Reconfigurability Function Deployment in Software Development

In the forthcoming highly dynamic and complex business environment high-speed and cost-effective development of software applications for targeting a precise, unique and momentary set of requirements (no more-no less) associated to a customized business case will bring sig-nificant benefits both for producers and users. This requires a life cycle change-oriented ap-proach in software development. In this respect, designing software with intrinsic evolutionary resources for reconfiguration represents the sound approach. A methodology for concurrent deployment of reconfigurability characteristics in software applications is introduced in this paper. Its potential is exemplified in a case study dealing with web-based software tools to support systematic product innovation projects.Reconfigurability, Software Development, Innovation, TRIZ, RAD

A research review of quality assessment for software

Measures were recommended to assess the quality of software submitted to the AdaNet program. The quality factors that are important to software reuse are explored and methods of evaluating those factors are discussed. Quality factors important to software reuse are: correctness, reliability, verifiability, understandability, modifiability, and certifiability. Certifiability is included because the documentation of many factors about a software component such as its efficiency, portability, and development history, constitute a class for factors important to some users, not important at all to other, and impossible for AdaNet to distinguish between a priori. The quality factors may be assessed in different ways. There are a few quantitative measures which have been shown to indicate software quality. However, it is believed that there exists many factors that indicate quality and have not been empirically validated due to their subjective nature. These subjective factors are characterized by the way in which they support the software engineering principles of abstraction, information hiding, modularity, localization, confirmability, uniformity, and completeness

A Field Comes of Age: Geometric Morphometrics in the 21st Century

Twenty years ago, Rohlf and Marcus proclaimed that a revolution in morphometrics was underway, where classic analyses based on sets of linear distances were being supplanted by geometric approaches making use of the coordinates of anatomical landmarks. Since that time the field of geometric morphometrics has matured into a rich and cohesive discipline for the study of shape variation and covariation. The development of the field is identified with the Procrustes paradigm, a methodological approach to shape analysis arising from the intersection of the statistical shape theory and analytical procedures for obtaining shape variables from landmark data. In this review we describe the Procrustes paradigm and the current methodological toolkit of geometric morphometrics. We highlight some of the theoretical advances that have occurred over the past ten years since our prior review (Adams et al., 2004), what types of anatomical structures are amenable to these approaches, and how they extend the reach of geometric morphometrics to more specialized applications for addressing particular biological hypotheses. We end with a discussion of some possible areas that are fertile ground for future development in the field

Why the short face? Developmental disintegration of the neurocranium drives convergent evolution in neotropical electric fishes

© 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Convergent evolution is widely viewed as strong evidence for the influence of natural selection on the origin of phenotypic design. However, the emerging evo-devo synthesis has highlighted other processes that may bias and direct phenotypic evolution in the presence of environmental and genetic variation. Developmental biases on the production of phenotypic variation may channel the evolution of convergent forms by limiting the range of phenotypes produced during ontogeny. Here, we study the evolution and convergence of brachycephalic and dolichocephalic skull shapes among 133 species of Neotropical electric fishes (Gymnotiformes: Teleostei) and identify potential developmental biases on phenotypic evolution. We plot the ontogenetic trajectories of neurocranial phenotypes in 17 species and document developmental modularity between the face and braincase regions of the skull. We recover a significant relationship between developmental covariation and relative skull length and a significant relationship between developmental covariation and ontogenetic disparity. We demonstrate that modularity and integration bias the production of phenotypes along the brachycephalic and dolichocephalic skull axis and contribute to multiple, independent evolutionary transformations to highly brachycephalic and dolichocephalic skull morphologies

Multi-objective community detection applied to social and COVID-19 constructed networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks. After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics. For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network. Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%