Automated knowledge capture in 2D and 3D design environments

In Life Cycle Engineering, it is vital that the engineering knowledge for the product is captured throughout its life cycle in a formal and structured manner. This will allow the information to be referred to in the future by engineers who did not work on the original design but are wanting to understand the reasons that certain design decisions were made. In the past, attempts were made to try to capture this knowledge by having the engineer record the knowledge manually during a design session. However, this is not only time-consuming but is also disruptive to the creative process. Therefore, the research presented in this paper is concerned with capturing design knowledge automatically using a traditional 2D design environment and also an immersive 3D design environment. The design knowledge is captured by continuously and non-intrusively logging the user during a design session and then storing this output in a structured eXtensible Markup Language (XML) format. Next, the XML data is analysed and the design processes that are involved can be visualised by the automatic generation of IDEF0 diagrams. Using this captured knowledge, it forms the basis of an interactive online assistance system to aid future users who are carrying out a similar design task

Label Dependent Evolutionary Feature Weighting for Remote Sensing Data

Nearest neighbour (NN) is a very common classifier used to develop important remote sensing products like land use and land cover (LULC) maps. Evolutive computation has often been used to obtain feature weighting in order to improve the results of the NN. In this paper, a new algorithm based on evolutionary computation which has been called Label Dependent Feature Weighting (LDFW) is proposed. The LDFW method transforms the feature space assigning different weights to every feature depending on each class. This multilevel feature weighting algorithm is tested on remote sensing data from fusion of sensors (LIDAR and orthophotography). The results show an improvement on the NN and resemble the results obtained with a neural network which is the best classifier for the study area

From crescent to mature virion: vaccinia virus assembly and maturation

ReviewVaccinia virus (VACV) has achieved unprecedented success as a live viral vaccine for smallpox which mitigated eradication of the disease. Vaccinia virus has a complex virion morphology and recent advances have been made to answer some of the key outstanding questions, in particular, the origin and biogenesis of the virion membrane, the transformation from immature virion (IV) to mature virus (MV), and the role of several novel genes, which were previously uncharacterized, but have now been shown to be essential for VACV virion formation. This new knowledge will undoubtedly contribute to the rational design of safe, immunogenic vaccine candidates, or effective antivirals in the future. This review endeavors to provide an update on our current knowledge of the VACV maturation processes with a specific focus on the initiation of VACV replication through to the formation of mature virions.Liang Liu, Tamara Cooper, Paul M. Howley, and John D. Haybal

It’s Not All about the Economy Stupid! Immigration and Subjective Well-Being in England

While much is known regarding the effects of immigration for objective outcomes, relatively little is known regarding the effects for perceived well-being. By exploiting spatial and temporal variation in the net-inflows of foreign-born individuals across local areas in England, we examine the relationship between immigration and natives’ subjective well-being as captured by the General Health Questionnaire (GHQ). We find small negative effects overall but that an analysis of the main effects masks significant differences across subgroups, with relatively older individuals, those with below-average household incomes, the unemployed and finally those without any formal educational qualifications experiencing much more substantive well-being losses than others. These observed well-being differentials are congruent with voting patterns evident in the recent UK referendum on EU membership. We put forward perceived as opposed to actual labour market competition and social identity as two potential explanations for the negative well-being impacts of immigration for natives

Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page

Phosphinecarboxamide based InZnP QDs – an air tolerant route to luminescent III–V semiconductors

We describe a new synthetic methodology for the preparation of high quality, emission tuneable InP-based quantum dots (QDs) using a solid, air- and moisture-tolerant primary phosphine as a group-V precursor. This presents a significantly simpler synthetic pathway compared to the state-of-the-art precursors currently employed in phosphide quantum dot synthesis which are volatile, dangerous and air-sensitive, e.g. P(Si(CH3)3)3

Genetic folding for solving multiclass SVM problems

Genetic Folding (GF) algorithm is a new class of evolutionary algorithms specialized for complicated computer problems. GF algorithm uses a linear sequence of numbers of genes structurally organized in integer numbers, separated with dots. The encoded chromosomes in the population are evaluated using a fitness function. The fittest chromosome survives and is subjected to modification by genetic operators. The creation of these encoded chromosomes, with the fitness functions and the genetic operators, allows the algorithm to perform with high efficiency in the genetic folding life cycle. Multi-classification problems have been chosen to illustrate the power and versatility of GF. In classification problems, the kernel function is important to construct binary and multi classifier for support vector machines. Different types of standard kernel functions have been compared with our proposed algorithm. Promising results have been shown in comparison to other published works

Evolving Gaussian Process Kernels for Translation Editing Effort Estimation

In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, a text that has been translated using a machine translation method. Recent developments in this area have shown that Gaussian Processes can be accurate for post-editing effort prediction. However, the Gaussian Process kernel has to be chosen in advance, and this choice in- fluences the quality of the prediction. In this paper, we propose a Genetic Programming algorithm to evolve kernels for Gaussian Processes. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and achieves predictions that, in many cases, outperform those obtained with fixed kernels.TIN2016-78365-

Who Needs Good Neighbours?

Abstract: Due to the increasing spatial dispersion of social networks, the association between neighbor relationships and quality of life has become more uncertain. Our analysis used instrumental variable modelling to reduce bias associated with residual confounding and reverse causation, in order to provide a more reliable examination of the effect of interaction with neighbors on subjective well-being than previous work. While the frames of reference for individuals’ socializing may have shifted outside the neighborhood, our analysis provides robust evidence that interaction with neighbors still matters a great deal for subjective well-being. A further important question to ask is if neighboring does affect well-being, then are there certain groups in society for whom contact with neighbors matters more? Our analysis suggests that there are, namely for those in a relationship, unemployed or retired. This means that while fostering contact with neighbors has the potential to significantly improve individual well-being, such policy efforts are likely to matter a good deal more in neighborhoods with relatively large numbers of geographically constrained social groups, such as the elderly and the unemployed. Key words: subjective well-being, neighborly interaction, social capita