bluuebunny/crossref_metadata_2025 huggingface dataset:
Analisis Teologis Tentang Makna Jenazah di Patadongkon Dalam Upacara Rambu Solo’ di Desa Orobua
Authors: Armianti Gebi
Published: July 2024

Tujuan penelitian ini ingin  mengkaji mengenai upacara Rambu Solo’ yang lebih berfokus kepada tradisi mayat dipatadongkon yang ada di Kabupaten Mamasa  Desa Orobua Kecamatan Sesenapadang, dan mendialogkan tradisi tersebut dengan Injil dalam agama Kristen. Tradisi dipatadongkon ini merupakan sebuah tradisi yang awalnya dilakukan oleh Aluk Todolo dan sampai saat ini masih dilakukan. Penelitian ini menggunakan metode penelitian yang kualitatif, dengan melibatkan tehnik wawancara dan observasi. Telaah literatur juga digunakan dalam pengumpulan data ini dengan melakukan pengumpulan referensi berupa buku, jurnal yang relevan dengan topik yang dibahas. Hasil penelitian ini ditemukan bahwa tradisi dipatadongkon ini dilakukan oleh masyarakat Desa Orobua, dulunya sebagai bentuk penghormatan kepada orang yang telah meninggal serta mereka menganggap bahwa orang yang telah meninggal tersebut dapat memberikan keselamatan, memberikan berkat dan juga memberikan penghiburan kepada keluarga yang ditinggalkan. Namun, sampai sekarang ini sebagian dari masyarakat Desa Orobua masih ada yang percaya dengan hal yang demikian.Kata Kunci: Rambu Solo’, Dipatadongkon, Orobua
Sistematização da aprendizagem do conteúdo jogo na Educação Física escolar com estudantes do 7º, 8º e 9º anos do ensino fundamental
Authors: Ramos Pricylla Carolina Queiroz de Moura , Melo Flávio Dantas Albuquerque , Melo Marcelo Soares Tavares de , Lorenzini Ana Rita
Published: July 2024

Fruto do Programa de Mestrado Profissional em Educação Física (PROEF) esta produção objetivou analisar como os estudantes dos anos finais do ensino fundamental sistematizam a aprendizagem do Jogo nas aulas de Educação Física. A metodologia buscou aproximação com o Materialismo Histórico-Dialético que orienta o trabalho educativo da Pedagogia Histórico-Crítica, da Psicologia Histórico-Cultural e da Educação Física Crítico-Superadora. Os sujeitos da investigação foram três turmas de estudantes (7º ao 9º ano do ensino fundamental) e a investigação utilizou a técnica da pesquisa-ação, o diário de campo e seminários. A análise dos dados foi realizada mediante as categorias dialéticas realidade-possibilidade e contradição. Os resultados demonstraram que: o 7º ano ampliou a sistematização da aprendizagem sobre jogo e os jogos de salão iniciando o processo de formação de conceitos; o 8º avançou na formação de conceitos sobre jogos populares por conseguirem constatar, interpretar, compreender e explicar suas características, seu conhecimento histórico-social; o 9º ano concretizou sua aprendizagem mediante o processo de abstração, demonstrando apropriação da generalidade do Jogo e das três manifestações trabalhadas ao longo da unidade didática. Concluiu-se que ocorreu a sistematização da aprendizagem do Jogo e houve um salto qualitativo que materializou a formação de conceitos, as generalizações elevando o nível do conhecimento e elaboração do pensamento nos estudantes, reiterando a relevância clássica do componente curricular Educação Física.
PENERAPAN STRATEGI MENTOR-MENTORING BERMEDIA LINKTREE UNTUK MENINGKATKAN ADOPTION RATE RAPOR PENDIDIKAN SEKOLAH BINAAN
Authors: Purwanto Wahyu
Published: July 2024

Penggunaan rapor pendidikan sebagai alat evaluasi kinerja pendidikan seringkali tidak optimal di sejumlah satuan pendidikan. Hal ini mengindikasikan rendahnya tingkat adopsi rapor pendidikan sebagai instrumen evaluasi yang efektif. Artikel ini mengeksplorasi penerapan pendampingan dengan strategi Mentoring-Monitoring sebagai upaya untuk meningkatkan adopsi rapor pendidikan. Metode penelitian yang digunakan melibatkan pendekatan deskriptif kualitatif pada 10 sekolah binaan. Hasil penelitian menunjukkan bahwa penerapan pendampingan dengan strategi Mentoring-Monitoring sangat efektif meningkatkan adopsi rapor pendidikan di satuan pendidikan binaan. Pasca kegiatan mentoring hasil adopsi rapor pendidikan meningkat dari 0 sekolah menjadi 10 sekolah atau meningkat 100% dalam adoption rate rapor pendidikan. Hal tersebut juga berdampak pada meningkatnya adoption rate kabupaten yaitu mengalami peningkatan 48,89% untuk indikator eksplorasi rapor pendidikan. Adapun langkah kegiatan Monitoring pada strategi Mentor-Monitor berfungsi untuk memastikan keberhasilan adoption rate rapor pendidikan dengan pemanfaatan secara kualitas sebagai bentuk upaya membenahi sekolah melalui perencanaan berbasis data.
The use of the method of remote space sensing of the Earth's surface in countering the illegal cultivation of narcotic plants
Authors: Батоев В.Б.
Published: July 2024

В статье рассматривается возможность использования в деятельности правоохранительных органов метода дистанционного космического зондирования земной поверхности в рамках осуществления противодействия незаконному культивированию наркосодержащих растений, а также при уничтожении незаконно выращенных их посевов и очагов произрастания. В работе представлены официальные статистические данные Главного информационно-аналитического центра МВД России по данному направлению деятельности. Автором обозначен ряд проблемных вопросов, устранение которых предлагается определить в качестве одного из основных направлений совершенствования деятельности правоохранительных органов по линии противодействия незаконному обороту наркотиков. В условиях цифровизации вопросов организации общественных процессов принятие государством во внимание необходимости совершенствования практики внедрения и использования дистанционного зондирования земной поверхности из космоса в сочетании с технологиями искусственного интеллекта должно обеспечить правоохранительные органы современным инструментарием противодействия сложившейся наркоситуации.

The article considers the possibility of using the method of remote space sensing of the Earth's surface in the activities of law enforcement agencies in the framework of countering the illegal cultivation of narcotic plants, as well as in the destruction of illegally grown crops and foci of their growth. The paper presents official statistical data of the Main Information and Analytical Center of the Ministry of Internal Affairs of Russia in this area of activity. The author identifies a number of problematic issues, the elimination of which is proposed to be identified as one of the main directions for improving the activities of law enforcement agencies in countering drug trafficking. In the context of digitalization of issues of the organization of social processes, the state's taking into account the need to improve the practice of introducing and using remote sensing of the Earth's surface from space in combination with artificial intelligence technologies should provide law enforcement agencies with modern tools to counter the current drug situation.
The procedural form of seizure medical documents during consideration of iatrogenic crime report
Authors: Ишмухаметова Е.Ш.
Published: July 2024

В статье рассмотрены вопросы законодательного урегулирования, следственно-судебная практика и позиция высших судов РФ о порядке получения следователем медицинских документов в ходе рассмотрения сообщения о ятрогенном преступлении. Отсутствие регламентированного порядка изъятия документов до возбуждения уголовного дела порождает процессуальные споры и различные подходы к этому мероприятию. Дополнительная сложность обусловлена наличием в таких документах охраняемой законом медицинской тайны, что на стадии расследования уголовного дела обязывает следователя получать судебное разрешение на производство выемки. Вместе с тем законодательство об охране здоровья предусматривает право третьих лиц получать меддокументы с согласия их владельца, а следователя – по запросу в ходе расследования дела. По мнению автора, следует признать законным и не нарушающим права и интересы участников процесса получение документов на основании запроса при наличии письменного согласия пациента, а равно производство выемки меддокументов у заявителя на основании постановления следователя.

The article discusses the issues of legislative regulation, investigative and judicial practice and the position of the supreme courts of the Russian Federation on the procedure for seizure medical documents by an investigator during consideration of iatrogenic crime report. The absence of a regulated procedure for the seizure of documents before the initiation of a criminal case generates procedural disputes and various approaches to this action. These documents also contains the legally protected medical secrecy that causes additional problems, because it obliges the investigator to take a court permit for the seizure during the investigation phase. At the same time, according to the legislation on health people can receive medical documents with the permission of their owner and the investigator have the right to make a request for such documents during the investigation. The author thinks it is legal to take medical documents with the investigator’s request if you have the written permission of the patient. It is also acceptable to seize medical documents from the patient without court order.
Some features of the disclosure and investigation of corruption-related crimes
Authors: Харченко С.В.
Published: July 2024

В статье раскрываются алгоритмы действий сотрудников оперативных подразделений, осуществляемых в ходе раскрытия и расследования преступлений коррупционной направленности на различных этапах. На первом этапе предлагается осуществление оперативно-разыскных, оперативно-технических и иных мероприятий, направленных на проверку первичной информации о преступлении коррупционной направленности. На втором этапе аргументируется необходимость изучения финансовой, производственно-хозяйственной деятельности конкретного объекта преимущественно с помощью источников оперативно-разыскной информации. Через их возможности, как правило, получают копии соответствующих документов, носящих в себе материальные следы преступления. При этом в ходе проверки информации особое внимание следует обращать на юридическую обоснованность проводимых банковских операций, осуществление процедуры закупки и т. д. На третьем этапе обосновываются меры, направленные на выяснение роли каждого участника преступления коррупционной направленности, установление механизма преступного посягательства, размера причиненного материального ущерба и иных обстоятельств, подлежащих доказыванию.

The article reveals the algorithms of actions of employees of operational units carried out during the disclosure and investigation of corruption-related crimes at various stages. At the first stage, it is proposed to carry out operational-investigative, operational-technical and other measures aimed at verifying primary information about a corruption-related crime. At the second stage, the need to study the financial, production and economic activities of a particular object is argued, mainly with the help of sources of operational investigative information. At the same time, during the verification of information, special attention should be paid to the legal validity of the banking operations carried out, the implementation of the procurement procedure, etc. At the third stage, measures are justified aimed at clarifying the role of each participant in a corruption-related crime, establishing the mechanism of criminal encroachment, the amount of material damage caused and other circumstances to be proved.
Scientific and organizational foundations of the state program to combat cyber extremism: the creative legacy of Vasily V. Bychkov
Authors: Прорвич В.А.
Published: July 2024

Результаты многолетних исследований важнейших особенностей современной киберпреступности позволили В.В. Бычкову создать научные и организационные основы государственной программы борьбы с киберэкстремизмом, включающей важнейшие уголовно-правовые, уголовно-процессуальные, криминалистические и иные аспекты следственных действий. Описаны подходы к созданию проблемно ориентированных информационных технологий для борьбы с киберэкстремизмом, выверенных с правовой точки зрения, позволяющие реализовать установки в данной сфере, сформированные высшим руководством страны. Предложены основные направления подготовки кадров, способных реализовать основные направления работы в рамках разработанной государственной программы.

The results of many years of research on the most important features of modern cybercrime allowed V.V. Bychkov to create the scientific and organizational foundations of the state program to combat cyber extremism, which includes the most important criminal law, criminal procedure, criminalistic and other aspects of investigative actions. The article describes approaches to the creation of problem-oriented information technologies to combat cyber extremism, verified from a legal point of view, allowing the implementation of installations in this area formed by the country's top leadership. The main directions of training personnel capable of implementing the main areas of work within the framework of the developed state program are proposed.
Ensuring continuity – to the 10th anniversary of the Sukharev Moscow academy of the Investigative Committee of the Russian Federation
Authors: Бессонов А.А.
Published: July 2024

Являясь первой из учреждённых Следственным комитетом Российской Федерации образовательной организацией, Московская академия Следственного комитета Российской Федерации имени А.Я. Сухарева отметила свой 10-летний юбилей. В статье кратко рассматриваются достижения её коллектива в образовательной, научной и воспитательной сферах. Отмечена роль вуза в профессиональной подготовке высококвалифицированных кадров системы Следственного комитета Российской Федерации, отражены сформировавшиеся связи с другими отечественными и зарубежными научными и образовательными организациями и учреждениями, намечены направления дальнейшего развития. Выражены слова благодарности Председателю Следственного комитета Российской Федерации Александру Ивановичу Бастрыкину и подразделениям центрального аппарата Следственного комитета Российской Федерации.

Being the first educational organization established by the Investigative Committee of the Russian Federation, the Sukharev Moscow academy of the Investigative Committee of the Russian Federation celebrated its 10th anniversary. The article briefly considers the achievements of the Academy’s staff in the educational, scientific and educational spheres. The role of the higher educational institution in the professional training of highly qualified personnel of the system of the Investigative Committee of the Russian Federation is noted, the formed bindings with other domestic and foreign scientific and educational organizations and institutions are reflected, the directions of further development are outlined. Words of gratitude are expressed to the Chairman of the Investigative Committee of the Russian Federation Alexander Ivanovich Bastrykin, and the divisions of the central apparatus of the Investigative Committee of the Russian Federation.
Historical and legal aspects of the genesis and formation of the institute of certification of law enforcement officers of the Russian Empire
Authors: Кобозев А.А.
Published: July 2024

В статье рассматриваются предыстория, генезис и становление процедуры аттестации государственных служащих правоохранительных органов Российской империи. Материалом для анализа стали научные и учебно-методические труды историков, социологов, педагогов и психологов, указы и иные правовые акты российских императоров, дореволюционные энциклопедические издания и большое количество научных исследований, посвященных изучению проблем аттестации государственных служащих России. По результатам ретроспективного анализа возникновения и развития правового регулирования аттестации военнослужащих и чинов государственной гражданской службы Российской империи автор показывает эволюцию системы аттестования отечественных государственных служащих, подчеркивает ее ключевую роль в формировании профессионального кадрового состава правоохранительной службы. В заключение обосновывается необходимость использования положительного опыта аттестации чиновников правоохранительных органов России императорского периода с учетом современных условий прохождения службы сотрудниками правоохранительных ведомств Российской Федерации.

The article examines the background, genesis and formation of the certification procedure for civil servants of law enforcement agencies of the Russian Empire. The materials for the analysis are the scientific and educational works of historians, sociologists, teachers and psychologists, decrees and other legal acts of the Russian emperors, pre-revolutionary encyclopedic publications and a large number of scientific studies devoted to the study of the problems of certification of Russian civil servants. Based on the results of a retrospective analysis of the emergence and development of legal regulation of certification of military personnel and ranks of the state civil service of the Russian Empire, the author shows the evolution of the certification system of domestic civil servants, emphasizes its key role in the formation of professional personnel of the law enforcement service. In conclusion, the necessity of using the positive experience of attestation of officials of law enforcement agencies of Russia of the imperial period, taking into account the modern conditions of service by law enforcement officers of the Russian Federation, is justified.
False conclusion of a legal expert: ways to identify
Authors: Пичугин С.А.
Published: July 2024

Заведомо ложный вывод эксперта связывается исключительно с умыслом и предполагает совершение действий, направленных на искажение или игнорирование в процессе экспертного исследования действительных признаков и свойств исследуемого объекта, положенных в основу вывода. В этом смысле понятие ложного вывода необходимо отграничивать от понятия экспертных ошибок, возникновение которых никак не может быть связано с умышленными действиями. И в первом, и во втором случае недостоверность вывода связана с неверной интерпретацией выявленных признаков, которые могут быть проверены на предмет соответствия их объективной действительности. В случае установления факта недостоверного вывода может быть назначена повторная судебная экспертиза, которая подвергнет анализу те признаки, которые ранее оценивались экспертом и на основании которых в первичной экспертизе был сформулирован неверный вывод. В статье рассматриваются ключевые положения заключений экспертов, которые могут наиболее часто содержать в себе ложные (недостоверные) сведения, влияющие на обоснованность сформулированного вывода.

A deliberately false conclusion of an expert is associated exclusively with intent and involves the commission of actions aimed at distorting or ignoring in the process of expert research the actual features and properties of the object under study, which form the basis of the conclusion. In this sense, the concept of a false conclusion must be distinguished from the concept of expert errors, the occurrence of which can in no way be associated with intentional actions. In both the first and second cases, the unreliability of the conclusion is associated with an incorrect interpretation of the identified signs, which can be checked for compliance with their objective reality. If the fact of an unreliable conclusion of the forensic examination is established, a second examination may be appointed, which will analyze those signs that were previously evaluated by the expert, and based on which an incorrect conclusion was formulated in the primary examination. The article discusses the key provisions of expert opinions, which may most often contain false (inaccurate) information that affects the validity of the formulated conclusion.
Issues in China's Alignment with DEPA Data Cross-Border Regulation
Authors: Xu Ziyu
Published: July 2024

In the global digital governance game, the Digital Economy Partnership Agreement (DEPA) features innovative designs for digital trade, aligning with China's goals of internal data sharing openness and external breaking of "rule locks". However, China currently exhibits significant legislative differences in the "Data Issues" section of DEPA's fourth module, adopting stricter regulatory measures for cross-border data flow and data localization. Addressing DEPA's "principle & exception" regulatory standards, this paper argues that legitimate public policy objectives can be aligned with DEPA through proportionality principles, WTO general exception clauses, etc. By innovating customs supervision methods and following the path of the Hainan Free Trade Port trial, connecting international trade new rules, orderly implementing law enforcement systems and supporting mechanisms to ensure data security and orderly flow, China can gradually align with DEPA.
On the Artistic Features of Venting Feelings with 500 Words in Going from Capital to County Fengxian
Authors: Zhao Congyi
Published: July 2024

Venting Feelings with 500 Words in Going from Capital to County Fengxian is a renowned poem by the Tang Dynasty poet Du Fu. Highly esteemed through the ages, it holds a significant place in the history of Chinese literature. This poem employs a unique narrative style and a rich array of expressive techniques, showcasing the poets profound and sincere emotions. It creates a melancholic poetic atmosphere, with a fluctuating plot that profoundly impacts the reader. It epitomizes the essence of "somber and forceful" style, conveying deep and complex ideological connotations, making it a treasure of classical Chinese poetry. This paper analyzes the artistic features of the poem from the perspectives of narrative characteristics, poetic style, and the integration of emotion and ambition.
Research on NBA winning percentage prediction
Authors: Li Junyi , Xu Yuntian , Xu Zizheng , Pan Luca , Zhang Shubo
Published: July 2024

The National Basketball Association, also known as the NBA, is a professional basketball league composed of 30 professional teams in North America and one of the four major professional sports leagues in the United States. This research delves into predicting winning percentages in NBA games using a data-driven approach. It utilizes three years worth of comprehensive game data from 30 NBA teams (2014-2016), including actual outcomes and expert predictions. The study aims to acquire valuable teamwork and data research skills while contributing to sports analytics. Data preparation involves cleaning and selecting recent seasons for model training. The prediction model utilizes an iterative algorithm to estimate team strength coefficients based on game results. Though the model shows promise, its accuracy falls slightly short compared to bookies predictions. The research provides valuable insights for predicting NBA game outcomes and suggests avenues for improving the models accuracy through additional variables
A study of collision vertex and centrality of simulated Au-Au Ion collision data sets at 200GeV
Authors: Liu Jason , Yan Hebe , Zhou Jarvis
Published: July 2024

Investigation of the properties of Quark-Gluon Plasma (QGP) is conducted mainly through heavy ion collisions at high energy levels. In this article, we studied simulated collision data via open data from CERN, constructed a geometric model of particle colliders with both Cartesian and spherical coordinates representing the collider, devised a transformation formula between the two coordinate systems, and determined the vertex of separate events through the Hough transform. After the determination of the vertex, we also classified events according to the centrality constant v_0 which described the mode of collision. It is worth noting that the significant noise in the data cannot be sufficiently cancelled using our processing algorithms, thus impacting the overall accuracy of results. Though the accuracy of the vertex location does not meet expectations, future optimizations in coding would improve the calculation process. With the quantitative model accurately constructed, coupled with sufficiently clear data, the results of this study would be more evident.
Reproduction of two studies discussing bias: Jury selection bias and Diving2000 findings
Authors: Guo Jiaqi , Mai Yunhao , Wang Chen , Hao Chentong , Wang Zilong
Published: July 2024

Unveiling the truths behind biases in critical decision-making processes has been an enduring challenge in various domains. This reproduction study endeavors to shed light on two intriguing topics: potential bias against education in jury selection and nationalistic judging in the 2000 Olympic diving competition. By rigorously reproducing influential papers and employing sophisticated statistical tools like t-tests and permutation tests, we aim to contribute robust evidence to the ongoing discourse on bias. Our findings challenge prevailing theories by revealing no statistically significant evidence of biases against education in jury selection, providing invaluable insights for policy discussions. Simultaneously, our investigation into nationalistic judging uncovers the presence of bias among certain judges, highlighting the impact on highly competitive events outcomes. As we delve into these compelling issues, our study reinforces the critical importance of impartiality in decision-making processes, offering key contributions to the understanding of biases and their implications in various domains. Our primary purpose in this paper is to precisely test the reproducibility of the methods employed in the original influential papers. By carefully reproducing their analyses using advanced statistical techniques like t-tests and permutation tests, we find that the reproducibility of both papers is quite high.
House price prediction using machine learning for Ames, Iowa
Authors: Ye Qiongwei
Published: July 2024

The real estate sector is pivotal to economic growth and plays a substantial role in the global GDP. In this technologically advanced age, the adoption of machine learning for accurate house price prediction is crucial. These models optimize decision-making for homeowners, sellers, and investors alike. This study represents a comprehensive exploration into the field of house price prediction within the context of Ames, Iowa, United States. The primary objective of this research is to construct a reliable and highly accurate predictive model, empowering individuals to estimate property values with unprecedented precision. The research encompasses three different machine learning algorithms: linear regression, random forest, and XGBoost. The use of the dataset from the reputed website helps improve the reliability of the result. Furthermore, the investigation extends to a detailed examination of the multifaceted determinants exerting a profound influence on house prices in the dynamic Ames real estate landscape, and determined that the factor that will influence the house price most is the total area of the house. Among all models, XGBoost produces the best result, which achieved an R-square of 0.8803. Moreover, the importance of each feature is also analyized using the feature ranking algorithm in random forest, showing that the overall quantity of the house, the living area of the house, and the total area of the basement are the top three factors that influence the house price most.
Analysis of implementation for advanced tools in sales prediction
Authors: Huang Yuan
Published: July 2024

As a matter of fact, since the 20th century, companies have started using statistical methods to predict the future sales of their product in order to adjust sales strategy as well as optimize the unitality of the corporations. In recent years, with the help of machine learning thanks to the rapid development of computation ability, firms could predict the future sales of their products with a much higher precision based on the state-of-art scenarios and models. To be specific, machine learning models such as Decision Trees help firms predict the trend of their sales with high efficiency. On this basis and with this in mind, this study will investigate the implementation and application of advanced tools in sales prediction. With the appearance of neuron networks such as LSTM-RNN, firms are now able to predict with high accuracy. According to the analysis, this study concludes the result of research carried out on Decision trees, GBDTs, as well as RNNs. At the same time, the current limitations and prospects are demonstrated and illustrated.
Deep learning model-driven financial risk prediction and analysis
Authors: Yang Tianyi , Li Ang , Xu Jiahao , Su Guangze , Wang Jiufan
Published: July 2024

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.
An introduction to mathematical algorithms and Artificial Intelligence
Authors: Yuan Yuchen , Liu Yiran
Published: July 2024

Artificial Intelligence (AI), as a hot topic in today's science and technology, has penetrated into every aspect of our lives. The relationship between AI and mathematics and algorithms has become increasingly close. Mathematics, as a basic science, provides the theoretical basis for AI algorithms and models and is an essential part of the development of AI. The algorithm is an element that acts as a bridge between mathematics and AI and is a series of steps taken to solve a particular problem or reach a clear result [1]. The purpose of this paper is to explore the application and impact of mathematics and algorithms in the field of AI, and to explore how AI can bring about far-reaching changes in our society and human development.
Comparing quantum convolutional and classical convolutional architectures
Authors: Ren Zhiyi
Published: July 2024

While machine learning technology evolves fast with the development of computing power and new algorithms, integrating theories of quantum mechanics gives novel opportunities to it. This paper focuses on the comparative analysis of Quantum Convolutional Neural Networks (QCNN) and traditional Convolutional Neural Networks (CNN), which are important techniques in the domain of machine learning, especially image processing. By doing a literature review and analysis, the comparison includes performance differences, application potential, and inherent challenges of QCNN. Although QCNN is comparably novel, it does exhibit superiority over CNN in specific cases theoretically like high dimensional and complex datasets. QCNN currently is facing problems due to hardware constraints and immaturity in quantum computing and doesn't show superior performance when running on actual quantum machines. With the development of quantum computing and hardware, QCNN has a strong potential for use in machine learning. The research compares the current performance of QCNN and CNN to make suggestions about applying them in real cases and guide the path for future research.
A convolutional transformer for EEG sleep stage classification
Authors: Li Zhuoran
Published: July 2024

Sleep is one of the most critical physiological processes in life, with profound implications for human health and functioning. Sleep stage classification is a fundamental task in sleep research, aiding in the understanding of the various stages of sleep and playing a crucial role in medical and health monitoring. Traditional Convolutional Neural Networks (CNNs) have limitations in handling long-range dependencies, focusing only on information within specific windows. For example, missing transitions between Rapid Eye Movement (REM) sleep stages and Non-Rapid Eye Movement (NREM) sleep stages. RNN may not effectively capture local features. For instance, RNNs might not effectively capture short awakenings or microarousals during sleep, which are crucial for accurate sleep stage classification. Subsequently, one limitation of transformers is their inefficiency in handling sequential data with long-range dependencies. To address these issues, we propose the Conformer model, which combines convolutional neural networks and transformers to efficiently capture both local and global dependencies in an electroencephalogram sequence. Conformer models employ various parameter reduction strategies, making the model more lightweight and suitable for embedded devices and low-resource environments. To validate our approach, we conducted comprehensive experiments using a widely collected single-channel EEG dataset and evaluated the model's performance. When compared to traditional methods and other deep learning approaches, our model demonstrates a significant advantage in capturing subtle features and patterns associated with different sleep stages.
Deep learning methods for cancer recognition
Authors: Shen Yiwei
Published: July 2024

Modern medical technology has been advancing continuously, and deep learning, a potent machine learning technique, demonstrates significant potential in the domain of cancer identification and diagnosis. This paper through methods of lieterature review, examines the distinctive abilities and advancements of deep learning in the recognition, classification, and segmentation of breast and lung cancer images. Deep learning has proven to be very suitable for medical image analysis and has the ability to aid and even autonomously make decisions in many stages of cancer diagnosis, including history taking, imaging, and biopsy. Furthermore, it possesses the capability to precisely identify, categorize, and divide photos pertaining to breast and lung cancer. Additionally, it has the capacity to identify and predict tumors based on gene expression profiles, so enhancing the efficacy of contemporary medicine in the identification and prevention of cancer. These findings have significant implications for cancer detection and treatment in contemporary healthcare, offering clinicians a potent tool to enhance diagnostic precision and treatment effectiveness.
Revolutionizing financial management: The impact of algorithmic methodologies
Authors: Chen Jiani
Published: July 2024

This paper explores the transformative role of algorithmic methodologies in modern financial management. It delves into the historical evolution of financial accounting, transitioning from traditional practices to advanced algorithmic approaches. The focus is on how algorithms enhance data analysis and reporting, forecasting accuracy, risk assessment and management, and strategic financial planning. The paper discusses the integration of sophisticated tools like machine learning, data analytics, and predictive modeling in financial decision-making processes. It highlights the benefits of these methodologies in terms of efficiency, accuracy, and adaptability to changing market conditions. Additionally, the paper examines the role of algorithms in addressing sustainability and ethical considerations within financial strategies. The conclusion underscores the critical role of algorithmic methodologies in shaping the future of financial management.
Navigating the currents of natural language processing: A comprehensive overview of modern techniques and applications
Authors: Zhou Yimin
Published: July 2024

Natural Language Processing (NLP) has been a trend over the years, and many young researchers are inspired to study this field. In this paper, the author conducts a comprehensive overview of NLP from rule-based systems to machine learning techniques. Besides, the core principles of NLP and the impact of deep learning architectures, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers, are explored. Additionally, this paper delves into significant NLP tasks, for instance, text classification, sentiment analysis, and machine translation. At the same time, the ethical considerations and challenges in NLP, including issues related to data bias and privacy, are also discussed. As a result, this research concludes that NLP is versatile and valuable, but it also needs to overcome challenges related to language ambiguity, data bias, privacy concerns, and so on. The ethical use of AI is necessary in order to ensure progress in NLP in the future.
Dynamic gesture recognition model based on FMCW radar and multidimensional feature fusion with dual-channel CNN and GRU
Authors: Ye Xiaowei
Published: July 2024

With the development of computers, gestures have also become a commonly used human-computer interaction, so it becomes important to recognize gestures correctly. The objective of this paper is to propose a deep learning model to improve the accuracy of low performance radar in dynamic gesture recognition.We use a dataset acquired by CAL60S244-AB radar to classify ten gestures. The model consists of a dual-channel convolutional neural network(CNN), Gate Recurrent Unit(GRU) and a classification module. The inputs to the model are feature vectors fused in four dimensions of distance, speed, receiving antenna channel and time, from which the dual-channel CNN can extract shallow and deep spatial information, and GRU can extract temporal information, and the combination of these two parts makes the model's classification accuracy improve. Through experiments, the accuracy of the model on the dataset reaches 95.1%, and the results show that the model proposed in this paper can improve the accuracy of dynamic gesture classification for low-precision radar, which verifies the effectiveness of the model.
Predicting cryptocurrency price trends using artificial intelligence methods
Authors: Zhang Xu
Published: July 2024

This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.
See you later: roost fidelity of the “Boomerang Bats”, Artibeus lituratus (Olfers, 1818) (Mammalia, Chiroptera)
Authors: Batista Carolina Blefari , Pereira Alan Deivid , Figueiredo Guilherme de Toledo , Lima Isaac Passos de
Published: July 2024

Animals exhibit sociability behaviors and spatial use patterns that are important for species survival. Bats are animals that exhibit complex patterns of aggregation of individuals, which can vary according to sex and age. Aggregation can be explained by active or passive mechanisms. In this behavioural note, we report a group of female Artibeus lituratus in the reproductive stage that exhibits aggregation behavior supporting the active mechanism hypothesis. The females returned to the same roost, for two reproductive season, maintaining the aggregation with the same individuals with which the social bonds were already established.
Analisis Pentingnya Pengelolaan Kurikulum di Sekolah Dasar
Authors: Prastiwi Maulia Widya , Nur Azizah Lintang , Kurniawan Zahri , Apriliana Gita , Susanto Bahtiyar Heru
Published: July 2024

Improving curriculum management standards must continually be pursued to achieve optimal curriculum management goals, particularly at the elementary school level. This study aims to explore the importance of curriculum management in elementary schools. The method used is qualitative with a literature review approach involving analysis of literature from various sources such as books, articles, and other documents. Miles and Huberman's analysis is employed to analyze qualitative data, including data reduction, data display, and conclusion drawing. The research findings indicate that curriculum management plays a crucial role in supporting educational sustainability. However, in practice, curriculum management faces various challenges, both from educators and students. Ongoing efforts are being made to ensure smooth curriculum implementation and minimize potential obstacles throughout the process
Detection of Lung Cancer Malignancy Types on CT-Scan Using the Convolutional Neural Network Method at PHC Hospital Surabaya
Authors: Kurniawan Kholilul Rohman , Setyati Endang , Chandra Francisca Haryanti
Published: July 2024

There are many uses for digital image processing, ranging from tumor and cancer detection in the body to reading blood cells. The rate of lung cancer represents about 13.27% of the total cancer cases, and this shows that lung cancer is the main type of disease in men. Lung cancer is one of the most dangerous and life-threatening diseases in the world. In Indonesia, lung cancer is more often detected when patients are at an advanced stage. Therefore, in this paper, we applied Deep Learning to solve a lung cancer malignant detection system; it is used to detect and classify nodule areas. So that lung cancer detection can be obtained with accurate results. This paper explains the working system for detecting lung cancer malignancies using a Convolutional Neural Network (CNN) and the model architecture for training the dataset using the EfficientNet model. This study collected 800 lung CT images from PHC Surabaya Hospital in DICOM format. A total of 13 layers with EfficientNet architecture and classification layers for each type of cancer class have been used in the model. The experimental results of the model achieved satisfactory results with an accuracy of 99.46%, with a maximum epoch of 30 and a mini-batch size of 128.
NGHIÊN CỨU NGUYÊN NHÂN THẤM NƯỚC, THẤM ION CLO VÀ GIẢI PHÁP SỬA CHỮA CHO CÁC BỂ CHỨA NƯỚC SẠCH
Authors: Vũ Quốc Vương
Published: July 2024

Hiện nay các bể chứa nước sạch dùng chủ yếu kết cấu bê tông cốt thép. Với kết cấu bê tông cốt thép sau một thời gian sử dụng có hiện tượng thấm nước, thấm iol Cl- dẫn đến công trình bị xuống cấp, giảm tuổi thọ, đặc biệt hiện tượng ăn mòn, xâm thực bê tông làm giảm chất lượng của nguồn nước cung cấp gây hậu quả nghiêm trọng đến sức khỏe người dân. Bài báo tiến hành khảo sát một số bể chứa nước ngọt để tìm ra nguyên nhân thấm nước, thấm iol Cl-, để từ đó đưa ra giải pháp sửa chữa hư hỏng của các bể chứa nước sạch.
Control of a Wind Turbine based on DFIG by Improved Direct Torque Control using Fuzzy Logic
Authors: Sayeh Karim Fathi , Tamalouzt Salah , Ziane Djamel , Sahri Younes , Deffaf Brahim , Lalouni Belaid Sofia
Published: July 2024

The capacity of doubly-fed induction generators (DFIGs) to regulate both active and reactive power makes them a well-liked option for wind turbines. Direct Torque Control (DTC) is a commonly employed method for managing DFIGs, but it has limitations such as torque and flux ripples, which can reduce efficiency and cause stress on the turbine components. This study proposes a novel approach that combines DTC with a fuzzy logic controller (FLC) to address these limitations. Fuzzy logic control is a rule-based technique that can handle complex systems and nonlinearities. By incorporating FLC into DTC, the proposed Fuzzy Logic-based DTC (FH-DTC) seeks to enhance the WT-DFIG system's overall performance. MATLAB/Simulink simulations are used to evaluate the effectiveness of FH-DTC. The results are expected to demonstrate that FH-DTC significantly reduces torque and flux ripples compared to conventional DTC, leading to improved efficiency, reduced stress on turbine components, and ultimately, better overall performance of the WT-DFIG system.
Clinical study of chemotherapy-related cognitive impairment in patients with non-Hodgkin lymphoma
Authors: Wang Qiang-Li , Xu Hai-Yan , Wang Yi , Wang Yin-Ling , Lin Pei-Nan , Chen Zhong-Lei
Published: July 2024

BACKGROUND

Chemotherapy for malignant tumors can cause brain changes and cognitive impairment, leading to chemotherapy-induced cognitive impairment (CICI). Current research on CICI has focused on breast cancer and Hodgkin’s lymphoma. Whether patients with non-Hodgkin’s lymphoma (NHL) undergoing chemotherapy have cognitive impairment has not been fully investigated.

AIM

To investigate whether NHL patients undergoing chemotherapy had cognitive impairments.

METHODS

The study included 100 NHL patients who were required to complete a comprehensive psychological scale including the Brief Psychiatric Examination Scale (MMSE) at two time points: before chemotherapy and within 2 wk of two chemotherapy courses. A language proficiency test (VFT), Symbol Number Pattern Test (SDMT), Clock Drawing Test (CDT), Abbreviated Daily Cognition Scale (ECog-12), Prospective and Retrospective Memory Questionnaire, and Karnofsky Performance Status were used to assess cognitive changes before and after chemotherapy.

RESULTS

The VFT scores for before treatment (BT) and after treatment (AT) groups were 45.20 ± 15.62, and 42.30 ± 17.53, respectively (t -2.16, P < 0.05). The CDT scores were 8 (3.5-9.25) for BT and 7 (2.5-9) for AT groups (Z -2.1, P < 0.05). Retrospective memory scores were 13.5 (9-17) for BT and 15 (13-18) for AT (Z -3.7, P < 0.01). The prospective memory scores were 12.63 ± 3.61 for BT and 14.43 ± 4.32 for AT groups (t -4.97, P < 0.01). The ECog-12 scores were 1.71 (1.25-2.08) for BT and 1.79 (1.42-2.08) for AT groups (Z -2.84, P < 0.01). The SDMT and MMSE values did not show a significant difference between BT and AT groups.

CONCLUSION

Compared to the AT group, the BT group showed impaired language, memory, and subjective cognition, but objective cognition and execution were not significantly affected.