A knowledge-based decision support system for roofing materials selection and cost estimating: a conceptual framework and data modelling

A plethora of materials is available to the modern day house designer but selecting the appropriate material is a complex task. It requires synthesising a multitude of performance criteria such as initial cost, maintenance cost, thermal performance and sustainability among others. This research aims to develop a Knowledge-based Decision support System for Material Selection (KDSMS) that facilitates the selection of optimal material for different sub elements of a roof design. The proposed system also has a facility for estimating roof cost based on the identified criteria. This paper presents the data modelling conceptual framework for the proposed system. The roof sub elements are modelled on the Building Cost Information Service (BCIS) Standard Form of Cost Analysis. This model consists of a knowledge base and a database to store different types of roofing materials with their corresponding performance characteristics and rankings. The system s knowledge is elicited from an extensive review of literature and the use of a domain expert forum. The proposed system employs the multi criteria decision method of TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution), to resolve the materials selection and optimisation problem. The KDSMS is currently being developed for the housing sector of Northern Ireland

A knowledge-based decision support system for roofing materials selection

Varieties of materials are available for roof housing construction but selecting the appropriate material is a complex and ponderous task. In order to choose the right material, a multitude of performance criteria would need to be considered. This research aims to develop a knowledge-based decision support system for material selection (KDSMS) to facilitate the selection of optimal material for different sub elements of roof design. This model consists of a knowledge base and databases to store different types of roofing materials with their corresponding performance characteristics. Knowledge is elicited from domain experts and extensive literature review. The proposed system employs the use of TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution) multiple criteria decision making method, to solve the materials selection and optimisation problem where initial cost, maintenance cost, thermal performance and sustainability criteria are considered among others. The proposed system is currently being developed for the housing sector in Northern Ireland. This paper presents and explains the framework of the proposed system

Process Logic for Verifying the Correctness of Business Process Models

Process verification is a key step in business process management. In this paper, we propose process logic as a new logical formalism and mathematical method to enable advanced process verification. We formally define the syntax and semantics of process logic, establish a formal relationship between process logic and graphical representation of process models, and transform the problem of verifying the correctness of process models into a problem of determining the validity of logic argument forms

Solutions of Initial Value Problems with Non-Singular, Caputo Type and Riemann-Liouville Type, Integro-Differential Operators

Motivated by the recent interest in generalized fractional order operators and their applications, we consider some classes of integro-differential initial value problems based on derivatives of the Riemann–Liouville and Caputo form, but with non-singular kernels. We show that, in general, the solutions to these initial value problems possess discontinuities at the origin. We also show how these initial value problems can be re-formulated to provide solutions that are continuous at the origin but this imposes further constraints on the system. Consideration of the intrinsic discontinuities, or constraints, in these initial value problems is important if they are to be employed in mathematical modelling applications

A Genome-Wide Association Study of Cocaine Use Disorder Accounting for Phenotypic Heterogeneity and Gene–Environment Interaction

Background: Phenotypic heterogeneity and complicated gene-environment interplay in etiology are among the primary factors that hinder the identification of genetic variants associated with cocaine use disorder. Methods: To detect novel genetic variants associated with cocaine use disorder, we derived disease traits with reduced phenotypic heterogeneity using cluster analysis of a study sample (n = 9965). We then used these traits in genome-wide association tests, performed separately for 2070 African Americans and 1570 European Americans, using a new mixed model that accounted for the moderating effects of 5 childhood environmental factors. We used an independent sample (918 African Americans, 1382 European Americans) for replication. Results: The cluster analysis yielded 5 cocaine use disorder subtypes, of which subtypes 4 (n = 3258) and 5 (n = 1916) comprised heavy cocaine users, had high heritability estimates (h2 = 0.66 and 0.64, respectively) and were used in association tests. Seven of the 13 identified genetic loci in the discovery phase were available in the replication sample. In African Americans, rs114492924 (discovery p = 1.23 x E-8), a single nucleotide polymorphism in LINC01411, was replicated in the replication sample (p = 3.63 x E-3). In a meta-analysis that combined the discovery and replication results, 3 loci in African Americans were significant genome-wide: rs10188036 in TRAK2 (p = 2.95 x E-8), del 1:15511771 in TMEM51 = 9.11 x E-10) and rs149843442 near LPHN2 (p = 3.50 x E-8). Limitations: Lack of data prevented us from replicating 6 of the 13 identified loci. Conclusion: Our results demonstrate the importance of considering phenotypic heterogeneity and gene-environment interplay in detecting genetic variations that contribute to cocaine use disorder, because new genetic loci have been identified using our novel analytic method

A Systematic Approach to Delay Functions

We present a systematic introduction to a class of functions that provide fundamental solutions for autonomous linear integer-order and fractional-order delay differential equations. These functions, referred to as delay functions, are defined through power series or fractional power series, with delays incorporated into their series representations. Using this approach, we have defined delay exponential functions, delay trigonometric functions and delay fractional Mittag-Leffler functions, among others. We obtained Laplace transforms of the delay functions and demonstrated how they can be employed in finding solutions to delay differential equations. Our results, which extend and unify previous work, offer a consistent framework for defining and using delay functions

Patterning of the MinD cell division protein in cells of arbitrary shape can be predicted using a heuristic dispersion relation

© 2016, Paul M. G. Curmi, et al. Many important cellular processes require the accurate positioning of subcellular structures. Underpinning many of these are protein systems that spontaneously generate spatiotemporal patterns. In some cases, these systems can be described by non-linear reaction-diffusion equations, however, a full description of such equations is rarely available. A well-studied patterning system is the Min protein system that underpins the positioning of the FtsZ contractile ring during cell division in Escherichia coli. Using a coordinate-free linear stability analysis, the reaction terms can be separated from the geometry of a cell. The reaction terms produce a dispersion relation that can be used to predict patterning on any cell shape and size. Applying linear stability analysis to an accurate mathematical model of the Min system shows that while it correctly predicts the onset of patterning, the dispersion relation fails to predict oscillations and quantitative mode transitions. However, we show that data from full solutions of the Min model can be used to generate a heuristic dispersion relation. We show that this heuristic dispersion relation can be used to approximate the Min protein patterning obtained by full simulations of the non-linear reaction-diffusion equations. Moreover, it also predicts Min patterning obtained from experiments where the shapes of E. coli cells have been deformed into rectangles or arbitrary shapes. Using this procedure, it should be possible to generate heuristic dispersion relations from protein patterning data or simulations for any patterning process and subsequently use these to predict patterning for arbitrary cell shapes

Micro-RNA and mRNA Profiles Associated with Ectopic Germinal Center Formation in Thymus Samples of Patients with Autoimmune Myas

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder caused by antibodies directed against proteins present at the post-synaptic surface of neuromuscular junction (NMJ). A characteristic pathology of patients with early onset MG is thymic hyperplasia with ectopic germinal centers (GC). However, mechanisms that trigger and maintain thymic hyperplasia are poorly characterized. Micro-RNAs (miRNA) are small, non-coding RNAs that are increasingly appreciated to be involved in the pathology of several autoimmune diseases. In order to determine the central mechanisms involved in the pathology, thymus samples from MG patients were assessed by histology and grouped based on appearance of GC compared to samples without them. MiRNA and mRNA were evaluated using GeneChip® miRNA 4.0 Array and GeneChip® Human Transcriptome Array 2.0, respectively. Partek Genomic Suite 6.6 and Transcript Analysis Console 2.0 programs were used for further analysis. Thirty-four mature miRNA and forty eight annotated mRNA transcripts were identified that were differentially expressed between the two groups with greater than 1.5 fold difference in expression (ANOVA p Our study shows that there is a distinct mRNA and miRNA expression pattern in the thymus and maintenance of autoimmunity is supported by regulatory pathways known to be involved in neoplasia

Differential mRNA expression in ectopic germinal centers of myasthenia gravis thymus

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder resulting in weakness of voluntary muscles. It is caused by antibodies directed against proteins present at the post-synaptic surface of neuromuscular junction (NMJ). A characteristic pathology of patients with early onset MG is thymic hyperplasia with ectopic germinal centers (GC). However, mechanisms that trigger and maintain thymic hyperplasia are poorly characterized. In order to determine the central mechanisms involved in the pathology, thymus samples from MG patients were assessed by histology and grouped based on appearance of GC compared to samples without them. We assessed the differential mRNA expression profiles between the two groups by GeneChip® Human Transcriptome Array 2.0. Partek Genomic Suite 6.6 and Transcript Analysis Console 2.0 programs were used for further analysis. Forty eight annotated mRNA transcripts were identified that were differentially expressed between the two groups with greater than 1.5 fold difference in expression (ANOVA p\u3c0.05). We verified their expression by RT-PCR. We identified Regulator of G protein Signaling 13 or RGS13 that is known to be expressed in GC B-cells and regulate responsiveness to chemokine signaling. Upregulation of RGS13 was found to be associated with specimens having GC. We verified its expression in GC by immunohistochemistry. Gene ontology (GO) enrichment analysis and Ingenuity Pathway Analysis (IPA) core analysis of differentially expressed genes indicate involvement of immune response regulation and cell proliferation pathways, indicating their importance in GC formation and regulation