Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.Comment: 41 pages, 14 figure

(5-Bromo-2-chloro­phen­yl)(4-ethoxy­phen­yl)methanone

In the title mol­ecule, C15H12BrClO2, the two benzene rings form a dihedral angle of 69.30 (3)°. In the crystal structure, weak inter­molecular C—H⋯O hydrogen bonds link mol­ecules into chains propagating along the b axis

China carbon emission accounts 2020-2021

In the past a few years, the outbreak of the COVID-19 epidemic has significantly changed global emission patterns and increased the challenges in emission reduction. However, a comprehensive analysis of the most recent trends of China's carbon emissions has not been conducted due to a lack of up-to-date emission accounts by regions and sectors. This study compiles the latest CO2 emission inventories for China and its 30 provinces during the epidemic (2020−2021), following the administrative-territorial approach from the International Panel on Climate Change (IPCC). Our inventories cover energy-related emissions from 17 types of fossil fuel combustion and cement production across 47 economic sectors. To provide a holistic view of emission patterns, we esitamted consumption-based emissions in China. We find that the COVID-19 epidemic led to a 50% reduction in the growth rate of territorial emissions in 2020 compared to 2019. This trend then reversed in 2021 as lockdown measures gradually relaxed. Our study reveals the impact of the rapid expansion of exports, driven by epidemic prevention materials and “stay-at-home economy” products on widening the differences between territorial- and consumption-based emissions. Our study offers a timely blueprint for designing strategies towards carbon peak and neutrality, especially in the context of sustainable recoveries and carbon mitigation post-pandemic.</p

Spatiotemporal evolution and drivers of carbon inequalities in urban agglomeration:An MLD-IDA inequality indicator decomposition

Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China’s Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future

Sentence Specified Dynamic Video Thumbnail Generation

With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences. Conventional video thumbnails are generated once purely based on the visual characteristics of videos, and then displayed as requested. Hence, such video thumbnails, without considering the users' searching intentions, cannot provide a meaningful snapshot of the video contents that users concern. In this paper, we define a distinctively new task, namely sentence specified dynamic video thumbnail generation, where the generated thumbnails not only provide a concise preview of the original video contents but also dynamically relate to the users' searching intentions with semantic correspondences to the users' query sentences. To tackle such a challenging task, we propose a novel graph convolved video thumbnail pointer (GTP). Specifically, GTP leverages a sentence specified video graph convolutional network to model both the sentence-video semantic interaction and the internal video relationships incorporated with the sentence information, based on which a temporal conditioned pointer network is then introduced to sequentially generate the sentence specified video thumbnails. Moreover, we annotate a new dataset based on ActivityNet Captions for the proposed new task, which consists of 10,000+ video-sentence pairs with each accompanied by an annotated sentence specified video thumbnail. We demonstrate that our proposed GTP outperforms several baseline methods on the created dataset, and thus believe that our initial results along with the release of the new dataset will inspire further research on sentence specified dynamic video thumbnail generation. Dataset and code are available at https://github.com/yytzsy/GTP

Patterns of CO2 emissions in 18 central Chinese cities from 2000 to 2014

With the Rise of Central China Plan, the central region has had a great opportunity to develop its economy and improve its original industrial structure. However, this region is also under pressure to protect its environment, keep its development sustainable and reduce carbon emissions. Therefore, accurately estimating the temporal and spatial dynamics of CO2 emissions and analysing the factors influencing these emissions are especially important. This paper estimates the CO2 emissions derived from the fossil fuel combustion and industrial processes of 18 central cities in China between 2000 and 2014. The results indicate that these 18 cities, which contain an average of 6.57% of the population and 7.91% of the GDP, contribute 13% of China's total CO2 emissions. The highest cumulative CO2 emissions from 2000 to 2014 were from Taiyuan and Wuhan, with values of 2268.57 and 1847.59 million tons, accounting for 19.21% and 15.64% of the total among these cities, respectively. Therefore, the CO2 emissions in the Taiyuan urban agglomeration and Wuhan urban agglomeration represented 28.53% and 20.14% of the total CO2 emissions from the 18 cities, respectively. The three cities in the Zhongyuan urban agglomeration also accounted for a second highest proportion of emissions at 23.51%. With the proposal and implementation of the Rise of Central China Plan in 2004, the annual average growth rate of total CO2 emissions gradually decreased and was lower in the periods from 2005 to 2010 (5.44%) and 2010 to 2014 (5.61%) compared with the rate prior to 2005 (12.23%). When the 47 socioeconomic sectors were classified into 12 categories, “power generation” contributed the most to the total cumulative CO2 emissions at 36.51%, followed by the “non-metal and metal industry”, “petroleum and chemical industry”, and “mining” sectors, representing emissions proportions of 29.81%, 14.79%, and 9.62%, respectively. Coal remains the primary fuel in central China, accounting for an average of 80.59% of the total CO2 emissions. Industrial processes also played a critical role in determining the CO2 emissions, with an average value of 7.3%. The average CO2 emissions per capita across the 18 cities increased from 6.14 metric tons in 2000 to 15.87 metric tons in 2014, corresponding to a 158.69% expansion. However, the average CO2 emission intensity decreased from 0.8 metric tons/1000 Yuan in 2000 to 0.52 metric tons/1000 Yuan in 2014 with some fluctuations. The changes in and industry contributions of carbon emissions were city specific, and the effects of population and economic development on CO2 emissions varied. Therefore, long-term climate change mitigation strategies should be adjusted for each city