Electronically Excited States in Model Complexes of Noble Metal Clusters with Carbon Nanodots

Russian Physics Journal - Theoretical calculations of excited states in the complexes of gold and silver three-atom nanoclusters with carbon quantum nanodots are performed using the M062X...     

Separation,Preconcentration, and Determination of Lead,Cadmium, and Iron Using Cloud Point Extraction with Flame Atomic Absorption Spectrometry

Journal of Applied Spectroscopy - The concentration of heavy metals in drinking water is an important standard for water quality evaluation and water pipeline corrosion detection. This research...     

Evolutionary neighborhood discovery algorithm for agricultural routing planning in multiple fields

Annals of Operations Research - In recent years, operations research in agriculture has improved the harvested yield, reduced the cost and time required for field operations, and maintained...     

Singular soliton,shock-wave,breather-stripe soliton,hybrid solutions and numerical simulations for a (2+1)-dimensional Caudrey–Dodd–Gibbon–Kotera–Sawada system in fluid mechanics

Nonlinear Dynamics - In this paper, a (2+1)-dimensional Caudrey–Dodd–Gibbon–Kotera–Sawada system is investigated in fluid mechanics via the symbolic computation. With the...     

基于无人机探测大柴旦地区近地层气溶胶特征

通过改装多旋翼无人机（UAV）和搭载各类载荷以及联合地基观测设备对大柴旦地区大气、环境以及气溶胶参数进行测量。利用获得的数据资料，对该地区近地层气溶胶粒子数浓度（即单位体积空气中气溶胶粒子的数目）、消光系数以及气象要素等特征进行了分析。结果表明，在大柴旦地区，近地层气溶胶粒子数浓度日变化显著，呈现双峰形态，气溶胶粒子数浓度的变化范围为75～220 cm^-3,消光系数的变化范围为0.004～0.038 km^-1;当风速小于6 m/s时，气溶胶粒子数浓度与风速呈负相关关系；当风速大于6 m/s时，二者呈正相关关系；相对湿度对气溶胶粒子的影响较小，这可能是由于该地区以沙尘型气溶胶为主，吸湿性较弱。本研究基于多旋翼无人机探测平台，可以有效地获得近地层精细化大气、环境结构，有助于研究人员了解该地区气溶胶的结构、变化特征以及建立气溶胶模式，同时也为气溶胶及大气环境参数探测方法提供了技术支撑及思路拓展。     

Finite-sample properties of estimators for first and second order autoregressive processes

Statistical Inference for Stochastic Processes - The class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily...     

Cost-allocation problems for fuzzy agents in a fixed-tree network

Fuzzy Optimization and Decision Making - Cost-allocation problems in a fixed network are concerned with distributing the costs for use by a group of clients who cooperate in order to reduce such...     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

Optimal Control of Nonlinear Fractional-Order Systems with Multiple Time-Varying Delays

Journal of Optimization Theory and Applications - This paper considers an optimal control problem governed by nonlinear fractional-order systems with multiple time-varying delays and subject to...     

Dynamic stability of a class of fractional-order nonlinear systems via fixed point theory

In this paper, the problem of the uniform stability for a class of fuzzy fractional-order genetic regulatory networks with random discrete delays, distributed delays, and parameter uncertainties is studied. Although there is a portion of literature on using fixed point theorems to study the stability of fractional neural networks, most of them required the fractional order to be in $\left(\frac{1}{2},1\right)$. However, the case of the fractional-order belonging to $(0,\frac{1}{2})$ has not been discussed. To solve it, this work proposes a novel idea of using fixed point theory to study the stability of fuzzy (0,1) order neural networks, the problem of the uniqueness of the solution of the considered genetic regulatory networks is resolved, and a novel sufficient condition to guarantee the uniform stability of above genetic regulatory networks is also derived. Eventually, an example is given to demonstrate that the obtained result is effective.