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Carbyne nanocrystal (CN), as a kind of one-dimensional (1D) sp-hybridized carbon allotrope, has attracted much attention due to its excellent optical and transport properties. However, the lattice properties of 1D CNs under high temperature remain unclear. In our work, we investigate the stability and lattice properties of 1DCNs based on the atomic-bond-relaxation (ABR) approach and first-principles calculations. We find the change of transverse vibration frequencies and bond kinks induced by the thermal effect plays a significant role in the mechanical properties of 1D CNs. We establish a relationship between kink angle and phonon vibration modes, giving a deep inside into high frequency and low-frequency vibration behavior of 1D CNs. Our results indicate the phonon vibration modes modulated by kinks under applied temperatures can lead to a negative thermal expansion behavior in the axial direction of 1D CNs, suggesting an effective way to control the thermal properties of 1D CNs for practical applications.

This paper investigates the thermal stability of metakaolin/fly ash (MK/FA) based geopolymers at elevated temperature of 500℃ and 800℃. The thermal stability of geopolymers is evaluated from the following aspects: apparent morphology, residual compressive strength, volumetric shrinkage, and microstructure. The results show that the incorporation of FA contributes to the reduction of specimen cracking while the increasing of equivalent Na2O leads to severe cracking. After experiencing 800°C, the compressive strength of all specimens remains above 70 % of that at room temperature, and the specimen with high equivalent Na2O exhibits higher compressive strength. The XRD and FTIR results indicate that the chemical component of most specimens remains stable at elevated temperatures without decomposition and formation of new crystalline phases. However, the MK/FA based geopolymer exhibit considerable volume shrinkage at elevated temperatures. The shrinkage ranges from 3.2 % to 4.9 % at 500°C and 5.1–14.2 % at 800°C for different specimens. The increase of equivalent Na2O from 8 % to 16 % results in a 174.8 % increase of shrinkage. Overall, MK/FA based geopolymers exhibit convincing thermal performance, and the specimens exhibit higher residual compressive strength and chemical stability after exposure to elevated temperatures.

Background Studies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear. Methods The NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks. Results There were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1. Conclusion The study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Abstract Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.

The human induced pluripotent stem cell (hiPSC) line was generated from peripheral blood mononuclear cells (PBMCs) isolated from a 1-year-old boy who suffering from congenital cataract (CC), carrying heterozygous mutations in BFSP1 and RHO. PBMCs from this patient were reprogrammed into hiPSCs using non-integrative Sendai viral vectors expressing OCT4, SOX2, KLF4 and C-MYC. CC-hiPSCs had normal karyotype and showed pluripotency both in vitro and in vivo. The CC-hiPSCs would supply an important cell model for studying the pathogenesis of CC.

Huge amounts of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in road sensor networks. Publish/subscribe system is one kind of important applications for analyzing and processing these huge mounts of data in road sensor networks, which is required to support millions of subscriptions and filter a message in milliseconds. Since the messages arrive continuously at a high speed, rapid processing of the messages is definitely a challenge. This paper mainly addresses the issue of parameterized spatio-textual publish/subscribe problem in road sensor networks. First, with considering both the network distance and textual similarity of the subscriptions and messages, the road network structure, together with the subscriptions and the messages will be partitioned and organized efficiently, and a combined index structure, called basic indexing architecture, is proposed. Second, several effective pruning techniques which consider both location information and textual information are presented to cut down the processing overhead. Moreover, by employing these pruning techniques into the basic indexing architecture, an more efficient index, called enhanced indexing architecture, is presented. Third, an efficient processing algorithm is designed to improve the scalability. Finally, extensive simulations are conducted to show the efficiency and scalability of the proposed methods in road sensor networks.

Hashing method is an efficient technique of multimedia security for content protection. It maps an image into a content-based compact code for denoting the image itself. While most existing algorithms focus on improving the classification between robustness and discrimination, little attention has been paid to geometric invariance under normal digital operations, and therefore results in quite fragile to geometric distortion when applied in image copy detection. In this article, a novel effective image hashing method is proposed based on geometric invariant vector distance in both spatial domain and frequency domain. First, the image is preprocessed by some joint operations to extract robust features. Then, the preprocessed image is randomly divided into several overlapping blocks under a secret key, and two different feature matrices are separately obtained in the spatial domain and frequency domain through invariant moment and low frequency discrete cosine transform coefficients. Furthermore, the invariant distances between vectors in feature matrices are calculated and quantified to form a compact hash code. We conduct various experiments to demonstrate that the proposed hashing not only reaches good classification between robustness and discrimination, but also resists most geometric distortion in image copy detection. In addition, both receiver operating characteristics curve comparisons and mean average precision in copy detection clearly illustrate that the proposed hashing method outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]