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In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

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In recent years, the development of abandoned mining land has become a focal point in landscape planning. However, during the development of abandoned mining land, there often exists a phenomenon of prioritizing economic considerations over ecological concerns, leading to a failure to achieve genuinely sustainable development. An ecological sensitivity assessment, guided by the principles of protection and development, provides an evaluation framework that directs planning strategies for abandoned mining land from the perspective of balanced development and conservation. To facilitate the development and construction of abandoned mining land, this paper utilizes GIS technology, on-site surveys, analytic hierarchy processes, etc. Taking the abandoned mining land in Haining, Zhejiang, China, as the research subject and considering its unique site conditions, ecological sensitivity is divided into topographic conditions, surface water systems, and plant landscapes. Ecological sensitivity factors are selected, and an ecological sensitivity assessment system is constructed from the perspectives of ecological conservation and sustainable development. Using ArcGIS 10.2 and Fragstats 4.2 software, landscape pattern analysis is conducted, exploring the relationship between landscape patterns and ecological sensitivity assessment results from the perspectives of landscape fragmentation, diversity, and aggregation. By comparing the results of single-factor sensitivity analysis and comprehensive sensitivity analysis, as well as landscape pattern indices before and after classifying ecologically sensitive areas, the practicality of the evaluation system is verified, facilitating planning studies and providing design recommendations for abandoned mining land. Landscape pattern indices serve as supplementary explanations for ecological sensitivity. Based on the results of ecological sensitivity assessment and landscape pattern indices, the ecological conservation levels in the research area are classified into five categories: the Level I Comprehensive Protection Zone, Level II Moderately Developed Zone, Level III Construction Suitable Zone, Level IV Core Construction Zone, and Level V Core Development Zone. These correspond to ecological protection zones, sightseeing experience zones, historical exhibition zones, core commercial zones, and themed amusement zones, respectively. The I-level sensitive area in the research area has the smallest range, while IV-level and VI-level sensitive areas have larger extents, exhibiting a high degree of overall landscape fragmentation but with diverse and dominant landscape types. Integrating ecological sensitivity assessment results and landscape pattern indices aids in delineating ecological conservation levels and regional functional recreation zones, guiding the rational recreation allocation of resources for abandoned mining land and promoting its development into a scenic area integrating ecology and tourism. [ABSTRACT FROM AUTHOR]

Digital literacy has become indispensable for individuals in the contemporary techno-centric world and imperative for governments to promote equitable growth. Using the fixed effects models to analyze a panel sample of 25 African countries from 2017 to 2021, the authors describe the overall situation of digital literacy in Africa and examine its relationship with ICT infrastructure, policy, and social inequality. The results show that the African countries exhibit a substantial and stable disparity in digital literacy. Availability of ICT infrastructure increases digital literacy, while social inequality decreases it. They also find that digital literacy and digital inclusion policies can overcome the barriers of availability and affordability of ICT infrastructure. In addition, digital literacy and digital inclusion policies have the potential to be mutually supportive, reinforcing each other's role in promoting social equality. [ABSTRACT FROM AUTHOR]

A novel composite carrier of folic acid (FA)- polyethyleneimine (PEI) -ethosome (Eth) (FA-PEI-Eth) was developed for the treatment of cancers through loading and taiting delivery of multidimg (including gene and other drugs) into cancer cells. Physical and chemical property tests were done to prove the grafting of the composite. Gel retardation test was done to determine the optimal ratio of DNA@ PEI complex,and cytocompatibility tests and tumor cell uptake tests were done to evaluate the efficiency of the composite. The results demonstrated that the FA-PEI-Eth could effectively deliver a gene and other drugs into tumor cells simultaneously, and suggested that this composite would be a promising carrier in tumor- targeted therapy applications. [ABSTRACT FROM AUTHOR]

• An adaptive filtering method more suitable for practical systems, which can improve the generalization ability of the system and achieve good control results; • Improvements to the MR-based BCI system by embedding a live view in the center of visual stimulation paradigm to avoid the output of erroneous commands when the object is out of the line of sight in remote control; • Development of a portable brain-computer interface system using a small size EEG acquisition device and wearable MR glasses. Brain-computer interface (BCI) systems based on mixed reality (MR) have promising applications in assisting people with disabilities to control manipulators. Using MR glasses instead of a computer screen to display visual stimulator can effectively avoid frequent switching of attention between the visual stimulator and the manipulator. When the manipulator moves out of the sight of the subject, the subject may not be able to control it accurately. Our system uses Microsoft Hololens2 as the display device to synchronize the command matrix with a live view of the mobile manipulator's position, thus tracking the position in real-time. Another problem in previous studies is that they have good accuracy in trained subjects, however, the accuracy drops dramatically when faced with untrained subjects, suggesting poor generalization capabilities. In our study, an adaptive filtering method combined with convolutional neural networks (CNN) is proposed, which has few learning parameters and fast convergence, and can improve the generalization ability of the system in the face of untrained subjects. When faced with untrained subjects, the average accuracy of our method was 93.04%, and the average ITR was 20.96 bits/min. All subjects can successfully complete the grasping task without colliding with obstacles. The results show that the BCI system developed in this study has strong practicability and high research significance. [ABSTRACT FROM AUTHOR]

Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.