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Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning. • Proposed state-of-the-art model to predict the radiotherapy dose intelligently. • Automatic prediction results were stable across medical centers. • Computer-aided dose prediction can accelerate treatment planning. • The model also showed good generalizability for endometrial cancer. [ABSTRACT FROM AUTHOR]

This paper mainly discusses the application of the particle swarm optimization in logistics distribution routing problems. Combining with the characteristics of logistics and distribution, it established a mathematical model of the distribution routing problem. Introducing three kinds of optimization strategies in the particle swarm optimization to optimize the particle swarm algorithm, constructing three different particle swarm algorithms of LinWPSO, SAPSO and RandWPSO, used respectively the standard of PSO, LinWPSO, SAPSO and RandWPSO to solve calculation cases of problems in logistics and route, the results showed that the performance of the LinWPSO, SAPSO and RandWPSO to solve vehicle routing problem is better than standard PSO. Performance of SAPSO is optimal, which can effectively solve vehicle routing problems of the logistics distribution, when the problem size increases, optimization advantages of the SAPSO will display fully, we can greatly shorten the delivery mileage by using the SAPSO to solve the logistics distribution routing problem. [ABSTRACT FROM AUTHOR]

One solution to the low specific power of hybrid electric vehicular batteries is a hybrid energy storage system (HESS) that takes advantage of the high specific power performance of ultra-capacitors. The design of a type of zero current transition (ZCT) soft switching bidirectional direct current-direct current (DC-DC) power converter that can be used as an ultra-capacitor-battery interface in an active parallel schema of a HESS is described. The circuit operation of the ZCT DC-DC power converter is depicted in detail. The HESS controller is designed as a two-layered hierarchical control structure: the first layer is responsible for working mode control of the HESS, and the second layer is responsible for DC-DC power converter control in which a fuzzy logic PID algorithmis employed. Simulation results indicate that this design is a potential solution to the problem of the low specific power of batteries, especially for regenerative braking and electric motor assist. The proposed active parallel schema with ZCT exhibits a significant advantage in power and energy decoupling. HESS with ZCT achieves better efficiency compared to the battery only operation. The experimental results validates the idea that the ultra-capacitor cooperates with the battery in acceleration mode. [ABSTRACT FROM AUTHOR]

The quadratic knapsack problem ( QKP) maximizes a quadratic objective function subject to a binary and linear capacity constraint. Due to its simple structure and challenging difficulty, it has been studied intensively during the last two decades. This paper first presents some global optimality conditions for ( QKP), which include necessary conditions and sufficient conditions. Then a local optimization method for ( QKP) is developed using the necessary global optimality condition. Finally a global optimization method for ( QKP) is proposed based on the sufficient global optimality condition, the local optimization method and an auxiliary function. Several numerical examples are given to illustrate the efficiency of the presented optimization methods. [ABSTRACT FROM AUTHOR]

In this article, we first introduce a definition of a local minimizer for the quadratic programs with {-1, 1} constraints and show that the existing necessary condition of global optimality in some recent literature is in fact the necessary and sufficient condition of local optimality under the present definition. A filled function and the corresponding filled function algorithm then are proposed to get the global minimizer of the concerned minimization problem. The existing sufficient condition of global optimality is employed to form one of the termination rules of the algorithm. Several numerical examples are reported to show the efficiency of the present method for solving quadratic programs with binary constraints. [ABSTRACT FROM AUTHOR]

Normally, the available transfer capability of the study transfer is obtained for a given base power flow. If once the node power changed, the available transfer capability calculation is carried out, a lot times of calculation will be needed, as the commercial software of managing and utilizing system transmission (MUST) does, which will cost a lot of computation time. Based on the processing of the available transfer capability by using distribution factors, the sensitivity of available transfer capability with respect to the node power is proposed. The sensitivity can be used to estimate the available transfer capability of the study transfer and to predict the change region of the available transfer capability when the node power changed. Practical system analysis verified the proposed algorithm. [ABSTRACT FROM AUTHOR]

Convexity is, without a doubt, one of the most desirable features in optimization. Many optimization problems that are nonconvex in their original settings may become convex after performing certain equivalent transformations. This paper studies the conditions for such hidden convexity. More specifically, some transformation-independent sufficient conditions have been derived for identifying hidden convexity. The derived sufficient conditions axe readily verifiable for quadratic optimization problems. The global minimizer of a hidden convex programming problem can be identified using a local search algorithm. [ABSTRACT FROM AUTHOR]

The pointwise sampling algorithm has been used in adaptive contrast enhancement (ACE) for nearly three decades. To overcome its computational overhead, various stepwise sampling algorithms have been proposed, including the highly overlapped interpolation contrast enhancement (HOICE) algorithm that addresses the artefacts, such as image distortion and blocking effects, of other stepwise sampling algorithms while keeping the advantage of speed. However, HOICE's internal mechanism has not been compared with traditional pointwise algorithms, which may affect the scope of its future application. The authors establish a bridge between traditional pointwise sampling and HOICE: they first mathematically analyse the internal mechanism of two typical sampling algorithms (pointwise and HOICE); and then quantitatively demonstrate the similarities between these two kinds of algorithms. Moreover, the results of quantitative measurement suggest that HOICE has an advantage over the pointwise algorithm in reducing the distortion along image edges. Therefore HOICE has the advantages of both pointwise and other stepwise algorithms making it a reliable sampling algorithm for ACE. [ABSTRACT FROM AUTHOR]

Electromagnetic inverse problems have been found in numerous research areas, such as medical imaging, non-destructive testing, geophysical prospecting and microwave tomography. These inverse problems can be considered as a large-scale nonlinear programming problem. In the paper, a quasi-Newton algorithm based on Broyden–Fletcher–Goldfarb–Shanno (BFGS) Hessian matrix updating formula is introduced and applied to solve two-dimensional (2-D) electromagnetic inverse problems. Numerical results of image reconstruction from the simulation data for some dielectric cylinders are given, and the efficiency of the proposed method for image reconstruction is evaluated. [ABSTRACT FROM AUTHOR]

A class of convexification and concavification methods are proposed for solving some classes of non-monotone optimization problems. It is shown that some classes of non-monotone optimization problems can be converted into better structured optimization problems, such as, concave minimization problems, reverse convex programming problems, and canonical D.C. programming problems by the proposed convexification and concavification methods. The equivalence between the original problem and the converted better structured optimization problem is established. [ABSTRACT FROM AUTHOR]

Optimal design and rehabilitation of a water distribution system is a constrained nonlinear optimization problem. A penalty function is often employed to transform a constrained into a nonconstrained optimization problem within the framework of a genetic algorithm search. A penalty factor is used for defining the penalty function and calculating the penalty cost for the solutions with constraint violation. Effective penalty factors vary from one optimization model to another. This paper introduces a self-adaptive penalty approach to artificially evolve both the penalty factor and the design solutions. Solution robustness is proposed and quantified, along with solution fitness, for representing the boundary between the infeasible and feasible solutions of a high-dimension optimization problem. The solution space of multiple dimensions is mapped onto a 2D space, providing significant insight into the self-adaptive penalty approach, which is compared with other constraint-handling techniques tested on a benchmark example. The results show that this approach is more effective than other penalty methods used for searching for optimal and near-optimal solutions. The self-adaptive penalty approach relieves modelers from tuning the penalty function and facilitates a practical optimization modeling for water distribution design. [ABSTRACT FROM AUTHOR]

This paper proposes an elastic demand network equilibrium model for networks with transit and walking modes. In Hong Kong, the multi-mode transit system services over 90% of the total journeys and the demand on it is continuously increasing. Transit and walking modes are related to each other as transit passengers have to walk to and from transit stops. In this paper, the multi-mode elastic-demand network equilibrium problem is formulated as a variational inequality problem where the combined mode and route choices are modeled in a hierarchical logit structures and the total travel demand for each origin-destination pair is explicitly given by an elastic demand function. In addition, the capacity constraint for transit vehicles and the effects of bi-directional flows on walkways are considered in the proposed model. All these congestion effects are taken into account for modeling the travel choices. A solution algorithm is developed to solve the multi-mode elastic-demand network equilibrium model. It is based on a Block Gauss-Seidel decomposition approach coupled with the method of successive averages. A numerical example is used to illustrate the application of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

In this paper, from a Hamiltonian point of view, the nonlinear optimal control problems are transformed into nonlinear two-point boundary value problems, and a symplectic adaptive algorithm based on the dual variational principle is proposed for solving the nonlinear two-point boundary value problem. The state and the costate variables within a time interval are approximated by using the Lagrange polynomial and the costate variables at two ends of the time interval are taken as independent variables. Then, based on the dual variational principle, the nonlinear two-point boundary value problems are replaced by a system of nonlinear equations which can preserve the symplectic structure of the nonlinear optimal control problem. Furthermore, the computational efficiency of the proposed symplectic algorithm is improved by using the adaptive multi-level iteration idea. The performance of the proposed algorithm is tested by the problems of Astrodynamics, such as the optimal orbital rendezvous problem and the optimal orbit transfer between halo orbits. [ABSTRACT FROM AUTHOR]

In view of the importance of the interfacial transition zone (ITZ) to the macro-properties of concrete, it is essential to determine the ITZ area fraction in concrete. Once the ITZ area fraction is known, the physicomechanical properties of concrete can be predicted based on the three-phase composite model. The intention of the present paper is to develop a numerical algorithm for the ITZ area fraction in concrete with elliptical aggregate particles. By introducing a contact function for two ellipses, the elliptical aggregate particles with various sizes are distributed within a square element with periodic boundary conditions. The Monte Carlo method is then adopted to evaluate the ITZ area fraction. After verifying the developed numerical algorithm with the analytical solution for the ITZ area fraction in concrete with circular aggregate particles, the effects of key factors that affect the ITZ area fraction are examined in a quantitative manner. It is found in the paper that the ITZ area fraction increases with the increase of the ITZ thickness, but decreases with the increase of the maximum aggregate diameter and the ratio between the major and minor axes of elliptical aggregate particles. It is also found that the aggregate gradation has a significant influence on the ITZ area fraction. The paper concludes that the numerical algorithm presented in the paper can predict the ITZ area fraction with reasonable accuracy. [ABSTRACT FROM AUTHOR]

A novel algorithm to recognise human identities via gait by body-worn accelerometers is presented. It uses acceleration information to measure human gait dynamics. Acceleration-based gait recognition is a non-intrusive biometric measurement, which is insensitive to changes of lighting conditions and viewpoint. The proposed algorithm first extracts signature points from gait acceleration signals, and then identifies the gait pattern using a signature point-based voting scheme. Experiments with a data set of 30 subjects show that the proposed algorithm significantly outperforms other existing methods and achieves a high recognition rate of 96.7% for the case of five accelerometers. [ABSTRACT FROM AUTHOR]

Recently taking advantage of the novel diffraction-enhanced imaging (DEI) method, one very effective and practical phase contrast imaging technique—a new x-ray computed tomography scheme based on DEI (DEI-CT) showed promising results, really superior to those of conventional CT imaging. In this letter, we show that the application of the conventional CT reconstruction algorithm is not always satisfactory for the new DEI-CT imaging and a new mathematical framework for imaging reconstruction is presented. Experimental data collected at the Beijing synchrotron radiation facility are also discussed using the new algorithm. [ABSTRACT FROM AUTHOR]

An absorbing fictitious boundary condition (FBC) is presented to generate an iterative domain decomposition method (DDM) for analyzing waveguide problems. The FBC for connecting the subdomains on a fictitious boundary is developed according to the actual field distribution in the waveguide to ensure the propagation of waves between adjacent subdomains. Numerical experiments demonstrate that the proposed iterative algorithm can acquire a convergent solution accurately. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 30: 252–254, 2001. [ABSTRACT FROM AUTHOR]