Hybrid PolyLingual Object Model: An Efficient and Seamless Integration of Java and Native Components on the Dalvik Virtual Machine

Copyright © 2014 Yukun Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. 1

Scalable parallel evolutionary optimisation based on high performance computing

Evolutionary algorithms (EAs) have been successfully applied to solve various challenging optimisation problems. Due to their stochastic nature, EAs typically require considerable time to find desirable solutions; especially for increasingly complex and large-scale problems. As a result, many works studied implementing EAs on parallel computing facilities to accelerate the time-consuming processes. Recently, the rapid development of modern parallel computing facilities such as the high performance computing (HPC) bring not only unprecedented computational capabilities but also challenges on designing parallel algorithms. This thesis mainly focuses on designing scalable parallel evolutionary optimisation (SPEO) frameworks which run efficiently on the HPC. Motivated by the interesting phenomenon that many EAs begin to employ increasingly large population sizes, this thesis firstly studies the effect of a large population size through comprehensive experiments. Numerical results indicate that a large population benefits to the solving of complex problems but requires a large number of maximal fitness evaluations (FEs). However, since sequential EAs usually requires a considerable computing time to achieve extensive FEs, we propose a scalable parallel evolutionary optimisation framework that can efficiently deploy parallel EAs over many CPU cores at CPU-only HPC. On the other hand, since EAs using a large number of FEs can produce massive useful information in the course of evolution, we design a surrogate-based approach to learn from this historical information and to better solve complex problems. Then this approach is implemented in parallel based on the proposed scalable parallel framework to achieve remarkable speedups. Since demanding a great computing power on CPU-only HPC is usually very expensive, we design a framework based on GPU-enabled HPC to improve the cost-effectiveness of parallel EAs. The proposed framework can efficiently accelerate parallel EAs using many GPUs and can achieve superior cost-effectiveness. However, since it is very challenging to correctly implement parallel EAs on the GPU, we propose a set of guidelines to verify the correctness of GPU-based EAs. In order to examine these guidelines, they are employed to verify a GPU-based brain storm optimisation that is also proposed in this thesis. In conclusion, the comprehensively experimental study is firstly conducted to investigate the impacts of a large population. After that, a SPEO framework based on CPU-only HPC is proposed and is employed to accelerate a time-consuming implementation of EA. Finally, the correctness verification of implementing EAs based on a single GPU is discussed and the SPEO framework is then extended to be deployed based on GPU-enabled HPC

Evolutionary framework with reinforcement learning-based mutation adaptation

Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of state-of-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.</p

Adaptive distributed differential evolution

Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation population, and balance population are co-evolved concurrently by using the master-slave multipopulation distributed framework. Different populations will adaptively choose their suitable mutation strategies based on the evolutionary state estimation to make full use of the feedback information from both individuals and the whole corresponding population. Besides, the historical successful experience and best solution improvement are collected and used to adaptively update the individual parameters (amplification factor F and crossover rate CR) and population parameter (population size N), respectively. The performance of ADDE is evaluated on all 30 widely used benchmark functions from the CEC 2014 test suite and all 22 widely used real-world application problems from the CEC 2011 test suite. The experimental results show that ADDE has great superiority compared with the other state-of-the-art DDE and adaptive differential evolution variants

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Electronic energy migration in Microtubules

The repeating arrangement of tubulin dimers confers great mechanical strength to microtubules, which are used as scaffolds for intracellular macromolecular transport in cells and exploited in biohybrid devices. The crystalline order in a microtubule, with lattice constants short enough to allow energy transfer between amino acid chromophores, is similar to synthetic structures designed for light harvesting. After photoexcitation, can these amino acid chromophores transfer excitation energy along the microtubule like a natural or artificial light-harvesting system? Here, we use tryptophan autofluorescence lifetimes to probe energy hopping between aromatic residues in tubulin and microtubules. By studying how the quencher concentration alters tryptophan autofluorescence lifetimes, we demonstrate that electronic energy can diffuse over 6.6 nm in microtubules. We discover that while diffusion lengths are influenced by tubulin polymerization state (free tubulin versus tubulin in the microtubule lattice), they are not significantly altered by the average number of protofilaments (13 versus 14). We also demonstrate that the presence of the anesthetics etomidate and isoflurane reduce exciton diffusion. Energy transport as explained by conventional Förster theory (accommodating for interactions between tryptophan and tyrosine residues) does not sufficiently explain our observations. Our studies indicate that microtubules are, unexpectedly, effective light harvesters

Normalizing Tumor Vasculature Using Sepiapterin to Increase Radiosensitivity

Our studies examine the role of nitric oxide synthase (NOS) in tumor vasculature. NOS is “uncoupled” in tumor cells, resulting in peroxynitrite (ONOO-) formation in lieu of nitric oxide (NO). NO signaling is critical for vascular function, thus uncoupling of eNOS in endothelial cells may partly explain the poor vasculature found within tumors. NOS can be “recoupled” through Sepiapterin (SP) treatment of tumor cells. We examined whether SP could normalize tumor vasculature, promoting radiosensitivity. Optoacoustic tomography of flank xenografts and spontaneous tumor models demonstrate that SP significantly enhances percent oxyhemoglobin in tumors. Immunohistochemical analysis of SP-treated tumors showed significant reduction in CD31 staining and significant increases in smooth muscle actin (SMA), both hallmarks of vascular normalization. SP resulted in over a two-fold increase in apoptosis with irradiation. These data demonstrate potential for SP as an adjuvant in cancer treatment. Future studies will examine drug uptake and mechanisms behind vascular normalization

Interação de derivados de benzenossulfonamida com Smyd3 usando um modelo teórico

Cancer is a serious public health problem worldwide. This clinical pathology is associated with the activation/release of several biomolecules, including the Smyd proteins family. In this way, some studies indicate that Smyd3 is associated with cancer cells growth. It is important to mention that some drugs act as Smyd3 inhibitors in the treat some cancers. However, their interaction is very confusing; for this reason, the aim of this research was to evaluate the theoretical interaction of benzenesulfonamide and their derivatives (compounds 2 to 28) using 7o2c protein, novobiocin, BAY-6035, EPZ031686 and BCI-121 drugs as theoretical tools in DockingServer program. The results showed differences in the aminoacid residues involved in the interaction of benzenesulfonamide and their derivatives with 7o2c protein surface compared with novobiocin, BAY-6035, EPZ031686 and BCI-121 drugs. In additions, the inhibition constant (Ki) for benzenesulfonamide derivatives 2, 7, 8, 13, 14, 17, 20, 21, 24 and 28 was very lower compared to benzenesulfonamide, novobiocin, BAY-6035, EPZ031686 and BCI-121. In conclusion, the benzenesulfonamide derivatives 2, 7, 8, 13, 14, 17, 20, 21, 24 and 28 could be a good alternative as Smyd3 inhibitors to decrease cancer cells growth.El cáncer es un grave problema de salud pública a nivel mundial. Esta patología clínica está asociada a la activación/liberación de varias biomoléculas, entre ellas las proteínas de la familia Smyd. De esta forma, algunos estudios indican que Smyd3 está asociado con el crecimiento de células cancerosas. Es importante mencionar que algunos medicamentos actúan como inhibidores de Smyd3 en el tratamiento de algunos tipos de cáncer. Sin embargo, su interacción es muy confusa; por tal motivo, el objetivo de esta investigación fue evaluar la interacción teórica de la bencenosulfonamida y sus derivados (compuestos 2 al 28) utilizando como herramientas teóricas en el programa DockingServer la proteína 7o2c, novobiocina, BAY-6035, EPZ031686 y BCI-121. . Los resultados mostraron diferencias en los residuos de aminoácidos involucrados en la interacción de la bencenosulfonamida y sus derivados con la superficie de la proteína 7o2c en comparación con los fármacos novobiocina, BAY-6035, EPZ031686 y BCI-121. Además, la constante de inhibición (Ki) para los derivados de bencenosulfonamida 2, 7, 8, 13, 14, 17, 20, 21, 24 y 28 fue mucho menor en comparación con bencenosulfonamida, novobiocina, BAY-6035, EPZ031686 y BCI-121. En conclusión, los derivados de bencenosulfonamida 2, 7, 8, 13, 14, 17, 20, 21, 24 y 28 pueden ser una buena alternativa como inhibidores de Smyd3 para disminuir el crecimiento de células cancerosas.O câncer é um grave problema de saúde pública em todo o mundo. Esta patologia clínica está associada à ativação/liberação de várias biomoléculas, incluindo as proteínas da família Smyd. Desta forma, alguns estudos indicam que o Smyd3 está associado ao crescimento de células cancerígenas. É importante mencionar que algumas drogas atuam como inibidores de Smyd3 no tratamento de alguns tipos de câncer. No entanto, sua interação é muito confusa; por esta razão, o objetivo desta pesquisa foi avaliar a interação teórica de benzenossulfonamida e seus derivados (compostos 2 a 28) usando a proteína 7o2c, novobiocina, BAY-6035, EPZ031686 e drogas BCI-121 como ferramentas teóricas no programa DockingServer. Os resultados mostraram diferenças nos resíduos de aminoácidos envolvidos na interação da benzenossulfonamida e seus derivados com a superfície da proteína 7o2c em comparação com as drogas novobiocina, BAY-6035, EPZ031686 e BCI-121. Além disso, a constante de inibição (Ki) para os derivados de benzenossulfonamida 2, 7, 8, 13, 14, 17, 20, 21, 24 e 28 foi muito menor em comparação com benzenossulfonamida, novobiocina, BAY-6035, EPZ031686 e BCI-121. Em conclusão, os derivados de benzenossulfonamida 2, 7, 8, 13, 14, 17, 20, 21, 24 e 28 podem ser uma boa alternativa como inibidores de Smyd3 para diminuir o crescimento de células cancerígenas