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In the Internet era, soccer teaching has gotten rid of the previous theoretical teaching mode and paid more attention to the use of video and multimedia for network teaching. In this paper, from the perspective of a fuzzy mathematical algorithm, the mathematical set algorithm is used to construct a fuzzy matrix through algorithm mapping, calculate the NMI value of the equivalent algorithm and the comprehensive rating weights of U1-U10 in the evaluation elements of soccer network teaching, and derive the fuzzy transformation value of the rating result of soccer network teaching platform as 83.7895. The probability distribution value is inferred to be 45.8% on average by creating the affiliation function and then calculating the search accuracy, platform recall, and teaching F1 metrics of the Zadeh mathematical operator. After calculating the weighted average value of B=87.6617 for the Zadeh mathematical operator through the metrics, an empirical analysis of the feasibility of invoking the fuzzy mathematical algorithm in the soccer teaching web platform was conducted. The results showed that the total number of students who wanted to continue using the platform was 36,530, accounting for 91.33% of the total number of students, indicating that the use of fuzzy mathematical algorithms to participate in the teaching of the online platform is significantly better than the traditional teaching model and is conducive to improving the effectiveness of student autonomy.

Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall = 100%, precision = 100%, F1 score = 100%; FC: recall = 100%, precision = 100%, F1 score = 100%), slalom walking (IC: recall = 100%, precision ≥ 99%, F1 score = 100%; FC: recall = 100%, precision ≥ 99%, F1 score = 100%), and turning (IC: recall ≥ 85%, precision ≥ 95%, F1 score ≥ 91%; FC: recall ≥ 84%, precision ≥ 95%, F1 score ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information. This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR? This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR. Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body's immune system and lead to the development of allergies. Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.

Leaf and fruit infections are the primary cause of the maximum harm to the crop, which decreases the quality and amount of the goods. To improve the productivity of plants, the timely identification of the infection is vital, which is a highly challenging task. Deep learning (DL) with image processing allows farmers to distinguish between healthy and infected crops. This work intends to identify healthy and diseased citrus leaf images using a convolutional neural network (CNN) on the Platform as a Service (PaaS) cloud. The dataset of five types of healthy and unhealthy citrus images was used, namely, black spot, melanose, canker, greening, and healthy. Furthermore, the four-transfer learning (TL) pre-trained deep CNN (DCNN) models, namely, ResNet152V2, InceptionResNetV2, DenseNet121, and DenseNet201, were used to classify the leaf type. The performance of the CNN and four DCNNs were assessed using the confusion matrix (accuracy, precision, recall, and F1-score) and receiver operating characteristic-area under the curve (ROC-AUC) curve. An augmentation technique was utilised to enhance the dataset images, which helped to improve the model’s performance and achieved an accuracy of 98% precision and recall and an F1 score of 99% and an ROC-AUC score of 0.99. Moreover, the suggested CNN has only 15 layers, 427317 parameters, and 1.68MB size, while DCNN models have more layers, parameters, and large size. The small-size CNN was deployed to the Platform as a Service (PaaS) cloud. The deployed model link is available on a smartphone to upload a citrus leaf image to the cloud, and the result is instantly available on a mobile screen.

Salt stress is one of the major environmental stress factors that affect and limit wheat production worldwide. Therefore, properly evaluating wheat genotypes during the germination stage could be one of the effective ways to improve yield. Currently, phenotypic identification platforms are widely used in the seed breeding process, which can improve the speed of detection compared with traditional methods. We developed the Wheat Seed Vigour Assessment System (WSVAS), which enables rapid and accurate detection of wheat seed germination using the lightweight convolutional neural network YOLOv4. The WSVAS system can automatically acquire, process and analyse image data of wheat varieties to evaluate the response of wheat seeds to salt stress under controlled environments. The WSVAS image acquisition system was set up to continuously acquire images of seeds of four wheat varieties under three types of salt stress. In this paper, we verified the accuracy of WSVAS by comparing manual scoring. The cumulative germination curves of wheat seeds of four genotypes under three salt stresses were also investigated. In this study, we compared three models, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLOv4. We found that YOLOv4 was the best model for wheat seed germination target detection, and the results showed that the model achieved an average detection accuracy (mAP) of 97.59%, a recall rate (Recall) of 97.35% and the detection speed was up to 6.82 FPS. This proved that the model could effectively detect the number of germinating seeds in wheat. In addition, the germination rate and germination index of the two indicators were highly correlated with germination vigour, indicating significant differences in salt tolerance amongst wheat varieties. WSVAS can quantify plant stress caused by salt stress and provides a powerful tool for salt-tolerant wheat breeding.

Background Resident immune cells are central in shaping the lung’s tissue-specific immunity. Precision-cut lung slices (PCLS) preserve the native tissue microenvironment and are therefore an excellent ex vivo model to analyze residency and functionality of resident memory T cells. Methods To study the modulation of tissue residency markers and T cell activation in the native lung niche, we treated PCLS with broad and T cell-specific stimuli and analyzed responses using flow cytometry and mediator secretion analysis. Using TGFβ, anti-CD3/CD28, IL-2 and a pool of MHC-I restricted peptides we analyzed cytokine secretion, CD4 + /CD8 + T cell ratios, and the expression of activation and residency markers. Results First, we characterized lung immune cell in PCLS which also revealed that resident memory T cells are abundant in PCLS. We showed that regulation of the tissue residency marker CD103 is dependent on TGFβ or IL-2 signaling in combination with T cell receptor engagement. Further, polyclonal activation of T cells in the tissue reduced tissue secretion of anti-inflammatory cytokines like TGFβ, while increasing the secretion of T cell-associated cytokines like IFNγ, IL-2, and Granzyme B. This shift was supported by an upregulation of T cell activation markers such as CD39, CD137, and Ki-67. Finally, treatment of PCLS with a pool of MHC-I-restricted peptides led to increased secretion of multiple inflammatory effector cytokines associated and a specific activation of tissue resident T cells. Conclusion Taken together, we have demonstrated that PCLS provide an excellent platform to modulate tissue resident T cell responses influenced by human lung tissue microenvironment.

Nowadays, various social media platforms, as well as wearable sensor devices, play a significant role in collecting data from patients for effective healthcare monitoring. However, continuous monitoring of patients using wearable sensor devices generates a huge amount of data and can be a complicated task to analyze efficiently. Therefore, this paper proposes a novel framework called Biased ReLU Neural Network-based Cat Hunting Optimization (BRNN-CHO) to classify the health condition of the patient. The proposed system consists of five phases: the data source phase, data collection phase, data pre-analysis phase, data pre-processing phase, and data classification phase. In the data source layer, various heterogeneous data including various medical records, social media platforms, and wearable sensor devices are addressed. The second data collection phase collects data regarding patients with blood pressure and diabetes. Then, in the data storage phase, the data collected from various medical records, social media platforms, and wearable sensor devices are uploaded to the big data cloud center. After uploading, the data is pre-analyzed and pre-processed to extract unwanted data. Finally, the pre-processed data is classified using BRNN-CHO to determine the health condition of the patient related to mental health, diabetes, and blood sugar, as well as blood pressure, with an enhanced accuracy rate. Experimental analysis is carried out and compared with various state-of-the-art techniques to determine the efficiency of the proposed system. When compared in terms of accuracy, F-measure, precision, and recall, the proposed BRNN-CHO model offers higher performance. The accuracy, recall, precision, and F1-measure of the proposed model are nearly equal to 95%, 90%, 92%, and 93%. The Root mean square error (RMSE), Mean absolute error (MAE), execution time, and latency of the proposed model are 30, 12, 1.38 s, and 1.8 s, respectively.

Effective nutrition policies require timely, accurate individual dietary consumption data; collection of such information has been hampered by cost and complexity of dietary surveys and lag in producing results. The objective of this work was to assess accuracy and cost-effectiveness of a streamlined, tablet-based dietary data collection platform for 24-hour individual dietary recalls (24HR) administered using INDDEX24 platform v. a pen-and-paper interview(PAPI) questionnaire, with weighed food record (WFR) as a benchmark. This cross-sectional comparative study included women 18–49 years old from rural Burkina Faso (n 116 INDDEX24; n 115 PAPI). A WFR was conducted; the following day, a 24HR was administered by different interviewers. Food consumption data were converted into nutrient intakes. Validity of 24HR estimates of nutrient and food group consumption was based on comparison with WFR using equivalence tests (group level) and percentages of participants within ranges of percentage error (individual level). Both modalities performed comparably estimating consumption of macro- and micronutrients, food groups and quantities (modalities’ divergence from WFR not significantly different). Accuracy of both modalities was acceptable (equivalence to WFR significant at P < 0·05) at group level for macronutrients, less so for micronutrients and individual-level consumption (percentage within ±20 % for WFR, 17–45 % for macronutrients, 5–17 % for micronutrients). INDDEX24 was more cost-effective than PAPI based on superior accuracy of a composite nutrient intake measure (but not gram amount or item count) due to lower time and personnel costs. INDDEX24 for 24HR dietary surveys linked to dietary reference data shows comparable accuracy to PAPI at lower cost.

Currently, there is a need to understand the characteristics of manual wheelchair propulsion patterns in the daily life of users and the impact of these patterns on repetitive strain injury of the shoulders. This study aimed to develop a method for remote and long-term monitoring of wheelchair propulsion techniques. We used a hand-mounted inertial measurement unit (IMU) to identify propulsion patterns in manual wheelchair users. IMU data was collected from 12 participants (7 males and 5 females), including 8 experienced and 4 inexperienced manual wheelchair users. We applied continuous wavelet transform (CWT) for feature extraction and used Support Vector Machine (SVM) and Multilayer Perceptron (MLP) Neural Network for pattern classification. SVM with a linear kernel achieved 89% accuracy, 78% F1-score, 78% precision, and 78% recall. SVM with a polynomial kernel achieved 94% accuracy, 88% F1-score, 88% precision, and 89% recall, while the MLP reached 95% accuracy, 89% F1-score, 89% precision, and 89% recall. Neither the participants’ wheelchair experience nor their gender significantly affected the performance of the classifiers. These findings suggest that the proposed IMU and propulsion patterns classification method can be used across different user profiles for remote and long-term monitoring of wheelchair propulsion patterns to better understand shoulder overuse risk in daily life.

Background This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms. Method This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels—medical institutional level and public level—and three types, namely data, technology, and services. The data are analyzed using SmartPLS2. Results (1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways. Conclusions The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.