Linear viscoelastic behaviours of bituminous mixtures and fiberglass geogrids interfaces

One major research topic is to characterize the mechanical behaviour of actual reinforced pavement structures from laboratory experimentation and take it into account for the design. This investigation aims to verify the effect of fiberglass geogrid presence on interface linear viscoelastic (LVE) behaviour separately and as a system along with the bituminous mixture layers. To conduct the research, two different fiberglass geogrids, with ultimate tensile strength (UTS) of 100 and 50 kN/m, and tack coat made of straight-run bitumen and modified by polymer were combined for the fabrication of three reinforced configurations. In addition, two unreinforced configurations were also fabricated. The first was a single layer slab and the second was a double-layered slab composed of two bituminous mixtures (same type) bonded layers by a tack coat. Complex modulus tests were carried out in specimens cored in two different directions, vertically (V) and horizontally (H) cored. The experimental data were fitted using the 2 Springs, 2 Parabolic Elements and 1 Dashpot (2S2P1D) model. The test results showed that all interfaces’ complex modulus obtained for V specimens were LVE. Moreover, complex viscous properties of the interfaces were obtained from the used binder. The interface containing polymer modification presented the highest stiffness.     

Ammonia gas sensing and magnetic permeability of enhanced surface area and high porosity lanthanum substituted Co–Zn nano ferrites

This work reports magnetic permeability and ammonia gas sensing characteristics of La³⁺ substituted Co–Zn nano ferrites possessing chemical formula Co_0.7Zn_0.3La_xFe_2-2xO₄ (x = 0–0.1) synthesized by a sol-gel route. Refinement of X-ray diffraction (XRD) patterns of the ferrite powders by the Rietveld technique has revealed the creation of single-phase spinel structure. The tenancy of constituent cations at tetrahedral/octahedral sites was obtained from the refinement of XRD. The crystallite sizes calculated from the W–H method vary from 20 to 24 nm. The scanning electron microscope (SEM) profiles of the ferrite samples were analyzed for the morphological details. The energy dispersive X-ray analysis (EDAX) patterns of the samples were obtained to test the elemental purity of the ferrites within their stoichiometry. The transmission electron microscope (TEM) image of the ferrite (x = 0.1) exhibits the spherical and oval shaped particles with a mean size of 20 nm. Fourier transform infra-red (FTIR) spectra were analyzed to confirm the superseding of La³⁺ cations at octahedral sites. The Brunauer-Emmett-Teller (BET) analysis of nitrogen adsorption-desorption isotherms of the ferrites was performed to investigate the porous structure and to determine the surface area of the nanocrystalline ferrites. The oxidation states of the constituent ions were confirmed by means of X-ray photoelectron spectroscopy (XPS). The complex permeability as a function of frequency was studied to explore the effects of structural parameters on the magnetic behaviour of the ferrites. Analysis of gas sensing properties of the ferrites have proved that the Co–Zn–La ferrite with controlled La composition can be utilized as an effective ammonia gas sensing material in commercial gas sensors.     

Microenvironment-Sensitive Fluorescent Ligand Binds Ascaris Telomere Antiparallel G-Quadruplex DNA with Blue-Shift and Enhanced Emission

The development of small molecules that can selectively target G-quadruplex (G4) DNAs has drawn considerable attention due to their unique physiological and pathological functions. However, only a few molecules have been found to selectively bind a particular G4 DNA structure. We have developed a fluorescence ligand Q1 , a molecular scaffold with a carbazole–pyridine core bridged by a phenylboronic acid side chain, that acts as a selective ascaris telomere antiparallel G4 DNA ASC20 ligand with about 18 nm blue-shifted and enhanced fluorescence intensity. Photophysical properties revealed that Q1 was sensitive to the microenvironment and gave the best selectivity to ASC20 with an equilibrium binding constant K_a=6.04×10⁵ M⁻¹. Time-resolved fluorescence studies also demonstrated that Q1 showed a longer fluorescence lifetime in the presence of ASC20. The binding characteristics of Q1 with ASC20 were shown in detail in a fluorescent intercalator displacement (FID) assay, a 2-Ap titration experiment and by molecular docking. Ligand Q1 could adopt an appropriate pose at terminal G-quartets of ASC20 through multiple interactions including π–π stacking between aromatic rings; this led to strong fluorescence enhancement. In addition, a co-staining image showed that Q1 is mainly distributed in the cytoplasm. Accordingly, this work provides insights for the development of ligands that selectively targeting a specific G4 DNA structure.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Electrical and magnetic properties of double perovskite: Y2CoMnO6

In this communication, the structural, micro-structural, dielectric, electrical, magnetic, and leakage-current characteristics of a double perovskite (Y₂CoMnO₆) ceramic material have been reported. The material was synthesized via a high-temperature mixed-oxide route. The compound crystallizes in a monoclinic structure which is confirmed from preliminary X-ray structural study. The morphological study by using scanning electron micrograph reveals the almost homogeneous distribution of grains throughout the surface of the sample. The nature of frequency-dependence of dielectric constant has been described by the Maxwell-Wagner model. The occurrence of a dielectric anomaly in the temperature dependence of dielectric permittivity study demonstrates the ferroelectric-paraelectric phase transition in the material. From the Nyquist plots, we found the existence of both grain and grain boundary effects. The frequency dependence of conductivity was studied by the Jonscher’s Power law, and the conduction phenomenon obeys the large overlapping polaron tunneling model. By using the Arrhenius equation, the activation energy has been calculated which is nearly equal to the energy required for the hoping of the electron. Both impedance and conductivity analysis demonstrate that the sample exhibits negative temperature coefficient of resistance (NTCR) properties indicating the semiconducting type of material at high temperatures. The anti-ferromagnetic character of the material is observed from the nature of magnetic hysteresis loop. The leakage current analysis suggests that the conduction process in the material follows the space charge limited conduction phenomenon. Such material will be helpful for modern electronic devices and spintronic applications.     

Enhanced electromagnetic microwave absorption of Fe/C/SiCN composite ceramics targeting in integrated structure and function

The Fe/C/SiCN composite ceramics were synthesized by polymer-derived method to obtain the integration of structure and functions. The electromagnetic waves (EMW) absorption properties at X and Ku bands were investigated. The addition of nano-sized Fe particles improved the magnetic loss and impedance matching, and the carbon nanotubes generated by the iron in-situ catalysis increased the internal relaxation polarization and interfacial polarization, which together improved the EMW absorption properties significantly. In particular, the Fe/C/SiCN-9 showed the optimum reflection loss (RL) of ?31.06 dB at 10.03 GHz with an effective absorption bandwidth (EAB, RL < ?10 dB) of 3.03 GHz at 2.51 mm, indicating the excellent EMW absorption properties of Fe/C/SiCN composite ceramics.     

Hybridization Networks of mRNA and Branched RNA Hybrids

Messenger RNA (mRNA) is emerging as an attractive biopolymer for therapy and vaccination. To become suitable for vaccination, mRNA is usually converted to a biomaterial, using cationic peptides, polymers or lipids. An alternative form of converting mRNA into a material is demonstrated that uses branched oligoribonucleotide hybrids with the ability to hybridize with one or more regions of the mRNA sequence. Two such hybrids with hexamer arms and adamantane tetraol as branching element were prepared by solution-phase synthesis. When a rabies mRNA was treated with the branched hybrids at 1 M NaCl concentration, biomaterials formed that contained both of the nucleic acids. These results show that branched oligoribonucleotides are an alternative to the often toxic reagents commonly used to formulate mRNA for medical applications.     

Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multi-phases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hard-ness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive expla-nation (SHAP) values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four fea-tures,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

基于K-means和SOM的水下传感器数据采集方法

随着海洋资源勘探和海洋污染物监控工作的开展，水文数据的监测和采集等已经成为重要的研究方向。其中，水下无线传感器网络在水文数据采集过程中起着举足轻重的作用。本文研究的是水下无线传感器二维监测网络模型中，传感器节点数据采集的问题，其设计方法是通过自组织映射（Self-organizing mapping，SOM）对传感器节点进行路径最优化处理，结合优化的路径图形和K-means算法找到路径内部聚合点，利用聚合点和传感器的节点得到传感器通信半径内的数据采集点，最后通过SOM得到水下机器人（Autonomous underwater vehicle，AUV）到各个数据采集点采集数据的最优路径。经过实验验证，在水下1 200 m×1 750 m范围内布置52个传感器节点的情景下，数据采集点相比于传感器节点路径规划采用相同的采集顺序得到的路径优化了6.7%；对数据采集点重新进行自组织路径规划得到的路径比传感器结点路径的最优解提高了12.2%。增加传感器节点的数量，其结果也大致相同，因此采用该方法可以提高水下机器人采集数据的效率。