Theoretical prediction of structure,electronic and optical properties of VH2 hydrogen storage material

The vanadium hydrides have better hydrogen storage capacity in comparison to the other metal hydrides. Although the structure of VH₂ hydride has been reported, the structural stability, electronic and optical properties of VH₂ hydride are unclear. To solve these problems, we apply the first-principles method to study the structural stability, electronic and optical properties of VH₂ hydrides. Similar to the metal dihydrides, four possible VH₂ hydrides such as the cubic (Fm-3m), tetragonal (I4/mmm), tetragonal (P4₂/mnm) and orthorhombic (Pnma) are designed. The result shows that the cubic VH₂ hydride is a thermodynamic and dynamical stability. In particular, the tetragonal (I4/mmm) and the orthorhombic (Pnma) VH₂ hydrides are firstly predicted. It is found that these VH₂ hydrides show metallic behavior. The electronic interaction of V (d-state)-H (s-state) is beneficial to improve the hydrogen storage in VH₂ hydride. In addition, the formation of V–H bond can improve the structural stability of VH₂ hydride. Based on the analysis of optical properties, it is found that all VH₂ hydrides show the ultraviolet response. Compared to the tetragonal and orthorhombic VH₂ hydrides, the cubic VH₂ hydride has better storage optical properties. Therefore, we believe that the VH₂ hydride is a promising hydrogen storage material.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

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。     

Structural,optical, and electrical properties of tin iodide-based vacancy-ordered-double perovskites synthesized via mechanochemical reaction

Over the recent past, lead-based halide perovskite materials have drawn significant attention due to their excellent optical and electrical properties for solar cells and optoelectronics applications. However, the toxicity of lead elements and instability under ambient conditions leads to develop alternative compositions. Herein, we report a novel mechanochemical synthesis of tin iodide-based double perovskites (A₂SnI₆; A = Rb⁺, Cs⁺, methylammonium, and formamidinium), and their structural, optical, and electrical properties are investigated. Importantly, we found that the hydrogen iodide (HI) addition during the ball-milling process minimizes secondary phase formation in the synthesized A₂SnI₆ powders. The effects of HI addition and the A-site substitution are investigated with respect to the lattice parameters, optical bandgaps, and electrical properties of the synthesized perovskite materials. Our results demonstrate essential information to improve the understanding of halide perovskite materials and develop efficient lead-free perovskite photoelectric devices.     

Determination of optical and structural properties of lithium silicate ceramics with different ratios of Sm doped

In this study, the synthesis and luminescence characterization of Samarium (Sm³⁺) doped lithium metasilicate (Li₂SiO₃) phosphor ceramic were investigated. It was presented and discussed the results obtained on the luminescence and other optical studies such as X-ray diffraction (XRD), optical absorption and luminescence properties of Li₂SiO₃:Sm³⁺ phosphor ceramic. The Li₂SiO₃ compound was shown a characteristic phase in XRD. The doping in the lithium compound was not having a significant effect on the basic crystal structure of the material. The maximum photoluminescence (PL) emission for Sm³⁺ doped Li₂SiO₃ was observed at 554, 583, 641, 725 nm and bore resemblance to the visible region of the spectrum. The glow curves of all synthesized materials have a complex peak structure after being irradiated with a ⁹⁰Sr–⁹⁰Y beta source. In addition, the peak between 400 and 600 nm was seen in the radioluminescence (RL) spectrum because of a wide peak thought to be caused by silicate.     

Influence of Al3+ doping for V5+ on the structural,optical, thermal and electrical properties of V2-xAlxO5-δ (x=0–0.20) ceramics

This paper reports an investigation on the structure-properties correlation of trivalent metal oxide (Al₂O₃)-doped V₂O₅ ceramics synthesized by the melt-quench technique. XRD patterns confirmed a single orthorhombic V₂O₅ phase formation with increasing strain on the doping of Al₂O₃ in place of V₂O₅ in the samples estimated by Williamson-Hall analysis. FTIR and Raman investigations revealed a structural change as [VO₅] polyhedra converts into [VO₄] polyhedra on the doping of Al₂O₃ into V₂O₅. The optical band gap was found in a wide semiconductor range as confirmed by UV–visible spectroscopy analysis. The thermal and conductivity behavior of the prepared samples were studied using thermal gravimetric analysis (TGA) and impedance analyzer, respectively. All the prepared ceramics exhibit good DC conductivity (0.22–0.36 Sm^-1) at 400 ?C. These materials can be considered for intermediate temperature solid oxide fuel cell (IT-SOFC)/battery applications due to their good conductivity and good thermal stability.     

Spray pressure variation effect on the properties of CdS thin films for photodetector applications

Herein, we report the photosensing property of CdS thin films. CdS thin films were coated onto glass substrates via a spray pyrolysis method using different spray pressures. Prepared films were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and optical and photoluminescence spectroscopy. XRD analysis demonstrated the growth of crystalline CdS films with crystallite sizes varying from 26 to 29 nm depending on the pressure. The SEM and EDAX analyses revealed nearly-stoichiometric CdS films with smooth surfaces and slight variation in grain morphology due to pressure changes. Optical measurements showed a direct bandgap varying from 2.37 eV to 2.42 eV due to pressure changes. A photodetector was also fabricated using the grown CdS films; the fabricated photodetector exhibited good performance depending on the spray pressure. A spray pressure of 1.5 GPa resulted in high photoresponsivity and external quantum efficiency.     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

Dispersive optical constants and electrical properties of nanocrystalline CuInS2 thin films prepared by chemical spray pyrolysis

Nanocrystalline CuInS₂ thin films were deposited on borosilicate glass substrates via chemical spray pyrolysis method. The structural, morphological, optical, and electrical properties were studied as a function of increasing annealing temperature from 250 to 350 ̊C. XRD analysis showed mixed phases at lower temperatures with the preferred orientation shifting towards the (112) chalcopyrite CuInS₂ plane at higher substrate temperature. The crystallite size increased slightly between 13 and 18 nm with increase in annealing temperature. The optical band gap was determined on basis of Tauc extrapolation method and the Wemple–Di-Domenico single oscillator model. Possible structural and quantum confinement effect may have resulted in relatively larger band gaps of 1.67–2.04 eV, relative to the bulk value of 1.5 eV. The presence of Cu_xS in the as-deposited and wurtzite peaks after annealing at 350 ^̊C play a role in influencing the optical and electrical properties of CuInS₂ thin films.