2024

This paper comprehensively reviews continuum robots and their application in medical domains. Unlike traditional rigid robots, continuum robots possess continuous, flexible structures that enhance dexterity and adaptability. Their compliant nature allows safer human-robot collaboration, while their deformability permits navigation through confined spaces and conformance to anatomy. These advantages make continuum robots promising for medical applications like minimally invasive surgery. The paper discusses continuum robots' fundamental principles, including kinematics, dynamics, mechanical design, modelling, and control strategies. It highlights the unique benefits of continuum robots compared to rigid counterparts, especially for medical use. Challenges such as design optimization, accurate control and modelling, sensing, miniaturization, and technology integration are also addressed. Enhancing manipulation capabilities, developing miniaturized continuum robots, achieving autonomous operation, integrating imaging modalities, and validating safety and efficacy through clinical trials are suggested for future work. This review offers valuable insights into continuum robotics technology and its immense potential to transform medical interventions through precise, minimally invasive procedures.
Research on the model of automatic recognition and natural language question-answer system for traditional Chinese medicine tongue images based on LLMs
Authors: Yang Yan , Yin Yunxia , Li Zhi
Published: February 2024

Large Language Models (LLMs) have recently demonstrated their potential in clinical applications by providing valuable medical knowledge and recommendations. Traditional Chinese tongue diagnosis, one of the "Four Diagnoses," is an essential method for traditional Chinese medicine diagnosis. This paper builds upon tongue image classification technology and utilizes natural language processing and image recognition techniques to enhance the discrimination and analysis of traditional Chinese tongue images through learning and inference. We propose a method to integrate LLMs into the tongue image automatic recognition model and use them in interactive question-answering by summarizing and reorganizing information in natural language text format.
Syntax-aware bidirectional decoding Neural Machine Translation model
Authors: Dong Jia
Published: February 2024

The mainstream model in neural machine translation, the Transformer, relies heavily on self-attention mechanisms for translation operations. This approach has significantly improved both accuracy and speed. However, there are still some challenges. For instance, it lacks the incorporation of linguistic knowledge and the ability to leverage syntactic structure information in natural language for translation, leading to issues such as mistranslation and omission. Addressing the limitations of the Transformer's autoregressive decoding, which decodes from left to right without fully utilizing context information and is prone to exposure bias, this paper proposes a syntax-aware bidirectional decoding neural machine translation model. By employing both forward and backward decoders, the generated decoding results can encompass contextual information. Additionally, the model integrates dependency syntax to generate target language sentences with syntactic guidance. Finally, an optimization strategy involving the Teacher Forcing mechanism is introduced to balance the discrepancies between the Teacher Forcing training phase and the autoregressive testing phase, thus alleviating exposure bias issues.
Performance analysis of GFSK modulation over AWGN channel
Authors: Zhang Xiaoyue
Published: February 2024

This paper analyses the performance of Gaussian Frequency Shift Keying (GFSK) modulation in Additive White Gaussian Noise (AWGN) channels. Using MATLAB 2022b for simulations, the study explores GFSK modulation principles, the role of Gaussian low-pass filters, the characteristics reflected by Power Spectral Density (PSD), and the influence of Bit Time-Bandwidth Product (BT) on Bit Error Rate (BER). It has been observed that the Gaussian filter restricts sidelobe amplitudes and enhances spectral efficiency. Through varying BT values, it is observed that higher BT values correlate with lower BER. Additionally, the study successfully reconstructs original baseband signals through sampling decisions, confirming GFSK modulation effectiveness.
Analysis of the prospective application of artificial intelligence in swimming
Authors: Yu Zhehao
Published: February 2024

Swimming has consistently maintained its status as a highly favored athletic pursuit for a span of one hundred years. In contemporary times, the burgeoning field of artificial intelligence (AI) has exhibited notable advancements, resulting in significant impacts across all domains. Examples of industries include finance, the service industry, and engineering. Furthermore, it has been implemented in various other sports previously. Presently, a predominant focus of scholarly inquiry is in the exploration of artificial intelligence (AI) applications within the realm of team sports, including disciplines such as basketball, volleyball, and rugby. Nevertheless, swimming shares certain characteristics with the aforementioned sports. In order to enhance the advancement of the swimming domain through the utilization of artificial intelligence (AI) technology, this essay will examine the feasibility of implementing certain AI applications that have been employed to support other sports, and will also introduce several AI technologies that have the potential to make distinctive contributions to the field of swimming.
Facial expression recognition with computer vision
Authors: Yu Huang
Published: February 2024

Facial Expression Recognition (FER) is a specialized field within the domains of computer vision and pattern recognition, which is dedicated to the automated identification and examination of facial expressions. Facial expression recognition (FER) has attracted considerable scholarly interest in recent years owing to its diverse array of applications and its potential ramifications across multiple disciplines, such as psychology, human-computer interaction, marketing, and security systems. The objective of this study is to present a thorough examination of the scholarly progression of FER, elucidating the significant achievements, approaches, and obstacles encountered by researchers in this domain. The study presents a selection of databases that are appropriate for Facial Expression Recognition (FER) and conducts a comparative analysis of these databases. The primary methodologies are examined, and recommendations are provided for each stage. In conclusion, this research presents several suggestions for addressing both obstacles and potential in future research endeavors.
Review of common spiking neural network simulation tools
Authors: Li Linyang
Published: February 2024

As a recognized third-generation neural network, the spiking neural network has high concurrency and complexity. Although the degree of research is still far from the previous generation of neural networks, spiking neural networks are excellent in performance and energy consumption. In this paper, the common spiking neural network simulation tools are reviewed. The most frequently used and mentioned tools are NEURON, NEST, and BRAIN. NEURON is more suitable for simulation based on biological applications, pays more attention to biological characteristics, and can support large-scale network simulation. Examples used in the official documentation are neural simulations of invertebrates and mammals. Large heterogeneous networks of point neurons or neurons with a few compartments are frequently simulated using NEST. In contrast to models that concentrate on the specific morphological and biophysical characteristics of individual neurons, NEST is appropriate for those that emphasize the dynamics, size, and structure of the nervous system. Brian was originally designed for research and teaching and is well suited as a teaching and presentation tool for simulating and observing the effects of different parameters for classical neural network projects such as picture classification. In addition, CSIM, SPLIT, SPINNAKER, and other tools also have their merits, but due to the low frequency of relevant references and lack of universality, this study will not give a detailed introduction.
Research of types and current state of machine translation
Authors: Wang Yanran
Published: February 2024

The background of machine translation dates back to the 1950s when scientists began exploring the use of computers for translation. Motivated by various challenges and needs, such as the high cost of manual translation and cross-cultural communication, machine translation has become a pivotal field. This overview delves into the research background, content, methods, key figures, conclusions, and future prospects of machine translation. It summarizes automatic evaluation metrics, corpus construction, and transfer learning, all of which contribute to enhancing translation performance. Currently, there are three mainstream categories of methods, which include rule-based translation, statistical translation, and neural network-based translation. The rule-based translation method relies on language rules and dictionaries for translation. Statistical machine translation involves the use of extensive bilingual corpora for identification and translation. The conclusion emphasizes the potential of neural machine translation, yet acknowledges challenges in diverse languages, low-resource languages, and specialized terminology.
Strategy optimization for breakout games based on improved BFS algorithm and image recognition techniques
Authors: Li Zekai , Lu Chang , Tong Andi
Published: February 2024

This paper develops a Dungeon Game and details the development process of the City Game. The Dungeon Game is an adventure-themed role-playing game in which the player takes on the role of a character he or she creates and engages in a variety of adventurous activities in a set fictional world. It also allows the use of various props to add interest and realism to the game. In the development of this game, the pathfinding algorithm is crucial to the character's movement and behavior, and the previous traditional pathfinding method can no longer meet the needs of the game, so we optimize the original algorithm, adding dynamic programming and backtracking method in the original algorithm to improve the original algorithm's pathfinding logic to make the character pathfinding in the game more reasonable, to improve the game's performance and user experience. performance and user experience. At the same time, this paper tries to incorporate an image recognition technique based on ANN neural network into the game by adding figures as images into the game, to enrich the content of the game and make it have more possibilities, which increases the playability for the players. In the path optimization test, although the response time is slightly inaccurate, the overall response time is gradually decreasing as the optimization algorithms improve. These algorithm improvements have a more significant increase in the efficiency of the game.
Current study on NeRF-based techniques in SLAM frameworks
Authors: Ying Kunpeng
Published: February 2024

This paper comprehensively examines the application and current research status of Neural Radiance Fields (NeRF) technology within Simultaneous Localization and Mapping (SLAM) systems. NeRF, which has gained significant attention since 2020, has evolved into a powerful method for reconstructing 3D scenes from images, offering advantages such as continuous scene representation and photorealistic novel view synthesis. However, it also comes with drawbacks, including substantial training data requirements, limited model generalization, and challenges in map scalability. In contrast, SLAM is a complex, real-time, efficient, and robust system capable of tracking camera motion and constructing environmental maps in real-time, with no limitations on map size. The integration of NeRF technology into SLAM enhances the capabilities of various modules, including Mapping, Tracking, Optimization, Loop Closure, and Localization, providing potential advantages. Beginning with an exploration of NeRFs fundamental principles and its inherent strengths and weaknesses, this paper delves into the significant implications of integrating NeRF into the SLAM pipeline. It addresses the challenges encountered during implementation and outlines potential future directions. The aim is to provide a clear elucidation of the evolving landscape of the combined NeRF and SLAM approach, serving as a reference for researchers interested in pursuing this research direction.
The performance of artificial intelligence in medical field
Authors: Zeng Kaiwen
Published: February 2024

In modern society, there is an increasing prevalence of individuals who are able to avail themselves of medical facilities for the purpose of receiving healthcare services. With the rise in population, there has been a corresponding decrease in the availability of medical resources for some disorders, leading to challenges in accurately diagnosing patients by healthcare professionals. According to specialists, artificial intelligence (AI) is being considered as a potential solution for addressing medical challenges. This paper mainly focuses on discussing the impact of artificial intelligence in the medical field. Through methods of literature review and analysis, this study explores the fundamental idea of AI and its use in the medical field. Besides, the paper also introduces the potential flaws behind AI in the medical field, and how will artificial intelligence help us further in the medical field. The study reveals that artificial intelligence is extensively employed within the medical sector. Through extensive training, AI has the potential to attain a considerable level of accuracy when it comes to diagnosing various ailments. The level of precision exhibited is akin to that observed in medical practitioners diagnoses. Nevertheless, artificial intelligence possesses certain limitations. For instance, in the context of privacy preservation, several patients exhibit a reluctance to divulge their symptoms. In the absence of adequate safeguards for information security, this situation might potentially lead to adverse consequences in the lives of these patients and their interpersonal interactions. Ultimately, AI continues to possess significant potential for advancement within the realm of medicine.
Cutting-edge research in physics engines: Exploring parallel technologies, algorithm insights, and upcoming trends
Authors: Che Chang
Published: February 2024

Physics engines, a quintessential product of computer science, have become indispensable tools in simulating physical phenomena through mathematical computations. Their prominence in game development, engineering simulations, and various physics applications is unquestionable. Ensuring their optimization is pivotal for meeting the rigorous demands of practical experiments. The significance of this study is twofold: theoretical foundations and practical necessities. From a theoretical standpoint, this paper delves into the core principles and architectural underpinnings of physics engines. We venture into the integration of multithreading and the application of niche algorithms within these engines. Further analysis illuminates how leveraging these technologies can drastically enhance the performance of a physics engine. In culmination, we propose a robust, feasible theoretical framework for physics engines, filling in the pivotal details. This exploration aims to galvanize and steer the evolution of physics engine design. Addressing practical necessities, our research emphasizes real-world applications of various physics engines. We examine tailored optimization strategies suited for specific needs and investigate methodologies to elevate the operational efficiency of specialized physics engines. This dimension of our study holds substantial real-world implications.
Overfitting of CNN model in cifar-10: Problem and solutions
Authors: Xia Zhangjie
Published: February 2024

CNN, proposed by Yann LeCun in the 1980s, has gained high attention and extensive research from both the academia and the industry. It has proved to be useful in a wide variety of fields, including image recognition, which aims to enable the computer to identify different objects in digital images. Despite its usefulness, problems like overfitting occur during the training process of a CNN model, which seriously harm the effectiveness of the model. Firstly, an initial CNN model is built to accomplish image recognition based on data from cifar-10. Secondly, after the presence of overfitting during the training and validation of the initial model, 4 methods, including shallower model, L2 regularization, dropout, data augmentation, are proposed to see how they handle overfitting respectively, and comparisons are made between different methods. Thirdly, the last three methods are combined to see how they handle overfitting together. Finally, conclusion and possible future work are presented. As it turns out, L2 regularization, dropout and data augmentation all reduce overfitting with some slight differences, but shallower model and the combined method cause underfitting rather than overfitting.
Stable flight control of UAV under atmospheric turbulence
Authors: Jicheng Yu
Published: February 2024

Unmanned aerial vehicles will be subjected to various environmental disturbances such as atmospheric turbulence during the execution of tasks, which will seriously affect the flight safety of UAV. By establishing UAV flight dynamics model and atmospheric turbulence model, and using PID algorithm to design controller, this paper studies the attitude and altitude control effect of UAV under atmospheric turbulence disturbance. The simulation results show that PID algorithm can effectively control the UAV's stable flight under atmospheric turbulence and ensure the safety of UAV.
Sentiment analysis based on BiLSTM with attention mechanism on Chinese comment with stickers
Authors: Bao Yuchen , Huang Hongyi , Meng Zizhou
Published: February 2024

As the Internet is progressively becoming larger and more intricate, more and more users of various social media choose to post their comments to express their opinions and thinking on those platforms. Analyzing the emotions contained in user comments holds great business value, helping to accurately perceive user consumption habits and improve user service levels. However, the use of emoticons and stickers in comments has increased dramatically in recent years, which brings new challenges to text sentiment analysis based on natural language processing. In this paper, in order to alleviate the above problems, we propose a method for analyzing the sentiment of Chinese comments based on the attention mechanism and BiLSTM. Specifically, we partitioned the original dataset from the Weibo platform according to the number and type of emoticons in the comments. By analyzing the actual data, the specific features of emojis that affect the performance of sentiment analysis are identified, and corresponding explanations are given. In addition, a hypothesis is proposed to quantify the impact of emoticons on model effectiveness. All the results demonstrate the effectiveness of our proposed method.
Design and implementation of computer network security detection and control system
Authors: Chen Xi
Published: February 2024

With the widespread application of computer network technology, computer network security defense capability has become a focus of attention in various industries. The extensive use of computer information systems in various sectors has significantly improved work efficiency but has also introduced security risks and management issues. The deployment of network security detection and control systems allows for effective security monitoring and management of computer operations and real-time network information. This paper presents a computer network security detection and control system based on human-computer interaction, which enables users to handle daily key business processes. The work principles and overall architecture of the network security detection and control system are analyzed and demonstrated. It offers functions such as filtering options, address rules, network security detection, network unreachability, overall traffic analysis, subnet definition, fault diagnosis, and security analysis. Testing and analysis indicate that the systems design achieves the intended goals. In the application of network security features, it can effectively combine various forces to enhance the quality and efficiency of network security, making it widely applicable in various industry units.
Data privacy and protection in communication networks
Authors: Ban Liyuan
Published: February 2024

This exhaustive study examines the crucial topic of protecting user data and maintaining privacy within the complex realm of communication networks. It meticulously analyzes the multifarious dimensions of data privacy, including its conceptual foundations, the significant dangers resulting from intrusions, and the fundamental principles of privacy protection. The investigation encompasses a comprehensive evaluation of concrete strategies such as data anonymization, de-identification techniques, granular data sharing controls, and the use of cryptography. Real-world case studies attest to the efficacy of these methods, providing tangible evidence of their practical viability. The article predicts the evolving landscape of privacy protection, which will be driven by the rapid advancement of technologies and the adaptive responses required by regulations and social responsibilities. The collaborative engagement of individuals, corporations, and governing bodies emerges as crucial in charting the future of data privacy, highlighting the inherent interplay between technological innovations, legal frameworks, and proactive user participation.
Practice and application of artificial intelligence technologies in the digital economy
Authors: Zhou Quan
Published: February 2024

With the continuous development of the digital economy, artificial intelligence technology is becoming more and more widely used in various fields. This paper mainly discusses the practice and application of artificial intelligence technology in the digital economy, including its application field, technical principle, implementation method, and future development trends. Through analyzing the current application status and existing problems of artificial intelligence technology in the digital economy, this paper puts forward some improvement measures and development suggestions, aiming to provide a reference for promoting the better application of artificial intelligence technology in the digital economy.
The application of backend database systems in precision marketing: A case study of Nike official online store
Authors: Yan Yueqi
Published: February 2024

In the era of big data, databases have become a powerful tool for enterprises to gain insight into consumer behavior and market dynamics. However, some enterprises, especially small and medium-sized ones, focus too much on data volume growth while ignoring the importance of data relevance, resulting in unsatisfactory marketing outcomes. Based on this, this paper provides an example of Nike official online store. With the assistance of structured query language (SQL), a backend database system is created under the Oracle11g R2 environment, and its functions are simulated and demonstrated. The system is capable of managing user, product, and order transaction information through basic functions such as adding, querying, modifying, and deleting. In addition, it serves as a foundation for Nike's precision marketing efforts through user profile construction, user behavior analysis, and demand forecasting based on basic statistical methods of counting, proportioning, and sorting.
Clinical diagnosis of overlapping symptoms in COVID-19 based on machine learning model
Authors: Zhang Jingxuan
Published: February 2024

The global pandemic COVID-19 erupted and infected an estimated 10% of the worlds population. Since vaccinations greatly reduced hospitalization rates, most countries removed the restrictive policies implemented to combat the virus. It has become a rather common illness with more than twelve thousand active hospitalizations. As a result, convenient COVID-19 diagnosis from diseases that display overlapping symptoms has become increasingly important. An effective method for patient self-diagnosis greatly reduces hospital presentation, saving time and medical resources. This study uses machine learning techniques to classify and predict several common respiratory diseases quickly and accurately. The author trains several machine learning models that attempt to predict four diseases based on their distinct clinical signs. An open-access database on Kaggle developed for this disease classification is selected and further processed via principal component analysis to decrease database dimension and pinpoint critical symptoms. Support Vector Machine Classifier (SVM), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF) models are used, and their performances are compared. Study results show that the LR model slightly outperforms the others. In conclusion, the effectiveness of the proposed method is proved for classifying the symptoms of patients with allergies, colds, flu, and Covid-19 in this study.
Kinetics of Nonenzymatic Browning Reactions in Pumpkin Puree During Storage
Authors: Karadeniz Feryal , Işık Betül , Kaya Soner , Aslanali Osman , Midilli Fatma
Published: February 2024

Pumpkins (Cucurbita moschata) are a great source of essential nutrients counting vitamins, minerals, carotenoids, and dietary fiber. Due to their healthy composition, it draws consumer attention. Pumpkin puree is used for preparation of baby foods, soups, flour, jam, jellies, and desserts. To assess the main quality parameters of the foods such as color and hydroxymethylfurfural (HMF) formation, CIE-L*a*b* color changes and HMF formation which is also an indicator of Maillard browning reactions were evaluated storing the pumpkin puree at 27°, 37° and 47°C for 17 weeks. Kinetic parameters for HMF formation and color changes were calculated. The findings showed that HMF quantity linearly increased with the temperature and storage duration following the reaction model of zero order. The values of b* coordinate, Lightness, Chroma, and hue lessened linearly as a* coordinate values increased linearly fitting zero order reaction kinetic. By means of Arrhenius equation, the temperature dependency of the rate constant of color variation was demonstrated and the values of the activation energy (kj mol-1) were calculated as 76.15, 30.60, 46.08, 28.44, 27.61 and 38.32 for HMF formation, Lightness, a* coordinate, b* coordinate, Chroma, and hue, respectively.
The Relationship Between Corporate Governance and Earnings Management: Evidence From Bangladesh
Authors: Uddin Md Helal
Published: February 2024

The main purpose of the research is to evaluate the effects of corporate governance on earnings management. The study is mainly based on secondary data, which was collected from the annual reports of non-financial organizations of Bangladesh from 2011-2021. The dependent variable of the study is earnings management, which was calculated based on accrual and real earnings management. The independent variable is the corporate governance index. The study uses the OLS regression technique to get empirical evidence. Empirical research demonstrates that corporate governance mechanisms have a substantial adverse effect on earnings management. The board-diversity index has a positive impact on accrual earnings management, whereas the audit committee index has a negative impact on it. The influence of the corporate governance index is more on discretionary accruals than real earnings manipulation. This study provides novel perspectives to the current body of knowledge on corporate governance and earnings management research.
Scoliosis Without Fusion and Increased Risk of Early Medical and Surgery-Related Complications After Total Hip Arthroplasty: A Propensity-score Analysis
Authors: Sequeira Sean B. , Hasenauer Mark D. , McKinstry Robert , Ebert Frank , Boucher Henry R.
Published: February 2024

Introduction:

There is a paucity of literature that examines how the abnormal spinopelvic alignment of scoliosis affects outcomes after total hip arthroplasty (THA) in the absence of a lumbar fusion.

Methods:

Patients with a history of scoliosis (idiopathic, adolescent, degenerative, or juvenile) without fusion and those without a history of scoliosis who underwent primary THA were identified using a large national database. Ninety-day incidence of various medical complications, emergency department (ED) visit, and readmission and 1-year incidence of surgery-related complications and cost of care were evaluated in both the scoliosis and control cohorts. Propensity score matching was used to control for patient demographic factors and comorbidities as covariates.

Results:

After propensity matching, 21,992 and 219,920 patients were identified in the scoliosis and control cohorts, respectively. Patients with scoliosis were at increased risk of several 90-day medical complications, including pulmonary embolism (odds ratio [OR] 1.96; P < 0.001), deep vein thrombosis (1.49; P < 0.001), transfusion (OR, 1.13; P < 0.001), pneumonia (OR, 1.37; P < 0.001), myocardial infarction (OR, 1.38; P = 0.008), sepsis (OR, 1.59; P < 0.001), acute anemia (OR, 1.21; P < 0.001), and urinary tract infection (OR, 1.1; P = 0.001). Patients with a history of scoliosis were at increased 1-year risk of revision (OR, 1.31; P < 0.001), periprosthetic joint infection (OR, 1.16; P = 0.0089), dislocation (OR, 1.581; P < 0.001), and aseptic loosening (OR, 1.39; P < 0.001) after THA. Patients with scoliosis without a history of fusion were more likely to return to the emergency department (OR, 1.26; P < 0.001) and be readmitted (OR, 1.78; P < 0.001) within 90 days of THA.

Discussion:

Patients with even a remote history of scoliosis without fusion are at increased risk of 90-day medical and surgery-related complications after hip arthroplasty. Hip and spine surgeons should collaborate in future studies to best understand how to optimize these patients for their adult reconstructive procedures.
Computer vision meets metaverse
Authors: Zakynthinou Vasiliki , Kanakaris Venetis , Vrochidou Eleni , Papakostas George A.
Published: February 2024

This comprehensive analysis delves into the historical progression and important technological and contemporary advancements of computer vision inside the metaverse. The metaverse, which can be characterized as an interactive virtual reality environment that mirrors the physical world, signifies a novel domain for the utilization of computer vision in various applications. These applications span from object identification and tracking to gesture recognition and augmented reality. Additionally, a thorough evaluation of specific case studies occurs to provide a deeper understanding of the subject. Despite notable progress, the incorporation and utilization of computer vision inside the metaverse present numerous obstacles, including computational expenses, apprehensions regarding data privacy, and the faithful replication of physical aspects of reality. Potential solutions are examined, including deep learning approaches, optimization strategies, and the formation of ethical guidelines. A comprehensive analysis of anticipated patterns within the industry is also included, with emphasis on the confluence of artificial intelligence, the Internet of Things, and blockchain technologies. These convergences are predicted to create substantial prospects for the advancement of the metaverse. This review culminates by offering a contemplation on the ethical considerations and duties that arise from the utilization of computer vision in the realm beyond mortal existence. Research findings demonstrate that these technologies have greatly augmented user engagement and immersion within the digital domain. Readers can enhance their understanding of the interdependent connection between computer vision and the metaverse through the present analysis of existing scholarly works. Thus, this study aspires to make a valuable contribution to the advancement of research in this new domain.
PENGARUH BRAND IMAGE TERHADAP KEPUTUSAN PEMBELIAN MOBIL MEREK AVANZA PADA PT TOYOTA SUMBER HARAPAN DI TANJUNG REDEB
Authors: Edwar Ready
Published: February 2024

The purpose of this study was to find out how much influence brand image has on purchasing decisions for a Toyota Avanza car at PT Toyota Sumber Harapan in Tanjung Redeb. 

The method used in this study is a simple regression method to see the effect of brand image on purchasing decisions for the Toyota Avanza brand car and using the correlation coefficient to see how much influence the brand image has on purchasing decisions for the Toyota Avanza brand car at PT Toyota Sumber  Harapan in Tanjung redeb.

 Based on the results of the analysis, it is known that the influence of brand image on purchasing decisions for

the Toyota Avanza car is classified as strong, judging from the value of r = 0.995. In addition, based on the calculation of the correlation coefficient, it shows the value of r square = 0.989 indicating that 98.9% of the

decision to purchase a Toyota Avanza car is influenced by brand image, while the remaining 1.10% is influenced by other factors not examined.
PENGARUH IMPLEMENTASI IFRS PADA LAPORAN KEUANGAN PERUSAHAAN DI INDONESIA
Authors: Khaerudin Taufiq
Published: February 2024

Indonesia has implemented IFRS as a financial reporting standard following its implementation in multiple countries, resulting in the elimination of certain provisions of accounting standards. This change in concept will have an impact on the development of accounting in Indonesia. The aim of this research is to investigate the influence of IFRS adoption on financial reporting in Indonesia, despite the fact that it has both positive and negative consequences for Indonesian companies. A literature review was conducted using a descriptive qualitative approach, and secondary data sources were utilized. The research findings indicate that businesses in the industry can significantly demonstrate the effects of standard modifications, making it a more efficient standard. The principle-based standard has been created in the form of industry-specific accounting guidelines, which can be utilized as a reference for preparing financial statements for industrial companies.
IDIOPATHIC (AUTOIMMUNE) THROMBOCYTOPENIC PURPURA: A CASE REPORT
Authors: Dias Ana Tânia Pereira , Do Nascimento Sidrayton Pereira , Nascimento Idernon Cândido , Castro Milenna Rodrigues da Cruz , Vieira Tainá Cavalcante , Mendes Jimmy Klawston Silva , Veloso Layanne Damasceno Silva , De Souza Paulo Marcilio Araújo , Duarte Diogo Ribeiro , Mauriz José Ribamar Alves Júnior , Cavalcante Felipe Henrique de Holanda
Published: February 2024

This report aims to describe a school patient, from the city of Juazeiro da Bahia, diagnosed with idiopathic thrombocytopenic purpura (autoimmune) and diagnosed with thrombocytopenia that had been sustained for months. Initially, he was diagnosed with dengue and anemia. Although this report describes a patient's favorable evolution, the relevance of carrying out future studies to evaluate the effects of thrombocytopenic purpura is highlighted. In short, this study aims to reference the most common findings through scientific literature as well as signs of worsening.
Analisis CR,DER,ROE DAN DPR TERHADAP RETURN SAHAM PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BEIPERIODE 2017 - 2021
Authors: Nasution Yuslinda , Septian Rizki Andri
Published: February 2024

Tujuan penelitian ini adalah untuk mengetahui pengaruh CR, DER, ROE, dan DPR terhadap Return Saham pada bisnis. Sampel terdiri dari 17 bisnis Sub sektor makanan dan minuman yang terdaftar di Bursa Efek Indonesia periode 2017-2021, dipilih melalui metode purposive sampling. Analisis data melibatkan pendekatan deskriptif dan regresi linier berganda. Menggunakan data bersifat sekunder. hasil pengujian secara parsial menyatakan bahwa  variabel Current Ratio (CR),Debt to Equity  Ratio (DER), Return on Equity (ROE) secara parsial berpengaruh terhadap Return Saham, dan Kebijakan Deviden (DPR) tidak berpengaruh terhadap Return Saham. Dan hasil pengujian secara keseluruhan membuktikan bahwa variabel Current Ratio (CR), Debt to Equity  Ratio (DER), Return on Equity (ROE), dan Kebijakan Deviden (DPR) keempatnya berpengaruh terhadap Return Saham
KUALITAS PELAYANAN, HARGA DAN LOKASI TERHADAP TINGAT KEPUASAN PELANGGAN PADA BIMBINGAN BELAJAR PALEM V
Authors: Simatupang Lucy Nancy , Karimah Nasikhatul
Published: February 2024

ABSTRACT
This study aims to determine the effect of service quality, price, and location on customer satisfaction at Palem V tutoring simultaneously or partially. The data used is primary data collected through the distribution of questionnaires to 100 respondents who joined the Palem V Tutoring. The quantitative method and the results of this study are based on the answers of respondents using a Likert scale. The analytical method used in this research is Instrument Test (Validity and reliability), Basic Normality Test, Classical Assumption Test (Normality, Multicollinearity, Autocorrelation, Heteroscedasticity), Hypothesis Test (Multiple linear regression analysis, F test, t test and Coefficient of Determination) . The results of this study indicate that Service Quality, Price, and Location by F Test (simultaneous) have an effect on and significant to Customer Satisfaction, and the results of the t-test (partial) Service Quality (X1) and Price (X2) have an effect and significant to Customer Satisfaction (Y ) while Location (X3) has no effect and is not significant on Customer Satisfaction (Y).
PENGARUH GAYA KEPEMIMPINAN DAN KOMPENSASI TERHADAP KINERJA KARYAWAN MELALUI MOTIVASI KERJA SEBAGAI VARIABLE INTERVENING PADA PT. KARUNIA JAYA GLOBAL
Authors: Prasetya GL Hery , Hendarto Rizki Tri
Published: February 2024

This research aims to determine leadership style and compensation on employee performance through work motivation as an intervening variable at PT. Karunia Jaya Global. The sample used in this research was employees of PT. Karunia Jaya Global has a sample size of 55 employees. Based on the hypothetical test results in Sub Structure Path Analysis 1, the results showed that leadership style and compensation had a direct positive and significant effect on work motivation. Meanwhile, the results of hypothesis testing in Sub Structure Path Analysis 2 showed that leadership style, compensation, and work motivation had a direct positive and significant effect on employee performance. Likewise, leadership style and compensation positively and significantly indirectly affect employee performance through work motivation as an intervening variable.
Keep in mind sex differences when prescribing psychotropic drugs
Authors: Mazza Marianna , De Berardis Domenico , Marano Giuseppe
Published: February 2024

Women represent the majority of patients with psychiatric diagnoses and also the largest users of psychotropic drugs. There are inevitable differences in efficacy, side effects and long-term treatment response between men and women. Psychopharmacological research needs to develop adequately powered animal and human trials aimed to consider pharmacokinetics and pharmacodynamics of central nervous system drugs in both male and female subjects. Healthcare professionals have the responsibility to prescribe sex-specific psychopharmacotherapies with a priority to differentiate between men and women in order to minimize adverse drugs reactions, to maximize therapeutic effectiveness and to provide personalized management of care.
Impaired implicit emotion regulation in patients with panic disorder: An event-related potential study on affect labeling
Authors: Wang Hai-Yang , Li Li-Zhu , Chang Yi , Pang Xiao-Mei , Zhang Bing-Wei
Published: February 2024

BACKGROUND

Panic disorder (PD) involves emotion dysregulation, but its underlying mechanisms remain poorly understood. Previous research suggests that implicit emotion regulation may play a central role in PD-related emotion dysregulation and symptom maintenance. However, there is a lack of studies exploring the neural mechanisms of implicit emotion regulation in PD using neurophysiological indicators.

AIM

To study the neural mechanisms of implicit emotion regulation in PD with event-related potentials (ERP).

METHODS

A total of 25 PD patients and 20 healthy controls (HC) underwent clinical eva-luations. The study utilized a case-control design with random sampling, selecting participants for the case group from March to December 2018. Participants performed an affect labeling task, using affect labeling as the experimental condition and gender labeling as the control condition. ERP and behavioral data were recorded to compare the late positive potential (LPP) within and between the groups.

RESULTS

Both PD and HC groups showed longer reaction times and decreased accuracy under the affect labeling. In the HC group, late LPP amplitudes exhibited a dynamic pattern of initial increase followed by decrease. Importantly, a significant group × condition interaction effect was observed. Simple effect analysis revealed a reduction in the differences of late LPP amplitudes between the affect labeling and gender labeling conditions in the PD group compared to the HC group. Furthermore, among PD patients under the affect labeling, the late LPP was negatively correlated with disease severity, symptom frequency, and intensity.

CONCLUSION

PD patients demonstrate abnormalities in implicit emotion regulation, hampering their ability to mobilize cognitive resources for downregulating negative emotions. The late LPP amplitude in response to affect labeling may serve as a potentially valuable clinical indicator of PD severity.
Risk factors for cognitive impairment in patients with chronic kidney disease
Authors: Wang Xiao-Hui , He Yong , Zhou Huan , Xiao Ting , Du Ran , Zhang Xin
Published: February 2024

BACKGROUND

Chronic kidney disease (CKD) patients have been found to be at risk of concurrent cognitive dysfunction in previous studies, which has now become an important public health issue of widespread concern.

AIM

To investigate the risk factors for concurrent cognitive dysfunction in patients with CKD.

METHODS

This is a prospective cohort study conducted among patients with CKD between October 2021 and March 2023. A questionnaire was formulated by literature review and expert consultation and included questions about age, sex, education level, per capita monthly household income, marital status, living condition, payment method, and hypertension.

RESULTS

Logistic regression analysis showed that patients aged 60-79 years [odds ratio (OR) = 1.561, P = 0.015] and ≥ 80 years (OR = 1.760, P = 0.013), participants with middle to high school education (OR = 0.820, P = 0.027), divorced or widowed individuals (OR = 1.37, P = 0.032), self-funded patients (OR = 2.368, P = 0.008), and patients with hypertension (OR = 2.011, P = 0.041) had a higher risk of cognitive impairment. The risk of cognitive impairment was lower for those with a college degree (OR = 0.435, P = 0.034) and married individuals.

CONCLUSION

The risk factors affecting cognitive dysfunction are age, 60-79 years and ≥ 80 years; education, primary school education or less; marital status, divorced or widowed; payment method, self-funded; hypertension; and CKD.
Maternidade de filho com deficiência e mercado de trabalho: vozes de mães que vivenciam essa complexa realidade
Authors: Oliveira Angela Maria Souza Silva Vidal de , Gonçalves Josiane Peres
Published: February 2024

Durante todo o processo de preparação para a maternidade, não existe entre as mulheres o desejo de ter um filho com alguma limitação, pois além de resultar em sofrimento familiar, poderia interferir no exercício profissional feminino, devendo a mãe ter que conciliar a profissão com as necessidades da criança. Assim, a pesquisa teve o objetivo de identificar os desafios que mães de filhos com deficiência enfrentam para atuar no mercado de trabalho. O referencial teórico baseia-se em discussões sobre a mulher, relações com o trabalho e maternidade de filhos com deficiência. A pesquisa de campo, de natureza qualitativa, foi realizada em um município do interior do Estado de Mato Grosso do Sul com quatro mães de pessoas com deficiência que trabalhavam fora de casa. Os resultados apontam que a maior dificuldade das mulheres foi se deparar com a deficiência dos filhos; algumas voltaram para o mercado de trabalho rapidamente por questões financeiras e outras aguardaram um tempo, para então se dedicar à carreira profissional; para as mães que participaram da pesquisa, a possibilidade de trabalhar fora era entendida como forma de obter equilíbrio emocional e estar de bem consigo mesmas. Constata-se que trabalhar fora de casa é considerado um fator importante para mulheres que são mães de filhos com deficiência.