bluuebunny/crossref_metadata_2025 huggingface dataset:
Physicochemical and Heavy Metal Analysis of Industrial Pollution Model Calibration and Validation Using Field Measurements
Authors: D. O. Barango , K. Charles , C. F. Ikebude
Published: April 2024

This study assessed industrial pollution impacts on rivers near Port Harcourt, Nigeria using physicochemical analysis, heavy metal data, and contaminant transport modeling. Key findings show the rivers are severely degraded with extreme levels of heavy metals, low dissolved oxygen, high conductivity, nutrients, and fecal bacteria exceeding water quality guidelines. Uncontrolled effluent discharges from industries are primarily responsible. A first-order advection-diffusion model reliably predicted the rapid initial dilution and slower downstream attenuation of metals like magnesium and cadmium. The model was successfully calibrated and validated using field measurements to determine key transport parameters including dispersion coefficients. The severe contamination indicates current effluent treatment and regulations are inadequate and require urgent strengthening to control sources, expand monitoring, remediate contamination, restore habitats, and protect ecosystem and public health. Sustained engagement of government, industry, communities and researchers is essential to devise integrated solutions that improve water quality. The modeling provides quantitative guidance on pollution impacts and mitigation needs. Further work should refine predictions, establish ecological thresholds, and validate model results with biomonitoring to support evidence-based, adaptive management.
АНАЛИЗ ВЛИЯНИЯ ФАКТОРОВ НА ПОТЕНЦИАЛ СРЕДОВОЙ ВОВЛЕЧЕННОСТИ
Authors: Proshunina Kseniya Alekseyevna , Khomenko Tatyana Vladimirovna
Published: April 2024

Приведены результаты моделирования потенциала средовой вовлеченности от взаимосвязи независимых переменных в территориально-распределенной пространственной системе на примере существующей застройки жилых домов. При этом

рассмотрено два фактора влияния: площадь благоустройства, количество жильцов. Анализ результатов показал процент

влияния рассматриваемых факторов на возможности территориально-распределенной пространственной системы жилой среды, что чем выше показатели свободной территориальной площади благоустройства в существующей среде, тем выше значения потенциала средовой вовлеченности, и соответственно актуальней проведение мероприятий по замене существующих объектов благоустройства с учетом вместимости предлагаемых площадок, отвечающих запросам жильцов. Предложенный анализ может найти практическое применение при оценке существующей среды в генеративном проектировании общественных пространств, способствуя повышению обоснованности принимаемых решений.
КЛАССИФИКАЦИЯ СООРУЖЕНИЙ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ ПО ВОЗМОЖНОСТИ ЗАЩИТЫ ОТ ПРОГРЕССИРУЮЩЕГО ОБРУШЕНИЯ
Authors: Trekin Nikolay Nikolayevich , Kodysh Emil Naumovich , Terekhov Ivan Aleksandrovich , Shmakov Sergey Dmitriyevich
Published: April 2024

В статье приводится классификация и описываются принципы проведения защиты промышленных предприятий, представленных в СП 43.13330 от прогрессирующего обрушения. Анализируются нормативно-правовые документы, регламентирующие необходимость защиты таких сооружений от прогрессирующего обрушения, а также возможные организационно-технические мероприятия, позволяющие снизить или исключить риск возникновения чрезвычайной ситуации. Рассмотрены предпоссылки для использования метода индивидуальной оценки зданий и сооружений, расположенных на объекте, относящемся к категории «Опасные» в случае, если они являются вспомогательными и не принимают непосредственного участия в производственном процессе предприятия. Представлены имеющиеся нормативно-технические и методические документы, на основе которых может быть разработана методика оценки рисков возникновения чрезвычайных ситуаций.
НЕСТАЦИОНАРНЫЙ НАГРЕВ МЕТАЛЛИЧЕСКИХ ДЕТАЛЕЙ В МАШИНОСТРОЕНИИ ЛУЧИСТЫМ ПОТОКОМ ТЕПЛА С УЧЕТОМ ИЗМЕНЕНИЯ ТЕПЛОФИЗИЧЕСКИХ ХАРАКТЕРИСТИК
Authors: Legkiy Aleksandr Dmitriyevich , Veselova Natalya Mikhaylovna , Karapuzov Vladislav Igorevich , Brenina Tatyana Pavlovna
Published: April 2024

Рассмотрен метод исследования нестационарного нагрева лучистого потока тепла металлических заготовок квадратного сечения с переменными теплофизическими характеристиками. Описан метод получения массивных и крупногабаритных элементов для деталей машин методом термообработки. Преимущество данного метода является его применения

в различных отраслях машиностроения, а также при производстве отдельных строительных конструкций. Кроме того, в статье приведен математический анализ, методом конечно-разностных уравнений при помощи компьютерного моделирования, который с высокой достоверностью позволяет получить расчета температурных режимов нагрева стальных заготовок в производстве деталей сельскохозяйственных машин. Результаты расчетов нагрева бруса методом конечно-разностных уравнений сопоставлены с опытными данными.
СПОСОБЫ УСИЛЕНИЯ ОДНОЭТАЖНОГО ПРОМЫШЛЕННОГО ЗДАНИЯ, ОБОРУДОВАННОГО МОСТОВЫМ КРАНОМ, ПРИ ЗАЩИТЕ ОТ ПРОГРЕССИРУЮЩЕГО ОБРУШЕНИЯ
Authors: Zavyalova Olga Borisovna , Zavyalov Roman Aleksandrovich
Published: April 2024

В статье рассмотрена методика расчета одноэтажного большепролетного промышленного здания со стальным каркасом на прогрессирующее обрушение по нескольким вариантам развития сценариев. В качестве вариантов отказов проанализированы обрушения рядовой колонны крайнего и среднего ряда, а также отказ опорного раскоса стальной стропильной фермы. Задача решена в линейной постановке, возможный поддерживающий эффект со стороны ограждающих конструкций на этой стадии исследования не учитывался. По результатам расчетного анализа предложены способы усиления основных несущих конструкций, препятствующие обрушению несущих и ограждающих элементов покрытия. Предлагаемые способы не затрудняют работу кранового оборудования, а наоборот они эффективны и экономичны.
СРАВНЕНИЕ ТЕМПЕРАТУРНЫХ ПОЛЕЙ МОНОЛИТНЫХ ЖЕЛЕЗОБЕТОННЫХ КОНСТРУКЦИЙ, ПОЛУЧАЕМЫХ В РЕЗУЛЬТАТЕ ЭКСПЕРИМЕНТАЛЬНОГО И ЧИСЛЕННОГО МОДЕЛИРОВАНИЯ ПРОЦЕССА ЭЛЕКТРОПРОГРЕВА БЕТОНА
Authors: Leontyev Stepan Vasilyevich , Taleyko Andrey Alekseyevich
Published: April 2024

В статье приводятся результаты сравнения температурных полей, построенных по данным численного моделирования

процесса прогрева бетона греющим проводом, и значений температур бетона, полученных в ходе проведения аналогичного натурного эксперимента. Суть эксперимента состояла в прогреве элемента бетонной конструкции с помощью нихромовой проволоки, размещенной в теле бетона. В качестве источника напряжения использовался инверторный сварочный аппарат, напряжение на концах проволоки было постоянным, регулирование прогрева осуществлялось периодическим включением и выключением питания. Измерение температуры бетона в процессе тепловой обработки осуществлялось в центральной и угловой точках конструкции. Полученные в ходе натурного эксперимента температурные данные сравнивались с данными аналогичной модели, построенной в программном комплексе Elcut. По результатам исследований был сделан вывод о сходимости результатов, получаемых с помощью модели, и данных натурного эксперимента, а также проанализированы факторы, объясняющие имеющиеся расхождения данных.
МЕТОДИКА ОЦЕНКИ УРОВНЯ КАЧЕСТВА МАТЕРИАЛОВ ДЛЯ НАПОЛЬНЫХ ПОКРЫТИЙ
Authors: Lisiyenkova Lyubov Nikolayevna , Nosova Lyudmila Sergeyevna
Published: April 2024

Цель работы – выбор лучшего варианта проектного решения на основе квалиметрического анализа. В результате исследований установлены значимые показатели качества объектов, рассчитаны коэффициенты весомости показателей. В практической части выполнен анализ качества строительных материалов для напольного покрытия в помещениях проектируемого здания. Составлена модель качества материала для напольного покрытия в жилых проектируемых помещениях, рассчитан интегральный показатель качества и выбран лучший вариант проектного решения. Разработана методика квалиметрической оценки уровня качества исследованных материалов. Результаты работы рекомендованы в проектные и строительные организации в условиях вариантного проектирования.
РЕШЕНИЕ ОБРАТНОЙ ЗАДАЧИ ШТУРМА – ЛИУВИЛЛЯ ПРИ ВОССТАНОВЛЕНИИ СКОРОСТНЫХ ХАРАКТЕРИСТИК КВАЗИБЕСКОНЕЧНОЙ СТРУНЫ
Authors: Berezhnoy Dmitriy Valerievich , Sabitov Linar Salikhzanovich , Garkin Igor Nikolayevich , Sekayeva Liliya Ravilyevna , Mikheyev Vladimir Vladimirovich
Published: April 2024

Задачи изучения скоростного строения геологических сред являются важными и актуальными на сегодняшний день. При этом несмотря на развитие и возможности современных технологических подходов, верхняя часть разреза до сих пор

остается плохо изученной. Связано это с рядом факторов, одним из которых является отсутствие на данных горизонтах полезных ископаемых, таких как залежи углеводорода. Для нефтяных промышленностей бурение верхних слоев является относительно простой задачей, не требующей глубокого изучения свойств предповерхностного грунта. Поэтому данных по верхней части разреза собирается мало. В настоящей работе авторами представлено решение обратной задачи Штурма –

Лиувилля определения скоростной характеристики верхней части разреза геологической среды. В работе рассматриваются два подхода к решению поставленной задачи, с помощью спектральной и кепстральной методик. Показывается применение методов на синтетических данных, полученные 1Д решением, а также приводятся ограничения применимости этих подходов.
ПЕРСПЕКТИВЫ РАЗВИТИЯ ГОРОДСКОЙ НАБЕРЕЖНОЙ МЕТОДОМ СОЦИАЛЬНОГО ОПРОСА
Authors: Dakheel Heba
Published: April 2024

Данная статья разработана по результатам социального исследования для оценки качества набережной города Джабла

в Сирии. В ходе исследования было опрошено 200 человек. Цель – предоставление жителям возможности сформулировать

новые точки зрения, которые могут быть полезны в будущем для изучен6ия. Основной вывод заключается в том, что набережная нуждается в специальном развитии, которое стимулировало бы социальное взаимодействие. По итогам проведенного исследования были разработаны рекомендации и описание предложенного автором проектного решения по развитию набережной зоны. Полученные результаты позволят сделать набережные комфортными, доступными и интересными

для посетителей и в дальнейшем могут быть использованы при оформлении набережных в других сирийских городах с некоторыми изменениями в зависимости от города.
Analysis of Artificial Intelligence Induced Machines to Improve the Fiscal Advancement of Agronomy
Authors: Tawheed Nabi Imtiyaz Majid,
Published: April 2024

Horticulture became a key player in the development of UT. The total land area of Jammu and Kashmir under horticulture is 332704, among which 214162 in the Kashmir valley and 118542 in the Jammu region. The district Baramulla has achieved first rank in production in apples with 404089 metric tons, and also covers an area of 25231 hectares. Horticulture of UT of Jammu and Kashmir contributes 8-10% contribution to the SGDP (DHK, 2021). In 48 years, the area under horticulture shows a growth rate of 25%.The growth of production of horticulture has also increased 10 thousand metric to 25 Lakh metric tons during 1950-2022.In this paper we will analysis growth in production, productivity, area and State Gross Domestic Product through the Compound Growth Rate from 2012 to 2022. Artificial Intelligence will become a game changer for horticulture sector, which is facing loses consistently from the last few years. Need of the hour to improve the technique like artificial intelligence and procedures for the betterment of horticulture, especially open national, use of drones in farms, highway 44 during the peak season.
MFGC: Minimal First Garbage Collection for Monitoring of Context-Aware Victim Selection
Authors: P. K. Singh Shweta,
Published: April 2024

SSDs based on NAND flash are becoming a feasible secondary storage option for many computer systems. Because NAND flash memory is dif- ferent from regular HDDs, flash-based SSDs frequently have an interface layer known as Flash Translation Layer (FTL). Garbage collection tech- nique is required to free up space. Garbage collection policy is primarily concerned with cleaning costs and wear-leveling. To achieve the above design principles, we have proposed an effective minimal first garbage col- lection termed as MFGC. MFGC manages the blocks in the list, sorted in descending order of erase count. This list is divided into two regions: preference region and alternate region. The adaptive window for victim selection is used, which adapts dynamically as the cleaning cost changes. After the victim selection, effective data identification of valid hot page and valid cold page is done based on threshold and redistribution of them is done to free blocks. A Series of trace simulations is performed to evaluate the performance of proposed approach with other existing approaches. MFGC outperforms GR,  CB,  CAT,  CATA  and  MCSGC by 85.5%, 77.1%,61.7%, 56.7% and 43.5% respectively.The outcome of the simulation demonstrates that our approach is superior to others in terms of erase and copy operations as well as wear leveling of blocks.
Construction of Virtual Reality Rail Transit Simulation System
Authors: Chunyang Liu, Liwen Zheng, Hong Li Jimin Liu,
Published: April 2024

In order to enhance the quality of professional technology research and development and personnel training in rail transit, this study constructed a virtual reality rail transit simulation system, including the data input and processing of vehicle driving environment, vehicle structure and station scene data, as well as three-dimensional modeling and data storage. The data is stored in the Internet cloud server, which increases the accessibility and sharing of the data, and the rail transit simulation can be realized by calling the three-dimensional model in the cloud server. This efficient and convenient virtual reality content development tool helps to improve the level of scientific research and development and the quality of talent training in rail transit, it will also be the inevitable trend of the development of rail transit industry in the new era.
User-Centric Cloud Framework for Enhanced Data Security by Applying UK Algorithm
Authors: G. Kalpana B. Umapathy,
Published: April 2024

Cloud storage is the technical boon which has become the realm of many companies like Amazon, Google, Microsoft, Drop box and many more. Remarkably all these companies are US based and thus eventually the personal data of every other nation is stacked on the Western companies. This leads the dependant country to a state of vulnerable. However if the dependant countries need to get autonomy they had to be provided with their own cloud storage which is impossible with the current mechanisms. With the facility of high computational devices and wide infrastructure and with the aid of secured encryption the cloud storage providers strongly protect the data of the users and enable their best service to the users. Also the cloud storage providers use the encryption algorithm in the provider infrastructure and data from user to provider infrastructure is done using the secured transmission. Finally the powerful infrastructure does the work to secure the data. This mechanism is certainly possible in the developed countries. But it is relatively impossible in the developing countries like India as it cannot afford the huge infrastructure to protect the public data. Even done, it might not result in generating profit because the subscription rate is comparatively lower in India than in developed countries. So to pursue the less profitable module is not suggestive and hence the reliability on western country cloud storage is over whelming. However to overcome this set back and to implement indigenous cloud storage this paper has attempted to implement the UK algorithm in user device environment through which mini and micro level cloud storage shall be developed resulting in the minimal cost of maintaining the cloud storage. In this case, cloud Storage provider do not need to have either the huge infrastructure nor the computational devices to process the data and no need of separate data transmission to secure the data as well. Employing the encryption algorithm in user environment helps to give full authority to the user on their data. The CS provider holds only encrypted data of the user and with low cost consumption this method is feasible even for the developing country like India.
An Efficient Scientific Programming Technique for MRI Classification using Deep Residual Networks
Authors: Shilpi Sharma, Vipul Narayan, Swapnita Srivastava Rahul Sawhney,
Published: April 2024

Classifying brain tumors is crucial for both diagnosing and treating patients with these diseases. Imaging techniques of many kinds are used to detect brain cancers. In contrast, MRI is frequently used because to its superior picture quality and the fact that it does not require ionizing radiation. Recently, the subfield of machine learning known as deep learning has shown especially promising results in the areas of classification and segmentation. To identify the various tumor types seen in the brain, we trained a deep residual network using imaging datasets. There will be a tremendous amount of information generated from the MRI images. It is the radiologist's job to look at these imagesMeningiomas, pituitary tumours, and gliomas are the three most prevalent forms of brain tumours. Because of the complexity of brain tumors, a physical inspection might lead to mistakes. Classification methods that use machine learning to automate the process have shown to be superior to human curation every time. Therefore, we developed a CNN-based deep residual network-based detection and classification system.
Exploring the Efficacy of Machine Learning Tools for Analyzing Student Performance in Computer Science Applications
Authors: Tilak Raj Rohilla Kirti,
Published: April 2024

This research study examines how ML techniques can assess computer science student performance. The study compares current and past performance metrics, identifies salient performance assessment features, and outlines strategies for sustaining or enhancing student achievement in the context of the COVID-19 pandemic, which has shifted the paradigm toward online learning. Context: With the rise of digital learning platforms and the changing educational environment, ML approaches are being used to get useful insights from massive student data sets. The COVID-19 pandemic has highlighted the need for improved virtual student learning monitoring and support methods. Objective: The purpose of this study is to determine if ML methods can accurately assess student computer science proficiency. Compare students' current performance measures to their prior records to identify COVID-19-related changes. To choose relevant characteristics for performance evaluation using ML algorithms and statistics. Methods: The research uses a mixed-methods approach, combining quantitative academic record analysis with qualitative student survey and interview data. Results: show substantial relationships between performance metrics and external influences, including remote learning. ML models identify academic performance factors, enabling targeted interventions and instructional improvements. Conclusion: Integrating ML technologies can improve computer science instruction and student achievement. Data-driven insights help instructors change teaching tactics, identify at-risk pupils, and create a dynamic learning environment that promotes academic success. ML techniques may help educational institutions negotiate distant learning and achieve inclusive, equitable, and successful results.
A Deep Learning Model for Neural Network Optimization for Glaucoma Classification Using Fundus and Oct Feature Fusion
Authors: Nagamani K Nanditha Krishna,
Published: April 2024

Irreversible vision loss is a common consequence of glaucoma, demands accurate and timely diagnosis for effective management. This research aims to enhance glaucoma classification accuracy by fusing information from two distinct imaging modalities like using deep learning explores the fusion of these modalities through an innovative neural network architecture with optimization. This approach combines Deep Stochastic Variational Autoencoder Convolution Neural Networks (DSVAECNN) and Adam optimization techniques to enable robust and accurate classification of glaucoma. A multi-path architecture is designed to accommodate both imaging as optical coherence tomography (OCT) radiomics features and fundus morphological features simultaneously. To ensure the effectiveness of the model, this study investigates the implementation of advanced optimization algorithm such as Adam, to expedite convergence and alleviate the risk of over fitting. The resulting model demonstrates improved generalization capabilities, critically for accurate diagnosis across diverse patient populations. A comprehensive OCT and fundus images dataset is used to evaluate the proposed approach from a representative cohort of glaucoma patients and healthy individuals. Quantitative various metrics including sensitivity, accuracy and specificity are employed toward appraise the accomplishment of the fusion-based classification model. Comparisons with current techniques show the dominance of the proposed move toward in accurately detecting glaucoma cases. This research provides the advancement of glaucoma diagnosis by effectively harnessing the synergy between fundus image and OCT scans and findings helpful information for clinics, ultimately facilitating early detection and personalized management of glaucoma, thus preserving vision for affected individuals.
Vehicle Steering Gear Sleeve Defect Detection Method Based on Machine Vision
Authors: Yi Liang, Hongwang Zhao, Xuebang Tang, Tingpeng Li
Published: April 2024

This project takes the steering gear sleeve as the research object, and uses computer vision technology to detect it. By using this method, the Canny operator is adaptive filtered and integrated with the watershed method, and many features of the image are extracted. SVM classifier was established for three representative defects, such as lack of material, burr and coating. It is used to identify the steering gear sleeve. A test platform is set up to automatically classify and remove defective parts of automobile steering gear sleeve based on UR5 robot. The accuracy and stability of detection are improved by an improved edge segmentation algorithm. The experimental results show that the system can diagnose the fault of the axle sleeve of the automobile steering gear and cooperate with the operating arm to make it possible to automatically detect and remove the fault products.
Research on Intelligent Navigation System Based on Aerospace Big Data Traffic System
Authors: Songchun Wang, Tiezhong Liu
Published: April 2024

This paper studies the navigation technology of the On-Orbit Servicing Spacecraft (OSS) and proposes an overall solution for the OSS navigation system. The composition of the OSS satellite navigation simulation system and simulation supporting environment are studied. Then a new SSUKF navigation filter is proposed based on the hyper spherical distributed feature sampling point transform algorithm (SSUT) and the non-scanning Kalman filter (UKF). The numerical simulation of the OSS system is studied based on MATLAB/RTW. Finally, the SSUKF algorithm and the conventional UKF algorithm are simulated digitally. The effectiveness and advanced nature of the hybrid Kalman filter applied to spacecraft autonomous navigation are verified.
Magneto-Mechanical Coupling Analysis of Automobile Brake by Wire System Based on Giant-Magnetostrictive Actuator
Authors: Xingjian Jia, Changlin Run, Changbao Chu
Published: April 2024

Addressing the drawbacks of traditional automotive braking systems, including long hydraulic lines, heavy weight, and slow response speed, a new type brake by wire system based on giant-magnetostrictive material is proposed, which consists of giant-magnetostrictive actuator module and mechanical drive module. In this paper, CATIA software is used to establish a three-dimensional model of the brake. In order to verify the reliability of the model, giant-magnetostrictive actuator is taken as the research object, and COMSOL software is used to simulate different working conditions of automobile braking. A Magneto-mechanical coupling model was established and Magneto-mechanical coupling analysis of the actuator was carried out. The simulation results show that the aluminum shell material is superior to the 45 steel shell material. The inductance direction of the actuator is approximately elliptical from top to bottom, with uniform distribution of magnetic flux density and stress. The output stress is 5e4N/m2, and the maximum elongation is 0.1071mm. Finally, the driving efficiency of GMA was verified on an optical isolation platform, and comparative experiments were conducted before and after optimization using instruments such as laser displacement samplers, Tesla meters, and force sensors. The experimental results indicate that by optimizing the housing material and structure of the actuator, the displacement increased from 0.05188mm to 0.1003mm, magnetic induction increased from 42.9mT to 79.9mT, resulting in an increase of 41.6% and 42.7% respectively, and the maximum output force was 4752.3N. It makes a certain contribution to the application of GMA to automotive braking field and further proves the effectiveness of GMA in the application of automobile brake by wire system.
Research on Improving the Optimization System of Agricultural and Forestry Resource Allocation Management Measures by Using Multi-Objective Optimization Algorithm
Authors: Chang Gao, Baogang Chen, HaiPing Si
Published: April 2024

At present, there are some problems in the use of forestry resource database, such as large amount of data and data centralization, which leads to the low efficiency of the system. Therefore, an application software platform for forestry informatization is built based on optimized Hadoop cluster. The system consists of four parts: permission management, file management, interface management and data processing. In this paper, a control strategy FDMDR is designed, which implements dynamic copy management according to user access frequency. A method of file access frequency limit based on DMDR is proposed, and the data in the system is processed dynamically. The experiment proves that the system can integrate traditional files, images, videos and other contents organically. The system has achieved a better integration of traditional agricultural information resources. It has good performance and scalability.
Variation of New Energy Vehicle Product Scores Based on Fuzzy Rough Set Theory and Cellular Automaton
Authors: Jiaxun You, Shouxi Wu, Fan Zhang
Published: April 2024

This study investigates the fluctuation in product scores of new energy vehicles (NEVs) using a combination of fuzzy rough set theory and cellular automaton. By integrating these two methodologies, we aim to provide a comprehensive understanding of how NEV product scores evolve over time. Firstly, the fuzzy rough set theory is employed to handle the uncertainty and imprecision inherent in NEV product evaluation, optimizing the selection of influential factors. Subsequently, a cellular automaton model is utilized to simulate the dynamic changes in NEV product scores, incorporating factors identified through fuzzy rough set theory. Through this combined approach, we can continuously monitor and analyze the variations in NEV product scores, enabling stakeholders to make informed decisions for improving product competitiveness and market performance. This study contributes to the advancement of methodologies for evaluating NEV products and offers insights into the dynamic nature of their competitive landscape.
A Novel SDN-Based Architecture of Task Offloading in Mobile Ad-Hoc Cloud
Authors: Wenjuan Shao
Published: April 2024

As the core function of mobile Ad-hoc cloud, task offloading has always been a research hotspot of mobile cloud computing, and the construction, offloading decision, task division and scheduling strategies of mobile Ad-hoc cloud directly affect the performance of offloading. In this paper, we propose a new mobile Ad-hoc cloud architecture based on SDN solving the performance bottleneck of opportunistic task offloading. The proposed solution constructs terminal clusters in Ad-hoc cloud to provide users with offloading service, and makes offloading decision and task allocation through SDN controller to achieve the effectiveness of task allocation and the rationality of resource scheduling. Simulation results show that compared with traditional scheduling method, our method has the advantages in offloading ratio and execution efficiency, and can be more effectively used in dynamic mobile edge network environment, to improve network performance and user service satisfaction.
Artificial Intelligence Based Flood and Landslide Disaster Monitoring and Dynamic Numerical Prediction System
Authors: Fajing Wang, Shuantao Dong, Xu Feng
Published: April 2024

Monitoring and dynamic numerical prediction of flood and landslide disasters are of great significance for reducing disaster risks, ensuring urban safety and stable development. The existing systems have limitations in real-time and dynamic performance, making it difficult to provide effective support for disaster prevention and management. To improve the efficiency of disaster monitoring, this article combined artificial intelligence (AI) to conduct in-depth research on the construction of flood and landslide disaster monitoring and dynamic numerical prediction systems. This article first analyzed the system functional requirements, and then designed the system architecture based on this, dividing it into three levels: user interface layer, application layer, and data layer. Finally, the BP (Back Propagation) neural network algorithm and dynamic numerical model were utilized to analyze the monitoring and dynamic numerical prediction of flood and landslide disasters. To verify the effectiveness of the system, this article compared it with GIS (Geographic Information System) based methods, and conducted testing and analysis on the system from monitoring errors, real-time monitoring, numerical simulation, and several levels. The results showed that in terms of real-time monitoring, compared with GIS based disaster prediction systems, the average response time of the AI based disaster monitoring and prediction system in this paper was shortened by 1.36 milliseconds. The conclusion indicated that the AI-based flood and landslide disaster monitoring and dynamic numerical prediction system in this article could effectively improve the efficiency of disaster monitoring and early warning, and help promote the efficient and intelligent development of disaster prevention
Real-time Gait Event Tracker (RGET): An Innovative Approach to Digital Human Modeling through Integration of Inertial Measurement Unit and Dynamic Feature Extraction
Authors: Tengxu Xia
Published: April 2024

The advancement of digital human modeling technology underscores the importance of accurately representing human characteristics and behaviors using motion capture technologies. This paper introduces the Real-time Gait Event Tracker (RGET), an advanced method aimed at precisely detecting gait events via an Inertial Measurement Unit (IMU), thereby supporting high-precision digital human modeling. RGET integrates planning techniques into continuous states and action spaces, utilizing first-order difference functions and sliding window techniques to effectively identify four key gait events: Heel Strike (HS), Toe Off (TO), Walking Start (WS), and Walking Pause (WP). The system efficiently manages IMU gait signal data through real-time queuing techniques and extracts dynamic features within time frames using positive/negative windowed first-order difference integrations. By adopting weighted sleep time methods and adaptive threshold decision rules, RGET accurately extracts gait event features from sequences. Not only does RGET support precise gait segmentation with an F1 score of 95.9%, but it also provides an innovative method for digital human modeling and its applications.
Research on Innovative Dissemination Models of Nantong’s Intangible Cultural Heritage under the Background of Big Data
Authors: Minli Shi, Xiaodong Zheng
Published: April 2024

Intangible cultural heritage is a product of human society’s spiritual civilization and a precious wealth left by history, carrying the wisdom of mankind and the civilization and splendour of human history. Nowadays, in the era of the development of cultural diversity, with the rise of a new generation of technologies such as Big Data and Artificial Intelligence, the audience of cultural heritage breaks the limitations of time and space, while the means and paths for the protection and inheritance of cultural heritage are becoming richer and richer. By summarizing the connotations and characteristics of intangible heritage, sorting out the changes and developments in its transmission methods, analyzing the opportunities for the dissemination of intangible heritage in the digital age, examining the challenges faced by Nantong’s intangible cultural heritage in its dissemination, and combining current hot topics, this study proposes new paths for the dissemination of intangible heritage in the digital era in Nantong. The aim is to achieve efficient and widespread transmission from multiple angles, providing new ideas for cultural construction and cultural identification.
Study on the Integration of Chinese Martial Arts Bing Dao in the Online + Offline Teaching Model at College Sports Education
Authors: Qixia Jia, Yongyong Wang, Pengxin Gu
Published: April 2024

At present, with the continuous development of science and technology and the continuous innovation of new educational technology, the online + offline education model is widely used in college physical education. It has been a long history for the Chinese Martial Arts Bing Dao to be an excellent Chinese traditional sports event. But the sports competition system of the Chinese Martial Arts Bing Dao has been formally established for less than 3 years. The Chinese Martial Arts Bing Dao has been developed rapidly in the past 3 years. Many training courses for coaches and referees of Chinese Martial Arts Bing Dao have been held. The rules on the competition of Chinese Martial Arts Bing Dao have been formulated and the National Chinese Martial Arts Bing Dao Championship has been successfully held. In addition, the Chinese University Wushu Long and short weapon Competition has been successfully held for three times. The integration of Chinese Martial Arts Bing Dao into college sports teaching plays a positive role in inheriting excellent traditional Chinese culture, enhancing cultural confidence and promoting the healthy physical and mental development of college students. Therefore, in college physical education, Chinese Martial Arts Bing Dao should be promoted and valued
A Comparative Study on the Aesthetics of Chinese and Russian War-themed Movies from the Perspective of Big Data
Authors: Xinlin Gao, Mingyue Sun
Published: April 2024

As an important component of national cinema, war-themed movies represent the strength of the national film industry and modern technology. From a historical perspective, Chinese war-themed films have been influenced by the aesthetics of Soviet cinema. This paper begins by exploring the historical origins and development of Chinese and Russian war-themed mainstream films, and conducts an aesthetic comparison from the aspects of ideological consciousness, humanistic reflection, new paradigms, and application of network big data technology. It elucidates the spiritual aspirations of Chinese and Russian war-themed films and grasps the development direction of such films in China.
A Feasible Flow-based Algorithm for Solving Classic Assignment Problem with Bottleneck
Authors: Yongwen Hu, Lixun Wang, Zejian Zhang
Published: April 2024

Classic assignment problem with bottleneck is a fundamental combinatorial optimization problem with numerous practical applications in diverse domains. In order to comprehensively explore the classic assignment problem with bottleneck and to develop efficient algorithm, a feasible flow-based algorithm is proposed for optimizing classic assignment problem with bottleneck. The bottleneck constraints are addressed by updating the bottleneck of the problem, and a feasible-flow approach is leveraged to efficiently identify optimal solutions within the network framework associated with such problems. To assess the effectiveness of our proposed algorithm, a comprehensive set of numerical experiments is carried out across various instances of classic assignment problem with bottleneck. Our numerical results demonstrate the superior performance of our algorithm, particularly when applied to problems with smaller cost scales, making it particularly suitable for addressing medium-sized to smaller-scale problems.
The Effect of Government Environmental Auditing on the Efficiency of Green Development in the Yangtze River Economic Belt under the Background of Big Data
Authors: Liuhua Fang, Chuanhao Wen Yun Qing, Wangying Xie,
Published: April 2024

The issue of environmental pollution has grown more noticeable due to the rapid expansion of our country's economy. The urgent demand for sustainable growth and the protection of ecological environment has arisen. In such a context, the role of governmental environmental audits is of utmost importance. This article is based on the background of the big data, from a variety of vantage points, this paper investigates the mechanisms and direct effects of government environmental assessments on green development efficacy in 108 localities along the Yangtze River Economic Belt (YREB). The study's results signify the following: (1) Government environmental evaluations substantially increase green development efficacy, even when no other control variables are taken into account. (2) The principal factors that impact such efficiency in the YREB are industrial structure upgrading and local government competition. (3) Robust tests conducted through variable substitution, estimation method replacement, and sample period alteration consistently yield significant results. This research holds significant implications for expanding the research perspective on governmental environmental audits, addressing ecological environmental issues, promoting high-quality green growth in the YREB, and achieving sustainable growth.
Research on The Application of Data Mining in Evaluation of Curriculum Teaching
Authors: Li Dandan
Published: April 2024

The effectiveness of moral education is the fundamental standard to test all the work in colleges and universities, evaluation of curriculum teaching is very important for teachers to accurately master students' learning effects .Data mining technology has great advantages in curriculum evaluation. Guided by the concept of OBE (Outcomes-based Education), this paper obtains evaluation data through data mining from the goal of talent training. The data is processed through data collection, data integration, data selection, data cleaning, data conversion ,then the AHP(Analytic Hierarchy Process)-FCE(Fuzzy Comprehensive Evaluation)  analysis model is constructed for empirical analysis, and the educational effect of big data and accounting courses is evaluated. Carrying out evaluation of curriculum teaching can promote teaching, improve the quality of personnel training, and achieve the goal of educating people.On the one hand, evaluation of curriculum teaching can provide an empirical basis to improve the talent training quality. On the other hand, it can provide theoretical reference for other majors to carry out curriculum teaching evaluation
Optimization Analysis of AI Intelligent English Teaching Strategies Based on Computer Virtual Reality Technology
Authors: Du Zenghui
Published: April 2024

The Internet of Things and other technology breakthroughs have a big impact on how English is taught (IoTV). The investigation of the actual English teaching process and the identification of student features come first. In order to use the interpolation technique with the student image-based feature detection method, we investigate the IoTV reform. As a result, we find a clever algorithm for IoTV that recognises student features more effectively. Artificial intelligence (AI) is the design technique used to create the intelligence algorithm for pupil feature recognition. A comprehensive multifunctional human-computer interaction system utilizes a variety of input and output streams. In addition to the standard computer keyboard, cursor clicking, and screen touching, the most recent speech and facial recognition technology can be employed for data input. Students learned to orally interact with the robot and act as a guide to various destinations. The Multimodal Interaction System for English Education (MMIEE) is an investigation into the use of network and artificial intelligence (AI) in teaching. Recurrent neural network (Rein RNN)-based Reinforced learning is utilized for the perpectual evaluation model to categorize the questions posed by teachers in terms of their content and kind, and to conduct experimental investigation. The results show that the proposed Rein_RNN model achieves 98.6% of accuracy in 4.3sec of mean evaluation time.
Adaptability of the Reform of Speaking Teaching Mode of Master's Foreign Language Based on Virtual Simulation Technology
Authors: Wang Zhuoxian
Published: April 2024

The speaking teaching mode for mastering foreign languages at the master's level has been significantly enhanced through the integration of virtual simulation technology. By leveraging immersive simulations, students are provided with realistic scenarios and contexts for language practice and skill development. Through virtual environments, learners engage in interactive dialogues, presentations, and role-playing exercises, simulating real-life communication scenarios. This mode allows students to apply linguistic knowledge and cultural understanding in a dynamic and risk-free setting, fostering confidence and fluency in speaking. Moreover, virtual simulation technology offers personalized feedback and assessment, enabling instructors to tailor instruction to individual learning needs and track progress effectively. This paper explores the adaptability of the reformed speaking teaching mode for master's foreign language education, leveraging virtual simulation technology and Automated Statistical Deep Learning (ASDL). The integration of virtual simulation technology enhances the speaking teaching mode by providing immersive and interactive language learning experiences. ASDL algorithms analyze large-scale linguistic data and learner interactions within virtual environments to provide personalized feedback and optimize teaching strategies. Through simulated experiments and empirical validations, the adaptability of the reformed teaching mode is assessed. Results indicate significant improvements in student engagement, speaking proficiency, and learning outcomes compared to traditional methods. Students participating in virtual simulation-based sessions demonstrated a remarkable 35% increase in speaking proficiency compared to those in traditional classroom settings. Furthermore, learner engagement levels surged by 40%, indicating heightened interest and active participation in language practice activities facilitated by virtual environments. Automated Statistical Deep Learning (ASDL) algorithms, embedded within the virtual simulation platform, provided personalized feedback tailored to individual learner needs, resulting in a 25% enhancement in learning outcomes.
Optimal Power Flow and Performance Analysis of SPV Penetration to IEEE Bus System Using MI Power
Authors: Santosh S. Raguwanshi, L.D. Arya Gaurav B. Patil,
Published: April 2024

The need to meet changing demand in an economical manner while making the best use of already-expensive electrical systems led to inescapable problems with overloading, increased power transfer coupled with extremely high system loss, and unstable voltage profiles that ultimately resulted in an unreliable system. Power distribution is impacted by the remarkable changes that are currently taking place in the use and provision of energy services. Numerous new avenues for the provision and use of electrical energy have been opened up as a result of developed and diverse distributed technologies, including distributed electricity generation. The emergence of powerful and affordable software has enabled us to cope with the fast-paced development in the power industry which requires updating resources with modern technologies to manage substations. The goal of the proposed work is to integrate solar photovoltaic and optimize the power flow, voltage profile, and real power loss through the building of a simulation model on Mi Power. Power flow analysis was used to calculate the actual power loss, which was then minimized through SPV placement. Additionally, the impact of PV penetration on single bus loading was assessed for all load buses operating in succession with either active, reactive, or combined active and reactive loads.
Regular paper Comparative Analysis of Deep Learning Models for Sentiment Analysis on IMDB Reviews
Authors: Lydia Suganya, Pratiksha Deshmukh, Kanak Pandit, Harshali Patil, Drashti Shrimal,
Published: April 2024

Within the domain of natural language processing, sentiment analysis assumes a fundamental position, facilitating the comprehension of societal perspectives and opinions, thereby playing a pivotal role in understanding public sentiment. In this study, an examination and comparison of deep learning architectures was conducted on IMDB movie reviews. We evaluated the performance of Basic Recurrent Neural Networks, Long Short-Term Memory (LSTM), Gated Recurrent Neural Unit (GRU), Bidirectional LSTM, Bidirectional GRU and 1D Convolutional Neural Networks (Conv1D) based on their training, validation, and testing accuracies. Our results indicate that while LSTM achieved the highest accuracy of 99.91% on the training data, GRU demonstrated superior performance (88.28%) on the validation dataset. Interestingly, Bidirectional GRU emerged as the top-performing model (87.54%) on the testing data, showcasing its robustness in generalizing to unseen instances. These findings highlight the importance of evaluating model performance across multiple datasets to assess their real-world effectiveness. Furthermore, our comparative analysis provides valuable understanding of the advantages and limitations of each model, offering practical guidance for selecting the optimal framework for sentiment analysis endeavors. Overall, this research contributes to the progress of such methodologies and deep learning approaches in natural language processing.
Application of SSA-KNN Algorithm in Fault Analysis of Power Metering Automation System
Authors: Yan Liu, Chaoran Bi, Yaoyu Wang Chengfei Qi,
Published: April 2024

In view of the great pressure of operation and maintenance of power energy data collection terminal after putting into operation, an intelligent fault diagnosis system for power equipment terminal based on computer was developed. This paper proposes an Agent based on autonomy, promotility, cooperation and interactivity. It also provides a flexible interactive communication interface and load balancing optimization method. It can meet a variety of data transmission specifications and a large number of data service transmission requirements. The SA-KNN algorithm is used to analyze the power quality. Methods The operation data of maintenance electricity were collected. Learning and cluster analysis are carried out by using relevant data, and the calculation model of automatic check device failure is established. At the same time, the outlier points in the collected data are used to accurately locate the fault points of the power grid. This method can realize the comprehensive fault diagnosis of the power grid by using the interaction between the power grid and each component.