Constraints and Countermeasures: To Promote Low-carbon Economy Development of Resource-based Cities

This article summarized the experience of low-carbon economy development in both foreign and domestic countries. Also, the present study proposed a low carbon system where the government plays the leading role in developing low-carbon economy, enterprises set up the goal of low-carbon operation, market instruments are used to adjust low-carbon operation, society layout is arranged in a low-carbon manner, and low-carbon consumption awareness among residents is well established. In addition, the existing problems and proposed countermeasures were discussed for low-carbon development of resource-based cities

Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR

<p>Abstract</p> <p>Background</p> <p>Real-time quantitative reverse transcription PCR (RT-qPCR) data needs to be normalized for its proper interpretation. Housekeeping genes are routinely employed for this purpose, but their expression level cannot be assumed to remain constant under all possible experimental conditions. Thus, a systematic validation of reference genes is required to ensure proper normalization. For soybean, only a small number of validated reference genes are available to date.</p> <p>Results</p> <p>A systematic comparison of 14 potential reference genes for soybean is presented. These included seven commonly used (<it>ACT2, ACT11, TUB4, TUA5, CYP, UBQ10, EF1b</it>) and seven new candidates (<it>SKIP16, MTP, PEPKR1, HDC, TIP41, UKN1, UKN2</it>). Expression stability was examined by RT-qPCR across 116 biological samples, representing tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars. Expression of all 14 genes was variable to some extent, but that of <it>SKIP16, UKN1 </it>and <it>UKN2 </it>was overall the most stable. A combination of <it>ACT11, UKN1 </it>and <it>UKN2 </it>would be appropriate as a reference panel for normalizing gene expression data among different tissues, whereas the combination SKIP16, UKN1 and MTP was most suitable for developmental stages. <it>ACT11, TUA5 </it>and <it>TIP41 </it>were the most stably expressed when the photoperiod was altered, and <it>TIP41, UKN1 </it>and <it>UKN2 </it>when the light quality was changed. For six different cultivars in long day (LD) and short day (SD), their expression stability did not vary significantly with <it>ACT11, UKN2 </it>and <it>TUB4 </it>being the most stable genes. The relative gene expression level of <it>GmFTL3</it>, an ortholog of Arabidopsis <it>FT </it>(<it>FLOWERING LOCUS T</it>) was detected to validate the reference genes selected in this study.</p> <p>Conclusion</p> <p>None of the candidate reference genes was uniformly expressed across all experimental conditions, and the most suitable reference genes are conditional-, tissue-specific-, developmental-, and cultivar-dependent. Most of the new reference genes performed better than the conventional housekeeping genes. These results should guide the selection of reference genes for gene expression studies in soybean.</p

GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences

Supervised and unsupervised deep trackers that rely on deep learning technologies are popular in recent years. Yet, they demand high computational complexity and a high memory cost. A green unsupervised single-object tracker, called GUSOT, that aims at object tracking for long videos under a resource-constrained environment is proposed in this work. Built upon a baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT contains two additional new modules: 1) lost object recovery, and 2) color-saliency-based shape proposal. They help resolve the tracking loss problem and offer a more flexible object proposal, respectively. Thus, they enable GUSOT to achieve higher tracking accuracy in the long run. We conduct experiments on the large-scale dataset LaSOT with long video sequences, and show that GUSOT offers a lightweight high-performance tracking solution that finds applications in mobile and edge computing platforms

Pixel Sampling for Style Preserving Face Pose Editing

The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc. In this paper, we take advantage of the well-known frontal/profile optical illusion and present a novel two-stage approach to solve the aforementioned dilemma, where the task of face pose manipulation is cast into face inpainting. By selectively sampling pixels from the input face and slightly adjust their relative locations with the proposed ``Pixel Attention Sampling" module, the face editing result faithfully keeps the identity information as well as the image style unchanged. By leveraging high-dimensional embedding at the inpainting stage, finer details are generated. Further, with the 3D facial landmarks as guidance, our method is able to manipulate face pose in three degrees of freedom, i.e., yaw, pitch, and roll, resulting in more flexible face pose editing than merely controlling the yaw angle as usually achieved by the current state-of-the-art. Both the qualitative and quantitative evaluations validate the superiority of the proposed approach

The Improvement and Application of Econometric Model for Reverse Spillover Effect of Technology Sourcing FDI

Domestic enterprises expect to obtain foreign advanced technology through foreign direct investment, but its reverse spillover effect has been the lack of effective measurement. This paper use foreign research results for reference, and tries to improve and apply the econometric model for reverse spillover effect of technology sourcing FDI.Key words: Technology sourcing FDI; Econometric model; Tes

Microstructure, mechanical properties and machinability of particulate reinforced Al matrix composites: a comparative study between SiC particles and high-entropy alloy particles

In this study, 2024Al matrix composites reinforced by SiC particles (SiC-2024Al) and nanocrystalline high-entropy alloy particles (HEA-2024Al) fabricated by powder metallurgy were systematically compared for the first time. There is a significant difference in microstructure and mechanical properties as well as machinability between two kinds of composites. In term of microstructure, when the volume fraction of reinforcements was 10%, both SiC-2024Al and HEA-2024Al composites showed a homogeneous particle distribution in the matrix. With the increase of reinforcement content, HEA-2024Al composites presented denser microstructure than that of SiC-2024Al composites. The composites with 10, 20 and 30 vol.% HEA reinforcements all showed better plasticity than that of the SiC-2024Al composites with same volume fraction of reinforcements, which was related with better particle distribution and interface bonding. However, the strength showed the opposite tendency in the two kinds of composites. Selecting 10SiC-2024Al and 10HEA-2024Al composites as examples to explore the difference in the yield strength of two kinds of composites, it is ascribed to the dislocation punched zones around interface between the Al matrix and reinforcements, which was analyzed in detail by a combination of calculation, nanoindentation tests and finite element analysis. Additionally, HEA-2024Al composites showed better machinability than those of SiC-2024Al composites. This work provides insight into the application of particulate reinforced Al matrix composites