Improved Newton-Raphson Methods for Solving Nonlinear Equations

In this paper, we mainly study the numerical algorithms for simple root of nonlinear equations based on Newton-Raphson method. Two modified Newton-Raphson methods for solving nonlinear equations are suggested. Both of the methods are free from second derivatives. Numerical examples are made to show the performance of the presented methods, and to compare with other ones. The numerical results illustrate that the proposed methods are more efficient and performs better than Newton-Raphson method

A Modified Newton-type Method with Order of Convergence Seven for Solving Nonlinear Equations

In this paper, we mainly study the iterative method for nonlinear equations. We present and analyze a modified seventh-order convergent Newton-type method for solving nonlinear equations. The method is free from second derivatives. Some numerical results illustrate that the proposed method is more efficient and performs better than the classicalNewton's method

Quantum Annealing Approach for the Optimal Real-time Traffic Control using QUBO

Traffic congestion is one of the major issues in urban areas, particularly when traffic loads exceed the roads capacity, resulting in higher petrol consumption and carbon emissions as well as delays and stress for road users. In Asia, the traffic situation can be further deteriorated by road sharing of scooters. How to control the traffic flow to mitigate the congestion has been one of the central issues in transportation research. In this study, we employ a quantum annealing approach to optimize the traffic signals control at a real-life intersection with mixed traffic flows of vehicles and scooters. Considering traffic flow is a continuous and emerging phenomenon, we used quadratic unconstrained binary optimization (QUBO) formalism for traffic optimization, which has a natural equivalence to the Ising model and can be solved efficiently on the quantum annealers, quantum computers or digital annealers. In this article, we first applied the QUBO traffic optimization to artificially generated traffic for a simple intersection, and then we used real-time traffic data to simulate a real Dongda-Keyuan intersection with dedicated cars and scooter lanes, as well as mixed scooter and car lanes. We introduced two types of traffic light control systems for traffic optimization C-QUBO and QUBO. Our rigorous QUBO optimizations show that C-QUBO and QUBO outperform the commonly used fixed cycle method, with QUBO outperforming C-QUBO in some instances. It has been found that QUBO optimization significantly relieves traffic congestion for the unbalanced traffic volume. Furthermore, we found that dynamic changes in traffic light signal duration greatly reduce traffic congestion.Comment: 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 24-26 November 202

Fabrication of multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors by using CF4 plasma treatment

Multianalyte CeO2 biosensors have been demonstrated to detect pH, glucose, and urine concentrations. To enhance the multianalyte sensing capability of these biosensors, CF4 plasma treatment was applied to create nanograin structures on the CeO2 membrane surface and thereby increase the contact surface area. Multiple material analyses indicated that crystallization or grainization caused by the incorporation of flourine atoms during plasma treatment might be related to the formation of the nanograins. Because of the changes in surface morphology and crystalline structures, the multianalyte sensing performance was considerably enhanced. Multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors exhibit potential for use in future biomedical sensing device applications

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).</p

N-(4-Nitro­pheneth­yl)formamide

The title compound, C9H10N2O3, was synthesized by direct N-formyl­ation of 4-nitro­phenethyl­amine hydro­chloride with formic acid and sodium formate in the absence of catalyst and solvent. In the crystal structure, mol­ecules are linked by inter­molecular N—H⋯O hydrogen-bond inter­actions into chains parallel to the a axis

An Automatic Method for Complete Triangular Mesh Conversion into Quadrilateral Mesh for Multiple Domain Geometry

This research developed an automatic two-dimensional finite element meshing system to resolve practical engineering problems in the fields of geology, hydrology, and water resources. This system first used the Delaunay triangulation method to create reasonable-density triangular mesh and then converted it into quadrilateral mesh by combining proper pairs of adjacent triangles. A series of combination patterns aiming at three cases were established. The effect of the number of boundary edges on the subsequent meshing procedures were studied and summarized. For the geometry with multiple domains an adjustment method is proposed to completely eliminate the residual triangles during quadrilateral meshing through adjusting the number of boundary edges in each loop to be even. A special boundary loop identification method is proposed for priority treatment. Corresponding treatment methods aimed at three different situations are established for common boundary loops. For a certain boundary loop with an odd number of boundary edges, the appropriate edge for new point insertion is determined by the position properties and relative density errors. Practical applications confirm that the method proposed in this paper could successfully implement the full conversion from the triangular mesh to the quadrilateral mesh

A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter

Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.</p