bluuebunny/crossref_metadata_2025 huggingface dataset:
Entrepreneurship and entrepreneurial innovation of university students, Amazonas, Peru
Authors: Davila Manuel Antonio Morante , Pantaleon Alex Javier Sanchez , Caro Omer Cruz , Reyes Elena Irene Bautista , Ocampo Patricia Escobedo
Published: April 2024

This study aims to investigate the influence of entrepreneurship on innovation competencies in students. Using a descriptive, non-experimental research design with a quantitative relational approach, the research involved a participant pool of students from the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, totaling 310 individuals. To stimulate entrepreneurship that contributes to economic growth, it is essential to nurture entrepreneurship competencies among university students. Data collection utilized a Likert scale questionnaire, and the analysis employed SPSS and PLS statistical software. Results revealed that 60.7% of students fell within the 21 to 25 age range, with 50% receiving an income between S/. 500 to S/. 1500 soles from their parents and 48.8% obtaining income from the Cuna Más program. The PLS findings reveal a substantial impact of entrepreneurship on university students' innovation competencies, with correlations of 0.756 for knowledge generation, 0.736 for knowledge assimilation, 0.699 for knowledge partnership, and 0.756 for reorganization of knowledge. This highlights the critical importance of emphasizing entrepreneurial skills in university education. The research addresses a global literature gap on the impact of entrepreneurial competencies in establishing innovative businesses, aiming to provide empirical evidence linking open innovation dynamics and entrepreneurship. Anticipated outcomes underscore how pedagogical approaches can enhance students' capacity to generate new value for organizations or society through fostering innovative thinking and behavior.
Democracia en tinieblas
Authors: Valdez Castro Reyna Guadalupe
Published: April 2024

José Woldenberg (2022). La democracia en tinieblas. México: Cal y Arena.
Organización Independiente Totonaca: plataforma política del gobierno indígena
Authors: Mendoza Mora Elizabeth , Hernández Limonchi María del Pilar , Arrocha Belinda Rodríguez
Published: April 2024

La Organización Independiente Totonaca (OIT) se creó, principalmente, por la falta de reconocimiento del derecho político-electoral de la población indígena. El objetivo del artículo es comprender el proceso de reconocimiento de la OIT como plataforma política para participar en las elecciones municipales de Huehuetla, Puebla, en 1989, con la finalidad de identificar las implicaciones político-electorales en la actualidad. Se utilizó la metodología cualitativa, desde la línea sociojurídica y el método histórico, y como técnica la entrevista no estructurada dirigida a miembros de la Organización. La unión con el Partido de la Revolución Democrática (PRD) fue fundamental para colocar a la OIT como plataforma política y social en las elecciones municipales de Huehuetla, pese a la intimidación por parte de los caciques locales.
A case of primary Sjögren syndrome masquerading as post-COVID Bickerstaff encephalitis
Authors: Lin Shawn Zhi Zheng , Ling Hua Chan , Tan You Jiang
Published: April 2024

We present a case of acute external ophthalmoplegia, hypersomnolence, and ataxia occurring 5 days after an episode of SARS-CoV-2 upper respiratory tract infection. The patient’s serum returned positive for anti-GD1a antibodies. Guided by precedent cases, she was empirically started on a course of intravenous (IV) immunoglobulin for possible para-infectious Bickerstaff brainstem encephalitis (BBE). However, she failed to respond and subsequently developed autoimmune hemolytic anemia as well as renal tubular acidosis (type 1). Corroborative history from her family elucidated constitutional and sicca symptoms and she was eventually diagnosed with primary Sjögren syndrome (pSS) complicated by brainstem and cerebellar involvement. She was treated with IV cyclophosphamide and rituximab, after which her neurological deficits completely resolved at two months.
MAD2L2 as a novel prognostic biomarker and its correlation with immune infiltrates in glioma
Authors: Chen Kaiqin , Li He , Chen Yongtai , Huang Hesen
Published: April 2024

Background: The most common type of central nervous system tumor is glioma, the identification of Glioma biomarkers is essential. MAD2L2, which is abbreviated as MAD2-Like2, collaborates with a number of proteins to carry out numerous essential cellular functions. Uncertain is the role of MAD2L2 in glioma. The role of MAD2L2 expression is examined for the first time in relation to gliomas occurrence and development in this study
Methods: A bioinformatics and clinicopathological analysis, immune infiltration analysis, and enrichment analysis were conducted based on TCGA and additional gene expression analysis. Using ssGSEA and TIMER, the immune response to MAD2L2 expression in glioma was analyzed statistically. This study also analyzed the effect of MAD2L2 overexpression on major chemotherapeutic medicines and the methylation status of glioma patients. CGGA, HPA data analysis, and K-M survival were also utilized to validate the results.
Results: MAD2L2 was a crucial independent prognostic factor for glioma patients. Correlations were found between MAD2L2 expression and age, IDH status, WHO grades and 1p/19q codeletion. MAD2L2 is intimately connected to the DNA replication, transcription, cell cycle, immune system, and signal transduction pathways, as determined by the GSEA. In glioma, The majority of MAD2L2 DNA methylation sites were hypomethylated, and the degree of methylation was associated with patient outcomes. Chemotherapy would benefit MAD2L2 overexpression patients. according to these results. The expression of MAD2L2 was associated with partial immune cell infiltration and co-expressed with immune-related genes and immune checkpoints.
Conclusion: Glioma is characterized by higher MAD2L2 expression, and high MAD2L2 expression is linked with a poor prognosis. MAD2L2 might participate in tumor development by regulating tumor-infiltrating cells inside the TME. MAD2L2 could possibly be an immunotherapeutic target.
Tubridge flow diverter for the treatment of recurrent cerebral aneurysms
Authors: Zhao Li , Wang Jiaxiong , Liu Hailong , Xie Dajiang
Published: April 2024

Background & Objective: The Tubridge flow diverter is a commonly used device in China for reconstructing the parental artery and occluding complex intracranial aneurysms. However, there is limited experience with the device in treating recurrent intracranial aneurysms. The purpose of this study is to assess the safety and effectiveness of the Tubridge flow diverter in treating recurrent single- coiling aneurysms.
Methods: The clinical records of patients with recurrent intracranial aneurysms who were treated with a Tubridge flow diverter were reviewed to show the therapeutic process, occlusion rate, and clinical outcomes associated with this treatment method.
Results: Five patients with recurrent aneurysms of the internal carotid artery after singe coiling were included in the study. The recurrent aneurysmal sac had a mean length and width of 8.34/4.30 mm. All five Tubridge flow diverters were implanted without any unfold failure. The last angiographic follow-up showed a complete occlusion rate of 100%. Five branch arteries were covered, and only one branch artery disappeared during follow-up. No cases of cerebral infarction or intracranial hemorrhage were found among the patients.
Conclusions: Our initial findings indicate that the Tubridge flow diverter could be a reliable and successful method for treating recurring single-coil aneurysms. We observed that the branch arteries were well-preserved, and no patients experienced cerebral infarction or hemorrhage. However, further research is necessary to establish clear indications and potential complications through a multicenter randomized controlled trial with long-term follow-up.
Use of CALLY index to predict aneurysmal subarachnoid hemorrhage patient outcome
Authors: Mutlucan Umut , Selvi Fatih , Bedel Cihan , Zortuk Ökkeş , Türk Cezmi cağrı , Korkut Mustafa
Published: April 2024

Background & Objective: Aneurysmal subarachnoid hemorrhage (aSAH) is a condition linked to elevated mortality and morbidity rates. Despite limited knowledge of the exact mechanism in aSAH patients, systemic inflammation is thought to have a significant role in cerebrovascular disease. The CALLY index is a prognostic marker utilized in many diseases, derived from parameters such as CRP, albumin, and lymphocytes. Nevertheless, there is limited research on its implementation in aSAH patients. This study aimed to investigate the CALLY index’s prognostic usefulness in aSAH patients.
Methods: The research cohort comprises patients who attended a tertiary teaching and research hospital’s emergency department between January 1st, 2021, and January 1st, 2022, with a confirmed diagnosis of aneurysmal rupture. Basic demographic data and associated comorbidities of the cohort, in addition to the CALLY index derived from CRP, albumin, and lymphocyte levels, were documented. A comparison of the effects of investigations and data on in-patient mortality between the groups was conducted.
Results: A total of 190 patients who met the inclusion criteria participated in the study. Patients with mortality were observed to have significantly lower CALLY Index scores than those in the other group (1.940 vs. 8.805). Regression analysis showed that mortality could be predicted based on the CALLY index (OR 0.393, 95% CI 0.1511-1.058, p=0.058). An AUC of 0.752, sensitivity of 78.2, specificity of 59.8 [p<0.001] was achieved by the optimal value for CALLY index.
Conclusion: Based on the findings of this study, it can be inferred that CALLY index is a simple marker which can serve as an indicator of mortality in aSAH patients.
Accuracy and reliability of wristband smartwatch-based estimation of circadian rhythm of heart rate in stroke patients
Authors: Yao Lin , Pan Xi , Yang Yiqun , Zhong Weiying , Li Jiaxuan , Wang Zhi , Xu Lan
Published: April 2024

Background & Objective: Stroke has a high incidence, disability, and mortality rate, which has a serious impact on the patients’ quality of life. Heart rate (HR) is valuable in the prognostic assessment of stroke patients. Continuous HR tracking helps to detect abnormal indicators and provide interventions timely to avoid disease progression. However, the cuff-based or electrode-based HR monitoring tools commonly used in clinic are not suitable for continuous monitoring in daily life. The aim of this study was to verify the accuracy and reliability of a wristband smartwatch in monitoring HR over 24-hour in stroke patients, including in terms of the circadian rhythm characteristics of HR.
Methods: 25 stroke patients underwent 24-hour HR monitoring by wearing a smartwatch and a clinical monitor. We use ambulatory blood pressure monitors and bedside monitors as the gold standard for clinical HR monitoring. The degree of association between the two measurements was compared using Pearson’s correlation coefficient. Bland-Altman analysis showed the accuracy of the smartwatch in HR monitoring. Intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to verify the reliability of the wristband smartwatch for repeated measurements.
Results: A total of 625 heart rate pairs were acquired. The smartwatches had an average error of 0.816 bpm in 24-hour, with no difference from standard monitors (P>0.05), indicating good accuracy. The intraday repeated measures ICC was 0.969 and the interday repeated measures ICC was 0.788, indicating good reliability.
Conclusions: The smartwatch is acceptable in detecting 24-hour ambulatory HR in stroke patients, and in the future, long-term HR monitoring with diurnal variations can be performed in daily life.
The effect of valproate on dystonic movement in a moyamoya patient: A case report
Authors: Chang Hyeyeon , Na Sang-Jun , Kim Yong Duk , Lee Sunghyun , Lee Sang-Soo , Shin Dong-Ick
Published: April 2024

Moyamoya disease (MMD) is a progressive cerebrovascular disorder caused by narrowing of the arteries located at the base of the brain in the basal ganglia. Dystonia is a relatively rare symptom of MMD, and little is known about the treatment of this symptom in MMD patients. We report a case of dystonia in an MMD patient that was alleviated after valproate treatment. A 57-year-old female patient with MMD presented with new-onset dystonia of the left upper extremity and left hemifacial spasm. Although she already suffered from mild cognitive dysfunction, there was no exacerbation when dystonia presented. She was treated with valproate 300 mg b.i.d, and the symptoms were alleviated. We discuss the efficacy of valproate in terms of anatomy and neurotransmitters. In addition, the patient’s symptoms are similar to those associated with LGI-1 encephalitis but are distinguished by the progression of the disease.
Study of the association between anemia and prognosis of moderate-to-severe traumatic brain injury
Authors: Yin Ruilan , Wei Xijian
Published: April 2024

Objective: To investigate the impact of admission anemia on the prognosis of patients with moderate- to-severe traumatic brain injury (TBI).
Methods: The clinical data of patients with moderate-to-severe TBI treated in the Department of Intensive Care Unit in our hospital from January 2020 to September 2022 were retrospectively analyzed. The patients were divided into admission non-anemia group (Hb ≥ 100 g/L) and admission anemia group (Hb<100 g/L) based on hemoglobin (Hb) levels. The Glasgow Coma Scale (GCS) score, first blood analysis, arterial blood gas, blood pressure, injury mechanism, pupillary reflex at admission and others were compared, multivariate logistic was used to analyse independent risk factors for patient death.
Results: One hundred and nine patients were included in the study, of which 17 died in the hospital and 52 had anemia at admission. Compared with patients in the admission non-anemia group, patients in the admission anemia group were younger, had lower admission GCS and trauma score (PTS) scores, higher mortality rates, and longer hospital stay (all P<0.05).Univariate analysis showed that moderate-to-severe TBI patients with admission anemia, admission hyperglycemia, GCS<8, PTS<8, abnormal pupillary reflex, and other organ injuries had higher mortality rates (all P<0.05) .Multivariate logistic regression analysis showed that admission anemia (OR=9.383, 95% CI=1.086~81.094, P=0.042), admission pupillary reflex abnormalities (OR=23.121,95% CI=3.680~145.270,P=0.001), and admission hyperglycemia (OR=15.181, 95% CI=2.019~114.160, P=0.008) are independent risk factors for death of Moderate-to-severe TBI patients.
Conclusions: Admission anemia is an independent risk factor for death of moderate-to-severe TBI patients, and it can prolong the length of hospital stay for such patients.
Evaluation of midwifery students’ health fatalism, knowledge, and attitudes toward patients with epilepsy in Eastern Turkey
Authors: Koç özlem , ÖZKAN Hava
Published: April 2024

Objective: This study was conducted to determine the relationship between midwifery students’ health fatalism, epilepsy knowledge, attitudes, and some sociodemographic variables.
Methods: The descriptive cross-sectional study was conducted with 270 midwifery department students who were studying at a state university and who agreed to participate in the study in eastern Turkey during the 2021–2022 academic year. The data were collected using a Sociodemographic Information Form, the Health Fatalism Scale (HFS), the Epilepsy Knowledge Scale (EKS), and the Epilepsy Attitude Scale (EAS).
Results: The midwifery students had total mean scores of 49.69±13.25, 7.71±3.69, and 54,87±8.13 for the HFS, EKS, and EAS, respectively. It was found that those who had social contact with epilepsy patients had significantly higher EKS and EAS scores, but no correlation with HFS.
Conclusion: The study of midwifery students in Eastern Turkey shows that close contact with epilepsy patients is associated with better knowledge of and attitude to epilepsy, although the high health fatalism remains.
Barreiras antropogênicas e conectividade do habitat
Authors: Barbosa Eliane Soares Santos
Published: April 2024

O presente estudo aborda os corredores ecológicos, uma estratégia crucial para a conservação da biodiversidade e a manutenção da integridade dos ecossistemas, focando em sua capacidade de conectar unidades de conservação, facilitar o movimento da biota e o fluxo genético, além de mitigar os impactos das mudanças climáticas e das barreiras antropogênicas. O objetivo central dessa análise é investigar a importância e a função dos corredores ecológicos no contexto da ecologia de paisagens e da conservação ambiental, considerando diferentes biomas e desafios ambientais. A justificativa para tal investigação reside na urgente necessidade de compreender e implementar medidas eficazes que promovam a resiliência dos ecossistemas e a coexistência harmoniosa entre a natureza e as atividades humanas, frente aos crescentes desafios impostos pela fragmentação de habitats e pelas alterações climáticas. Para isso, a metodologia incluiu uma revisão bibliográfica com abordagem qualitativa do problema, utilizando análise crítica de conteúdo dos dados. Os principais resultados da pesquisa evidenciaram que as barreiras antropogênicas desempenham papel de impacto significativo na fragmentação dos habitats, especialmente nos corredores ecológicos, essenciais para a conectividade da fauna e flora. Além disso, constatou-se que as mudanças climáticas têm contribuído para intensificar essas barreiras, tornando-as mais severas e difíceis de transpor para as espécies. Identificou-se, contudo, uma série de obstáculos à conectividade do habitat, incluindo a urbanização, a fragmentação causada por estradas e o desmatamento. Para mitigar esses impactos, foram propostas estratégias de restauração ecológica, visando recuperar e reconectar áreas fragmentadas, promovendo a livre circulação das espécies e a restauração da biodiversidade. A conclusão do estudo reafirma a importância dos corredores ecológicos como elementos vitais no planejamento da conservação ambiental, destacando seu papel na promoção da conectividade do habitat, na preservação da diversidade biológica, e no desenvolvimento de estratégias sustentáveis que assegurem a saúde e a resiliência dos ecossistemas naturais, contribuindo significativamente para a conservação da biodiversidade em uma escala global.
Violência na escola: um estudo preliminar indicativo de polí-ticas públicas para as cidades educadoras
Authors: Bacila Maria Sílvia
Published: April 2024

A violência na escola é um fenômeno complexo e impactante, que abrange comportamentos agressivos, ameaçadores e prejudiciais no ambiente escolar, como bullying, agressões físicas, verbais, discriminação e vandalismo. Este estudo teve como objetivo geral compreender a natureza da violência na escola e como objetivos específicos identificar os tipos de violência prevalentes em cidades educadoras, bem como avaliar a percepção dos gestores sobre seu impacto no ambiente escolar e desempenho dos estudantes. Realizado por meio de uma pesquisa do tipo levantamento, a população-alvo foram gestores educacionais de cidades educadoras pertencentes à Rede Brasileira de Cidades Educadoras (sendo 9 respondentes de um total de 33 cidades membro), utilizando a técnica de coleta de dados por questionários via Google Forms. A análise dos dados foi qualitativa e/ou quantitativa. Embasado em autores como Abamovay e Rua (2002), Guimarães (1998), Maldonado (2011), Matos e Gonçalves (2009), Teixeira e Kassouf (2015), o estudo focou em 9 cidades membros da rede brasileira de cidades educadoras, comprometidas com a promoção da educação. Os resultados destacaram a importância das políticas públicas em cidades educadoras para promover ambientes escolares seguros e acolhedores, visando à prevenção da violência e ao melhor desempenho acadêmico dos estudantes. Para estudos futuros, recomenda-se explorar mais a fundo as causas subjacentes da violência nas escolas, bem como investigar estratégias eficazes de prevenção e intervenção. Além disso, seria relevante estudar a relação entre a violência escolar e o desempenho acadêmico a longo prazo, assim como investigar o papel das famílias e da comunidade na promoção de ambientes escolares seguros e saudáveis.
Determinação do coeficiente de reaeração (k2) em pequeno rio de encosta: estudo de caso
Authors: Forgiarini Francisco Rossarolla
Published: April 2024

O balanço de oxigênio nos cursos d’água é regido pela subtração dos processos de desoxigenação e reaeração. Esta dinâmica é basicamente regulada pelo coeficiente de reaeração (K2) que demonstra o potencial de recuperação natural da concentração de oxigênio dissolvido. Este processo é de difícil quantificação devido à influência das características hidráulicas de cada córrego. Desta forma o objetivo deste trabalho foi de quantificar o coeficiente de reaeração para um arroio de encosta localizado no município de Silveira Martins, RS e avaliar os valores encontrados com os disponíveis na bibliografia. O trecho do curso de água estudado possui 1630 metros de comprimento e apresenta nas suas extremidades altitudes de 216 e 407 metros, apresentando uma declividade média de 118,6 m/km. Foram coletadas amostras de água em dois pontos durante cinco eventos de chuva com diferentes vazões. O coeficiente de reaeração foi estimado com a utilização de três modelos, dois modelos de previsão do coeficiente de reaeração (Owens et al, 1964; e Parker e Gay, 1987) e a partir do modelo de autodepuração de Streeter e Phelps. Os resultados encontrados com a utilização dos modelos de previsão de K2 não foram representativos para as características hidráulicas do córrego em estudo, pois os valores foram extremamente altos (superiores à 34,491 dia-1). Já o modelo de autodepuração de Streeter e Phelps apresentou resultados semelhantes aos disponíveis na bibliografia para cursos d’água com características semelhantes ao córrego estudado, demonstrando que os valores calculados possam ser considerados válidos. O ganho de OD foi maior nos eventos ocorridos nas estações de outono e inverno. Isto pode ser justificado devido a serem os eventos onde as temperaturas estavam mais amenas, fazendo com que o ganho de oxigênio fosse superior que nos outros eventos que ocorreram na primavera e verão.
Research on a Convolutional Neural Network Based Digestive Tumor Diagnosis System and Software Supply Chain Design
Authors: Huang Yuting , Zhang Jie , Yuan Yezhi , Fan Pei , Wang Jianbo , Li Liangyu , Qin Yi
Published: April 2024

The research team uses convolutional neural networks to recognize images of gastrointestinal endoscopy and classify them according to their pathological prediction types. This allows clinical doctors to predict their pathological classification in advance through convolutional neural networks when obtaining images of gastrointestinal endoscopy. The research team conducted supply and demand simulations for this algorithm and studied the design of its e-commerce system using information technology. The team reported on the above results.
Rent Price Prediction with Advanced Machine Learning Methods: A Comparison of California and Texas
Authors: Chan Hong Ru
Published: April 2024

The forecast of rent prices in dynamic housing markets is of fundamental importance to renters, landlords, investors, and politicians alike. Machine learning models offer flexibility, excel at modeling complex relationships, and provide outstanding forecast precision. This study compares advanced machine learning models, extreme gradient boosting regressor (XGBoost), light gradient boosting machine (LightGBM), random forest, ridge regression, and two ensemble approaches, to predict California and Texas rent prices. The two ensemble approaches include a hybrid regression of averaging base models and a 2-level stacked generalization model. The results revealed that a stacked generalization ensemble with base models random forest, XGBoost, LightGBM, and meta-model ridge regression achieved the best performance for the California dataset by generating the lowest MSE and highest R2 of 46116.3 and 0.8858, respectively. In contrast, random forest outperformed both ensemble models with the lowest MSE and MAPE of 18401.93 and 9.7003%, respectively, and the highest R2 of 0.7992. These methodologies can assess future rental property worth, serve as indicators for market dynamics, and aid in establishing real estate policies, thereby providing practical guidance to individuals, businesses, and policymakers.
Research On the Development of Intelligent Face Recognition System for Business Competition in Chatgpt Industry
Authors: Wang Ruoxi
Published: April 2024

Artificial intelligence has become a key area of today's international competition. Analysis of the development trend of artificial intelligence and the strategies of major powers is crucial to winning this competition. This paper refers to (GCN) and Laplace's (LBP) feature equation in ChatGPT. Firstly, an image processing method based on LBP is proposed. GCN analyzes the temporal and spatial correlation of character attributes in the time domain. This method can capture both short-term and long-term temporal and situational correlations simultaneously, effectively improving the extraction of temporal and situational correlations. Then, text analysis determines the implementation intentions, measures and prospects of the EU's "human-centered" artificial intelligence competition strategy. It also evaluates its strategic effects from the relevant reactions of its main competitor, the United States, and infers the weaknesses and risks existing in the strategy.
Implementation Of Machine Learning and Artificial Intelligence for Music Composition
Authors: Yang Xiaohan
Published: April 2024

With the rapid developemnt of machine learning techiniques, it has been widely adopyed in various fields. This paper explores the models and applications of machine learning in music composition. It traces the evolution of computer music from its early days with electronic music pioneers to its contemporary implementations, showcasing the integration of cutting-edge technologies. Various models and techniques employed in music composition, including rule-based systems, deep learning, and stochastic composition methods, are thoroughly examined. Furthermore, significant challenges posed by copyright issues are addressed, along with potential solutions to overcome them. Looking ahead, one envisions a future where computer music achieves greater emotional depth, accessibility, and customization despite inherent complexities. Advances in copyright management and licensing systems are expected to diversify the pool of musical compositions available for training machine learning models, resulting in more emotionally resonant and diverse computer-generated music. This study sheds light on the potential of combining computer music with machine learning, providing valuable insights into the intricate world of music composition and technological innovation for future music creators and enthusiasts.
Drivable Area Detection: a Comparative Study of Algorithms Based on Deep Learning
Authors: Cao Fanghua
Published: April 2024

Early techniques, frequently based on computer vision, classified drivable zones into four types: sensor-based, rule-based, map-based, and traditional machine learning (ML). Due to the limitations of these methods, they were later replaced by deep learning-based methods. The advancements in methods for segmenting drivable areas, encompassing both conventional computer vision-based approaches and deep learning-based algorithms are discussed in this article. This paper aims to conduct comparative research on the principles underlying Fast-SCNN, EDANet, and D3NET algorithms along with their performances on the CityScapes dataset. The accuracy and efficiency of the drivable area segmentation task deserve significant attention. Furthermore, a comparative study is conducted separately on PC and embedded platforms to facilitate readers in selecting appropriate datasets and validation platforms. Lastly, the existing challenges as well as future trends pertaining to these algorithms are discussed. This study's comparisons offer valuable insights into the strengths and limitations of the reviewed methods, thereby aiding in the construction of the algorithm model.
BCI Application in Stroke Rehabilitation: Robotic Assisted System
Authors: Yuan Junmeng
Published: April 2024

Stroke remains a pervasive global health concern, exacting enduring motor, cognitive, and emotional tolls on affected individuals. The evolving landscape of neural interfaces has recently shown promising strides in reinstating lost sensorimotor functions. Non-invasive brain-computer interfaces (BCIs) have gained widespread recognition for their inherent advantages—simplicity, safety, and cost-effectiveness. The ongoing refinement of BCIs involves addressing previous technological and neurophysiological limitations, focusing on understanding distinctive neurophysiological alterations observed in individuals with disabilities. The exploration of brain connectivity attributes is pivotal in implementing BCI-based control systems. Emerging as a notable prospect for post-stroke motor rehabilitation, Brain-Computer Interface (BCI)-based therapy has made significant progress in restoring sensorimotor function. Revolutionary advancements have been made to traditional brain-computer interface (BCI) systems, which rely on brain signals obtained from electroencephalography (EEG) and rule-based translation algorithms. This comprehensive review delves into the realm of BCI Robotic Assisted Systems, offering a nuanced analysis of contributions from diverse researchers. It meticulously dissects these systems' advantages and limitations, providing valuable insights into their efficacy. Ongoing endeavors within the field prioritize enhancing the portability, simplicity, and cost-effectiveness of BCI technology, ultimately refining its overall usability. As research progresses, emphasizing larger sample sizes, there is a tangible potential to augment the reliability of BCI systems for stroke rehabilitation significantly. This trajectory holds promise for extending the benefits of BCI technology to a broader spectrum of patients, marking a transformative leap in neurorehabilitation methodologiest.
Forecasting The Population of China From 2020 To 2025 Based on Random Forest and Linear Regression
Authors: Wang Weibo
Published: April 2024

The population dynamics of a country do play a vital role in its economic and political development. The COVID-19 epidemic has significantly affected the world's population. However, most people only care about the negative influence caused by COVID-19 but ignore the positive influence. This article uses two machine learning models, the random forest model, and the linear regression model, to predict the population change in China if there were no COVID-19 pandemic. With the predicted results, this article can compare the potential positive impact of the pandemic. This paper tries to fit two popular models, namely, Random Roest and Linear Regression to forecast the population of China from 2020 to 2025. The historical birth rate, death rate, and GDP growth rate of China are collected as features adding to the models to decline the error. For the Random Forest model, this paper set up an ensemble of decision trees to predict the future population of China. For the Linear Regression model, our features and population fit a linear connection. The findings of two models are compared in this article, and it is suggested that the random forest model is more suited for population forecasting. In addition, according to this study, the COVID-19 pandemic has some favorable effects on economic growth and birth rates. The article emphasizes the need to not only focus on the negative effects of the pandemic. Furthermore, the article points out that the linear regression model has poor fitting results for non-linear relationships. It suggests exploring more non-linear models for prediction and considering more influential parameters.
Research And Application Analysis of Multimodal Emotion Recognition Methods Based on Speech, Text, And Facial Expressions
Authors: Sun Jiaqi
Published: April 2024

In this study, the focus is primarily on the diverse methods for recognizing human emotions through language, text, and facial expressions via computational technology. Emphasizing the real-world applicability of these techniques, the paper underscores the significance of multimodal emotion recognition in areas such as human-computer interaction, psychology, and emotion analytics. Multimodal methods, which combine data from various sources like voice tone, facial cues, and textual context, offer a robust approach for discerning nuanced emotional states. Compared to single-mode analysis, these multimodal techniques tend to produce more accurate and comprehensive results, bridging the gaps left by any one mode in isolation. As technology increasingly integrates with daily human activity, the importance of nuanced, reliable emotion recognition is becoming paramount for fostering more natural and empathic machine-human interactions. Moreover, in the realm of psychology, these methods offer groundbreaking possibilities for diagnosis and treatment. By discussing the future applications and methodologies of multimodal emotion recognition, this paper aims to provide a comprehensive roadmap for both academic research and practical applications in the evolving landscape of emotion-aware computing.
Visual perception and deep learning in smart homes and the Internet of Things
Authors: Li Jiamiao
Published: April 2024

In recent years, IOT (Internet of Things) has been widely used in the field of smart homes. IOT technology is an important stage in the development process of the "digital information age", with a history of more than ten years of development. It is widely used in the communication industry, especially in the field of smart homes. The basic principle of DL is to establish a multi-layer neural learning network, which learns a large amount of data to obtain better features and improve the accuracy of classification and recognition. This article conducts research on the visual perception of smart homes based on DL (Deep Learning) and IOT. Utilizing an intelligent gateway to achieve the mutual conversion of the underlying ZigBee protocol and in ternet protocol, the cloud service platform is interacted with by the Netty middleware and gateway. The entire system is centered around the Internet cloud platform, connecting the underlying IOT devices and the mobile network platform of the application layer to achieve the construction of a smart home system. The device then collects the user's EEG signals in real-time and hands them over to a trained specific network for classification. Finally, based on the classification results, the serial device sends signals to the smart home terminal to achieve the final practical operation actions, such as turning on the lights and fans.
Biomedical Engineering Research: Computer Translating and Teaching System and Neural Network
Authors: Liu Yueyin , Si Weichen , Huang Xvshang , Li Liangyu , Qin Yi , Lin Yixian
Published: April 2024

In the research on medical students, the research team obtained the idea of using English teaching to cultivate interdisciplinary talents, and established a computer teaching model to predict the incidence rate of cancer through the use of computer engineering based in-depth learning and oncology. Through the situation of susceptible gene mutation and computer imaging after chromosome staining, an algorithm teaching model using convolutional neural network and linear function is proposed. This model solves the problem that computer engineering students lack knowledge of medicine. This model can enhance medical students' understanding of applied mathematics and neural networks, provide new ideas for the medical community to comprehensively promote in-depth learning, and design social experiments, carry out data analysis and data visualization. The research team analyzed and reported this situation, summarized and communicated.
Generative AI: An In-depth Exploration of Methods, Uses, and Challenges
Authors: Wang Shang
Published: April 2024

Recently, artificial intelligence has surged to the forefront of computer science, with generative AI emerging as the most sought-after research area. The success of generative AI hinges on advancements in algorithms, training frameworks, and data. Among these, training algorithms are of paramount importance. This article aims to shed light on several leading training algorithms in the domain. Generative AI has left a profound imprint on a myriad of industries. Intelligent publishing, advertising content creation, and finance are just a few sectors that have been revolutionized by this technology. As with all significant technological shifts, generative AI is not without its challenges. The implications it holds for employment, intellectual property rights, as well as security and privacy concerns, are profound. It's vital for stakeholders in the AI domain and beyond to consider and address these challenges. As generative AI continues to integrate more deeply into industries and our daily lives, proactive steps need to be taken to ensure ethical, secure, and equitable use. This not only guarantees the continued growth and trust in the technology but also safeguards society's values and norms in the face of rapid innovation.
Enhancing Well-being and User Experience: Electric Sticky Notes, UI Design Colors, Login Preferences, and Clear Webpages
Authors: Huang Rangpei
Published: April 2024

This paper examines various essential elements of digital design that have a significant impact on user experience and well-being. The present study explores the efficacy of electric sticky notes in reducing anxiety. It clarifies the potential of writing notes as a self-treatment approach that can decrease anxious thoughts and enhance mental wellness. Also, this paper discusses the role of color in user interface (UI) design and its profound psychological effects on users. It emphasizes how color choices can influence emotions, perceptions, and behavior in user interface/user experience (UI/UX) design, emphasizing the importance of strategic color selection to enhance user engagement. The paper also discusses the significance of website login preferences, explaining how personalized settings and optimized login processes contribute to a smoother user experience and heightened security. Lastly, it underscores the importance of a clear and organized webpage layout, demonstrating how a clean and simple design promotes improved user comprehension, satisfaction, and overall browsing experience. By examining these aspects of digital design, this paper provides valuable insights into enhancing both user experience and psychological well-being in the age of technology.
Analysis of Image Data Augmentation in Smart Agriculture
Authors: Liu Mudong
Published: April 2024

The demand for agricultural production automation in smart agriculture has made deep learning widely used in tasks, e.g., disease identification, weed-crop segment and fruit detection based on image processing. However, in agricultural production environments, there are limitations in data acquisition, so data enhancement technology has also been widely used. This study introduces two data enhancement methods commonly used in smart agriculture, namely image transformation and GAN-based, and analyzes their specific applications and roles in three common task scenarios: disease identification, weed-crop segment, and fruit detection. According to the analysis, the data enhancement is mainly used to deal with class imbalance problems and over-fitting problems, and can significantly improve model performance in specific scenarios; the complexity of tasks and image structures has an impact on the application effects of different methods, and image segmentation tasks. It is necessary to increase the number of image modes, so methods such as GAN-based are adopted. However, due to the complexity of the image, fruit detection uses simple transformation methods to achieve better results. In addition, data enhancement methods designed for specific tasks, such as simulating synthetic images, can also achieve good results. However, data augmentation technology still has mode collapse problems and is too cumbersome in its application, which points out the direction for subsequent research. Overall, this research sorts out the applications of common data enhancement methods, analyzes the characteristics and limitations of these methods, and hopes to provide guidance for subsequent research.
Research On Traffic Sign Detection Based on Transformer and Yolov5
Authors: Xie Zhonghao
Published: April 2024

YOLOX-Swin algorithm takes Swin-Transformer as the backbone network of YOLOX to extract traffic sign image features, obtain enough global context information through mobile window, and use multiple self-attention mechanism; use YOLOX own path enhanced feature pyramid network to extract and integrate multi-scale feature information including lower information of traffic sign to improve the detection accuracy of small target traffic sign. Because the small target traffic signs occupy fewer pixels in the image, and considering that Transformer requires more training samples than the convolutional network, the original copy and paste method is improved to increase the number of traffic signs samples to further improve the accuracy of object detection. The test results on the TT100K dataset show that the proposed object detection method has higher object detection accuracy than several other methods, and can meet the requirements of accuracy and real-time object detection.
Application Programming Based on Human-Computer Interaction and Software Modularity
Authors: Liang Haoyu
Published: April 2024

In the 21st century, cutting-edge information technologies such as artificial intelligence, blockchain and big data have profoundly affected the way human society is organised, the form of interaction and even the social structure. With the miniaturisation, mobility, intelligence and embedding of machines, the relationship between humans and machines has undergone fundamental changes. Human-computer interaction has gradually become one of the most important forms of interaction in human production and life, and human daily life centred on human-computer interaction has also undergone great changes. Various applications have been introduced into the lives of human beings, and at present, it would be difficult for human beings to carry out their daily lives without these applications as a vehicle for the development of information technology. However, complicated applications do not really enhance the usage experience in many cases, redundant information also ignores the real needs of users in this paper, which will focus on human-computer interaction, from the four aspects of human-computer interaction (HCI) HCI modes, HCI interfaces, HCI devices, HCI modalities, HCI patterns to analysethen borrow the concept of software modularity to optimise the current applications, and put forward some suggestions for improvement. This will make the application more suitable for the user's experience and reduce the burden of programme managers.
Multi-Technique Integration in Brain-Computer Interface-VR: Challenges and Opportunities
Authors: Li Haoqi
Published: April 2024

Nowadays, BCI-VR (Brain-Computer Interface with Virtual Reality) integration is a hot technology. There is an urgent need for immersive experiences and better neurorehabilitation tools, so researchers are focusing on BCI-VR integration technology. However, BCI-VR technology still has many challenges and difficulties, such as the difficulty of decoding brain signals, the reliance on traditional reading modes, and ethical issues. In this paper, the researcher analyzes the current application prospects and development potential of BCI-VR integration technology, explores other possibilities of multimodal BCI-VR integration technology (e.g., AI, robotics, electrical stimulation modulation, and other mechanisms), and then analyzes its improvement techniques mainly in neural signal modulation and user experience. Through this analysis, the researcher concluded that large-scale interdisciplinary collaborations need to be established in order to reach further BCI-VR integration and bring a stronger immersive experience.
Prediction of Anorexia Risk Based on Decision Tree
Authors: Xie Jinyu
Published: April 2024

Anorexia is a kind of eating disorder caused by the cognitive distortions of patients. Under the condition, patients' physical functions can be impaired by extreme weight control behaviors. The prediction of anorexia can help patients with early detection and prediction of the risk of disease, then decrease the damage to the body and the psychological burden. In recent years, machine learning models achieve promising performances in prediction problems. In this paper, after trying on the Adaboost model, Extratree model, and decision tree model, this research decides to use the decision tree model. It is suitable for Boolean-type data classification to do data analysis and hence achieves outstanding performances. By building a decision tree model and referring to the correlation between different features, this research concludes data training. People who have the problem with liver firm and spleen palpable are more likely to gain anorexia. Fatigue and malaise have little relationship with anorexia.
Advanced Deep Learning Models for Discriminating Real
Authors: Lin Jiaguo
Published: April 2024

The rapid development of the field of artificial intelligence has brought many significant innovations across many domains. Nevertheless, it has also led to an increasing number of online crimes, particularly those involving the misuse of synthetic images. This study focuses on the critical issue of discriminating between real and fake faces using several advanced deep learning models. The models that are used are Convolutional Neural Networks (CNN), ResNet-50, and InceptionV3, and these models are used on a dataset containing both real and synthetically generated face images. Based on the result, the improved InceptionV3 model achieves the highest accuracy rate with 92.08%, followed by the improved ResNet-50 model at 89.37%. Additionally, the second CNN model eventually reaches an accuracy of 85.99%. This study emphasizes the importance of using advanced deep learning models to fight against online discrimination and fraud. In the future, data augmentation and model refinement will be added to further enhance the accuracy and robustness of real and fake faces discrimination tasks. Moreover, this research contributes valuable insights for the future development of deep learning in order to improve more on the field of network security and personal privacy protection.
Broadcast Swin Transformer for Music Genre Classification
Authors: Duan Yu
Published: April 2024

As a matter of fact, with the rapid development of computation ability as well as machine learning models, music genre classification (MGC) has been widely explored in recent years, which is crucial to the development of modern digital music media platforms. With this in mind, this paper proposes a novel architecture called Broadcast Swin Transformer (BST). To be specific, it adds the Broadcast Mechanism to Swin Transformer, which can effectively convey as well as utilize the low-level information of the spectrogram at multiple scales. According to the analysis, the model has been experimented on Mel-spectrograms extracted from the audio dataset GTZAN with a Top-1 accuracy of 99.0%. At the same time, its excellent performance has also been demonstrated and evaluated in ablation study and comparison with the state-of-the-art methods. In the meantime, the current limitations as well as further prospects are presented as well. Overall, these results shed light on guiding further exploration of music genre classification.