bluuebunny/crossref_metadata_2025 huggingface dataset:
Mecanismos de participación ciudadana y el gobierno de México : 2019-2022
Authors: Montoya Vargas Fernando
Published: March 2024

Citizen participation mechanisms refer to legally constituted dialogue spaces to deli berate or negotiate with public institutions. Here we offer the partial results of a broader inves tigation of these mechanisms against the government of Mexico between 2019 and 2022. The mechanisms that prevail in Mexico are indicated, with their main characteristics.
EFFECTIVENESS OF MULLIGAN TAPING VERSUS KINESIO TAPING WITH COMMON USE OF ULTRASOUND THERAPY IN TENNIS ELBOW OF CRICKET PLAYERS: A RANDOMIZED CLINICAL STUDY
Authors: Bhattacharyya Dr. Satyendra Nath , Mallick Dr. Amartya , Mandal Dr. Subhajit , Mandal Dr.Soumyajit
Published: March 2024

BACKGROUND: The purpose of this study was to compare the effectiveness of Mulligan taping versus kinesio taping with common use of ultrasound therapy in managing tennis elbow. METHODS: Thirty cricketers aged between 25 to 35 years, presenting with tennis elbow with symptom duration within one month, were randomized into two groups. The Group-A received Mulligan taping with ultrasound therapy and Group-B received kinesio taping with ultrasound therapy. Treatment duration was 3 times a weekly four weeks. OUTCOME MEASURES: Data was obtained on pain intensity levels via Visual Analog Scale (VAS) grip strength via measuring Pain Free Grip Strength (PFGS) and functional status measured by the Patient-Rated Tennis Elbow Evaluation (PRTEE). RESULTS: Result shows that significant improvement in VAS (p=.04), PFGS (p=.02) and PRTEE (p=.01). CONCLUSION: The results of this study demonstrate that kinesio taping with ultrasound therapy is a superior treatment approach compared to Mulligan taping with ultrasound therapy in managing tennis elbow.
Young people's mental health: facing revolution aggravated by new technologies
Authors: Morgado Eduardo Martins , Távora Carla Gonçalves
Published: March 2024

Young people are suffering from depression, anxiety, fatalism and may develop suicidal tendencies. The objective is to follow the paths defined by Dairymple, Lukianoff and Haidt regarding the reality of young people. The causes are not fully understood, however, the solution is to help children develop healthy trajectories to minimize mental disorders or suicidal thoughts and behaviors. We also know that schools are a source of tension and that they need to become healthier environments. We know that we are in the middle of a technological revolution, which, through Automation, affects employment and family income. And finally, the most important thing is the interest and commitment to improving communication and family support, family and community ties, as well as children's ties to school, so that they feel safe and connected.
Education and school meals: a way to feed knowledge and cur hunger
Authors: Martins Gleicillaine Porto Giron , Caruso Sérgio
Published: March 2024

This article has as objective to comprehend questions related school lunch school in teaching environment for student basic education. The public policy, the guaranty of laws to proporcion the dues feed security are fundamental for individuals to assimilate the knowledge that education proporcion, above contributing to appreciation of lunch regional development through local. Analyzing the influence of school lunch security lunch of public  school students, we served for the PNAE, allows us to gain knowledge of   entire program, that is, education, school, lunch, regional development and agriculture. This study demonstrates the relevance of school meals so that students can not only have access to lanch in its physical form in sufficient quantities and nutritional values, but also  social and economic forms.
The role of technological modernization in Brazil's border security: a study on the Integrated Border Monitoring System (SISFRON)
Authors: Henrique Júnior Moacir , Giolo Junior Cildo , Martins Bernardi Coelho Pablo , Rodrigues Magalhães de Melo Laiane
Published: March 2024

The central purpose of this study is to conduct a scientific analysis of the influence of technological innovations on crime mitigation and the enhancement of public security in the border zone of Brazil. This examination aims to understand how the adoption of cutting-edge technologies can contribute to reducing criminal activity and strengthening security in the country's border areas. This study is of great relevance as it seeks to assess the importance of implementing the Integrated Border Monitoring System (SISFRON) as a preventive and repressive measure against crimes in the border regions between Brazil and other Latin American countries. SISFRON is essential as it integrates the Armed Forces, government agencies, and federative states, playing a crucial role in National Defense. The methodology employed in this work is based on a deductive approach, involving qualitative bibliographic and documentary research. The analysis of institutional documents, mapping of key actors from institutions involved in border protection, and investigating their role in promoting public security and defending Brazilian borders are fundamental steps in this research. By concluding this study, it is expected to confirm that SISFRON emerges as a significant tool of the National Defense Policy. It not only focuses on internal security and strengthening territorial defense in border areas but also serves as a link for cooperation between neighboring countries, enabling the sharing of information and joint actions in combating various forms of transboundary illicit activities.
Aquatic and semiaquatic mammals occurring in the brazilian Amazon: Diversity, Habitats and Extinction
Authors: Souza Thamara Nunes , Braga Carlos Elias de Souza , Gutjahr Ana Lúcia Nunes
Published: March 2024

The Amazon has a megabiodiversity, among which the species of aquatic and semi-aquatic mammals that are distributed in the Amazon rivers stand out, whose human actions cause damage to their habitats. Therefore, the objective of this study was to understand the species of aquatic and semi-aquatic mammals endemic to or occurring in the Amazon region, considering the impacts on their diversity, habitat, and conservation status. To this end, a systematic bibliographical review was conducted using the keywords “aquatic mammals of the Amazon” and “aquatic mammals of the Amazon”, without making a historical cut. The species found were common, endemic, and non-endemic, with rare observations in the Brazilian Amazon, totaling 15 aquatic and two semi-aquatic species. It was observed that all of them are included in the list of endangered species, some with a less worrying classification and others in danger of extinction, threatened mainly by human actions. Therefore, it is necessary for human beings to become more aware of nature because of the great importance of these animals for Amazonian aquatic ecosystems.
Household Energy Conservation Practices Used in Chalimbana Ward, Chongwe District
Authors:   The University of Zambia, Chewetu Alfred , Nyanga Progress H ,   The University of Zambia
Published: March 2024

Energy conservation is a subject that has continued to attract global attention as it essential in ensuring that the growing energy demand does not pose a threat to the environment. In view of this, the aim of this study was to find out the household energy conservation practices used in Chalimbana ward of Chongwe district, Zambia. The study adopted a convergent mixed method research design. Qualitative data from 10 purposively sampled key informants was collected through in-depth-interviews using semi-structured interview guides. Additionally, quantitative data from 175 households selected through cluster sampling technique was collected using a questionnaire. The qualitative data was analysed though content analysis whereas quantitative data was analysed through descriptive statistics and multiple linear regression. The study found that most households in Chalimbana ward depend on traditional biomass energy (Charcoal and firewood). Through multiple linear regression, the study found that expenditure on household energy consumption in the study area is influenced is influenced by social economic factors such as family size, house size and education. To conserve energy, the households in this area use a variety of conservation methods of which the most used involves switching or putting off unused household energy appliances or sources. The energy used among these households causes air pollution, habitat loss, land use change, exposure to hazardous waste among other environmental atrocities While the study findings show that households in Chalimbana ward do practice energy conservation, this is not done at a substantive level that can yield sustainable environmental, economic and social benefits of household energy conservation.
Nutritive value of selected naturally growing dry season pastures in flood plains of Kafue river in Southern Zambia
Authors:   The University of Zambia, Gondwe Smart , Chibinga Oswin Chifungwe ,   The University of Zambia, Kanyinji Francisco ,   The University of Zambia
Published: March 2024

Wetlands are an important source of pastures for livestock forage in the dry season in arid and semi-arid regions. In this study forage samples were collected during the dry season (September to November) to evaluate the nutritive value for potential to supply livestock requirements during dry season under the transhumance system of livestock production in the Kafue Flood Plains in Mazabuka district of Southern Province. The frequency, height and cover of the grass species in upper and lower parts of the grazing area were measured. Proximate components, fibre fractions and in vitro dry matter digestibility were determined using standard laboratory procedures. The grass species found in the upper land were Acroceras macrum, and Vetiveria nigritana at the frequency of 60 and 56 percent, respectively. Panicum repens, Polygonum spp and Cyperus papyrus are the grass species that were found in the lower area at a frequency of 44, 52 and 80 percent. Ground cover was 69 and 100 % for upper and lower area, respectively. The Crude Protein was significantly different among the species Vetiveria nigritana (7.90%) and Polygonum salicifolium (8.46 %), Cyperus papyrus (6.12%) and were also higher than for Panicum repens (3.37%) and Acroceras macrum (3.32%) (p ˂ 0.05). Crude fiber ranged from 12.8% (Polygonum salicifolium) to 34.6% (Vetiveria nigritana). Polygonum salicifolium had higher in vitro dry matter digestibility (INVDMD) (50.8%) followed by Vetiveria nigritana (46.4%), Cyperus papyrus (44.5%), Panicum repens (39.9%) and Acroceras macrum (39.7%) at (p ˂ 0.05). These results clearly shown that the some of the grass species of the Kafue flat have good nutrition content and therefore are a good source of nutrition for ruminants in the dry season
Os desafios para absorção de nutrientes no paciente bariátrico: análise abrangente de literatura
Authors: Linhares Mateus Rodrigues , Cunha Júlia Camargo Gonçalves , Santana Leticia Cerqueira de , Silva Nayá Pinto de Rezende Nobre da , Gomes Wallace da Silva , Camêlo Laíse Barcelos Vieira , Faleiro Danniela Príscylla Vasconcelos , Gomes Vinicius Lopes Nunes , Mesquita Gustavo Soares , Lima Natália Camelo de , Rosso José Gustavo , Albuquerque Tácio Souza Cavalcante , Costa Gabriel Tavares Maciel Luz , Sabatke Guilherme , Leite Letícia Lima , Moreira Paulo Roberto Rocha , Lucian Camili Vitória
Published: March 2024

A cirurgia bariátrica é um tratamento eficaz para os pacientes com obesidade mórbida, no entanto, por mais seguros e comuns que os procedimentos bariátricos se tornaram, muitas complicações ainda podem resultar. Essas complicações variam dependendo do tipo de procedimento realizado (disabsortivo ou restritivo) e muitas vezes são distúrbios nutricionais da superfície absortiva alterada do trato gastrointestinal e diminuição da capacidade do estômago. Dessa forma, com o crescente corpo de literatura sobre a absorção de nutrientes no paciente bariátrico, foi possível a realização de uma revisão integrativa de literatura por meio da plataforma PubMed, com seleção e análise criteriosa dos artigos, a fim de revisar e analisar as evidências atuais sobre os desafios para absorção de nutrientes no paciente bariátrico. Nesta revisão foi identificado que deficiências nutricionais são alterações comuns, tanto pré cirurgia, como no pós cirúrgico, tendo relação com a microbiota intestinal, adesão às medicações e suplementos prescritas, assim como as alterações anatômicas causadas por cada técnica da cirurgia bariátrica.  Deficiências na vitamina D após procedimentos disabsortivos, como o bypass gástrico Roux-en-Y, podem resultar em hipocalcemia subsequente e desmineralização óssea, as anemias podem se apresentar após a cirurgia por absorção inadequada de vitamina B12  e ferro, e também deficiências de vitaminas lipossolúveis e hidrossolúveis, são achados comuns no pré e pós cirurgia bariátrica.   Assim, conclui-se que com a alta prevalência dessas deficiências, o teste de micronutriente de base e a triagem pós-operatória são recomendados em muitos casos. Além disso, o tratamento suplementar geralmente requer doses mais altas do que as recomendadas para adultos saudáveis.
Estratégias didáticas para o fomento da argumentação jurídi-ca: prática do júri simulado
Authors: Aragão Alice Iracema Melo , Viganò Ana Patrícia Holanda , Araújo Júnior Francisco de Assis de
Published: March 2024

Um dos desafios do bacharel em Direito consiste em desenvolver a capacidade argumentativa visando estimular o raciocínio jurídico prudencial na resolução dos conflitos, bem como estabelecer uma relação de ensino e aprendizagem associado às necessidades e expectativas dos educandos torna-se uma preocupação dos educadores. Todavia, o modelo educacional brasileiro privilegia o domínio do conhecimento aliado às experiências profissionais dos docentes. Nesse contexto, para o exercício de uma carreira jurídica é esperado que o jurista tenha conhecimento, desenvolva habilidades e domine técnicas argumentativas, sendo o papel da universidade contribuir nesse sentido. Para tanto, através de um estudo bibliográfico, documental, qualitativo e descritivo, incluindo entrevistas junto aos coordenadores e alunos dos cursos de Direito nas universidades privadas de Fortaleza, no período de 01 de setembro a 30 de novembro de 2023, buscou-se propor uma estratégia didática de ensino, com o uso de metodologias ativas e fomento da argumentação, mediante a aplicação da ferramenta Júri Simulado. O objetivo principal desta pesquisa consiste em propor uma estratégia didática de ensino, com o uso de metodologias ativas na disciplina de Argumentação Jurídica na graduação em Direito, aproximando a prática à teoria jurídica, possibilitando uma maior absorção e construção do conhecimento pelo aluno. Ainda apresenta como objetivos específicos: destacar a importância do estudo sobre argumentação jurídica na graduação em Direito; demonstrar a necessidade da oferta da disciplina argumentação jurídica na grade curricular da graduação em Direito; apresentar e discutir o uso das metodologias ativas no ensino da disciplina de Argumentação Jurídica, e especial no que trata do uso da simulação do júri. Como resultado, constatou-se que o estudo da argumentação jurídica é primordial porque promove o estímulo de habilidades cognitivas e éticas, notadamente a resolução de conflitos e o raciocínio jurídico prudencial.
A educação em espaços de privação de liberdade
Authors: Naves Flávia Cunha Rios
Published: March 2024

No Brasil, o sistema carcerário enfrenta uma realidade de precariedade, superlotação, violência, opressão e morte.  A cada ano o número de presos vem se multiplicando, de forma que a oferta de uma educação de qualidade nesse contexto, voltada a promoção e humanização dos indivíduos que ali se encontram, geraria reflexos nesse contingente populacional, uma vez que a educação está relacionada com o processo de ressocialização do preso, que por sua vez, gera impacto na diminuição dos índices de reincidência. É diante desse cenário que se reconhece a importância do estudo acerca da educação em espaços de privação de liberdade. Trata-se de uma pesquisa bibliográfica e documental desenvolvida em abordagem qualitativa. Para tanto, recorreu-se a buscas nas bases de dados Scientific Electronic Library Online (SciELO) e Biblioteca Digital Brasileira de Teses e Dissertações (BDTD) e ao estudo de documentos que disciplinam a questão, por exemplo, a Constituição Federal (Brasil, 1988), a Lei de Diretrizes e Bases da Educação (Brasil, 1996), a Lei de Execução Penal (Brasil, 1984) e outras. Como resultados, constatou-se que a realidade do sistema prisional envolve muitos desafios e paradoxos em relação à educação da prisão e à educação na prisão; o que torna extremamente árdua a tarefa de conciliar ideais de punição, educação e ressocialização. Constatou-se também um abismo existente entre as previsões legais que garantem o acesso à educação e sua efetividade prática; o que nos leva a crer que, diante de todas as particularidades que envolvem esse contexto, temos um longo caminho pela frente, a fim de que a educação seja um instrumento de transformação na vida das pessoas que ali se encontram.
Statistical comparative analysis between strength parameters of a bauxite mining tailing through laboratory tests and numerical simulation
Authors: Barbosa Amanda Silva , Nierwinski Helena Paula
Published: March 2024

Tailings dams are the primary global way to solve the problem of low storage availability for tailings resulting from mining. However, the recent ruptures of this reservoir in Brazil, such Mariana and Brumadinho dams, draw attention to the importance of studies determining the geomechanical characteristics and behavior of mining tailings. With the implementation of Law nº 14066/2020, which establishes the National Dam Safety Policy, there was an increase in demands for the monitoring and inspecting dams and the imposition of bills. Given this, triaxial tests were carried out to characterize the mechanical behavior of bauxite mining tailing from a Brazilian deposit. Confining pressure of 25, 50, 100, and 200 kPa was applied. Moreover, data were obtained for applying numerical simulation modeling based on the parameters determined for this material. Therefore, this paper describes a comparison using exploratory data analysis between the results obtained in the laboratory by the triaxial test and the results obtained by the finite element method (FEM) analysis with the aid of the Abaqus software. Statistical analysis showed a greater discrepancy between laboratory and numerical simulation results. The most significant differences observed between laboratory and numerical simulation results occurred for the lowest stress levels. The greatest differences observed between laboratory and numerical simulation results occurred for the lowest stress levels. The difference was significant also in terms of behavior. This fact highlights the need for extensive geotechnical investigation to characterize the material adequately and obtain parameters to simulate the correct tailing behavior and develop a safe design for storage structures.
Discussing the Ethnicity of Tchaikovsky's 1812 Overture through the Professional Lens of Musicology
Authors: Wang Yangyanru
Published: March 2024

The scope of my dissertation is: the sections of the 1812 Overture that are characterized by Russian folk music; the connection between the Russo-French War of 1812 and the 1812 Overture; and the composer Tchaikovsky's inspiration and compositional process for writing the 1812 Overture. The purpose of the research is: to discuss the embodiment of Russian musical style in the 1812 Overture through the knowledge and understanding of folk music. In the process of writing the article, the first step is to listen to the audio of the 1812 Overture, watch the video of the 1812 Overture, and find out the characteristic passages of Russian music and the use of characteristic instruments in the video of the 1812 Overture, and combine them with the same passages in the score of the 1812 Overture. Next, collect literature on the 1812 Overture, Tchaikovsky, Russian folk music, and definitions of folk music. And begin to organize the framework of the thesis and categories the literature. Finally write the paper, revise the final draft and submit the course paper. The result is a discussion and critical response to the reasons for the controversy of the Russian national music of the composer Tchaikovsky by the people of Russia itself by analyzing the national character of the 1812 Overture. The conclusion is that the 1812 Overture is a strong confirmation of the composer Tchaikovsky's expertise in using not only Western European symphonic music, but also Russian musical styles in his musical compositions.
Research on Street Landscape Design Along Urban Rail Transit Lines
Authors: Zhao Wenjin , Halabi Khairun Nisa Mustaffa
Published: March 2024

The study on the landscape design of urban streets along the rail transit lines is an important topic in urban planning and development. With the acceleration of urbanization, the construction and improvement of urban rail transit systems have become a top priority. However, there are numerous challenges and issues in the landscape design of streets along rail transit lines. In recent years, governments and relevant departments have actively promoted the construction of urban green streets and integrated ecological concepts into landscape design to address the pressures and challenges brought about by urbanization. Drawing on the experiences of ecological city construction, the establishment of urban green streets has become an effective approach to solving the functional and environmental problems of urban streets. By integrating green infrastructure into street design, it is possible to effectively manage stormwater and improve the street environment. Research indicates that the root cause of urban street flooding lies in the increase of impervious surface area, which alters the surface hydrological conditions and impedes the collection and utilization of rainwater. Therefore, the design of urban green streets should focus on functional zoning, awareness of stormwater management, and integration of urban cultural landscapes. Through the application of these design methods, not only can the existing problems of urban streets be solved, but valuable design experience and guidance can also be provided for future urban green street construction.
Research on the Creative Application of Memphis Style in Illustration Design
Authors: Jia Xinhao
Published: March 2024

This paper explores the relevance of the use of dragon elements in illustration design and Memphis style visual design. Through the study of the symbolic meaning of dragon elements in different cultures and the origin and characteristics of Memphis style, combined with the actual case study of illustration design, this paper discusses the correlation and interaction between dragon elements and Memphis style visual design. Research has found that the clever incorporation of dragons in illustration designs, combined with Memphis-style geometry and bright colors, can create creative, unique and appealing visuals. These findings provide new creative ideas and inspiration for illustration design.
Target Detection and Segmentation Technology for Zero-shot Learning
Authors: Lou Zongzhi , Chen Linlin , Guo Tian , Wang Zhizhong , Qiu Yuxuan , Liang Jinyang
Published: March 2024

Zero-shot learning (ZSL) in the field of computer vision refers to enabling the model to recognize and understand categories that have not been encountered during the training phase. It is particularly critical for object detection and segmentation tasks, because these tasks require the model to have good generalization capabilities to unknown categories. Object detection requires the model to determine the location of the object, while segmentation further requires the precise demarcation of the object's boundaries. In ZSL research, knowledge representation and transfer are core issues. Researchers have tried to use semantic attributes as a knowledge bridge to connect categories seen during the training phase and categories not seen during the testing phase. These attributes may be color, shape, etc., but this method requires accurate attribute annotation, which is often not easy to achieve in practice. Therefore, researchers have begun to explore the use of non-visual information such as knowledge maps and text descriptions to enrich the recognition capabilities of models, but this also introduces the challenge of information integration and alignment. At present, ZSL has made certain progress in target detection and segmentation tasks, but there is still a significant gap compared with traditional supervised learning. This is mainly due to the limited ability of ZSL models to generalize to new categories. To this end, researchers have begun to explore combining ZSL with other technologies, such as generative adversarial networks (GANs) and reinforcement learning, to enhance the model's detection and segmentation capabilities for new categories. Future research needs to focus on several aspects. The first is how to design a more effective knowledge representation and transfer mechanism so that the model can better utilize existing knowledge. The second step is to develop new algorithms to improve the performance of ZSL in complex environments. In addition, research should focus on how to reduce the dependence on computing resources so that the ZSL method can run effectively in resource-limited environments. In summary, the research on target detection and segmentation technology of zero-shot learning is a cutting-edge topic in the field of computer vision. Despite the challenges, with the deepening of research, we expect these technologies to contribute to improving the generalization ability and intelligence level of computer vision systems.
Determination of Viscosity, Syneresis and Acceptability of Optimized Yoghurt Analogues from African Yambean (Sphenostylis stenocarpa Hams) and Malted Red Rice (Oryza glabberima)
Authors:   Department of Food Science and Technology, Rivers State University, Nkpolu-Oroworukwo, P.M.B 5080, Port Harcourt, Rivers State., Okoronkwo Blessing Chisomuaga , Barber Lucretia I. ,   Department of Food Science and Technology, Rivers State University, Nkpolu-Oroworukwo, P.M.B 5080, Port Harcourt, Rivers State., Obinna-Echem Patience C. ,   Department of Food Science and Technology, Rivers State University, Nkpolu-Oroworukwo, P.M.B 5080, Port Harcourt, Rivers State.
Published: March 2024

Yoghurt analogues were produced using African yambean (Sphenostylis stenocarpa Hams) and malted red rice (Oryza glabberima). Optimization of the mixture components and process factor was carried out using combined optimal mixture design, viscosity, syneresis and acceptability as responses were analysed. The fitted regression model for viscosity, syneresis and acceptability were all significant (P<0.05) with coefficient of determination (R2) values of 0.9992, 0.9983 and 0.9904 % respectively. The coefficient of variation (cv) values were 0.992, 6.58 and 6.58 % respectively. The lack-of-fit for the models were not significant (P < 0.05). The suitable mathematical models developed for the optimization of the fermentation variables in African yambean and malted red rice yoghurt is highly recommended for the development of a novel non-dairy probiotic yoghurt.
Implementasi Media Boneka Tangan Dan Media Audio Drama Dalam Melatih Kemampuan Bahasa Anak dPaud-Kb-Tk Islam Sabrina Sidoarjo
Authors: Amelia Juli , Masturoh Umi
Published: March 2024

Penerapan media boneka tangan dan media audio drama merupakan perkembangan yang menjadi orientasi dalam pengembangan bahasa anak yang harus dikembangkan sejak usia dini. Pada kegiatan tersebut dapat mengembangkan perkembangan Bahasa.. Penelitian ini merupakan penelitian menggunakan pendekatan deskriptif, dengan jenis penelitian kualitatif. Dalam mengumpulkan data yang diperlukan penulis melakukan dengan cara observasi, wawancara dan dokumentasi. Analisis data yang digunakan interpretasi mendalam dimulai dengan pengumpulan data dan penarikan kesimpulan. Berdasarkan hasil analisis pada kegiatan pembelajaran di Ra Bakti 3 Sukosewu Sukorejo Ponorogo dapat peneliti simpulkan bahwa pengembangan bahasa anak pada PAUD – KB – TK Islam Sabrina adalah sebanyak 83,3% siswa yang berkembang sesuai harapan dan 16,6%  siswa berkembang sangat baik dalam menjawab pertanyaan berdasarkan cerita yang telah diperdengarkan. Anak dapat menceritakan kembali cerita dalam kemampuan berbahasa dari 12 siswa terdapat  75 % siswa yang berkembang sesuai harapan dan 25 %  siswa berkembang sangat baik. perkembangan kemampuan bahasa anak dalam menyimpulkan kembali isi cerita secara sederhana dari 12 siswa terdapat  83,3% siswa yang berkembang sesuai harapan dan 16,6%  siswa berkembang sangat baik. Peneliti telah menerapkan dua media tersebut dengan semaksimal mungkin dalam proses pembelajaran sehingga hasil yang dicapai oleh anak sangat baik aspek-aspek perkembangan bahasa anak yang meliputi kosakata, sintaksis (tata bahasa), semantik (penggunaan kata), dan fonem (perangakan bunyi) telah berkembang dengan baik sesuai dengan tingkat pencapaian perkembangan yang harus dicapai oleh anak yang sesuai dengan usia anak.
A Hybrid Meta-Heuristic Approach for Test Case Prioritization and Optimization
Authors:   Department of Information and Communication, Al-Khwarizmi College of Engineering, University of Baghdad,  Iraq, Heba Heba ,   Department of Computer Engineering, College of Engineering, University of Baghdad, Iraq, Younis Mohammed Issam
Published: March 2024

The application of the test case prioritization method is a key part of system testing intended to think it through and sort out the issues early in the development stage. Traditional prioritization techniques frequently fail to take into account the complexities of big-scale test suites, growing systems and time constraints, therefore cannot fully fix this problem. The proposed study here will deal with a meta-heuristic hybrid method that focuses on addressing the challenges of the modern time. The strategy utilizes genetic algorithms alongside a black hole as a means to create a smooth tradeoff between exploring numerous possibilities and exploiting the best one. The proposed hybrid algorithm of genetic black hole (HGBH) uses the capabilities of considering the imperatives such as code coverage, fault finding rate and execution time from search algorithms in our hybrid approach to refine test cases considerations repetitively. The strategy accomplished this by putting experiments on a large-scale project of industrial software developed. The hybrid meta-heuristic technique ends up being better than the routine techniques. It helps in higher code coverage, which, in turn, enables to detect crucial defects at an early stage and also to allocate the testing resources in a better way. In particular, the best APFD value was 0.9321, which was achieved in 6 generations with 4.879 seconds the value to which the computer was run. Besides these, , the approach resulted in the mean value of APFD as 0.9247 and 0.9302 seconds which took from 10.509 seconds to 30.372 seconds. The carried out experiment proves the feasibility of this approach in implementing complex systems and consistently detecting the changes, enabling it to adapt to rapidly changing systems. In the end, this research provides us with a new hybrid meta-heuristic way of test case prioritization and optimization, which, in turn, helps to tackle the obstacles caused by large-scale test cases and constantly changing systems.
New algebraic approach towards interval-valued neutrosophic cubic vague set based on subbisemiring over bisemiring
Authors:   Department of Mathematics, Shanmuga Industries Arts and Science College, Affiliated to Thiruvalluvar University, Tiruvannamalai, Tamil Nadu, India, 606603., K. K. ,   Department of Mathematics, Faculty of Science, Zarqa University, Zarqa 13110, Jordan.,   Department of Mathematics, Faculty of Science and Technology, Irbid National University, P.O. Box: 2600 Irbid, Jordan,   Department of Mathematics, Faculty of Science and Technology, Irbid National University, P.O. Box: 2600 Irbid, Jordan,   Department of Mathematics, Shanmuga Industries Arts and Science College, Affiliated to Thiruvalluvar University, Tiruvannamalai, Tamil Nadu, India, 606603.,   Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai-602105, India,   Department of Mathematics, Al- Ameen Engineering College, Erode., Gharib Gharib , Al-Husban Abdallah Al , Soudi Maha Al , K. K. Lenin Muthu. , Palanikumar Murugan , Sundareswari K.
Published: March 2024

We introduces the concept of a interval-valued neutrosophic cubic vague subbisemiring (IVNCVSBS), level sets of IVNCVSBS of a bisemiring. IVNCVSBSs are the new extension of neutrosophic subbisemirings and SBS over bisemirings. Let be a neutrosophic vague subset in $X$, we show that is a IVNCVSBS of X if and only if all non empty level set is a SBS of X. Let be a IVNCVSBS of a bisemiring X and strongest cubic neutrosophic vague relation of X, we prove that is a IVNCVSBS of X × X. Let be any IVNCVSBS of X, prove that pseudo cubic neutrosophic vague coset is a IVNCVSBS of X. Let 1, 2,...,n be the family of IVNCVSBS of X1, X2,..., Xn respectively.The homomorphic image of every IVNCVSBS is a IVNCVSBS. The homomorphic pre-image of every IVNCVSBS is a IVNCVSBS. Examples are provided to strengthen our results.
GravNet: A novel deep learning model with nonlinear filter for gravitational wave detection
Authors: Nie Qianheng
Published: March 2024

Gravitational waves (GW) detected by LIGO, VIRGO, and upcoming facilities have ushered in a transformative era for astronomy and physics. However, these cosmic ripples present unique challenges. Most GW signals are not only weak but also fleeting, lasting mere seconds. This poses a significant hurdle to current search strategies. The prevalent matched filtering technique, while effective, demands an exhaustive search through a template bank, slowing down data processing.To overcome these limitations, machine learning, particularly Convolutional Neural Networks (CNNs), has emerged as a solution. Recent studies demonstrate that CNNs surpass traditional matched-filtering methods in detecting weak GW signals, extending beyond the training set parameters. Nonetheless, optimizing these deep learning models and assessing their robustness in GW signal detection remains essential. In this study, we explore various methods to enhance CNN models effectiveness using simulated data from three gravitational wave interferometers. Our investigation spans denoising techniques, CNN architectures, and pretrained AI models. Notably, the Constant-Q transform (CQT) outperforms the Fast Fourier transform in denoising raw gravitational signals. Furthermore, employing the pretrained model EfficientNet enhances GW detection efficiency. Our proposed CNN model, GravNet, combines CQT, EfficientNet, and an optimized CNN structure. GravNet achieves an impressive 76.5% accuracy and 0.85 AUC. This innovative approach offers valuable insights into harnessing deep learning models for more efficient and accurate gravitational wave detection and analysis.
Federated learning algorithm-based skin cancer detection
Authors: Hu Jingyue
Published: March 2024

Owing to the oversight regarding training data privacy within the realm of Deep Learning (DL), there have been inadvertent data leaks containing personal information, resulting in consequential impacts on data providers. Consequently, safeguarding data privacy throughout the deep learning process emerges as a paramount concern. In this paper, the author suggests the integration of FedAvg into the training procedure as a measure to ensure data security and privacy. In the experiments, the author first applied data augmentation to equalize the various samples in the dataset, then simulated four users using a Central Processing Unit (CPU) with four cores and established a network architecture starting with DenseNet201. Each user cloned all parameters of global model and received an equal portion of the dataset. After updating the parameters locally, the weights were aggregated by averaging and passed back to the global model. Additionally, the author introduced learning rate annealer to help the model converge better. The experimental results demonstrate that incorporating FedAvg indeed saves training time and achieves excellent performance in skin cancer classification. Despite a slight loss in accuracy, the algorithm can address privacy concerns, making the use of FedAvg highly valuable.
Machine learning with oversampling for space debris classification based on radar cross section
Authors: Zhang Yining
Published: March 2024

Over the past few years, the likelihood of collision of space objects increases as the quantity of space debris rises. Space debris classification and identification becomes more crucial to space assets security and space situation awareness. Radar cross section (RCS), one of the essential arguments for tracking space debris, was measured by European Incoherent Scatter Scientific Association (EISCAT) and other radar systems. This study investigates the effectiveness of seven machine learning methods employed to address the classification of space objects based on RCS data from European Space Agency (ESA). To tackle the Class-imbalance issue in this study (the ratio of space debris to non-debris is approximately 5:1 in the dataset), three oversampling techniques are employed, including: Synthetic Minority Oversampling Technique (SMOTE), synthetic minority oversampling technique-support vector machine (SMOTE-SVM) and Adaptive Synthetic Sampling (ADASYN). The experiments show that, in the test set, the combination of SVM with SMOTE-SVM oversampling techniques can reach the accuracy of 99.7%, the precision of 98.7% and the recall of 99.4% which is better than the rest of models.
Investigation the influence related to parameters configuration of Generative Adversarial Networks in face image generation
Authors: He Yuebo
Published: March 2024

Due to the excellent performance of Generative Adversarial Networks (GAN) for age regression on face images, it is particularly important to explore the effect of different parameters on model training. In this study, the origin and development of Artificial Intelligence (AI) is first discussed, from which the concept and principles of GAN are derived. This is followed by a brief introduction of the UTKface dataset used in this research, and the Conditional Adversarial Autoencoder (CAAE) framework based on the GAN technique. The division of labor and roles of the encoder, generator, and the two discriminators in the model are described. The various learning rates as well as batch size combinations attempted in this study are then illustrated, and the training results of the model are shown in the form of graphs and plots of the loss value function. A situation where the model stops learning is highlighted in the results, which is similar to pattern descent in GAN, and is shown to be characterized by the inability of the discriminator to successfully recognize it. Ultimately, drawing from the acquired outcomes, it can be deduced that employing a larger batch size serves to enhance the pace of model training. It is advisable to concurrently elevate the learning rate by an equivalent factor when augmenting the batch size, thereby ensuring a consistent trajectory for model convergence.
Generative Adversarial Networks-based solution for improving medical data quality and insufficiency
Authors: Gao Chenxi
Published: March 2024

As big data brings intelligent solutions and innovations to various fields, the goal of this research is to solve the problem of poor-quality and insufficient datasets in the medical field, to help poor areas can access to high quality and rich medical datasets as well. This study focuses on solving the current problem by utilizing variants of generative adversarial network, Super Resolution Generative Adversarial Network (SRGAN) and Deep Convolutional Generative Adversarial Network (DCGAN). In this study, OpenCV is employed to introduce fuzziness to the Brain Tumor MRI Dataset, resulting in a blurred dataset. Subsequently, the research utilizes both the unaltered and blurred datasets to train the SRGAN model, which is then applied to enhance the low-quality dataset through inpainting. Moving forward, the original dataset, low-quality dataset, and the improved dataset are each used independently to train the DCGAN model. In order to compare the difference between the produced image datasets and the real dataset, the FID Score is separately computed. The results of the study found that by training DCGAN with SRGAN repaired medical dataset, the naked eye can observe that the medical image dataset is significantly clearer and there is a reduction in Frchet Inception Distance (FID) Score. Therefore, by using SRGAN and DCGAN the current problem of low quality and small quantity of datasets in the medical field can be solved, which increase the potential possibilities of big data in artificial intelligence filed of medicine.
Research on the analog-to-digital hybrid square wave encoder signal error compensator
Authors: Zhang Shouhuan
Published: March 2024

Photoelectric encoders are important electronic components, widely used in motion control systems, position measurement systems, and can measure angular displacement or displacement with high accuracy. Encoders widely used today output analog sine waves or digital square waves. The resolution of the encoder determines the measurement accuracy of the encoder, because the sine wave encoder can be more easily subdivided, which gradually replaces the square wave encoder in many occasions, but the square wave encoder is still widely used in the system with low demand for accuracy and low cost. The prerequisite for high subdivision of the output of the square wave encoder is good signal quality. Through the study of the signal output by the ideal encoder, this paper proposes the characteristics of distinguishing the error signal from the ideal signal, divides the error into two categories, designs a filter with two steps to compensate separately, and uses Simulink to establish a simulation model for verification.
Enhancing GPU performance and energy efficiency: Innovative strategies for sustainable computing
Authors: Shen He
Published: March 2024

With the rapid advancement of science and information technology, Graphics Processing Units (GPUs) have become indispensable tools in contemporary scientific research and industrial production. This paper delves into optimizing GPU performance and energy efficiency, with a specific focus on five aspects: core count and layout, memory hierarchy and cache design, clock speed, specialized hardware units for specific tasks, and interconnections among GPU components. Increasing core count generally enhances GPU performance but also elevates energy consumption. Optimizing core count and layout strikes a balance between performance and energy use. Memory hierarchy and cache design are crucial for handling the inherent parallelism of GPU architecture; optimizing these aspects boosts GPU efficiency and sustainability. Clock speed is a vital performance indicator, with the right speed achieving an optimal balance between performance and energy use. Tailoring hardware units for specific tasks enhances computational efficiency and lowers energy consumption. Optimizing interconnections between GPU components enhances data transfer efficiency, yielding higher performance. Through comprehensive research and optimization in these five areas, this paper introduces innovative strategies and techniques to boost GPU performance and reduce energy consumption, laying the foundation for sustainable computing development.
A comparative study of deep convolutional neural network-based facial expression regression
Authors: Sun Zhengyuan
Published: March 2024

Facial expression is an important way for people to express their emotions. Recently, with the advancement of the computer vision, facial expression recognition has become a current research hotspot and has made significant progress, which can be utilized in the interaction between humans and computers, emotional computing and other computer vision fields, the evolution of artificial intelligence and deep learning has better promoted the research of facial expression recognition. Conventional approaches are largely based on machine learning, which leverages artificial way for feature extraction. The extracted facial expression features are interfered by human factors, so that the trained classifier cannot effectively interpret the expression information, which ultimately leads to insufficient model generalization ability and low recognition accuracy. However, deep learning-based facial expression recognition in real scenes is still affected by factors such as human pose, different degrees of facial occlusion, background environment and light interference, and the recognition accuracy still needs to be further improved. This paper focuses on representative convolutional neural network architectures and summarizes the strengths, weaknesses, and innovations of these networks, through which the advancement of neural network architectures shows the great potential of the direction.
Enhancing customer segmentation in Chinese city commercial banks: A machine learning approach
Authors: Si Yuancheng , Xu Chunming
Published: March 2024

This paper examines the progression and implementation of customer segmentation theory in banking, focusing particularly on an improved RFM (Recency, Frequency, Monetary) model. It highlights the essential role of identifying high-value customers for the sustained growth and success of commercial banks. The study elucidates the pivotal role of Customer Relationship Management (CRM) and detailed management in the realm of retail banking as crucial for achieving success. By analyzing real-case scenarios from the Chinese banking sector, this paper illuminates the evolving nature of customer segmentation theory and its varied applications. The objective is to provide insightful and practical recommendations for commercial banks aiming to develop or enhance their customer segmentation frameworks. This research contributes both to the theoretical understanding of customer segmentation and offers a pragmatic guide for banks seeking to improve their customer management approaches in a market that is becoming increasingly competitive.
Genres Classification of Popular Songs Listening by Using Keras
Authors: Tarımer İlhan , Karadağ Buse Cennet
Published: March 2024

Listening to the music affects the brain in ways which might help to promote the human health and arrange various diseases symptoms. Music is a phenomenon that is intertwined at every stage of human life. In the modern era music is shaped by the combination of an incredible number of genres, some of which are contemporary, and some come from the previous times. The music genre represents a collection of musical works that develop according to a certain shape, expression and technique. The music genre of interest varies from person to person in society. Most listeners today do not know what kind of music they listen to. In this study, sound features were extracted from music data and the Keras model was trained using these attributes. The correct classification rate of a music genre of the trained model was determined as 71.66%. Mel Frequency Cepstral Coefficients (MFCC), Mel Spectrogram, Chroma Vector and Tonnetz methods in the Librosa library were used to extract sound properties from music data. Using the features probed by means of the library, the most listened songs with Shazam in Türkiye were categorized in with TensorFlow/Keras. Many methods can be used in classification. It is uncertain which method the researchers should opt. It has been emphasized that classification of the genres of newly released songs by using Keras in this study. At result, it is said that the study has presented a sound processing are Keras classification of musical parts.