A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Tissue-specific alternative splicing and expression of ATP1B2 gene

The Na+-K+-ATPase is an essential transport enzyme expressed in all animal tissues, where it generates ion gradients to maintain membrane potential and drive the transport of other solutes. It also balances metabolism and body temperature. In this study, the characterization of three novel bovine ATP1B2 splice variants, designated as ATP1B2-AS1, ATP1B2-AS2, and ATP1B2-AS3, is discussed. All three novel splice isoforms were derived from a complete transcript (ATP1B2-complete) by alternative splicing. The pattern of splicing to produce the ATP1B2-AS1 and ATP1B2-AS2 isoforms was intron retention; these isoforms were found in liver, kidney, muscle and breast tissues. For the ATP1B2-AS3 isoform, splicing was by exon inclusion and this isoform was only found in muscle tissue. As demonstrated by real-time polymerase chain reaction, the isoforms were all expressed at significantly lower levels than the complete ATP1B2 gene transcript in all the tissues studied. After heat-stress, the expression levels of the different transcripts were lower in different tissues; however, the expression of the ATP1B2-complete transcript increased in heart and lung tissues. The results of this research provide some useful information for further studies into the function of the bovine ATP1B2 gene. Alternative splicing (AS) is recognized as the major contributor to protein diversity from limited gene pool. ATP1B2-AS2 was the splice of intron retention found from ATP1B2 in liver, kidney, muscle and breast tissues. In the study, ATP1B2-AS2 showed that many of the amino acid residues were in an unfavorable energy environment. It is interesting to speculate that this may be the perfect transcript to respond to heat-stress. So, AS may become the appropriate pathway to tackle heat-stress and reduce the economic losses in cows.Key words: ATP1B2 gene, alternative splicing, alternative splicing mechanism

IDENTIFICATION AND RECOGNIZATION OF BAMBOO BASED ON CROSS-SECTIONAL IMAGES USING COMPUTER VISION

Identification of bamboo is of great importance to its conservation and uses. However, identify bamboo manually is complicated, expensive, and time-consuming. Here, we analyze the most evident and characteristic anatomical elements of cross section images, that’s a particularly vital breakthrough point. Meanwhile, we present a novel approach with respect to the automatic identification of bamboo on the basis of the cross-sectional images through computer vision.Two diverse transfer learning strategies were applied for the learning process, namely fine-tuning with fully connected layers and all layers, the results indicated that fine-tuning with all layers being trained with the dataset consisting of cross-sectional images of bamboo is an effective tool to identify and recognize intergenericbamboo, 100% accuracy on the training dataset was achieved while 98.7% accuracy was output on the testing dataset, suggesting the proposed method is quite effective and feasible, it’s beneficial to identify bamboo and protect bamboo in coutilization. More collection of bamboo species in the dataset in the near futuremight make EfficientNet more promising for identifying bamboo.  

Tensile Properties of Single Rattan Fibers

The longitudinal tensile strength of single fibers of four rattan species, namely C. simplicifolius, C. nambariensis Becc. var. yingjiangensis, C. nambariensis var. xishuangbannaensis, and C. yunnanensis, was studied using a custom-built short vegetable fiber mechanical tester. The stress-strain curves produced by the four different rattans showed two distinct phases: a steep, straight segment in the initial phase followed by a straight line with a lower slope up to the breaking point. The respective average values for tensile elastic modulus, tensile strength, and elongation at breaking point of C. simplicifolius, C. nambariensis.var. xishuangbannaensis, C. yunnanensis, and C. nambariensis var. yingjiangensis canes were 10.61, 10.05, 9.10, and 9.54 GPa; 603, 566, 464, and 539 MPa; and 17.00, 17.24, 16.44, and 21.08%. The length position of the single fibers in the cane had variable effects on the three aforementioned properties for all four sampled rattan species. The tensile properties of C. simplicifolius fibers were highest. Compared with wood and bamboo, modulus of elasticity and tensile strength of the studied rattans were much lower, whereas elongation at breaking point of single rattan fibers was generally higher

Improving Photostability and Antifungal Performance of Bamboo with Nanostructured Zinc Oxide

We report on the formation of zinc oxide (ZnO) films with various morphologies on bamboo to simultaneously furnish it with excellent photostability and antifungal properties. A simple two-step process was adopted, consisting of generation of ZnO seeds on the bamboo surface followed by solution treatment to promote crystal growth. Effect of reaction conditions on film morphologies was systematically investigated. Results indicate morphologies of ZnO films can be tailored from nanoparticles to nanostructured networks and irregular aggregates at the micron scale with different crystallinities through specific combinations of reaction conditions. The photostability and antifungal performances of coated bamboo were greatly improved and highly dependent on both crystallinity and morphologies of ZnO films

Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)

Background: Developing an accurate computer-aided diagnosis (CAD) system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (DWPT) to extract wavelet packet coefficients from MR brain images. Next, Shannon entropy (SE) and Tsallis entropy (TE) were harnessed to obtain entropy features from DWPT coefficients. Finally, generalized eigenvalue proximate support vector machine (GEPSVM), and GEPSVM with radial basis function (RBF) kernel, were employed as classifier. We tested the four proposed diagnosis methods (DWPT + SE + GEPSVM, DWPT + TE + GEPSVM, DWPT + SE + GEPSVM + RBF, and DWPT + TE + GEPSVM + RBF) on three benchmark datasets of Dataset-66, Dataset-160, and Dataset-255. Results: The 10 repetition of K-fold stratified cross validation results showed the proposed DWPT + TE + GEPSVM + RBF method excelled not only other three proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the DWPT + TE + GEPSVM + RBF method achieved accuracy of 100%, 100%, and 99.53% on Dataset-66, Dataset-160, and Dataset-255, respectively. For Dataset-255, the offline learning cost 8.4430s and online prediction cost merely 0.1059s. Conclusions: We have proved the effectiveness of the proposed method, which achieved nearly 100% accuracy over three benchmark datasets

Binary Structuring Elements Decomposition Based on an Improved Recursive Dilation-Union Model and RSAPSO Method

This paper proposed an improved approach to decompose structuring elements of an arbitrary shape. For the model of this method, we use an improved dilation-union model, adding a new termination criterion, as the sum of 3-by-3 matrix should be less than 5. Next for the algorithm of this method, we introduced in the restarted simulated annealing particle swarm optimization method. The experiments demonstrate that our method can find better results than Park's method, Anelli's method, Shih's SGA method, and Zhang's MFSGA method. Besides, our method gave the best decomposition tree of different SE shapes including “ship,” “car,” “heart,” “umbrella,” “vase,” “tree,” “cat,” “V,” “bomb,” and “cup.

Genetic Pattern Search and Its Application to Brain Image Classification

A novel global optimization method, based on the combination of genetic algorithm (GA) and generalized pattern search (PS) algorithm, is proposed to find global minimal points more effectively and rapidly. The idea lies in the facts that GA tends to be quite good at finding generally good global solutions, but quite inefficient in finding the last few mutations for the absolute optimum, and that PS is quite efficient in finding absolute optimum in a limited region. The novel algorithm, named as genetic pattern search (GPS), employs the GA as the search method at every step of PS. Experiments on five different classical benchmark functions (consisting of Hump, Powell, Rosenbrock, Schaffer, and Woods) demonstrate that the proposed GPS is superior to improved GA and improved PS with respect to success rate. We applied the GPS to the classification of normal and abnormal structural brain MRI images. The results indicate that GPS exceeds BP, MBP, IGA, and IPS in terms of classification accuracy. This suggests that GPS is an effective and viable global optimization method and can be applied to brain MRI classification.</jats:p