Review of Performance Pay at County Level Public Hospitals

Performance pay in county-level public hospitals is an important part of the reform of public hospitals, which largely determines the success or failure of the reform of public hospitals. This paper reviews the concept of performance pay, the recognition of hospitals and staff, the main performance pay model, current situation, design principles and ideas in County-level Public hospitals, and provides some reference for future research and reform direction of performance pay in County-level Public hospitals

Rethinking Alignment in Video Super-Resolution Transformers

The alignment of adjacent frames is considered an essential operation in video super-resolution (VSR). Advanced VSR models, including the latest VSR Transformers, are generally equipped with well-designed alignment modules. However, the progress of the self-attention mechanism may violate this common sense. In this paper, we rethink the role of alignment in VSR Transformers and make several counter-intuitive observations. Our experiments show that: (i) VSR Transformers can directly utilize multi-frame information from unaligned videos, and (ii) existing alignment methods are sometimes harmful to VSR Transformers. These observations indicate that we can further improve the performance of VSR Transformers simply by removing the alignment module and adopting a larger attention window. Nevertheless, such designs will dramatically increase the computational burden, and cannot deal with large motions. Therefore, we propose a new and efficient alignment method called patch alignment, which aligns image patches instead of pixels. VSR Transformers equipped with patch alignment could demonstrate state-of-the-art performance on multiple benchmarks. Our work provides valuable insights on how multi-frame information is used in VSR and how to select alignment methods for different networks/datasets. Codes and models will be released at https://github.com/XPixelGroup/RethinkVSRAlignment.Comment: This paper has been accepted for NeurIPS 202

Potential of Repurposing Recycled Concrete for Road Paving: Flexural Strength (FS) Modeling by a Novel Systematic and Evolved RF-FA Model

Concrete can be recycled after certain processing technologies for use in pavement engineering but the flexural strength (FS) is difficult to predict accurately in the design process. This study proposes a novel systematic and evolved approach to estimate the FS of recycled concrete. The proposed methods are conducted based on the random forest (RF) model as well as the firefly algorithm (FA), where the latter is employed to tune the hyperparameters of the RF model. For this purpose, data sets were collected from previously published literature for the training and verification of the model, and the accuracy of the model was verified by the fitting effect of the predicted and actual values. The results showed that the proposed hybrid machine learning model has a good fitting effect on the predicted and actual values; the calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the proposed model to determine the FS of the recycling concrete. In addition, the study analyzed the sensitivity of the FS of recycled concrete to input variables, and the results showed that effective water-cement ratio (WC), water absorption of recycling concrete (WAR), and water absorption of natural aggregate (WAN) show more obvious influences on FS, so these factors should be paid more attention in future pavement design using the recycling of concrete

Review of Performance Pay at County Level Public Hospitals

Performance pay in county-level public hospitals is an important part of the reform of public hospitals, which largely determines the success or failure of the reform of public hospitals. This paper reviews the concept of performance pay, the recognition of hospitals and staff, the main performance pay model, current situation, design principles and ideas in County-level Public hospitals, and provides some reference for future research and reform direction of performance pay in County-level Public hospitals

An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated

Identification of differentially expressed genes in sorghum (Sorghum bicolor) brown midrib mutants

Sorghum, a species able to produce a high yield of biomass and tolerate both drought and poor soil fertility, is considered to be a potential bioenergy crop candidate. The reduced lignin content characteristic of brown midrib (bmr) mutants improves the efficiency of bioethanol conversion from biomass. Suppression subtractive hybridization combined with cDNA microarray profiling was performed to characterize differential gene expression in a set of 13 bmr mutants, which accumulate significantly less lignin than the wildtype plant BTx623. Among the 153 differentially expressed genes identified, 43 were upregulated and 110 down regulated in the mutants. A semiquantitative RT–PCR analysis applied to 12 of these genes largely validated the microarray analysis data. The transcript abundance of genes encoding L-phenylalanine ammonia lyase and cinnamyl alcohol dehydrogenase was less in the mutants than in the wild type, consistent with the expectation that both enzymes are associated with lignin synthesis. However, the gene responsible for the lignin synthesis enzyme cinnamic acid 4-hydroxylase was upregulated in the mutants, indicating that the production of monolignol from L-phenylalanine may involve more than one pathway. The identity of the differentially expressed genes could be useful for breeding sorghum with improved efficiency of bioethanol conversion from lignocellulosic biomass

Depth Estimation of Monocular PCB Image Based on Self-Supervised Convolution Network

To improve the accuracy of using deep neural networks to predict the depth information of a single image, we proposed an unsupervised convolutional neural network for single-image depth estimation. Firstly, the network is improved by introducing a dense residual module into the encoding and decoding structure. Secondly, the optimized hybrid attention module is introduced into the network. Finally, stereo image is used as the training data of the network to realize the end-to-end single-image depth estimation. The experimental results on KITTI and Cityscapes data sets show that compared with some classical algorithms, our proposed method can obtain better accuracy and lower error. In addition, we train our models on PCB data sets in industrial environments. Experiments in several scenarios verify the generalization ability of the proposed method and the excellent performance of the model

Study on the dynamic natural ventilation rates of occupied residence

The residential natural ventilation rates have a significant impact on indoor thermal comfort and air quality and building energy consumption. The characteristics of the indoor-outdoor temperature difference and wind pressure change over time, as well as the occupants' window opening behavior and the use of HVAC systems, resulting in the residential air change rate being dynamic with time. Many previous studies of residential ventilation measured the steady-state air change rates, which does not reflect the actual dynamic characteristics. In this study, a field measurement was conducted in a bedroom of one natural ventilation residential building in Beijing with continuously monitoring the CO2 concentration, indoor air temperature, and outdoor meteorological parameters for one year. Using the CO2 released by occupants as a tracer gas, the extended Kalman filter based on the Transient Mass Balance Equation (TMBE) was adopted to calculate the dynamic air change rate. This method can effectively filter the CO2 concentration measurement noise. The trend of air change rate with each influencing factor was analyzed. This study is expected to lay the foundation for future studies of dynamic air change rates in residential buildings

Mitigating Artifacts in Real-World Video Super-resolution Models

The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, hidden states tend to contain unpleasant artifacts and propagate them to restored frames. In this circumstance, our analyses show that such artifacts can be largely alleviated when the hidden state is replaced with a cleaner counterpart. Based on the observations, we propose a Hidden State Attention (HSA) module to mitigate artifacts in real-world video super-resolution. Specifically, we first adopt various cheap filters to produce a hidden state pool. For example, Gaussian blur filters are for smoothing artifacts while sharpening filters are for enhancing details. To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated. Equipped with HSA, our proposed method, namely FastRealVSR, is able to achieve 2x speedup while obtaining better performance than Real-BasicVSR. Codes will be available at https://github.com/TencentARC/FastRealVSR