Acoustic emission activity from the two main types of small multi-cylinder diesel engine fuel injection systems

Effective fuel injector operation and efficient combustion are two of the most critical aspects when Diesel engine performance, efficiency and reliability are considered. Indeed, it is widely acknowledged that fuel injection equipment faults lead to increased fuel consumption, reduced power, greater levels of exhaust emissions and even unexpected engine failure. Previous investigations have identified fuel injector related acoustic emission activity as being caused by mechanisms such as fuel line pressure build-up; fuel flow through injector nozzles, injector needle opening and closing impacts and premixed combustion related pulses. Few of these investigations however, have attempted to categorise the close association and interrelation that exists between fuel injection equipment function and the acoustic emission generating mechanisms. Consequently, a significant amount of ambiguity remains in the interpretation and categorisation of injector related AE activity with respect to the functional characteristics of specific fuel injection equipment. The investigation presented addresses this ambiguity by detailing a study in which AE signals were recorded and analysed from two different Diesel engines employing the two commonly encountered yet fundamentally different types of fuel injection equipment. Results from tests in which faults were induced into fuel injector nozzles from both indirect-injection and direct-injection engines show that functional differences between the main types of fuel injection equipment results in acoustic emission activity which can be specifically related to the type of fuel injection equipment used.

‘Works well with others’ : examining the different types of small group learning approaches and their implications for law student learning outcomes

In the current regulatory climate, there is increasing expectation that law schools will be able to demonstrate students’ acquisition of learning outcomes regarding collaboration skills. We argue that this is best achieved through a stepped and structured whole-of-curriculum approach to small group learning. ‘Group work’ provides deep learning and opportunities to develop professional skills, but these benefits are not always realised for law students. An issue is that what is meant by ‘group work’ is not always clear, resulting in a learning regime that may not support the attainment of desired outcomes. This paper describes different types of ‘group work', each associated with distinct learning outcomes. It suggests that ‘group work’ as an umbrella term to describe these types is confusing, as it provides little indication to students and teachers of the type of learning that is valued and is expected to take place. ‘Small group learning’ is a preferable general descriptor. Identifying different types of small group learning allows law schools to develop and demonstrate a scaffolded, sequential and incremental approach to fostering law students’ collaboration skills. To support learning and the acquisition of higherorder skills, different types of small group learning are more appropriate at certain stages of the program. This structured approach is consistent with social cognitive theory, which suggests that with the guidance of a supportive teacher, students can develop skills and confidence in one type of activity which then enhances motivation to participate in another.

Clinical use of diodes and micro-chambers to obtain accurate small field output factor measurements

There have been substantial advances in small field dosimetry techniques and technologies, over the last decade, which have dramatically improved the achievable accuracy of small field dose measurements. This educational note aims to help radiation oncology medical physicists to apply some of these advances in clinical practice. The evaluation of a set of small field output factors (total scatter factors) is used to exemplify a detailed measurement and simulation procedure and as a basis for discussing the possible effects of simplifying that procedure. Field output factors were measured with an unshielded diode and a micro-ionisation chamber, at the centre of a set of square fields defined by a micro-multileaf collimator. Nominal field sizes investigated ranged from 6×6 to 98×98 mm2. Diode measurements in fields smaller than 30 mm across were corrected using response factors calculated using Monte Carlo simulations of the full diode geometry and daisy-chained to match micro-chamber measurements at intermediate field sizes. Diode measurements in fields smaller than 15 mm across were repeated twelve times over three separate measurement sessions, to evaluate the to evaluate the reproducibility of the radiation field size and its correspondence with the nominal field size. The five readings that contributed to each measurement on each day varied by up to 0.26%, for the “very small” fields smaller than 15 mm, and 0.18% for the fields larger than 15 mm. The diode response factors calculated for the unshielded diode agreed with previously published results, within 1.6%. The measured dimensions of the very small fields differed by up to 0.3 mm, across the different measurement sessions, contributing an uncertainty of up to 1.2% to the very small field output factors. The overall uncertainties in the field output factors were 1.8% for the very small fields and 1.1% for the fields larger than 15 mm across. Recommended steps for acquiring small field output factor measurements for use in radiotherapy treatment planning system beam configuration data are provided.

Learning the learning rate for prediction with expert advice

Most standard algorithms for prediction with expert advice depend on a parameter called the learning rate. This learning rate needs to be large enough to fit the data well, but small enough to prevent overfitting. For the exponential weights algorithm, a sequence of prior work has established theoretical guarantees for higher and higher data-dependent tunings of the learning rate, which allow for increasingly aggressive learning. But in practice such theoretical tunings often still perform worse (as measured by their regret) than ad hoc tuning with an even higher learning rate. To close the gap between theory and practice we introduce an approach to learn the learning rate. Up to a factor that is at most (poly)logarithmic in the number of experts and the inverse of the learning rate, our method performs as well as if we would know the empirically best learning rate from a large range that includes both conservative small values and values that are much higher than those for which formal guarantees were previously available. Our method employs a grid of learning rates, yet runs in linear time regardless of the size of the grid.

The small business CGT concessions: Evidence from the perspective of the tax practitioner

On 21 September 1999 Division 152 was inserted into the Income Tax Assessment Act (1997) (ITAA 1997). Division 152 contains the small business CGT concessions, which enables eligible small business taxpayers to reduce the amount of tax payable on capital gains arising from certain CGT events that occur after 11:45 am on 21 September 1999. One of the principal objectives of the legislation is to provide a concessionary regime for small business owners who do not have the same ability to access the concessionary superannuation regime generally available to employees. When announcing the introduction of the concessions the then Federal Treasurer, Mr Peter Costello, specifically stated that the objective of Division 152 was to provide ‘small business people with access to funds for retirement or expansion’. The purpose of this article is to: one, assess the extent to which small business taxpayers understand the CGT small business concessions, particularly when considering the sale of their business; two, determine which of the four small business CGT concessions are most commonly adopted and/or recommended by tax practitioners to clients; and three, to determine whether the superannuation changes in relation to the capping of the concessional superannuation thresholds have had an impact on the use of the small business retirement concession.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

System design for a small autonomous helicopter

The detailed system design of a small experimental autonomous helicopter is described. The system requires no ground-to-helicopter communications and hence all automation hardware is on-board the helicopter. All elements of the system are described including the control computer, the flight computer (the helicopter-to-control-computer interface), the sensors and the software. A number of critical implementation issues are also discussed.

Towards strongly consistent online HMM parameter estimation using one-step Kerridge inaccuracy

In this paper, we propose a novel online hidden Markov model (HMM) parameter estimator based on the new information-theoretic concept of one-step Kerridge inaccuracy (OKI). Under several regulatory conditions, we establish a convergence result (and some limited strong consistency results) for our proposed online OKI-based parameter estimator. In simulation studies, we illustrate the global convergence behaviour of our proposed estimator and provide a counter-example illustrating the local convergence of other popular HMM parameter estimators.

Corporate-cooperative management of fisheries: A potential alternative governance structure for low value small fisheries?

While the economic and environmental benefits of fisheries management are well accepted, the costs of effective management in low value fisheries, including the research necessary to underpin such management, may be considerable relative to the total economic benefits they may generate. Co-management is often seen as a panacea in low value fisheries. Increasing fisher participation increases legitimacy of management decision in the absence of detailed scientific input. However, where only a small number of operators exist, the potential benefits of co-management are negated by the high transaction cost to the individual fishers engaging in the management process. From an economic perspective, sole ownership has been identified as the management structure which can best achieve biological and economic sustainability. Moving low value fisheries with a small number of participants to a corporate-cooperative management model may come close to achieving these sole ownership benefits, with lower transaction costs. In this paper we look at the applicability of different management models with industry involvement to low value fisheries with a small number of participants. We provide an illustration as to how a fishery could be transitioned to a corporate-cooperative management model that captures the key benefits of sole management at a low cost and is consistent with societal objectives.

Combination chemotherapy for primary small intestinal lymphoma in the Middle East

Twelve patients with primary small intestinal lymphoma were followed prospectively for 3 years. Endoscopic abnormalities were diagnostic of lymphoma in all cases where the duodenum was involved (83%). In three cases (25%) the disease extended to the stomach. One patient (8%) had diffuse small cell cleaved and 11 (92%) diffuse large cell lymphoma stages I (8%), II (25%), III (58%) and IV (8%). Nine of them were unresectable and primarily treated with combination chemotherapy; 67% achieved complete remission, 22% partial response and 11% no response. Only one patient relapsed and achieved a second remission. All complete remission patients are currently alive and free of disease at a median follow-up of 36 months. Overall survival for all patients is 58%, and disease-free survival is 50%. No instance of chemotherapy-related bleeding or perforation was seen. Tetracycline was necessary for the treatment of IPSID-associated diarrhea and malabsorption in spite of cytotoxic chemotherapy.

Vascular endothelial growth factor is an autocrine growth factor, signaling through neuropilin-1 in non-small cell lung cancer

Background The VEGF pathway has become an important therapeutic target in lung cancer, where VEGF has long been established as a potent pro-angiogenic growth factor expressed by many types of tumors. While Bevacizumab (Avastin) has proven successful in increasing the objective tumor response rate and in prolonging progression and overall survival in patients with NSCLC, the survival benefit is however relatively short and the majority of patients eventually relapse. The current use of tyrosine kinase inhibitors alone and in combination with chemotherapy has been underwhelming, highlighting an urgent need for new targeted therapies. In this study, we examined the mechanisms of VEGF-mediated survival in NSCLC cells and the role of the Neuropilin receptors in this process. Methods NSCLC cells were screened for expression of VEGF and its receptors. The effects of recombinant VEGF and its blockade on lung tumor cell proliferation and cell cycle were examined. Phosphorylation of Akt and Erk1/2 proteins was examined by high content analysis and confocal microscopy. The effects of silencing VEGF on cell proliferation and survival signaling were also assessed. A Neuropilin-1 stable-transfected cell line was generated. Cell growth characteristics in addition to pAkt and pErk1/2 signaling were studied in response to VEGF and its blockade. Tumor growth studies were carried out in nude mice following subcutaneous injection of NP1 over-expressing cells. Results Inhibition of the VEGF pathway with anti-VEGF and anti-VEGFR-2 antibodies or siRNA to VEGF, NP1 and NP2 resulted in growth inhibition of NP1 positive tumor cell lines associated with down-regulation of PI3K and MAPK kinase signaling. Stable transfection of NP1 negative cells with NP1 induced proliferation in vitro, which was further enhanced by exogenous VEGF. In vivo, NP1 over-expressing cells significantly increased tumor growth in xenografts compared to controls. Conclusions Our data demonstrate that VEGF is an autocrine growth factor in NSCLC signaling, at least in part, through NP1. Targeting this VEGF receptor may offer potential as a novel therapeutic approach and also support the evaluation of the role of NP1 as a biomarker predicting sensitivity or resistance to VEGF and VEGFR-targeted therapies in the clinical arena.

The role of innovation intermediaries in collaborative networks of small and medium-sized enterprises

The major challenge of European Union’s agricultural industry is to ensure sustainable supply of quality food that meets the demands of a rapidly growing population, changing dietary patterns, increased competition for land use, and environmental concerns. Investments in research and innovation, which facilitate integration of external knowledge in food chain operations, are crucial to undertaking such challenges. This paper addresses how SMEs successfully innovate within collaborative networks with the assistance of innovation intermediaries. In particular, we explore the roles of innovation intermediaries in knowledge acquisition, knowledge assimilation, knowledge, transformation, and knowledge exploitation in open innovation initiatives from the wine industry through the theoretical lens of absorptive capacity. Based on two case studies from the wine industry, we identified seven key activities performed by innovation intermediaries that complement SMEs’ ability to successfully leverage external sources of knowledge for innovation purposes. These activities are articulation of knowledge needs and innovation capabilities, facilitation of social interactions, establishment of complementary links, implementation of governance structures, conflict management, enhancement of transparency, and mediation of communication. Our in-depth qualitative study of two innovation intermediaries in the wine industry has several important implications that contribute to research and practice.

Small firm training: Just meeting the day-to-day needs of the business

Purpose The purpose of this paper is to explore what the attitudes of small firm owner-managers are to developing the skills of their key resources and then examine how these and other factors affect owner-managers’ preferences for training these employees. Design/methodology/approach This study of training in small road transport firms in West Australia is cast in light of the literature on human resource management in small firms underpinned by insights drawn using the resource based view of the firm. Small firms (less than 20 people) dominate this industry, while the increasing freight task, and extreme distances between West Australian ports, towns and mines highlight this sectors’ importance. Survey results from 39 small road transport firms and interviews with nine owner-managers are analysed. Findings Legislative, regulatory and licensing requirements were shown to be a key determinant of skills development. Employers ensured that basic standards for employee certification and qualification were met, as the penalty for not doing so would be too high. Regulations drove the need for certain types of training – licenses, fatigue management, occupational health and safety, handling dangerous goods, the Maritime Security Identification Card card, forklift license, mine site inductions – while owner-managers knew where to get the training their staff needed. Although regulation appeared most visible in prescribing what happened in relation to training for drivers, the relevance of owner-managers’ attitudes could not be ignored, nor could conditions in the firms external environment as this shaped how these requirements were met. Research limitations/implications The RBV is useful in showing how skill development enabled similarity in skills across firms, while the attitudes owner-managers and economic and social conditions meant what happened in firms around skill development varied. The importance of small firm owner-managers’ attitudes are clearly highlighted and shown to influence organizational decisions and choices around training, but these were not independent of the regulatory framework and the economic and social conditions within which the firm operated. The small firms in this study did engage workers in formal training when necessary but it was put in the context of the idiosyncratic approach of the owner-manager and the day-to-day needs of the firm. “Training” was essentially about ensuring certain types of skills were held by employees and then passing on knowledge to ensure the behavior of employees was consistent with the owner-manager’s vision for the firm in its current environment. Originality/value Ways industry and government can encourage training activity that goes beyond the day-to-day firm needs are suggested.

Test-retest reliability of graph theory measures of structural brain connectivity

The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and 'small-world' properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.

Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design

The total entropy utility function is considered for the dual purpose of Bayesian design for model discrimination and parameter estimation. A sequential design setting is proposed where it is shown how to efficiently estimate the total entropy utility for a wide variety of data types. Utility estimation relies on forming particle approximations to a number of intractable integrals which is afforded by the use of the sequential Monte Carlo algorithm for Bayesian inference. A number of motivating examples are considered for demonstrating the performance of total entropy in comparison to utilities for model discrimination and parameter estimation. The results suggest that the total entropy utility selects designs which are efficient under both experimental goals with little compromise in achieving either goal. As such, the total entropy utility is advocated as a general utility for Bayesian design in the presence of model uncertainty.