Guidelines for the verification and validation of expert system software and conventional software: Bibliography. Volume 8

This volume contains all of the technical references found in Volumes 1-7 concerning the development of guidelines for the verification and validation of expert systems, knowledge-based systems, other AI systems, object-oriented systems, and conventional systems

The Source Size Dependence on the M_hadron Applying Fermi and Bose Statistics and I-Spin Invariance

The emission volume sizes of pions and Kaons, r_{\pi^\pm \pi^\pm} and r_{K^\pm K^\pm}, measured in the hadronic Z^0 decays via the Bose-Einstein Correlations (BEC), and the recent measurements of r_{\Lambda\Lambda} obtained by through the Pauli exclusion principle are used to study the r dependence on the hadron mass. A clear r_{\pi^\pm \pi^\pm} > r_{K^\pm K^\pm} > r_{\Lambda \Lambda} hierarchy is observed which seems to disagree with the basic string (LUND) model expectation. An adequate description of r(m) is obtained via the Heisenberg uncertainty relations and also by Local Parton Hadron Duality approach using a general QCD potential. These lead to a relation of the type r(m) ~ Constant/sqrt{m}. The present lack of knowledge on the f_o(980) decay rate to the K^0\bar{K}^0 channel prohibits the use of the r_{K^0_SK^0_S} in the r(m) analysis. The use of a generalised BEC and I-spin invariance, which predicts an BEC enhancement also in the K^{\pm}K^0 and \pi^{\pm}\pi^0 systems, should in the future help to include the r_{K^0_SK^0_S} in the r(m) analysis.Comment: 7 pages, 4 figures, Based on an invited talk given by G. Alexander at the XXIX Int. Symp. on Multiparticle Dynamics, 9-13 August 1999, Providence RI, USA. (to be published in the proceedings of this conference

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Impact of Landuse Morphology on Urban Transportation

People, cities, nations and the world, in general, would remain largely underdeveloped without transportation systems. However, Transportation puts significant pressure on land use and poses a great challenge to urban sustainability in developing countries. This study examines the influence of Land use structure on Intra-urban transportation in the developing city of cities in the West African sub-region – using Enugu city as a case study. The study uses a descriptive research method. A survey was carried out in six districts within the Enugu metropolis based on a stratified, purposive sampling technique. Questionnaires were used as data collection instruments; 400 respondence participated in the study employing Yamane equation. Furthermore, a twelve-hour (7 am to 7 pm) traffic count was conducted to assess traffic volume. The study finding revealed that Transportation within the urban areas is significantly impacted by Land-use structure, city morphology, neighbourhood characteristics in terms of population and residential density of the city. The hypothesis suggests no significant difference between the various land uses across the Enugu metropolis (p = 0.129). It was also discovered that an average of 122,431 Passenger Car Units (PCU) constantly ply the metropolis roads to service a total population of 564,725 daily, indicated a high rate of car dependency. The study surmises that land use generates vehicular traffic, which impacts the socio-economic environment and the effectiveness of the transportation system. The significance of this study is that the findings contribute to the existing knowledge base that would advance stratic policy formation towards acceleration of the uptake of sustainable urban transportation systems in the region. Doi: 10.28991/cej-2021-03091758 Full Text: PD

Big tranSMART for clinical decision making

Molecular profiling data based patient stratification plays a key role in clinical decision making, such as identification of disease subgroups and prediction of treatment responses of individual subjects. Many existing knowledge management systems like tranSMART enable scientists to do such analysis. But in the big data era, molecular profiling data size increases sharply due to new biological techniques, such as next generation sequencing. None of the existing storage systems work well while considering the three ”V” features of big data (Volume, Variety, and Velocity). New Key Value data stores like Apache HBase and Google Bigtable can provide high speed queries by the Key. These databases can be modeled as Distributed Ordered Table (DOT), which horizontally partitions a table into regions and distributes regions to region servers by the Key. However, none of existing data models work well for DOT. A Collaborative Genomic Data Model (CGDM) has been designed to solve all these is- sues. CGDM creates three Collaborative Global Clustering Index Tables to improve the data query velocity. Microarray implementation of CGDM on HBase performed up to 246, 7 and 20 times faster than the relational data model on HBase, MySQL Cluster and MongoDB. Single nucleotide polymorphism implementation of CGDM on HBase outperformed the relational model on HBase and MySQL Cluster by up to 351 and 9 times. Raw sequence implementation of CGDM on HBase gains up to 440-fold and 22-fold speedup, compared to the sequence alignment map format implemented in HBase and a binary alignment map server. The integration into tranSMART shows up to 7-fold speedup in the data export function. In addition, a popular hierarchical clustering algorithm in tranSMART has been used as an application to indicate how CGDM can influence the velocity of the algorithm. The optimized method using CGDM performs more than 7 times faster than the same method using the relational model implemented in MySQL Cluster.Open Acces

Tracing the Bioavailability of Three-Dimensional Graphene Foam in Biological Tissues

This is the final version of the article. Available from MDPI via the DOI in this record.Graphene-based materials with a three-dimensional (3D) framework have been investigated for a variety of biomedical applications because of their 3D morphology, excellent physiochemical properties, volume stability, and their controllable degradation rate. Current knowledge on the toxicological implications and bioavailability of graphene foam (GF) has major uncertainties surrounding the fate and behavior of GF in exposed environments. Bioavailability, uptake, and partitioning could have potential effects on the behavior of GF in living organisms, which has not yet been investigated. Here, we report a pilot toxicology study on 3D GF in common carps. Our results showed that GF did not show any noticeable toxicity in common carps, and the antioxidant enzymatic activities, biochemical and blood parameters persisted within the standard series. Further histological imaging revealed that GF remained within liver and kidney macrophages for 7 days without showing obvious toxicity. An in vivo study also demonstrated a direct interaction between GF and biological systems, verifying its eco-friendly nature and high biocompatibility.This work was supported by EPSRC Centre for Doctoral Training in Metamaterials, XM2 (Grant No. EP/L015331/1) University of Exeter EX4 4QF, United Kingdom

Characterization of wash-off from urban impervious surfaces and SuDS design criteria for source control under semi-arid conditions

[EN] Knowledge about pollutant wash-off from urban impervious surfaces is a key feature for developing effective management strategies. Accordingly, further information is required about urban areas under semi-arid climate conditions at the sub-catchment scale. This is important for designing source control systems for pollution. In this study, a characterization of pollutant wash-off has been performed over sixteen months, at the sub-catchment scale for urban roads as impervious surfaces. The study was conducted in Valencia, Spain, a city with a Mediterranean climate. The results show high event mean concentrations for suspended solids (98 mg/l), organic matter (142 mgCOD/l, 25 mg BOD5/l), nutrients (3.7mgTN/l, 0.4 mg TP/l), and metals (0.23, 0.32, 0.62 and 0.17 mg/l for Cu, Ni, Pb, and Zn, respectively). The results of the runoff characterization highlight the need to control this pollution at its source, separately from wastewater because of their different characteristics. The wash-off, defined in terms of mobilized mass (g/m(2)) fits well with both process-based and statistical models, with the runoff volume and rainfall depth being the main explanatory variables. Based on these results and using information collected from hydrographs and pollutographs, an approach for sizing sustainable urban drainage systems (SuDS), focusing on water quality and quantity variables, has been proposed. By setting a concentration-based target (TSS discharged to receiving waters < 35 mg/l), the results indicate that for a SuDS type detention basin (DB), an off-line configuration performs better than an on-line configuration. The resulting design criterion, expressed as SuDS volume per unit catchment area, assuming a DB type SuDS, varies between 7 and 10 l/m(2).This research was funded through the SUPRIS-SUReS projects (Ref. BIA2015-65240-C2-1-R MINECO/ERDF, UE) and SUPRIS-SUPeI (Ref. BIA2015-65240-C2-2-R MINECO/ERDF, UE), financed by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF) and by the project IMBORNAL (Ref. SP20120732), financed by Universitat Politecnica de Valencia.Andrés Doménech, I.; Hernández Crespo, C.; Martín Monerris, M.; Andrés-Valeri, VC. (2018). Characterization of wash-off from urban impervious surfaces and SuDS design criteria for source control under semi-arid conditions. The Science of The Total Environment. 612:1320-1328. https://doi.org/10.1016/j.scitotenv.2017.09.011S1320132861

Towards a Protein-Protein Interaction information extraction system: recognizing named entities

[EN] The majority of biological functions of any living being are related to Protein Protein Interactions (PPI). PPI discoveries are reported in form of research publications whose volume grows day after day. Consequently, automatic PPI information extraction systems are a pressing need for biologists. In this paper we are mainly concerned with the named entity detection module of PPIES (the PPI information extraction system we are implementing) which recognizes twelve entity types relevant in PPI context. It is composed of two sub-modules: a dictionary look-up with extensive normalization and acronym detection, and a Conditional Random Field classifier. The dictionary look-up module has been tested with Interaction Method Task (IMT), and it improves by approximately 10% the current solutions that do not use Machine Learning (ML). The second module has been used to create a classifier using the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA 04) data set. It does not use any external resources, or complex or ad hoc post-processing, and obtains 77.25%, 75.04% and 76.13 for precision, recall, and F1-measure, respectively, improving all previous results obtained for this data set.This work has been funded by MICINN, Spain, as part of the "Juan de la Cierva" Program and the Project DIANA-Applications (TIN2012-38603-C02-01), as well as the by the European Commission as part of the WIQ-EI IRSES Project (Grant No. 269180) within the FP 7 Marie Curie People Framework.Danger Mercaderes, RM.; Pla Santamaría, F.; Molina Marco, A.; Rosso, P. (2014). Towards a Protein-Protein Interaction information extraction system: recognizing named entities. Knowledge-Based Systems. 57:104-118. https://doi.org/10.1016/j.knosys.2013.12.010S1041185