A New Optimized Hybridization Approach for in silico High Throughput Molecular Docking on FPGA Platform

BACKGROUND

The development process of a new drug should be a subject of continuous evolution and rapid improvement as drugs are essential to treat a wide range of diseases of which many are life-threatening. The advances in technology resulted in a novel track in drug discovery and development known as in silico drug design. The molecular docking phase plays a vital role in in silico drug development process. In this phase, thousands of 3D conformations of both the ligand and receptor are generated and the best conformations that create the most stable drug-receptor complex are determined. The speed in finding accurate and high-quality complexes depends on the efficiency of the search function in the molecular docking procedure.

OBJECTIVE

The objective of this research is to propose and implement a novel hybrid approach called hABCDE to replace the EMC searching part inside the BUDE docking algorithm. This helps in reaching the best solution in a much accelerated time and higher solution quality compared to using the ABC and DE algorithms separately.

METHODS

In this work, we have employed a new approach of hybridization between the Artificial Bee Colony (ABC) algorithm and the Differential Evolution (DE) algorithm as an alternative searching part of the Bristol University Docking Engine (BUDE) in order to accelerate the search for higher quality solutions. Moreover, the proposed docking approach was implemented on Field Programmable Gate Array (FPGA) parallel platform using Vivado High-Level Synthesis Tool (HLST) in order to optimize and enhance the execution time and overall efficiency. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach.

RESULTS

The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The results showed that the execution time for the BUDE with the new proposed hybridization approach was improved by 9,236 times.

CONCLUSION

Our novel approach was significantly effective to improve the functionality of docking algorithms (Bristol University Docking Engine (BUDE)).

Penalized variable selection in multi-parameter regression survival modeling

Standard survival models such as the proportional hazards model contain a single regression component, corresponding to the scale of the hazard. In contrast, we consider the so-called "multi-parameter regression" approach whereby covariates enter the model through multiple distributional parameters simultaneously, for example, scale and shape parameters. This approach has previously been shown to achieve flexibility with relatively low model complexity. However, beyond a stepwise type selection method, variable selection methods are underdeveloped in the multi-parameter regression survival modeling setting. Therefore, we propose penalized multi-parameter regression estimation procedures using the following penalties: least absolute shrinkage and selection operator, smoothly clipped absolute deviation, and adaptive least absolute shrinkage and selection operator. We compare these procedures using extensive simulation studies and an application to data from an observational lung cancer study; the Weibull multi-parameter regression model is used throughout as a running example.

Application of Multiple-Optimization Filtering Algorithm in Remote Sensing Image Denoising

Denoising remote sensing images is crucial in the application and research of remote sensing imagery. Noise in remote sensing images originates from sensor characteristics, signal transmission, and environmental conditions, among which Gaussian noise is the most common type. In this paper, we proposed a multiple-optimization bilateral filtering (MOBF) algorithm based on edge detection and differential evolution (DE) methods. The proposed algorithm optimizes the spatial domain filtering kernel and the spatial domain Gaussian kernel by using the standard deviation and width of the edge response. By employing the DE algorithm, the individuals in the population based on the standard deviation of the gray value domain are subjected to iterative mutation, crossover, and selection operations to refine the latent solution vectors and determine the optimal color space for optimizing the standard deviation of the pixel range domain kernel. As a result, the MOBF algorithm, which does not require any parameter input, is realized. To verify the feasibility and effectiveness of the proposed algorithm, denoising experiments were conducted on remote sensing images by using evaluation metrics such as the mean squared error, peak signal-to-noise ratio, and structural similarity index. The experimental results revealed that the MOBF algorithm outperforms traditional algorithms for all three evaluation metrics.

Simulation and Experiment of Active Vibration Control Based on Flexible Piezoelectric MFC Composed of PZT and PI Layer

In order to improve the vibration suppression effect of the flexible beam system, active control based on soft piezoelectric macro-fiber composites (MFCs) consisting of polyimide (PI) sheet and lead zirconate titanate (PZT) is used to reduce the vibration. The vibration control system is composed of a flexible beam, a sensing piezoelectric MFC plate, and an actuated piezoelectric MFC plate. The dynamic coupling model of the flexible beam system is established according to the theory of structural mechanics and the piezoelectric stress equation. A linear quadratic optimal controller (LQR) is designed based on the optimal control theory. An optimization method, designed based on a differential evolution algorithm, is utilized for the selection of weighted matrix Q. Additionally, according to theoretical research, an experimental platform is built, and vibration active control experiments are carried out on piezoelectric flexible beams under conditions of instantaneous disturbance and continuous disturbance. The results show that the vibration of flexible beams is effectively suppressed under different disturbances. The amplitudes of the piezoelectric flexible beams are reduced by 94.4% and 65.4% under the conditions of instantaneous and continuous disturbances with LQR control.

A mutation operator self-adaptive differential evolution particle swarm optimization algorithm for USV navigation

To keep the global search capability and robustness for unmanned surface vessel (USV) path planning, an improved differential evolution particle swarm optimization algorithm (DePSO) is proposed in this paper. In the optimization process, approach to optimal value in particle swarm optimization algorithm (PSO) and mutation, hybridization, selection operation in differential evolution algorithm (DE) are combined, and the mutation factor is self-adjusted. First, the particle population is initialized and the optimization objective is determined, the individual and global optimal values are updated. Then differential variation is conducted to produces new variables and cross over with the current individual, the scaling factor is adjusted adaptively with the number of iterations in the mutation process, particle population is updated according to the hybridization results. Finally, the convergence of the algorithm is determined according to the decision standard. Numerical simulation results show that, compared with conventional PSO and DE, the proposed algorithm can effectively reduce the path intersection points, and thus greatly shorten the overall path length.

Optimization of Spatial Land Use Patterns with Low Carbon Target: A Case Study of Sanmenxia, China

Land use change is an important factor in atmospheric carbon emissions. Most of the existing studies focus on modeling the land use pattern for a certain period of time in the future and calculating and analyzing carbon emissions. However, few studies have optimized the spatial pattern of land use from the perspective of the impact of carbon emission constraints on land use structure. Therefore, in this study, the effects of land use change on carbon emissions from 1990 to 2020 were modeled using a carbon flow model for Sanmenxia, Henan, China, as an example. Then, the land use carbon emission function under the low carbon target was constructed, and the differential evolution (DE) algorithm was used to obtain the optimized land use quantity structure. Finally, the PLUS model was used to predict the optimal spatial configuration of land use patterns to minimize carbon emissions. The study produced three major results. (1) From 1990 to 2020, the structural change of land use in Sanmenxia mainly occurred between cultivated land, forest land, grassland and construction land. During this period of land use change, the carbon emissions from construction land first increased and then decreased, but despite the decrease, carbon emissions still exceeded carbon sinks, and the carbon metabolism of land use was still far from equilibrium. (2) Between 2010 and 2020, the area of cultivated land began to decrease, and the area of forest land rapidly increased, and land-use-related carbon emissions showed negative growth. This showed that the structural adjustment of energy consumption in Sanmenxia during the period decreased carbon emissions in comparison with the previous period. (3) A comparison of predicted optimized land use patterns with land use patterns in an as-is development scenario showed a decrease in construction land area of 23.05 km² in 2030 with a steady increase in forest land area and a decrease in total carbon emission of 20.43 t. The newly converted construction land in the optimized land use pattern was concentrated in the ribbon-clustered towns built during urban expansion along the Shaanling basin of the Yellow River and the Mianchi-Yima industrial development area.

Machine Learning and Swarm Optimization Algorithm in Temperature Compensation of Pressure Sensors

The main temperature compensation method for MEMS piezoresistive pressure sensors is software compensation, which processes the sensor data using various algorithms to improve the output accuracy. However, there are few algorithms designed for sensors with specific ranges, most of which ignore the operating characteristics of the sensors themselves. In this paper, we propose three temperature compensation methods based on swarm optimization algorithms fused with machine learning for three different ranges of sensors and explore the partitioning ratio of the calibration dataset on Sensor A. The results show that different algorithms are suitable for pressure sensors of different ranges. An optimal compensation effect was achieved on Sensor A when the splitting ratio was 33.3%, where the zero-drift coefficient was 2.88 × 10^-7/°C and the sensitivity temperature coefficient was 4.52 × 10^-6/°C. The algorithms were compared with other algorithms in the literature to verify their superiority. The optimal segmentation ratio obtained from the experimental investigation is consistent with the sensor operating temperature interval and exhibits a strong innovation.

MODECP: A Multi-Objective Based Approach for Solving Distributed Controller Placement Problem in Software Defined Network

Software-Defined Network is an emerging networking paradigm that enables intelligent and flexible network management. Specifically, the design of the control plane is crucial. Therefore, in order to avoid a single point of failure, multiple controllers are deployed constantly in a distributed manner on the control plane. In this paper, we propose a controller placement approach based on multiple objectives (MODECP), including network delay, network security, load-balancing rate, and link occupancy. In the controller placement stage, an improved multi-objective differential evolution algorithm is proposed to search for controllers' positions and assign switches to controllers reasonably. Furthermore, an improved affinity propagation algorithm is proposed to obtain the number of controllers placed in the network partition stage, comprehensively considering the delay, node security, and load. Simulations are performed based on several topologies from Internet Topology Zoo. Extensive results show that the proposed algorithm can realize trade-offs among multiple objectives and improve network performance in delay, security, controller load, and link occupancy compared to the single-objective based approach. Moreover, compared with the genetic algorithm and random placement algorithm, the proposed algorithm performs better with low latency, high security, low load rate, and low link overhead.

Structural Design and Optimization of a Resonant Micro-Accelerometer Based on Electrostatic Stiffness by an Improved Differential Evolution Algorithm

This study designed an in-plane resonant micro-accelerometer based on electrostatic stiffness. The accelerometer adopts a one-piece proof mass structure; two double-folded beam resonators are symmetrically distributed inside the proof mass, and only one displacement is introduced under the action of acceleration, which reduces the influence of processing errors on the performance of the accelerometer. The two resonators form a differential structure that can diminish the impact of common-mode errors. This accelerometer realizes the separation of the introduction of electrostatic stiffness and the detection of resonant frequency, which is conducive to the decoupling of accelerometer signals. An improved differential evolution algorithm was developed to optimize the scale factor of the accelerometer. Through the final elimination principle, excellent individuals are preserved, and the most suitable parameters are allocated to the surviving individuals to stimulate the offspring to find the globally optimal ability. The algorithm not only maintains the global optimality but also reduces the computational complexity of the algorithm and deterministically realizes the optimization of the accelerometer scale factor. The electrostatic stiffness resonant micro-accelerometer was fabricated by deep dry silicon-on-glass (DDSOG) technology. The unloaded resonant frequency of the accelerometer resonant beam was between 24 and 26 kHz, and the scale factor of the packaged accelerometer was between 54 and 59 Hz/g. The average error between the optimization result and the actual scale factor was 7.03%. The experimental results verified the rationality of the structural design.

A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm's performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.

Absolute Positioning Accuracy Improvement in an Industrial Robot

The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

Differential Evolution for the Optimization of DMSO-Free Cryoprotectants: Influence of Control Parameters

This study presents the influence of control parameters including population (NP) size, mutation factor (F), crossover (Cr), and four types of differential evolution (DE) algorithms including random, best, local-to-best, and local-to-best with self-adaptive (SA) modification for the purpose of optimizing the compositions of dimethylsufloxide (DMSO)-free cryoprotectants. Post-thaw recovery of Jurkat cells cryopreserved with two DMSO-free cryoprotectants at a cooling rate of 1 °C/min displayed a nonlinear, four-dimensional structure with multiple saddle nodes, which was a suitable training model to tune the control parameters and select the most appropriate type of differential evolution algorithm. Self-adaptive modification presented better performance in terms of optimization accuracy and sensitivity of mutation factor and crossover among the four different types of algorithms tested. Specifically, the classical type of differential evolution algorithm exhibited a wide acceptance to mutation factor and crossover. The optimization performance is more sensitive to mutation than crossover and the optimization accuracy is proportional to the population size. Increasing population size also reduces the sensitivity of the algorithm to the value of the mutation factor and crossover. The analysis of optimization accuracy and convergence speed suggests larger population size with F > 0.7 and Cr > 0.3 are well suited for use with cryopreservation optimization purposes. The tuned differential evolution algorithm is validated through finding global maximums of other two DMSO-free cryoprotectant formulation datasets. The results of these studies can be used to help more efficiently determine the optimal composition of multicomponent DMSO-free cryoprotectants in the future.