bluuebunny/crossref_metadata_2025 huggingface dataset:
Visible-Near Infrared–Short-Wave Infrared Spectroscopy and Mineral Mapping of Hydrothermal Alteration Zones at the Brejuí W-Mo Skarn Deposit, Seridó Mobile Belt, Borborema Province, Brazil
Authors: Corrêa da Costa Manoel Augusto , Carneiro Naleto João Luís , Perrotta Mônica Mazzini , Soares Monteiro Lena Virgínia , de Souza Filho Carlos Roberto
Published: July 2024

The Brejuí W-Mo skarn deposit is the main scheelite deposit of the Seridó tungsten province (Borborema province, NE Brazil). It constitutes the largest Brazilian W ore reserve. The orebodies are hosted in the metasedimentary Jucurutu Formation of the Seridó Group (650–610 Ma), close to the margin of the Neoproterozoic Acari pluton (Brasiliano orogeny). Skarns include prograde and retrograde mineral assemblages. Infrared spectral analysis shows that the skarns mainly reflect retrograde mineral assemblages, formed in three different hydrothermal alteration stages with overprinting. Mineral phases spectrally detected include (1) vesuvianite, actinolite, and phlogopite (alteration stage 1), (2) epidote, prehnite, and illite (alteration stage 2), and (3) laumontite, montmorillonite, chlorite, and gypsum (alteration stage 3; main ore zone). Phlogopite chloritization and actinolite recrystallization were observed in the main W-Mo skarn orebodies. Chloritization is marked by a displacement in the Fe-OH–related absorption wavelength (2,246.5–2,250.5 nm). A spectral index using spectral mixtures of laumontite + montmorillonite + actinolite is proposed here to map mineralized skarn layers (WO3 + Mo ≥ 0.1%) in drill cores. It was used to vector to richer mineralized bodies of the Brejuí deposit successfully and may be applied to similar skarn deposits with the same aim.
Autorregulación del aprendizaje (ARA) en la educación superior: variables que inciden en su desarrollo
Authors: Lara-Sotomayor Juan Eduardo , Suárez-Erazo Rosa Tatiana , Carrera-Gallardo Andrea Estefanía
Published: July 2024

La autorregulación como proceso autónomo de aprendizaje en estudiantes universitarios es crucial y de alta importancia en los actuales momentos en donde los indicadores de rendimiento académico permiten la aprobación y promoción de este. El objetivo principal del artículo fue:  describir como en el transcurso del tiempo fue evolucionando el concepto de Autorregulación del Aprendizaje (ARA) y la revisión sistemática de las variables que inciden en su desarrollo como la motivación académica y los niveles de comprensión lectora, para lo cual se abordarán autores que tratan desde su perspectiva los elementos que forman parte del proceso de autorregulación en el trabajo autónomo de aprendizaje. Basados en la revisión bibliográfica, es factible decir que las variables con mayor incidencia en la ARA en la Educación Superior son: mediación pedagógica, andamiaje, tutorías y ayudas de parte del docente hacia el estudiante; por lo tanto, ésta se constituye en predictor del rendimiento académico.
Metodología de enseñanza del compostaje de residuos sólidos orgánicos
Authors: Chinachi Sotalin Carlos , Mayorga-Román Mario Gonzalo , Peñuela-Jara Diana Raquel
Published: July 2024

La investigación tiene por objetivo diseñar una metodología para mejorar la enseñanza del compostaje de residuos sólidos orgánicos a estudiantes de tercer año de bachillerato técnico. La metodología que se empleó para el desarrollo de esta investigación tuvo un enfoque cualitativo y nivel descriptivo; por medio de un cuestionario estructurado se encuestaron a 17 estudiantes que tomaron la asignatura agropecuaria. Los resultados mostraron que la forma de enseñar del docente tiene varios aspectos positivos, pero deben ser fortalecidos para lograr la inclusión de todos los estudiantes. Por esta razón la propuesta metodológica está enfocada a la mejora de la enseñanza del compostaje de residuos sólidos orgánicos y formada por cuatro fases: fundamentos teóricos, ciclo del compostaje, recursos multimedia y proyectos colaborativos. Cada una de estas fases integran al aprendizaje interactivo, participación activa, enfoque multidisciplinar y la evaluación formativa como estrategias pedagógicas para su desarrollo. Así mismo la activación de conocimientos previos, retroalimentación permanente y la evaluación como proceso de enseñanza y mejora son los ejes transversales de toda la metodología. La metodología busca mejorar la comprensión de los conceptos de compostaje y fomentar una conciencia ambiental entre los estudiantes, contribuyendo a la formación de ciudadanos más responsables y comprometidos con la sostenibilidad ambiental.
RANCANG BANGUN SISTEM PENDINGIN DAN PENCUCI PADA MESIN PENGOLAH BIODIESEL DENGAN BAHAN BAKU MINYAK JELANTAH
Authors: Rahmawaty Made , Fauzan Muhammad Hilmy , Hendriko Hendriko
Published: July 2024

Terdapat tiga tahapan pengolahan biodiesel berbasis minyak jelantah yaitu proses esterifikasi, pendinginan dan pemurnian FAME, dan filtrasi biodiesel mentah. Waktu yang dibutuhkan untuk pemurnian FAME sekitar dua hingga enam jam karena harus mengikuti proses sedimentasi beberapa kali. Oleh karena itu, penelitian ini bertujuan mengembangkan mesin pemurnian biodiesel yang dilengkapi dengan sistem pendingin. Sistem pendingin bertujuan untuk mempercepat proses sedimentasi. Sistem pendingin menggunakan modul termoelektrik yang dipasang pada pipa dari tangki esterifikasi ke tangki pemurnian. Mesin ini juga dilengkapi dengan sistem kontrol dan katup yang digerakkan oleh motor untuk melakukan proses secara otomatis. Rancangan mesin pemurnian biodiesel telah berhasil dikembangkan dan telah mengikuti serangkaian pengujian. Hasil pengujian akurasi pengukur aliran menunjukkan bahwa mereka bekerja dengan benar. Penerapan sistem pendingin terbukti mengurangi waktu proses sedimentasi menjadi 20 menit yang pada akhirnya mempersingkat waktu proses pemurnian biodiesel. Selain itu, sistem kontrol memungkinkan alat beroperasi secara otomatis, sehingga mengurangi kebutuhan operator secara signifikan.
ANALISIS PENGARUH SURFACE ROUGHNESS, TEMPERATUR SUBSTRAT, DAN JENIS MATERIAL ABRASIVE TERHADAP BIAYA PROSES MENGGUNAKAN METODE TAGUCHI
Authors: Rachman Farizi , Mutiara Arun Sukma , Indrawan Rizal , Setiawan Tri Andi , Bisono Fipka , Purnomo Dhika Aditya , Hamzah Fais
Published: July 2024

Proses manufaktur finishing dan painting merupakan tahapan pengerjaan proyek di PT. Lintech Duta Pratama. Kesalahan dalam proses pengecatan dapat menyebabkan munculnya cacat pada produk. Produk yang cacat perlu diperbaiki sebelum dapat dikirim ke pelanggan. Perbaikan yang berulang-ulang menambah biaya proses dan menghambat pengiriman produk. Oleh karena itu, untuk mendapatkan kualitas produk yang bagus dan menekan biaya proses maka riset untuk mendapatkan setting parameter yang optimal dibutuhkan. Parameter proses yang dianalisis mencakup kekasaran permukaan, suhu substrat, dan jenis material abrasif. Metode Taguchi yang digunakan untuk perancangan eksperimen ini adalah matriks ortogonal . Berdasarkan hasil ekseprimen, didapatkan setting parameter proses yang optimal terhadap biaya proses yaitu Kekasaran permukaan pada level 1 dengan nilai 50 μm, suhu pada level 2 dengan angka 34 °C, dan jenis material abrasif pada level 2 menggunakan steel grit.
An ultra-wideband antenna design optimization for biomedical applications using artificial neural networks
Authors: Benmelouka Rania Ibtissam , Tighilt Yamina , Karaçuha Kamil , Hamida Abdelhak Ferhat , Slimani Asma
Published: July 2024

This paper presents an innovative method based on Artificial Neural Networks (ANN) for optimizing the design of microstrip antennas using HFSS software. The method leverages a dataset generated from comprehensive full-wave electromagnetic simulations to accurately predict antenna performance. In the ANN model,  coefficients of reflection as  input parameter, and five output parameters representing key geometrical variables of the antenna are utilized. The study demonstrates how ANNs effectively predict these variables, thereby enhancing design efficiency and significantly reducing the computational time required for antenna optimization. By providing a robust and efficient framework, the results highlight the promising potential of ANNs in optimizing antenna designs for diverse applications, including medical and wireless communication systems. Specifically, the proposed Ultra-Wideband (UWB) antenna operates over a frequency range from 5.17 GHz to 7.02 GHz and maintains an S11 value of -20.49 dB at 5.8 GHz within the Industrial, Scientific, and Medical (ISM) bands. This research contributes valuable insights into advancing electromagnetic performance through efficient antenna design methodologies.
Impacto da oxidação anódica na adesão de microrganismos e células sanguíneas: uma análise comparativa de tratamentos de superfície
Authors: Oliveira Marcelly Anny Gonçalves de , Souza Gelson Biscaia de , Santos Fabio André dos , Pinheiro Luis Antonio , Pinto Shelon Cristina Souza
Published: July 2024

Na osseointegração dos implantes dentários, a adesão das células sanguíneas marca o início do processo de cicatrização, enquanto a adesão de microrganismos pode indicar o potencial desenvolvimento de doenças peri-implantares. O tipo de tratamento de superfície aplicado exerce um papel crucial nessas dinâmicas. Neste estudo, foi avaliado implantes de titânio usinados sem tratamento de superfície, submetidos a duplo ataque ácido, duplo ataque ácido com camada de hidroxiapatita (HANano), e oxidação anódica, comparando sua capacidade de aderência de microrganismos e células sanguíneas. A oxidação anódica foi realizada em colaboração com o departamento de Física da UEPG. Para avaliar a aderência de Pseudomonas aeruginosa, foram realizadas análises de formação de biofilme e espectrofotometria. Quanto à aderência de células sanguíneas, procedimentos com etapas de aderência e análise por microscopia eletrônica de varredura. A análise estatística utilizando o teste não paramétrico de Kruskal-Wallis indicou ausência de diferença significativa entre os grupos em relação à adesão de microrganismos, porém, observou-se uma diferença significativa na adesão de células sanguíneas para o grupo submetido à oxidação anódica (p=0,04). Concluímos que não há diferença estatisticamente significativa entre os grupos em relação à adesão de microrganismos, mas há diferença significativa na adesão de células sanguíneas. Estudos futuros devem continuar a explorar e compreender essas complexas interações para promover o avanço no desenvolvimento de materiais mais seguros, eficazes e sustentáveis. O design de implantes com superfícies que promovam a osseointegração e inibam a adesão e crescimento bacteriano é de suma importância.
Peningkatan Kapabilitas Organisasi melalui Pendampingan Implementasi Gerakan Sumsel Mandiri Pangan (GSMP)
Authors: Atrika Iriani  , Bambang Suprianto
Published: July 2024

The South Sumatra Independent Food Movement (GSMP) Mentoring aims to improve the organizational capabilities of farmer communities in South Sumatra, through improving Organizational Capability. The main problem faced is the suboptimal field assistance related to plant and fish cultivation. A participatory approach is applied through socialization, formation of working groups, identification of needs, preparation of action plans, training, and technical assistance, as well as development of market access. The results of the study showed a significant increase in farmer welfare, marked by an increase in average income of 25%, an increase in food production of 20%, and strengthening local capacity. This program has also succeeded in increasing farmer access to agricultural technology and wider markets, in accordance with the theory of community empowerment, human capital, and market linkage. Recommendations for the sustainability of this program include the application of a participatory approach and ongoing assistance in other community empowerment initiatives
EVALUATING ERCP OUTCOMES: GENERAL ANESTHESIA VS. CONSCIOUS SEDATION IN A TERTIARY CARE SETTING
Authors: WAHEED A , BUTT TW , GUL MA , FAISAL M
Published: July 2024

Endoscopic Retrograde Cholangiopancreatography (ERCP) is a critical technique for diagnosing and treating conditions affecting the biliary and pancreatic ducts. Recently, there has been a shift towards employing general anesthesia instead of conscious sedation for these interventions to enhance comfort and efficiency, particularly in intricate cases. This investigation evaluates the decision-making process behind general anesthesia over conscious sedation and assesses their comparative effectiveness and associated complication rates. Methods: Conducted as a retrospective cohort study at Akhtar Saeed Medical and Dental College Lahore, Pakistan, in the period from November 2023 to April 2024, this research involved 380 ERCP patients equally divided into two groups: one administered general anesthesia and the other, conscious sedation. Comprehensive data collection included patient demographics, specific criteria before the procedure, procedural details, and outcomes. The analysis utilized SPSS version 26.0, applying independent t-tests and chi-square tests for continuous and categorical variables, respectively, alongside logistic regression to pinpoint procedural outcomes and complications predictors. Results: Both the general anesthesia and conscious sedation groups, averaging ages of 55.2 and 54.8 years, respectively, showed no significant disparity in BMI, ASA scores, or reasons for ERCP. Success rates of the procedures did not significantly differ, standing at 85% for general anesthesia and 83% for conscious sedation, with complication rates at 20% and 18%, respectively. Metrics such as post-procedure pain, hospitalization duration, and patient satisfaction remained statistically similar across both groups. Conclusions: The study concludes that general anesthesia for ERCP is just as effective and safe as conscious sedation, with both methods showing equivalent success and complication rates. These results advocate for a tailored, patient-centric approach in selecting anesthesia techniques for ERCP, ensuring no procedural effectiveness or patient safety compromise.
Improved random forest model for smoke detection and early warning based on sparrow search algorithm
Authors: Gao Jun , Jia Duo , Hu Nan
Published: July 2024

Smoke detection technology is of great significance in the field of fire safety, aiming at the timely detection of smoke released from fires or other combustion events to safeguard people's lives and properties. In this paper, a smart smoke alarm device is developed based on AI algorithm using UCL open source dataset, which is processed by AI recognition after collecting data through IoT to achieve accurate judgement of smoke situation. The dataset is divided by 6:4 ratio, and the training set confusion matrix shows that all 1159 smoke alarm tests are correctly predicted, of which 445 times predict no smoke and 714 times predict smoke, with an accuracy rate of 100%. The test set confusion matrix shows that only 1 out of 1158 tests was incorrectly predicted, of which 184 predicted no smoke, 312 predicted smoke, and 1 was misjudged as no smoke even though it should have been smoke, with an accuracy rate of 99.7%. The successful application of this technology provides a reliable guarantee for fire warning and demonstrates the great potential of AI in the field of security.
Research on software reliability enhancement methods based on iterative development
Authors: Huang Shuang
Published: July 2024

Iterative development is an agile software development methodology that speeds up the development process and increases the flexibility of software delivery. However, the current software requirements change extremely fast, which leads to its increasing uncertainty, and iterative development also faces the challenge of software reliability. The software reliability enhancement method based on iterative development is researched and discussed for the current situation of insufficient reliability in the traditional software development process. In traditional software development, one-time delivery pressure and overall design complexity often lead to software functional defects and instability, affecting software reliability. The iterative development method, on the other hand, improves software reliability by decomposing the software development process into multiple iterative cycles, each of which contains requirements analysis, design, coding, and testing, updating the software functionality in smaller increments, and gradually improving the software product. This study proposes a software reliability improvement methodology by exploring and analyzing the key factors related to reliability in the iterative development process. This dissertation can establish a reliable and high-quality software development process through the steps of meticulous requirements planning and management, iterative development and testing, automated testing and continuous integration, code quality control, defect management and continuous improvement, user feedback and user engagement, and continuous learning and technology enhancement.
Automating pouring process in precision casting
Authors: Feng Xiang
Published: July 2024

Standing at the intersection of industry 4.0, most traditional manufacturers, especially those produce non-standard parts, are still facing the challenges from multiple aspects on the implementation of automations, that indicates a significant and necessary step towards their upgrading. The potential performance improvement that could be brought by the automation may be continuingly squeezed as the increasement of complexity when dealing with the various targets. This article is extended by a general concept of implementing automation on the metal pouring process of precision casting, aims to explore an efficient and robust automation solution with the integration of human-robots collaboration and the adoption of computer science techniques. The implementation emphasizes the reduction of unnecessary complexities from each working step, the applied algorithms, such as Object Bounding, Greedy Strategy and Last-In-First-Out, have been correspondingly tailored based on the characteristics of its engaged working steps and illustrated by the flowcharts. Both the adaptability and practicability of the automation are expected to be enhanced with the principles of constructing easy-interactive frames, allowing a certain degree of human intervention, and proactively utilizing the matured algorithms.
Research on the interactive design of intelligent assisted speech software
Authors: Wang Tianyi
Published: July 2024

Nowadays, people need to make all kinds of speeches and presentations, both at school and at work. With the continuous development of science and technology, there is a lot of software on the market to assist people in making speeches. The aim of this article is to introduce the conception of a new type of presentation aid software and to analyse the development process of such software. Users from different backgrounds have evaluated the existing speech assistance software on the market, and this evaluation information will be based on the user reviews on the official website of the software download. Through the requirement analysis, the main problems and demands faced by the users in the presentation process will be identified. The software will be analysed and researched in terms of its design purpose, interface design, core functions, user experience and other aspects. The research conducted in this thesis provides users with new presentation assistance software that enables them to make more confident and smooth presentations. At the same time, this research provides useful references and discussions for the design and development of intelligent aids.
Interpretability analysis in transformers based on attention visualization
Authors: Guo Yuxi
Published: July 2024

Self-attention is the core idea of the transformer, a kind of special structure for models to understand sentences and texts. Transformer is growing fast, but the model's internal unknowns are still out of control. In this work, the research visualizes self-attention and observes those self-attentions in some transformers. Through observation, there are five types of self-attention connections. The research classifies them as Parallel self-attention head, Radioactive self-attention head, Homogeneous self-attention head, X-type self-attention head, and Compound self-attention head. The Parallel self-attention head is the most important. The combination of different types will affect the performance of the transformer. Visualizations can indicate the location of different types. The results show that some homogeneous heads should be more varied in that case the model will perform better. A new training method is called local head training method, and the local training method may be useful during training transformer. The purpose of this study is to lay the foundation for model biology, to take other perspectives to understand transformers, and to fine-tune training methods.
Management and prediction of employee turnover in enterprises based on big data analytics and machine learning
Authors: Zhang Jiaxuan , Cao Zinan , Qu Haichen , Wang Meng
Published: July 2024

In the context of aging population and social transformation, employees also put forward higher demands on corporate culture and values, which also increases the risk of employee turnover. This paper explores the correlation between various indicators and leavers through statistical analysis and visualization of various data indicators and the distribution of leavers and non-leavers on various indicators by using whether they left the company as a classification criterion. In addition, this paper also carries out Pearson correlation analysis for each indicator and draws a correlation heat map to quantitatively explore the correlation between indicators. In order to predict whether employees will leave the company, this paper uses random forest model, support vector machine model, KNN model, plain Bayesian model and logistic regression model for training and testing. The results show that the best prediction in terms of employee turnover is the Random Forest model with a prediction accuracy of 98.8%. This was followed by the Support Vector Machine model with an accuracy of 95.1%. In addition, the KNN model also achieved an accuracy of 94.8%. Ordinary Bayesian model and logistic regression model have lower accuracy rates of 80.4% and 77.2% only. This is of great significance for enterprises to realize sustainable development, and is worthy of in-depth study and practice by enterprise managers.
Exploring the impact of HCI on smart buildings: A comprehensive literature review on user experience and sustainability
Authors: Zhou Jingwen
Published: July 2024

This paper provides insights into the application of human-computer interaction (HCI) principles in the field of smart building design, particularly their impact on improving occupant comfort, building energy efficiency, and the user interaction experience. The research context focuses on advances in HCI and the importance of smart buildings in enhancing quality of life and sustainability. The research theme focuses on how HCI principles can be utilized to improve the user interface design of smart buildings in order to promote more effective user-building system interactions. Through an extensive review of existing literature in the area of HCI and smart buildings, including recent research papers, case study reports, and technical overviews, this study concentrates on analyzing examples of HCI applications in smart lighting, temperature control systems, and energy management. Through this comprehensive literature review, this paper aims to illustrate how HCI principles can provide theoretical and practical guidance for the design of more humanized, interactive and environmentally friendly smart buildings, thereby providing insights and recommendations for future smart building design and research.
Weather forecast image classification based on KNN, CNN, and VGG19
Authors: Meng Chunzhu
Published: July 2024

Image classification is an important technology in the field of computer vision, with the main objective of categorizing input images into different classes. It is used in many areas, such as weather forecasting. The significance of weather forecast image classification spans several areas, including enhancing weather forecast accuracy, aiding agriculture, optimizing the energy sector, ensuring transportation safety, and informing urban planning and construction. In this study, the author investigates the precision and efficiency of three machine learning algorithmsConvolutional Neural Network (CNN), K-Nearest Neighbor (KNN), and Visual Geometry Group 19 (VGG19)in classifying weather forecast images. The research meticulously elucidates the methodologies underlying each algorithm, outlining their distinct architectures, training processes, and feature extraction mechanisms. The research highlights that the self-built CNN model excels in accuracy and efficiency, while VGG19 achieves higher accuracy (93%) with longer training time. In contrast, the KNN model shows shorter training time (32.50 seconds) with slightly lower accuracy (80%).
Mapping and animating Star Wars Millennium Falcon hyperspace jumping base on Blender
Authors: Qi Pengcheng
Published: July 2024

Science fiction movies are fictional, which means that most of the elements portrayed are not real but rather imaginative. For most space operas, writers often devise creative solutions to overcome the limitations imposed by the speed of light, such as the concept of hyperspace in "Star Wars" to bypass speed restrictions. This study aimed to celebrate the 47th Force Day in 2024 by using the non-commercial software Blender to recreate the iconic Disney version of hyperspace jumping visual effects. The research effort included reproducing both the hyperspace flyby scene and the scene of a spaceship jumping out of hyperspace, showcasing stunning visual effects achieved through Blender's rendering and special effects capabilities. In the context of the Disney version of hyperspace jumping featuring a swirling blue tunnel, the research team meticulously modeled and rendered the spaceship's traversal through hyperspace within Blender. Additionally, the paper employed special effects techniques to recreate the spectacular moment of the spaceship emerging from hyperspace, capturing the visual allure of Disney's interpretation of hyperspace jumping. The results demonstrate the feasibility of this endeavor, albeit with potential technical flaws.
Research on the application of Internet of Things (IoT) in supply chain management
Authors: Cao Jiarui
Published: July 2024

As an emerging technology, the Internet of Things has attracted widespread attention as soon as it was proposed and has been widely used in various fields. Internet of Things technology provides a unique advantage for supply chain management. It can intelligently manage the supply chain and, at the same time, transmit information to customers in real-time, allowing customers to quickly obtain information about goods, making the entire supply chain more efficient. Chain management has become more scientific and reasonable. This article first gives a brief introduction to the Internet of Things, and then conducts an in-depth analysis and discussion of its application in supply chain management, hoping to promote the development of SCM in our country.
Semantic-based neural network repair for AI models in dynamic environments
Authors: Chen Xiaoyu
Published: July 2024

This paper addresses a critical challenge in the field of artificial intelligence (AI) and deep learning (DL) - the debugging and repair of AI models within dynamic environments. The rapid development of frameworks like TensorFlow or PyTorch can introduce version incompatibilities, potentially breaking existing code. To tackle this issue, it can be seen that a semantic-based neural network repair method that leverages an existing semantics called ExAIS is proposed, defined in the Prolog logic programming language. This method identifies architectural and parameter errors in AI models and generates debug messages to facilitate the repair process. Key contributions of this paper include introducing a novel semantic-based AI model repair method capable of suggesting multiple changes to rectify inefficiencies in AI models. We evaluate the effectiveness of our approach in a real-world context by collecting a set of real bugs from AI developers. The background section provides an overview of AI framework testing, machine learning, neural architecture search (NAS), and deep learning. The methodology section details the repair method based on ExAIS semantics, covering error identification and repair suggestions as the two main steps. The evaluation section showcases the repair effectiveness on automatically generated and manually written models, emphasizing the applicability and efficiency of the approach.
The application of Verilog in the development of casual games
Authors: Yan Ziqi
Published: July 2024

Verilog is a hardware description language (HDL) that is widely used in digital circuit design and simulation. Its development is closely related to computer science and electrical engineering. Verilog gained popularity in the early 1980s as digital circuit designs became increasingly complex, requiring more efficient circuit design and verification tools. At the same time, rapid advances in computer hardware also stimulated the demand for digital circuit design languages. Furthermore, the popularity and adoption of Verilog highlight the growing necessity for digitisation, automation, and intelligence in modern society. As digital technology continues to advance across various industries, the need for effective and dependable digital circuit design languages is also increasing. This paper delves into the complex process of recreating the timeless arcade classic Pac-Man on the Spartan 3E FPGA platform using hardware description and digital circuit techniques and the Verilog programming language. Through a comprehensive review of existing literature and research, this study investigates the fusion of traditional game design principles with state-of-the-art hardware programming methods, demonstrating the seamless integration of software-driven game mechanics with hardware-based implementation. Through careful design and coding strategies, Pac-Man's basic functionality, such as maze traversal, ghost AI, and pellet consumption, is faithfully replicated using Verilog modules customized for the Spartan 3E FPGA board. By bridging the realms of game development and hardware engineering, this paper not only showcases the versatility of Field Programmable Gate Array (FPGA) technology in entertainment applications but also underscores the interdisciplinary nature of modern computing endeavours.
Development and validation of a deep learning-based model for diabetes diagnosis
Authors: Chen Zhixin
Published: July 2024

Diabetes is a systemic disease caused by hyperglycemia, and the number of people with diabetes worldwide may reach 1.31 billion by 2050. The traditional diagnosis of early diabetes is difficult and inaccurate. Computer-aided automatic method has been widely applied in diabetes diagnosis at early stage. In order to realize automatic diagnosis of diabetes, this study proposed a deep neural network-based model for diabetes diagnosis, and its performance was compared with those of other classical machine leaning models. After data cleaning, Synthetic Minority Over-sampling Technique (SMOTE) sampling and feature selecting of the diabetes dataset, multiple models were applied to the features such as Body Mass Index (BMI), Age and other features for prediction. The results showed that Deep Neural Network (DNN) is the best diagnostic solution for diabetes with excellent performance with an accuracy of 99.5%. More specific features will be considered to ensure the accuracy and credibility of clinical diagnosis of diabetes.
Enterprise cloud resource optimization and management based on cloud operations
Authors: Wu Binbin , Gong Yulu , Zheng Haotian , Zhang Yifan , Huang Jiaxin , Xu Jingyu
Published: July 2024

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.
A survey of the application and technical improvement of the multi-armed bandit
Authors: Tong Ruoyi
Published: July 2024

In recent years, the multi-armed bandit (MAB) model has been widely used and has shown excellent performance. This article provides an overview of the applications and technical improvements of the multi-armed bandit machine problem. First, an overview of the multi-armed bandit problem is presented, including the explanation of a general modeling approach and several existing common algorithms, such as -greedy, ETC, UCB, and Thompson sampling. Then, the real-life applications of the multi-armed bandit model are explored, covering the fields of recommender systems, healthcare, and finance. Then, some improved algorithms and models are summarized by addressing the problems encountered in different application domains, including the multi-armed bandit considering multiple objectives, the mortal multi-armed bandits, the multi-armed bandit considering contextual side information, combinatorial multi-armed bandits. Finally, the characteristics, trends of changes among different algorithms, and applicable scenarios are summarized and discussed.
The prospect and metaphysical analysis of conscious artificial intelligence
Authors: Lyu Nian
Published: July 2024

Artificial intelligence, also known as AI, has led the trend of evolution in the past and future decades, and the potential of consciousness artificial intelligence emerges as a renovative field to address. The computer machine aims to process repetitive and tedious tasks for humans since its concept was first developed. Whether AI is conscious does not raise unprecedented discussion before the appearance of the concept of machine learning. After it appears, the machine, instead of merely passing in input and generating output, is able to learn while processing the information, which resembles a human's learning process. Therefore, this paper delves into the complex terrain of AI to explore the theoretical possibility of endowing machines with consciousness and addresses the future concerns and potentials of AI. Illustrating through the aspects of ethical concerns, metaphysical perspectives on consciousness, and the latest advancements in AI, the study critically examines whether machines can possess a consciousness similar to human understanding.
The impact of artificial intelligence represented by ChatGPT on UI design
Authors: Zhang Ruoxiao
Published: July 2024

The rise of Artificial Intelligence (AI), notably represented by ChatGPT, has greatly influenced User Interface (UI) design. ChatGPT, an advanced language model, aids designers by suggesting ideas, generating interface images, providing feedback, and simulating user interactions. Its integration into UI design processes has improved efficiency, allowing designers to explore more design options quickly. Additionally, ChatGPT facilitates user research by simulating conversations with potential users, gathering valuable feedback on user experience. However, there are also a lot of challenges, even risks, of using artificial intelligence to help designers, including maintaining design quality, protecting creativity, and ensuring designs remain user-centered. Moreover, a lot of people worry that with the rapid development of ChatGPT, many UI designers will be out of work in the future. This article discusses the influence of artificial intelligence represented by ChatGPT on UI design, including the advantages and challenges of AI in UI design. There is no doubt that as AI technology evolves, its role in UI design will expand rapidly, reshaping the digital experience in the future. But the question of whether AI will cause a lot of designers to lose jobs needs time to give us an answer.
Research on the application and prospect of RFID technology
Authors: Sun Lujie
Published: July 2024

As Radio Frequency Identification (RFID) has the advantages of fast scanning speed, small size, large data storage, high security, and other technologies, it is widely used in various fields to upgrade working efficiency. Meanwhile, as one of the research topics of the Internet of Things (IoT), Radio Frequency Identification also provides the possibility for the development and application scope of IoT. Based on the application principle of Radio Frequency Identification technology, this paper discusses the application of Radio Frequency Identification in the two fields of intelligent libraries and intelligent logistics through literature review and theoretical analysis. Through the analysis of the current situation of RFID application in the library management and logistics industry, this paper discusses the advantages and shortcomings of radio frequency identification technology, and according to the existing problems the future direction of development of the corresponding improvement proposals to promote the further improvement of the application of radio frequency identification technology.
Analysis of artificial intelligence models for the smart home industry
Authors: Chen Yufeng , Ren Yuheng
Published: July 2024

Since ChatGPT's release, large AI models have gained significant attention, particularly in the integration with Smart Home 3.0, marking a new research direction. This paper reviews cutting-edge research on the intersection of smart homes and large AI models, highlighting challenges and trends. We focus on data, models, and execution to explore the advancement of smart home platforms globally. We discuss data collection and feature research progress within smart homes, using intelligent voice assistants like Amazon Alexa and Google Bard as examples. We examine the applications, potentials, and challenges of AI models in smart homes and offer insights into future applications of large AI models in this field.
Design and implementation of real-time log analysis based on ELK
Authors: Lyu Bin
Published: July 2024

With the rapid development of the IT industry, the integrated solution of real-time log analysis has become a member of the new generation of IT operation and maintenance structure. The traditional manual log analysis system is no longer suitable for the rapid development of the operation and maintenance system. The log analysis software commonly used in enterprises includes Elasticsearch, Logstash, and Kibana systems. The advantage is that it is free and open source.
"Design and implementation of real-time log analysis based on ELK" was born for system maintenance and developers to understand server software and hardware information through logs, and check the cumbersome operations in the configuration process. Because frequent analysis of the log can understand the load of the server, performance security, so as to take timely measures to correct errors. If you manage dozens or hundreds of servers, there is no centralized log management system to collect and summarize the logs on all servers, which causes great difficulties for operation and maintenance managers. "Design and implementation of real-time log analysis based on ELK" uses Elasticsearch, Logstash, and Kibana to achieve unified collection and classification of server logs, which can solve the defects of traditional log analysis platforms, improve the efficiency of server application positioning problems, and help enterprises solve data center problems running problems of related applications.
Improved AdaBoost for virtual reality experience prediction based on Long Short-Term Memory network
Authors: Fan Wenhan , Ding Zhicheng , Huang Ruixin , Zhou Chang , Zhang Xuyang
Published: July 2024

A classification prediction algorithm based on Long Short-Term Memory Network (LSTM) improved AdaBoost is used to predict virtual reality (VR) user experience. The dataset is randomly divided into training and test sets in the ratio of 7:3.During the training process, the model's loss value decreases from 0.65 to 0.31, which shows that the model gradually reduces the discrepancy between the prediction results and the actual labels, and improves the accuracy and generalisation ability. The final loss value of 0.31 indicates that the model fits the training data well, and is able to make predictions and classifications more accurately. The confusion matrix for the training set shows a total of 177 correct predictions and 52 incorrect predictions, with an accuracy of 77%, precision of 88%, recall of 77% and f1 score of 82%. The confusion matrix for the test set shows a total of 167 correct and 53 incorrect predictions with 75% accuracy, 87% precision, 57% recall and 69% f1 score. In summary, the classification prediction algorithm based on LSTM with improved AdaBoost shows good prediction ability for virtual reality user experience. This study is of great significance to enhance the application of virtual reality technology in user experience. By combining LSTM and AdaBoost algorithms, significant progress has been made in user experience prediction, which not only improves the accuracy and generalisation ability of the model, but also provides useful insights for related research in the field of virtual reality. This approach can help developers better understand user requirements, optimise virtual reality product design, and enhance user satisfaction, promoting the wide application of virtual reality technology in various fields.
The Role of Pediatric Dental Clinicians in Addressing Rare Diseases
Authors: Namineni Srinivas
Published: July 2024

Rare diseases, often referred to as orphan diseases, are conditions that affect a small percentage of the population. Despite their rarity, these diseases collectively impact millions of people worldwide, posing significant challenges for patients, families, and healthcare providers. Pediatric dental clinicians play a crucial role in the early detection and management of these conditions, underscoring the need for heightened awareness and education within the dental community.
Pengaruh Etika Kepemimpinan Terhadap Kinerja Guru di MAN 2 Sijunjung
Authors: Alwis Dona Ana Yori , Hendriani Suswati
Published: July 2024

This research aims to examine the influence of ethical leadership on teacher performance at MAN 2 Sijunjung. Leadership ethics includes the application of the principles of honesty, justice, responsibility, and transparency in a leader's actions. In the educational context, leadership can create a positive and supportive work environment, which ultimately influences improving teacher performance. The research method used is a qualitative approach with a descriptive design. Data was collected through in-depth interviews with teachers and madrasa leaders, direct observation in the work environment, and analysis of internal organizational documents. Research results show that teachers who work under ethical leaders report higher levels of motivation, a greater sense of job satisfaction, and better performance. Teachers feel more valued and supported, which encourages them to work harder and achieve better results. Apart from that, ethical leadership also contributes to the formation of a strong organizational culture, which encourages cooperation, openness, and innovation. The conclusion of this research is that ethical leadership has a significant influence on teacher performance at MAN 2 Sijunjung. Therefore, it is important for educational institutions to strengthen ethical leadership as part of a strategy to improve the quality of education
Perencanaan Karir dalam Pendidikan Islam (Studi Literatur)
Authors: Aprima Septi Gia , Jamilus Jamilus
Published: July 2024

Islamic education. The research approach used is qualitative with a literature study method. The main source of research data is research articles published through various journals and indexed by google scholar. Data collection was carried out using the keyword "career in Islamic education". Articles used as primary data sources are articles published in the last 10 years. The data analysis used is qualitative data analysis consisting of data reduction, data presentation, and conclusion drawing. The findings of this study are that career planning in Islamic education begins with a deep understanding of oneself. Individuals are encouraged to understand their potential, interests, talents, and values, in line with Islamic teachings that emphasize the importance of introspection and self-discovery. With a deep understanding of themselves, individuals can identify career goals that align with religious values and qualified personal aspirations.
Analisis Bahan Ajar Anatomi Fisiologi Tubuh Manusia (Karya Daniel Suranta Ginting, Et Al)
Authors: Waruwu Sela Marselina , Zega Listen Arahan , Harefa Lindawati , Ndraha Fita Kristiani , Lase Natalia Kristiani
Published: July 2024

In the world of education, teaching materials have a crucial role in the teaching and learning process. Teaching materials include all types of materials used by teachers or instructors to support learning activities in the classroom. Thus, teaching materials are prepared to achieve the desired goals, namely mastering the necessary competencies or subcompetencies along with all their aspects. The purpose of this study is to analyze the identity, content and advantages and disadvantages of a teaching material. The method used in this study is the research method of the pustka study. The results of this study are teaching materials on Physiological Anatomy of the Human Body" by Daniel Suranta Ginting, et al, presenting material about the working system of human body organs and explaining the basics of physiological anatomy and metabolic processes of the human body. This book summarizes eleven discussion materials about the organ system of the human body from the musculelenal, endocrine, lymphatic, urinary, reproductive, respiratory, digestive, and metabolic systems of the human body. This teaching material is written by eleven writers from various professional backgrounds and is compiled in collaboration and cooperation from various authors.