Mesh simplification algorithm based on edge curvature metrics and local optimization

Aiming at the problem that the mesh simplification algorithm loses the geometric features of the model in large-scale simplification,an improved half-edge collapse mesh simplification algorithm is proposed.The concept of approximate measurement of edge curvature is introduced,and the edge curvature is added to the error measure,so that the order of half-edge collapse of the mesh is changed,and the simplified details of the mesh model can be preserved accurately.At the same time,by analyzing the quality of simplified triangular mesh,optimizing triangular mesh locally,reducing the amount of narrow triangles,the quality of the simplified model is improved.The proposed algorithm was tested on Cow model,Car model and Bunny model,and compared with another three algorithms,one of them is a classical mesh simplification algorithm based on edge collapse,the other is an improved algorithm of the classical one.The experimental results show that the improved algorithm can better retain the detail features of the original model at the same reduction ratio,and has reasonable mesh allocation,fast execution speed and small error.

AHEVCVideo Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

Developing high-efficiency base editors by combining optimized synergistic core components with new types of nuclear localization signal peptide

The clustered regularly interspaced short palindromic repeats(CRISPR)–CRISPR-associated protein(Cas) system has been widely used for genome editing. In this system, the cytosine base editor(CBE) and adenine base editor(ABE) allow generating precise and irreversible base mutations in a programmable manner and have been used in many different types of cells and organisms. However, their applications are limited by low editing efficiency at certain genomic target sites or at specific target cytosine(C) or adenine(A) residues. Using a strategy of combining optimized synergistic core components, we developed a new multiplex super-assembled ABE(sABE) in rice that showed higher base-editing efficiency than previously developed ABEs. We also designed a new type of nuclear localization signal(NLS) comprising a FLAG epitope tag with four copies of a codon-optimized NLS(F4NLS^(r2)) to generate another ABE named F4NLS-sABE. This new NLS increased editing efficiency or edited additional A at several target sites. A new multiplex super-assembled CBE(sCBE) and F4NLS^(r2) involved F4NLS-sCBE were also created using the same strategy. F4NLS-sCBE was proven to be much more efficient than sCBE in rice. These optimized base editors will serve as powerful genome-editing tools for basic research or molecular breeding in rice and will provide a reference for the development of superior editing tools for other plants or animals.

Whole-process design and experimental validation of landing gear lower drag stay with global/local linked driven optimization strategy

Landing gear lower drag stay is a key component which connects fuselage and landing gear and directly effects the safety and performance of aircraft takeoff and landing. To effectively design the lower drag stay and reduce the weight of landing gear, Global/local Linked Driven Optimization Strategy(GLDOS) was developed to conduct the overall process design of lower drag stay in respect of optimization thought. The whole-process optimization involves two stages of structural conceptual design and detailed design. In the structural conceptual design, the landing gear lower drag stay was globally topologically optimized by adopting multiple starting points algorithm. In the detailed design, the local size and shape of landing gear lower drag stay were globally optimized by the gradient optimization strategy. The GLDOS method adopts different optimization strategies for different optimization stages to acquire the optimum design effect. Through the experimental validation, the weight of the optimized lower dray stay with the developed GLDOS is reduced by 16.79% while keeping enough strength and stiffness, which satisfies the requirements of engineering design under the typical loading conditions. The proposed GLDOS is validated to be accurate and efficient in optimization scheme and design cycles. The efforts of this paper provide a whole-process optimization approach regarding different optimization technologies in different design phases, which is significant in reducing structural weight and enhance design tp wid 1 precision for complex structures in aircrafts.

Optimization Methods for Mixed Integer Weakly Concave Programming Problems

In this paper,we consider a class of mixed integer weakly concave programming problems(MIWCPP)consisting of minimizing a difference of a quadratic function and a convex function.A new necessary global optimality conditions for MIWCPP is presented in this paper.A new local optimization method for MIWCPP is designed based on the necessary global optimality conditions,which is different from the traditional local optimization method.A global optimization method is proposed by combining some auxiliary functions and the new local optimization method.Furthermore,numerical examples are also presented to show that the proposed global optimization method for MIWCPP is efficient.

Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II

As distributed energy storage equipments, electric vehicles (EVs) have great potential for applications in power systems. Meanwhile, reasonable optimization of the charging time of EVs can reduce the users’ expense. Thus, the schedule of the EV load requires multi-objective optimization. A diversity-maximization non-dominated sorting genetic algorithm (DM-NSGA)-II is developed to perform multi-objective optimization by considering the power load profile, the users’charging cost, and battery degradation. Furthermore, a real-time locally optimal schedule is adopted by utilizing a flexible time scale. The case study illustrates that the proposed DM-NSGA-II can prevent being trapped in a relatively limited region so as to diversify the optimal results and provide trade-off solutions to decision makers. The simulation analysis shows that the variable time scale can continuously involve the present EVs in the real-time optimization rather than rely on the forecasting data. The schedule of the EV load is more practical without the loss of accuracy.

OPTIMAL INTERIOR AND LOCAL ERROR ESTIMATES OF A RECOVERED GRADIENT OF LINEAR ELEMENTS ON NONUNIFORM TRIANGULATIONS

We examine a simple averaging formula for the gradieni of linear finite elemelitsin Rd whose interpolation order in the Lq-norm is O(h2) for d < 2q and nonuniformtriangulations. For elliptic problems in R2 we derive an interior superconvergencefor the averaged gradient over quasiuniform triangulations. Local error estimatesup to a regular part of the boundary and the effect of numerical integration arealso investigated.

Enhancing performance of oppositional BBO using the current optimum(COOBBO)for TSP problems

Purpose-The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum(COOBBO)algorithm.Design/methodology/approach-The improvement measures tested in this paper include different initialization approaches,crossover approaches,local optimization approaches,and greedy approaches.Eight well-known traveling salesman problems(TSP)are employed for performance verification.Four comparison criteria are recoded and compared to analyze the contribution of each modified method.Findings-Experiment results illustrate that the combination model of“25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy”may be the best choice of all when considering both the overall algorithm performance and computation overhead.Originality/value-When solving TSP with varying scales,these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees.And an appropriate combination model may make the fullest possible contribution.

Curvature-aware simplification for point-sampled geometry

We propose a novel curvature-aware simplification technique for point-sampled geometry based on the locally optimal projection(LOP) operator.Our algorithm includes two new developments.First,a weight term related to surface variation at each point is introduced to the classic LOP operator.It produces output points with a spatially adaptive distribution.Second,for speeding up the convergence of our method,an initialization process is proposed based on geometry-aware stochastic sampling.Owing to the initialization,the relaxation process achieves a faster convergence rate than those initialized by uniform sampling.Our simplification method possesses a number of distinguishing features.In particular,it provides resilience to noise and outliers,and an intuitively controllable distribution of simplification.Finally,we show the results of our approach with publicly available point cloud data,and compare the results with those obtained using previous methods.Our method outperforms these methods on raw scanned data.

Optimal two-impulse space interception with multiple constraints

We consider optimal two-impulse space interception problems with multiple constraints.The multiple constraints are imposed on the terminal position of a space interceptor,impulse and impact instants,and the component-wise magnitudes of velocity impulses.These optimization problems are formulated as multi-point boundary value problems and solved by the calculus of variations.Slackness variable methods are used to convert all inequality constraints into equality constraints so that the Lagrange multiplier method can be used.A new dynamic slackness variable method is presented.As a result,an indirect optimization method is developed.Subsequently,our method is used to solve the two-impulse space interception problems of free-flight ballistic missiles.A number of conclusions for local optimal solutions have been drawn based on highly accurate numerical solutions.Specifically,by numerical examples,we show that when time and velocity impulse constraints are imposed,optimal two-impulse solutions may occur;if two-impulse instants are free,then a two-impulse space interception problem with velocity impulse constraints may degenerate to a one-impulse case.

Projection-based High-dimensional Sign Test

This article is concerned with the high-dimensional location testing problem.For highdimensional settings,traditional multivariate-sign-based tests perform poorly or become infeasible since their Type I error rates are far away from nominal levels.Several modifications have been proposed to address this challenging issue and shown to perform well.However,most of modified sign-based tests abandon all the correlation information,and this results in power loss in certain cases.We propose a projection weighted sign test to utilize the correlation information.Under mild conditions,we derive the optimal direction and weights with which the proposed projection test possesses asymptotically and locally best power under alternatives.Benefiting from using the sample-splitting idea for estimating the optimal direction,the proposed test is able to retain type-I error rates pretty well with asymptotic distributions,while it can be also highly competitive in terms of robustness.Its advantage relative to existing methods is demonstrated in numerical simulations and a real data example.

TOWARD SUSTAINABLE MAIZE PRODUCTION FOR SMALLHOLDERS THROUGH OPTIMIZED STRATEGIES IN NORTH CHINA

Agricultural production by smallholders is crucial for ensuring food provision in China.However,smallholders face a series of challenges on their farms including high-to-excess resource inputs,low use efficiency,as well as negative environmental impacts,which may be unfavorable for sustainable agriculture production.This study developed a county-level sustainability assessment of maize production in Hebei,China,by applying multiple data sources in combination with emergy,carbon footprint,nitrogen footprint and costbenefit analyses.Scenario analysis was applied to explore the localized implementation strategies to achieve the sustainable farming system.The results show that the average emergy sustainability index(ESI)of maize at 2.31 is relatively low.The average greenhouse gas(GHG)emissions and reactive nitrogen(Nr)losses are 0.15 g·kcal^(−1) CO_(2)-eq and 3.75 mg·kcal^(−1) N,respectively.The average cost and net income are 12,700 and 4340 CNY·ha^(−1),respectively.These results indicate a great potential to improve the environmental-economic sustainability of the maize production system of smallholders.In addition,the environmental and economic indicators calculated from the maize production show a substantial spatial heterogeneity among counties,indicating a requirement for different optimization strategies to improve the environment-economy sustainability at a finer scale.Based on the multiple scenario analysis,optimal strategies targeting each county are proposed.By adopting the optimal strategies,the average ESI and net income could increase by 32%and 83%,respectively,and the average GHG emissions and Nr losses reduce by 33%and 35%,respectively.These findings provide an important reference for adopting different strategies to achieve environmenteconomy sustainability for smallholders production systems with diverse landscapes in North China and propose a transition pathway toward achieving agriculture sustainability for smallholders worldwide.