bluuebunny/crossref_metadata_2025 huggingface dataset:
Beyond RNA-binding domains: determinants of protein–RNA binding
Authors: Zigdon Inbal , Carmi Miri , Brodsky Sagie , Rosenwaser Zohar , Barkai Naama , Jonas Felix
Published: October 2024

RNA-binding proteins (RBPs) are composed of RNA-binding domains (RBDs) often linked via intrinsically disordered regions (IDRs). Structural and biochemical analyses have shown that disordered linkers contribute to RNA binding by orienting the adjacent RBDs and also characterized certain disordered repeats that directly contact the RNA. However, the relative contribution of IDRs and predicted RBDs to the in vivo binding pattern is poorly explored. Here, we upscaled the RNA-tagging method to map the transcriptome-wide binding of 16 RBPs in budding yeast. We then performed extensive sequence mutations to distinguish binding determinants within predicted RBDs and the surrounding IDRs in eight of these. The majority of the predicted RBDs tested were not individually essential for mRNA binding. However, multiple IDRs that lacked predicted RNA-binding potential appeared essential for binding affinity or specificity. Our results provide new insights into the function of poorly studied RBPs and emphasize the complex and distributed encoding of RBP–RNA interaction in vivo.
O bem-assessorado: o caso da cidade de Sucupira
Authors: Amaral Alexandre Ribeiro , Souza Antônio José Inácio de , Oliveira Daniele Cecília Frota , Araújo Hugo Tavares
Published: October 2024

Informamos que as notas de ensino dos casos possuem acesso restrito e são exclusivas para docentes e instrutores vinculados à uma instituição acadêmica. Saiba mais.
Sucupiraprev
Authors: Silva Carlos Alves da , Soares Francisco Alberto de Oliveira , Araujo João Afonso da Silva , Freitas Márcio Osório de , Santos Marcos Malcher
Published: October 2024

Informamos que as notas de ensino dos casos possuem acesso restrito e são exclusivas para docentes e instrutores vinculados à uma instituição acadêmica. Saiba mais.
Hierarchical Genomic Analysis of Susceptibility to Schizophrenia in Sudanese multi-case families
Authors: Saeed Azza , Ahmed Lamees , Abuswar Safa , Salih Mudathir , Hussein Ayman , Eltigani Amel , Elgizouli Magdeldin , E.Ibrahim Muntaser
Published: October 2024

Background Schizophrenia, with its diverse and complex presentation, is a prime candidate for genetic investigation. Its heritability in both familial and sporadic cases, clinical overlap with other psychiatric conditions, and individual variations in response to treatment contribute to its complexity. Numerous genes and associated biochemical pathways show significant differences at the population, familial, and individual levels. Additionally, schizophrenia may represent an evolutionary trade-off for human brain development, creativity, and intellectual performance. Material and Methods This case pseudo-control study analyzed the whole genomes/exomes of seven Sudanese families with multiple siblings affected by schizophrenia. We examined shared variants among family members, including both cases and controls, and unique variants shared between patients but not controls. These variants were filtered based on their impact on protein function, expression levels, allele frequencies, ACMG classification for rare variants, and disease associations. Networks were created to identify central genes and common biological pathways. Results and Discussion The examination of this complex disorder in Sudanese families revealed numerous variants, both common and rare, showing differences between families and between our population and those reported in the literature. This highlights the challenge of accounting for the known heritability of the disease. Our hierarchical approach demonstrates that schizophrenia’s etiology involves the cumulative effect of various interacting variants in an ascending order of influence. Common variants are shared among all samples, while rare variants are shared among two or three families, most of which are associated with schizophrenia. In conclusion The significance lies not merely in the number of detected variants but in understanding their interactive roles, step by step, to reveal the complete picture of the disease’s phenotype.
Data Mining Technology for Smart Campus in Behavior Association Analysis of College Students
Authors: Zhang Jun , Kuang Yunxin , Zhou Jian
Published: October 2024

The data in smart campuses is complex and massive, with insufficient utilization, and existing data processing methods have many limitations. Therefore, in order to improve the efficiency of data processing in universities and assist in student management, a data processing method integrating cluster analysis and association rule mining is proposed. The proposed method is divided into two parts. Firstly, an improved K-Means model based on information entropy and density optimization is constructed for clustering analysis of student consumption, learning, and other data; Secondly, use the improved Mapping Apriori to obtain the correlation between student grades, consumption records, and learning behavior. The clustering results on student consumption data show that the average accuracy of ED-K-Means clustering is 97.41%, which is 12.8%, 8.5% and 4.0% higher than the comparison algorithm. The result of the correlation between consumption level and achievement shows that when the amount of consumption is less than 1500 yuan, the student’s achievement is directly proportional to the amount of consumption. Therefore, the proposed method can effectively mine and analyze student behavior data, which has important practical significance for intelligent management in universities.
Application of LSTM-based Body Fatigue Detection Algorithm in Tai Chi Training
Authors: Fan Zhehua , Tong Jinmao
Published: October 2024

Tai Chi training sessions are often lengthy, and athletes are prone to experiencing fatigue during the process. Timely detection of body fatigue can help athletes prevent injuries caused by excessive fatigue. This study combines Long Short-Term Memory (LSTM) networks with facial muscle activity detection models to propose a novel fatigue detection algorithm. In this algorithm, the limitations of LSTM networks in capturing future information are addressed by introducing an improved LSTM network model and combining attention mechanisms to highlight the important features of physical fatigue. This study utilizes hyperspectral imaging technology to extract real-time muscle fatigue signals from the faces of subjects in the dataset. Performance validation of the proposed model shows that it effectively extracts facial fatigue features with a detection accuracy of 97.67% and a recall rate of 96.78%, outperforming other existing models in this field. The model constructed through research has excellent performance and has broad application prospects in current and future technological development due to its high flexibility and adaptability, providing support and innovation momentum for different industries.
B-ERAC: Blockchain-Enabled Role-Based Access Control for Secure IoT Device Communication
Authors: Khan Neelam Saleem , Naaz Mir Roohie , Chishti Mohammad Ahsan , Saleem Mahreen
Published: October 2024

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework’s robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms.
A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm
Authors: Lopez-Cueva Milton , Apaza-Cutipa Renzo , Araujo-Cotacallpa Rene L. , Sasank V V S , Rangasamy Rajasekar , Sengan Sudhakar
Published: October 2024

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.
Evaluation of Athlete Physical Fitness Based on Deep Learning
Authors: Lv YouYang
Published: October 2024

In order to promote deep learning in physical education for students, guided by the concept of deep learning, the author integrates the concept of deep learning into flipped classrooms. The author proposes a flipped classroom design and classroom implementation process for promoting deep learning, and conducts a semester long experimental research based on basketball courses, continuously optimizing the design during this period.Through the analysis of various parameters appearing in the operation, the test results are satisfactory. The study found that students in three classes were physically trained by using the classroom teaching method of ”deep learning”. The classroom teaching mode based on deep learning achieved the best teaching results in the first stage. One habit that inspires real emotion in children is their pace: running in different directions. Classroom teaching based on deep learning can make students understand the importance of cross-movement. Thus, after experiment, there was no significant difference between the flipped classes and the traditional class teaching method in the cross run compared to before experiment. The effectiveness of flipped classroom design for promoting deep learning has been verified, achieving a promoting effect on students’ deep learning. The flipped classroom designed by this research institute can effectively promote learners to achieve deep learning, specifically manifested as: The physical fitness level of students has improved, the level of cognitive structure has improved, and students are more able to learn independently. The goal of deep learning for students has been achieved.
Intelligent Recommendation Algorithm for Product Information on E-commerce Platforms Based on Robot Customer Service Automatic Q&A
Authors: Fu Rong , Zhou Xiaoyan
Published: October 2024

In order to enhance the shopping experience of customers and retain them, thereby increasing sales volume, the author proposes the research topic of an intelligent recommendation system for product information on e-commerce platforms based on robot customer service automatic question answering. Firstly, starting directly from the question itself, the system can provide feedback to customers by simply segmenting the questions submitted by customers and matching them with semantic templates; Secondly, the system automatically builds and updates the user’s personalized knowledge base, using this to predict user purchasing tendencies and achieve the function of recommending products to customers. The implementation of the intelligent shopping robot system has passed 365 question and answer tests on 5G mobile phone sales terms, and is feasible in the professional field. The experimental results indicate that, when the number of training corpora increased to 300, the accuracy of the system was 0.85, 0.90, and 0.98 using 100, 2003, and 300 tests respectively. Such a system is perfect for natural language processing, so we can improve the system by expanding and improving the knowledge base. The intelligent shopping robot recommendation system studied by the author is still in the analysis and demonstration stage, sincerely hope that the processing method used in this project can have reference significance for similar recommendation systems in the near future.
Two Energy-Efficient Backhauling Solutions for Small Cell Networks of 5G Using Green Communications
Authors: Xue Haili
Published: October 2024

To meet the problem of ever-increasing wireless data congestion, the fifth-generation (5G) wireless communication system is projected to employ tiny cellphone networks for the needs of consumers heavily. One of the most significant elements of 5G networks will be sustainable interactions, as the quantity of power utilized by the information and communication technology (ICT) sector is expected to rise significantly by the end of the century. Therefore, scientists have focused much attention in recent years on developing strategies for designing small cell networks that use electricity optimistically. In addition, service providers need energy-efficient backing-up technologies to aid in using packed tiny cells. This research presents an interaction model that is well-suited to 5G HetNets and has a low power footprint. The model here accounts for an internet connection’s contact and backup portions. We create and present a mathematical framework to calculate the optimum number of tiny mitochondria that need to be maintained active at numerous hours of the day to minimize power consumption while still satisfying quality of service requirements set by customers. Our investigation into the backhaul’s electrical consumption led us to discover and put forward two energy-saving backing-up methods for 5G HetNets. Computer simulations show that the provided sustainable communication framework can reduce energy consumption by as much as 49% compared to the status quo.
Construction Method of Information Security Detection based on Clustering Algorithm
Authors: Chen Shaobo
Published: October 2024

A method for K-prototype clustering, which can process mixed data types, is proposed first. An algorithm for information security evaluation of hybrid clusters based on K-prototypes was constructed. This method makes full use of the excellent global optimization performance of PSO and effectively solves the defect that the K-protection function quickly falls into local optimization. Simulation results show that the proposed method can effectively prevent local extreme values and improve overall performance.
The Application of Deep Learning in Sports Training
Authors: Yan Fangling , Peng Qiuping
Published: October 2024

In response to the problems of overfitting, susceptibility to interference information, and insufficient feature expression ability in existing deep learning methods for sports action recognition, the author proposes a deep learning sports action recognition method that integrates attention mechanism. This method proposes a video data augmentation algorithm in data preprocessing to reduce the risk of model overfitting. Then, during the video frame sampling process, the existing sampling algorithms are improved to effectively suppress the influence of interference information. In the special section, the network residual consolidation is proposed to improve the feature extraction capacity of the structure. The Long Term Time Transform (LSTM) network is used to solve the problem of the global correlation of the spatial correlation, and the classification algorithm is achieved by Softmax. and the classification algorithm is proposed. The experimental results show that the recognition rates of this method on UCFYouTube, KTH, and HMDB-51 data are 96.73%, 98.07%, and 64.82%, respectively.
Online Monitoring System of Electromechanical Transient Simulation Data of Distribution Network Based on Edge Computing
Authors: Zhang Junliu
Published: October 2024

The current distribution network is developing toward the direction of information and intelligent distribution Internet of Things (IoT). In order to explore the online monitoring system for transient electromechanical simulation of distribution networks, this paper is based on the equivalent model of generator sets. Firstly, it describes the relevant theoretical knowledge of edge computing, designs the distribution IoT based on edge computing, and briefly introduces its network architecture. Secondly, based on the discrete-time domain equivalent model of generators, the electromechanical transient simulation distribution network is constructed by introducing the machine network division of the power network. Finally, the Western System Coordinating Council (WSCC)-3 units and 9 nodes are taken as an example, and simulation experiments are conducted under two fault simulation conditions to verify the effectiveness of the simulation model. The results show that under the two fault simulation conditions, the results of equivalent model simulation of generator terminal voltage and relative power angle change are like those of the Power System Analysis Software Package (PSASP). The changing trend of the two is similar and stable. After the PSASP results are stabilized at a value, the simulation results fluctuate extremely up and down the value. For example, under fault condition 1, the changing trend of the relative power angle of the No. 2 generator is first fluctuating and then becomes stable. After violent fluctuation, the relative power angle tends to be stable from about 20s to -12.35°. The result of PSASP software is stable at -12.35°. The transient electromechanical simulation of the generator equivalent model can provide some ideas for the online monitoring system of the distribution network.
Research on Mental Health Assessment and Intervention Methods for College Students based on Big Data Analysis
Authors: Ji Xiang
Published: October 2024

This study introduces the novel Fuzzy-Enhanced Predictive Neural System for Student Mental Health (FEPS-MH) approach to mental health assessment and intervention for college students. FEPS-MH synergistically combines a Backpropagation (BP) Neural Network with a Deep Fuzzy-Based Neural Network (DFBNN), leveraging the strengths of both systems to handle the complexities of mental health data in the context of big data analytics. The BP Neural Network, known for its effective learning and generalization capabilities, is integrated with the DFBNN to process imprecise, uncertain, or subjective data, typical in mental health assessments. The core objective of FEPS-MH is to provide a more accurate, robust, and sensitive analysis of mental health states, incorporating the nuanced variations and uncertainties inherent in psychological data. This system is designed to analyze a vast array of data sources, including but not limited to, behavioral patterns, self-reported questionnaires, and social media interactions, to identify potential mental health issues among college students. FEPS-MH’s capabilities extend beyond mere assessment; it is also equipped to recommend personalized intervention strategies. Utilizing big data analysis, the system not only predicts potential mental health crises but also suggests tailored intervention approaches based on the unique psychological profile of each student. This study demonstrates the feasibility and effectiveness of FEPS-MH through a series of tests and validations using real-world data. The results indicate a significant improvement in both the accuracy of mental health assessments and the efficacy of suggested interventions. FEPS-MH stands as a promising tool for educational institutions, offering a data-driven, sensitive, and comprehensive approach to student mental health care. Its implementation could revolutionize the field of mental health support in college environments, making it a vital asset for proactive psychological wellness in educational settings.
Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network
Authors: Yao Hongwei , Wu Wanxian
Published: October 2024

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.
Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms
Authors: Li LingLing , Wang PeiGang , Niu XueBiao
Published: October 2024

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.
Analysis of Virtual Reality-based Music Education Experience and its Impact on Learning Outcomes
Authors: Sun Fangjie
Published: October 2024

This study aims to analyze the impact of virtual reality (VR)-based music education on learning outcomes, integrating the strengths of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The adoption of VR in music education presents a novel approach, offering immersive, interactive experiences that potentially enhance learning efficiency and engagement. Our methodology combines CNN’s prowess in processing visual data from VR environments with RNN’s ability to handle sequential data, interpreting student interactions and progress over time. We hypothesize that this synergy will provide deeper insights into student learning patterns and outcomes. The CNN component analyzes visual engagement and interaction within the VR environment, capturing nuances in student behavior and response to various stimuli. Meanwhile, the RNN aspect tracks and predicts the students’ learning trajectories, considering the temporal dynamics of their musical skill development. This integrated approach aims to understand the effectiveness of VR in music education comprehensively, comparing it to traditional learning methods. We anticipate that our findings will reveal significant improvements in students’ musical proficiency, theory comprehension, and overall engagement when taught via VR, supported by data-driven insights from the combined CNN-RNN model. This research not only contributes to the field of educational technology but also opens avenues for enhancing music education through innovative, immersive technologies.
Research on Automatic Proofreading Algorithm for English Translation Based on Neural Networks
Authors: Liu Xiaoshan
Published: October 2024

In this proposed study, we explore the development and implementation of an innovative proofreading algorithm aimed at enhancing the accuracy of English translation. This algorithm leverages the capabilities of Convolutional Neural Networks (CNN) integrated with a fuzzy logic approach, offering a novel perspective in the realm of linguistic accuracy and consistency in translations. The core objective of this research is to address the prevalent challenges in automatic translation, such as context misinterpretation and semantic errors, by employing a fuzzy-based CNN model. This model is meticulously trained and tested using a diverse dataset of English translations, enabling it to learn and adapt to various linguistic nuances. Our results demonstrate a significant improvement in the proofreading accuracy, outperforming existing methods in terms of efficiency and reliability. The research highlights the potential of combining neural networks with fuzzy logic to create more sophisticated and context-aware translation tools. While our findings mark a considerable advancement in automatic translation proofreading, we also acknowledge the scope for further enhancements. Future work could involve refining the algorithm, expanding its applicability to other languages, and integrating it into real-world translation software. This research contributes to the evolving landscape of automated translation, presenting a promising solution for achieving higher translation fidelity.
The Application of Deep Learning in Sports Data Analysis
Authors: He Jin
Published: October 2024

In order to ensure that students have a deeper and more thorough understanding of knowledge and skills, the author takes deep learning as the direction and integrates it into the real-time evaluation of basketball classrooms, organically combining the two. Deep learning provides a new theoretical perspective and driving force for the optimization of real-time evaluation, and points out the direction for promoting evaluation effectiveness; The optimization of real-time evaluation provides a feasible path for achieving deep learning for students. After the implementation of the real-time evaluation plan between the experimental group and the control group, the comparison results of basketball skills tests were obtained: before and after the experiment, the experimental group’s 60 second self throwing and self grabbing, vigorous dribbling, comprehensive dribbling, teaching competitions, and teaching lectures all showed significant differences in test indicators; Before and after the experiment, the control group showed significant differences in the four test indicators of 60 second self throwing and self grabbing, vigorous dribbling, comprehensive dribbling, and teaching lectures. The teaching competition indicators also showed significant differences, all of which were improved. Comparison results of deep learning abilities: Before and after the experiment, there were significant differences in the dimensions of experimental combination ability and learning perseverance, while there were significant differences in the dimensions of communication ability, self-learning ability, and total score of deep learning ability; Before and after the experiment, there was a significant difference in the dimensions of control group work ability, while there was no significant difference in the dimensions of self-directed learning ability, learning perseverance, and communication ability. Overall, there was a very significant difference in deep learning ability. Overall, the experimental group showed a more significant improvement in deep learning ability compared to the control group. The real-time evaluation plan for sports education professional basketball classrooms based on deep learning is more effective in improving students’ basketball skills and deep learning abilities than the conventional real-time evaluation plan for sports education professional basketball classrooms. The design of the plan has certain effectiveness and feasibility.
Application of Facial Analysis Based on Convolutional Neural Network and Iterative Decision Tree for Teaching Evaluation in Smart Classrrom
Authors: Hui Jiang , Fu Wentao , Zhang Jian
Published: October 2024

The creation of intelligent classrooms has been expedited by the rapid advancement of the Internet and computer vision, and intelligent teaching has increased the interactivity and effectiveness of learning. Teachers’ teaching and students’ classroom learning state ultimately affect the teaching effect. Students’ facial expressions during class can reflect emotional changes and the current learning state. The computer camera in the smart classroom collects students’ face image data, uses texture-based information, edge-based information, geometric information, and global and local feature extraction to identify and analyze and process the students’ facial expressions. Research has shown that the combination of expression recognition and an intelligent teaching classroom can accurately identify and analyze students’ emotions and learning status, and can effectively evaluate the teaching effect of the intelligent classroom, which helps to improve teaching quality and learning efficiency. Therefore, applying facial expression recognition in the intelligent teaching classroom has far-reaching significance.
A Personalized Teaching System for College English Based on Big Data and Artificial Intelligence
Authors: Li Xiaojie
Published: October 2024

In the ultra-modern digital age, the schooling sector is experiencing a transformative shift driven by the convergence of massive records and artificial Intelligence (AI). This paper offers a singular initiative—a ”personalised teaching machine for university English” that harnesses the electricity of these technologies to revolutionise how English language practice is delivered at the college level. The system is designed to address the various ways of gaining knowledge of the desires of students and adapting dynamically to their talent degrees, styles, and possibilities. The centre of this device lies in its capability to collect, technique, and analyse extensive amounts of records associated with students’ language acquisition journey. By leveraging massive statistics techniques, the machine captures and interprets college students’ interactions with direct materials, assignments, exams, and peer interactions. Concurrently, the latest AI algorithms, which include herbal Language Processing (NLP) and gadget learning (ML), are employed to create a responsive and smart learning environment. The system tailors mastering pathways for personal college students, ensuring they get hold of content material and sporting events aligned with their cutting-edge scalability stages. Ordinary formative checks are performed to gauge college students’ progress, taking into consideration timely interventions and changes in teaching techniques. This progressive customised coaching device for university English now not only enhances language proficiency but additionally promotes self-directed getting-to-know and empowers educators with information-pushed insights. The amalgamation of large facts and AI promises to reshape English language education’s panorama, paving the way for an extra personalised, green, and effective pedagogical method in university settings. In conclusion, this study illuminates the transformative ability of harnessing massive records and AI in schooling, with precise relevance to language instruction. The machine is a pioneering model for personalised, adaptive, and information-centric teaching methodologies in higher training.
Research on Broadband Measurement Method of Power System based on Wavelet Transform
Authors: Li Jin , Zhao Huashi , Yang Yuanwei , Zhou Huafeng , Gu Huijie , Xu Danli , Li Yang , Liu Kemeng
Published: October 2024

This study delves into the exploration of broadband measurement techniques for power systems, utilizing wavelet transform as a foundational tool for signal analysis. The research rigorously evaluates the efficacy of several machine learning algorithms, namely Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest, in interpreting and analyzing broadband signals within power systems. Through a detailed analytical process, the performance of each algorithm is meticulously assessed based on several critical metrics: accuracy, precision, recall, and F1-score. The research investigates broadband measurement methods for power systems using wavelet transform and evaluates the performance of Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Results show SVM achieving an accuracy of 85%, precision of 86%, recall of 82%, and F1-score of 84%. ANN yields 82% accuracy, 84% precision, 78% recall, and 81% F1 score. KNN demonstrates 87% accuracy, 88% precision, 84% recall, and 86% F1 score. DT achieves 79% accuracy, 80% precision, 75% recall, and 77% F1 score. Overall, the study provides insights into machine learning algorithms’ effectiveness in broadband power system measurement.
Design and Implementation of a Visual Logging and Automatic Modeling Tool for Camp Distribution Connection based on Deep Learning Algorithms
Authors: Jia Yong , Zhang Junwei , Suo Siyuan , Xiao Chun , Liang Yue , Zhang Shiyi
Published: October 2024

This particular offers a visual logging and automatic modelling apparatus for camp distribution connection employing state-of-the-art deep learning techniques. The gadget that emerged from an interdisciplinary approach inspired by ideas related to data security, quantum computing, and environmental monitoring points to an upward trajectory in increasing the accuracy and efficiency of compassionate coordination in settings for camp distribution. Experiments on the dataset show how successfully connection modelling, anomaly detection, as well as semantic segmentation, are done to generate a more cohesive model. Its reputation is further highlighted by the corresponding study of works from various domains, which finds that it has accuracy, precision, and F1 score measurements above 0.88 per task. As a directed investigation area in non-stipulated regions turns into significant scientific research, it contributes an ever-more significant role in leading authorities through which to make some administrative efforts to optimise such research, as disciplined researchers currently have sufficient knowledge of contemporary trends. Compared to present use, the equipment is more accurate and has superior review values. The research can achieve unprecedented computing efficiency, as seen by its 12-hour setup time and processing velocity measurement of 20 milliseconds per recorded picture.
Research on Optimization of Visual Object Tracking Algorithm Based on Deep Learning
Authors: Liu Xiaolong , Rodelas Nelson C.
Published: October 2024

The appearance of deep gaining knowledge has extensively advanced the sphere of visible item tracking, permitting greater robust and accurate monitoring of items throughout complex scenes. This study optimises a visual item tracking set of rules based on the Siamese region inspiration network (Siamese RPN) monitoring algorithm, aiming to beautify its efficiency and effectiveness in actual-time packages. The Siamese RPN algorithm, acknowledged for its stability among accuracy and velocity because of its architecture that mixes the Siamese network for characteristic extraction with a vicinity suggestion community for item localisation, provides a promising basis for improvement. This examination introduces numerous optimisations to the authentic Siamese RPN framework. First, we endorse an improved feature extraction model that leverages a more efficient deep neural community structure, lowering computational load while preserving excessive accuracy. 2nd, we optimise the place concept mechanism by incorporating an adaptive anchor scaling method that dynamically adjusts the scale and ratio of anchors based on the object’s scale variations, enhancing the tracking accuracy across distinctive object sizes and aspect ratios. Moreover, we introduce a unique training method that employs an aggregate of actual global and synthetically generated statistics to beautify the robustness of the monitoring algorithm towards various demanding situations, including occlusions, speedy moves, and illumination adjustments. The effectiveness of the proposed optimizations is evaluated through complete experiments on numerous benchmark datasets, consisting of OTB, VOT, and LaSOT, demonstrating extensive upgrades in tracking accuracy and speed as compared to the authentic Siamese RPN algorithm and different modern-day tracking techniques. The outcomes of this study no longer underscore the potential of optimised Siamese RPN algorithms in visible item monitoring but additionally lay the basis for future explorations into actual-time, green, and strong tracking systems. Those improvements keep great promise for a wide variety of packages, from surveillance and protection to autonomous cars and augmented truth structures, where particular and dependable item monitoring is paramount.
An Enhanced RSA Algorithm to Counter Repetitive Ciphertext Threats Empowering User-centric Security
Authors: Balani Vishal , Kharya Chaitanya , Shivhare Shiv Naresh , Singh Thipendra P.
Published: October 2024

The technology-driven modern age emphasizes the security and privacy of communication. Through this paper, we delve deep into the need for user-centric security within cloud-based environments. The need for enhancement in encryption arises due to the increasing cases of data breaches and insider threats in cloud-based environments recently. The focus is laid upon the use of RSA encryption, end-to-end encryption, and anonymous messaging to address security-based concerns. The primary focus of this research is to develop a comprehensive security system to ensure the confidentiality and authenticity of text-based messages shared in the cloud. The proposed improved RSA algorithm, as suggested, incorporates three prime numbers in the key generation process. To address repetitive ciphertext, the proposed algorithm involves adding the index of each character in the plaintext string to the character’s integer value before encryption. Conversely, during decryption, the same index is subtracted. This proposed algorithm has been utilized in a practical scenario, specifically in the implementation of a chat application. This paper presents a proof-of-concept for the proposed enhanced version of the RSA algorithm, accompanied by a thorough comparison and analysis of computational times across various bit lengths. Increase in data security at a cost of minor increase in computation time was observed through this research.
A Multi-Agent Reinforcement Learning Blockchain Framework for Improving Vehicular Internet of Things Cybersecurity
Authors: Alyoubi Adel
Published: October 2024

The Vehicular Internet of Things (VIoT) is a novel idea in the field of connected transportation systems that defines a new paradigm. Nevertheless, even the most modern and complex system will require additional and more powerful layers to protect the conversation from interception and the data from leakage. The centralized models have problems like trust problems and that there might be vulnerabilities and that is why there are attempts to integrate decentralization in operation. The first challenge in the VIoT networks is that the security and openness of such a connection and data transfer are still not well developed. Another issue is the security and dependability of the interaction between the vehicles and the infrastructure, although this issue is magnified by the size of the VIoT network. This research is a blockchain and game theory base research that uses Multi-Agent Reinforcement Learning (MARL) to improve the security and efficiency of the VIoT ecosystem. The technology of blockchain gives a distributed ledger where data cannot be altered or erased. Moreover, the MARL architecture allows for the realisation of better decisions for each of the members of the network. To this, the set that was made up of the smart contracts, Vehicle Units (VUs) and the decentralized servers that form the proposed architecture would be added to allow for the right flow and processing of the data. The blockchain’s decentralized nature provides a guarantee for all secure, immutable data transfers and transparent transactions throughout the network of the VIoT. MARL enables agents to learn and acquire the best strategies as they pass through time, which leads to secure and effective communication among entities. Besides, the implementation of lightweight cryptography techniques and strategic selections according to game theory help to protect and improve the performance of the security system of the VIoT ecosystem.
The Teaching Effect Evaluation of Big Data Analysis in Music Education Reform
Authors: Deng Qiuyue
Published: October 2024

At present, the evaluation of the implementation effect of the music teaching reform project is a hot topic. This project establishes a teaching reform implementation effect evaluation model based on improving the accuracy of teaching reform plan effectiveness evaluation. This project first analyzes the current research status of the impact evaluation of the implementation of music education reform projects and builds a set of evaluation systems. The evaluation method of teaching reform plan implementation effect based on a ”big data-driven - support vector machine” was constructed. The simulation experiment verifies that this method can evaluate the implementation of the teaching reform plan well. The relative error of the evaluation results is small. It is practical in improving the effectiveness evaluation of the teaching reform plan. After verification, it is found that the research results of this project are feasible.
MPC Optimization Algorithm and Strategy for HVAC System under Smart City Construction
Authors: Wang Lei
Published: October 2024

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139◦ C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1◦ C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings.
Automobile Intelligent Vehicle-machine and Human-computer Interaction System based on Big Data
Authors: Wang Quanyu , Zhang Yao
Published: October 2024

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable.