bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting project duration using a coupled artificial neural network and Taguchi method approach
Authors: Bendada Larbi , Brioua Mourad , Morakchi Mohamed Razi , Djouani Ibrahim
Published: July 2024

Accurate project duration prediction is increasingly important for management because it defines the expected timeline for project realization. This study utilizes an integrated approach combining neural networks with the Taguchi method to forecast the time required to complete projects within predetermined deadlines. The methodology involves modelling and simulating the network of project activities to estimate the total average project duration. ration. The neural network model uses input variables such as success probability, effort, and learning factor to predict the total time necessary for project completion. The total average project duration is the output variable that is critical during the design phase. Subsequently, the Taguchi method optimizes the neural network’s outputs, incorporating mean squared error (MSE) values to enhance predictive accuracy. accuracy. This study underscores the efficacy of artificial neural networks (ANNs) as predictive tools, demonstrating their ability to meticulously estimate project duration. The proposed method’s efficiency and applicability are demonstrated by MATLAB simulation analyses, highlighting its effectiveness in precise deadline estimation. In the realm of engineering, ANNs stand tall as formidable predictive tools, their efficacy underscored by this study’s successful application. By harnessing ANNs and simulation data, this research crafts a predictive model adept at estimating the average total duration of projects. Through meticulous consideration of crucial parameters like the probability of success and effort factors, the model emerges as a beacon of accuracy within the design domain. Future research will explore the effects of additional parameters on activity networks and alternative transfer functions, as well as the potential integration of reinforcement algorithms to improve resource allocation, risk management, and project outcomes through online training data.
Docentes mães na educação básica: vivências intra e interpessoais na transição do ensino presencial para o remoto no período da pandemia da Covid-19
Authors: Leite Acácia de Carvalho Monteiro , Marçal Pedro Henrique Ferreira , Rodrigues Suely Maria , Vilarino Maria Terezinha Bretas , Silva Leonardo Oliveira Leão e , Brandão Marileny Boechat Frauches
Published: July 2024

O isolamento social causado pela pandemia da COVID-19 resultou na suspensão das aulas presenciais em instituições de ensino no Brasil e no mundo, exigindo que docentes adaptassem o planejamento pedagógico presencial para remoto. Este estudo teve por objetivo analisar as repercussões da transição do ensino presencial para o remoto nas vivências de docentes mães na educação básica no período da pandemia da COVID-19. Tratou-se de um estudo observacional, descritivo, de abordagem qualitativa realizado no distrito de Santo Agostinho, município de Água Doce do Norte/ES. Participaram oito docentes mães com filhos na faixa etária de 0 a 15 anos, que atuaram no ensino remoto entre março de 2020 a dezembro de 2021. A coleta de dados foi realizada por meio de entrevistas semiestruturadas, cujo conteúdo foi analisado por meio da Análise de Conteúdo de Bardin, focando na temática: Influência da transição do ensino presencial para o remoto nas vivências intra e interpessoais das docentes. As vivências intra e interpessoais compartilhadas pelas participantes evidenciaram desafios, como conciliar as responsabilidades familiares, profissionais e sociais, com a falta de delimitação entre o ambiente de trabalho e o domiciliar, sobrecarga de trabalho e utilização de diferentes tecnologias para o processo ensino-aprendizagem. Estratégias resilientes foram desenvolvidas para equilibrar os papéis de docente, mulher e mãe. A transição gerou consequências positivas e negativas, todavia ofereceu oportunidades de crescimento para as docentes. Conclui-se que apesar dos desafios, a transição criou oportunidades para conexões e solidariedade entre colegas e familiares, fortalecendo laços de colaboração e empatia nas relações interpessoais.
Assisting autonomous precision medical rehabilitation through gesture recognition devices
Authors: Pan Yuchen
Published: July 2024

With the aging of the population and the increase of various chronic diseases, there is a growing demand for effective and accessible medical rehab solutions. In order to overcome the above problems, this project innovatively adopts the gesture recognition technology to design and fabricate the realization of an autonomous accurate medical rehab gesture recognition assistance system. In terms of hardware, this paper uses Arduino as the main controller, Paj7620 as the gesture recognition device, and OLED 0.96 display as the system display. In terms of software, this paper utilizes Arduino IDE software to successfully write the code and realize all the functions. In terms of combining with actual medical rehab needs, the team chose elbow fracture rehab as a typical type of rehab, and designed a three-device obtuse triangle placement scheme for precise monitoring of rehab training. Initial simulation test results show that over 70% of participants reported an improvement in their rehabilitation outcomes, and about 90% found the system easy to use. The results of this project will directly benefit patients requiring long-term rehabilitation.
A review of recent image synthesis techniques
Authors: Hu Chengwei
Published: July 2024

Deep neural networks typically demand a substantial volume of labeled training data. Nonetheless, the process of gathering and annotating this training data is frequently labor-intensive and time-consuming. One way to address this issue is through Image Composition. Image composition refers to the process of combining multiple visual elements or components. It may involve techniques like image synthesis or generation, where algorithms generate new images by combining elements from existing ones.
This paper aims to overview the recent advancements in Image Composition based on deep networks and relevant applications. It offers an extensive overview of the concepts, principles, and techniques related to Image Composition. Subsequently, it delves into two distinct levels at which image composition can be accomplished: specifically, with 2D elements and 3D objects. Furthermore, the method of achieving composition with point clouds is presented.
Large language models privacy and security
Authors: Yang Jidong
Published: July 2024

The advancement of large language models (LLMs) has yielded significant advancements across various domains. Nevertheless, this progress has also raised crucial concerns regarding privacy and security. The paper does a comprehensive literature study to thoroughly examine the fundamental principles of LLM. It also provides a detailed examination of the characteristics and application fields of various LLMs, with a particular focus on Transformer. Furthermore, this study places emphasis on the examination of privacy concerns that may emerge in the context of LLM's handling of personal and sensitive data. It also explores the potential hazards associated with information leakage and misuse, as well as the existing privacy safeguards and the obstacles encountered in their implementation. Overall, LLM has made significant advancements in technology. However, it is imperative to acknowledge the importance of doing research on safeguarding privacy and enhancing security. These aspects are vital for guaranteeing the sustained development and public confidence in LLM technology.
Research on single target search and rescue in underwater environment based on machine learning
Authors: Ma Nannan , Jin Lina , Shi Wenhui
Published: July 2024

Machine learning has demonstrated its exceptional performance in addressing complex problems across various domains. This paper proposes an underwater search-rescue system based on the artificial immune algorithm, which effectively solve the problem of finding the optimal local search network in unknown underwater environments and shorten searching time. The system comprises multiple underwater search-rescue robots that form a global search network. Once target victims are identified, the global search network is broken down to form one or more local search networks to track and rescue them. To evaluate the robustness and effectiveness of the system, three indicators were designed: searching time, energy consumption, and tracking distance, and a simulation experiment with a single target is conducted. The results demonstrate that under the identical conditions, the artificial immune algorithm outperforms other methods in underwater target search-rescue tasks.
A-star algorithm vs. Q-learning algorithm for path planning: A comparative analysis
Authors: Gao Junxiao
Published: July 2024

In robotics research, the core focus is on path planning, as it's essential for robots to navigate according to human objectives to be valuable. This paper provides an in-depth analysis of the commonly used A-Star algorithm and Q-learning algorithm in mobile robot path planning. It specifically examines the advantages and disadvantages of both the A-Star algorithm and the Q-learning algorithm, highlighting their roles in the advancement of robotic intelligence. The A-star algorithm is an algorithm with a heuristic function to guide path selection direction. Also, the A-star algorithm is guaranteed to identify the most efficient path in terms of traffic cost when facing static obstacles. However, its performance degrades when dealing with trap nodes. Specifically, the algorithm occupies a large amount of memory and takes a long computational time. In contrast, the Q-learning algorithm is effective for learning continuous actions with the same network data structure for local path planning tasks.
Detecting unfriendly memes: A machine learning approach for content moderation
Authors: Huang Ziao
Published: July 2024

The article presents a machine learning approach to address concerns about the dissemination of unfriendly content through memes. Specifically, it focuses on detecting unfriendly memes for content moderation purposes. It will compare three different methods which respectively focusing on textual content, image content and the combination of text and image content. The approach utilizes advanced machine learning models to distinguish between friendly and unfriendly content by leveraging both textual and visual elements of memes. State-of-the-art techniques such as Optical Character Recognition (OCR) for text extraction and Convolutional Neural Networks (CNNs) for image analysis are employed. Additionally, recurrent neural networks (RNN), gated recurrent units (GRU), and long short-term memory (LSTM) models are utilized for text classification. MLP is used in the classification process of combination. The models' performance is evaluated using objective measurements such as accuracy, ROC curves, and confusion matrices. The results demonstrate the effectiveness of the proposed approach in identifying unfriendly content and its implications for content detection on social media platforms.
A review of methods for alleviating hallucination issues in large language models
Authors: Yin Zhibo
Published: July 2024

Large language models have demonstrated impressive language processing capabilities in recent years, exhibiting unparalleled excellence in the field of natural language processing. However, the generated text sometimes contains hallucinations, which is the text that contradicts the knowledge in the real world, the context, and the user input. This problem is mainly due to the inherent limitations of the method itself in aspects such as data quality, the model training process, and the model generation process. The issue of hallucinations has always been closely monitored by the academic community. It is widely recognized that its potential consequences should not be underestimated. This paper systematically summarizes the research on the causes of hallucinations in large language models, and introduces mainstream classification methods as well as current measures to address the issue of hallucinations. To be more specific, the article divides the causes of hallucinations into two categories: 1. hallucinations come from the training process and 2. hallucinations come from the generation process. Also, 4 typical types of causes for the former and 5 typical types of causes for the latter are provided. Simultaneously, a detailed discussion of 16 methods to mitigate hallucinations that arise in the generation process is offered. Finally, this paper also discusses inherent flaws that may exist in large language models, aiming to help people gain a more comprehensive understanding and research into hallucinations and large language models. In general, the text details about the hallucinations that exist in the large language model. Meanwhile, according to the previous research, it is pointed out that it is difficult for the large language model based on autoregressive method for token prediction to avoid the hallucinations completely.
Analyzing the impact of financial news on the stock market using Natural Language Processing techniques
Authors: Wang Yinhao
Published: July 2024

In the current financial domain, the impact of financial news on the stock market has become increasingly significant. Its rapid dissemination speed and wide coverage have made it a key factor in influencing investor sentiment and decision-making. Therefore, this study aims to explore how to utilize Natural Language Processing (NLP) technologies to analyze the impact of financial news on the stock market. The research involves collecting multiple sources of financial news and corresponding stock market data, and employs methods such as text preprocessing, sentiment analysis, and event extraction to process and analyze the news content. By constructing machine learning models, this study attempts to quantify the sentiment orientation of financial news and the impact of specific events on stock market volatility. The research questions focus on how to effectively extract valuable information from financial news and predict its specific impact on the stock market. The results indicate that sentiment orientation and key events in financial news are significantly correlated with short-term fluctuations in the stock market, especially for news reports on specific industries or companies. Moreover, the application of deep learning models further enhances the accuracy of predicting stock market reactions. This study not only provides financial market analysts with a new analytical tool but also offers a new perspective on understanding how financial news affects the stock market.
The application and development of toon shading technology in mobile games
Authors: Zheng Xi
Published: July 2024

With the continuous development of the gaming market, the toon shading technology in mobile games is gradually moving towards integration with Physically Based Rendering (PBR) technologies, giving rise to a trend of hybrid toon shading. This paper aims to discuss the development reasons of hybrid toon shading technology in mobile games, and compare the technical requirements and practical applications of toon shading and hybrid toon shading in the mobile gaming context. Firstly, the paper introduces the characteristics of toon shading and its relationship with non-photorealistic rendering (NPR) and PBR. Subsequently, examples of toon shading applications in mobile games are provided, along with a discussion on the two advantages of toon shading technology in the mobile gaming domain. The fundamental process of toon shading is then outlined, followed by an explanation of the limitations of traditional toon shading in contemporary mobile games. Then, introduce the development reasons of hybrid toon shading technology and the general process of hybrid toon shading, compare the technical differences between hybrid toon shading and toon shading. And then introduce the advantages and disadvantages and current application status of hybrid toon shading. In conclusion, the paper emphasizes that hybrid toon shading is a feasible approach warranting active development, and also discusses the future of the two-dimensional game market.
Automating the training and deployment of models in MLOps by integrating systems with machine learning
Authors: Liang Penghao , Song Bo , Zhan Xiaoan , Chen Zhou , Yuan Jiaqiang
Published: July 2024

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices.
Rumor detection in networks based on the BERT-CNN approach
Authors: Huang Deming , Yang Sibo , Gao Shengwei
Published: July 2024

As of June 2023, China has witnessed a surge in internet users, reaching 1.079 billion, with an internet penetration rate of 76.4%. This rapid expansion of social media and online communication channels has not only enhanced the ease of information sharing but has also intensified the spread of rumors and misinformation. This trend poses substantial threats to social order and public safety. In response to this challenge, this work introduces an innovative approach to detect online rumors, utilizing the BERT-CNN methodology. By harnessing the contextual understanding capabilities of the BERT model and the efficient feature extraction prowess of CNN models, the study has crafted a framework that seamlessly integrates these technologies. This integrated approach proves effective in accurately identifying and categorizing rumors in online data. Through extensive experiments conducted on various real-world datasets, a comparative performance analysis had conducted against existing technologies. The results reveal significant enhancements in key metrics such as accuracy, recall, and F1 score when compared to traditional methods. This affirms the efficacy of the BERT-CNN approach in the domain of online rumor detection. This research not only introduces a fresh perspective to rumor detection technology but also furnishes a robust tool to tackle the challenges associated with the dissemination of rumors in the digital age.
Analysis of machine learning-based music recommendation system using Spotify datasets
Authors: Li Xinyue
Published: July 2024

The field of musical exploration grows rapidly with the rapid development of technology in the digital era. In this paper, the application of machine learning in music recommendation systems is mainly focused on and the existing data sets about Spotify are used to analyze and show how machine learning achieves musical personality recommendations. This paper mainly uses principal component analysis and the K-means clustering algorithm to realize the goal. Principal component analysis (PCA) helps people to reduce the dimensionality of the whole dataset, and this method helps people to retain key information and reduce the complexity of the data. The k-means clustering algorithm divides the songs into different clusters. These clusters show consistency in musical features, and the dots will cluster together. By visualization of the clusters, people can deeply interpret the relationship between distinct music genres and discover the areas of music and songs that users are interested in. This research not only explains how music exploration is implemented but also promotes the development of personalized music recommendation systems.
Recommendation algorithms influencing factors on college students' continuance use intentionA S-O-R analysis based on Douyin
Authors: Chen Yiyang
Published: July 2024

Nowadays, some applications are having difficulty in retaining users after pulling them in. To enhance user stickiness, algorithms are widely used by major applications due to their advantage of accurate push. Recommendation algorithms have changed the Internets business logic and information dissemination modes from pursuing "information bombing" to "precision strike". Based on the S-O-R model, this paper investigates the influence of recommendation algorithm characteristics (recommendation quality, personalization, usability, and user trust) on college students' satisfaction and continuance use intention of Douyin. Structural equation modelling analysis was conducted in this paper with the help of a questionnaire survey and partial least squares (PLS). It was found that personalization positively affects user attitude and user cognition; usability positively affects user cognition and emotional response; and user trust positively affects user attitude, user cognition and emotional response. While none of the stimuli, recommendation quality, has a significant effect on users' psychological emotions. User attitude, cognition, and emotional response all significantly affect user satisfaction, which in turn significantly affects user continuance use intention. This study provides a quantitative study for understanding user behaviors on social media platforms, and offers suggestions for the optimization of recommendation systems.
Feasibility study of underwater drilling robot based on razor clam
Authors: Luo Baichun
Published: July 2024

Razor acis lives in the low tidal area of the inner bay and thrives in the area with mild infiltration of fresh water. It uses its rhythmic contraction and foot burp and its unique anatomy to make holes. The extraordinary ability of razor clams to effectively penetrate the sediment has inspired innovation in the design of underwater drilling robots. Underwater drilling robots are currently widely used in the industrial world, such as underwater geological exploration, underwater shipwreck exploration, etc. However, the existing models of these robots mainly have a rigid structural design, which limits the flexibility of their drilling components, while their rough appearance also hinders their drilling efficiency. In response to these limitations, this paper proposes a robot with a soft, flexible body, and other enhanced functions designed to simulate a soft razor clam, replicate its peristaltic motion, minimize surface friction, and thus achieve superior drilling capability. This paper focuses on the development of software, segmented structure design, and studies designed to reduce surface friction and explore the mechanism of biomimetic motion.
Pengaruh Kualitas Pelayanan dan Semangat Kerja Terhadap Kepuasan Masyarakat dengan Kecerdasan Sosial Sebagai Variabel Intervening pada Kantor Camat Tilatang Kamang
Authors: Suwardi Suwardi , Yulihasri Yulihasri
Published: July 2024

The purpose of this research is to determine the influence of service quality and work enthusiasm on community satisfaction with social intelligence as an intervening variable at the Tilatang Kamang sub-district office. The method used is an analytical or reception survey with data analysis techniques of outer model testing, inner model testing and hypothesis testing. The sample was 100 residents of Tilatang Kamang District. The results obtained show that there is a positive and significant influence of service quality on social intelligence at the Tilatang Kamang District Head Office. There is a positive and significant influence of work enthusiasm on social intelligence at the Tilatang Kamang Subdistrict Office. There is a positive and significant influence of service quality on community satisfaction at the Tilatang Kamang District Head Office. There is a positive and significant influence of work morale on community satisfaction at the Tilatang Kamang District Head Office. There is a positive and significant influence of social intelligence on community satisfaction at the Tilatang Kamang District Head Office. Social intelligence is able to mediate the influence of service quality on community satisfaction at the Tilatang Kamang District Head Office. Social intelligence is able to mediate the influence of work morale on community satisfaction at the Tilatang Kamang District Head Office
Pelaksanaan Program Tahfidz Al-Qur’an di MAN 2 Palembang
Authors: Dwiyana Annisa , Niswah Choirun , Ibrahim Ibrahim
Published: July 2024

The aim of this research is to understand the implementation of the tahfidz program at MAN 2 Palembang and to identify the supporting and inhibiting factors during the implementation of the Qur’an memorization program at MAN 2 Palembang. This research employs a qualitative research method, and the informants include the head of the madrasa, the head of the Darul Ulum tahfidz house, tahfidz supervisors, and students. The data collection techniques used in this study are observation, interviews, and documentation. The data analysis techniques employed are data reduction, data presentation, and conclusion drawing. The validity of the data is tested using triangulation, which includes source triangulation, technique triangulation, and time triangulation. The results of the research indicate that the tahfidz Qur’an program at MAN 2 Palembang is carried out through direction, motivation, communication, and coordination in each process, starting with determining the program's objectives, defining success indicators, setting the implementation schedule, and appointing program coordinators. The supporting factors include adequate facilities and infrastructure, quality human resources, the presence of rewards and punishments, and the program's timing. The inhibiting factors include the students' own attitudes and their surrounding environment
Analisis Kritis Critical Book Review Buku Ajar Anatomi Fisiologi Karya Heni Puji Wahyuningsih dan Yuni Kusmiyati
Authors: Gea Fifitriani , Lahagu Albertus , Hulu Adil Hati , Halawa Desheriasti , Lase Natalia Kristiani
Published: July 2024

Critical book review is one of the mandatory assignments given to students to review a book in the sense of focusing on evaluation (explanation, interpretation, and analysis) of the book's strengths and weaknesses and how the book affects the way of thinking and increases understanding of a field of study. Teaching materials for Physiological Anatomy by Heni Puji Wahyuningsih and Yuni Kusmiyati are a comprehensive learning resource to understand the structure and function of the human body. This book discusses various organ systems in detail, from the cellular to the macroscopic level. Its clear explanations and informative pictures make this book easy for readers to understand. This study aims to find out the discussion of teaching materials in detail. This study uses a literature study method, namely collecting data from various literature or scientific references and physiological anatomy books. The results of the critical analysis of the research are teaching materials discussing the physiological anatomy of the human body, where anatomy is the study of the structure and relationships between body parts of living things, and physiology is the study of the study of the function of body parts
Strategi Pengelolaan Sumber Daya Manusia untuk Menekan Perpindahan Tugas Pegawai
Authors: Sari Intan Permata , Novalinda Novalinda , Syafwandi Syafwandi
Published: July 2024

This study aims to find out human resource management strategies to reduce the transfer of employee duties. This research is a qualitative descriptive research, descriptive research does not provide treatment, manipulation or alteration to the variables studied, but describes a condition as it is. Data collection is carried out through observation, interviews, and documentation. Data analysis was carried out qualitatively consisting of data reduction, data presentation, and conclusion drawing. As a result of the previous discussions, the author concludes that effective HR management strategies to suppress the transfer of employee duties are a key factor in the success of the organization. By implementing the right strategies in human resource management, organizations can reap significant benefits, from increased productivity to better employee retention. It is important for organizations to continue to improve strategies in effective human resource management to reduce the transfer of employee duties in accordance with the development of the times and employee needs.
Pengembangan Media E-Komik Edukatif pada Mata Pelajaran IPAS Kelas V Sekolah Dasar
Authors: Sandika Citra Rahma , Hendri Nofri , V Novrianti , Amilia Winanda
Published: July 2024

This research aims to develop educational e-comic media as a learning medium for science and science subjects in class V SDN 07 Pulau Air using the ADDIE model which includes five stages, namely Analysis, Design, Development, Implementation and Evaluation. Data collection was carried out by observation, interviews, questionnaires, and documentation. Data analysis in the research carried out validity tests, practicality tests and effectiveness tests. The validity test was carried out by 3 validators consisting of 1 material validator and 2 media validators. Product trials were carried out by 25 class V.A students at SDN 07 Pulau Air with the aim of finding out the practicality of the product being developed, and effectiveness testing was carried out by distributing pretest and posttest questions. The results of research on the development of educational e-comic media obtained an average score for material of 4.92 with very suitable criteria, and 4.95 for media with very suitable categories. The students' practicality test results obtained a total average of 4.5 in the very practical category. The results of the effectiveness test obtained t_count of 11.52 and t_table= 2.064. Thus we get t_count > t_table. Based on the results of validity, practicality, and effectiveness tests, it can be concluded that the use of digital e-comic media in science and science learning is very suitable for use and has a significant effect on the learning process
Efektivitas Model Pembelajaran Kooperatif Tipe Numbered Head Together (NHT) Berbantuan Alat Peraga dalam Meningkatkan Kemampuan Berpikir Kritis Siswa pada Pembelajaran Matematika
Authors: Susilawati Susi , Tio Heriyana
Published: July 2024

The study aims to test the effectiveness of a Numbered Head Together (NHT) cooperative learning model with a tool in improving students' ability to think critically about learning mathematics. The research method used is quantitative with the type of Quasi-experimental research. The study population was class X students of SMA Negeri 1 Cigugur in the 2023-2024 school year, with samples taken using purposive sampling technique, namely classes X1 and X2 with a total of 64 students and the statistical test used was t-test. The instruments used included testing students' critical thinking skills and elevating students' responses. Data analyzed using test instruments and analysis of mathematical critical thinking abilities. The results of the study showed that there was a difference in average mathematical critical thinking skills between students who obtained cooperative NHT-type learning with a tool compared to students who gained conventional learning. Normality tests and homogenity tests show that the N-Gain Score data are distributed normally and the data variance is homogeneous. The results of the hypothesis test showed a significant improvement in students' ability to think critically in the experimental group. Students' response to the NHT learning model was also positive
Simulation and Implementation of Moving Platform for Flight Simulator
Authors: Yousif Elkhidir Tay Allah , AbdElbagi Omer Eldany
Published: July 2024

Flight simulator is a virtual reality system capable of simulating an environment of a flying machine for a pilot. This work presented a simulation and implementation example of a six degree of freedom moving platform as a part of a flight simulator, most flight simulators adopted the Stewart platform as a moving platform. Therefore, a mathematical model of the Stewart platform is derived by solving inverse kinematics problem, to find the length of all legs for a given desired position and orientation. The mathematical model is verified and simulated using MATLAB software. Low-cost commercially available components are provided for the constructed prototype. Additionally, interfacing between hardware and software components is accomplished by Arduino Uno. Obtained results showed that the range of translational motion is and cm along x, y, and z respectively, as well as, the rotational range is +_5,+-3 and +-10 about the mentioned three axes.
Derivation of Both Klein-Gordon Equation and Time Dependent Schrdinger Equation From Classical Wave Function
Authors: Tagabo Mobark , Hessen ALawad Wail , Elrayah Adil , Medani Imad Aldin
Published: July 2024

In this study, both of Klein-Gordon equation and time dependent Schrdinger equation are directly derived from De Broglie hypothesis (? = h=p). Besides that, a new formula of Klein-  ordon equation was obtained by used two equations, i.e., the classical wave function and relativistic total energy relation hw = (h2k2)=2m+V ?. Moreover, a new equation of time dependent Schrdinger (TDSE) in three-dimension 3D was derived and evidently obtained. Thus, previous result is corresponding to (TDSE) in 3D calculated by using two equations, (i.e., Einstein energy and Newton second law of mechanics).
Generalizing the Telescoping Procedures for Power Series to Reduce the Degree of Polynomials
Authors: AbdElhalim Said Mohammed , Abdallah Hashim Mohsin Hassan , Abdallah Obaid Hasim
Published: July 2024

By using the Telescoping Procedures for Power Series (TPPS), the degree of any given polynomial can be decreased by one degree on the interval [-1; 1], with only a slight accuracy loss, the reason for this is that, the (TPPS) depends on the Chebyshev polynomials, which have a minimum maximum-absolute value that is distributed uniformly over [-1; 1]. In this paper, we generalized the (TPPS) with the aim to reduce the degree of an arbitrary polynomial for any degree and in any interval [a; b]. The generalization was in the form of a theorem and the theorem was proven using the Induction. The new technique called Generalized Telescoping Procedures for Power Series (GTPPS). We also conducted a study of the error bound of the theorem in the intervals [-1; 1], and [a; b]. The authors believe that this paper paves the way for a comprehensive set of researches and applications in the fields of approximation theory, optimization theory, computer simulation and other computation techniques to issues in numerous scientific disciplines.
Classifying of Isolated Handwritten Arabic Characters (on-line) Using Machine Learning Methods
Authors: Albadawi Mohamed Mosadag , Bashir Kamal , Osman Seif Al-Din Fattouh
Published: July 2024

The prediction of Arabic handwritten characters is one of the fascinating matters in the area of artificial intelligence (AI) and machine learning, especially in the case of on-line handwriting. In the handwriting recognition system, characters are classified according to their particular categories by utilizing -as input- the values of the features extracted from them. Our major objective in this paper is to classify the isolated characters for online Arabic handwriting (? to ?) using machine learning techniques with the features from EDMs (Edge Direction Matrixes) as opposed to EEDMs (Extended Edge Direction Matrixes). With respect to all classification methods used in this study, EEDMs with machine learning techniques had better performance than EDMs in recognizing isolated Arabic online characters.
Động lực làm việc của điều dưỡng và một số yếu tố liên quan tại Khoa Hồi sức tích cực, Bệnh viện Việt Đức năm 2023
Authors: Nguyễn Trường Giang , Nguyễn Việt Minh , Lương Quang Thùy , Nguyễn Hải Hà Trang , Phạm Thị Ngọc , Lê Tuyết Nhung , Nguyễn Thị Hường , Dương Thị Bình Minh
Published: July 2024

Mục tiêu: Mô tả động lực làm việc và xác định một số yếu tố liên quan của điều dưỡng tại khoa Hồi sức tích cực, Bệnh viện Việt Đức.
Đối tượng và phương pháp nghiên cứu: Nghiên cứu mô tả cắt ngang trên 86 điều dưỡng làm việc tại khoa Hồi sức tích cực I và II, Bệnh viện Việt Đức từ tháng 04 đến tháng 12 năm 2023. Mẫu phiếu phỏng vấn có cấu trúc được xây dựng dựa trên tham khảo những tài liệu, nghiên cứu có liên quan đến động lực làm việc trong và ngoài nước.
Kết quả: Tỉ lệ điều dưỡng có động lực làm việc là 40,7%. Có mối liên quan có ý nghĩa thống kê về động lực làm việc chung với các yếu tố: tuổi, người thu nhập chính trong gia đình, thu nhập, thời gian công tác, loại hình lao động (p < 0,05).
Kết luận: Nghiên cứu cho thấy động lực làm việc của điều dưỡng tại Khoa hồi sức tích cực Bệnh viện Việt Đức chưa cao.
The Unveiling Distress: Harnessing NLP and Deep Learning to Identify Suicidal Signals in Tweets
Authors: Feroze S. Atruba , Feroze S.M Bazif , Abbasi Uzma
Published: July 2024

The rise of social media platforms has provided researchers with unprecedented access to vast amounts of user-generated content, offering a unique opportunity to explore various aspects of human behavior, including mental health. This paper presents a novel approach to identifying suicidal signals in tweets using Natural Language Processing (NLP) techniques and Deep Learning algorithms. We propose a multi-step methodology that involves data collection, preprocessing, feature extraction, and classification. Leveraging state-of-the-art deep learning architectures such as recurrent neural networks (RNNs) and transformer models, our approach aims to accurately detect linguistic patterns indicative of suicidal ideation and distress. We evaluate the effectiveness of our method using a large dataset of annotated tweets and demonstrate promising results in terms of both precision and recall. Furthermore, we discuss the ethical implications and potential applications of our research in suicide prevention and mental health support systems.
Reformation of the Almajiri Educational System in Nigeria Through the Maqashid Al-Shari’ah Paradigm
Authors: Lateef Abiola Muideen , Tijani Abdul-Lateef Aremu , Ahmad Muhammad Bashir
Published: July 2024

The Almajiri phenomenon is one of the pandemic matters affecting Muslims in the North, specifically Nigeria as a nation. It depicts a condition whereby children of school age, ostensibly enrolled to study under Mallams, roam about the streets in tattered clothes, begging, scavenging, and doing all sorts of odd jobs. While Islam places a high premium on education, emerging issues about The Almajiri system of education have heightened the need to re-examine the concept from the perspective of the essence, purpose, goal, and objective of Islamic law (Maqasid al-Shari’ah). To this extent, the practice of the Almajiri system of education as obtainable in Nigeria should be subjected to the balance of Maqasid as-Shari’ah to ascertain its acceptability and fecundity. This paper therefore attempts to assess and appraise the Almajiri educational system in Nigeria to reform its operation and recommend steps towards attaining the overall educational goals of Islam as enshrined in the Shari’ah. The researcher adopts both historical and narrative research methods in the work. This article finds a strong correlation between the Almajiri system of education and Maqasid as-Shari’ah.  The research concludes that there is a dire need for Nigerian Muslims to adopt the Maqasid as-Shari’ah as the guiding principle for the reformation and operation of the Almajiri educational system, particularly in the northern part of the country.
Russian-Vietnamese Partnership in Progress: on the Occasion of 30th Anniversary of the Treaty on the Foundations of Friendly Relations
Authors: Mazyrin Vladimir M.
Published: July 2024

The review of a series of scientific and practical events held in April-June 2024 and dedicated to the 30th anniversary of the Treaty on the foundations of friendly relations between Russia and Vietnam assesses the evolution, current state, problems and prospects of these relations, the impact of international events on them, and the active foreign policy of Hanoi and Moscow. In particular, the author highlights main reports of experts on Vietnam at the plenary conference of the Moscow Academic Economic Forum on the topic “Intensification of economic ties between Russia and the countries of Asia and Africa”, the ninth Vietnam Day at MGIMO of the Russian Foreign Ministry, the round table “Russia-Vietnam” in IOS RAS, the international conference “Mekong: problems of the region and possible solutions” in ICCA RAS. Vietnam's participation in the multilateral formats of Russian foreign policy was also briefly reviewed, and it was recognized as extremely restrained. Vietnam's participation in the multilateral formats of Russian foreign policy was briefly reviewed too, and it was recognized as extremely restrained. According to experts, the results of cooperation between the two countries lag far behind their official format and actual potential.
Technology Platforms for Industrial Chemistry
Authors: Mlčoch Antonín , Šilhan Martin , Souček Ivan
Published: July 2024

The aim of the article is to inform about the benefits of technological platforms for industrial chemistry. The contribution is dedicated to the memory of the first and long-time chairman of SUSCHEM CZ, Dr. Ladislav Novák.
Commercial Vessel Ballast Water Management (2021-2022): Report to Congress June 3, 2024
Authors:   National Ballast Information Clearinghouse,   United States Coast Guard
Published: July 2024

The 2018 Vessel Incidental Discharge Act (VIDA)1 requires an annual report on the effectiveness of national ballast water management (BWM) requirements. This report provides analyses of (1) the patterns of ballast water (BW) delivery and management; (2) Coast Guard’s compliance and enforcement actions regarding BWM regulations; and (3) patterns of biological invasions by marine and estuarine aquatic nuisance species (ANS). It covers the two-year period from January 2021 through December 2022 to demonstrate the effectiveness of the Coast Guard’s statutorily mandated BWM regulatory program and validates that enforcement efforts continue to increase BWM and reduce the risk of introducing ANS into U.S. waters.
The Impact of AI Perception on Daily Life Satisfaction among Elderly: Dual Mediating Effects of Information Production Sharing and Networkin
Authors: Kim Yunhui , Nam Jungmin ,   Global Venture Research Institute
Published: July 2024

This study explored in-depth the relationship between the elderly's awareness of AI and their daily satisfaction, information production sharing, networking, and social capital. The study used panel data from the '2023 Digital Information Gap Survey' conducted by the National Institute for Intelligence and Information Society (NIA), and analyzed 1,910 respondents out of 2,300 people aged 55 and over, excluding 390 non-respondents. Descriptive statistics, correlation, and multiple regression analyses were conducted using SPSS 23.0 and SPSS Process Macro, and the results of the study are as follows. AI awareness among the elderly has a significant effect on daily life satisfaction, and there is a direct dual mediation effect of information sharing and networking between AI awareness and daily life satisfaction, and a moderated mediation effect of social capital between AI awareness and information sharing production. This study will contribute to the establishment of a social support system and draw implications for promoting digital inclusion and strengthening social connections by effectively utilizing A.I. technology to prevent the elderly from being left behind in a changing society, and provide new perspectives that have not been addressed in previous studies.
Pendidikan Kesehatan Pentingnya Zat Besi Bagi Remaja Untuk Mencegah Terjadinya Anemia di MA Atqia Kabupaten Bondowoso
Authors: Saadah Miftahus , Puspitasari Ratna , Alfian Linda
Published: July 2024

Latar Belakang :Kekurangan zat besi dalam tubuh atau yang disebut anemia merupakan masalah kesehatan yang sering ditemui di negara maju dan berkembang. Keadaan kesehatan dan gizi kelompok usia 10-24 tahun di Indonesia masih memprihatinkan. Prevalensi anemia pada remaja putri (rematri) usia 10-19 tahun sebesar 30 %. Tubuh membutuhkan zat besi untuk mempercepat pertumbuhan dan perkembangan,hal ini menyebabkan rematri yang menderita anemia juga berisiko mengalami anemia saat hamil. Tujuan : Kegiatan ini bertujuan memberikan edukasi, informasi serta meningkatkan kesadaran pada remaja bahwa zat besi sangat penting dalam tubuh. Metode : Metode dalam kegiatan ini menggunakan KIE (Pendidikan kesehatan) yang terdiri dari tahap persiapan, implementasi dan evaluasi. Hasil : Kegiatan ini dilakukan oleh dosen bersama mahasiswa Akademi Kebidanan Dharma Praja Bondowoso yang dihadiri oleh 32 orang siswa MA Atqia dengan pemberian materi, pre test dan post test. Hasil pre test 6 siswa (19%) mengetahui manfaat zat besi dalam tubuh dan hasil post test mengalami kenaikan signifikan sebanyak 28 siswa (87%) mampu menjawab pertanyaan seputar zat besi. Kesimpulan : Diharapkan kegiatan ini dapat dilakukan secara berkelanjutan untuk menambah pengetahuan, pemahaman serta kesadaran siswa dalam mengatur kebutuhan zat besi dalam tubuhnya.
Perbedaan Obat Berdasarkan Nama Generik dan Nama Dagang pada Masyarakat Desa Genteng Kulon
Authors: Anung Kustriyani  , Dita Amanda Deviani  , Stephanie Devi Artemisia  , Azmi Prasasti  , Titis Sriyanti  , Nina Wijiani  , Riyan Dwi Prasetyawan  , Abi Mas Udianto  , Sari Prayudeni  , Ima Fitria Lestari  , Nur Hidayati
Published: July 2024

Drugs are substances, guide substances, and biological products that are used to influence or investigate physiological systems or pathological states. Medicines can have generic names and trade names. People think that drugs with generic names produce lower therapeutic effects than trade names, as is the case in the Genteng Kulon community. The aim of this community service activity is to provide information regarding the differences between medicines based on generic names and trade names to the Genteng Kulon Village community. The method used was counseling and giving leaflets. Apart from that, people also do pre-test and post-test questions. This community service activity was attended by 50 representatives of posyandu figures in Genteng Kulon Village. The result of community service activities is that representatives of posyandu figures receive information regarding the differences in medicines based on generic names and trade names. People can use drugs with generic names without worrying about efficacy, especially for people who receive drug services from BPJS. There is an increase in the post test score compared to the pre test.