bluuebunny/crossref_metadata_2025 huggingface dataset:
Educação socioambiental: desafios e contribuições do Programa Nacional Escolas Sustentáveis
Authors: Oliveira Alexandre Nicolette Sodré , Schweickardt Kátia Helena Serafina Cruz
Published: January 2024

As questões ambientais têm alcançado cada vez mais relevância nos debates e discussões políticas, econômicas e sociais. No âmbito educacional, tem sido objeto de atenção pela necessidade de formação para o desenvolvimento de valores e atitudes condizentes com a sustentabilidade socioambiental. Nesta perspectiva, este artigo é parte de uma tese de doutorado que tem por objetivo geral compreender as contribuições do Programa Nacional Escolas Sustentáveis (PNES) para que escolas públicas participantes se tornassem territórios educadores sustentáveis. Trata-se de uma pesquisa bibliográfica de base qualitativa. Os resultados indicam que, a nível nacional, apesar dos desafios enfrentados no processo de sua implementação e desenvolvimento, o PNES conseguiu estimular uma maior integração entre escola e comunidade, envolvendo os estudantes em atividades socioambientais.
Physical-mechanical properties of MDP panels with the addition of melamine paper in the internal layer
Authors: Franco Mariana Pires , Silva Maria Fernanda Felippe , Prates Glaucia Aparecida , Gali Lesley Carina do Lago Attadia , Savi Antônio Francisco , Favarim Higor Rogério , Caraschi José Cláudio , De Campos Cristiane Inácio
Published: January 2024

Particleboard panels find extensive application in the furniture sector, but their low stability in contact with water hinders their utilization. Simultaneously, the trimmings of melamine paper, exhibiting hydrophobic properties, used in panel finishing, represent unused industrial waste. This study aimed to evaluate the physical-mechanical properties of MDP (Medium Density Particleboard) panels produced with Pinus taeda and castor oil-based polyurethane resin with the addition of different proportions of melamine paper (0, 5, and 10%). The properties of density, moisture content, thickness swelling, water absorption, static bending, internal bonding, and screw withdrawal resistance, on face and top, were evaluated, following normative documents. At the end of the tests, statistical analysis was conducted at a 5% significance level to check any statistical differences among the means. The addition of melamine paper caused to an increase in panel density and moisture content, but all treatments remained within the ideal range. Thickness swelling and water absorption decreased, while all mechanical properties improved with an increase in the quantity of added melamine paper. Therefore, the use of melamine paper in MDP panels is viable and beneficial, notably the treatment with a 10% addition of melamine paper.
More Topography, Astronomy, and Geometry at the Newark Earthworks
Authors: Hively Ray , Horn Robert
Published: January 2024

New geometrical, photographic, topographic, and astronomical evidence is presented that links the architectural plan of the earthworks to the lunar standstills, solstices, and local terrain. The data are integrated with previous research to form a comprehensive interpretation of the site. Analysis shows that all eight sides of the Octagon can be used for observations of the 18.6-year lunar standstill cycle. The locations of obliterated earthwork features are established using historic aerial photos and maps. Solstice/standstill alignments are documented and support the Hively-Horn TAG hypothesis. When at its zenith during the Hopewell epoch, the Milky Way is shown to have intersected the horizon at the extreme rise/set points of the Moon at the major standstill. The scale, precision, and sophistication of the encoding of standstill/solstice alignments in the earthwork design and in its relation to the surrounding terrain is an achievement of the Hopewell builders with no known precedent in the prehistoric world.
Shrines to the Buffalo Spirit: Meteorites and Meteorite Shrines on the Plains
Authors: Blakeslee Donald J.
Published: January 2024

Native understandings of and uses for meteorites constitute a woefully understudied topic. This article reviews evidence from the Great Plains, referring to information from other regions in order to document patterns. Some meteorites clearly were shrines; others were situated in sacred places. Still others have been found far from their fall sites, curated in mortuary features and archaeological sites or placed along trails and in association with prominent landmarks. Some show signs of prehistoric manipulation, including heating.
Pendampingan Pembuatan Perangkat Pembelajaran Berbasis Proyek Penguatan Profil Pelajar Pancasila (P5) Pada Guru Di Kalisat
Authors: Indah Rahayu Panglipur  , Eric Dwi Putra  , Sunardi Sunardi  , Erfan Yudianto
Published: January 2024

The program This education mentoring program is an ongoing effort in which different stakeholders work together to achieve the goal of better education for the community. for the Community The school curriculum based on the project of strengthening the profile of pencasila learners (P5) has been implemented in the aftermath of the pandemic. It is necessary to provide assistance in making tools to prospective teachers. The condition of teachers in the school The condition of teachers in the school is still only 20% who have understood the implementation of the P5 while the rest still do not understand P5 at all. The purpose of this activity is to provide provisions and assist teachers to be able to develop appropriate learning tools. appropriate learning tools. One of the benefits that teachers get from this mentoring activity is refreshing their knowledge. knowledge refreshing. This benefit has a positive impact and will definitely improve the quality of the school in the future. school in the future
Green Intellectual Capital, Asset Growth on Stock Return: Role Financial Performance as Intervening
Authors: Agustina Sri , Ruhiyat Endang , Sugiyanto Sugiyanto
Published: January 2024

Purpose – The Purpose of study was  to determine the effect of green intellectual capital on stock returns with financial performance as an intervening variable. Metodelogy – This study uses a sample of agricultural sector companies listed on the Indonesia Stock Exchange during the 2018-2022 period. The sampling technique used purposive technique, the samples obtained were 20 agricultural sector companies or 100 data observations. The analysis method used is panel data regression analysis and path analysis using Eviews.12. Findings – Based on the research results, it shows that together green intellectual capital, asset growth and financial performance have an effect on stock returns. Meanwhile, partially asset growth has a positive and significant effect on stock returns. Financial performance has a positive and significant effect on stock returns. Green intellectual capital has no effect on stock returns. Green Intellectual capital has a positive and significant effect on financial performance. Asset growth has no effect on financial performance. Financial performance is able to mediate the effect of green intellectual capital on stock returns. Financial performance is unable to mediate the effect of asset growth on stock returns.
KNOWLEDGE, ATTITUDE, AND PRACTICE REGARDING IMMUNIZATION AMONG MOTHERS OF INFANT AND PRESCHOOL CHILDREN AT A TERTIARY CARE HOSPITAL, IN KARACHI.
Authors: KHAN M , AKBER JU , SOHRAB S , KHAN AH , HAQ MHU , ALI AA
Published: January 2024

The objective of this research was to determine the knowledge, attitude, and practice of mothers of infant and preschool children regarding immunization. The study will be conducted at a tertiary care hospital in Karachi, and a total of 119 people will be recruited for KAP research. The mothers' level of education, beliefs, and behaviors will be assessed along with factors such as a child's age, gender, place of birth, number of siblings, marital status, educational level, employment status, and monthly income. The principal investigator will gather information using a self-designed questionnaire that includes questions on demographics and immunization-related knowledge. The results of the study indicate that 68.9% of the mothers had knowledge of immunization, whereas 31.1% did not. In addition, 68.1% of the mothers were aware of the Expanded Program on Immunization (EPI), while 31.9% were not.  Regarding knowledge of the EPI 1st dose, 69.7% of the mothers had information about it, while 30.3% did not. Only 28.6% of the respondents had knowledge of diseases that could occur in the absence of vaccination, whereas 71.4% did not. In terms of specific diseases, 5.9% were aware of diphtheria, 5.9% of polio, 5% of tetanus, and 11.8% of influenza. However, 71.4% of the respondents did not have knowledge of diseases that could occur in the absence of vaccination. Furthermore, 35.3% of the parents expressed fear of immunization for their children, while 64.7% did not. Among those who had fears, 52.9% believed that there was no benefit to immunization, and 47.1% were concerned about harm to their children. In conclusion, the study found that more than half of the mothers who expressed fear of immunization did so because they believed there was no benefit. This highlights the need for more education and awareness campaigns to help parents understand the importance of immunization in preventing diseases and keeping their children healthy.
New type of Diophantine neutrosophic aggregation operators and its extension
Authors:   Department of Mathematics, Saveetha School of Engineering, Saveetha University, Saveetha Institute of Medical and Technical Sciences, Chennai-602105, India, Palanikumar M. ,   Department of Mathematics, Bharath Institute of Higher Education and Research, Tamil Nadu, Chennai-600073, India,   Department of Mathematics, Al-Ameen Engineering College, Erode.,   Fuzzy Algebras and Decision-Making Problems Research Unit, School of Science, University of Phayao, 19 Moo 2, Tambon Mae Ka, Amphur Mueang, Phayao 56000, Thailand., Arulmozhi K. , Sundareswari3 K. , Iampan Aiyared
Published: January 2024

We introduce the concept of new type of Diophantine neutrosophic set. A Diophatine neutrosophic set is the new type of a neutrosophic set and Diophatine fuzzy set (DioFS). We discuss Diophantine neutrosophic weighted averaging (DioNWA), Diophantine neutrosophic weighted geometric (DioNWG), generalized Diophantine neutrosophic weighted averaging(GDioNWA), generalized Diophantine neutrosophic weighted geometric (GDioNWG). In this article, we define the Euclidean distance (ED), Hamming distance (HD) and operator laws. By analyzing new type of Diophantine neutrosophic set through algebraic operations, we discuss its properties.
A Specific Category of Harmonic Functions Characterized By A Generalized Komatu Operator in Conjunction With The (R-K) Integral Operator and Applications to Neutrosophic Complex Field
Authors:   Department of Mathematics, College of Education for Pure Sciences, the University of Babylon, Iraq, Saheb Audy Hatim ,   Department of Mathematics and computer applications, College of Science, Al Muthanna University, Iraq, Buti Rafid Habib
Published: January 2024

In our work, we introduced a distinct subclass of univalent harmonic functions referred to as a subclass of chiral functions. These functions are defined by combining the generalized Komatu operator with the integral operator (R − K), which has positive coefficients within the unit disc A. Also, we generalize the same subclass into neutrosophic complex numbers. Throughout our investigation, we establish several properties associated with these functions, including coefficient estimates, the convex formula, the integral operator, and the Hadamard product. On the other hand, we present the Neutrosophic convex formula and the neutrosophic integral operator.
Research on the Classification and Application of Physical Education Teaching Mode by Neutrosophic Analytic Hierarchy Process
Authors:   Universidad Nacional Federico Villareal, Lima, Perú; Universidad Nacional Mayor de San Marcos, Lima, Perú, Gamboa-Cruzado Javier Gamboa ,   Universidad Nacional Federico Villareal, Lima, Perú,   Universidad Nacional Federico Villareal, Lima, Perú,   Universidad Nacional Micaela Bastidas de Apurímac, Apurímac, Perú,   Universidad Nacional Mayor de San Marcos, Lima, Perú, Morante-Palomino Eduardo Morante , Rivero Cristina Alzamora , Bendezú María Limaú , Fernández Dante Manuel M. Fern�
Published: January 2024

The goal of this paper is to classify and application of Physical Education (PE). PE growth rapidly these days due to rapid development in information technology. This rapid turn over the sports, training and physical education. So, this paper identifies the application of PE by using the Multi-Criteria Decision Making (MCDM) concept. This problem contains many criteria and sub-criteria. This paper proposed the Analytic Hierarchy Process (AHP) to determine the weights of criteria and sub-criteria. The AHP method was used under a neutrosophic environment to deal with uncertainty in this problem. An example is provided to show the outcomes of the proposed method.
Innovative Perspective on Neutrosophic Cubic Z-Algebras
Authors:   Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai-602105, India, Devi G. Nisha ,   Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai-602105, India,   Department of Mathematics, Prathyusha Engineering College (Autonomous), Thiruvallur-602025, India,   Department of Mathematics, Muthurangam Government Arts College, Vellore-632002, India,   Department of Mathematics, School of Science, University of Phayao, 19 Moo 2, Tambon Mae Ka, Amphur Mueang, Phayao 56000, Thailand, Hemavathi P. , Vinodkumar R. , Muralikrishna P. , Iampan Aiyared
Published: January 2024

This study explores an innovative perspective on neutrosophic cubic Z-algebras, delving into the theoretical framework within mathematical structures. Through a comprehensive analysis, we uncover unique insights that contribute to the advancement of algebraic methodologies, particularly in handling uncertainties represented by neutrosophic elements. This work aims to present the idea of neutrosophic cubic sets in Z-algebras, as well as the usage of false membership function, truth, and indeterminacy in Z-algebras. Further, the results on -union, -intersection, -union, and -intersection of neutrosophic cubic Z-subalgebras are provided. This paper also discusses homomorphisms of Z-algebras and its associated characteristics.
Garden Cities
Authors: Vijay Ameeth
Published: January 2024

This chapter introduces the themes and archive of Topothesia through a comparative analysis of Ebenezer Howard’s Garden Cities of To-morrow (1902) and William Morris’s novel News from Nowhere (1890). The professional infrastructure of town planning that develops in the early twentieth century (journals, conferences, university positions, government offices, etc.) emerges as a response to the problem of the slum, a figure that condenses economic inequality in a spatial form. In isolating this figure, incipient planners, Howard included, sought to resolve and reconcile the effects of capitalism through a conception of future places whose realization could be brought about by the art of planning rather than through political agitation. I read Howard in comparison with Morris’s News from Nowhere, which imagines a post-revolutionary London characterized by social cooperation, collective economic production, and verdant sensuality. The incipient fields of planning and design were highly influenced by the work of Morris and the arts and crafts movement as a whole; at the same time, this influence amounted, I argue, to a co-option of its aesthetics without its political animus.
Geographies of Discontent
Authors: Vijay Ameeth
Published: January 2024

This chapter asks how dislocations of space and identity operate not only in urban but also in provincial areas. I analyze this dynamic through Caryl Phillips’s novel A Distant Shore (2003), which in many ways prefigured the debate about Britain’s geographic fracture in the wake of the 2016 referendum to leave the European Union. Bringing together research from geography on the effects of rural gentrification and the impact of tourist economies, I show how the prerogatives of planning have imagined futures of productive and socially cohesive knowledge economies even in non-urban spaces, but ironically created conditions of economic inequality and racial hostility. In doing so, I turn to perhaps the most unlikely geography of postcolonial analysis—the provincial flashpoints of Brexit—in order to demonstrate the reach and extent of the planning imaginary and its ongoing, neocolonial mode. Without ceasing to be a speculative opening-up, spatial development also creates “geographies of discontent,” places and communities marked less by integration than by disintegration.
Narrating Humanity
Authors: Franklin Cynthia
Published: January 2024

Narrating Humanity makes a critical intervention into contemporary crises in the US about who counts as human. To enable this intervention, the author proposes a new analytical language centered on “narrative humanity,” “narrated humanity,” and “grounded narrative humanity,” and she foregrounds concepts of the human that emerge from movement politics. Hegemonic narratives about who counts as human, or “narrative humanity,” determine who lives and dies. At the same time, in the face of crushing violence, humans breathe life into new and resurgent stories of human being, becoming, and belonging. These stories, termed “narrated humanity” and “grounded narrative humanity” by Franklin, participate in contemporary political movements. They enable ways of being human necessary for not only surviving but thriving. Through chapters focused on Hurricane Katrina, Black Lives Matter, the Palestinian-led Boycott, Divestment and Sanctions movement, and the Native Hawaiian movement to protect Mauna a Wākea, Franklin reveals how life writing can help materialize ways of being human inspired by contemporary political movements that are based on queer kinship, inter/national solidarity, abolitionist care, and decolonial connectivity among humans, more-than-humans, land, and waters. Engaging writers, artists, and activists who inspire radical forms of relationality, she comes to write side-by-side with them in her own acts of narrated humanity, refusing the boundaries between autobiography, community-based activism, and literary and cultural criticism.
Introduction
Authors: Franklin Cynthia G.
Published: January 2024

This introduction lays out the book’s key arguments and its organizational structure. After exploring how life writing helps shape our understandings of who and what count as human, it looks to how life narratives that participate in liberatory political movements can counter hegemonic forms of dehumanization and envision ways of being human based on queer kinship, inter/national solidarity, abolitionist care, and decolonial connectivity among humans, more-than-humans, land, and waters. It defines the analytics of “narrative humanity,” “narrated humanity,” and “grounded narrative humanity” that this book conceptualizes as ways to understand the importance of narrating lives, and it situates this book’s theoretical interventions and understandings of “the human” in relation to contemporary theory, especially Barbara Harlow’s Resistance Literature, Samera Esmeir’s Juridical Humanity, and theories emerging from Black, Indigenous, American, and life writing studies.
Cadmium concentration in cocoa beans produced in agroforestry systems of small producers in Panama
Authors: Villalaz-Pérez Jhon A. , Casanoves Fernando , Villarreal-Núñez José E. , Santo-Pineda Adolfo , Gutiérrez-Lezcano Abiel , Merino Agustín
Published: January 2024

Aim of study: To calculate Cd concentration in cocoa plants and evaluate its relationship with available Cd and other soil properties. Area of study: Almirante, Bocas del Toro province, Panama, in 2020-2021. Material and methods: The study was carried out in 21 plots of eight cocoa-producing farms. The total area of each sampled plot was 300 m2. Soil samples were taken at a depth of 30 cm, and samples of the leaves and fruits of cocoa trees were also taken. Descriptive statistics and correlation analyses were carried out for soil variables and Cd in plants. The relationship between bioavailable Cd and soil physicochemical variables and between soil variables and Cd in plants was evaluated. Multiple linear regression was performed using the backward selection method. Main results: The pH was acidic (5.1) and the organic matter content of the soil was greater than 3%, suitable for immobilizing Cd from the soil. Total and bioavailable Cd averaged 0.10 mg kg-1 and 0.02 mg kg-1 respectively. The Cd levels in cocoa leaves exceeded the recommended levels of 0.5 mg kg-1. The Cd concentration in the cocoa bean was low (0.25 mg kg-1). Research highlights: The levels of bioavailable Cd found do not exceed the United States Environment Agency toxic limits in soil. The level of Cd found in the cocoa bean is below the limit of 0.8 mg kg-1 which is taken as a reference for chocolate, with total dry matter content ≥ 50% of the CODEX Alimentarius.
Predicting stock prices through deep learning techniques
Authors: Peng Chengszu
Published: January 2024

Stock price movements are linked to lots of factors, whether it is a statement by a celebrity or an event of the magnitude of Covid-19. This paper will mainly focus on CNN (Convolutional neural network) and RNN (recurrent neural network), which are two ways to do research on stock prediction. It will discuss the challenges and limitations associated with using neural networks for stock prediction, including data preprocessing, model training, and generalization to different market conditions. The results of this study will provide insights into the potential of CNNs and RNNs for stock prediction.
A review of motion generation technology
Authors: Yang Zhe
Published: January 2024

Nowadays, deep learning and neural network-related research play a very important role in the widely use of artificial intelligence -related technologies, Among them, the hot development in the direction of generative adversarial networks (GAN) has given birth to many generation-related techniques. For example, MoCoGAN is based on the implementation principle of GAN, which enables video generation of different actions of the same character or the same action of different characters, through the method that decompose video into actions and content. This paper introduces the history and principles of MoCoGAN, starting from the prospect of using MoCoGAN in artificial intelligence (AI) industry and the technical challenges that need to be overcome in the future application of action generation. Besides, this paper also discusses the two main issues of how to improve the quality of video generation using MoCoGAN and the input conditions that are the most central problem to GAN networks. By summarizing the optimization solutions of other researchers in these two areas in recent years, this paper searches the core problems need to be solved and propose a broad prospect for future video generation techniques that can be implemented by using MoCoGAN in human-computer interaction (HCI) area.
Research on space attitude recognition and control method for soft robotics based on neural networks
Authors: Wang Dahai
Published: January 2024

In the modern field of biomedical robotics, there is increasing attention on the use of soft robots as carriers for surgical devices. Compared to rigid robots, soft robots exhibit more complex postures and work environments, requiring new methods for recognizing and controlling their postures within biological organisms. This paper investigates the recognition and control of the posture of soft robots using a neural network learning approach, utilizing a soft robot equipped with resistive sensors. By establishing the relationship between changes in resistance values and posture variations, the study successfully achieves the identification and control of the soft robot's posture. The obtained posture data based on resistance values are validated for reliability. Thus, by combining resistive sensors with soft robots and employing neural network analysis, the recognition and control of soft robots postures within biological organisms can be achieved.
Literature review on the application of numerical model in improving face recognition performance
Authors: Yu Xiangning
Published: January 2024

As an important biometric technology, the applications of face recognition technology have been applied in many fields. With the development of related technology, the application of different algorithms has improved the face recognition technology in different situations. This paper mainly reviews three mathematical models of face recognition based on neural networks, namely genetic algorithm and artificial neural network (GA-ANN), principal component analysis method and feed-forward neural networks (PCA-FNN) and Hidden Markov Models (HMMs) and State-Action-Reward-State-Action (SARSA). In this paper, three models are introduced and analysed in detail. Through analysis, it has been concluded that the GA-ANN improves classification accuracy, the PCA-FNN is used in face recognition with constant posture and the use of HMM-SARSA improves the accuracy of face recognition.
Application of CNN in computer vision
Authors: Yan Jiamiao
Published: January 2024

Today's deep learning continues to be hot, and the application of machine learning can be seen in more and more fields. A neural network model called a Convolutional Neural Network (CNN) was created to imitate the structure of the human brain. It is a convolution operation that maps the relationship between input features and output features to a two-dimensional in the vector space of , the network can effectively process the input data. CNN emerged to solve the computational bottleneck problem faced by traditional networks. This paper discusses the application of the deep learning model CNN in image classification, target detection and face recognition. In these fields, models are continuously proposed, and architectures in each field are constantly emerging. Among them will be the classic architecture of CNN in this field. These classic architectures have their advantages, but there will also be improvements brought about by the shortcomings of the classic architecture. Through the application of these different fields, we can see that CNN-based deep learning can help various fields, and the efficiency will be improved, but it is not perfect and needs continuous improvement.
Application and comparison of decision tree algorithm and K-Nearest Neighbors algorithm in heart disease prediction
Authors: Wang Yiran
Published: January 2024

In the past two decades, rapid industrialization and urbanization have led to tremendous economic growth and an improvement in people's living standards. However, the impact of people's irregular lifestyles and habits on their health has gradually emerged. Among them, cardiovascular diseases have become particularly prominent, with increasing incidence and mortality rates, especially in developing countries. Heart disease is a major cause of the rising death rates. Early-stage prediction of heart disease poses a major challenge in clinical analysis. Today, the adoption of appropriate decision support systems to achieve cost reduction in clinical trials has become a future development trend for many hospitals. This study compares decision tree classification and K-nearest neighbors (KNN) classification algorithms to seek better diagnostic performance for heart disease. The existing dataset of heart disease patients from the Cleveland database is used to te3st and demonstrate the performance of all algorithms, providing support for the establishment of a heart disease prediction system. This, in turn, can assist doctors in making more accurate diagnoses and timely interventions before the onset of heart disease, thereby reducing the mortality rate of heart disease from the source.
An optimized approach to speech transcription using blind source separation and speech-to-text machine learning models
Authors: Yin Chaoyang
Published: January 2024

The use of speech-to-text transcription has a multitude of applications in various industries, including accessibility support, language processing, and automatic subtitling. In recent years, there has been greater interest in incorporating automatic speech source separation features to improve the accuracy and efficiency of transcription mechanisms. This paper aims to design a transcription mechanism that utilizes DUET algorithm to separate speech sources in a stereo setup. The separated sources are then transcribed into text using a machine learning model. The study evaluates the effectiveness of this approach using a dataset of speech recordings. The results of the study indicate high accuracy in speech separation and transcription, highlighting the potential of this approach for practical applications. However, the study also revealed potential issues with the mechanism, indicating the need for further exploration and refinement. These findings indicate the potential of the proposed approach for practical applications, and propose insight for further development and researches in this area.
Application and analysis of machine learning in handwritten digit recognition
Authors: Mo Zichun
Published: January 2024

It appears from the information that Character recognition research is currently focused on handwritten digit recognition, a significant subfield of optical character recognition, i.e. the use of computers to recognise and process digital information. In today's increasingly mainstream computer and data era, handwritten numeric recognition can simplify the process of paper-based offices, reduce the intensity of work when analysing data statistics afterwards and improve work efficiency. There are many algorithms to achieve recognition, each with different recognition accuracy, implementation efficiency and application scope. Based on the basic concepts described above, this thesis investigates the efficiency and accuracy of three algorithms - template matching, SVM and deep learning - in recognising handwritten digits with different sample sizes. The models or kernel functions currently used to process data of varying complexity and restricted scenarios also require continuous improvement to ensure accuracy, making it all the more important to discuss them in detail.
Prediction-based early warning system for overflow of people in metro stations
Authors: Lu Di
Published: January 2024

As cities rapidly urbanize, handling the management of crowded transportation hubs, notably metro stations, has become an immediate concern. High passenger traffic can lead to severe risks, including stampedes. While many past passenger flow forecasting systems aim to enhance prediction accuracy, the inherently noisy nature of passenger flow data makes it challenging for existing technologies to provide stable and precise predictions, making passenger flow management based solely on these models risky. This paper introduces a novel system that mitigates this risk by integrating a predictive model with managerial methods. The proposed management framework formulates a unique model for each station, determining a risk deviation coefficient grounded in the station's historical prediction accuracy. Station management is then holistically executed based on this coefficient and the predictive model. This paper employs the LSTM model for station-specific passenger flow prediction and defines the risk-related parameter , taking prediction accuracy into account. This adjusted LSTM prediction is then utilized to proactively streamline resource allocation, targeting improved passenger safety and overall station experience.
An evaluation of the discrete logarithm cryptosystem
Authors: Chen Yansheng
Published: January 2024

In modern society, the unauthorized disclosure of digital information such as military files, business details, and personal data can gravely endanger the security and privacy rights of an individual or group. Such risks highlight the significance of cryptosystems sets of computational algorithms used for safe data transit. This paper specifically explores the algorithms of the discrete logarithm cryptosystem from a number theory perspective and investigates the cryptosystems overall strengths and weaknesses as a result of their designs. In the end, from various algorithms such as ElGamal encryption and decryption, it was concluded that the use of discrete logarithms and large primes significantly contributed to the advantages and disadvantages of the system, respectively.
Design of flight control system of Quad Tilt Rotor UAV based on PID algorithm
Authors: Chen Weilian
Published: January 2024

This research focuses on the quadrotor tiltrotor unmanned aerial vehicle and considers various usage scenarios such as battlefield and transportation environments, as well as flight stability issues in complex meteorological environments. A PID control algorithm for rotor angle control of quadrotor tiltrotor unmanned aerial vehicles is designed, which is suitable for different usage scenarios and meteorological conditions.
Research on predicting football matches based on handicap data and BPNN
Authors: Hu Jiahao
Published: January 2024

Football is one of the most influential sports in the world, and billions of people around the globe pay much attention to the football matches. With the growing popularity of football and the continuous development of the football betting industry, the prediction of the outcomes of football matches has become a hot topic in the commercial operations of sports especially footballs in recent years. It is also an important subject of academic research. In this paper, we develop a football match result prediction model based on the back propagation neural network. We take the German Bundesliga competitions as the research object in this paper. In addition to utilizing historical statistic data and team attributes from previous matches, we also incorporate a new dataset, known as handicap data, which refers to the odds data of the football matches, as the input layer of the BPNN (back propagation neural networks) for prediction. We also innovatively use varying numbers of hidden nodes, which greatly improves the prediction accuracy and stability of the model. Experimental results indicate that the average prediction accuracy of this football match prediction model is around 57.2%, with the highest prediction accuracy reaching 59.8% and the lowest prediction accuracy at 53.8%. The prediction model demonstrates relative stability, with no significant fluctuations in prediction accuracy.
Interaction mode enables user perception recognition and perception optimization: An AI human-computer interaction study
Authors: Shen Yizhe
Published: January 2024

Artificial intelligent (AI) has various ways of human-computer interaction, but most of them overlook the recognition of human perception. If the interaction mode is combined with psychology, the user's mood change can be identified by the user's subtle expression, movement change and voice tone change, so as to provide corresponding services and improve the user experience. Statistical analysis of human responses to different situations in cognitive psychology, incorporating them into human-computer interaction methods. The current human-computer interaction modes in products tend to be standardized, and focusing user experience on user perception will bring special experiences to users. Emotional recognition is a cross disciplinary discipline with broad application prospects, but it has not yet reached a mature stage and requires corpus enrichment, theoretical strengthening, and method innovation. The era of artificial intelligence is leading a new wave of technological progress, and emotion recognition, as an important topic in the field of artificial intelligence, can help computer intelligence recognize human emotions and make human-computer interaction more friendly. In the near future, research on emotion recognition technology will make greater progress and be better applied to practical products.
The effectiveness of PCA and various hyperparameter settings in SVM and KNN for wine quality estimation
Authors: He Siyi
Published: January 2024

Wine is popular around the world and wine quality evaluation is focused by the wine companies. Wine quality prediction through machine learning is expected to mitigate the waste of time and money of artificial wine quality prediction. Previous researches focused on simple applications and comparisons of the machine learning methods on the wine dataset, but the exploration of optimal parameters of models lacked. Therefore, this research mainly aimed to determine wine quality based on known data by implementing various machine learning models and find the optimal model for predicting the wine quality. For the optimal model, the detailed value of parameter and setting are aimed to be explored. This paper trained five machine learning algorithms and tested them on a wine dataset. The impact of standardization on different machine learning models was tested. Except for decision tree and AdaBoost, standardization is an effective method to improve the performances of different methods. Support vector machine (SVM) with rbf kernel performed best among different SVM classifiers. K-nearest neighbor (KNN) of twenty-five neighborhood points combined with principal component analysis (PCA) of five principal components showed 90.94% accuracy and it is the optimal algorithm.
Impact of algorithmic and data structure implementation to game development
Authors: Xie Yuzhe , Zheng Junjie , Chen Kerui
Published: January 2024

Many game developers tend to build their proprietary game engines with various algorithmic and data structure implementations. This work discusses a game made using the Pygame library that utilizes a maze generation algorithm, collision detection algorithm, and 2d shadow drawing algorithm to analyze how programming implementation could impact the game's representation, optimization, and future development. In this paper, various algorithms and data structures are compared based on their characteristic and performance, which leads to an analysis of how the game would differ from its current state if alternative implementations are used. It also addresses the issues raised by algorithms and data structures based on their interdependence relationship.
Optimizing road sign detection using the segment anything model for background pixel exclusion
Authors: Liu Zhimeng
Published: January 2024

Road sign recognition plays a great role in automatic driving. At present, for the task of road sign recognition, the structure related to CNN is mainly used to classify the detected road signs. However, due to the complexity of road conditions and weather, the detected images often contain complex background pixels, which affects the accuracy of classification. Due to the release of segment anything model, there is a powerful tool for eliminating background pixels quickly and conveniently. We optimize the data to retain only the road sign part of the images, and directly let the model train the image without back- ground pixels to realize the recognition task. This paper constructs two CNN classification models with the same structure through comparative experiments. One model uses the segmented data set for training, and the other model uses the original data set for training. The performance of the two models is evaluated and compared to verify the optimization effect brought by the segmented data set. It is found that segment anything model can accurately cut most of the images in the data set. And the performance of the model trained by the segmented data set is better than that of the model using the original data set. Moreover, the optimized model can achieve high accuracy in less training times.
Elastic distributed dataset RDD (Resilient Distributed Datasets)
Authors: Li Yiwen
Published: January 2024

In recent years, the amount of data to be processed has become larger and larger, and it is difficult for a single processor to handle data, so we need to work hard to develop the expansion mode of cluster mode. Mapreduce, Storm, etc. are a solution for clusters to process data. However, we still have parallel processing, user resource sharing and other issues to be addressed. RDD data model is the key to solve these problems. In this paper, RDD is applied to Spark for detailed expansion, and project experiments are carried out to study the RDD data model. The programming language used in this article is python. The advantage of studying distributed iterative computing is that during the calculation process, the task calculation fails and can be recovered. The default is to allow four failures recoverable. If the stage fails, one can recover, and if the partition fails, one can also recover. Multiple partitions can improve parallelism and efficiency. After a shuffle, a partition failed because the many-to-one relationship needs to be recalculated.
The wearable tactile feedback system and its application in the Virtual Reality
Authors: Li Jiawei
Published: January 2024

Virtual reality provides users with an interactable environment based on the real world. In addition, the users can use wearable devices to detect the environmental conditions and project real-world body movement to the virtual world. The development of Virtual Reality highly depends on the hardware devices. In that way, the wearable tactile feedback system is considered as a reasonable choice. The article will mainly focus on three parts about the feedback system. First, how the system works and what kind of sensors and hardware devices are contained in the system will be discussed. How they are connected will be investigated as well. Secondly, three articles about the researches done by others will be introduced, including the sensors' materials, the system's improvement, and the application and future of the system. Finally, the particular application of the system in Virtual Reality will be presented. The research of passage will be greatly valuable for the further study of the wearable tactile feedback system in Virtual Reality.
Application of ultrasound diagnosis in cardiac and liver diseases
Authors: Bao Zheng
Published: January 2024

Ultrasonography is a non-invasive imaging modality widely used to evaluate and diagnose many diseases. The purpose of this article is to provide a more detailed overview of the use of ultrasound in the diagnosis of heart and liver disease. In addition to a basic introduction to heart and liver disease, this article will explore specific applications of ultrasound in specific heart and liver diseases. In terms of heart disease, ultrasound plays an important role in the diagnosis of coronary atherosclerotic heart disease, valvular heart disease and congenital heart disease. Ultrasound can assess the degree of narrowing and blood flow in coronary arteries to help determine a patient's cardiovascular risk. For valvular heart disease, ultrasound can detect and evaluate the function and abnormality of the heart valves, providing information about valve stenosis or regurgitation. For congenital heart disease, ultrasound can reveal structural abnormalities and hemodynamic changes in the heart, helping to determine the type and severity of the lesion. In liver disease, ultrasound plays an important role in the evaluation of pathologies such as cirrhosis and hepatocellular carcinoma. Ultrasound can assess the shape, size, and texture of the liver, help detect and diagnose cirrhosis, and assess liver function and disease progression. In addition, ultrasound is also of great significance for the early diagnosis and monitoring of hepatocellular carcinoma, which can reveal the location, size and blood flow of the tumor. The role of ultrasound in diagnosing and monitoring disease will expand further with advances in technologies such as artificial intelligence and elastography. In conclusion, ultrasound plays a key role in the assessment, diagnosis and monitoring of heart and liver diseases and has many advantages. With the further development of technology and the expansion of application, the position of ultrasound in clinical practice will be further strengthened.
Development analysis of intelligent robots in manufacturing industry
Authors: xin Wang
Published: January 2024

With the continuous development of the manufacturing industry, the application of intelligent robots is becoming more and more extensive. Many emerging technologies can be applied to intelligent robots. Typical intelligent robots still have room for further improvement in terms of automatic control and adaptation to the surrounding environment. And independent learning of production tasks and realization of human-computer interaction is the future development direction of industrial robots. In view of these deficiencies, the development of intelligent robots is particularly important. This paper introduces the application fields, key technologies and needs of intelligent robots, and explains the important position of intelligent robots in the future manufacturing industry. The functions of intelligent robot control, perception and human-computer interaction based on artificial intelligence technology are studied. Possible future challenges, limitations and problems will be presented at the end of this paper.
Evaluation of dimensionality reduction and unsupervised clustering methods in breast datasets
Authors: Liu JunFan , He Weide , Wang YuXin , Zhang BoYi
Published: January 2024

This paper delves into the issues related to handling high-dimensional data in massive datasets, such as computational challenges and uneven data distribution owing to diminished data point density. Various dimensionality reduction techniques such as Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Diffusion Maps are discussed and evaluated for their efficiency in extracting crucial data features. This aids in gaining a comprehensive understanding of the data. The study also examines unsupervised clustering methods like K-means, DBSCAN, and spectral clustering. By integrating these clustering methods with dimensionality reduction techniques, we aim to uncover potential synergies. The principles and methodology behind spectral clustering and unsupervised nonlinear diffusion learning are further dissected. Various datasets are employed to evaluate the efficiency of these techniques empirically. The final section of the paper comprises an evaluation of the clustering results and a discussion on potential avenues for future research.
Analysis of concrete architecture of Bitcoin Core
Authors: Zheng Yiwen
Published: January 2024

Bitcoin Core serves as the foundational software responsible for verifying and validating all transactions and blocks within the Bitcoin blockchain, hence upholding the security and integrity of the whole Bitcoin network. This study extensively examines the internal mechanisms, structure, and conceptual framework of Bitcoin Core. This study aims to conduct a comprehensive analysis of the Bitcoin Core architecture, with a focus on evaluating its capacity to fulfill the rigorous demands of a decentralized Bitcoin network. This study not only provides a comprehensive understanding of the essential elements that drive the Bitcoin network, but it also explores the numerous factors that have a substantial impact on the operational availability of Bitcoin nodes. All of these factors are essential for ensuring the efficient operation of the Bitcoin network, encompassing the physical environment, architectural designs of nodes, and maintenance requirements. This study provides a comprehensive analysis of the underlying mechanisms of Bitcoin, shedding light on its inherent robustness and the factors that contribute to its consistent performance within the ever-changing landscape of digital currencies. The objective of this endeavor is to furnish a detailed exposition of the mechanics of Bitcoin, so enhancing its prevalence and familiarity among individuals.