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The purpose of this study was to fully explore the mechanical properties of five different doses of an Argon–Oxygen Decarburization slag mixture in an unconfined compressive strength test. The peak stress, elastic modulus, and stress–strain curve of the mixture were studied for 90 days. Based on the experimental data and according to the theory of damage mechanics, the concept of damage threshold (t) was introduced to construct a damage constitutive model. Referring to the damage threshold of concrete, that of the mixture was determined to be 0.7 times higher than the peak strain, and the correlation coefficient between the established model and the test curve was above 0.85. These results indicate that the addition of AOD slag and fly ash can cause hydration reactions, increase the quantity of hydration products, and enhance the peak stress and elastic modulus of the mixture. The maximum increases were 94.9% and 43.1%, respectively. Parameters a and b reflect the peak stress and brittleness of the mixture, respectively. The incorporation of Argon–Oxygen Decarburization slag can make the mixture less brittle and improve its properties. The incorporation of Argon–Oxygen Decarburization slag can protect the mixture from damage. The maximum decrease is 40.2%.

The traditional malware research is mainly based on its recognition and detection as a breakthrough point, without focusing on its propagation trends or predicting the subsequently infected nodes. The complexity of network structure, diversity of network nodes, and sparsity of data all pose difficulties in predicting propagation. This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN) to address the aforementioned problems. First, to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network, a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information. The influence of the relationship between nodes on the infection intensity is also analyzed. Second, given the diversity and complexity of the content and structure of infected and normal nodes in the network, considering the advantages of representation learning in data feature extraction, the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes. This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional, dense, and real-valued representation learning for the characteristics of propagation spatial data. We also design a new method, Tensor2vec, to learn the potential structural features of malware propagation. Finally, considering the convolution ability of GCN for non-Euclidean data, we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier. The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation.

To promote resourceful utilization of argon oxygen decarburization (AOD) slag, this research developed a new three-ash stabilized recycled aggregate with AOD slag, cement, fly ash (FA), and recycled aggregate (RA) as raw materials. The AOD slag was adopted as an equal mass replacement for fly ash. The application of this aggregate in a road base layer was investigated in terms of its mechanical properties and mechanistic analysis. First, based on a cement: FA ratio of 1:4, 20 sets of mixed proportion schemes were designed for four kinds of cement dosage and AOD slag replacement rates (R/%). Through compaction tests and the 7-day unconfined compressive strength test, it was found that a 3% cement dosage met the engineering requirements. Then, the unconfined compressive strength test, indirect tensile strength test, compressive rebound modulus test, and expansion rate test were carried out at different age thresholds. The results showed that the mixture’s strength, modulus, and expansion rate increased initially and then stabilized with age, while the strength and modulus initially increased and then decreased with increasing R. Secondly, based on X-ray diffraction (XRD) and scanning electron microscopy (SEM) used to analyze the mechanism, it was found that the strength, modulus, and expansion rate of the new material can be promoted by blending AOD slag, due to its ability to fully stimulate the hydration reaction and pozzolanic reaction of the binder. Finally, based on the strength and modulus results, R = 3% was identified as the optimal ratio, which provides a reference point for the effective application of AOD slag and RA in road base materials.

Alopecia areata is an autoimmune disease characterized by the immune system attacking self hair follicles, mainly in the scalp. There is no complete cure, and the pathogenesis is still not fully understood. Here, sequencing of skin tissues collected from 1-month-old coarse- and fine-wool lambs identified miR-199a-3p as the only small RNA significantly overexpressed in the fine-wool group, suggesting a role in hair follicle development. MiR-199a-3p expression was concentrated in the dermal papillae cells of sheep hair follicles, along with enhanced β-catenin expression and the inhibition of PTPRF protein expression. We also successfully constructed a mouse model of alopecia areata by intracutaneous injection with an miR-199a-3p antagomir. Injection of the miR-199a-3p agomir resulted in hair growth and earlier anagen entry. Conversely, local injection with the miR-199a-3p antagomir resulted in suppressed hair growth at the injection site, upregulation of immune system-related genes, and downregulation of hair follicle development-related genes. In vivo and in vitro analyses demonstrated that miR-199a-3p regulates hair follicle development through the PTPRF/β-catenin axis. In conclusion, a mouse model of alopecia areata was successfully established by downregulation of a small RNA, suggesting the potential value of miR-199a-3p in the study of alopecia diseases. The regulatory role of miR-199a-3p in the PTPRF/β-catenin axis was confirmed, further demonstrating the link between alopecia areata and the Wnt-signaling pathway.

In social networks, many complex factors affect the prediction of user forwarding behavior. This paper proposes an improved SVM prediction method for user forwarding behavior of hot topics to improve prediction accuracy. Firstly, we consider that the improved Cuckoo Search algorithm can select the optimal penalty parameters and kernel function parameters to optimize the SVM and thus predict the user's forwarding behavior. Secondly, this paper considers the factors that affect the user forwarding behavior comprehensively from the user's own factors and external factors. Finally, based on the characteristics of the user's forwarding behavior changing over time, the time-slicing method is used to predict the trend of hot topics. Experiments show that the method can accurately predict the user's forwarding behavior and can sense the trend of hot topics.

β-catenin is a conserved molecule that plays an important role in hair follicle development. In this study, we generated skin-specific overexpression of ovine β-catenin in transgenic mice by pronuclear microinjection. Results of polymerase chain reaction (PCR) testing and Southern blot showed that the ovine β-catenin gene was successfully transferred into mice, and the exogenous β-catenin gene was passed down from the first to sixth generations. Furthermore, real-time fluorescent quantitative PCR (qRT-PCR) and western blot analysis showed that β-catenin mRNA was specifically expressed in the skin of transgenic mice. The analysis of F6 phenotypes showed that overexpression of β-catenin could increase hair follicle density by prematurely promoting the catagen-to-anagen transition. The results showed that ovine β-catenin could also promote hair follicle development in mice. We, therefore, demonstrate domestication traits in animals.

The microbial community performs vital functions in the intestinal system of animals. Modulation of the gut microbiota structure can indirectly or directly affect gut health and host metabolism. Aohan fine-wool sheep grow in semi-desert grasslands in China and show excellent stress tolerance. In this study, we amplified 16S rRNA gene to investigate the dynamic distribution and adaptability of the gut microbiome in the duodenum, jejunum, ileum, cecum, colon, and rectum of seven Aohan fine-wool sheep at 12 months. The results showed that the microbial composition and diversity of the ileum and the large intestine (collectively termed the hindgut) were close together, and the genetic distance and functional projections between them were similar. Meanwhile, the diversity index results revealed that the bacterial richness and diversity of the hindgut were significantly higher than those of the foregut. We found that from the foregut to the hindgut, the dominant bacteria changed from Proteobacteria to Bacteroidetes. In LEfSe analysis, Succiniclasticum was found to be significantly abundant bacteria in the foregut and was involved in succinic acid metabolism. Ruminococcaceae and Caldicoprobacteraceae were significantly abundant in hindgut, which can degrade cellulose polysaccharides in the large intestine and produce beneficial metabolites. Moreover, Coriobacteriaceae and Eggthellaceae are involved in flavonoid metabolism and polyphenol production. Interestingly, these unique bacteria have not been reported in Mongolian sheep or other sheep breeds. Collectively, the gut microbiota of Aohan fine-wool sheep is one of the keys to adapting to the semi-desert grassland environment. Our results provide new insights into the role of gut microbiota in improving stress tolerance and gut health in sheep.

In our study, a set of lambs with coarse wool type all over their bodies were discovered within a full-sib family during an embryo transfer experiment of merino fine wool sheep. The difference between coarse and fine wool traits were studied from the perspective of RNA modification-N6-methyladenosine. A total of 31,153 peaks were collected, including 15,968 peaks in coarse skin samples and 15,185 peaks in fine skin samples. In addition, 7208 genes were differentially m6A methylated, including 4167 upregulated and 3041 downregulated in coarse skin samples. Four key genes (EDAR, FGF5, TCHH, KRT2) were obtained by comprehensive analysis of the MeRIP-seq and RNA sequence, which are closely related to primary wool follicle morphogenesis and development. The PI3K/AKT pathway was enriched through different m6A-related genes. These results provided new insights to understand the role of epigenetics in wool sheep domestication and breeding.

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.

β-Catenin is an evolutionarily conserved molecule in the canonical Wnt signaling pathway, which controls decisive steps in embryogenesis and functions as a crucial effector in the development of hair follicles. However, the molecular mechanisms underlying wool production have not been fully elucidated. In this study, we investigated the effects of ovine β-catenin on wool follicles of transgenic sheep produced by pronuclear microinjection with a skin-specific promoter of human keratin14 (k14). Both polymerase chain reaction and Southern blot analysis showed that the sheep carried the ovine β-catenin gene and that the β-catenin gene could be stably inherited. To study the molecular responses to high expression of β-catenin, high-throughput RNA-seq technology was employed using three transgenic sheep and their wild-type siblings. These findings suggest that β-catenin normally plays an important role in wool follicle development by activating the downstream genes of the Wnt pathway and enhancing the expression of keratin protein genes and keratin-associated protein genes.

Action potential (AP) generation in inhibitory interneurons is critical for cortical excitation-inhibition balance and information processing. However, it remains unclear what determines AP initiation in different interneurons. We focused on two predominant interneuron types in neocortex: parvalbumin (PV)- and somatostatin (SST)-expressing neurons. Patch-clamp recording from mouse prefrontal cortical slices showed that axonal but not somatic Na+ channels exhibit different voltage-dependent properties. The minimal activation voltage of axonal channels in SST was substantially higher (∼7 mV) than in PV cells, consistent with differences in AP thresholds. A more mixed distribution of high- and low-threshold channel subtypes at the axon initial segment (AIS) of SST cells may lead to these differences. Surprisingly, NaV1.2 was found accumulated at AIS of SST but not PV cells; reducing NaV1.2-mediated currents in interneurons promoted recurrent network activity. Together, our results reveal the molecular identity of axonal Na+ channels in interneurons and their contribution to AP generation and regulation of network activity.

As a complementary technique of the BDD-based approach, bounded model checking (BMC) has been successfully applied to LTL symbolic model checking. However, the expressiveness of LTL is rather limited, and some important properties cannot be captured by such logic. In this paper, we present a semantic BMC encoding approach to deal with the mixture of ETLf and ETLl. Since such kind of temporal logic involves both finite and looping automata as connectives, all regular properties can be succinctly specified with it. The presented algorithm is integrated into the model checker ENuSMV, and the approach is evaluated via conducting a series of imperial experiments.

Effective cash management is key in banking operations and has implications for cost control, customer service, and risk management. As transactions become more diverse, manual forecasting methods have become inadequate for accurate vault cash forecasting, which involves extensive data analysis. To address this challenge, the banking industry has adopted FinTech tools based on big data and deep learning for various client services. These methods are generally accurate but perform poorly in cases with extreme events, for which data are scarce. In this study, we propose a time series prediction model with long short-term memory and an attention mechanism that effectively predicts the presence of extreme values. We applied extreme value theory to define the extreme value loss for extreme situations and use a sliding window to process time series data. The enhanced extreme value loss function in our model yields improved prediction accuracy for time series data. We evaluated the proposed model against previous methods in evaluation experiments on data from three branches of a commercial bank in Taiwan, where the vault cash data of each exhibited extreme values. The proposed model was highly accurate: it had a lower mean absolute percentage error and higher trend accuracy than competing methods on a majority of time series, and it was also more accurate in predicting extreme values in time series data. [ABSTRACT FROM AUTHOR]

Implementing environmental regulations and developing a green economy is conducive to building a resource-saving, environmentfriendly society and is of great significance to promoting the transformation and upgrade of national economic development. Therefore, this paper selects the panel data of 41 prefecture-level cities in the Yangtze River Delta region from 2007 to 2019. Based on the 'pollution paradise hypothesis' and 'Porter hypothesis', this paper analyses the relationship amongst environmental regulation (ER), regional innovation (RI) and environmental pollution (EP) by using the fixed-effect model. Results show ER policies can effectively accelerate RI and regional pollution control, and RI has a positive regulatory influence on the effect of ER on EP. The research conclusion clarifies the relevance amongst ER, regional technological innovation and regional pollution control, and confirms ER can be used as a reverse force mechanism to promote regional pollution control. This paper has practical significance for the rational implementation of ER policy, the promotion of RI and the realization of green economic development. [ABSTRACT FROM AUTHOR]