Strain engineering of selective chemical adsorption on monolayer MoS2

Nanomaterials are prone to influence by chemical adsorption because of their large surface to volume ratios. This enables sensitive detection of adsorbed chemical species which, in turn, can tune the property of the host material. Recent studies discovered that single and multi-layer molybdenum disulfide (MoS2) films are ultra-sensitive to several important environmental molecules. Here we report new findings from ab inito calculations that reveal substantially enhanced adsorption of NO and NH3 on strained monolayer MoS2 with significant impact on the properties of the adsorbates and the MoS2 layer. The magnetic moment of adsorbed NO can be tuned between 0 and 1 μB; strain also induces an electronic phase transition between half-metal and metal. Adsorption of NH3 weakens the MoS2 layer considerably, which explains the large discrepancy between the experimentally measured strength and breaking strain of MoS2 films and previous theoretical predictions. On the other hand, adsorption of NO2, CO, and CO2 is insensitive to the strain condition in the MoS2 layer. This contrasting behavior allows sensitive strain engineering of selective chemical adsorption on MoS2 with effective tuning of mechanical, electronic, and magnetic properties. These results suggest new design strategies for constructing MoS2-based ultrahigh-sensitivity nanoscale sensors and electromechanical devices.

Critical success factors for the implementation of a knowledge management system in a knowledge-based engineering firm

This research identifies factors that are crucial to the success of a knowledge management system (KMS) implementation in a prominent Australian engineering consultancy firm. The study employs the Delphi method to solicit the opinions of experienced market leaders in the Australian construction industry, and then benchmarks the organisational profile of the consultancy firm against the Delphi findings. From this comparative case study, recommendations are made pertaining to the organisational and cultural changes required within the consultancy firm in order to improve its readiness to successfully implement a KMS.

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

A bi-lineage conducive scaffold for osteochondral defect regeneration

Because cartilage and bone tissues have different lineage-specific biological properties, it is challenging to fabricate a single type of scaffold that can biologically fulfill the requirements for regeneration of these two lineages simultaneously within osteochondral defects. To overcome this challenge, a lithium-containing mesoporous bioglass (Li-MBG) scaffold is developed. The efficacy and mechanism of Li-MBG for regeneration of osteochondral defects are systematically investigated. Histological and micro-CT results show that Li-MBG scaffolds significantly enhance the regeneration of subchondral bone and hyaline cartilage-like tissues as compared to pure MBG scaffolds, upon implantation in rabbit osteochondral defects for 8 and 16 weeks. Further investigation demonstrates that the released Li+ ions from the Li-MBG scaffolds may play a key role in stimulating the regeneration of osteochondral defects. The corresponding mechanistic pathways involve Li+ ions enhancing the proliferation and osteogenic differentiation of bone mesenchymal stem cells (BMSCs) through activation of the Wnt signalling pathway, as well as Li+ ions protecting chondrocytes and cartilage tissues from the inflammatory osteoarthritis (OA) environment through activation of autophagy. These findings suggest that the incorporation of Li+ ions into bioactive MBG scaffolds is a viable strategy for fabricating bi-lineage conducive scaffolds that enhance regeneration of osteochondral defects.

Starting out in STEM : a study of young men and women in first year science, technology, engineering and mathematics courses

In late 2011, first year university students in science, technology, engineering and mathematics (STEM) courses across Australia were invited to participate in the international Interests and Recruitment in Science (IRIS) study. IRIS investigates the influences on young people's decisions to choose university STEM courses and their subsequent experiences of these courses. The study also has a particular focus on the motivations and experiences of young women in courses such as physics, IT and engineering given the low rates of female participation in these fields. Around 3500 students from 30 Australian universities contributed their views on the relative importance of various school and non-school influences on their decisions, as well as insights into their experiences of university STEM courses so far. It is hoped that their contributions will help improve recruitment, retention and gender equity in STEM higher education and careers.

Agility in consumer retail: sense-response alignment through the eyes of customers

In hyper competition, firms that are agile: sensing and responding better to customer requirements tend to be more successful and achieve supernormal profits. In spite of the widely accepted importance of customer agility, research is limited on this construct. The limited research also has predominantly focussed on the firm’s perspective of agility. However, we propose that the customers are better positioned to determine how well a firm is responding to their requirements (aka a firm’s customer agility). Taking the customers’ stand point, we address the issue of sense and respond alignment in two perspectives-matching and mediating. Based on data collected from customers in a field study, we tested hypothesis pertaining to the two methods of alignment using polynomial regression and response surface methodology. The results provide a good explanation for the role of both forms of alignment on customer satisfaction. Implication for research and practice are discussed.

Digital tablets as a tool for blended learning in power engineering education

Digital tablets have been identified as a tool for enabling blended learning and supporting online teaching and learning. A small scale trial was undertaken to assess the effectiveness of this technology when applied to power engineering education. Critical findings and experiences gained from this trial, including potential benefits, presentation techniques and the resulting student feedback are presented in this paper.

An exploration of the professional competencies required in engineering asset management

Engineering asset management (EAM) is a rapidly growing and developing field. However, efforts to select and develop engineers in this area are complicated by our lack of understanding of the full range of competencies required to perform. This exploratory study sought to clarify and categorise the professional competencies required of individuals at different hierarchical levels within EAM. Data from 14 field interviews, 61 online surveys, and 10 expert panel interviews were used to develop an initial professional competency framework. Overall, nine competency clusters were identified. These clusters indicate that engineers working in this field need to be able to collaborate and influence others, complete objectives within organisational guidelines, and be able to manage themselves effectively. Limitations and potential uses of this framework in engineering education and research are discussed.

Should I stay or should I go? The challenges and opportunities of moving between university systems

The scientific job market has evolved to a truly globalized market. This is epitomized not only by the English language being the de facto scientific language but also by the increasing share of native language journals that are being offered in multiple languages or have or will fully converted to English (such as, for example, the BISE journal in 2015). Similarly, a plethora of exchange programs exists that allow students and academic staff to visit other institutions and exchange knowledge, ideas, and learning opportunities. While student migration across scientific institutions is an established phenomenon (Gribble, 2008) with ample structures, policies, and schemes such as ERASMUS1 in place, academic staff migration between countries is still a challenge, even if exchange programs exist (Enders, 1998). One reason may be that different career paths, varying teaching loads and different evaluation schemes for what constitutes scientific excellence are notable. This also influences the decision of where to start and continue an academic career. While the university systems themselves have been examined previously (Galliers and Whitley, 2007; Lyytinen et al., 2007) and while there is knowledge about career requirements in different university systems (Dennis et al., 2006; Dean et al., 2011; Loos et al., 2013; Recker, 2013), we still do not know much about individual and contextual decisions of academics that either consider or execute a migration between university systems.

A Satellite Selection Algorithm for Achieving High Reliability of Ambiguity Resolution with GPS and Beidou Constellations

Reliability of carrier phase ambiguity resolution (AR) of an integer least-squares (ILS) problem depends on ambiguity success rate (ASR), which in practice can be well approximated by the success probability of integer bootstrapping solutions. With the current GPS constellation, sufficiently high ASR of geometry-based model can only be achievable at certain percentage of time. As a result, high reliability of AR cannot be assured by the single constellation. In the event of dual constellations system (DCS), for example, GPS and Beidou, which provide more satellites in view, users can expect significant performance benefits such as AR reliability and high precision positioning solutions. Simply using all the satellites in view for AR and positioning is a straightforward solution, but does not necessarily lead to high reliability as it is hoped. The paper presents an alternative approach that selects a subset of the visible satellites to achieve a higher reliability performance of the AR solutions in a multi-GNSS environment, instead of using all the satellites. Traditionally, satellite selection algorithms are mostly based on the position dilution of precision (PDOP) in order to meet accuracy requirements. In this contribution, some reliability criteria are introduced for GNSS satellite selection, and a novel satellite selection algorithm for reliable ambiguity resolution (SARA) is developed. The SARA algorithm allows receivers to select a subset of satellites for achieving high ASR such as above 0.99. Numerical results from a simulated dual constellation cases show that with the SARA procedure, the percentages of ASR values in excess of 0.99 and the percentages of ratio-test values passing the threshold 3 are both higher than those directly using all satellites in view, particularly in the case of dual-constellation, the percentages of ASRs (>0.99) and ratio-test values (>3) could be as high as 98.0 and 98.5 % respectively, compared to 18.1 and 25.0 % without satellite selection process. It is also worth noting that the implementation of SARA is simple and the computation time is low, which can be applied in most real-time data processing applications.

Is intent-aware expected reciprocal rank sufficient to evaluate diversity?

In this paper we define two models of users that require diversity in search results; these models are theoretically grounded in the notion of intrinsic and extrinsic diversity. We then examine Intent-Aware Expected Reciprocal Rank (ERR-IA), one of the official measures used to assess diversity in TREC 2011-12, with respect to the proposed user models. By analyzing ranking preferences as expressed by the user models and those estimated by ERR-IA, we investigate whether ERR-IA assesses document rankings according to the requirements of the diversity retrieval task expressed by the two models. Empirical results demonstrate that ERR-IA neglects query-intents coverage by attributing excessive importance to redundant relevant documents. ERR-IA behavior is contrary to the user models that require measures to first assess diversity through the coverage of intents, and then assess the redundancy of relevant intents. Furthermore, diversity should be considered separately from document relevance and the documents positions in the ranking.

Ethnographic notes on visualization practices in tissue engineering

Visual information is central to several of the scientific disciplines. This paper studies how scientists working in a multidisciplinary field produce scientific evidence through building and manipulating scientific visualizations. Using ethnographic methods, we studied visualization practices of eight scientists working in the domain of tissue engineering research. Tissue engineering is an upcoming field of research that deals with replacing or regenerating human cells, tissues, or organs to restore or establish normal function. We spent 3 months in the field, where we recorded laboratory sessions of these scientists and used semi-structured interviews to get an insight into their visualization practices. From our results, we elicit two themes characterizing their visualization practices: multiplicity and physicality. In this article, we provide several examples of scientists’ visualization practices to describe these two themes and show that multimodality of such practices plays an important role in scientific visualization.

Constrained control design for dynamic positioning of marine vehicles with control allocation

In this paper, we address the control design problem of positioning of over-actuated marine vehicles with control allocation. The proposed design is based on a combined position and velocity loops in a multi-variable anti-windup implementation together with a control allocation mapping. The vehicle modelling is considered with appropriate simplifications related to low-speed manoeuvring hydrodynamics and vehicle symmetry. The control design is considered together with a control allocation mapping. We derive analytical tuning rules based on requirements of closed-loop stability and performance. The anti- windup implementation of the controller is obtained by mapping the actuator-force constraint set into a constraint set for the generalized forces. This approach ensures that actuation capacity is not violated by constraining the generalized control forces; thus, the control allocation is simplified since it can be formulated as an unconstrained problem. The mapping can also be modified on-line based on actuator availability to provide actuator-failure accommodation. We provide a proof of the closed-loop stability and illustrate the performance using simulation scenarios for an open-frame underwater vehicle.

Design of an integrated platform for V2X wireless communications and positioning supporting C-ITS safety applications

In this paper, an integrated inter-vehicles wireless communications and positioning system supporting alternate positioning techniques is proposed to meet the requirements of safety applications of Cooperative Intelligent Transportation Systems (C-ITS). Recent advances have repeatedly demonstrated that road safety problems can be to a large extent addressed via a range of technologies including wireless communications and positioning in vehicular environments. The novel communication stack utilizing a dedicated frequency spectrum (e.g. at 5.9 GHz band), known as Dedicated Short-Range Communications (DSRC), has been particularly designed for Wireless Access in Vehicular Environments (WAVE) to support safety applications in highly dynamic environments. Global Navigation Satellite Systems (GNSS) is another essential enabler to support safety on rail and roads. Although current vehicle navigation systems such as single frequency Global Positioning System (GPS) receivers can provide route guidance with 5-10 meters (road-level) position accuracy, positioning systems utilized in C-ITS must provide position solutions with lane-level and even in-lane-level accuracies based on the requirements of safety applications. This article reviews the issues and technical approaches that are involved in designing a vehicular safety communications and positioning architecture; it also provides technological solutions to further improve vehicular safety by integrating the DSRC and GNSS-based positioning technologies.