EMID: An Emotional Aligned Dataset in Audio-Visual Modality

In this paper, we propose Emotionally paired Music and Image Dataset (EMID), a novel dataset designed for the emotional matching of music and images, to facilitate auditory-visual cross-modal tasks such as generation and retrieval. Unlike existing approaches that primarily focus on semantic correlations or roughly divided emotional relations, EMID emphasizes the significance of emotional consistency between music and images using an advanced 13-dimension emotional model. By incorporating emotional alignment into the dataset, it aims to establish pairs that closely align with human perceptual understanding, thereby raising the performance of auditory-visual cross-modal tasks. We also design a supplemental module named EMI-Adapter to optimize existing cross-modal alignment methods. To validate the effectiveness of the EMID, we conduct a psychological experiment, which has demonstrated that considering the emotional relationship between the two modalities effectively improves the accuracy of matching in abstract perspective. This research lays the foundation for future cross-modal research in domains such as psychotherapy and contributes to advancing the understanding and utilization of emotions in cross-modal alignment. The EMID dataset is available at https://github.com/ecnu-aigc/EMID

Divergent Leading Factors in Energy-Related CO2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region

Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012

The aim of this paper is to identify the correlations between energy consumption and the factors that control usage in the city of Tangshan. To do this, we first analyze the current status of Tangshan’s economic development and energy consumption, and then applied the logarithmic mean Divisia index to identify the factors affecting the changes in energy consumption of all sectors. The findings are summarized as follows: (1) secondary industry accounts for an extremely high percentage of industry in Tangshan city, much higher than the national average; from 2007 to 2012, the proportion of secondary industry increased in Tangshan city; (2) Tangshan’s energy consumption in 2013 was nearly twice that in 2005. Coal and coke coal consumption was responsible for 96.2% of total energy consumption in 2005 and 95.1% in 2013; (3) Tangshan’s energy intensity decreased from 3.00 tce/thousand Yuan in 2005 to 1.85 tce/thousand Yuan in 2013. However, the energy intensity of Tangshan was far more than the average for China, and the decline in Tangshan’s energy intensity was much slower than the average for China; (4) The technical effect plays a dominant role in decreasing energy consumption in most sectors, and the scale effect is the most important contributor to increasing energy consumption in all sectors. Input structural and final use structural effects play different roles in energy consumption in different sectors

What Drove Changes in the Embodied Energy Consumption of Guangdong’s Exports from 2007–2012?

China’s economy has been highly reliant on exports in recent years, with Guangdong its biggest province in export trade volume. Despite the global financial crisis of 2008, exports from Guangdong continued to increase significantly; however, the energy consumption embodied in exports is unknown. In this study, we investigate the changes of energy embodied in exports from 2007 to 2012 in Guangdong Province. We use EIO (Environmental Input-Output) and LMDI (Logarithmic Mean Divisia Index) method to find out the drivers of such changes embodied in total exports and export of each sector. Our results show: Firstly, from 2007 to 2012, the export structure in Guangdong has changed, reflecting in low energy intensity industry experiencing faster growth in exports than high energy intensity industry. Secondly, the growth rate of embodied energy consumption in Guangdong’s exports is slowing, with average annual growth from 2007 to 2012 of 6.8%. Thirdly, though Guangdong’s exports grew significantly, the energy consumption embodied therein decreased by 23% from 2007 to 2012, representing a drop of 50.51 Mtce. Finally, the most prominent change driver differed across sectors: For low value-added industries, such as metal smelting and rolling, the main contributor was export structure change, whereas for high value-added industries, such as communications, computers, and other electronic equipment, the main contributor was technical change. Guangdong is playing a leading role in industrial upgrading in China, and this has made the embodied energy consumption decreased obviously in Guangdong. It will be interesting to further investigate the trends of embodied energy consumption of other provinces in China, as this would give us deeper understanding of Chinese resource and environment problems

Correction: Consumption substitution and change of household indirect energy consumption in China between 1997 and 2012.

[This corrects the article DOI: 10.1371/journal.pone.0221664.]

Consumption substitution and change of household indirect energy consumption in China between 1997 and 2012.

With the rapid growth in the Chinese economy in recent decades, household incomes as well as consumption of goods and services have also steadily increased. This has resulted in growing demand for energy consumption across the economy. It has been suggested that consumption upgrades in tandem with substitutions might exert an impact on mitigating this growth. The input-output method was applied in this study to analyze variations in household indirect energy consumption between 1997 and 2012. The impact of consumption substitution on change was also determined using a two-tier structural decomposition analysis, in which the second-tier is a further decomposition based on first-tier results. The results show that the indirect energy use caused by household consumption makes up between 75% and 78% of total household energy demand and that this increased 161.2% over the study period. First-tier decomposition results reveal that this change was mostly caused by household consumption scale and energy intensity effects. Second-tier decomposition results reveal strong evidence for consumption substitution between energy-intensive industries and non-energy-intensive ones and that this can have an impact on reducing household indirect consumption. Household consumption therefore plays a prominent role in total energy consumption. Transforming to non-energy-intensive or services led consumption patterns should therefore be encouraged by the Chinese government in order to achieve conservation goals

SFA-MDEN: Semantic-Feature-Aided Monocular Depth Estimation Network Using Dual Branches

Monocular depth estimation based on unsupervised learning has attracted great attention due to the rising demand for lightweight monocular vision sensors. Inspired by multi-task learning, semantic information has been used to improve the monocular depth estimation models. However, multi-task learning is still limited by multi-type annotations. As far as we know, there are scarcely any large public datasets that provide all the necessary information. Therefore, we propose a novel network architecture Semantic-Feature-Aided Monocular Depth Estimation Network (SFA-MDEN) to extract multi-resolution depth features and semantic features, which are merged and fed into the decoder, with the goal of predicting depth with the support of semantics. Instead of using loss functions to relate the semantics and depth, the fusion of feature maps for semantics and depth is employed to predict the monocular depth. Therefore, two accessible datasets with similar topics for depth estimation and semantic segmentation can meet the requirements of SFA-MDEN for training sets. We explored the performance of the proposed SFA-MDEN with experiments on different datasets, including KITTI, Make3D, and our own dataset BHDE-v1. The experimental results demonstrate that SFA-MDEN achieves competitive accuracy and generalization capacity compared to state-of-the-art methods

Analysis of the Interprovincial Embodied Carbon Flow Network of China’s Exports

This is the dataset for "Analysis of the Interprovincial Embodied Carbon Flow Network of China’s Exports".</p