In functional linear regression, a supervised version of functional principal components analysis (FPCA) can automatically estimate the leading functional principal components (FPCs), which not only represent the major source of variation of the functional predictor but also are simultaneously correlated with the response. However, the existing supervised FPCA (sFPCA) is only applicable to single modal functional data. In this paper, we propose a weighted version of supervised FPCA (w-sFPCA) by considering the adaptive weighting of multi-modal functional predictors. The new w-sFPCA not only assigns corresponding weights to each modal of functional predictors, but also automatically estimates the leading FPCs associated with response variables, representing the main sources of variation in functional predictors. The method is demonstrated to have a better prediction accuracy than the conventional sFPCA method by using one real application on meteorological data and two carefully designed simulation studies.

This study describes the analysis of nanofluid flow due to trapping along porous annulus with cilia in the presence of thermal radiation and heat absorption effects. In this examination, a partial differential equation is formulated to represent the two-dimensional movement of nanofluid. This system is developed in the cylindrical coordinates system and thereafter, a long wavelength approximation is introduced to attain the simplified form of the system. Emerging parameters are attained with nanoparticle’s effects in the system. Effective thermal conductivity models for heat transfer analysis are utilized. Numerical solution has been calculated for pressure rise and pressure gradient. Velocity and temperature distribution is also plotted for physical parameters. It is important to find that the trend of velocity is increasing by improving the quantity of nanoparticle concentration, radiation and mixed convection parameters. By increasing the size of the radius, velocity profile and pressure gradient have decreasing trend.

The process of screening and developing new drugs through experiments is very slow and requires a lot of manpower and material resources, and the use of computer-aided prediction of the molecular properties of drugs can greatly save time and cost of drug development. Therefore, in order to enable anti-breast cancer candidate drugs to have good biological activity and ADMET properties for inhibiting ERα, the random forest classifier was first used for the collected 1974 compounds to screen the top 20 molecular descriptors with the most significant effects on biological activity. Then a QSAR model was established using this and ${\rm pIC}_{50}$ value as characteristic data. The biological activity values of 50 new compounds were predicted via the PSO optimized BP neural network, with the model fit of 0.8337 and the root mean square error of 0.7315, which were more consistent with the actual values than the predicted results of the BP neural network. Subsequently, in order to improve the success rate of drug development, the ADMET classification prediction model was constructed using PSO to optimize the SVM based on the existing ADMET property data. The algorithm cross-validation CV accuracy rate reached 94.0767%, and the prediction accuracy rates of the five index models were all above 79%. The results show that the proposed model has better prediction performance than the benchmark model, and the adopted prediction strategy is effective, which can provide reference for the discovery and development of anti-breast cancer drugs.

This paper proposed a Marching Trapezoidal Polyhedrons 3D modeling algorithm (MTPD) based on the cone-shaped spatial distribution of Doppler weather radar base data. In this algorithm, a trapezoidal polyhedron was introduced to replace the cube in the conventional modeling algorithm as a basic volume element for modelling. On the other hand, the hexahedral index or tetrahedral index was selected as the construction model for the 3D iso-surface based on the difference in spatial range for 3D radar modeling, to balance the efficiency of the algorithm and the precision of the modeling results. Based on this algorithm, a Doppler weather radar 3D visualization platform was developed using WebGL technology. The results revealed that the algorithm significantly improved efficiency without compromising the precision of 3D modeling when compared with the conventional modeling algorithm based on radar grid data. The durations for the algorithms were reduced by 1.9 seconds and 0.7 seconds, respectively under the hexahedral index mode and the tetrahedral index mode, while under the tetrahedral index mode, the 3D echo structure was more continuous with a higher level of precision. The Doppler weather 3D radar visualization platform based on the B/S architecture could provide a cross-platform 3D radar display, thus visualize the 3D structure of convective cloud effectively.

To address the large error and low efficiency of traditional manual design in cable laying task, the computer-aided design optimized by Ant Colony Algorithm (ACA) is applied to cable laying path planning. The shortest path for cable laying is solved via the ACA's multi terminal path calculation for complex path planning. Furthermore, the planarized cable laying path is optimized via Gompertz function in aspects of pheromone restriction and self-adaptive adjustment of volatilization factor, thus improves the ACA in both convergence speed and global performance. The simulation results show that the optimized ant colony algorithm can quickly obtain the shortest cable laying path in the task of substation digital 3D cable laying, which saves the cost of manpower and materials, and improves the design accuracy.