bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic food web model based on Lotka Volterra model
Authors: You Xinyi , Wu Jiaying
Published: August 2024

Lampreys are regarded as parasites with significant impacts on the ecosystem in some lake habitats, and analyzing the relationship between the population characteristics of lampreys and the ecosystem is of great significance for improving the ecological environment and maintaining the ecological balance. The sex ratio of lampreys varies depending on the external environment. To gain insight into the issue of sex ratios of lampreys in ecosystems and their dependence on local conditions, this paper firstly develops a Lotka-Volterra model to characterize the effects of changes in the sex ratio of lampreys on predators. The dynamics of male and female lampreys were modeled separately. A reproduction rate impact factor k was introduced to describe the effect of the poison TFM on lamprey populations, considering human intervention. A pattern of change in predator population size was derived: the proportion of males was negatively correlated with the number of predators. Considering that the larvae and adults of the lamprey are in different ecological niches, the model was further optimized to establish a dynamic food web model containing multiple trophic levels, and to derive the changes in the number of groups in the ecosystem over time. These data were used to construct a comprehensive evaluation index of ecological stability, establish an AHP-PCA integrated model, calculate the weights of each index, and analyze the comprehensive evaluation index. It was concluded that the proportion of males in the lamprey population fluctuated regularly over time, and when the proportion of males in the lamprey population increased, the comprehensive evaluation index declined, forming a fluctuating trend like that of the lamprey population.
AI-Driven ChatGPT Applications for Enhancing Music Education
Authors: Sun Xiaofan
Published: August 2024

Dut to the limitations inherent in traditional music education approaches, the remarkable predictive and analytical prowess of artificial intelligence has been integrated to amplify the quality of music education. This paper investigates the integration of ChatGPT, an emerging machine learning technology based on deep learning and Natural Language Processing, with music education, and analyzes various approaches. These include the utilization of contemporary spectral analysis techniques to deconstruct songs and the adoption of three deep neural network models: SIConvNe, JointEmbedNe, and DistMatNet, to evaluate music. By combining the GPT architecture with Constraint Satisfaction Programming, ABC notation, and Domain-Specific Language technology frameworks, differentiated music exercises are generated. By employing a Large Language Model, centered around the LLaMA2 model, and refining its capabilities through ChatGPT pre-training and fine-tuning, the understanding and generation of music are continually enhanced. Finally, to address the challenges of inadequate explainability, algorithmic limitations, and low applicability in this field, this study discussed the combination of domain expertise with expert systems for model training, and employs Layer-wise Relevance Propagation and Local Interpretable Model-Agnostic Explanations to increase system transparency. Integrating audio analysis and visualization technologies, expanding system capabilities to accommodate a broader range of exercises, and developing algorithms to facilitate more interactive and personalized learning tools all serve to bolster the system's adaptability and flexibility. Utilizing domain-adaptive techniques, transfer learning, and multi-task learning methods, the GPT model's adaptability and generalization capability are strengthened, enabling it to skillfully generate pieces in varying styles based on a broader music corpus.
The influence of network structure on different gradient descent optimization algorithms
Authors: Yan Lingyun
Published: August 2024

The gradient optimization algorithms and network architectures play pivotal roles in the field of artificial intelligence. However, there is limited research comparing multiple optimization algorithms across different network structures. In this paper, the effectiveness of several optimization techniques in image processing tasks is investigated, along with an investigation of their effects on different neural network architectures. LeNet, AlexNet, and the backpropagation neural network are the three popular neural network architectures included in the selection, along with three different optimization algorithms. An extensive assessment of training loss, test accuracy, convergence time, and other metrics was carried out to determine how well these algorithms worked in various network designs through rigorous experimentation on image datasets. The results show complex differences in the impact of optimization techniques across various neural network configurations, providing essential information for the choice of the best algorithms and network structures.
Research for Car Price Prediction Base on Machine Learning
Authors: Jiang Xianshun
Published: August 2024

This report details a machine learning project aimed at predicting car prices, which is crucial and meaningful for business owners to forecast the market, avoid financial loss, and for buyers to have a rough overview of each car’s price. To accomplish the goal, this project using the Car Price Prediction Challenge dataset from Kaggle. The dataset includes a variety of automobile attributes such as brand, model, year of manufacture, engine specifications, mileage, and other relevant factors. The project's goal was to create an accurate and reliable model to predict car prices based on these characteristics. To accomplish this, the paper utilized multiple machine-learning algorithms and techniques to analyze the dataset. This research’s findings highlighted the effectiveness of these machine-learning methods in car price prediction. The random forest regressor emerged as the best-performing model, achieving high accuracy with a low mean squared error and a high R-squared value. Additionally, the feature importance analysis sheds light on the key factors influencing car prices, offering a deeper understanding of market dynamics.
Exploring Deep Learning Strategies and Prospective Developments
Authors: Sun Yi
Published: August 2024

Single Image Super-Resolution (SISR) is a process designed to transform Low-Resolution (LR) images into High-Resolution (HR) counterparts. This technology finds critical applications in various sectors, including gaming, photography, and medical imaging. With the advent and widespread success of deep learning, this approach has been increasingly applied in the realm of SISR. Deep learning-based SISR models are primarily categorized into three types based on their nonlinear module structures: Convolutional Neural Network (CNN)-based models, Generative Adversarial Network (GAN)-based models, and Transformer-based models. This paper presents a comprehensive overview of several emblematic models within each category. An in-depth analysis and comparison of their structural nuances and experimental outcomes are provided. This comparison elucidates how enhancements in network architectures and refined loss function optimizations contribute substantially to advancements in performance. Concluding with an analysis of current models, the paper outlines potential avenues for future exploration and development in the field of SISR, indicating a promising trajectory for further technological advancements.
An Analytical Approach to Predicting Dynamics in Sports Matches Based on Machine Learning Models
Authors: Zhang Yuxuan , Kang Zixin , Lyu Wenjie
Published: August 2024

Sports events have been a significant component of global culture and economics, and analyzing the fluctuations in athletes' performance is one crucial factor in predicting the result of matches. Quantitative research on players’ momentum has long been a popular topic. Building upon prior research, we have developed a mathematical model based on two combined machine learning methods. Specifically, this paper utilized a random forest model to analyze the importance of various metrics of athletes during matches. This text then applied support vector machines for regression prediction of match scores, using data from Wimbledon Championships for model training, and finally yield satisfactory results. The paper further validated this model on a test dataset, demonstrating its robust predictive capabilities. This not only represents a significant application of artificial intelligence in the realm of sports but also holds the potential for substantial economic and cultural benefits for coaching teams and society in the future.
Optimization design and application scenario research of low power digital integrated circuit
Authors: Zhao Wenqi
Published: August 2024

This paper explores the importance of low-power digital integrated circuits (IC) in technological development and everyday applications, especially their key role in modern electronic devices such as smartphones and advanced medical devices. With the advent of the digital age, the design efficiency and energy efficiency of low-power IC have a direct impact on device performance and sustainability. Based on the design basis of low power IC, CMOS technology and its power consumption model are discussed in detail, and dynamic and static power consumption is reduced by means of dynamic voltage frequency regulation, multi-threshold CMOS technology and clock gating technology. In addition, the paper explores in depth the verification and optimization process of low-power designs, emphasizing the importance of experiments and simulations in accurately evaluating and adjusting power consumption. Finally, the paper focuses on the key applications of low-power design in mobile devices and medical devices and presents some practical application cases. The research in this paper will help to promote the wide application of low-power IC in different fields and contribute to scientific and technological progress and social needs.
The Use of Computer Graphics in Interactive Media Design, a Research
Authors: Tang Qianyu
Published: August 2024

In order to better meet user needs, user-oriented interactive design introduces computer graphics technology to generate, process, and transmit graphical information more effectively, thereby enhancing the practicality, simplicity, convenience, and enjoyment of interactive media. This paper aims to discuss the application of computer graphics in interactive media design. In order to analyze and compare the use of different computer graphics (CG) technologies in different scenarios, this paper classifies the interactive media into three categories according to the different focuses of the purposes, namely distributed interaction media, commercial interaction media and informational interaction media, and discusses the application of computer graphics in interactive media design with specific examples. This paper analyzes and compares the use of different CG technologies in interactive media according to their different purposes, namely distributed interaction media, commercial interaction media and informational interaction media. The paper concludes with a summary of the current applications of computer graphics in interactive media design, and an outlook on the future use of CG technology in interactive media design.
Research on heliostat field design based on mesh search optimization algorithm
Authors: Luo Lei
Published: August 2024

Nowadays, heliostat field has been gradually put into practical production, how to design heliostat field to make the highest efficiency of light energy utilization is an important problem. By establishing the adjustment model of heliostat, the design problem of heliostat field is studied by using the mesh search optimization algorithm. First, the position of the absorption tower is determined, so that the heliostat is closely arranged, and then the annual average output thermal power per unit mirror area is as large as possible as the objective function. The optimization model is established for the size requirements, installation height and center distance of the heliostat as the constraint conditions, and then the grid search algorithm is used to solve the problem and remove the heliostat with low output thermal power. Until the selected 60MW rated average annual thermal output power is reached, the result is that the average annual thermal output power per unit area of the mirror is 0.6011kw/m2 under the same heliostat size and the average annual thermal output power per unit area of the mirror is 0.6138kw/m2 under different heliostat sizes. The feasibility and accuracy of the model are verified by a concrete example, which lays a foundation for the design of defining the mirror field.
Performance Evaluation of Intelligent Driving Emotion Recognition Model based on Synthetic Dataset in Real Scenes
Authors: Li Hancheng , Li Yurui , Qian Yaowen
Published: August 2024

The paper aims to explore the feasibility of using artificially generated facial expressions with different emotions to enhance the features and increase the data volume for emotion recognition in the context of intelligent cockpit. The paper first introduces the background and significance of the research, which is motivated by the increasing number of private cars in China, the development of intelligent cockpit technology, and the importance of emotion recognition for driving safety. The paper then reviews the existing literature on emotion recognition based on facial recognition, and points out the challenges and limitations of using real datasets, such as ImageNet, which may have high cost, low quality, privacy issues, and inaccurate annotations. The study suggests utilizing synthetic facial expressions that convey a range of emotions, created through advanced deep learning algorithms, as a solution to enhance the precision and reliability of the emotion detection system. The paper further examines the prospective applications and effects of the recommended technique pertaining to the realm of intelligent automotive cockpits and the associated vehicular journey.  The paper concludes by summarizing the main contributions and limitations of the research, and suggesting some directions for future work.
Dynamic research of tennis match based on machine learning
Authors: Wu Xiangyu , Xu Haoxuan , Zeng Xuan
Published: August 2024

The study employed the Adaboost algorithm to identify optimal indicators of momentum, demonstrating that momentum could significantly predict the likelihood of a player's subsequent wins or losses. This study offers a novel exploration into the dynamics of tennis matches, focusing on the concept of "momentum" and its impact on match outcomes. Through an analysis of the 2023 Wimbledon Men’s Singles final, the research demonstrates how momentum significantly influences the likelihood of a player's success, challenging traditional perceptions of match progression as merely a sequence of random events. By employing advanced statistical methods, including the Adaboost algorithm for identifying key indicators of momentum and the Wald-Wolfowitz Runs Test to examine the randomness of winning streaks, the findings reveal a substantial effect of momentum on the game's flow. The analysis further utilizes the CatBoost algorithm to pinpoint factors critical in shifting momentum between players, such as set scores, serving dynamics, and return depths. These indicators provide concrete evidence against the randomness of match dynamics, suggesting that strategic adjustments based on these factors can potentially alter the course of a match. The implications of this research extend beyond academic interest, offering practical insights for players and coaches in strategizing to leverage momentum for competitive advantage. By debunking the myth of randomness in tennis match outcomes, this study underscores the importance of psychological and strategic elements in sports performance, providing a foundation for further research and practical applications in the field of sports science and coaching.
Optimization of submarine submersible search and rescue path based on simulated annealing
Authors: Yue Yujun , Dong Xingqi
Published: August 2024

The aim of this paper is to apply models and algorithms to address the problem of searching and rescuing lost underwater vehicles. This paper main work is divided into three steps: establishing a geographic model, establishing a simulation model, and optimizing the search model. A model is built to predict the drift path of underwater vehicles in the ocean, and an optimization search model based on simulated annealing algorithm is used to find the optimal search and rescue strategy to locate the underwater vehicle in the shortest time possible. Experimental results demonstrate that by utilizing simulated annealing iterations and spatial physics analysis methods, it is possible to predict the path of underwater vehicles and identify the optimal search points, thereby improving rescue efficiency and rapidly locating the lost underwater vehicle.
Advancements in Traffic Sign Recognition and Detection: Harnessing the Power of Intelligent Systems
Authors: Huang Jiaxi , Dong Yaokun , Zhang Yichen
Published: August 2024

This paper delves into the current research landscape of traffic sign recognition and detection, aiming for a comprehensive understanding of the field's developmental trajectory, significant achievements, and persistent enigmas. This is achieved through an extensive review of pertinent literature from both domestic and international sources. Within the research backdrop, it explores the diverse methodologies and challenges encountered in traffic sign detection and recognition, with a specific emphasis on algorithms powered by deep learning. The paper provides an in-depth analysis of the notable advancements these deep learning-based algorithms have achieved in recent years, marking a significant stride in the field. Furthermore, it scrutinizes the implications of these advancements for the future of automated and intelligent transportation systems. Building on this analysis, the paper forecasts future research directions, intending to offer a referential and guiding framework for subsequent, more profound investigations. This comprehensive approach not only showcases the current state of the art but also lights the path for future explorations that could revolutionize the landscape of traffic sign recognition and its application in autonomous driving technologies.
A Study of Property Insurance Sustainability Based on ARIMA and CGDAM-WRIR Models
Authors: Zhang Jiaxuan , Rong Jiuji
Published: August 2024

The property insurance industry faces significant challenges against the backdrop of the high incidence of extreme weather events around the world. This study focuses on insurers writing policies in high-risk areas and explores how communities and real estate developers are adapting their insurance models to enhance property resilience. We provide an in-depth analysis of the impact of climate change on the sustainability of the insurance industry and use ARIMA and CGDAM-WRIR models to predict the potential losses and impacts of future extreme weather events. Finally, polynomial fitting is used to analyze the effects of earthquakes on buildings, the economy, and human life. Findings suggest that increased demand for insurance is causing insurers to re-evaluate underwriting strategies, especially in high-risk areas. Community engagement and real estate development strategies are critical in reducing costs and expanding coverage, and governments, insurers and communities are called upon to work together to effectively address climate change challenges.
Exploiting Feature Space Manipulation for Image Generation in Deep Convolutional Generative Adversarial Networks
Authors: Yang Yujie
Published: August 2024

Image generation and manipulation are important research directions in the field of computer vision and graphics. Traditional methods of image generation and manipulation often necessitate substantial manual intervention or are constrained by the quality and diversity of the dataset, leading to a lack of authenticity and control in the images produced. This paper provides an extensive examination of image generation and manipulation using Deep Convolutional Generative Adversarial Networks (DCGAN) and feature weighting operations. The model successfully captured the basic structure of a face. Furthermore, the color and texture of the images are quite realistic, indicating that the model has learned some basic features of the face. In terms of feature weighting operations, specific features are successfully transferred from one image to another. This research contributes valuable insights to the field of computer vision and graphics and paves the way for future advancements in image generation and manipulation. Ethical considerations and efficient training methods are also discussed as important aspects for future research.
Analysis of the influencing factors and protection schemes of cultural relics protection based on multiple assessment models
Authors: Zhai Haojing
Published: August 2024

The choice of a cultural heritage conservation scheme is crucial for the protection of cultural relics. A scientific cultural relics protection scheme can effectively reduce the risks and protection costs, and realize the sustainable and efficient development of cultural relics protection. In order to be able to choose the protection scheme more scientifically, this paper establishes an extreme weather risk assessment model based on the entropy weight method, a local underwriting value evaluation model based on grey prediction and other methods, and a cultural relics protection value evaluation model based on the entropy weight-fuzzy comprehensive evaluation method, and finally applies the results to the cultural relics protection scheme based on the analytic hierarchy process, so as to select a suitable protection scheme. This paper takes the protection of Tulou in Fujian Province as an example, and uses the model of this paper to analyze its protection scheme.
The Application of SLAM Based on Extended Kalman Filteration
Authors: Wen Hoiho
Published: August 2024

In this paper, there is a brief introduction about Kalman filteration. Then, analyze the working principle of Simultaneous Localization and Mapping, also called Concurrent Localization and Mapping. Give an introduction about different types of SLAM and the sensors required. After then, give some applications to the SLAM. A practical solution of laparoscopic sequence for surgery, which applied some feature that occur in the realistic use and overcome these obstacles. After then introduce a optimizing method for SLAM to be applied to embedded systems, using coprocessor to solve the calculation problem. Then is another algorithm that can be applied with SLAM to adapt different environments in space, which can automatically adjust the output voltage of different component to meet the scarcity of resources in space. Also introduce an application of algorithm to split the mapping of large scale to small scale map, which can increase the smoothness for closing loop route. Then suit the need for realistic world, give conclusion about different way of implementing multiple robots SLAM problem.
PELATIHAN PENYUSUNAN KONTEN MEDIA SOSIAL BAGI PEMUDA DESA UNTUK PROMOSI WISATA DESA WONOKITRI
Authors: Yascya Marcya Nurfarida , Widyowati Weny
Published: August 2024

The training activity for preparing social media content for young people to promote tourism in Wonokitri Village is integrated with the Gerak Milenial program series which involves the participation of students from various universities individually as facilitators or instructors. This training activity is a medium that provides opportunities for Wonokitri Village youth to learn from students and vice versa, students can learn from the community directly. In this case, students can develop thinking in helping to find solutions to problems that occur in the lives of local communities. Through analysis of the situation and conditions in the field, it was found that efforts to promote village potential were still very lacking, especially the lack of involvement of village youth. The younger generation is expected to be more technologically literate and more easily utilize this technology to help improve tourism promotion. To improve these abilities, especially in the use of cell phone technology with its various applications, the author was involved in training in preparing social media content targeting the younger generation in Wonokitri Village so that they could hone their abilities in creating creative social media content to promote the tourism potential of Wonokitri Village. The training was attended by eleven village youth who were provided with a pre-test, material, and a simple post-test. Through this training, participants realized the importance of creating content on social media as an effort to attract tourists to visit Wonokitri Village.
IMPLEMENTASI PROGRAM INOVASI BUAH CABE CIPELANG UNTUK PEMBANGUNAN DESA CIPELANG
Authors: Saputra Egi Adi , Suryatna Undang
Published: August 2024

Pesona pedesaan yang asri yang dikelilingi taman nasional Halimun Salak dan Gunung Gede Pangrango, Terdapat sebuah desa yang cukup luas dengan menyimpan tantangan yang begitu besar dalam menangani pengelolaan sampah secara tepat dalam pengelolaan sampah organik maupun sampah non-Organik. Tujuan pengabdian untuk mendeskripsikan kegiatan kuliah kerja nyata dan menilai keberhasilan kegiatan di Desa Cipelang dalam pengelolaan sampah.  Penelitian pengabdian menggunakan survei untuk mengukur kesadaran masyarakat tentang pembuangan sampah. Program inovasi "Buah Cabe Cipelang" dirancang untuk meningkatkan kesadaran kebersihan di desa tersebut. Program ini berhasil mengedukasi pelajar sekolah dasar tentang pengelolaan sampah, termasuk pemanfaatan sampah plastik untuk daur ulang. Sebelumnya program, 68,1% masyarakat tidak memahami pemilahan sampah dengan benar. Hasil program akan kemampuan masyarakat naik menjadi 14,6%, sementara pengetahuan meningkat menjadi 85,4%. Studi ini melibatkan 30 anak (20 perempuan, 10 laki-laki), dengan pengumpulan data dilakukan melalui pertanyaan dan edukasi dalam program "Buah Cabe Cipelang".
Kata kunci: Masyarakat, Pengelolaan sampah, Pemberdayaan
Brain-Driven Prosthetics: Advancements in Neuroprosthetic Engineering
Authors: Krishna Pasupuleti Murali
Published: August 2024

Abstract: This chapter explores the latest advancements in neuroprosthetic engineering, with a focus on brain-driven prosthetics that enable direct communication between the brain and prosthetic devices. It begins by defining neuroprosthetics and tracing their historical development, highlighting key milestones that have shaped the field. The chapter delves into the neuroscience behind brain-computer interfaces (BCIs), explaining how neural signals are captured, processed, and translated into prosthetic movements. It also covers engineering innovations, such as advanced materials, sensory feedback mechanisms, and wireless technologies, that are enhancing the functionality and user experience of neuroprosthetics. Clinical applications are discussed through case studies, illustrating the real-world impact of these technologies on individuals with limb loss and neurological disorders. The chapter addresses ethical and regulatory considerations, emphasizing the importance of patient consent, data privacy, and the societal implications of widespread neuroprosthetic use. Finally, the chapter looks to the future, exploring emerging technologies and the potential for neuroprosthetics to evolve beyond medical applications into human augmentation. Keywords: Brain-Driven Prosthetics, Neuroprosthetic Engineering, Brain-Computer Interfaces (BCIs), Neural Signal Processing, Sensory Feedback, Wireless Neuroprosthetics, Limb Prosthetics, Neurological Disorders, Rehabilitation Technologies, Ethical Considerations, Regulatory Challenges, Emerging Technologies, Human Augmentation, Neuroplasticity and AI-Driven Prosthetics.
University Teachers’ Academic Promises in Adapting Leadership Patterns Towards Academic Excellence
Authors: Shafqat Saira , Muhammad Zafar Jam , Hussain Bhadroo Mazhar
Published: August 2024

The objective of the present research was to identify the university teachers' academic promises in adapting leadership patterns towards academic excellence. The quantitative method was used for this survey and descriptive nature study. The research population contained all faculty members, heads of departments, and chairpersons of KFUIT. Three hundred twenty participants were selected for data collection to contribute to this study. Among these participants, the two hundred eighty were faculty members, and forty were heads of departments and chairpersons of KFUIT. For the present research study, the researcher used a questionnaire for data collection. The questionnaire contained two parts, comprising both close-ended and open-ended questions. The validity of the questionnaire was ensured through the opinion of the experts, and the questionnaire's reliability was calculated using Corn-batch Alpha. The researcher meets with research participants face-to-face to collect data. The Statistical Package for Social Sciences (SPSS) later analyzed the data. The analyzed data was presented in numerical form in tables. Based on research findings, some recommendations are given.
What is Intercultural Communicative Competence in English Language Teaching? Examining the Discourse of Thai Pre-Service English Teachers
Authors: Sairattanain Jariya
Published: August 2024

Due to globalization, intercultural communicative competence (ICC) has become important. To integrate ICC into the classroom, appropriate perceptions towards this construct are needed. To know and understand what these perceptions might be, this study examined pre-service English teachers’ views collected through an online open-ended survey; a total of 53 respondents completed the survey. Analysis of the data was done through a corpus approach; specifically, keywords were derived from comparison with a benchmark corpus. Subsequently, the contexts of occurrence of these keywords were analysed. This study the analysed the first five keywords, which were “we,” “language,” “different,” “culture,” and “students.” Based on the contexts of occurrence, this study found that the pre-service teachers perceived ICC as a notion that can be taught through specific pedagogical practices; that ICC is driven by differences in language and culture; and that students have the role of enhancing their ICC knowledge and skills. The findings present a contextual understanding of ICC, which may not necessarily be aligned with other cultural settings.
From “Suem Sao” to “Rok Suem Sao” and After
Authors: Palakawongsa na Ayudhya Bandhukavi
Published: August 2024

This ethnographic study explored the subjectification and psychiatrization journeys of Bangkok youths who went from realizing they were suffering from Suem Sao (Depression) to being diagnosed with Rok Suem Sao (Major Depressive Disorder). Through ethnographic interviews, the research examined how cultural perceptions and societal stigma influenced the acceptance and internalization of their depression diagnosis and its social and personal consequences. Focusing on seven young adults, a complex interplay between culture and medical diagnosis was revealed. Firstly, pre-psychiatrization, participants generally accepted their psychiatric labels, often due to the perceived authority and credibility of professionals. Secondly, during psychiatrization, some participants felt dissatisfied with the treatment as doctors strongly relied on prescriptions and lacked communication skills, particularly incorporating inappropriate scenarios of Dhamma into the treatment or unintentionally gaslighting conversation. Thirdly, post-psychiatrization, this study documented the pervasive stigma associated with depression, which significantly impacts an individual’s willingness to seek help, tell others, and their subsequent treatment experiences, such as discrimination, feelings of hiding, and burden from having depression, particularly in academic environments. Participants reported a range of responses to their diagnoses, from relief at having their feelings validated by doctors to continued struggles with accepting their condition amidst ongoing stigmatizations.
IA´ Tools for the development of investigative skills
Authors: Gaviria Alvarado Mayra Alejandra
Published: August 2024

This article explores how the artificial intelligence (IA) it is transforming the education in natural sciences by means of strategies pedagogic innovators. The IA allows the learning personalization, adjusting the content and the rhythm to the individual necessities of the students, what improves the understanding and retention of complex concepts significantly. Also, the use of simulations and virtual models believe interactive and visual learning environments, enriching the educational experience. These tools also foment the development of critical and creative skills, promoting a more active and collaborative approach in the resolution of scientific problems. On the whole, these strategies not only improve the effectiveness of learning, but rather they also prepare the students to face the challenges of the XXI century with a solid base in science and technology.
Hospital processes optimization based on artificial intelligence
Authors: Sánchez Suárez Yasniel , Alawi Abdulmuneem Mohammed , Leyva Ricardo Sonia Emilia
Published: August 2024

Artificial intelligence is revolutionizing hospital management by optimizing critical processes to improve operational efficiency. The automation of administrative tasks allows reducing errors and streamlining the flow of patients and work, which translates into lower costs and better use of hospital resources. The objective is to analyze research related to the optimization of hospital processes based on artificial intelligence. The research paradigm was qualitative-quantitative, the focus of this research was based on a bibliometric analysis, which was complemented with a documentary review in databases of high international and Latin American impact in the period from 2010 to 2024. The trend of the research was towards an increase, where research in the area of medicine and computer sciences predominated. A keyword co-occurrence and citation analysis were carried out to identify possible lines of research. It was identified that monitoring and predictive analytics technologies based on artificial intelligence enable proactive management of patients' health, preventing complications and optimizing resource allocation. These tools also facilitate the personalization of care, adjusting treatments according to the specific needs of each patient. The implementation of artificial intelligence in hospital processes is a crucial tool for improving operational efficiency and reducing costs through the automation of administrative tasks, resulting in a smoother and more effective operation
Use of AI to improve the teaching-learning process in children with special abilities
Authors: Rodríguez Torres Esteban , Comas Rodríguez Raúl , Tovar Briñez Edwin
Published: August 2024

Through adaptive and assistive technologies, AI enables deep personalization of learning, as well as adjusting content and pacing based on each student's individual needs. These systems not only optimize the delivery of educational material, but also offer new forms of interaction and accessibility for students with physical, visual and hearing disabilities. The research was conducted with the purpose of exploring how artificial intelligence (AI) has revolutionized special education. The results indicate that the implementation of tools such as speech recognition, brain-computer interfaces and text-to-speech software significantly improves student autonomy and participation in the classroom. However, the data also highlights the importance of addressing ethical and accessibility issues, ensuring that these technological advances benefit all students equitably and without compromising their security or privacy. The inquiry concluded that, while AI presents transformative opportunities for special education, its integration requires thoughtful approaches that prioritize inclusion and equity.
Design and implementation of an IoT monitoring system for the optimization of solar stills for water desalination
Authors: Pimienta Barros Roger David
Published: August 2024

The project "Design and Implementation of an IoT Monitoring System for the Optimization of Solar Distillers in Water Desalination" sought to improve the efficiency of desalination in La Guajira, a region with critical water scarcity. The objective was to develop an IoT system to optimize solar stills, offering a sustainable solution. A prototype solar still with IoT monitoring was built. The study included the creation of circuits to integrate sensors and an HTML dashboard to visualize real-time variables, such as internal and external temperatures, humidity, and water level in the basin, facilitating the calculation of efficiency. The IoT monitoring system proved to be effective in increasing efficiency and providing valuable data for design decisions, marking a step towards water autonomy.
Color in images: a machine vision approach to the measurement of CIEL*a*b* coordinates in bovine loins
Authors: Torres Vargas Olga Lucía , Valencia Buitrago Mateo
Published: August 2024

Electronic machine vision systems bring together a set of technologies and techniques used to capture, process and analyze images to perform a specific task, such as object or measurement pattern recognition. These systems rely on image processing and machine learning algorithms to interpret visual information. Therefore, the objective of this research was the construction of an electronic machine vision system (SVA) for color analysis in bovine (longisimus dorsi) loins based on the CIEL*a*b* color space. The VAS implementation was carried out using the programming language Python3.9 programming language and the color parameters obtained were compared with those obtained on a Minolta CR-400 colorimeter (CM). Both systems were synchronized to provide the user with information about the color coordinates in the samples of loins stored for 6 days at 4°C. The results obtained showed no significant differences. The results obtained showed no significant differences in the values of the L* parameter, while b* and a* showed significant differences during the storage time of the loins. These results are attributed to the oxidation process of the myoglobin and to factors such as breed, feeding and slaughtering process of the cattle, which affect the color of the samples. The results obtained indicate that VAS could be used for the determination of color during the storage of beef loins in real time, offering a non-invasive and low-cost solution to the actors in the meat chain.Keywords: image analysis, beef, colorimeter, artificial vision system.
Digital skills in the use of artificial intelligence tools for the formulation of formative research projects from the TECSIS Research Seminar.
Authors: Cardenas Espinosa Ruben Dario , Caicedo-Eraso Julio César , Epifanía Huerta Andres David
Published: August 2024

Artificial intelligence (AI) has promoted a change in the way research and innovation (R&D) is performed bringing new perspectives, automating time and routine tasks contributing to the generation of knowledge. To take full advantage of the potential of AI, it is proposed to develop strategic digital skills for the use of artificial intelligence tools in the formulation of formative research projects from the TECSIS Research Seminar. The methodology corresponds to a qualitative research with a descriptive analytical approach with a cross-sectional approach in four stages: analysis, design, evaluation and dissemination. The population under study includes the students of the special programs of Computer Engineering and Systems Technology of the University of Caldas. The expected result is the characterization of strategic digital skills of use for the formulation of formative research projects. This project will contribute in the development of a methodological route that exposes in a practical and applied way the strategic digital skills of use of AI tools for the formulation of formative research projects for the special programs of the University.
Pisau Hukum Dan Kotak Suara: Studi Evaluatif Penegakan Hukum Dalam Pemilu 2024
Authors: Mahdi Hidayatullah
Published: August 2024

Penelitian ini mengkaji efektivitas penegakan hukum dalam Pemilu 2024 di Indonesia, dengan fokus pada peran Badan Pengawas Pemilu (Bawaslu) dan Sentra Penegakan Hukum Terpadu (Gakkumdu). Analisis normatif yuridis terhadap Undang-Undang Nomor 7 Tahun 2017 tentang Pemilihan Umum dan berbagai sumber terkait mengungkapkan beberapa tantangan utama dalam penegakan hukum pemilu. Ini mencakup kompleksitas pembuktian kasus politik uang, perbedaan interpretasi regulasi antar lembaga, dan keterbatasan sumber daya. Studi ini juga mengevaluasi efektivitas sanksi hukum dalam memberikan efek jera terhadap pelaku pelanggaran pemilu. Temuan menunjukkan bahwa penerapan sanksi belum sepenuhnya efektif karena ringannya hukuman dan inkonsistensi dalam penerapannya. Penelitian ini mengidentifikasi beberapa faktor penyebab kesenjangan antara regulasi dan implementasi, termasuk keterbatasan sumber daya manusia dan anggaran Bawaslu, ego sektoral antar lembaga penegak hukum, serta kurangnya pemahaman masyarakat tentang bentuk-bentuk pelanggaran pemilu. Analisis terhadap kinerja Sentra Gakkumdu mengungkapkan tantangan dalam koordinasi antar lembaga dan keterbatasan infrastruktur, terutama di daerah terpencil. Untuk meningkatkan integritas pemilu, penelitian ini merekomendasikan penguatan regulasi Gakkumdu, peningkatan kapasitas personel melalui pelatihan intensif, dan optimalisasi peran teknologi informasi dalam pengawasan dan penegakan hukum pemilu. Studi ini juga menyoroti pentingnya penerapan sanksi yang lebih tegas dan konsisten, serta peningkatan transparansi dalam proses penegakan hukum pemilu. Lebih lanjut, penelitian ini mengusulkan penguatan mekanisme rehabilitasi dan reintegrasi bagi pelaku tindak pidana pemilu, serta peningkatan kerjasama internasional dalam pertukaran praktik terbaik penegakan hukum pemilu. Implementasi rekomendasi ini diharapkan dapat berkontribusi pada penyelenggaraan pemilu yang lebih berintegritas dan demokratis di Indonesia, sesuai dengan semangat Undang-Undang Nomor 7 Tahun 2017 dan cita-cita reformasi.
Biomechanical characteristics of lower limbs in Tai Chi Novices with different squatting depths: A pilot study
Authors: Li Wenlong , Zhou Zhifeng , Ji Mengchen , Quan Wenjing , Liang Minjun , Baker Julien S. , Liu Yuwei
Published: August 2024

This study aimed to analyze the biomechanical characteristics of the lower limbs in Tai Chi novices performing the Part the Wild Horse’s Mane (PWHM) movement at varying squatting depths to identify potential risk factors for joint pain and injuries. Eight Tai Chi novices, with an average age of 20.75 years participated in this study. Joint angles, joint moments, ground reaction forces (GRF), center of gravity (COG), and muscle force were measured during PWHM at various squat depths. Data were analyzed using the one-way ANOVA in Open-Source Statistical Parameter Mapping in MATLAB, with corrected post-hoc tests using the Bonferroni correction. Different squat depths resulted in differences in joint angles, joint moments, joint range of motion (ROM), COG, and muscle force (p < 0.05); however, no difference was observed in the joint stiffness or GRF. In comparison with a high squat depth, both low and medium squat depths exhibited greater peak knee and hip flexion angles, while the low squat depth also demonstrated a larger ROM in ankle inversion–extension, knee flexion-extension, hip flexion-extension, and hip adduction-abduction (p < 0.05). Compared with a low squat depth, a high squat depth resulted in smaller peak ankle inversion, ankle internal rotation, knee external rotation, hip extension, hip adduction moment, and smaller muscle force in the semitendinosus, rectus femoris, rectus femoris, medial gastrocnemius, and tibialis anterior muscles (p < 0.05). Different squat depths led to differences in lower limb biomechanics among Tai Chi novices. A low squat depth can bring more health benefits to Tai Chi novices; however, the higher demand for muscle strength may increase the load on the joints, causing joint pain or even injury.
Rethinking and Reintegrating the Nostalgia of Displacement: A Literature Review
Authors:   Xavier University, Gallamaso Jane
Published: August 2024

This study explores human displacement and the role of nostalgia in connecting displaced individuals to their past attachments. While acknowledging the potential negative aspects of nostalgia, the study emphasizes its inherent role in human existence and argues against completely dismissing nostalgia in favor of the present. Nostalgia, when approached thoughtfully, can provide a vital sense of continuity in one’s identity, place, self, and history. However, it is crucial to avoid romanticizing or overindulging in nostalgia, as an excessive focus on the past can restrict personal growth and hinder adaptation to new environments. The study employs a purely qualitative approach, using descriptive data analysis to identify recurring themes related to nostalgia within displacement experiences. Results revealed that nostalgia offers displaced individuals’ comfort, meaning, and a sense of identity, but it can also act as a barrier to integration into new environments. Therefore, a careful balance between embracing the present and cherishing the past is essential for successful resettlement. The study concludes that there is a need to re-evaluate and understand nostalgia and its role in the present and recommends the development of programs and initiatives to help displaced individuals process their past while building skills for their present and future.
Digital Marketing: A Study of Its Relationship with Digital Entrepreneurship in Peru
Authors: Urribarri Dra. Africa Calanchez , Vera Kerwin Chávez , Verdesoto German Jazmany Zambrano , Reyes Johan Méndez , Medina Dra. Ana Padrón
Published: August 2024

The main objective of this research was to examine the relationship between digital marketing and digital entrepreneurship in Peru. A quantitative methodology was used with a descriptive-correlational causal and non-experimental design. The sample consisted of 384 Peruvian entrepreneurs using digital tools, selected through non-probabilistic snowball sampling. Data were collected through an online questionnaire and analyzed using partial least squares (PLS) structural equation modeling (SEM). The results revealed a positive and significant relationship between digital marketing and digital entrepreneurship (β=0.5805, p<0.001). A strong relationship was found between digital marketing and its channels and media (β=0.7499, p<0.001), as well as with its applications (β=0.4962, p<0.001). Digital entrepreneurship showed a positive effect on forms of online presence (β=0.5086, p<0.001) and alternative digital business models (β=0.2857, p<0.001). It was concluded that digital marketing plays a crucial role in the success of digital ventures in Peru, highlighting the importance of developing competencies in this area, properly selecting communication channels and establishing a strong online presence.