Gene Transfer of Calcitonin Gene-Related Peptide Inhibits Macrophages and Inflammatory Mediators in Vein Graft Disease

Vein graft disease is a chronic inflammatory disease and limits the late results of coronary revascularization. Calcitonin gene-related peptide (CGRP) inhibits macrophages infiltrated and inflammatory mediators, we hypothesized that transfected CGRP gene inhibits macrophages infiltrated and inflammatory mediators in vein graft disease. Autologous rabbit jugular vein grafts were incubated ex vivo in a solution of mosaic adeno-associated virus vectors containing CGRP gene (AAV2/1.CGRP) 、escherichia coli lac Z gene (AAV2/1.LacZ) or saline and then interposed in the carotid artery. Intima/media ratio were evaluated at postoperative 4 weeks, Macrophages were marked with CD68 antibody by immunocytochemistry. Inflammatory mediators were mensurated with real-time PCR. Neointimal thickening was significantly suppressed in AAV2/1.CGRP group. Macrophages infiltrated and inflammatory mediators monocyte chemoattractant protein-1 (MCP-1)、tumor necrosis factorα(TNF-α)、inducible nitricoxide synthase (iNOS)、matrix metalloproteinase-9 (MMP-9) was significantly suppressed in AAV2/1.CGRP group.Gene transfected AAV2/1.CGRP suppressed neointimal hyperplasia in vein graft disease by suppressed macrophages infiltrated and inflammatory mediators

A Joint Doppler Frequency Shift and DOA Estimation Algorithm Based on Sparse Representations for Colocated TDM-MIMO Radar

We address the problem of a new joint Doppler frequency shift (DFS) and direction of arrival (DOA) estimation for colocated TDM-MIMO radar that is a novel technology applied to autocruise and safety driving system in recent years. The signal model of colocated TDM-MIMO radar with few transmitter or receiver channels is depicted and “time varying steering vector” model is proved. Inspired by sparse representations theory, we present a new processing scheme for joint DFS and DOA estimation based on the new input signal model of colocated TDM-MIMO radar. An ultracomplete redundancy dictionary for angle-frequency space is founded in order to complete sparse representations of the input signal. The SVD-SR algorithm which stands for joint estimation based on sparse representations using SVD decomposition with OMP algorithm and the improved M-FOCUSS algorithm which combines the classical M-FOCUSS with joint sparse recovery spectrum are applied to the new signal model’s calculation to solve the multiple measurement vectors (MMV) problem. The improved M-FOCUSS algorithm can work more robust than SVD-SR and JS-SR algorithms in the aspects of coherent signals resolution and estimation accuracy. Finally, simulation experiments have shown that the proposed algorithms and schemes are feasible and can be further applied to practical application

Shear behaviour and design of diagonally stiffened stainless steel plate girders

The shear behaviour of stainless steel plate girders investigated in this study is related to the introduction of diagonal stiffeners. Four plate girders with or without diagonal stiffeners were fabricated by welding hot-rolled stainless steel plates of different thicknesses. All the plate girders were tested to failure subject to shear loading. The critical shear buckling stress, ultimate resistance and post-peak response were recorded and carefully analysed. Elaborate finite element (FE) models were developed by means of the FE software package ABAQUS and were validated against the test results. A comprehensive parametric analysis of key parameters including the web aspect ratio and slenderness, the flexural stiffness of diagonal stiffeners and the material properties was further conducted, reflecting the influence of these parameters on structural responses. The critical shear buckling stresses obtained from eigenvalue buckling analysis were compared with theoretical predictions of the elastic buckling stress, and a new calculation approach for the shear buckling coefficient of diagonally stiffened web panel that can account for the flexural stiffness of diagonal stiffeners and the effective restraint from flanges was proposed. Based on the obtained ultimate shear resistances, the existing design methods were assessed; and a new design proposal within the framework of EN 1993-1-4+A1 for predicting the shear resistance of diagonally stiffened stainless steel plate girders was developed. In this novel method, the shear contributions from diagonal stiffeners and the shear buckling coefficients for diagonally stiffened web panels were revised. The reliability of the new proposal was also confirmed by further statistical analysis

Typhoon Intensity Prediction with Vision Transformer

Predicting typhoon intensity accurately across space and time is crucial for issuing timely disaster warnings and facilitating emergency response. This has vast potential for minimizing life losses and property damages as well as reducing economic and environmental impacts. Leveraging satellite imagery for scenario analysis is effective but also introduces additional challenges due to the complex relations among clouds and the highly dynamic context. Existing deep learning methods in this domain rely on convolutional neural networks (CNNs), which suffer from limited per-layer receptive fields. This limitation hinders their ability to capture long-range dependencies and global contextual knowledge during inference. In response, we introduce a novel approach, namely "Typhoon Intensity Transformer" (Tint), which leverages self-attention mechanisms with global receptive fields per layer. Tint adopts a sequence-to-sequence feature representation learning perspective. It begins by cutting a given satellite image into a sequence of patches and recursively employs self-attention operations to extract both local and global contextual relations between all patch pairs simultaneously, thereby enhancing per-patch feature representation learning. Extensive experiments on a publicly available typhoon benchmark validate the efficacy of Tint in comparison with both state-of-the-art deep learning and conventional meteorological methods. Our code is available at https://github.com/chen-huanxin/Tint.Comment: 8 pages, 2 figures, accepted by Tackling Climate Change with Machine Learning: workshop at NeurIPS 202

Fault diagnosis of refrigerant charge based on PCA and decision tree for variable refrigerant flow systems

Variable refrigerant flow (VRF) systems are easily subjected to performance degradation due to refrigerant leakage, mechanical failure or improper maintenance after years of operation. Ideal VRF systems should equip with fault detection and diagnosis (FDD) program to sustain its normal operation. This paper presents the fault diagnosis method for refrigerant charge faults of variable refrigerant flow (VRF) systems. It is developed based on the principal component analysis (PCA) feature extraction method and the decision tree (DT) classification algorithm. Nine refrigerant charge schemes are implemented on the VRF system in the laboratory, which contain the normal and faulty refrigerant charge conditions. In addition, data of the online operating VRF systems are collected in this work. Firstly, data from both experimental VRF system and online operating systems are pre-processed by outlier cleaning, feature extraction and data normalization, because the original data of the VRF system usually has poor quality and complex structure. Secondly, the fault diagnosis model based on the PCA-DT method is built using the data of the experimental VRF system. In this step, the PCA method is used to obtain a new data sample which includes four comprehensive features, then the new data sample are randomly split into training and testing sets as the input of DT classifier for fault diagnosis. Thirdly, the advantages of the PCA-DT method is validated using the experimental data of different fault severity levels. Results show that the combined use of PCA and DT methods can achieve better fault diagnosis efficiency than the single decision tree method. Further, the robustness of the PCA-DT method in online fault diagnosis is verified using the data from online VRF systems. The online VRF systems have the same or different number of indoor units as the trained (experimental) VRF system. The PCA-DT method also shows desirable goodness on the online fault diagnosis process. In this sense, this work provides a promising fault diagnosis strategy for refrigerant charge faults of VRF system application

An Advanced Multicarrier Residential Energy Hub System Based on Mixed Integer Linear Programming

This work proposes a multicarrier energy hub system with the objective of minimizing the economy cost and the CO2 emissions of a residential building without sacrificing the household comfort and increasing the exploitation of renewable energy in daily life. The energy hub combines the electrical grid and natural gas network, a gas boiler, a heat pump, a photovoltaic plant, and a photovoltaic/thermal (PV/T) system. In addition, to increase the overall performance of the system, a battery-based energy storage system is integrated. To evaluate the optimal capacity of each energy hub component, an optimization scheduling process and the optimization problem have been solved with the YALMIP platform in the MATLAB environment. The result showed that this advanced system not only can decrease the economic cost and CO2 emissions but also reduce the impact to electrical grid

The co-benefits of clean air and low-carbon policies on heavy metal emission reductions from coal-fired power plants in china

China has implemented a series of measures to address air pollutants and carbon emissions from coal-fired power plants, which can mitigate toxic heavy metal emissions simultaneously. By integrating plant-level information and energy activity data, we investigated the co-benefits of clean air and low-carbon policies by compiling a detailed inventory of historical heavy mental emissions (i.e., Hg, Pb, Cd, Cr, Ni, Sb, Mn, Co, Cu, Zn, As, and Se) for China's coal-fired power plants during 2005–2020. Several scenarios were then designed to assess the evolution of heavy metal emissions for each coal-fired power plant with consideration given to the coal washing rate, air pollution control devices, operational hours and lifetime. The total emissions decreased from 12.9 thousand tons in 2005 to 8.8 thousand tons in 2020, which was mainly due to the widely installation of upgraded end-of-pipe devices and the decommissioning of small and emission-intensive plants, especially in Sichuan, Jiangsu and Zhejiang. Scenario analysis shows that reducing the operational lifetime to 20 years is the most effective measure to reduce national HM emissions, but the effects differ widely between regions. This study provides insights for the precise co-control of both heavy metals and carbon emissions, which is highly important for meeting the requirements of the Minamata Convention and carbon neutrality