bluuebunny/crossref_metadata_2025 huggingface dataset:
Makerspace: Membangun Budaya Literasi Numerasi Sejak Dini Di Lingkungan Keluarga Bagi Perempuan Desa Padelegan Pamekasan
Authors: Wulandari Rika
Published: February 2024

Pademawu termasuk kecamatan yang memberikan andil cukup besar dalam perolehan rapor pendidikan daerah khususnya di Kabupaten Pamekasan. Pada Rapor Pendidikan Publik tahun 2022, kemampuan literasi dan numerasi anak-anak khususnya di sekolah dasar masih di bawah kompetensi minimum. Berdasarkan hal tersebut, perlu adanya upaya yang selaras antara pendidikan formal dan pendidikan informal (keluarga) untuk dapat meningkatkan kemampuan literasi dan numerasi anak-anak agar kelak mereka dapat beradaptasi dengan perubahan zaman yang begitu cepat. Salah satu langkah yang dapat dilakukan yaitu pelatihan pemberdayaan dan pengoptimalan peran ibu sebagai madrasah pertama dan utama bagi anak-anaknya, dalam melatih kemampuan literasi dan numerasi sejak dini di lingkungan keluarga melalui pembiasaan dan pengenalan anak dalam lingkungan yang kaya teks (makerspace).
What makes servant leadership effective for female employees? Servant leadership application during the COVID-19 pandemic
Authors: Fakhri Mahendra , Raharja Sam'UnJaja , Purnomo Margo , Rivani Rivani
Published: February 2024

This study delves into the intricate dynamics of servant leadership and its profound impact on sustaining employee motivation amidst the unprecedented challenges posed by the COVID-19 pandemic. It sets out to meticulously examine and discern the pivotal factors that underpin the remarkable efficacy of servant leadership practices, particularly centered around female employees in an Indonesian setting, with a deliberate emphasis on women leaders. The research aims to understand the effectiveness of servant leadership among female employees in Indonesia, with a specific focus on women in leadership roles. The study includes a comprehensive sample of 310 respondents and utilizes confirmatory factor analysis within the Structural Equation Modeling-Partial Least Squares framework to determine the key factors influencing servant leadership effectiveness. The findings of the study suggest that one of the most influential factors in driving the effectiveness of servant leadership is helping subordinates grow and succeed. On the other hand, emotional healing has the least influential impact on enhancing servant leadership practices compared to other dimensions. The study's findings provide valuable insights for organizations, especially in navigating the difficulties presented by the COVID-19 pandemic. Understanding the importance of fostering employees' personal growth and success, as well as acknowledging the limited impact of emotional healing, can inform leadership approaches to sustain workforce motivation during times of uncertainty. These results highlight the significance of cultivating employees' development within a framework of servant leadership, ultimately leading to a more resilient and motivated workforce, with particular relevance for female employees.
HUBUNGAN TINGKAT PENGETAHUAN DENGAN KECEMASAN PERAWAT DALAM PEMBERIAN KEMOTERAPI PADA PASIEN KANKER DI RUANG RAWAT INAP RUMAH SAKIT KANKER DHARMAIS
Authors: Trifani Dini , Sari Dewi Nur Puspita , Mukhaira Inna
Published: February 2024

Pendahuluan: Kanker merupakan suatu ancaman serius dan pengobatan kemoterapi insiden dan angka kematiannya terus merayap naik. Perawat merupakan garda terdepan dalam pelaksanaan kemoterapi yang dilakukan di Rumah Sakit. Kecemasan sebelum tindakan medis khususnya pada perawat yang memberikan terapi kemoterapi tidak bisa dihindari. Sehingga diperlukan pengetahuan perawat dalam melakukan tindakan kemoterapi tanpa ada rasa khawatir dan takut. Tujuan: Tujuan penelitian ini untuk mengetahui hubungan tingkat pengetahuan terhadap kecemasan perawat dalam pemberian kemoterapi pada pasien kanker di Ruang Rawat Inap RS Kanker Dharmais. Metode: Desain penelitian ini deskriptif kuantitatif yang menggunakan desain analisis korelasi dengan pendekatan cross-sectional. Sampel sebanyak 92 responden. Hasil: Hasil penelitian didapatkan tingkat pengetahuan baik (62,0%) tidak cemas (65.2%). Ada hubungan tingkat pengetahuan terhadap kecemasan perawat dalam pemberian kemoterapi pada pasien kanker di ruang rawat inap Rumah Sakit Kanker Dharmais dengan uji Chi Square didapatkan p-value: 0,000 (α:<0,05). Kesimpulan: Ada hubungan tingkat pengetahuan terhadap kecemasan perawat dalam pemberian kemoterapi pada pasien kanker di ruang rawat inap Rumah Sakit Kanker Dharmais. Saran: Diharapkan pelayanan keperawatan dapat memfasilitasi dalam upaya peningkatan pengetahuan perawat tentang tindakan kemoterapi yang aman.
Analisis Faktor Gaya Hidup Keluarga dengan Kejadian Hipertensi pada Usia Dewasa Muda di Desa Sungai Paring Wilayah Kerja UPTD Puskesmas Jambu Hilir Tahun 2023
Authors: Widniah Any Zahrotul , Putri Helmania
Published: February 2024

Latar belakang : Hipertensi merupakan salah satu masalah Kesehatan yang cukup berbahaya diseluruh dunia karena hipertensi merupakan faktor resiko yang mempngaruhi penyakit kardiovaskuler.
Tujuan: Penelitian ini bertujuan untuk Menganalisis faktor gaya hidup dewasa muda di desa Sungai paring di wilayah UPTD PUSKESMAS jambu hilir
Metode : Jenis Penelitian ini adalah korelasional cross sectional. Populasi berjumlah 82 orang. Pengambilan sampel dengan teknik total sampling, intrumen penelitian yang di gunakan adalah kuesioner.
Hasil : Adanya hubungan pada gaya hidup seperti aktivitas fisik dengan nilai (p<0,32) pola makan dengan nilai (p<0,31), kebiasaan istirahat dengan nilai (p<,45), kebiasaan merokok dengan nilai (p<0,035) terhadap kejadian hipertensi pada usia dewasa muda dan pada kebiasaan konsumsi alkohol dengan nilai (p<0,399) karena <0,05 tidak terdapat hubungan
Kesimpulan : Terdapat hubungan yang signifikan antara aktivitas fisik, pola makan, kebiasaan istirahat dan merokok dengan kejadian hipertensi sedangkan konsumsi alcohol tidak terdapat hubungan dengan kejadian hipertensi pada usia dewasa muda di desa Sungai paring
Produção e qualidade bromatológica dos sorgos forrageiros BRS 658 e Volumax, cultivados na Zona da Mata Alagoana, nordeste do Brasil
Authors: De Oliveira Mauro Wagner , Rodrigues Thiago Camacho , Borges Sávio Tavares Ferreira , Santos Dalmo de Freitas , Bhatt Rajan , Da Silva Maria Karlla Verônica , Santos Izabella Letícia Araújo
Published: February 2024

No presente estudo foram avaliados o estado nutricional, o acúmulo de matéria seca e a qualidade bromatológica de dois híbridos de sorgo forrageiro: BRS 658 e Volumax. A pesquisa foi conduzida em área experimental do Campus de Engenharias e Ciências Agrárias, em Rio Largo, AL, nordeste do Brasil. O delineamento experimental foi o de blocos ao acaso, com quatro repetições. A adubação química, aplicada no fundo do sulco de plantio, foi apenas de fósforo, na dose de 70 kg por hectare, para evitar salinização próximo às sementes. Em cobertura aplicou-se 250 kg de nitrogênio e 250 kg de potássio por hectare. As parcelas foram constituídas de cinco sulcos de 5,0 metros de comprimento, espaçadas em 0,70 m. Na fase de emissão da panícula, foram coletadas folhas para avaliação do estado nutricional das plantas. Não houve efeito de híbrido de sorgo para os teores de nutrientes no limbo foliar. À exceção do magnésio e do cobre, os teores foliares dos demais elementos encontravam-se em concentrações consideradas adequadas para o sorgo. O BRS658 e o Volumax tiveram alta produtividade sendo a produção de matéria natural e de matéria seca pelos dois híbridos semelhantes, com valores médios de 47 e 13 t por hectare, respectivamente. Para a qualidade bromatológica também não houve diferença entre o BRS658 e o Volumax, tendo-se obtido valor médio de proteína bruta, fibra insolúvel em detergente neutro (FDN), e de fibra insolúvel em detergente ácido (FDA), respectivamente de 80, 555 e 342 g por kg de matéria seca.
Composição bromatológica e alocação da matéria seca no híbrido de milho P3862H, cultivado em solo de alta fertilidade
Authors: De Oliveira Mauro Wagner , Rodrigues Thiago Camacho , Lopes Kêmelly Athla da Silva , Santos Dalmo de Freitas , Paes Reinaldo de Alencar , Santos Hariane Luiz , Nogueira Carlos Henrique de Castro
Published: February 2024

No presente estudo foi avaliada a produção de forragem, remoção de nutrientes e a composição química da forragem do híbrido de milho P3862H, cultivado em solo de alta fertilidade. Na semeadura aplicou-se 400 kg do adubo 08-28-16 por hectare, e em cobertura, 700 kg por hectare do adubo 30-00-20. Na fase de grãos farináceos duros avaliou-se o acúmulo e a partição da matéria seca, o acúmulo de macronutrientes e a qualidade bromatológica da forragem, amostrando-se em sete pontos da lavoura, áreas de 2,1 m2 (3,0 m de sulco x 0,7 m de espaçamento). A produtividade da lavoura foi elevada, tendo-se obtido média de 22,49 t de matéria seca por hectare, com valores oscilando de 20,45 a 24,79 t ha-1. A remoção de nutrientes também foi alta, tanto devido a concentração dos elementos na forragem, quanto a elevada produção de matéria seca. Para o nitrogênio, fósforo e potássio foram obtidos valores médios de 344, 68 e 349 kg ha-1. A qualidade bromatológica da forragem também foi muito boa com valores médios de 96, 396, 243 e 47 g kg-1, respectivamente, para proteína bruta, FDN, FDA e lignina. Em média, 58,27% da biomassa aérea do P3862H estava alocada na espiga.
História dilemática como proposta metodológica na investigação do juízo moral de estudantes: em pauta, o preconceito religioso
Authors: Gonçalves Carlos Eduardo de Souza , De Oliveira Francismara Neves , Thomas Silvana , De Godoi Guilherme Aparecido , Sanchez Júnior Sidney Lopes , Aleixo Ana Carolina Mexia , Do Amaral Leila Rute Oliveira Gurgel , De Oliveira Ricardo Furtado
Published: February 2024

O artigo apresenta uma pesquisa de abordagem qualitativa, na modalidade de estudo de caso, apoiada no método clínico-crítico piagetiano e nas discussões sobre o desenvolvimento sociomoral. Sua problemática relaciona-se com a investigação de mecanismos subjacentes ao pensamento preconceituoso. Objetivou investigar a aplicação de uma história dilemática como apoio instrumental na análise das construções de juízo moral por parte de dois estudantes do Ensino Médio de um campus do Instituto Federal do Paraná quanto às dimensões psicológicas cognitivas e afetivas. Enfatizou-se o aspecto metodológico da história dilemática em intervenções educacionais que tenham como meta a promoção do desenvolvimento de raciocínios e sentimentos morais. Nos resultados são compartilhados os procedimentos elaborados para analisar elementos das dimensões psicológicas nos juízos de valor moral, bem como as respostas dos participantes por meio das quais foi possível constatar argumentos moralmente mais elaborados (P1), como a exigência de respeito mútuo entre pessoas de diferentes crenças, e menos elaborados (P2), como as obrigações que um docente deve cumprir apenas por obediência unilateral a currículos ou superiores na hierarquia escolar. Concluímos que histórias dilemáticas podem ser utilizadas como instrumentos metodológicos na investigação das construções de juízos morais, bem como meios de intervenção para o desenvolvimento de construções morais entre estudantes.
Meaning and significance of the continued training of physical education teachers at the Londrina Municipal Education Bureau
Authors: De Oliveira Priscila Aparecida Silva , Palma Ângela Pereira Teixeira Victoria , Palma José Augusto Victoria
Published: February 2024

Continued teacher training is a topic that has been studied by several authors and can be understood as a process of complex and continuous development of reflection and construction about the profession and not a product ready to be consumed. The Municipal Department of Education of Londrina (SME) is responsible for the continued training of all teachers who work in the municipal education network. The present study sought to find out, with the Physical Education teachers of the said network, what the meaning and meaning attributed by them, of the continuous training offered by the SME. For this we used a questionnaire with statements associated with Likert scale of agreement and the theme was divided into three categories: Conception of continuous training; Conception of teaching; Relationship of professional interest-need and offer by SME. For the application of the questionnaire, an email was sent with a link to the form to the 159 teachers from the municipal network who are taking part in the continuous physical education training and 81 teachers participated in the research. The data were analyzed according to the higher incidence of responses that pointed out that teachers consider the continuous training process important for professional development and that it is capable of producing changes in the teaching identity, that they have the conception of being professionals of education and that this requires continuous training, that the continuous training offered by the SME contributes to the professional growth of teachers and that it should prioritize moments of exchange of experiences. We believe that the points raised from the answers should be discussed with the group of teachers and we conclude that it is necessary for the teacher to attribute meaning and meaning to this process. We also concluded that continuing education programs have much to contribute to professional development, based on reflections that favor the construction of teaching identity.
Research Center and Olympics as a strategy for science education in the centers Educates More of Maranhão
Authors: Miranda Marcos Eduardo , De Assis Raquel Melo , Mendonça Sidney Fernandes , Alves Thiago Gomes
Published: February 2024

This article addresses the implementation of the Research and Olympics Center (NPO) in the Educa Mais Centers in the State of Maranhão, part of the Full-Time Public Education Networks. The main objective was to analyze the results obtained by students and teachers after the introduction of the Center, focusing on participation and awards in knowledge olympiads and scientific events. The data collected revealed a significant increase in the engagement of Educa Mais Centers in challenging academic activities after the implementation of the NPO. The significant participation of students and teachers in Olympiads and scientific events indicated a clear interest in deepening knowledge and expanding educational horizons. Also noteworthy is the considerable increase in the number of awards, indicating not only recognition of merit, but also encouraging participation in other activities, with different methodologies. This positive scenario suggests that the Research and Olympics Center not only introduces an innovative approach to teaching, but also promotes a school culture that values academic excellence. The focus on research and participation in Olympiads not only challenges students intellectually, but also develops fundamental skills and competencies such as critical thinking, problem solving, networking and teamwork. When considering the longevity and effectiveness of this implementation, constant monitoring of results is essential, identifying areas for improvement and necessary adjustments. The dissemination of these positive results not only celebrates the success of the initiative, but also serves as an inspiring model for other educational institutions seeking to improve educational standards and encourage student participation in enriching activities beyond the conventional curriculum. This study highlights the importance of innovative pedagogical strategies to promote the academic and scientific development of students, contributing to the formation of a more capable generation to face future challenges.
Per clava orientalis. Mace Heads and Kistens in the Collection of the National Museum of Transylvanian History
Authors:   Facultatea de Istorie și Filosofie, Universitatea Babeș-Bolyai, Cluj-Napoca, Fărcaș Andrei Octavian
Published: February 2024

Over the past few years, the number of mace heads discovered in the collections of the Transylvanian museums has gradually increased. The National Museum of Transylvanian History makes no exception. The reorganization and re‑evaluation of its collections brought together 20 mace heads, three kistens, and one kisten chain, all of them unpublished so far. With the help and generosity of the museum staff, I compiled, analyzed, and dated these artifacts using, where possible, the existing typo‑chronologies. For the significant number of maces and kistens where no classification exists, I took on the analysis of relevant analogies.
Cultural Goods Related to the Cult of Poet Sándor Petőfi from the Collections of the National Museum of Transylvanian History
Authors:   Muzeul Național de Istorie a Transilvaniei, Cluj-Napoca, Mitu Melinda
Published: February 2024

The study aims to present several cultural goods from the patrimony of the National Museum of Transylvanian History (Muzeul Național de Istorie a Transilvaniei – MNIT) that reflect the cult of poet Sándor Petőfi among Hungarians during the nineteenth century. Sándor Petőfi (1823–1849) is one of the most representative poets of Hungarian literature and one of the most important personalities of the Hungarian Revolution of 1848–1849. Almost all the objects presented throughout this paper, and nowadays included in MNIT’s patrimony, pertained to the collections of two museums that had functioned in Cluj during the nineteenth century: the Transylvanian National Museum (Erdélyi Nemzeti Múzeum) and the Museum of Relics of the 1848–49 Revolution (1848‒49. Országos Történelmi és Ereklye‑Múzeum). The present paper was elaborated on the occasion of celebrating 200 years since the birth of Sándor Petőfi, an event marked by MNIT through the organization of two temporary exhibitions.
Adrenalectomía Bilateral en un Paciente de 15 Años con Feocromocitoma Adrenal
Authors: Bardales Hugo , González Gustavo , Rivas Paula
Published: February 2024

Objetivo: Reportar el caso de una paciente con diagnóstico de feocromocitoma adrenal bilateral sometida a adrenalectomía abierta bilateral. Presentación de caso: Paciente femenina de 15 años de edad que consultó con  cuadro de sudoración y palpitaciones de cinco meses de evolución. Al examen físico se documenta hipertensión arterial, diaforesis y alteraciones visuales por lo que se indica la realización de estudios complementarios; estos últimos revelan presencia de tumor adrenal bilateral T2,N0,M0. Se decide realizar adrenalectomía bilateral abierta. Paciente se traslada a la unidad de cuidados intensivos con uso de aminas vasoactivas. La evolución clínica es adecuada por lo que se egresa exitosamente con uso de esteroides y plan de seguimiento ambulatorio en 6 semanas. Histopatología confirma presencia de feocromocitoma confinado a las glándulas suprarrenales, sin extensión capsular y sin invasión linfovascular. Discusión: Los feocromocitomas son tumores neuroendocrinos infrecuentes originados en las células enterocromafines adrenales que pueden producir síndromes simpaticomiméticos. El tratamiento inicial consiste en el bloqueo farmacológico pero el tratamiento definitivo es la resección tumoral completa. La adrenalectomía videolaparoscópica es el tratamiento quirúrgico de elección en la mayoría de casos. El abordaje abierto se indica en pacientes con tumores >6-8cm, de localización compleja o con compromiso de órganos circundantes. Conclusión: El feocromocitoma es una neoplasia adrenal infrecuente y muchas veces silente cuyo tratamiento definitivo es la resección tumoral completa. El abordaje adecuado implica el manejo integral de la enfermedad por un equipo multidisciplinar que involucre médicos internistas, cardiólogos, urólogos, nefrólogos, entre otros.
Automated valuation of used sailboat prices based on random forest regression modeling
Authors: Ju Minhan
Published: February 2024

This study presents a machine learning method for regression prediction of used sailboat prices. The dataset contains attributes such as brand, length, year, and listing price of the sailboat, and the dataset is preprocessed by removing irrelevant fields and normalizing the data. A random forest model is constructed and evaluated against several models such as gradient boosting and neural networks through k-fold cross-validation. Random Forest performs well compared to other models. The ensemble approach of the algorithm effectively modeled the complex nonlinear relationships in the data. Rigorous validation ensures the generalizability of the model. The Random Forest model outperforms traditional manual assessments in terms of the accuracy of price assessments. This data-driven solution allows customers to value sailboats on their own and avoid paying excessive fees. It also allows sailboat companies to develop automated pricing systems to speed up operations. This research provides a powerful machine-learning approach for accurately predicting used sailboat prices. These techniques can be extended to other regression tasks. Further work includes refining the model and deploying real-world applications.
Optimization of human-machine interface for fatigue driving problem
Authors: Wei Jinwu , Sun Zhenning , Chen Hanyu
Published: February 2024

Amidst rapid motorization, the surge in serious traffic accidents has raised concerns about the significant contribution of fatigued driving to road safety. However, the current vehicle-machine interface for fatigue driving reminder is relatively simplistic and plays a weak role. This study aims to optimize the functionality of traditional in-vehicle HMIs by exploring the key factors of human-computer interaction (HCI) and developing targeted user interfaces to effectively alert and reduce driver fatigue. A quantitative analysis based on previous experimental data is conducted to model the correlation between interface design factors (such as simplicity and feedback clarity) and physical fatigue parameters. An integrated user interface with fatigue alerts, rest area navigation, driver assistance, air conditioning settings, and voice control modules is proposed. Compared to the traditional interface, the improved user interface is evaluated in simulated driving conditions using an A/B experiment. The new user interface is expected to demonstrate improved effectiveness in relieving driver fatigue by providing clear visual, audio and haptic feedback. This research contributes a structured methodology for applying HCI principles to optimize in-vehicle interface design for mitigating driver fatigue, providing a framework to inform future interface development and enhance road safety.
Evolution of CNNs in human tracking applications
Authors: Chen Yancheng
Published: February 2024

Tracking the movement of objects in videos or footage from CCTV systems plays an integral role in crime investigations, surveillance, security predictions, and many other domains. Historically, this task was primarily entrusted to dedicated observers or analysts who would be summoned to review pre-recorded footage post-event. With the advent of machine learning and AI, convolutional neural networks (CNNs) have paved the way for computers to augment human capabilities in analyzing streaming videos or archived recordings. Among the various tracking methodologies that machine learning offers, YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Networks), along with its iterations, stand out as some of the most reliable and precise. However, the scope of analysis often extends beyond these techniques. To enhance accuracy and provide an adept classification mechanism, the Deep SORT (Simple Online and Realtime Tracking) algorithm emerges as pivotal. Its synergy with human detection remains a significant area of discussion and will be deliberated upon in this study. This review aims to elucidate the intricacies of these state-of-the-art methods and their interplay in modern tracking systems.
Exploring methods to enhance network security through artificial intelligence
Authors: Tang Yanni
Published: February 2024

In the dynamic landscape of cybersecurity, traditional rule-based systems find themselves frequently outstripped by the intricacy, variety, and mutable nature of cyber threats. This paper explores the capabilities of Machine Learning (ML) in detecting cyber-attacks, offering a fresh perspective to fortify cyber defense mechanisms. Through its unparalleled strengths in data analysis, pattern discernment, and outcome prediction, Machine Learning emerges as a promising ally in grappling with the multifaceted challenges posed by cyber adversaries. The exploration zeroes in on the potential of utilizing machine learning for cyber-attack detection, spotlighting supervised learning algorithms such as SVM and Random Forest. Experimental findings robustly underscore the value of Machine Learning in identifying potential cyber threats. In conclusion, the transformative potential of Machine Learning in the domain of cyber-attack detection is evident. Equipped with the prowess to derive insights from vast data sets, swiftly adapt to changing parameters, and preemptively recognize threats, Machine Learning promises to redefine the paradigms of cybersecurity. As the digital expanse continues to evolve, defense mechanisms must also evolve, with Machine Learning serving as a pivotal tool in this endeavor.
Long-term and short-term memory network based movie comment sentiment analysis
Authors: Bi Ruoxue
Published: February 2024

This paper proposes an emotional analysis method of movie reviews based on Long-term and Short-term Memory(LSTM) Network model. Emotional analysis is widely used in movie recommendation system, which can recommend and judge movies by understanding the audiences emotional response to movies. However, due to the characteristics of movie text and the complexity of emotional expression, traditional methods such as machine learning have limitations and shortcomings in emotional analysis. However, the LSTM models better memory is utilized by the method proposed in this paper and the ability to capture the long-term correlation in movie texts, which obviously improves the accuracy and reliability of emotional analysis, and demonstrates the advantages of the LSTM model in emotional analysis compared to the traditional model. Future research can further explore other deep learning models and algorithms, so as to make emotional analysis more accurate and provide users with reliable movie recommendation information.
Efficient parallelism in Breadth-First Search: A comprehensive analysis and implementation
Authors: Li Jianlin
Published: February 2024

The Breadth-First Search (BFS) entails a systematic traversal of a given graph, G = (V, E), layer by layer, starting from a specific vertex. Recognized as a cornerstone methodology for graph exploration, the importance of BFS has skyrocketed, especially with the increasing demands of graph-based data processing. However, as the vertex count expands, traditional serial implementations reveal their limitations, faltering in terms of time and space efficiency. This paper aims to contrast the efficiencies of standard BFS with its parallelized iteration. Introducing a shared-memory model of level-synchronous parallel BFS, the approach integrates optimizations to navigate the challenges posed by implicit barriers and critical sections. Employing the Graph500 benchmark, this parallel methodology is meticulously evaluated, highlighting the speedup concerning various thread counts. Initial findings unveil a compelling pattern: speedup generally correlates positively with the number of active threads. However, if the thread count breaches the system's inherent capacity, the speedup hits a plateau, showing only marginal fluctuations without significant increases. These statistical revelations not only vouch for the advantages of BFS parallelization but also emphasize a critical insight: judiciously increasing thread count, up to a system-specified limit, can yield peak efficiency.
New psychological education: Leveraging the power of ChatGPT and artificial intelligence
Authors: Lin Yechao
Published: February 2024

Due to various reasons, the fact is that psychological problems are becoming increasingly serious worldwide. In recent years, Artificial Intelligence (AI) and chatbot have developed rapidly. With the advancement of natural language processing, AI driven chat robots have become an innovative tool that can enhance learning experiences and provide support in various fields. In the field of psychological education, ChatGPT is expected to promote interactive and personalized learning environments. It is possible and necessary to try using them for psychological education. This article analyzes the application of chatgpt in psychology and how AI is used in psychological counseling. These chat robots can simulate conversations similar to humans, allowing students to participate in the conversation and receive immediate feedback. In addition, their ability to adjust their reactions according to personal needs promotes autonomous learning and cultivates students' autonomy. Subsequently, this article outlines the specific applications of ChatGPT in psychological education, including mental health support, cognitive assessment, and virtual reality therapy. Finally, this article discusses some of the disadvantages of ChatGPT, such as appearing correct but actually having the wrong answer. In summary, this article discusses the potential of ChatGPT and artificial intelligence in psychological education. With interactive and personalized learning experiences, AI driven chat robots can supplement traditional teaching methods and empower students in their educational journey. However, in order to ensure the responsible and effective integration of artificial intelligence in psychological education, careful implementation and continuous evaluation are necessary.
Improvement of the recommendation system based on the multi-armed bandit algorithm
Authors: Li Youxuan
Published: February 2024

In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this paper, the author studies the improvement of the recommendation system based on the multi-armed bandit algorithm. The Liner Upper Confidence Bound (LinUCB), Collaborative Filtering Bandits (COFIBA), and Context-Aware clustering of Bandits (CAB) algorithms are analyzed. It is found that the MAB algorithm can get a good maximum total revenue regardless of the content value after going through the cold start stage. In the case of a particularly large amount of content, the CAB algorithm achieves the greatest effect.
Study and methodology exploration of high-potassium glass and lead-barium glass classification patterns based on cluster analysis and decision tree
Authors: Xu Ruixuan , Xiong Xin , Hou Yingying , Wu Yilan , Wang Yuhua
Published: February 2024

This study aims to analyze the classification patterns of high-potassium glass and lead-barium glass. It involves preprocessing the data through cluster analysis and employing machine learning decision trees and K-means clustering models for in-depth analysis. In the clustering analysis model, we start by performing a cluster difference analysis on the feature attributes of high-potassium glass and lead-barium glass to obtain initial classification results. Subsequently, we use machine learning decision tree models to partition the data into training and testing sets to explore the classification patterns.
Construct a K-means clustering model by iteratively determining the initial cluster centroids' positions using an algorithm and calculating the silhouette coefficient for different numbers of clusters.This process aids in determining the appropriate number of clusters and the initial cluster centroids' positions. The results indicate significant differences in various features between high-potassium glass and lead-barium glass, and the employment of decision trees and K-means models successfully classifies artifacts. In conclusion, this study provides a robust method for the classification of high-potassium glass and lead-barium glass, offering valuable insights for research and application in related fields.
Research advanced in Chinese word segmentation methods and challenges
Authors: Du Guancheng
Published: February 2024

Chinese word segmentation refers to the process of dividing a sequence of Chinese characters into individual words. It constitutes a fundamental component of Chinese natural language processing. Due to the intricacies of the Chinese language, Chinese word segmentation has garnered significant attention from researchers. Based on a review of historical literature, segmentation methods can be broadly categorized into rule-based, statistical, semantic-based, and comprehension-based approaches. With the advancement of machine learning, neural networks have emerged as the mainstream algorithm for word segmentation. However, Chinese presents several unique challenges, leading to segmentation results that are less effective compared to morphological analysis in languages like English. Moreover, word segmentation faces new challenges such as dependency on the quality and scale of corpora, as well as domain-specific segmentation in diverse fields. Addressing these emerging challenges will undoubtedly become a focal point in future research endeavors in this field. This review provides a comprehensive summary of existing methods, discusses the current state of Chinese word segmentation, and outlines directions for addressing the evolving complexities in the field. As Chinese language processing continues to advance, finding robust solutions for accurate word segmentation remains a critical area of research.
Inclusive games: Accessible game design for the visually impaired
Authors: Deng Yiqi
Published: February 2024

Accessible game design is a key effort aimed at creating games that transcend physical and cognitive limitations. By carefully integrating a series of functions and strategies, designers empower players with various abilities to participate in the interactive field. Resolve visual barriers through high contrast visual effects, scalable fonts, and alternative color dependent information. Auditory elements are accompanied by visual cues or subtitles to ensure that the game narrative can be understood by auditory impairments. By utilizing customizable controls, motion sensitivity adjustments, and optional input methods, motion challenges are overcome. Recognize cognitive diversity by providing clear explanations, intuitive interfaces, and adjustable rhythms. This inclusive approach promotes innovation and encourages developers to explore new paths in game design, resonating with a wider range of users. As the gaming industry places increasing emphasis on usability, it emphasizes the industry's commitment to equal participation, while enhancing the creative process of each participant and enriching game gameplay.
Investigation of recent advances in exercise-enhanced knowledge tracing models
Authors: Zheng Yilin
Published: February 2024

Exercising plays a significant role in Knowledge Tracing (KT), however, the majority of KT models struggle to effectively extract the abundant information embedded within students' exercise histories, leading to a prevalent issue of information erosion. Hence, it is vital for exploring methods to mine the data from the exercises to better forecast students' future performance. This paper is a review that aims to discuss recent improvements in KT models on mining information existing in exercises. This paper will briefly discuss DKT, a popular approach to knowledge tracing, and its limitation on mining information from exercises. And then, this review will introduce four exercise-enhanced knowledge tracing models including EERNN, EKT, HGKT and Concept-Aware Deep Knowledge Tracing. EERNN and EKT can track students knowledge acquisition by using vectors to represent knowledge concepts. HGKT goes a step further by examining the interconnectedness among different exercises based on EERNN and EKT. Concept-Aware DKT is an improvement of DKVMN, the other approach of KT, by considering the influence of knowledge concepts and corresponding exercises in more detail. Finally, the applications of exercise-enhanced KT models will be covered.
A research of artificial intelligence game agent application
Authors: Lan Yuqi , Li Zhenghao
Published: February 2024

Currently, large language models are on the rise with breakthrough progress in artificial intelligence. Existing reviews of AI game agents have not covered these latest developments, requiring a combing and analysis of the newest research advancements in game AI agents. This paper summarizes the application scenarios of game AI agents in four aspects: combat AI, Non-Player Character (NPC) interaction, automated testing, and Artificial General Intelligence (AGI) testing. In combat AI, there is a progressive developmental trend, with the introduction of Monte Carlo tree search and reinforcement learning enabling AI game agents to fully surpass humans in traditional board games. In NPC interaction, full AI is unnecessary. Game developers only need to incorporate AI for abilities related to player experience to increase appeal, with controllable generation results. In automated testing, game AI agents lack generalizability for testing so far. In AGI testing, academia has helpfully explored general game AI, but capabilities remain limited to certain games. Introducing large language models to game AI agents shows unprecedented capabilities. Finally, this paper provides an outlook on the hot topics and future directions of this research subject.
Application of artificial intelligence in the field of Traditional Chinese Medicine health preservation in the era of big data
Authors: Li Jiawei
Published: February 2024

TCM means Traditional Chinese Medicine. TCM Museums are becoming more and more common with peoples increasing demand for health concepts. However, because there is no unified industry standard and the quality of projects is uneven, potential customers in need are discouraged. With the rapid development of information technology, we are in an era of big data filled with a large amount of information. In the context of the big data era, artificial intelligence, as a cutting-edge technology, complements each other, and its integrated application is widely used in many industry fields. This paper discusses the application of artificial intelligence in the field of TCM health preservation in the era of big data from three aspects: health monitoring, personalized recommendation of TCM health preservation scheme and publicity of TCM health preservation knowledge. From the discussion of these three aspects, we can explore more possibilities of integrating big data and artificial intelligence into TCM health preservation, which can not only help TCM Museums improve service quality, but also effectively realize peoples personalized self-health management.
Investigation of progress and application related to Multi-Armed Bandit algorithms
Authors: Liu Zizhuo
Published: February 2024

This paper discusses four Multi-armed Bandit algorithms: Explore-then-Commit (ETC), Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling algorithm. ETC algorithm aims to spend the majority of rounds on the best arm, but it can lead to a suboptimal outcome if the environment changes rapidly. The Epsilon-Greedy algorithm is designed to explore and exploit simultaneously, while it often tries sub-optimal arm even after the algorithm finds the best arm. Thus, the Epsilon-Greedy algorithm performs well when the environment continuously changes. UCB algorithm is one of the most used Multi-armed Bandit algorithms because it can rapidly narrow the potential optimal decisions in a wide range of scenarios; however, the algorithm can be influenced by some specific pattern of reward distribution or noise presenting in the environment. Thompson Sampling algorithm is also one of the most common algorithms in the Multi-armed Bandit algorithm due to its simplicity, effectiveness, and adaptability to various reward distributions. The Thompson Sampling algorithm performs well in multiple scenarios because it explores and exploits simultaneously, but its variance is greater than the three algorithms mentioned above. Today, Multi-armed bandit algorithms are widely used in advertisement, health care, and website and app optimization. Finally, the Multi-armed Bandit algorithms are rapidly replacing the traditional algorithms; in the future, the advanced Multi-armed Bandit algorithm, contextual Multi-armed Bandit algorithm, will gradually replace the old one.
Survey of dynamic pricing based on Multi-Armed Bandit algorithms
Authors: Qu Jiaming
Published: February 2024

Dynamic pricing seeks to determine the most optimal selling price for a product or service, taking into account factors like limited supply and uncertain demand. This study aims to provide a comprehensive exploration of dynamic pricing using the multi-armed bandit problem framework in various contexts. The investigation highlights the prevalence of Thompson sampling in dynamic pricing scenarios with a Bayesian backdrop, where the seller possesses prior knowledge of demand functions. On the other hand, in non-Bayesian situations, the Upper Confidence Bound (UCB) algorithm family gains traction due to their favorable regret bounds. As markets often exhibit temporal fluctuations, the domain of non-stationary multi-armed bandits within dynamic pricing emerges as crucial. Future research directions include enhancing traditional multi-armed bandit algorithms to suit online learning settings, especially those involving dynamic reward distributions. Additionally, merging prior insights into demand functions with contextual multi-armed bandit approaches holds promise for advancing dynamic pricing strategies. In conclusion, this study sheds light on dynamic pricing through the lens of multi-armed bandit problems, offering insights and pathways for further exploration.
The investigation of progress and application related to chatbot in psychology
Authors: Li Zhiran
Published: February 2024

The discipline of psychology plays a pivotal role in contributing to the development of scientific knowledge and the advancement of society, as well as being a prominent area of scientific research globally. In recent years, an increasingly number of artificial intelligence technologies have been combined with psychology technologies, aiming to facilitate in promoting the development of peoples mental health through artificial intelligence technology. Numerous chatbot technologies have emerged over the years, and these chatbots have excellent comprehensive capabilities and have been widely used in the field of psychology. However, there is little literature reviewing the application of chatbots in the field of psychology, and this review aims to summarize the technologies of chatbots and their application in psychology in this last several years, in order to help subsequent scholars understand the relevant research results in this field more quickly. This study summarizes some of the chatbot approaches in recent years and divides them into four parts, which are 1) ChatGPT 2) Chatbot 3) Accessing personality and emotions 4) Mental health support, and this study also further describes the advances, shortcomings, and future perspectives of the research in this field. and future perspectives in this field, hoping to be helpful to subsequent scholars.
Parallel implementation of Wiener's attack on RSA: Algorithm design and performance evaluation
Authors: Lu Jiaming
Published: February 2024

The robustness of the RSA cryptosystem is intrinsically tied to the choice of public and private keys. As pinpointed by M. Wiener, should the private decryption exponent, d, be improperly chosen-either disproportionately large or unduly small in relation to the public key n-an adversary could feasibly deduce the private keys within a practical time span. In this paper, the algorithm invented by Wiener is revisited and find a way to parallelize the attacking algorithm by using OpenMP. The algorithm is based on the proof made in Wieners article, that if d<1/3 n^(1/4), then the private key d is the denominator of one of the convergent of CF(e/n). Using a constant set of private keys (e,n), an extensive series of simulated attacks employing Wieners method was conducted on a laptop to determine the optimal thread count for executing the parallel algorithm. The results indicate that the algorithms execution speed can be enhanced by a factor of 1.5607 (rounded to five significant figures). Specifically, while the sequential version of the algorithm averaged a runtime of 3518939.04 nanoseconds, its parallel counterpart averaged 2240493.28 nanoseconds.
Movie sentiment analysis based on Long Short-Term Memory Network
Authors: Li Siyao , Qin Rui , Zhou Zijian
Published: February 2024

An important task in the study of Natural Language Processing (NLP) is the analysis of movie reviews. It finishes the task of classifying movie review texts into sentiment, such as positive, negative or neutral sentiment. Previous works mainly follow the pipeline of LSTM (Long Short-Term Memory Network). The network model is a variant of Recurrent Neural Network (RNN) and particularly suitable for processing natural language texts. Though existing LSTM-based works have improved the performance significantly, we argue that most of them deal with the problem of analyzing the sentiment of movie reviews while ignore analyze the model performance in different application scenarios, such as different lengths of the reviews and the frequency of sentiment adverbs in the reviews. To alleviate the above issue, in this paper, we constructed a simple LSTM model containing an embedding layer, a batch normalization layer, a dropout layer, a one-dimensional convolutional layer, a maximal pooling layer, a bi-directional LSTM layer and a fully connected layer. We used the existing IMDB movie review dataset to train the model, and selected two research scenarios of movie review length and frequency of occurrence of sentiment adverbs to test the model, respectively. From the experimental results, we proposed a model for the scenarios in which the LSTM model handles the problem of sentiment analysis with respect to the dataset construction, model stability and generalization ability, text fragment processing, data preprocessing and feature extraction, model optimization and improvement.
The design of library database management system based on MySQL
Authors: Wei Ziang
Published: February 2024

Database-manage-system has become a modern management among different enterprises. The enterprises data need managed by a well-performed system. Also, the database is an essential need for a modern library. In this case, more and more people start using different way to build the system which is suitable for the enterprises. The library system has become a famous one which need a large database system to encounter the huge amount of data. In this article, it shows the usage of database-manage-system in a local library and how does the system work. Also, the introduction of the function of MySQL usage in this application. Finally, after the test of the data and running the programme in MySQL, the database-manage-system realize the function of list the book, check of availability, make reservations and store different users status. In this essay, through the test of data and the code, a database-manage-system is designed. This database-manage-system could complete basic function which contain check status, make appointments and locate books.