Technology strategy for re-engineering design and construction

Automation technology can provide construction firms with a number of competitive advantages. Technology strategy guides a firm's approach to all technology, including automation. Engineering management educators, researchers, and construction industry professionals need improved understanding of how technology affects results, and how to better target investments to improve competitive performance. A more formal approach to the concept of technology strategy can benefit the construction manager in his efforts to remain competitive in increasingly hostile markets. This paper recommends consideration of five specific dimensions of technology strategy within the overall parameters of market conditions, firm capabilities and goals, and stage of technology evolution. Examples of the application of this framework in the formulation of technology strategy are provided for CAD applications, co-ordinated positioning technology and advanced falsework and formwork mechanisation to support construction field operations. Results from this continuing line of research can assist managers in making complex and difficult decisions regarding reengineering construction processes in using new construction technology and benefit future researchers by providing new tools for analysis. Through managing technology to best suit the existing capabilities of their firm, and addressing the market forces, engineering managers can better face the increasingly competitive environment in which they operate.

Data-Driven Text Generation using Neural Networks & Provenance is Complicated and Boring — Is there a solution?

Title: Data-Driven Text Generation using Neural Networks Speaker: Pavlos Vougiouklis, University of Southampton Abstract: Recent work on neural networks shows their great potential at tackling a wide variety of Natural Language Processing (NLP) tasks. This talk will focus on the Natural Language Generation (NLG) problem and, more specifically, on the extend to which neural network language models could be employed for context-sensitive and data-driven text generation. In addition, a neural network architecture for response generation in social media along with the training methods that enable it to capture contextual information and effectively participate in public conversations will be discussed. Speaker Bio: Pavlos Vougiouklis obtained his 5-year Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2013. He was awarded an MSc degree in Software Engineering from the University of Southampton in 2014. In 2015, he joined the Web and Internet Science (WAIS) research group of the University of Southampton and he is currently working towards the acquisition of his PhD degree in the field of Neural Network Approaches for Natural Language Processing. Title: Provenance is Complicated and Boring — Is there a solution? Speaker: Darren Richardson, University of Southampton Abstract: Paper trails, auditing, and accountability — arguably not the sexiest terms in computer science. But then you discover that you've possibly been eating horse-meat, and the importance of provenance becomes almost palpable. Having accepted that we should be creating provenance-enabled systems, the challenge of then communicating that provenance to casual users is not trivial: users should not have to have a detailed working knowledge of your system, and they certainly shouldn't be expected to understand the data model. So how, then, do you give users an insight into the provenance, without having to build a bespoke system for each and every different provenance installation? Speaker Bio: Darren is a final year Computer Science PhD student. He completed his undergraduate degree in Electronic Engineering at Southampton in 2012.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

Design and construction of fixed bed pyrolysis system and plum seed pyrolysis for bio-oil production

This work investigated the production of bio oil from plum seed (Zyziphus jujuba) by fixed bed pyrolysis technology. A fixed bed pyrolysis system has been designed and fabricated for production of bio oil. The major components of the system are: fixed bed reactor, liquid condenser and liquid collector. Nitrogen gas was used to maintain the inert atmosphere in the reactor where the pyrolysis reaction takes place. The feedstock considered in this study is plum seed as it is available waste material in Bangladesh. The reactor is heated by means of a cylindrical biomass external heater. Rice husk was used as the energy source. The products are oil, char and gas. The parameters varied are reactor bed temperature, running time and feed particle size. The parameters are found to influence the product yields significantly. The maximum liquid yield of 39 wt% at 5200C for a feed particle size of 2.36-4.75 mm and a gas flow rate of 8 liter/min with a running time of 120 minute. The pyrolysis oil obtained at these optimum process conditions are analyzed for some of their properties as an alternative fuel. The density of the liquid was closer with diesel. The viscosity of the plum seed liquid was lower than that of the conventional fuels. The calorific value of the pyrolysis oil is one half of the diesel fuel.

Engineering based problem solving in the middle school : design and construction with simple machines

Incorporating engineering concepts into middle school curriculum is seen as an effective way to improve students’ problem-solving skills. A selection of findings is reported from a science, technology, engineering and mathematics (STEM)-based unit in which students in the second year (grade 8) of a three-year longitudinal study explored engineering concepts and principles pertaining to the functioning of simple machines. The culminating activity, the focus of this paper, required the students to design, construct, test, and evaluate a trebuchet catapult. We consider findings from one of the schools, a co-educational school, where we traced the design process developments of four student groups from two classes. The students’ descriptions and explanations of the simple machines used in their catapult design are examined, together with how they rated various aspects of their engineering designs. Included in the findings are students’ understanding of how their simple machines were simulated by the resources supplied and how the machines interacted in forming a complex machine. An ability to link physical materials with abstract concepts and an awareness of design constraints on their constructions were apparent, although a desire to create a ‘‘perfect’’ catapult despite limitations in the physical materials rather than a prototype for testing concepts was evident. Feedback from teacher interviews added further insights into the students’ developments as well as the teachers’ professional learning. An evolving framework for introducing engineering education in the pre-secondary years is proposed.

The SCRAMSPACE I scramjet flight design and construction

The design activities of the development of the SCRAMSPACE I scramjet-powered free-flight experiment are described in this paper. The objectives of this flight are first described together with the definition of the primary, secondary and tertiary experiments. The Scramjet configuration studied is first discussed together with the rocket motor system selected for this flight. The different flight sequences are then explained, highlighting the SCRAMSPACE I free-flyer separation and re-orientation procedures. A design trade-off study is then described considering vehicle stability, packaging, thermo-structural analysis and trajectory, discussing the alignment of the predicted performance with the mission scientific requirements. The global system architecture and instrumentation of the vehicle are then explained. The conclusions of this design phase are that a vehicle design has been produced which is able to meet the mission scientific goals and the procurement & construction of the vehicle are ongoing.

Design and construction of an in-plant activation cassette for transgene expression and recombinant protein production in plants

Virus-based transgene expression systems have become particularly valuable for recombinant protein production in plants. The dual-module in-plant activation (INPACT) expression platform consists of a uniquely designed split-gene cassette incorporating the cis replication elements of Tobacco yellow dwarf geminivirus (TYDV) and an ethanol-inducible activation cassette encoding the TYDV Rep and RepA replication-associated proteins. The INPACT system is essentially tailored for recombinant protein production in stably transformed plants and provides both inducible and high-level transient transgene expression with the potential to be adapted to diverse crop species. The construction of a novel split-gene cassette, the inducible nature of the system and the ability to amplify transgene expression via rolling-circle replication differentiates this system from other DNA- and RNA-based virus vector systems used for stable or transient recombinant protein production in plants. Here we provide a detailed protocol describing the design and construction of a split-gene INPACT cassette, and we highlight factors that may influence optimal activation and amplification of gene expression in transgenic plants. By using Nicotiana tabacum, the protocol takes 6-9 months to complete, and recombinant proteins expressed using INPACT can accumulate to up to 10% of the leaf total soluble protein.

Book Review: "Safe Design and Construction of Machinery – Regulation, Practice and Performance [First edition]"

With Safe Design and Construction of Machinery, the author presents the results of empirical studies into this significant aspect of safety science in a very readable, well-structured format. The book contains 436 references, 17 tables, one figure and a comprehensive index. Liz Bluff addresses a complex and important, but often neglected domain in OHS – the safety of machinery – in a holistic and profound, yet evidence based analysis; with many applied cases from her studies, which make the book accessible and a pleasant lecture. Although research that led to this remarkable publication might have been primarily focused on the regulators, this book can be highly recommended to all OHS academics and practitioners. It provides an important contribution to the body of knowledge in OHS, and establishes one of the few Australian in-depth insights into the significance of machinery producers, rather than machinery users in the wider framework of risk management. The author bases this fresh perspective on the well-established European Machinery Safety guidelines, and grounds her mixed-methods research predominantly in qualitative analysis of motivation and knowledge, which eventually leads to specific safety outcomes. It should be noted that both European and Australian legal aspects are investigated and considered, as both equally apply to many machinery exporters. A detailed description of the research design and methods can be found in an appendix. Overall, the unique combination of quantitative safety performance data and qualitative analysis of safety behaviours form a valuable addition to the understanding of machinery safety. The author must be congratulated on making these complex relationships transparent to the reader through her meticulous inquiry.

The Design and Construction of Synthetic Protein Mimics

A strategy for the modular construction of synthetic protein mimics based on the ability non-protein amino acids to act as stereochemical directors of polypeptide chain folding, is described. The use of alpha-aminoisobutyric acid (Aib) to construct stereochemically rigid helices has been exemplified by crystallographic and spectroscopic studies of several apolar peptides, ranging in length from seven to sixteen residues. The problem of linker design in elaborating alpha,alpha motifs has been considered. Analysis of protein crystal structure data provides a guide to choosing linking sequences. Attempts at constructing linked helical motifs using linking Gly-Pro segments have been described. The use of flexible linkers, like epsilon-aminocaproic acid has been examined and the crystallographic and solution state analysis of a linked helix motif has been presented. The use of bulky sidechain modifications on a helical scaffold, as a means of generating putative binding sites has been exemplified by a crystal structure of a peptide packed in a parallel zipper arrangement.