Self Adaptive Artificial Bee Colony for Global Numerical Optimization

AbstractThe ABC algorithm has been used in many practical cases and has demonstrated good convergence rate. It produces the new solution according to the stochastic variance process. In this process, the magnitudes of the perturbation are important since it can affect the new solution. In this paper, we propose a self adaptive artificial bee colony, called self adaptive ABC, for the global numerical optimization. A new self adaptive perturbation is introduced in the basic ABC algorithm, in order to improve the convergence rates. 23 benchmark functions are employed in verifying the performance of self adaptive ABC. Experimental results indicate our approach is effective and efficient. Compared with other algorithms, self adaptive ABC performs better than, or at least comparable to the basic ABC algorithm and other state-of-the-art approaches from literature when considering the quality of the solution obtained

Efficacy of autologous bone marrow buffy coat grafting combined with core decompression in patients with avascular necrosis of femoral head: a prospective, double-blinded, randomized, controlled study

Introduction Avascular necrosis of femoral head (ANFH) is a progressive disease that often leads to hip joint dysfunction and even disability in young patients. Although the standard treatment, which is core decompression, has the advantage of minimal invasion, the efficacy is variable. Recent studies have shown that implantation of bone marrow containing osteogenic precursors into necrotic lesion of ANFH may be promising for the treatment of ANFH. Methods A prospective, double-blinded, randomized controlled trial was conducted to examine the effect of bone-marrow buffy coat (BBC) grafting combined with core decompression for the treatment of ANFH. Forty-five patients (53 hips) with Ficat stage I to III ANFH were recruited. The hips were allocated to the control group (core decompression + autologous bone graft) or treatment group (core decompression + autologous bone graft with BBC). Both patients and assessors were blinded to the treatment options. The clinical symptoms and disease progression were assessed as the primary and secondary outcomes. Results At the final follow-up (24 months), there was a significant relief in pain (P \u3c0.05) and clinical joint symptoms as measured by the Lequesne index (P \u3c0.05) and Western Ontario and McMaster Universities Arthritis Index (P \u3c0.05) in the treatment group. In addition, 33.3% of the hips in the control group have deteriorated to the next stage after 24 months post-procedure, whereas only 8% in the treatment group had further deterioration (P \u3c0.05). More importantly, the non-progression rates for stage I/II hips were 100% in the treatment group and 66.7% in the control group. Conclusion Implantation of the autologous BBC grafting combined with core decompression is effective to prevent further progression for the early stages of ANFH. Trial registration ClinicalTrials.gov identifier NCT01613612. Registered 13 December 2011

Battery Protective Electric Vehicle Charging Management in Renewable Energy System

The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.</p

Online Battery Protective Energy Management for Energy-Transportation Nexus

Grid-connected electric vehicles (GEVs) and energy-transportation nexus bring a bright prospect to improve the penetration of renewable energy and the economy of microgrids (MGs). However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile MG states. This article develops a novel online battery anti-aging energy management method for energy-transportation nexus by using a novel deep reinforcement learning (DRL) framework. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modeling the impact of number of cycles, depth of discharge, and charge and discharge rate. The established life loss model is used to evaluate battery anti-aging effectiveness of agent actions. The coordination of GEVs charging is modeled as multiobjective learning by using a DRL algorithm. The training objective is to maximize renewable penetration while reducing MG power fluctuations and vehicle battery aging costs. The developed energy-transportation nexus energy management method is verified to be effective in optimal power balancing and battery anti-aging control on a MG in the U.K. This article provides an efficient and economical tool for MG power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition.</p

Optimal power system design and energy management for more electric aircrafts

Recent developments in fuel cell (FC) and battery energy storage technologies bring a promising perspective for improving the economy and endurance of electric aircraft. However, aircraft power system configuration and power distribution strategies should be reasonably designed to enable this benefit. This paper is the first attempt to investigate the optimal energy storage system sizing and power distribution strategies for electric aircraft with hybrid FC and battery propulsion systems. First, a novel integrated energy management and parameter sizing (IEMPS) framework is established to co-design aircraft hardware and control algorithms. Under the IEMPS framework, a new real-time power distribution algorithm with a flexible ratio is established to facilitate integrated parameter optimization, which can adapt to different power system configurations. Based on the comprehensive analysis of hydrogen economy, FC aging cost, and aircraft stability, a multi-objective parameter optimization model is established to decide the size of aircraft energy storage systems and hyper-parameters in the power controller. The X-57 Maxwell, an experimental electric aircraft designed by NASA, is employed to verify the developed methods. This work provides a novel power system configuration, sizing, and power management method for future commercial aircraft design, and it can further promote the aviation electrification process.</p

Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction

We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly. We establish theoretically proven convergence guarantees in the stochastic convex settings, based on primal-dual methods. We evaluate the regularized effect of our new optimizers on three large-scale real-world ad click datasets with state-of-the-art deep learning models. The experimental results reveal that compared with the original optimizers with the post-processing procedure which uses the magnitude pruning method, the performance of the models can be significantly improved on the same sparsity level. Furthermore, in comparison to the cases without magnitude pruning, our methods can achieve extremely high sparsity with significantly better or highly competitive performance. The code is available at https://github.com/intelligent-machine-learning/dlrover/blob/master/tfplus.Comment: 24 pages. Published as a conference paper at ECML PKDD 2021. This version includes Appendix which was not included in the published version because of page limi

Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First Principle and Deep Learning Molecular Dynamics Simulations

Complex strain status can exist in 2D materials during their synthesis process, resulting in significant impacts on the physical and chemical properties. Despite their prevalence in experiments, their influence on the material properties and the corresponding mechanism are often understudied due to the lack of effective simulation methods. In this work, we investigated the effects of bending, rippling, and bubbling on the ferroelectric domains in In2Se3 monolayer by density functional theory (DFT) and deep learning molecular dynamics (DLMD) simulations. The analysis of the tube model shows that bending deformation imparts asymmetry into the system, and the polarization direction tends to orient towards the tensile side, which has a lower energy state than the opposite polarization direction. The energy barrier for polarization switching can be reduced by compressive strain according DFT results. The dynamics of the polarization switching is investigated by the DLMD simulations. The influence of curvature and temperature on the switching time follows the Arrhenius-style function. For the complex strain status in the rippling and bubbling model, the lifetime of the local transient polarization is analyzed by the autocorrelation function, and the size of the stable polarization domain is identified. Local curvature and temperature can influence the local polarization dynamics following the proposed Arrhenius-style equation. Through cross-scale simulations, this study demonstrates the capability of deep-learning potentials in simulating polarization for ferroelectric materials. It further reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering

Reactions of Chinese adults to warning labels on cigarette packages: A survey in Jiangsu Province

<p>Abstract</p> <p>Background</p> <p>To compare reactions to warning labels presented on cigarette packages with a specific focus on whether the new Chinese warning labels are better than the old labels and international labels.</p> <p>Methods</p> <p>Participants aged 18 and over were recruited in two cities of Jiangsu Province in 2008, and 876 face-to-face interviews were completed. Participants were shown six types of warning labels found on cigarette packages. They comprised one old Chinese label, one new label used within the Chinese market, and one Chinese overseas label and three foreign brand labels. Participants were asked about the impact of the warning labels on: their knowledge of harm from smoking, giving cigarettes as a gift, and quitting smoking.</p> <p>Results</p> <p>Compared with the old Chinese label, a higher proportion of participants said the new label provided clear information on harm caused by smoking (31.2% vs 18.3%). Participants were less likely to give cigarettes with the new label on the package compared with the old label (25.2% vs 20.8%). These proportions were higher when compared to the international labels. Overall, 26.8% of participants would quit smoking based on information from the old label and 31.5% from the new label. When comparing the Chinese overseas label and other foreign labels to the new Chinese label with regard to providing knowledge of harm warning, impact of quitting smoking and giving cigarettes as a gift, the overseas labels were more effective.</p> <p>Conclusion</p> <p>Both the old and the new Chinese warning label are not effective in this target population.</p