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Recycled aggregate concrete (RAC) exhibits inferior mechanical and durability properties owing to the deterioration of the recycled coarse aggregate (RCA) surface quality. To improve the surface properties of RCA, the reinforcement efficiency of RAC, and the maneuverability of the surface treatment method, this study used magnesium phosphate cement (MPC), a clinker-free low-carbon cement with excellent bonding properties, to precoat RCA under three-day pre-conditioning. Moreover, variable amounts of fly ash (FA) or granulated blast furnace slag (GBFS) were utilized to partly substitute MPC to enhance the compressive strength and chloride ion penetration resistance. Subsequently, FA–MPC and GBFS–MPC hybrid slurries with the best comprehensive performance were selected to coat the RCA for optimal reinforcement. The crushing value and water absorption of RCA, as well as the mechanical strengths and durability of RAC, were investigated, and microstructures around interfaces were studied via BSE-EDS and microhardness analysis to reveal the strengthening mechanism. The results indicated that the comprehensive property of strengthening paste was enhanced significantly through substituting MPC with 10% FA or GBFS. Surface coating resulted in a maximum reduction of 8.15% in the crushing value, while the water absorption barely changed. In addition, modified RAC outperformed untreated RAC regarding compressive strength, splitting tensile strength, and chloride ion penetration resistance with maximum optimization efficiencies of 31.58%, 49.75%, and 43.11%, respectively. It was also evidenced that the improved MPC paste properties enhanced the performance of modified RAC. Microanalysis revealed that MPC pastes exhibited an excellent bond with RCA or new mortar, and the newly formed interfacial transition zone between MPC and the fresh mortar exhibited a dense microstructure and outstanding micro-mechanical properties supported with an increase in the average microhardness value of 30.2–33.4%. Therefore, MPC pastes incorporating an appropriate mineral admixture have enormous potential to be utilized as effective RCA surface treatment materials and improve the operability of RCA application in practice. [ABSTRACT FROM AUTHOR]

Mulberry (Morus alba), a widely distributed economic plant, can withstand long-term flooding stress. However, the regulatory gene network underlying this tolerance is unknown. In the present study, mulberry plants were subjected to submergence stress. Subsequently, mulberry leaves were collected to perform quantitative reverse-transcription PCR (qRT-PCR) and transcriptome analysis. Genes encoding ascorbate peroxidase and glutathione S-transferase were significantly upregulated after submergence stress, indicating that they could protect the mulberry plant from flood damage by mediating ROS homeostasis. Genes that regulate starch and sucrose metabolism; genes encoding pyruvate kinase, alcohol dehydrogenase, and pyruvate decarboxylase (enzymes involved in glycolysis and ethanol fermentation); and genes encoding malate dehydrogenase and ATPase (enzymes involved in the TCA cycle) were also obviously upregulated. Hence, these genes likely played a key role in mitigating energy shortage during flooding stress. In addition, genes associated with ethylene, cytokinin, abscisic acid, and MAPK signaling; genes involved in phenylpropanoid biosynthesis; and transcription factor genes also showed upregulation under flooding stress in mulberry plants. These results provide further insights into the adaptation mechanisms and genetics of submergence tolerance in mulberry plants and could aid in the molecular breeding of these plants. [ABSTRACT FROM AUTHOR]

Video anomaly detection is widely applied in modern society, which is achieved by sensors such as surveillance cameras. This paper learns anomalies by exploiting videos under the fully unsupervised setting. To avoid massive computation caused by back-prorogation in existing methods, we propose a novel efficient three-stage unsupervised anomaly detection method. In the first stage, we adopt random projection instead of autoencoder or its variants in previous works. Then we formulate the optimization goal as a least-square regression problem which has a closed-form solution, leading to less computational cost. The discriminative reconstruction losses of normal and abnormal events encourage us to roughly estimate normality that can be further sifted in the second stage with one-class support vector machine. In the third stage, to eliminate the instability caused by random parameter initializations, ensemble technology is performed to combine multiple anomaly detectors' scores. To the best of our knowledge, it is the first time that unsupervised ensemble technology is introduced to video anomaly detection research. As demonstrated by the experimental results on several video anomaly detection benchmark datasets, our algorithm robustly surpasses the recent unsupervised methods and performs even better than some supervised approaches. In addition, we achieve comparable performance contrast with the state-of-the-art unsupervised method with much less running time, indicating the effectiveness, efficiency, and robustness of our proposed approach. [ABSTRACT FROM AUTHOR]

With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to solve the incomplete multi-view clustering problem. Firstly, to eliminate the noise existing in the original space, we transform complete original data into latent representations which contribute to better graph construction for each view. Then, we incorporate feature extraction and incomplete graph fusion into a unified framework, whereas two processes can negotiate with each other, serving for graph learning tasks. A sparse regularization is imposed on the complete graph to make it more robust to the view-inconsistency. Besides, the importance of different views is automatically learned, further guiding the construction of the complete graph. An effective iterative algorithm is proposed to solve the resulting optimization problem with convergence. Compared with the existing state-of-the-art methods, the experiment results on several real-world datasets demonstrate the effectiveness and advancement of our proposed method. [ABSTRACT FROM AUTHOR]

In the iron and steel enterprises, efficient utilization of byproduct gas is of great significance for energy conservation and emission reduction. This work presents a fuzzy optimal scheduling model for byproduct gas system. Compared with previous work, uncertainties in byproduct gas systems are taken into consideration. In our model, uncertain factors in byproduct systems are described by fuzzy variables and gasholder level constraints are formulated as fuzzy chance constraints. The economy and reliability of byproduct gas system scheduling are sensitive to different confidence levels. To provide a reference for operators to determine a proper confidence level, the risk cost is defined to quantify the risk of byproduct gas shortage and emission during the scheduling process. The best confidence level is determined through the trade-off between operation cost and risk cost. The experiment results demonstrated that the proposed method can reduce the risk and give a more reasonable optimal scheduling scheme compared with deterministic optimal scheduling. [ABSTRACT FROM AUTHOR]

Geese feather production and the quality of downy feathers are additional economically important traits in the geese industry. However, little information is available about the molecular mechanisms fundamental to feather formation and the quality of feathers in geese. This study conducted de novo transcriptome sequencing analysis of two related geese species using the Illumina 4000 platform to determine the genes involved in embryonic skin feather follicle development. A total of 165,564,278 for Anser anser and 144,595,262 for Anser cygnoides clean reads were generated, which were further assembled into 77,134 unigenes with an average length of 906 base pairs in Anser anser and 66,041 unigenes with an average length of 922 base pairs in Anser cygnoides. To recognize the potential regulatory roles of differentially expressed genes (DEGs) during geese embryonic skin feather follicle development, the obtained unigenes were annotated to Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analysis. In both species, GO and KOG had shown similar distribution patterns during functional annotation except for KEGG, which showed significant variation in signaling enrichment. Anser asnser was significantly enriched in the calcium signaling pathway, whereas Anser cygnoides was significantly enriched with glycerolipid metabolism. Further analysis indicated that 14,227 gene families were conserved between the species, among which a total of 20,715 specific gene families were identified. Comparative RNA-Seq data analysis may reveal inclusive knowledge to assist in the identification of genetic regulators at a molecular level to improve feather quality production in geese and other poultry species. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the changes in the goose embryo transcriptome during feather development. RNA-Sequencing (RNA-Seq) was used to find the transcriptome profiles of feather follicles from three stages of embryonic dorsal skin at embryonic day 13, 18, and 28 (E13, E18, E28). The results showed that 3001, 6634, and 13,780 genes were differently expressed in three stages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that differentially expressed genes (DEGs) in E13 vs. E18 were significantly mapped into the GO term of extracellular structure organization and the pathway of extracellular matrix (ECM)-receptor interaction. In E18 vs. E28, the top significantly mapped into GO term was the single-organism developmental process; the pathway was also the ECM-receptor interaction. DEGs in E13 vs. E28 were significantly mapped into the GO term of the multicellular organismal process and the pathway of cell adhesion molecules. Subsequently, the union of DEGs was categorized by succession cluster into eight profiles, which were then grouped into four ideal profiles. Lastly, the seven genes spatio-temporal expression pattern was confirmed by real-time PCR. Our findings advocate that interleukin 20 receptor subunit alpha (IL20RA), interleukin 6 receptor (IL6R), interleukin 1 receptor type 1 (IL-1R1), Wnt family member 3A (WNT3A), insulin-like growth factor binding protein 3 (IGFBP3), bone morphogenetic protein 7 (BMP7), and secreted-frizzled related protein 2 (SFRP2) might possibly play vital roles in skin and feather follicle development and growth processes. [ABSTRACT FROM AUTHOR]