Homoepitaxial regrowth habits of ZnO nanowire arrays

Synthetic regrowth of ZnO nanowires [NWs] under a similar chemical vapor transport and condensation [CVTC] process can produce abundant ZnO nanostructures which are not possible by a single CVTC step. In this work, we report three different regrowth modes of ZnO NWs: axial growth, radial growth, and both directions. The different growth modes seem to be determined by the properties of initial ZnO NW templates. By varying the growth parameters in the first-step CVTC process, ZnO nanostructures (e.g., nanoantenna) with drastically different morphologies can be obtained with distinct photoluminescence properties. The results have implications in guiding the rational synthesis of various ZnO NW heterostructures

Observation of strong attenuation within the photonic band gap of multiconnected networks

We theoretically and experimentally study a photonic band gap (PBG) material made of coaxial cables. The coaxial cables are waveguides for the electromagnetic waves and provide paths for direct wave interference within the material. Using multiconnected coaxial cables to form a unit cell, we realize PBGs via (i) direct interference between the waveguides within each cell and (ii) scattering among different cells. We systematically investigate the transmission of EM waves in our PBG materials and discuss the mechanism of band gap formation. We observe experimentally for the first time the wide band gap with strong attenuation caused by direct destructive interference

Spatiotemporal evolution and multi-scenario prediction of habitat quality in the Yellow River Basin

IntroductionThe Yellow River Basin (YRB) is not only a vital area for maintaining ecological security but also a key area for China’s economic and social development. Understanding its land-use change trends and habitat quality change patterns is essential for regional ecological conservation and effective resource allocation.MethodsThis study used the patch-generating land-use simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models to analyze and predict the spatial and temporal trends of habitat quality in the YRB from 2000 to 2030 under natural development (ND) and ecological conservation and high-quality development (ECD) scenarios. The PLUS model was used to predict land-use change in 2030 under different scenarios, after which the InVEST model was used to obtain the habitat quality distribution characteristics from the 2000–2030 period.Results(1) The mean values of habitat quality in the YRB in 2000, 2010, and 2020 were 0.6849, 0.6992, and 0.7001, respectively. The mean habitat quality values were moderately high. Spatial distribution characteristics were high in the west and low in the east and along the water. In 2030, habitat quality (0.6993) started to decline under ND, whereas under ECD, there was an indication of substantial improvement in habitat quality (0.7186). (2) The mean habitat degradation values in 2000, 2010, and 2020 were 0.0223, 0.0219, and 0.0231, respectively. The level of habitat degradation showed a decreasing trend, followed by an increasing trend with a stable spatial distribution pattern. The mean level of habitat degradation in 2030 (0.0241) continued to increase under ND, while a substantial decrease in the level of habitat degradation occurred under ECD (0.0214), suggesting that the level of habitat degradation could be effectively contained under the ECD scenario. (3) During the study period, the conversion of building land—both negative and positive—had the most pronounced impact on habitat quality per unit area. Further, the conversion of grassland was shown to be a key land transformation that may either lead to the deterioration or improvement of the ecological environment. The results provide scientifific theoretical support and a decision basis for ecological conservation and the high-quality development of the YRB

Deciphering RNA structural diversity and systematic phylogeny from microbial metagenomes

Metagenomics has been employed to systematically sequence, classify, analyze and manipulate the entire genetic material isolated from environmental samples. Finding genes within metagenomic sequences remains a formidable challenge, and noncoding RNA genes other than those encoding rRNA and tRNA are not well annotated in metagenomic projects. In this work, we identify, validate and analyze the genes coding for RNase P RNA (P RNA) from all published metagenomic projects. P RNA is the RNA subunit of a ubiquitous endoribonuclease RNase P that consists of one RNA subunit and one or more protein subunits. The bacterial P RNAs are classified into two types, Type A and Type B, based on the constituents of the structure involved in precursor tRNA binding. Archaeal P RNAs are classified into Type A and Type M, whereas the Type A is ancestral and close to Type A bacterial P RNA. Bacterial and some archaeal P RNAs are catalytically active without protein subunits, capable of cleaving precursor tRNA transcripts to produce their mature 5′-termini. We have found 328 distinctive P RNAs (320 bacterial and 8 archaeal) from all published metagenomics sequences, which led us to expand by 60% the total number of this catalytic RNA from prokaryotes. Surprisingly, all newly identified P RNAs from metagenomics sequences are Type A, i.e. neither Type B bacterial nor Type M archaeal P RNAs are found. We experimentally validate the authenticity of an archaeal P RNA from Sargasso Sea. One of the distinctive features of some new P RNAs is that the P2 stem has kinked nucleotides in its 5′ strand. We find that the single nucleotide J2/3 joint region linking the P2 and P3 stem that was used to distinguish a bacterial P RNA from an archaeal one is no longer applicable, i.e. some archaeal P RNAs have only one nucleotide in the J2/3 joint. We also discuss the phylogenetic analysis based on covariance model of P RNA that offers a few advantages over the one based on 16S rRNA

Malicious URL Detection Based on Improved Multilayer Recurrent Convolutional Neural Network Model

The traditional malicious uniform resource locator (URL) detection method excessively relies on the matching rules formulated by the network security personnel, which is hard to fully express the text information of the URL. Thus, an improved multilayer recurrent convolutional neural network model based on the YOLO algorithm is proposed to detect malicious URL in this paper. First, single characters are mapped to dense vectors using word embedding, and the dense vectors are participated in the training process of the whole model according to the structural characteristics of the URL in the method. Then, the CSPDarknet neural network model based on the improved YOLO algorithm is proposed to extract features of the URL. Finally, the extracted features are used to evaluate malicious URL by the bidirectional LSTM recurrent neural network algorithm. In order to verify the validity of the algorithm, a total of 200,000 URLs are collected, including 100,000 normal URLs labeled “good” and 100,000 malicious URLs labeled “bad”. The experimental results show that the method detects malicious URLs more quickly and effectively and has high accuracy, high recall rate, and high accuracy compared with Text-RCNN, BRNN, and other models

Significant enhancement of the selectivity of propylene epoxidation for propylene oxide: A molecular oxygen mechanism

As an attractive and environmentally friendly process for propylene oxide (PO) production, direct epoxidation of propylene (DEP) with molecular oxygen catalyzed by metal-based catalysts such as Ag and Cu has drawn much attention, but remains one of the biggest challenges in chemistry.</p

Shaping characteristics of excavation contours in sequential controlled fracture blasting of rock-anchored beams in Shuangjiangkou underground powerhouse

Abstract Influences of high in-situ stress generally need to be considered when excavating deep underground caverns. The dynamic fracture behaviors of rocks under blast loads were investigated by using the rock-anchored beam excavation in underground powerhouses of Shuangjiangkou Hydropower Station in Sichuan Province, China as the engineering background. To solve the problems of the poor blasting breakage effect of rocks and the difficulty in protecting surrounding rocks during excavation, mechanical properties of granite under static and dynamic loads were investigated and the sequential controlled fracture blasting (SCFB) method was adopted during in-situ tests. Based on the Riedel-Hiermaier-Thoma constitutive model and the strength criterion, software LS-DYNA was employed to simulate the dynamic propagation of blasting-induced cracks. The contour shaping effect obtained via numerical simulation is generally consistent with the test results. The results show that SCFB can to some extent control the direction of crack initiation and rock fracture behavior of the blasthole wall cracks and the spacing of successive bursting holes is about 10 times the diameter of the blastholes when the cracks between the blastholes are shaped the best effect. Moreover, the magnitude and direction of principal in-situ stress can both affect the propagation path and length of blasting-induced cracks. The results of the research on the excavation and construction of deeply buried underground caverns have a certain reference value

Temperature Effect on the Microstructures and Optical Properties of ZnO Nanowires

Temperature effects on the microstructures and optical properties of ZnO nanowires were investigated extensively. Furnace calibration results showed that temperatures inside the tube furnace had Gaussian distribution. Both radial and axial growth rates of ZnO nanowires in the Arrhenius plot fall on straight lines. With the increase of temperatures, ZnO nanowires exhibited larger diameters, better crystal qualities, and preferred growth along 〈0002〉 directions. Enhanced UV emission with the increase of growth temperatures was observed, while green emission was greatly reduced