bluuebunny/crossref_metadata_2025 huggingface dataset:
Fault-tolerant Integration of Charge and Discharge Information Security for Multi-Scale Time Series Models
Authors: Nan Zhang, Bin Wu, Jian Zhang, Ming Zhao, Junhao Xiao Yongsheng Zhao,
Published: April 2024

In this paper, the power mode of winding and converter topology are integrated. Considering the filter characteristics and electromagnetic torque control of the system during charging/discharging, the integrated structures of one-phase full bridge, three-phase full bridge and three-phase four-arm bridge are studied. The single-phase all-bridge integrated structure can completely suppress the electromagnetic torque generated by the motor during charging and discharging, and make the filtering effect of the system better. In this project, a multi-scale full convolutional deep neural network method based on time series is studied. The multi-scale full convolution time series model is used for training, and the future continuous signals can be monitored according to the trained model based on the power signal data collected from high-voltage power lines. Experiments show that the proposed method can effectively extract the required data.
Design of Privacy Protection System for Target Search Based on Selection Algorithm and Sustainable Iteration Index
Authors: Tao Liu, Lihong Hao, Jiwei Tang Wei Zheng,
Published: April 2024

The data self-destruct method based on privacy cloud proposed in this paper can realize the automatic destruction of cloud data after expiration, and based on this method, a self-destruct image protection system is implemented. This project intends to combine the adaptive scale invariant Feature conversion (SIFT) extraction of ciphertext images with binary SIFT to ensure the confidentiality of the images uploaded to the server in the ciphertext space. The security protection of data is realized using Paillier homomorphic encryption technology. The feature of SIFT is extracted from the encrypted region, and it is expressed in binary form to reduce the amount of computation and memory overhead. The experimental results show that this method can effectively prevent the automatic destruction of images after expiration, so as to ensure that the user's personal information is not leaked.
Digital Transformation in Physical Education: The Application of Intelligent Technology in Enhancing the Effectiveness of Sports Teaching
Authors: Gang Liu Leqiang Zhang,
Published: April 2024

With the deep development of digital transformation, intelligent technology has become a key force in promoting the innovation and development of physical education. This study aims to explore the application of intelligent technologies such as Virtual Reality (VR), Augmented Reality (AR), Artificial Intelligence (AI), and big data analytics in physical education, as well as how these technologies optimize teaching methods, improve learning efficiency, and stimulate student interest. Through literature review and questionnaire survey, this paper systematically assesses the current application and challenges of intelligent technology in sports teaching. Furthermore, using qualitative and quantitative research methods, the teaching effects of utilizing intelligent technology versus traditional teaching methods in different educational environments were collected and analyzed. The study finds that the integration of intelligent technology significantly enhances the interactivity and personalization of sports teaching, helps to increase student motivation and participation, and promotes the improvement of learning outcomes. However, there are challenges such as resource limitations, teacher training, and student adaptability in the process of implementing technology. The paper concludes with strategies and suggestions for these challenges, aiming to provide reference and guidance for the digital transformation of future physical education.
Research on Computer Classification Algorithm of Concrete Crack Based on Deep Learning
Authors: Yetao Cong Juanjuan Wang, Xin'e Yan,
Published: April 2024

In combination with transfer learning under the framework of Unet semantic partitioning, VGG16 pre-trained neural network enhanced encoder is used to extract multi-level high-level semantic information. The cross-entropy loss function is used to eliminate the imbalance between samples, and finally the crack shape is accurately semitone. Combining with the theory of computer vision, a quantitative calculation method of the crack region, length, width and other geometric characteristic parameters based on the binary segmentation template is proposed. Finally, the self-developed dam concrete crack image is taken as an example to carry out simulation and comparison test to check the correctness and superiority of the research results of this project. The research results will reveal that the crack recognition based on deep neural network can achieve a high recognition rate. The calculation results of fracture characteristic parameters meet the requirement of detection accuracy. The research results of this paper are expected to provide a new technical means for the quality control of dam concrete structure.
Comparative Analysis of use of Machine Learning Algorithm for Prediction of Sales
Authors: Sandeep Musale, Snehal Bhosale Ravikant Suryawanshi,
Published: April 2024

Predicting sales holds immense importance for businesses, particularly for large retailers. It significantly influences decision-making processes and their subsequent execution. For big retail stores engaged in marketing and sales, accurate sales prediction is crucial for effectively allocating resources, estimating sales revenue, and devising future strategies. It empowers these stores to grasp market trends, make informed decisions, and adapt to evolving conditions, ensuring long-term viability and prosperity. Given the dynamic nature of trends and market conditions, traditional forecasting methods face significant challenges in keeping pace with rapid changes. This is where the application of machine learning algorithms becomes indispensable, especially when dealing with large and complex datasets. This paper delves into the exploration of various machine learning algorithms and models to predict sales, conducting comparative analyses to assess their performance.The overarching aim is to identify the most suitable algorithm or combination of algorithms through comprehensive analysis. Furthermore, the paper scrutinizes how the characteristics of the dataset impact the performance of these algorithms, offering valuable insights for selecting the most appropriate machine learning algorithm based on performance analysis. By leveraging these insights, businesses can enhance their sales prediction capabilities and make informed decisions, thereby maximizing their success in the marketplace.
Performance of Modular Multilevel Converter in Electric Vehicles Charging Station
Authors: P.V.S.Aditya, Md. Mustafa S.Jaanaa Rubavathy,
Published: April 2024

The awareness of air pollution is the main reason for the development of Electric Vehicles. Hence, the many number of Electric vehicle connected to grid produce power quality problem in conversion of AC-DC.  In this paper, to avoid this drawback, the MATLAB/SIMULINK model of Modular Multi Level Converter is implemented in charging station. MMC not only provide bidirectional flow but also improve power quality. The converter works on boost mode and buck mode. In G2V, the boost mode is for charging the battery of electric vehicle and buck mode is discharging the battery.         For, the G2V, the buck mode reduces the voltage to charge the battery with current or voltage constant which is about the state of the charge of the battery. The out of the battery is pure dc with mmc which replace the filter also. It increases the life of the battery. The model of the MMC-based charger for electric vehicle was created in MATLAB/Simulink, and simulation results shows the output power of the MMC and circulation current is controlled.
Development and application of a substation work ticket inspection tool based on big data cloud platform
Authors: Ling Qing
Published: April 2024

In the context of the digital transformation of the power grid, based on information data mining and information fusion technology, fully utilizing big data cloud platforms to develop and apply substation work ticket inspection tools, innovating application data scenarios in the deep implementation of safety production, solving the problems of traditional work ticket inspection content and process timeliness. Through the application of substation work ticket inspection models, it is fully verified that substation work ticket inspection tools improve inspection efficiency and quality, and play a positive role in reducing personal and equipment risks during on-site operations.
The Effectiveness of ChatGPT in Translating Chunky Construction Texts in Chinese Political Discourse
Authors: Shaolong Liu Huan Cao,
Published: April 2024

From the theoretical perspective of Chinese-English temporal-spatial cognitive differences, this study employs a self-constructed bilingual parallel corpus of political discourse totaling one million words. It extracts chunky construction texts with latent agents in the source language (Chinese) and corresponding texts of target language translations (English) for a comparative experiment between ChatGPT translations and those of human translators. Driven by the “Human-AI Interaction Model”, three different prompts were set up to conduct three rounds of translation testing with ChatGPT. The study discovers: 1) ChatGPT has limitations in actively understanding the implicit elements of the source Chinese texts that are driven by a strong spatial preference, which are also reflected in the target language English translations; 2) The “Human-AI Interaction Model” can guide and train ChatGPT effectively through continuous optimization of prompts, enhancing AI abilities in understanding the source language and achieving effective target language translation; 3) Currently, ChatGPT cannot replace human translators in translating chunky discourses of Chinese, but the collaborative interaction between human translators and AI is highly effective. By optimizing the design of prompt instructions, this research offers cognitive reference and operational paradigms to improve ChatGPT’s recognition and cognitive reconstruction capabilities of Chinese-English temporal-spatial differences, providing insights for future research, translation practice, and teaching in related areas.
Research on the Path of Digital Technology Empowering Teaching Reform in Japanese Majors in Chinese Universities---A Case Study of ZYU
Authors: Chen Xu
Published: April 2024

With the rapid development of information technology, the application of digital technology in foreign language teaching is becoming increasingly widespread. Driven by the Chinese government’s policies on education digitalization, Japanese majors at Chinese higher education institutions are actively embracing digital technology to drive teaching reform. This paper, taking Zhejiang Yuexiu University (ZYU) as an example, discusses how digital technology can empower teaching reform in Japanese majors, analyzes the attempts and shortcomings of the current digitalization reform of Japanese majors, and proposes specific reform paths in conjunction with China’s national policies and the trend of educational digitalization development.
Analysis of the Key Factors of User Behavior Measurement in Unexpected Events based on Feature Extraction and Selection
Authors: Chen Ying
Published: April 2024

Artificial intelligence is important for people’s daily life. In recent years, short video platforms have risen rapidly, and TikTok APP is one of the most outstanding among them. AI tools are applied to support their big data recommendation algorithm. In the era in which everyone is using the Internet, the public can often find the reflection of all kinds of attitudes and behaviors when unexpected events occur, and this influence will inevitably become more and more important as well as draw more and more attention. Therefore, taking the key factors of the user's behavior on the TikTok platform as the research object, this thesis takes “3.21 China Eastern Airlines Passenger Plane Accident” event as the background to extend the topic. Then, the existing data of TikTok platform such as Cicada Mama is used to capture and analyze the key factors of users’ behavior in the backdrop of unexpected events, such as user attention, user influence, the heat of the event itself. Next, according to AI methods of statistics-related data, the calculation method for each factor is proposed. Finally, the analytic hierarchy process is used to establish the method of key factor measurement, to analyze the weight of each factor in the emergency.
The Application of Informatization Teaching and Flipped Classroom in the Teaching of Traditional Culture in College Languages
Authors: Nan Jiang, Yuping Ou Jie Cheng, Zhimei Liu, Fang Zhou,
Published: April 2024

For the teaching of traditional Chinese culture in universities, fully utilizing existing resources for innovation is crucial to its teaching effectiveness. Hence, this study advocates for the utilization of information technology and the flipped classroom methodology in university-level traditional Chinese culture education. By thoroughly examining the fundamental goals of teaching traditional Chinese culture at universities, this paper suggests employing information technology to develop diverse educational scenarios. These scenarios encompass generating cultural contexts, simulating cultural experiences, enhancing interactive exchanges, and broadening the scope of educational materials, all aimed at enriching the pedagogical approach to traditional Chinese culture. In the stage of designing flipped classrooms for traditional Chinese culture teaching in universities, the ARCS theory was introduced. Guided by attention motivation, association motivation, confidence motivation, and satisfaction motivation, research was conducted on course content design, teaching methods and means, learning experience, feedback, and evaluation. Through the integrated application of information technology and flipped classroom strategies, our objective is to diversify the pedagogical approaches for traditional culture. This initiative is designed to boost students' comprehension and learning outcomes regarding traditional culture, foster their enthusiasm and knowledge depth in traditional Chinese cultural studies, and encourage the preservation and evolution of traditional cultural heritage.
Unlocking Vital Professional Skills of EFL Educators to Cultivate Student English Core Competencies in the Digital Era
Authors: Kun Huang, Ahmad Johari Bin Sihes Qihua Sun, Ping Qu,
Published: April 2024

With the rapid development of artificial intelligence (AI) technology, the education sector has witnessed transformative changes. In the domain of secondary English language teaching, the advent and implementation of intelligent teaching systems have necessitated a reevaluation of Teacher Professional Competency (TPC). However, there is limited research exploring English as a Foreign Language (EFL) teachers’ perspectives on the TPCs required for fostering student English core competencies in the ear of AI. This study aimed to investigate the need for relevant TPCs among English teachers. A sample of 1089 English teachers from 335 schools across China participated in this research. The findings identified a total of 12 TPCs, with variations observed based on gender, years of teaching experience, school type, school level, and school location. Notably, Teaching Design Competency (TDC), English Knowledge and Application Competency (EKAC), and Teaching Implementation and Management Competency (TIMC) were identified as the most crucial TPCs. These results provide valuable insights into the components of EFL teacher professional competencies and offer guidance for competency-based teacher digital education programs.
Emerging Trends in Sports and Artificial Intelligence: A Scientometric Analysis in Citespace
Authors: Xiangyu Wang, Jun Chu Yue Ren, Shaowei Wang,
Published: April 2024

This study endeavors to utilize CiteSpace software to construct and examine co-citation networks of references within the domains of sports and artificial intelligence (AI), gathered from the Web of Science. The aim is to delineate the progression of AI applications in sports and to pinpoint current research focal points. Analyzing a corpus of 2,493 papers sourced from the Web of Science via CiteSpace, the findings reveal a year-on-year escalation in the application of AI within sports over the last twelve years (2010 to 2021). Machine learning has found extensive application in sports, notably in the analysis of athlete behavior, prediction of match outcomes, and physiological monitoring, among other areas. The evolution of sports and AI has witnessed two significant fluctuations within the past twelve years, specifically in 2011 and 2014. The modularity shift in 2011 indicated an uptick in the analysis of tasks and the identification of human activities. The year 2014 was marked as a pivotal moment with the advent of visualization utilization in team sports and wearable technologies. The study underscores the presence of numerous unexplored intellectual avenues within the sports and AI domain, which warrant further exploration in future research endeavors. Key milestones spanning from 2010 to 2021 are elucidated through visual analysis.
Near-neighbor Propagation Clustering Algorithm Based on Cuckoo Search
Authors: Guodong Li Yao Wang, Fuguo Liu,
Published: April 2024

In this paper, a nearest neighbor propagation clustering algorithm (CSB-AP) based on cuckoo search is proposed to solve the problem of poor parameter setting of the AP algorithm. A population-intelligent optimized cuckoo search algorithm is introduced into the AP algorithm to find the appropriate parameters. Two important parameters in the nearest neighbor propagation algorithm are taken as the location of the bird’s nest. The BWP value index is introduced as the bird’s nest fitness in the process of intelligent search, and the minimum value is obtained as the final fitness through the reverse mechanism. The final parameters are substituted into the calculation as the best parameters. The CSB-AP algorithm is verified by the UCI data set, and compared with the traditional AP nearest neighbor propagation clustering algorithm, it is found that the CSB-AP algorithm proposed in this paper is better than the traditional AP nearest neighbor propagation clustering algorithm. By comparison, it can be found that the clustering result obtained by the CSB-AP algorithm is closer to the actual result, and it can be known according to the results of the BWP index, contour coefficient, Recall, and F-measure. The improved algorithm can significantly improve the clustering quality and clustering performance.
Research On the Path of High Quality Development Enabled by Big Data Technology in the Curved Area of The Yellow River
Authors: Minjie Wang Jiangxin He,
Published: April 2024

Big data is a key factor of production to promote the current social development, and the good use of big data technology is a must to achieve high-quality development. Jiziwan of the Yellow River is a strategic overlapping area of the western development and the overland Silk Road, which has very important strategic value both in terms of geographical space and historical development. Therefore, it is particularly important to enable the high-quality development of the Yellow River Jiziwan region with big data technology. By analyzing the relevant data of the high-quality development of the main provinces of the Yellow River Bend in recent years, this paper makes a comparison of the gross product of the region in recent years, the added value of the three major industries, and the R&D investment of the industrial enterprises, so as to get the results of the high-quality development of the Yellow River Bend area and the existing problems. Through these analysis results, this paper puts forward some solutions: using big data technology to optimize regional industrial structure, promote high-quality ecological development and promote innovative development. These optimization paths help promote the importance of the digital economy and promote high-quality, sustainable and coordinated development in the region.
Analysis and Value Evaluation of Carbon Emission Reduction Strategies in the Power Industry Based on Real Options Theory and XGboost Algorithm
Authors: Guodong Li
Published: April 2024

China, as the world’s largest energy consumer, has made significant commitments regarding carbon peaking and carbon neutrality. In order to realize the goal of “dual carbon,” this paper proposes a method for analyzing and evaluating carbon emission reduction strategies in the electric power industry based on physical options theory and the XGBoost algorithm. Starting from the study of carbon emission reduction strategies for power enterprises, the model is oriented towards investment in energy-saving and emission reduction technologies for thermal power, aiming to construct the least-cost carbon emission reduction strategy. Additionally, it comprehensively considers changes in carbon trading mechanisms and the uncertainty of investment budgets for emission reduction, integrating machine learning algorithms to assess the emission reduction value of enterprises during the research period. The aim is to provide theoretical guidance and strategic suggestions for existing thermal power enterprises to cope with the implementation of carbon trading mechanisms. Finally, through case analysis, the effectiveness of this method is validated.
Research on the Influence of Network Culture Group on College Students' Ideological and Political Education from the Perspective of Big Data Analysis
Authors: Zhanwei Tian Tao Jiang, Yuxuan Yan,
Published: April 2024

Today's society has entered the era of big data. Big data technology has been applied to ideological and political education in colleges and universities. The application of big data analysis in the field of ideological and political education in colleges and universities can promote the inclusive expansion, individualized teaching and expansion of ideological and political education in colleges and universities. Internet new media provides unprecedented space and potential for cultural innovation in the youth circle, and expands the path of production, dissemination, consumption and participation of cultural individuals and groups. The network culture circle group has gradually emerged and developed into the main place for the breeding and spread of network public opinion, which has an important impact on the ideological and political education of college students. From the perspective of big data analysis, colleges and universities need to do a good job in reshaping educational concepts, improving political positions, enhancing public opinion monitoring, and strengthening position management, and explore the path of ideological and political education for college students from the perspective of network cultural circles, so as to do a good job in ideological and political education for college students.
The Numerical Simulation of Marangoni Problems Employing Multi-phase Parallel SPH Method
Authors: Dudou Wang, Yuxiang Liu Lintao Zhang, Hongfu Qiang, Yujie Zhu,
Published: April 2024

When facing microscale problems, the phenomenon of droplet movement caused by temperature gradients is usually attributed to the Marangoni effect. This study first constructed a multiphase smoothed particle fluid dynamics (SPH) system structure, integrating continuous surface force (CSF) model and multiphase model to explore the behavior of droplets under Marangoni effect. To further improve computational efficiency, this study also utilized the Open Multi Processing (OpenMP) parallel computing framework to perform parallel optimization on the SPH algorithm. Subsequently, the effectiveness of the constructed SPH framework in addressing thermal capillary phenomena was verified by simulating the Marangoni migration phenomenon of silicone oil droplets in fluorine solutions. In addition, this study also investigated the polymerization process of two droplets in microchannels under the Marangoni effect, and obtained the complete dynamic changes of droplets from motion to polymerization. During this period, we conducted a detailed analysis of the variation process of the velocity vector field, the spatial distribution of the temperature field, and the evolution characteristics of the surface tension gradient during the droplet movement process, thereby comprehensively revealing how the Marangoni effect drives the phenomenon of droplet movement and aggregation.
Goal Progression, Trait Changes, and Practice Paths of Artificial Intelligence Ethics Education of Professional Courses in Higher Education
Authors: Wenqi Zhang Dake Qian,
Published: April 2024

Artificial Intelligence as an emerging technology in a large number of applications at the same time gradually revealed many ethical issues. Artificial Intelligence ethics education has become an important initiative and a key link in the implementation of the fundamental task of moral education in Chinese higher education institutions, however, Artificial Intelligence ethics education in professional courses often lags behind the education of course knowledge and skills, and it is difficult to achieve the integration of the three educational requirements. This paper proposes that Artificial Intelligence ethics education in professional courses should meet the requirements of the progression of course teaching objectives and adapt to the changes of educational traits from explicit and implicit to integration, and puts forward a practical path of Artificial Intelligence ethics education in professional courses that is consistent with the objectives, adapted to the traits, and constructed with multiple synergies according to the progression of the educational objectives of the professional course system and the changes of the educational traits. This paper also proposes the following suggestions for ethical education in professional courses: in order to achieve the goals of ethical teaching, it is necessary to establish connections between courses at different stages of cultivation, so that each course forms a hierarchical, collaborative, and supportive relationship. After the relationship is established, it is necessary to strengthen the collaborative participation of various subjects and corresponding practical guarantees in the specific teaching practice. This helps to form a system of Artificial Intelligence ethics education throughout the whole process, which in turn improves students' Artificial Intelligence ethics literacy.
AUTOMATED GARBAGE SEPARATION USING AI BASED ROBOTIC ARM
Authors: Rajeshree Shinde, Shridevi Vasekar, Vaishali A. Patil Jayashree P. Tamkhade, Anup Ingale, Ketki P. Kshirsagar,
Published: April 2024

This paper aims to present an automated garbage separation system using a deep learning approach and integrating it with a robotic arm. The system is designed to separate different types of garbage, such as plastics, metals, and glass, based on images captured by sensors and analyzed using a deep learning model. The system is then integrated with a robotic arm that picks up and sorts the garbage based on the model's predictions. This system involves data collection, preprocessing, model building, training, testing, and deployment, as well as robotics engineering and sensor technology. The system has potential applications in waste management and recycling, including municipal waste management, recycling facilities, industrial waste management, landfills, and smart cities. It aims to improve the efficiency, accuracy, and safety of waste management systems and promote sustainability.
Brain Tumor Detection Using Classification Model
Authors: Saumitra Patil, Aryan Paratakke, Archita Kulkarni Latika Pinjarkar, Poorva Agrawal, Gagandeep Kaur,
Published: April 2024

This study aims to develop automated methods for detecting and segmenting brain tumors in MRI scans using neural networks and conventional image processing. Accurately identifying tumor location and boundaries is crucial for diagnosis and determining the most effective treatment approach. Manual review by radiologists is time-consuming and subjective. However, recent advances in deep learning now enable more efficient analysis of large MRI datasets.
A variety of neural network models have been explored for tumor segmentation, including recurrent networks, autoencoders and convolutional networks. RNNs are well-suited to capture contextual information across sequential MRI slices. Autoencoders perform unsupervised feature learning from the images. CNNs have shown promise due to their ability to directly learn visual patterns from pixel values. Unsupervised clustering techniques such as fuzzy c-means and k-means are also investigated to group tumor pixels based on intensity similarity without labeled data.
Previous research combining these techniques has achieved high segmentation accuracy between 84-97% using additional image processing steps. Automated segmentation provides benefits over manual inspection such as reduced workload and more consistent delineation of tumor margins. It can precisely outline growths to aid radiologists in diagnosis and pathologists in assessing malignancy.
The detection and segmentation of brain tumors has applications in both radiology and oncology. Radiologists could benefit from faster scan review and automated report generation. Neurosurgeons would gain insights into a tumor's size, location and infiltration to determine the most suitable surgical approach. Oncologists could utilize quantitative tumor features to select the most targeted radiation therapy or chemotherapy protocols. Overall, continued advances in deep and machine learning hold promise to improve diagnosis and management of brain cancer patients.
Planning, Design, and Execution of a Quantum Incremental Clustering Algorithm System for the Examination of Unsupervised Data
Authors: Vikrant Chole, Nitin J. Janwe, Narendra Chaudhari Vaibhav D. Dabhade, Pornima  Niranjane, Prarthana A. Deshkar,
Published: April 2024

The exponential growth of data in various domains has underscored the need for efficient unsupervised learning algorithms to discover hidden patterns and structures within large datasets. Traditional clustering algorithms often face challenges in handling dynamic datasets and require periodic retraining, making them less suitable for real-time applications. In response to these challenges, this research introduces a Quantum Incremental Clustering Algorithm System (QICAS) designed to adapt to evolving datasets and provide timely insights into the underlying structures. The proposed QICAS is based on quantum computing principles, leveraging the unique properties of qubits and quantum superposition to perform clustering tasks more efficiently than classical counterparts. The research focuses on the planning, design, and execution of the QICAS, ensuring a comprehensive understanding of the algorithm's capabilities and performance.
Clinical Applications of Machine Learning in the Diagnosis, Classification and Prediction of Heart Failure
Authors: Rabie Ahmed Eman H. Abd-Elkawy,
Published: April 2024

Heart failure (HF) remains a leading cause of mortality and morbidity worldwide, necessitating innovative approaches to its diagnosis, classification, and management. This paper explores the transformative potential of machine learning (ML) technologies in the realm of cardiology, with a particular focus on heart failure. Through a comprehensive review and analysis, we examine the application of various ML algorithms in enhancing the accuracy and efficiency of HF diagnosis, the nuanced classification of its types and stages, and the predictive modeling of patient outcomes. The synthesis of findings highlights the integration of ML with traditional clinical practices, underscoring the improved diagnostic and prognostic capabilities thus afforded. Additionally, the paper addresses the challenges of data quality, privacy concerns, and the integration of ML tools into existing healthcare systems. By presenting case studies and emerging trends, we illuminate the path forward in leveraging big data and AI to revolutionize heart failure care. This research not only underscores the significant strides made in applying ML to heart failure but also charts a course for future investigations and clinical implementations that could further enhance patient outcomes and healthcare efficiencies.
Performance of Flood Early Detection System (FEDS) and Artificial Neural Network on Predicting Flood in the City
Authors: Subekti Agung Wahyudi Biantoro,SI Wahyudi ,Moh. Faiqun Ni'am,
Published: April 2024

An integrated flood detection system that is easily accessible to the public is one of the efforts to reduce the impact of flooding in flood-prone locations. The performance of flood detectors integrated with the internet helps make it easy Public for access information about possibility happening flood. Information about bulk rain, high water level, the water discharge will Becomes indication possibility happening downstream flooding. Tool prototype detector flood this be equipped with temperature and humidity sensor as addition information for society. Research results show that performance tool detector flood already good because capable give information related to data that can be made indication possibility happening flood. Sensors used have score small mistake and after calibrated got score constant for standardize results testing. Bulk sensor test results rain produces an average error of 3% and after calibration obtained constant of 1.03. High sensor test results water level has an average error of 1.07% and after calibrated obtained score constant of 0.98. Humidity sensor testing have the average error value is 3% and after calibration obtained score constant 1.03
Advanced Framework for Multi-Modal Healthcare Data Integration: Leveraging HPC with GPU Computing and CNN Architecture in CDSS
Authors: S. Sagar Imambi Santosh Kumar,
Published: April 2024

In this study, we shall be looking at the challenges involved in integrating multi-modal healthcare data in the clinical decision support systems (CDSS). We propose the Automated Multi-Modal Data Integration (AMMI-CDSS) algorithm, which will utilize the latest high-performance computing (HPC) techniques such as the Convolutional Neural Network (CNN) architecture and the Graphics Processing Unit (GPU) computing to provide precise and rapid analysis. Which features will be extracted, multi-modal data will be merged, data will be prepared and algorithms developed in a distributed computing environment. We illustrate how AMMI-CDSS through the use of real world datasets such as wearable sensors data, medical imaging, genetic data, and electronic health records (EHRs), can improve the clinical decision support. By performing harmonization of the diverse data sources into a unique dataset after thorough data preprocessing and complex calculations, AMMI-CDSS provides the analysis with better quality and coherence.
Our study allow us to make conclusion about how HPC-based CDSS models can be compared to conventional machine learning ones using their scalability and performance as key metrics. We enrich CDSS with the methodical framework for one-by-one testing and evaluation of proposed models and multi-modal healthcare data analysis. Future research might explore novel methods for integrating diverse types of healthcare data, as well as enhancing the HPC-based CDSS models by keeping them up-to-date.
La ecología de medios: un ambiente y un territorio complejos por reconocer
Authors: Islas-Carmona Octavio , Gutiérrez-Cortés Fernando Ignacio , Arribas-Urrutia Amaia , Strate Lance
Published: April 2024

La ecología de medios (Media Ecology) es el tema central del volumen 27, número 1, de la revista científica Palabra Clave, el cual comprende diez textos. En la presentación del número abordamos la ecología de los medios como metadisciplina compleja que de ninguna manera puede ser encorsetada en el imaginario de la comunicología y destacamos la extensión y amplitud de su objeto de estudio, que comprende desde las primeras tecnologías desplegadas por el Homo Sapiens hasta la inteligencia artificial.
SSSCIP’s Perspective on the cyber-attacks unfolded in the context of the military conflict between Russia and Ukraine (January 2022 – January 2024)
Authors: OLTEANU Mihai
Published: April 2024

This paper evaluates the reports of the SSSCIP regarding cyber-attacks carried out against Ukraine from January 2022 to January 2024. From the exploitation of the CaddyWiper malware, attributed by SSSCIP to APT SANDWORM, to the sophisticated campaigns of the FSB and the cyber-attack on Kyivstar, the paper provides an insight into Russian-origin cyber-attacks against Ukraine, as reported by the main Ukrainian authority in the field, SSSCIP.The purpose of the article is to identify how SSSCIP reported cyber-attacks on Ukrainian IT&C infrastructures, the completeness of the published data, and the way the campaigns are presented. To achieve this goal, all SSSCIP reports from the reference period were evaluated, and only those that materialized and affected IT&C infrastructures were included in the study. In conclusion, the paper will primarily highlight the limitations of SSSCIP reports and, secondarily, SSSCIP’s perspective on the domains most frequently targeted by cyber-attacks and the capabilities of Russian actors.
Travel Intelligence: Enhancing Counterterrorism and National Security
Authors: KANELLOPOULOS Anastasios-Nikolaos , STAVROPOULOS Dimitrios
Published: April 2024

In the contemporary landscape of global security challenges, addressing the intricate dynamics of Counterterrorism and National Security is paramount. This paper emphasizes the pivotal role of Travel Intelligence (TRAVINT) in enhancing Counterterrorism (CT) strategy and improving security measures. Eventually, through a comparative analysis between theoretical frameworks and practical applications, supplemented by case studies of Hezbollah and Hamas, the study examines the significance of TRAVINT, in proactively identifying and preventing potential Terrorism and National Security.
Operațiile tactice în mediul montan, în contextul cerințelor câmpului de luptă contemporan
Authors: NISTORESCU Claudiu Valer
Published: April 2024

Integrarea noilor tehnologii la nivelul operațiilor militare afectează întregul proces al acestora, imprimându-le o serie de particularități, modelând, totodată, caracterul războiului. Precizia și raza lungă de acțiune a noilor sisteme de armament, utilizarea extensivă a senzorilor multispectrali, îmbunătățirea permanentă a relației sensor-to-shooter, dezvoltarea capabilităților fără pilot sunt doar câteva dintre realitățile câmpului de luptă modern. În acest context,marcat de o schimbare de paradigmă în ceea ce privește relația manevră – foc – protecție, se pune întrebarea dacă operațiile militare executate în mediul montan își mai păstrează relevanța în ecuația câmpului de luptă contemporan.De regulă, limitate spațial, acest tip de operații sunt supuse transformării din perspectivă doctrinară și operațională. În acest sens, printr-o explorare longitudinală și interpretativă a operațiilor terestre, dar și printr-o analiză calitativă a cerințelor câmpului de luptă contemporan, am identificat și am descris repere în ceea ce privește adaptarea forțelor de vânători de munte și a operațiilor executate de către acestea.
The theory of the regional security complex — Case study, the riparian states of the Black Sea
Authors: GHENADE Adrian , ONU Elena
Published: April 2024

Known in Antiquity as the Pontus Euxinus, the Black Sea has been the bridge between European and Eastern civilizations since ancient times. Possessing a multi-varied mosaic of cultures, the Black Sea area has facilitated over the centuries, both the development of commercial and political relations and the maintenance and production of conflicts, being like the Sword of Damocles. Located at the intersection of three security zones (Euro-Atlantic, Russian, and Eastern), the Black Sea is currently a vulnerable space in terms of security. Heir to Byzantine culture, most of the riparian states have a complicated internal and external policy, being caught between the idealism of the Western world and the realism of the Eastern European space. At the same time, the revisionism of the Russian Federation and Turkey in terms of foreign policy will also mean a change in the dynamics of the relations between the states bordering the Black Sea, which could result either in its return to the status of a Russian lake or in a division of the spheres of influence between the Russian Federation and Turkey. In this sense, in order to analyze the future security dynamics of the riparian states, we used the theory of the regional security complex, which we consider very appropriate in our study of the Black Sea region.
استخدام وسائل التواصل الاجتماعي لدعم ممارسات الاتصالات الحكومية تحليل من المستوى الثاني
Authors: السيد سالم شيماء
Published: April 2024

تغير شكل ممارسات الاتصال الحكومي حول العالم، من خلال تبني أنماط الاتصال الرقمي، وخلق مفاهيم مبتكرة للممارسات لدي القائمين على الاتصال الحكومي؛ وترتكز هذه المفاهيم على التفاعلية، وتطوير المحتوى الإعلامي الرقمي، وتوظيف الموارد الرقمية لتطوير آليات العمل بما يزيد من كفاءة الأداء الاتصالي للمؤسسات الحكومية. لذا زاد اعتماد تلك المؤسسات على منصات التواصل الاجتماعي لدعم قيم الانفتاح والشفافية والتفاعلية، مما ساهم في زيادة مشاركة الجمهور في صنع القرارات الحكومية، وتحسين كفاءة ممارساتها الإدارية والاتصالية. تنتمي هذه الدراسة من حيث المنهج إلى دراسات تحليل المستوى الثاني Meta-Analysis ، وتسعى لتحليل نماذج من البحوث العربية والأجنبية في مجال استخدام وسائل التواصل الاجتماعي لدعم الاتصالات المؤسسية الحكومية (59 بحثاً في الفترة ما بين عام 2010 إلى عام 2021 (، وذلك بهدف رصد التطورات العلمية والنتائج القيمة في هذا المجال، والتعرف على المناهج والأطر النظرية والمعرفية التي اعتمد عليها، في محاولة لتقديم رؤية مقترحة لتطوير البحوث العربية في هذا المجال على مستوى أجندة القضايا البحثية، الأطر النظرية، والمناهج والأدوات البحثية.

الكلمات المفتاحية: وسائل التواصل الاجتماعي؛ الاتصال المؤسسي؛ الاتصال الحكومي؛ الاتصال الرقمي؛ تحليل من المستوى الثاني.
The Role of Institutions in the Civilization Schism (the Case of Christianity)
Authors: Barakhvostov P. A.
Published: April 2024

The geopolitical transformations of recent decades have added to the topicality of the study оf civilizational differences between the East and the West relevant. Religion is often cited as a contributing factor to these differences. This can be confirmed by the example of the split of the Christian Church into the Roman Catholic and Orthodox churches and, as a consequence, the emergence of the Western and the Eastern Christian civilizations. As a rule, the authors “isolate” religion from the social system, considering it independently of all other spheres, as the primary factor that triggers the emergence of civilization. In this work, the emergence of a new Western and Eastern Christian, is considered within the framework of the institutional approach, as a process of formation of a certain complex institutional structure that regulates the life of society and consists of a whole complex of interconnected and interdependent economic, political, and socio-cultural institutions, including religion. This process can begin in the bowels of the “mother” civilization, causing its crisis. The stages of development and resolution of such a crisis are analyzed using the example of a split in the united Christian world using a matrix approach. This approach assumes that civilization is characterized by a civilizational matrix formed by an institutional core that simultaneously contains market and redistributive institutions on the basis of dominance/complementarity, and a shell that includes cultural-religious and national-demographic characteristics, the naturalclimatic and material-technological environment. The necessity of both strengthening dominant institutions in the institutional core and creating balancers (complementary institutions) for the sustainable development of civilization is shown.
Simplifying Rogue Scholar Infrastructure
Authors: Fenner Martin
Published: April 2024

From the start last year one important goal for the Rogue Scholar science blog archive was to make it easy to use for blog authors and readers. Today I want to focus on another aspect: keep it simple to run Rogue Scholar infrastructure. To address that goal I started development work last week to further simplify one important aspect of Rogue Scholar infrastructure: metadata conversion.
Penguatan Kompetensi Santri Melalui Pendidikan Kewirausahaan: Sebuah Literature Review
Authors: Siswantoro Siswantoro
Published: April 2024

Pesantren sebagai lembaga pendidikan memiliki potensi untuk diberdayakan. Salah satu usaha untuk memberdayakan pesantren adalah dengan membangun pendidikan pesantren yang berorientasi pada entrepreneurships untuk meningkatkan kompetensi lulusan pesantren. Tujuan penelitian ini adalah menyajikan informasi mengenai pemberdayaan pesantren melalui konsep pendidikan kewirausahaan dalam usaha meningkatkan kompetensi kewirausahaan santri. Metode penelitian berjenis kualitatif dengan pendekatan studi pustaka (library research). Hasil penelitian menunjukan bahwa pemberdayaan pesantren melalui konsep kewirausahaan telah banyak dilakukan di berbagai pesantren di Indonesia dan terbukti telah memberikan dampak positif bagi peningkatan kompetensi kewirausahaan santri dan kemandirian ekonomi pesantren. Pelaksanaan pendidikan kewirausahaan di pesantren dapat dilakukan melalui empat tahapan yaitu mengenali potensi usaha pesantren, penanaman modal, peningkatan kapabilitas pengurus pesantren, dan pendidikan kewirausahaan. Untuk mengimplementasikan pendidikan kewirausahaan pesantren diperlukan kontribusi dari berbagai stakeholder seperti pemerintah, swasta, masyarakat dan perguruan tinggi