Effect of APS on Hormones Regulating Blood Glucose in Active Rats

The paper aims to discuss the influence of Astragalus Polysacharin (APS) on hormones regulating blood glucose in active rats. The experiment was conducted to detect the plasma insulin and glucagon concentrations in swimming rats in different states. The result of the experiment showed that the ASP-injected rat group had higher plasma insulin and glucagon concentration, compared with that of the pure-water-drinking rat group (control group). After one-hour swimming, the APS-injected rat group had higher glucagon concentration than that of the control group (P<0.05); when just fatigued, the APS-injected group showed evidently higher plasma insulin concentration, compared with the control group (P<0.01). The conclusion is drawn that APS can enhance the release of plasma insulin and glucagon in active rats; promote their compatibility effect in the process of glycogen synthesis and storage, hence increasing glycogen reserves. It can delay fatigue caused by hypoglycemia, and accelerate physical recovery from exercise-induced fatigue

Integrated Appraisal Management System for PE Undergraduates of General Universities

Integrated appraisal management system concerning undergraduates involves, based on systematic principles, via the computer technology, making statistical analysis of the database information about students according to the set standard, and then obtaining overall appraisal scores of each individual, displaying them with charts and report forms as well. The overall appraisal scores serve as the grounds of assessing undergraduates’ qualities. The advantages of the system are obvious: enormously saving time, labor and financial resources traditionally used for undergraduates’ appraisal and management, and improving working efficiency accordingly. Key words: higher education management; integrated appraisal; information syste

Diffusion–reaction–induced stress in moving boundary cylindrical Li-ion battery electrodes

Lithium (Li) inserted into or extracted from the electrode in Li-ion battery causes stress which may cause fracture of the electrode. A moving boundary model in a cylindrical Li-ion battery electrode accounting for reversible electrochemical reaction is obtained. The volumetric change created by Li diffusion and formation of reversible reaction product would generate the diffusion–reaction-induced stress in the electrode. The constitutive relation among Li concentration, reaction product, and stress is derived, and the numerical solutions of the concentration, reaction product, and stress fields are obtained. The effects of phase transformation and reversible electrochemical reaction on Li diffusion and stress in a cylindrical Li-ion battery electrode are analyzed

Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion

Infrared and visible image fusion plays a vital role in the field of computer vision. Previous approaches make efforts to design various fusion rules in the loss functions. However, these experimental designed fusion rules make the methods more and more complex. Besides, most of them only focus on boosting the visual effects, thus showing unsatisfactory performance for the follow-up high-level vision tasks. To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules. In addition, to achieve a better semantic understanding of the feature fusion process, a fusion block based on the transformer is presented in a multi-scale manner. Moreover, we devise a regularization loss function, together with a training strategy, to fully use semantic guidance from the high-level vision tasks. Compared with state-of-the-art methods, our method does not depend on the hand-crafted fusion loss function. Still, it achieves superior performance on visual quality along with the follow-up high-level vision tasks

Integrated Platform for Whole Building HVAC System Automation and Simulation

Integrated optimal control strategies can reduce the overall building HVAC system energy consumption as well as improved air quality resulting in improved health and cognitive function for the occupants. However, it is time consuming to quantitatively evaluate the design-intended building HVAC automation system performance before on-site deployment, because: 1) the building and HVAC system design specs are in 2D or 3D drawings that require significant efforts to develop the system steady state or dynamic models based on them; 2) the building HVAC control strategies are designed and implemented in building automation (BA) system that could not smoothly connect with the building HVAC system steady state or dynamic models for performance evaluation through close-loop simulation. This paper presents the tool chain of an integrated simulation platform for building HVAC system automation and simulation as well as its implementation in a real case. First, building information from a Revit BIM model is automatically parsed to an EnergyPlus building energy model. Second, the HVAC system model is quickly populated with a scalable HVAC system library in Dymola. Third, the HVAC controls are developed in WebCTRL, a building HVAC automation system by Automated Logic Corporation (ALC). Finally, both the building energy model and HVAC system model are wrapped up as Functional Mock-up Units (FMU) and connected with embedded simulator in WebCTRL to perform close-loop building automation system performance simulation. A real case study, a chiller plant system in a hotel building, is conducted to verify the scalability and benefit of the developed tool chain. The case study demonstrates the values in identifying both HVAC automation system design-intended control issues and improvement areas for integrated optimal controls. This platform enables testing of building HVAC control strategies before on-site deployment, which reduces the labor and time required for building HVAC control development-to-market process and ensure the delivering quality. Furthermore, this platform can be calibrated with metered real-time data from the specific building HVAC system and serve as its “digital twin” that empowers the system fault detection, diagnostics and predictive maintenance

Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI

Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior knowledge is incorporated into the separators for disentangling each modality of information by the graph convolutional networks (GCN). And a disentangled cosine distance loss is devised to ensure the disentanglement's effectiveness. Moreover, the hierarchical representation fusion module is designed to effectively maximize the combination of relevant and effective features between modalities, which makes the generated structural-functional connectivity more robust and discriminative in the cognitive disease analysis. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed model performs better than competing approaches in terms of classification evaluation. In general, the proposed HSCF model is a promising model for generating brain structural-functional connectivities and identifying abnormal brain connections as cognitive disease progresses.Comment: 4 figure

The green GDP accounting system based on the BP neural network: an environmental pollution perspective

Introduction: The green GDP accounting system has become the focus of sustainable development, but a comprehensive accounting of environmental pollution cost and resource depletion cost has not yet been formed.Methods: This study measures environmental pollution cost and resource loss cost, and establishes the green GDP accounting system based on the SEEA-2012. To analyze the environmental effects brought by the adoption of green GDP accounting system, a BP neural network model including green GDP, traditional GDP and global climate indicators is constructed to predict the global climate changes.Results: The empirical results show that after the adoption of the green GDP accounting system, the global climate extreme weather can be reduced, the sea level will be lowered, and the climate problem is thus alleviated

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance's contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario.Comment: Accepted by COLING 2022. The 29th International Conference on Computational Linguistics, Gyeongju, Republic of Kore

Does SYNTAX score II predict poor myocardial perfusion in ST-segmen

Background: SYNTAX score II (SS-II) has been demonstrated to predict long-term outcomes in unprotected left main or multiple vessels in patients with coronary artery disease. However, its prognostic value for patients with ST-segment elevation myocardial infarction (STEMI) remains unknown. The poor myocardial perfusion (myocardial blush grade [MBG] 0/1) after primary percutaneous coronary intervention (pPCI) has a negative prognostic value in patients with STEMI. We aimed to assess SS-II and its possible relationships with MBG 0/1 in patients with STEMI treated with pPCI. Methods: The study included 477 patients with STEMI who underwent pPCI between October 2010 and May 2014. SYNTAX Score II and MBG were determined in all patients. Myocardial blush grade were divided into MBG 0/1 (poor myocardial perfusion) and MBG 2/3 (normal myocardial perfusion). Patients were divided into three tertiles: SS-IIlow (￡ 20), SS-IIintermediate (20–26) and SS-IIhigh (≥ 26). Results: Compared with the SS-IIintermediate and SS-IIlow tertiles, the SS-IIhigh tertile had more MBG 0/1 (46.1%, 32.1% and 21.8%, p &lt; 0.001, respectively). On multivariate logistic regression analysis, SS-II was an independent predictor of MBG 0/1 (hazard ratio 1.084, 95% confidence interval 1.050–1.119, p &lt; 0.001). Receiver operating characteristic analysis identified SS-II &gt; 24 as the best cut-off value predicting MBG 0/1 (sensitivity of 66%, specificity of 54%). Conclusions: High SS-II is an independent predictor of MBG 0/1 in patients with STEMI undergoing pPCI.