The politics of foreign aid: A median voter perspective

The paper presents a model where the median voter in the donor country determines the support of foreign aid. It is first established that an individual in the donor country is affected by the direct benefits (due to altruism) and costs (due to taxes) of giving aid, and by the indirect benefits or costs of a change in the terms of trade. Then it is shown that the latter effect works through changing both the donor country's average income and its distribution of income. Given the stylized facts of a capital-abundant donor country and relatively capital-poor median voter, it is shown how redistribution-of-income effects soften the impact of terms-of-trade changes on the political support for foreign aid.

Tied aid, unemployment and welfare

The effect of foreign aid on the welfare levels of both the recipient and the donor country has been a much analysed topic for research in both the theory of international trade and development economics. In the development economics literature, concerns have been raised since the 1960s on the possible adverse effect of foreign aid on domestic savings and growth.1 The trade theory literature in this respect is much older and dates back to the 1920s when Professors Keynes and Ohlin debated on the effect of foreign aid on international terms of trade.2 Ever since, the terms of trade effect has been the cornerstone in the analysis of the welfare effect of foreign aid in the trade theory literature.3 After some early confusion, it is now well established that in a Walrasian stable world economy with two countries, a necessary condition for foreign aid to have perverse effects is that there is some distortion in either of the two countries.4 It is also known that, under normality and substitutability of goods, untied aid cannot be strictly Pareto-improving in a tariff distorted world.5

Welfare effects of aid under quantitative trade restrictions

The paper examines the effects of tied-aid on the welfare of both the donor and the recipient countries. We depart from the previous literature by assuming preexistence of quantitative trade distortions. To mitigate these distortions the donor country provides aid that is tied to the rationed good. Conditions for the presence of the transfer paradox and of the enrichment of both countries are derived and interpreted under the stability of the system. Furthermore, we show that whereas untied aid cannot increase global welfare, tied-aid unambiguously does so.

Joint evaluation of multiple speech patterns for speech recognition and training

We are addressing the novel problem of jointly evaluating multiple speech patterns for automatic speech recognition and training. We propose solutions based on both the non-parametric dynamic time warping (DTW) algorithm, and the parametric hidden Markov model (HMM). We show that a hybrid approach is quite effective for the application of noisy speech recognition. We extend the concept to HMM training wherein some patterns may be noisy or distorted. Utilizing the concept of ``virtual pattern'' developed for joint evaluation, we propose selective iterative training of HMMs. Evaluating these algorithms for burst/transient noisy speech and isolated word recognition, significant improvement in recognition accuracy is obtained using the new algorithms over those which do not utilize the joint evaluation strategy.

Pheo-type: A diagnostic gene-expression assay for the classification of pheochromocytoma and paraganglioma

Context: Pheochromocytomas and paragangliomas (PPGLs) are heritable neoplasms that can be classified into gene-expression subtypes corresponding to their underlying specific genetic drivers. Objective: This study aimed to develop a diagnostic and research tool (Pheo-type) capable of classifying PPGL tumors into gene-expression subtypes that could be used to guide and interpret genetic testing, determine surveillance programs, and aid in elucidation of PPGL biology. Design: A compendium of published microarray data representing 205 PPGL tumors was used for the selection of subtype-specific genes that were then translated to the Nanostring gene-expression platform. A support vector machine was trained on the microarray dataset and then tested on an independent Nanostring dataset representing 38 familial and sporadic cases of PPGL of known genotype (RET, NF1, TMEM127, MAX, HRAS, VHL, and SDHx). Different classifier models involving between three and six subtypes were compared for their discrimination potential. Results: A gene set of 46 genes and six endogenous controls was selected representing six known PPGL subtypes; RTK1–3 (RET, NF1, TMEM127, and HRAS), MAX-like, VHL, and SDHx. Of 38 test cases, 34 (90%) were correctly predicted to six subtypes based on the known genotype to gene-expression subtype association. Removal of the RTK2 subtype from training, characterized by an admixture of tumor and normal adrenal cortex, improved the classification accuracy (35/38). Consolidation of RTK and pseudohypoxic PPGL subtypes to four- and then three-class architectures improved the classification accuracy for clinical application. Conclusions: The Pheo-type gene-expression assay is a reliable method for predicting PPGL genotype using routine diagnostic tumor samples.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.

Functional Imagery Training to reduce snacking: Testing a novel motivational intervention based on Elaborated Intrusion theory

Functional Imagery Training (FIT) is a new theory-based, manualized intervention that trains positive goal imagery. Multisensory episodic imagery of proximal personal goals is elicited and practised, to sustain motivation and compete with less functional cravings. This study tested the impact of a single session of FIT plus a booster phone call on snacking. In a stepped-wedge design, 45 participants who wanted to lose weight or reduce snacking were randomly assigned to receive a session of FIT immediately or after a 2-week delay. High-sugar and high-fat snacks were recorded using timeline follow back for the previous 3 days, at baseline, 2 and 4 weeks. At 2 weeks, snacking was lower in the immediate group than in the delayed group, and the reduction after FIT was replicated in the delayed group between 2 and 4 weeks. Frequencies of motivational thoughts about snack reduction rose following FIT for both groups, and this change correlated with reductions in snacking and weight loss. By showing that FIT can support change in eating behaviours, these findings show its potential as a motivational intervention for weight management.

Lowering grain boundary resistance of BaZr0.8Y0.2O3−δ with LiNO3 sintering-aid improves proton conductivity for fuel cell operation

A novel sintering additive based on LiNO3 was used to overcome the drawbacks of poor sinterability and low grain boundary conductivity in BaZr0.8Y0.2O3-δ (BZY20) protonic conductors. The Li-additive totally evaporated during the sintering process at 1600°C for 6 h, which led to highly dense BZY20 pellets (96.5% of the theoretical value). The proton conductivity values of BZY20 with Li sintering-aid were significantly larger than the values reported for BZY sintered with other metal oxides, due to the fast proton transport in the "clean" grain boundaries and grain interior. The total conductivity of BZY20-Li in wet Ar was 4.45 × 10-3 S cm-1 at 600°C. Based on the improved sinterability, anode-supported fuel cells with 25 μm-thick BZY20-Li electrolyte membranes were fabricated by a co-firing technique. The peak power density obtained at 700°C for a BZY-Ni/BZY20-Li/La0.6Sr0.4Co0.2Fe 0.8O3-δ (LSCF)-BZY cell was 53 mW cm-2, which is significantly larger than the values reported for fuel cells using electrolytes made of BZY sintered with the addition of ZnO and CuO, confirming the advantage of using Li as a sintering aid.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.