Search

This research delves into the critical evaluation of the “Party Building + Civic and Political” program within higher education, addressing the sector’s contemporary challenges and opportunities. By implementing the entropy value method and the efficacy function model, this Analysis spans an experimental cohort of 38 students from College J, scrutinizing their autonomous learning proficiency, educational outcomes, and the integrative impact of Party building on civic and political education. The empirical evidence showcases a substantial elevation in independent learning abilities among participants (with improvements ranging from 1.181 to 1.765, markedly surpassing the control group’s 0.257 to 0.563) and a remarkable increase in the coupling degree from 0.1542 in 2012 to 0.8186 in 2023. These results underscore the significant strides made in student learning and teaching quality through the “Party Building + Civic and Political” model, advocating its utility in enhancing the comprehensive capabilities of students and innovating higher education methodologies.

In this study, we successfully realized the interaction design of virtual Civics teaching scene through multiresolution hierarchical detail (LOD) model, and constructed an immersive teaching environment using texture mapping technology. Combined with the gesture recognition and training of Adaboost classifier, this paper proposes an innovative interaction model for virtual Civics and Politics teaching. Through in-depth Analysis of the virtual interactive teaching elements of Civics courses in colleges and universities, this study conducted a covariance analysis of the aspects of creativity, critical thinking, metacognition, self-efficacy, and scientific ideological reasoning. Then it explored the effects of different places of origin and types of schools on the acceptance of virtual Civics courses by the students. It was found that among the science and engineering and teacher training students, the science and engineering students paid more attention to the virtual Civics teaching (3.36 and 3.05, respectively). In addition, compared with traditional Civics teaching, virtual practice teaching significantly increased students’ motivation (3.79), proving that Civics courses incorporating virtual interaction design have significant advantages in improving learning attractiveness.

BackgroundIschemic heart disease (IHD) is a major global health concern, and its burden among young adults aged 25–49 years remains underexplored. This study aims to provide a comprehensive assessment of the global burden and trends of IHD over the past 30 years (1990–2019) among this age group, as well as to analyze the health inequalities related to socioeconomic development.MethodsData from Global Burden of Disease Study 2019 (GBD 2019) were utilized to analyze the prevalence, mortality, and disability-adjusted life years (DALYs) rate of IHD among young adults globally. Joinpoint regression analysis was applied to examine the trends over the study period. Health inequality analysis was performed to investigate the disparities in IHD burden related to the Socio-Demographic Index (SDI) of countries.ResultsAccording to GBD 2019 data, in 2019, the global numbers of young adults with IHD cases, deaths, and DALYs were 18,050,671 (95% UI, 15,551,940–21,254,746), 597,137 (548,250–647,778), and 28,692,968 (26,397,448–31,178,464), respectively, accounting for 9.15%, 6.53%, and 15.7% of the total global cases. Over the past 30 years, the mortality [AAPC = −0.4%, 95% CI (−0.7% to −0.1%)] and DALYs rate [AAPC = −0.3%, 95% CI (−0.6% to −0.1%)] of IHD among young adults decreased, while the prevalence rate [AAPC = 0.4%, 95% CI (0.4%–0.4%)] and YLDs rate [AAPC = 0.4%, 95% CI (0.3%–0.4%)] increased. Furthermore, countries with lower levels of socio-demographic index (SDI) disproportionately bore a higher burden of IHD among young adults. The inequality slope index for young adult IHD shifted from −56.6 [95% CI (−480.4–370.2)] in 1990 to −583.0 [95% CI (−996.8 to −169.2)] in 2019, and the concentration index moved from −8.2 [95% CI (−8.5 to −7.9)] in 1990 to −13.2 [95% CI (−13.9 to −12.4)] in 2019.ConclusionsWhile the mortality and DALYs rate of IHD among global young adults have decreased over the past 30 years, the degree of inequality related to SDI among countries has continued to increase. Decision-makers in various countries should allocate resources wisely and implement effective strategies to improve the burden of young adults IHD globally and address the health inequalities associated with it.

Background: As observed in recent genetic studies, PITX2 is one of the most popular genes with atrial fibrillation; single nucleotide polymorphism (rs2200733) at chromosome 4q25 (near PITX2) is found to be strongly associated with atrial fibrillation, but it has a difference among Chinese Han population. The basic aim of conducting this study is to find the correlation between PITX2 gene polymorphism and the risk of atrial fibrillation and to identify the possibility for early diagnosis of silent atrial fibrillation and high-risk atrial fibrillation. Methods: The study included 98 cases of atrial fibrillation patients and 88 non-atrial fibrillation patients in Affiliated Hospital of Yangzhou University were enrolled in a case-control study. The single nucleotide polymorphism of rs2200733 at 4q25 near PITX2 was genotyped by polymerase chain reaction-restriction fragment length polymorphism analysis. Results: A total of 98 patients with atrial fibrillation were genotyped, and the following frequencies were included in genotype percentages (44.9%, 50%, and 5.1%) while distribution of significant single nucleotide polymorphism rs2200733 consisted (29.55%, 53.41%, and 17.05%) which showed (χ2 = 9.159, P =.01). There was no significant difference in TC genotype frequency (P =.642), frequency of T allele (χ2 = 7.447, P =.006), and T allele was 1.806 times that of the control group (odds ratio = 1.806, 95% CI = 1.179-2.766, P =.006). According to logistic regression analysis, following results were concluded for TC genotype (odds ratio = 3.128, 95% CI = 1.053-9.287, P =.04), or TT genotype (odds ratio = 5.077, 95% CI = 1.653-15.595, P =.005) increased the risk of atrial fibrillation. Conclusions: The genotype and allele frequency distribution of rs2200733 (T/C) near PITX2 is different in the atrial fibrillation group and the control group. The T allele is a risk factor for atrial fibrillation. Compared with the CC genotype, the TT genotype increased the risk of atrial fibrillation.

IntroductionObstructive sleep apnea syndrome (OSAS) is a chronic disorder characterized by recurring episode obstruction and collapse of upper airways during sleep, leading to hypoxia and sleep disruption. OSAS is commonly associated with an increased prevalence of hypertension. The underlying mechanism in OSA with hypertension is related to intermittent hypoxia. This hypoxia induces endothelial dysfunction, overactivity of sympathetic effects, oxidative stress, and systemic inflammation. Hypoxemia triggers the sympathetic process's overactivity, leading to the development of resistant hypertension in OSA. Thus, we hypothesize to evaluate the association between resistant hypertension and OSA.MethodsThe PubMed, ClinicalTrails.gov, CINAHL, Google Scholar, Cochrane Library, and Science Direct databases were searched from 2000 to January 2022 for studies demonstrating the association between resistant hypertension and OSA. The eligible articles underwent quality appraisal, meta-analysis, and heterogeneity assessment.ResultsThis study comprises seven studies, including 2,541 patients ranged from 20 to 70 years. The pooled analysis of six studies demonstrated that OSAS patients with a history of increased age, gender, obesity, and smoking status are at an increased risk for resistant hypertension (OR: 4.16 [3.07, 5.64], I2:0%) than the non-OSAS patients. Similarly, the pooled effect demonstrated that patients with OSAS were at an increased risk of resistant hypertension (OR: 3.34 [2.44, 4.58]; I2:0%) than the non-OSAS patients when all associated risk factors were adjusted using multivariate analysis.ConclusionThis study concludes that OSAS patients with or without related risk factors demonstrated increased risk for resistant hypertension.

As the most commonly used attack strategy by Botnets, the Domain Generation Algorithm (DGA) has strong invisibility and variability. Using deep learning models to detect different families of DGA domain names can improve the network defense ability against hackers. However, this task faces an extremely imbalanced sample size among different DGA categories, which leads to low classification accuracy for small sample categories and even classification failure for some categories. To address this issue, we introduce the long-tailed concept and augment the data of small sample categories by transferring pre-trained knowledge. Firstly, we propose the Data Balanced Review Method (DBRM) to reduce the sample size difference between the categories, thus a relatively balanced dataset for transfer learning is generated. Secondly, we propose the Knowledge Transfer Model (KTM) to enhance the knowledge of the small sample categories. KTM uses a multi-stage transfer to transfer weights from the big sample categories to the small sample categories. Furthermore, we propose the Knowledge Distillation Transfer Model (KDTM) to relieve the catastrophic forgetting problem caused by transfer learning, which adds knowledge distillation loss based on the KTM. The experimental results show that KDTM can significantly improve the classification performance of all categories, especially the small sample categories. It can achieve a state-of-the-art macro average F1 score of 84.5%. The robustness of the KDTM model is verified using three DGA datasets that follow the Pareto distributions. [ABSTRACT FROM AUTHOR]

Objective. Due to non-invasive imaging and the multimodality of magnetic resonance imaging (MRI) images, MRI-based multi-modal brain tumor segmentation (MBTS) studies have attracted more and more attention in recent years. With the great success of convolutional neural networks in various computer vision tasks, lots of MBTS models have been proposed to address the technical challenges of MBTS. However, the problem of limited data collection usually exists in MBTS tasks, making existing studies typically have difficulty in fully exploring the multi-modal MRI images to mine complementary information among different modalities. Approach. We propose a novel quaternion mutual learning strategy (QMLS), which consists of a voxel-wise lesion knowledge mutual learning mechanism (VLKML mechanism) and a quaternion multi-modal feature learning module (QMFL module). Specifically, the VLKML mechanism allows the networks to converge to a robust minimum so that aggressive data augmentation techniques can be applied to expand the limited data fully. In particular, the quaternion-valued QMFL module treats different modalities as components of quaternions to sufficiently learn complementary information among different modalities on the hypercomplex domain while significantly reducing the number of parameters by about 75%. Main results. Extensive experiments on the dataset BraTS 2020 and BraTS 2019 indicate that QMLS achieves superior results to current popular methods with less computational cost. Significance. We propose a novel algorithm for brain tumor segmentation task that achieves better performance with fewer parameters, which helps the clinical application of automatic brain tumor segmentation. [ABSTRACT FROM AUTHOR]

With the growth of data in the real world, datasets often encounter the problem of long-tailed distribution of class sample sizes. In long-tailed image recognition, existing solutions usually adopt a class rebalancing strategy, such as reweighting based on the effective sample size of each class, which leans towards common classes in terms of higher accuracy. However, increasing the accuracy of rare classes while maintaining the accuracy of common classes is the key to solving the problem of long-tailed image recognition. This research explores a direction that balances the accuracy of both common and rare classes simultaneously. Firstly, a two-stage training is adopted, motivated by the use of transfer learning to balance features of common and rare classes. Secondly, a balanced weight function called Balanced Focal Softmax (BFS) loss is proposed, which combines balanced softmax loss focusing on common classes with balanced focal loss focusing on rare classes to achieve dual balance in long-tailed image recognition. Subsequently, a Balanced Weight Learning Mechanism (BWLM) to further utilize the feature of weight decay is proposed, where the weight decay as the weight balancing technique for the BFS loss tends to make the model learn smaller balanced weights by punishing the larger weights. Through extensive experiments on five long-tailed image datasets, it proves that transferring the weights from the first stage to the second stage can alleviate the bias of the naive models toward common classes. The proposed BWLM not only balances the weights of common and rare classes, but also greatly improves the accuracy of long-tailed image recognition and outperforms many state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]