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

A Deep Learning Method for Dynamic Process Modeling of Real Landslides Based on Fourier Neural Operator

Abstract The conventional numerical solvers for partial differential equations encounter a formidable challenge, as their computational efficiency and accuracy are heavily contingent on grid size. Recently, machine learning (ML) has exhibited substantial promise in addressing partial differential equations. Nevertheless, substantial hurdles persist in practical applications. In this work, we endeavor to establish a deep learning framework founded on the Fourier neural operator (FNO) for resolving the intricacies of simulating real landslide dynamic processes. Our findings demonstrate that the current FNO approach adeptly replicates landslide dynamic processes and boasts exceptional computational efficiency. Additionally, it is noteworthy that this data‐driven ML methodology can seamlessly incorporate data from other experimental sources or numerical simulation techniques. Consequently, this work underscores the significant potential of utilizing ML methodologies to supplant conventional numerical simulation methods

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

3D Twin Formation Mechanism from Onion Carbon

The synthesis of diamonds, in particular that of ultrahard nanotwin diamonds, has been a long-standing goal. The high pressure triggers the sp2 carbon, such as graphite, to transit to the ultrahard sp3 diamond under high-temperature conditions. Due to the high-energy barrier for phase transition, controlling particle size is highly challenging. In this work, we employed large-scale MD simulations to study the microstructure evolution of onion carbon under HTHP conditions. We found that the three-dimensional (3D) twin developed at the apexes of onion carbon particles and propagated along the cross-layer direction. Interestingly, the grains are effectively controlled in nanosize by the high density of the 3D twin structure. Verifying the influence of curvature in 3D twin formation, hollow onion carbon systems were also investigated in HTHP, which exhibits fewer 3D twin structures due to their lower curvature. 3D twin structures extensively promoted the hardness in shear deformation simulations, thus offering valuable insights into the synthesis of ultrahard material

3D Twin Formation Mechanism from Onion Carbon

The synthesis of diamonds, in particular that of ultrahard nanotwin diamonds, has been a long-standing goal. The high pressure triggers the sp2 carbon, such as graphite, to transit to the ultrahard sp3 diamond under high-temperature conditions. Due to the high-energy barrier for phase transition, controlling particle size is highly challenging. In this work, we employed large-scale MD simulations to study the microstructure evolution of onion carbon under HTHP conditions. We found that the three-dimensional (3D) twin developed at the apexes of onion carbon particles and propagated along the cross-layer direction. Interestingly, the grains are effectively controlled in nanosize by the high density of the 3D twin structure. Verifying the influence of curvature in 3D twin formation, hollow onion carbon systems were also investigated in HTHP, which exhibits fewer 3D twin structures due to their lower curvature. 3D twin structures extensively promoted the hardness in shear deformation simulations, thus offering valuable insights into the synthesis of ultrahard material

3D Twin Formation Mechanism from Onion Carbon

The synthesis of diamonds, in particular that of ultrahard nanotwin diamonds, has been a long-standing goal. The high pressure triggers the sp2 carbon, such as graphite, to transit to the ultrahard sp3 diamond under high-temperature conditions. Due to the high-energy barrier for phase transition, controlling particle size is highly challenging. In this work, we employed large-scale MD simulations to study the microstructure evolution of onion carbon under HTHP conditions. We found that the three-dimensional (3D) twin developed at the apexes of onion carbon particles and propagated along the cross-layer direction. Interestingly, the grains are effectively controlled in nanosize by the high density of the 3D twin structure. Verifying the influence of curvature in 3D twin formation, hollow onion carbon systems were also investigated in HTHP, which exhibits fewer 3D twin structures due to their lower curvature. 3D twin structures extensively promoted the hardness in shear deformation simulations, thus offering valuable insights into the synthesis of ultrahard material