The International Society for the Study of Vascular Anomalies (ISSVA) ontology

The International Society for the Study of Vascular Anomalies (ISSVA) provides a classification for vascular anomalies that enables specialists to unambiguously classify diagnoses. This classification is only available in PDF format and is not machine-readable, nor does it provide unique identifiers that allow for structured registration. In this paper, we describe the process of transforming the ISSVA classification into an ontology. We also describe the structure of this ontology, as well as two applications of the ontology using examples from the domain of rare disease research. We used the expertise of an ontology expert and clinician during the development process. We semi-automatically added mappings to relevant external ontologies using automated ontology matching systems and manual assessment by experts. The ISSVA ontology should contribute to making data for vascular anomaly research more Findable, Accessible, Interoperable, and Reusable (FAIR). The ontology is available at https://bioportal.bioontology.org/ontologies/ISSVA.     

The effect of particle size distribution on the microstructure and properties of Al2O3 ceramics formed by stereolithography

Stereolithography (SL) shows advantages for preparing alumina-based ceramics with complex structures. The effects of the particle size distribution, which strongly influence the sintering properties in ceramic SL, have not been systematically explored until now. Herein, the influence of the particle size distribution on SL-manufactured alumina ceramics was investigated, including bending strength at room temperature, post-sintering shrinkage, porosity, and microstructural morphology. Seven particle size distributions of alumina ceramics were studied (in μm/μm: 30/5, 20/3, 10/2, 5/2, 5/0.8, 3/0.5, and 2/0.3); a coarse:fine particle ratio of 6:4 was maintained. At the same sintering temperature, the degree of sintering was greater for finer particle sizes. The particle size distribution had a larger influence on flexural strength, porosity and shrinkage than sintering temperature when the particle size distribution difference reached 10-fold but was weaker for 10 μm/2 μm, 5 μm/2 μm and 5 μm/0.8 μm. The sintering shrinkage characteristics of cuboid samples with different particle sizes were studied. The use of coarse particles influenced the accuracy of small-scale samples. When the particle size was comparable to the sample width, such as 30 μm/5 μm and 5 mm, the width shrinkage was consistent with the height shrinkage. When the particle size was much smaller than the sample width, such as 2 μm/0.3 μm and 5 mm, the width shrinkage was consistent with the length shrinkage. The results of this study provide meaningful guidance for future research on applications of SL and precise control of alumina ceramics through particle gradation.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

TCBR-HMM: An HMM-based text classifier with a CBR system

This paper presents an innovative solution to model distributed adaptive systems in biomedical environments. We present an original TCBR-HMM (Text Case Based Reasoning-Hidden Markov Model) for biomedical text classification based on document content. The main goal is to propose a more effective classifier than current methods in this environment where the model needs to be adapted to new documents in an iterative learning frame. To demonstrate its achievement, we include a set of experiments, which have been performed on OSHUMED corpus. Our classifier is compared with Naive Bayes and SVM techniques, commonly used in text classification tasks. The results suggest that the TCBR-HMM Model is indeed more suitable for document classification. The model is empirically and statistically comparable to the SVM classifier and outperforms it in terms of time efficiency.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

Studying local liquid velocity in liquid–solid packed bed using the newly developed X-ray DIR technique

Combination of X-ray Digital Industrial Radiography (DIR) and Particle Tracking Velocimetry (PTV) techniques for local liquid velocity measurement (V_LL) has been newly developed and successfully applied for trickle bed reactor (TBR). The technique was validated against newly developed fiber optical probe technique. This work attempts to highlight the applicability of this newly developed technique on a liquid–solid packed bed reactor. In this work, liquid was represented by water and solids were represented by EPS beads. The EPS beads were chosen because of its low density property. Three superficial liquid velocities (V_SL) were applied to the system. The experiment was replicated four times. The digital industrial radiography (DIR) consists of a complementary metal oxide semiconductor (CMOS) digital detector and X-ray source. Results of this work suggest that the technique has been successfully applied and comparable with previous work that has been done in the literature. It also suggests that there will be a maximum measurable interstitial liquid velocity when it travel inside the packed bed. The measured V_LL can have a maximum range that is between 4 and 4.7 times that of its V_SL. For V_SL=0.42±±2%, the V_LL-Max is in between 1.7 cm/s and 1.9 cm/s, V_SL=0.84±±2%, the V_LL-Max is in between 3.6 cm/s and 4.0 cm/s, and for V_SL=1.11±±2%, the V_LL-Max is in between 4.3 cm/s and 4.8 cm/s.     

基于神经网络算法的井下裂缝诊断与堵漏技术

在钻井过程中，常常钻遇不同宽度的井下地层裂缝。钻遇裂缝时容易发生钻井液漏失现象，甚至发生钻井液失返现象，严重影响了安全、高效钻井。目前裂缝封堵的方法常存在封堵成功率不高、堵漏承压能力低的问题，其中一个重要的原因是对井下地层的裂缝宽度等特征认识不清。基于地层裂缝产生的岩石力学机理，确定影响裂缝宽度关键的6个力学和工程因素，并利用神经网络计算的非线性、大数据特点建立了井下地层裂缝宽度的分析模型，模型包含输入层、输出层和3个隐藏层。通过该模型诊断井下裂缝宽度，提高了计算精度，平均误差仅为2.09%，最大误差为5.88%，解决钻井现场仅凭经验判断裂缝误差较大和依靠成像测井成本较高的问题。同时根据神经网络模型诊断得到的裂缝宽度优化堵漏材料的粒径配比，提高了裂缝内的架桥封堵强度和架桥的稳定性，封堵层的承压能力达到12.8 MPa，反向承压能力达到4.5 MPa。现场堵漏试验最高憋压10 MPa，经过封堵作业后大排量循环不漏，达到了裂缝性地层高效堵漏的目的，堵漏一次成功。