Spatial and temporal phylogeny of border disease virus in pyrenean chamois (Rupicapra p. Pyrenaica)

Border disease virus (BDV) affects a wide range of ruminants worldwide, mainly domestic sheep and goat. Since 2001 several outbreaks of disease associated to BDV infection have been described in Pyrenean chamois (Rupicapra pyrenaica pyrenaica) in Spain, France and Andorra. In order to reconstruct the most probable places of origin and pathways of dispersion of BDV among Pyrenean chamois, a phylogenetic analysis of 95 BDV 5'untranslated sequences has been performed on chamois and domestic ungulates, including novel sequences and retrieved from public databases, using a Bayesian Markov Chain Monte Carlo method. Discrete and continuous space phylogeography have been applied on chamois sequences dataset, using centroid positions and latitude and longitude coordinates of the animals, respectively. The estimated mean evolutionary rate of BDV sequences was 2.9x10(-3) subs/site/year (95% HPD: 1.5-4.6x10(-3)). All the Pyrenean chamois isolates clustered in a unique highly significant clade, that originated from BDV-4a ovine clade. The introduction from sheep (dated back to the early 90s) generated a founder effect on the chamois population and the most probable place of origin of Pyrenean chamois BDV was estimated at coordinates 42.42 N and 1.9 E. The pathways of virus dispersion showed two main routes: the first started on the early 90s of the past century with a westward direction and the second arise in Central Pyrenees. The virus spread westward for more than 125 km and southward for about 50km and the estimated epidemic diffusion rate was about 13.1 km/year (95% HPD 5.2-21.4 km/year). The strong spatial structure, with strains from a single locality segregating together in homogeneous groups, and the significant pathways of viral dispersion among the areas, allowed to reconstruct both events of infection in a single area and of migrations, occurring between neighboring areas

Applications integration for manufacturing control systems with particular reference to software interoperability issues

The introduction and adoption of contemporary computer aided manufacturing control systems (MCS) can help rationalise and improve the productivity of manufacturing related activities. Such activities include product design, process planning and production management with CAD, CAPP and CAPM. However, they tend to be domain specific and would generally have been designed as stand-alone systems where there is a serious lack of consideration for integration requirements with other manufacturing activities outside the area of immediate concern. As a result, "islands of computerisation" exist which exhibit deficiencies and constraints that inhibit or complicate subsequent interoperation among typical MCS components. As a result of these interoperability constraints, contemporary forms of MCS typically yield sub-optimal benefits and do not promote synergy on an enterprise-wide basis. The move towards more integrated manufacturing systems, which requires advances in software interoperability, is becoming a strategic issue. Here the primary aim is to realise greater functional synergy between software components which span engineering, production and management activities and systems. Hence information of global interest needs to be shared across conventional functional boundaries between enterprise functions. The main thrust of this research study is to derive a new generation of MCS in which software components can "functionally interact" and share common information through accessing distributed data repositories in an efficient, highly flexible and standardised manner. It addresses problems of information fragmentation and the lack of formalism, as well as issues relating to flexibly structuring interactions between threads of functionality embedded within the various components. The emphasis is on the: • definition of generic information models which underpin the sharing of common data among production planning, product design, finite capacity scheduling and cell control systems. • development of an effective framework to manage functional interaction between MCS components, thereby coordinating their combined activities. • "soft" or flexible integration of the MCS activities over an integrating infrastructure in order to (i) help simplify typical integration problems found when using contemporary interconnection methods for applications integration; and (ii) enable their reconfiguration and incremental development. In order to facilitate adaptability in response to changing needs, these systems must also be engineered to enable reconfigurability over their life cycle. Thus within the scope of this research study a new methodology and software toolset have been developed to formally structure and support implementation, run-time and change processes. The tool set combines the use of IDEFO (for activity based or functional modelling), IDEFIX (for entity-attribute relationship modelling), and EXPRESS (for information modelling). This research includes a pragmatic but effective means of dealing with legacyl software, which often may be a vital source of readily available information which supports the operation of the manufacturing enterprise. The pragmatism and medium term relevance of the research study has promoted particular interest and collaboration from software manufacturers and industrial practitioners. Proof of concept studies have been carried out to implement and evaluate the developed mechanisms and software toolset

OPTIMIZATION OF wasteWOIMA’s MODULAR POWER PLANT INSTALLATION USING 3D SIMULATION AND VIRTUAL REALITY

The primary objective of this research is to optimize module installation procedure for Woima’s modular WTE power plant by making a Three-Dimensional (3D) simulation of the power plant and inspecting the model using Virtual Reality (VR). This thesis ad-dresses the research question on how to use 3D simulation to reduce time and cost dur-ing the construction. Therefore, this thesis suggests an installation procedure where each module fits into the other as it were a lego puzzle. This proposed building procedure is previewed as 3D simulation that could then be used to optimize the construction of wasteWOIMA’s modular power plant. This research work employs the use of primary data, CAD blueprints, sourced from the case company, while secondary data was sourced from books, online repository, aca-demic and scientific journals. To uphold the credibility of this research, it utilized both experimental and case study research strategies to conceptualize the simulation of the power plant which can be previewed on Virtual Reality (VR) glasses. VR model inspec-tion provides an immersive and real-life scale experience. The entire 3D simulation was done using SketchUp software, a 3D modelling and simulation tool. A VR session was also carried out during the research to help identify areas of possible improvement with safety, cost and quality, using HTC Vive VR glasses and Symmetry Alpha software. The outcome of this research shows that 3D simulation, especially when combined with VR models inspection, can help to optimize models to prevent errors during actual con-struction work, hence, saving cost and reducing lead time

Gap analysis of research, technology, & development activities

Most energy usage of buildings throughout their life cycle is during the operational stage (~80%). The decisions made in the conception and design stages of new buildings, as well as in renovation stages of existing buildings, influence about 80% of the total life cycle energy consumption. The impact of user behaviour and real-time control is in the range of 20%. ICT has been identified as one possible means to design, optimize, regulate and control energy use within existing and future (smart) buildings. This books presents a collection of best practices, gap analysis of current research and technology development activities, a research roadmap, and a series of recommendations for ICT supported energy efficiency in buildings. Key research, technology, and development priorities include: integrated design and production management; intelligent and integrated control; user awareness and decision support; energy management and trading; integration Technologies. The vision for ICT supported energy efficiency of buildings in the short, medium, and long term is advocated as follows: Short term: Buildings meet the energy efficiency requirements of regulations and users; Medium term: The energy performance of buildings is optimised considering the whole life cycle; Long term: New business models are driven by energy efficient “prosumer” buildings at district level – long term

Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Breast cancer (BC) is the common type of cancer among females. Mortality from BC could be decreased by identifying and diagnosing it atan earlierphase. Different imaging modalities are used to detect BC, like mammography. Even withproven records as a BC screening tool, mammography istime-consuming and hasconstraints, namely lower sensitivity in women with dense breast tissue. Computer-Aided Diagnosis or Detection (CAD) system assistsaproficient radiologist to identifyBC at an earlier stage. Recently, the advancementin deep learning (DL)methodsareemployed to mammography assist radiologists to increase accuracy and efficiency. Therefore, this study presents a metaheuristic-based hyperparameter optimization with deep learning-based breast cancer detection on mammogram images (MHODL-BCDMI) technique. The presented MHODL-BCDMI technique mainly focused on the recognition and classification of breast cancer on digital mammograms. To achieve this, the MHODL-BCDMI technique employs pre-processing in two stages: Wiener Filter (WF) based noise elimination and contrast enhancement. Besides, the MHODL-BCDMI technique exploits densely connected networks (DenseNet201) model for feature extraction purposes. For BC classification and detection, a hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model is used. Furthermore, three hyperparameter optimizers are employed namely cat swarm optimization (CSO), harmony search algorithm (HSA), and hybrid grey wolf whale optimization algorithm (HGWWOA). Finally, the U2Net segmentation approach is used for the classification of benign and malignant types of cancer. The experimental analysis of the MHODL-BCDMI method is tested on a digital mammogram image dataset and the outcomes are assessed in terms of diverse metrics. The simulation results highlighted the enhanced cancer detection performance of the MHODL-BCDMI technique over other recent algorithms

Impact of Product Modularity on Mass Customization Capability: An Exploratory Study of Contextual Factors

This study examines how the impact of product modularity on the mass customization capability is moderated by several contextual factors, such as the firms’ information system capacity (ISC), teamwork (TW), multifunctional employees (MFE), and organizational structure (flat or hierarchical) (OSF). Data from 238 firms located in multiple countries across three different industry groups were analyzed to test the moderated regression models and the hypotheses. The results showed that the product modularity strongly impacts the mass customization capability (MCC). Compared to ISC, the social contextual variables, such as TW, MFE, and OSF, have stronger moderating effects on the impact of the product modularity on the mass customization capability. In addition, ISC helps MCC solely for firms with flat organizational structures. Overall, our study suggests that manufacturers who desire to become mass customizers should create flat, nimble organizations with employees who are trained in several different tasks and are adept at teamwork

Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments