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Abstract Although deep convolution neural network (DCNN) has achieved great success in computer vision field, such models are considered to lack interpretability in decision‐making. One of fundamental issues is that its decision mechanism is considered to be a “black‐box” operation. The authors design the binary tree structure convolution (BTSC) module and control the activation level of particular neurons to build the interpretable DCNN model. First, the authors design a BTSC module, in which each parent node generates two independent child layers, and then integrate them into a normal DCNN model. The main advantages of the BTSC are as follows: 1) child nodes of the different parent nodes do not interfere with each other; 2) parent and child nodes can inherit knowledge. Second, considering the activation level of neurons, the authors design an information coding objective to guide neural nodes to learn the particular information coding that is expected. Through the experiments, the authors can verify that: 1) the decision‐making made by both the ResNet and DenseNet models can be explained well based on the "decision information flow path" (known as the decision‐path) formed in the BTSC module; 2) the decision‐path can reasonably interpret the decision reversal mechanism (Robustness mechanism) of the DCNN model; 3) the credibility of decision‐making can be measured by the matching degree between the actual and expected decision‐path.

Abstract Background In several eukaryotes, DNA methylation occurs within the coding regions of many genes, termed gene body methylation (GbM). Whereas the role of DNA methylation on the silencing of transposons and repetitive DNA is well understood, gene body methylation is not associated with transcriptional repression, and its biological importance remains unclear. Results We report a newly discovered type of GbM in plants, which is under constitutive addition and removal by dynamic methylation modifiers in all cells, including the germline. Methylation at Dynamic GbM genes is removed by the DRDD demethylation pathway and added by an unknown source of de novo methylation, most likely the maintenance methyltransferase MET1. We show that the Dynamic GbM state is present at homologous genes across divergent lineages spanning over 100 million years, indicating evolutionary conservation. We demonstrate that Dynamic GbM is tightly associated with the presence of a promoter or regulatory chromatin state within the gene body, in contrast to other gene body methylated genes. We find Dynamic GbM is associated with enhanced gene expression plasticity across development and diverse physiological conditions, whereas stably methylated GbM genes exhibit reduced plasticity. Dynamic GbM genes exhibit reduced dynamic range in drdd mutants, indicating a causal link between DNA demethylation and enhanced gene expression plasticity. Conclusions We propose a new model for GbM in regulating gene expression plasticity, including a novel type of GbM in which increased gene expression plasticity is associated with the activity of DNA methylation writers and erasers and the enrichment of a regulatory chromatin state.

Abstract This paper focuses on the development trend of industrial carbon emissions in Bengbu city, Anhui Province in the next ten years, and how to help the industry reach the carbon peak as soon as possible. The research process and conclusions are as follows: (1) Through literature review and carbon emission index method, five main factors affecting industrial carbon emission are identified. (2) The resistance model is used to analyze the main resistance factors of industrial carbon emission reduction in Bengbu city. (3) Based on the existing data of Bengbu city from 2011 to 2020, the grey prediction EGM (1,1) model is used to predict the industrial carbon emissions of Bengbu city from 2021 to 2030. The results show that among the five factors, the urbanization rate has the most significant impact on industrial carbon emissions, while energy intensity has the least impact. Bengbu’s industrial carbon emissions will continue to increase in the next decade, but the growth rate will be flat. Based on the findings of the analysis, specific recommendations on urbanization development, energy structure, and industrial structure of Bengbu city are put forward.

This study presents a novel soft-switching inverter distinguished by a simplified topology and an innovative modulation approach. The design aims to optimize the energy conversion processes commonly found in auxiliary snubber circuits. By minimizing the number of auxiliary switches, the control method is streamlined, thereby enhancing system reliability and cost-efficiency. The principles of operation and conditions for soft-switching are thoroughly analyzed using equivalent circuit models. A 3 kW/16 kHz inverter prototype was constructed, and the experimental results confirm the effectiveness and benefits of the proposed inverter.

Abstract In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, these deep neural models are still considered as “black box” for most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how CNNs process these features. It is widely believed that CNN models combine low‐level features to form complex shapes until the object can be readily classified, however, several recent studies have argued that texture features are more important than other features. In this paper, we assume that the importance of certain features varies depending on specific tasks, that is, specific tasks exhibit feature bias. We designed two classification tasks based on human intuition to train deep neural models to identify the anticipated biases. We designed experiments comprising many tasks to test these biases in the Res Net and Dense Net models. From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased towards a specific anticipated feature.

Xenia effects describe the genetic contribution of pollen to seed phenotypes. Here the authors show that paternal imprinting of Ded1 contributes to the xenia effect in maize by setting the pace of endosperm development.

In order to actively respond to the global climate and environmental challenges, and to help achieve the goal of carbon peaking and carbon neutrality, China aims to achieve carbon peaking by 2030. As the main contributor of energy consumption, construction industry transformation is imminent. This paper focuses on the development trend of carbon emissions in Anhui construction industry in the next 20 years, and how to help Anhui construction industry achieve the carbon peak target. The research process and conclusions are as follows: 1) Through literature review and gray correlation analysis, seven important factors affecting the construction industry are identified. 2) The gray prediction EGM(1,1) model was adopted to forecast the carbon emissions of the construction industry in Anhui Province from 2021 to 2040. The research results show that carbon emissions will increase year by year in the next 20 years. 3) The EGM-BP neural network model was used to further predict the carbon emissions from the construction industry in 2021–2040. It is expected that the carbon emissions from construction industry in Anhui Province will show an “inverted U″ trend in the next 20 years, and is expected to reach its peak in 2031, after which it will start to decline. Based on the results of the above study, we provide support paths for achieving the carbon peak in the construction industry from the perspective of improving and developing the construction system, and provide policy suggestions for relevant departments to accelerate the construction industry and carbon peak construction in Anhui Province.

The environmental problems with climate change have become the focus of social attention. Based on STIRPAT model, ridge regression analysis is used to analyze the impact of five factors, such as resident population, per capita GDP, energy intensity, energy structure and urbanization rate, on the carbon emissions of Anhui Province. Scenario-analysis is used to set up three scenarios to forecast the carbon emissions of Anhui Province from 2020 to 2035. The results show that when other economic conditions remain unchanged, the energy structure has the most significant impact on carbon emissions, followed by the number of permanent residents. Under the high carbon scenario, there is no peak. Under the medium carbon scenario, Anhui's carbon emissions will peak in 2027, and under the low carbon scenario, the peak time will be advanced to 2025. Finally, the policy recommendations are put forward from the energy structure and urbanization development. [ABSTRACT FROM AUTHOR]

With the accelerating process of urbanization, carbon emission reduction plays an indispensable role in the development of low-carbon economy. The paper puts forward the research topic of carbon emission in Anhui Province under low-carbon development. According to the relevant energy consumption data of Anhui Province from 2010 to 2019, the carbon emission was calculated. Based on the GM (1,1) model, the growth trend of carbon emission in the next ten years (2020-2029) is predicted, and the main influencing factors of carbon emission in Anhui Province were analyzed by using the grey correlation analysis method. The results show that the annual carbon emissions in Anhui Province will continue to grow, but the growth rate will slow down; Energy efficiency, urbanization rate, GDP of the secondary industry, GDP per capita and GDP of the primary industry had a decreasing effect on carbon emissions. Based on the research results, the paper proposes the following viewpoints to reverse the carbon emission dilemma of Anhui Province, namely, optimizing the energy consumption structure, improving the energy utilization efficiency, promoting the upgrading of industrial structure, creating a low-carbon industrial system of new urbanization pattern, and promoting the green sustainable development. [ABSTRACT FROM AUTHOR]