Lightweight Agents, Intelligent Mobile Agent and RPC Schemes: A Comparative Analysis

This paper presents the performance comparison of Lightweight Agents, Single Mobile Intelligent Agents and Remote Procedure Call which are tools for implementing communication in a distributed computing environment. Routing algorithms for each scheme is modeled based on TSP. The performance comparison among the three schemes is based on bandwidth overhead with retransmission, system throughput and system latency. The mathematical model for each performance metric is presented, from which mathematical model is derived for each scheme for comparison. The simulation results show that the LWAs has better performance than the other two schemes in terms of small bandwidth retransmission overhead, high system throughput and low system latency. The Bernoulli random variable is used to model the failure rate of the simulated network which is assumed to have probability of success p = 85% and the probability of failure q = 15%. The network availability is realized by multiplicative pseudorandom number generator during the simulation. The results of simulation are presented

A fuzzy semantic information retrieval system for transactional applications

In this paper, we present an information retrieval system based on the concept of fuzzy logic to relate vague and uncertain objects with un-sharp boundaries. The simple but comprehensive user interface of the system permits the entering of uncertain specifications in query forms. The system was modelled and simulated in a Matlab environment; its implementation was carried out using Borland C++ Builder. The result of the performance measure of the system using precision and recall rates is encouraging. Similarly, the smaller amount of more precise information retrieved by the system will positively impact the response time perceived by the users

The Efficacy of using Gravity Concentration Methods in Value Addition to Ajabanoko Iron Ore for use in Nigerian Smelting Plants towards Metallurgical Purposes

This research work x - rays the use of gravity separation methods – Wifely shaking tabling and Air flotation (Kip Kelly air floating machine) to produce a high iron ore concentrate that could be used in both Blast furnace and Direct Reduced Induction (DRI) operation as a feed towards the production of Nigerian iron and steel. Crude iron ore assaying 40.72%Fe was beneficiated by using dry air floating processing method and wet wilfley shaking tabling process after characterization and the determination of liberation size of the iron ore and grinding of the crude iron ore to this liberation size of -500 + 355µm.The result revealed that the use of Air floating method gave 67.95%Fe, low acid oxide gangue of 7.3%, while Wilfley shaking tabling gave 51.16%Fe, low acid oxide gangue of 18.6%. It does shows that Air float tabling machine met the required standard grade of iron (≥ 65%Fe) content needed as a charge for the furnace operation, while, the assay and recovery obtain of wilfley shaking tabling could not meet the standard requirement for iron and steel production in the country, Nigeria

Salvaging building materials in a circular economy: A BIM-based whole-life performance estimator

© 2017 The Author(s) The aim of this study is to develop a BIM-based Whole-life Performance Estimator (BWPE) for appraising the salvage performance of structural components of buildings right from the design stage. A review of the extant literature was carried out to identify factors that influence salvage performance of structural components of buildings during their useful life. Thereafter, a mathematical modelling approach was adopted to develop BWPE using the identified factors and principle/concept of Weibull reliability distribution for manufactured products. The model was implemented in Building Information Modelling (BIM) environment and it was tested using case study design. Accordingly, the whole-life salvage performance profiles of the case study building were generated. The results show that building design with steel structure, demountable connections, and prefabricated assemblies produce recoverable materials that are mostly reusable. The study reveals that BWPE is an objective means for determining how much of recoverable materials from buildings are reusable and recyclable at the end of its useful life. BWPE will therefore provide a decision support mechanism for the architects and designers to analyse the implication of designs decision on the salvage performance of buildings over time. It will also be useful to the demolition engineers and consultants to generate pre-demolition audit when the building gets to end of its life

Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy

© 2019 Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circular economy agendas require that buildings must have minimal impact on the environment across the entire lifecycle. In this study therefore, a disassembly and deconstruction analytics system is developed to provide buildings’ end-of-life performance assessment from the design stage. The system architecture builds on the existing building information modelling capabilities in managing building design and construction process. The architecture is made up of four different layers namely (i) Data storage layer, (ii) Semantic layer, (iii) Analytics and functional models layer and (iv) Application layer. The four layers are logically connected to function as a single system. Three key functionalities of the disassembly and deconstruction analytics system namely (i) Building Whole Life Performance Analytics (ii) Building Element Deconstruction Analytics and (iii) Design for Deconstruction Advisor are implemented as plug-in in Revit 2017. Three scenarios of a case study building design were used to test and evaluate the performance of the system. The results show that building information modelling software capabilities can be extended to provide a platform for assessing the performance of building designs in respect of the circular economy principle of keeping the embodied energy of materials perpetually in an economy. The disassembly and deconstruction analytics system would ensure that buildings are designed with design for disassembly and deconstruction principles that guarantee efficient materials recovery in mind. The disassembly and deconstruction analytics tool could also serve as a decision support platform that government and planners can use to evaluate the level of compliance of building designs to circular economy and sustainability requirements

Investigating profitability performance of construction projects using big data: A project analytics approach

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities of existing project intelligence tools used within the industry. Several tasks in the project lifecycle bear implications for the efficient planning and delivery of construction projects. Setting up right profit margins and its continuous tracking as projects progress are vital management tasks that require data-driven decision support. Existing profit estimation measures use a company or industry wide benchmarks to guide these decisions. These benchmarks are oftentimes unreliable as they do not factor in project-specific variations. As a result, projects are wrongly estimated using uniform rates that eventually end up with entirely unusual margins either due to underspends or overruns. This study proposed a project analytics approach where Big Data is harnessed to understand the profitability distribution of different types of construction projects. To this end, Big Data architecture is recommended, and a prototype implementation is shown to store and analyse large amounts of projects data. Our data analysis revealed that profit margins evolve, and the profitability performance varies across several project attributes. These insights shall be incorporated as knowledge to machine learning algorithms to predict project margins accurately. The proposed approach enabled the fast exploration of data to understand the underlying pattern in the profitability performance for different types of construction projects

Human plasma and serum extracellular small RNA reference profiles and their clinical utility

Circulating extracellular RNAs (exRNAs) have the potential to serve as biomarkers for a wide range of medical conditions. However, limitations in existing exRNA isolation methods and a lack of knowledge on parameters affecting exRNA variability in human samples may hinder their successful discovery and clinical implementation. Using combinations of denaturants, reducing agents, proteolysis, and revised organic extraction, we developed an automated, high-throughput approach for recovery of exRNAs and exDNA from the same biofluid sample. We applied this method to characterize exRNAs from 312 plasma and serum samples collected from 13 healthy volunteers at 12 time points over a 2-month period. Small RNA cDNA library sequencing identified nearly twofold increased epithelial-, muscle-, and neuroendocrine-cell–specific miRNAs in females, while fasting and hormonal cycle showed little effect. External standardization helped to detect quantitative differences in erythrocyte and platelet-specific miRNA contributions and in miRNA concentrations between biofluids. It also helped to identify a study participant with a unique exRNA phenotype featuring a miRNA signature of up to 20-fold elevated endocrine-cell–specific miRNAs and twofold elevated total miRNA concentrations stable for over 1 year. Collectively, these results demonstrate an efficient and quantitative method to discern exRNA phenotypes and suggest that plasma and serum RNA profiles are stable over months and can be routinely monitored in long-term clinical studies

Cloud computing in construction industry: Use cases, benefits and challenges

Cloud computing technologies have revolutionised several industries (such as aerospace, manufacturing, automobile, retail, etc.) for several years. Although the construction industry is well placed to also leverage these technologies for competitive and operational advantage, the diffusion of the technologies in the industry follows a steep curve. This study therefore highlights the current contributions and use cases of cloud computing technologies in construction practices. As such, a systematic review was carried out using ninety-two (92) peer-reviewed publications, published within a ten-year period of 2009-2019. A key highlight of the research findings is that cloud computing is an innovation delivery enabler for other emerging technologies (building information modelling, internet of things, virtual reality, augmented reality, big data analytics, mobile computing) in the construction industry. As such, this paper brings to the fore, current and future application areas of cloud computing vis-à-vis other emerging technologies in the construction industry. The paper also identifies barriers to the broader adoption of cloud computing in the construction industry and discusses strategies for overcoming these barriers

Complete Genome Sequence of a Hepatitis E Virus Genotype 1e Strain from an Outbreak in Nigeria, 2017

Hepatitis E virus genotype 1 (HEV-1) is associated with large epidemics. Notably, HEV subtype 1e (HEV-1e) has caused HEV outbreaks in sub-Saharan Africa. We report here the second full-length genome sequence of an HEV-1e strain (NG/17-0503) from a recent outbreak in Nigeria in 2017. It shares 94.2% identity with an HEV-1e strain from Chad.Peer Reviewe