Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation

This study aims to develop and evaluate an innovative simulation algorithm for generating thick-slice CT images that closely resemble actual images in the AAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE) metrics, with the hypothesis that our simulation would produce images more congruent with their real counterparts. Our proposed method demonstrated substantial enhancements in terms of both PSNR and RMSE over other simulation methods. The highest PSNR values were obtained with the proposed method, yielding 49.7369 $\pm$ 2.5223 and 48.5801 $\pm$ 7.3271 for D45 and B30 reconstruction kernels, respectively. The proposed method also registered the lowest RMSE with values of 0.0068 $\pm$ 0.0020 and 0.0108 $\pm$ 0.0099 for D45 and B30, respectively, indicating a distribution more closely aligned with the authentic thick-slice image. Further validation of the proposed simulation algorithm was conducted using the TCIA LDCT-and-Projection-data dataset. The generated images were then leveraged to train four distinct super-resolution (SR) models, which were subsequently evaluated using the real thick-slice images from the 2016 Low Dose CT Grand Challenge dataset. When trained with data produced by our novel algorithm, all four SR models exhibited enhanced performance.Comment: 11 pages, 4 figure

A Survey on Datasets for Decision-making of Autonomous Vehicle

Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could not cope with well, data-driven decision-making approaches have aroused more and more focus. The datasets to be used in developing data-driven methods dramatically influences the performance of decision-making, hence it is necessary to have a comprehensive insight into the existing datasets. From the aspects of collection sources, driving data can be divided into vehicle, environment, and driver related data. This study compares the state-of-the-art datasets of these three categories and summarizes their features including sensors used, annotation, and driving scenarios. Based on the characteristics of the datasets, this survey also concludes the potential applications of datasets on various aspects of AV decision-making, assisting researchers to find appropriate ones to support their own research. The future trends of AV dataset development are summarized

Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning

Zero-Shot Learning has been a highlighted research topic in both vision and language areas. Recently, most existing methods adopt structured knowledge information to model explicit correlations among categories and use deep graph convolutional network to propagate information between different categories. However, it is difficult to add new categories to existing structured knowledge graph, and deep graph convolutional network suffers from over-smoothing problem. In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. Our semantic enhanced knowledge graph can further enhance the correlations among categories and make it easy to absorb new categories. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN), which can effectively alleviate the problem of over-smoothing. Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method, and establish a new state-of-the-art on zero-shot learning. Moreover, our results on the large-scale ImageNet-21K with various feature extraction networks show that our method has better generalization and robustness

Transcriptome and Physiological Analyses for Revealing Genes Involved in Wheat Response to Endoplasmic Reticulum Stress.

BACKGROUND: Wheat production is largely restricted by adverse environmental stresses. Under many undesirable conditions, endoplasmic reticulum (ER) stress can be induced. However, the physiological and molecular responses of wheat to ER stress remain poorly understood. We used dithiothreitol (DTT) and tauroursodeoxycholic acid (TUDCA) to induce or suppress ER stress in wheat cells, respectively, with the aim to reveal the molecular background of ER stress responses using a combined approach of transcriptional profiling and morpho-physiological characterization. METHODS: To understand the mechanism of wheat response to ER stress, three wheat cultivars were used in our pre-experiments. Among them, the cultivar with a moderate stress tolerance, Yunong211 was used in the following experiments. We used DTT (7.5 mM) to induce ER stress and TUDCA (25 μg·mL RESULTS: Morpho-physiological results showed DTT significantly reduced plant height and biomass, decreased contents of chlorophyll and water, increased electrolyte leakage rate and antioxidant enzymes activity, and accelerated the cell death ratio, whereas these changes were all remarkably alleviated after TUDCA co-treatment. Therefore, RNA sequencing was performed to determine the genes involved in regulating wheat response to stress. Transcriptomic analysis revealed that 8204 genes were differentially expressed in three treatment groups. Among these genes, 158 photosynthesis-related genes, 42 antioxidant enzyme genes, 318 plant hormone-related genes and 457 transcription factors (TFs) may play vital roles in regulating wheat response to ER stress. Based on the comprehensive analysis, we propose a hypothetical model to elucidate possible mechanisms of how plants adapt to environmental stresses. CONCLUSIONS: We identified several important genes that may play vital roles in wheat responding to ER stress. This work should lay the foundations of future studies in plant response to environmental stresses

Osmotic Stress Induced Cell Death in Wheat Is Alleviated by Tauroursodeoxycholic Acid and Involves Endoplasmic Reticulum Stress–Related Gene Expression

Although, tauroursodeoxycholic acid (TUDCA) has been widely studied in mammalian cells because of its role in inhibiting apoptosis, its effects on plants remain almost unknown, especially in the case of crops such as wheat. In this study, we conducted a series of experiments to explore the effects and mechanisms of action of TUDCA on wheat growth and cell death induced by osmotic stress. Our results show that TUDCA: (1) ameliorates the impact of osmotic stress on wheat height, fresh weight, and water content; (2) alleviates the decrease in chlorophyll content as well as membrane damage caused by osmotic stress; (3) decreases the accumulation of reactive oxygen species (ROS) by increasing the activity of antioxidant enzymes under osmotic stress; and (4) to some extent alleviates osmotic stress–induced cell death probably by regulating endoplasmic reticulum (ER) stress–related gene expression, for example expression of the basic leucine zipper genes bZIP60B and bZIP60D, the binding proteins BiP1 and BiP2, the protein disulfide isomerase PDIL8-1, and the glucose-regulated protein GRP94. We also propose a model that illustrates how TUDCA alleviates osmotic stress–related wheat cell death, which provides an important theoretical basis for improving plant stress adaptation and elucidates the mechanisms of ER stress–related plant osmotic stress resistance

How Do Government Policies Promote Green Housing Diffusion in China? A Complex Network Game Context

To reduce energy consumption and environmental pollution in the construction industry, many countries have focused on the development of green housing (GH), which is a type of green building for residential use. In China, the local governments have introduced various incentive policies to encourage the development of GH; however, its scale is still small and unevenly distributed. This implies a necessity to optimize the policies that apply to the GH incentive. To promote GH diffusion, we built an evolutionary game model on a complex network to analyze the impacts of government policies on GH pricing and demand and the profits of real estate enterprises developing GH. By implementing simulations, we further explored the incentive effect and operational mechanism of the government policies. The results show that the subsidy policy, the preferential policy for GH, and the restriction policy for ordinary housing can effectively promote the diffusion of GH to 0.6752, 0.506, and 0.5137 respectively. Meanwhile, the incentive effect of the enterprise subsidy policy and GH preferential policy gradually decreases with the increase in policy strength. In terms of the demand side, the consumer subsidy policy could promote GH diffusion to 0.7097. If the subsidy is below 120 CNY/m2, the effect of the consumer subsidy policy is less powerful than that of the enterprises subsidy policy; conversely, the former is slightly more effective than the latter. The outcome of the study has managerial implications on governmental decision-making, especially on the strategy design of incentive policies for GH

ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

Abstract The node deployment in mobile sensor networks (MSNs) is mostly performed in a random method. However, a large number of redundant nodes may exist due to the randomness. As a result, severe data congestion may be caused and the quality of coverage (QoC) is undermined. In order to solve this QoC problem, we propose an Energy-efficient Nonlinear Coverage Control Protocol (ENCP). This protocol utilizes the normal distribution to calculate the minimal number of sensors which is required to guarantee coverage over the monitoring area. We also balance the node energy consumption and achieve the collaborative scheduling among all the sensor nodes. Meanwhile, when a certain QoC is guaranteed, we present the calculation model for the normal distribution of the sensing ranges and the proportional relationship between different parameters in the QoC function. Finally, simulation results show that the ENCP could not only improve the network QoC and network coverage rate but also effectively control the energy exhaustion at the nodes. Therefore, the network lifetime can be effectively prolonged