SINGAPORE’S DIRECT INVESTMENT IN SRI LANKA: PAST EXPERIENCE AND FUTURE PROSPECTS

Apart from aggregate accounting of Singapore’s investment abroad, case studies on the performance of these investments in individual countries hardly exist. This paper is an attempt to compile such a study by focusing on Singapore’s investment in Sri Lanka. Singapore is one of the largest foreign investors in Sri Lanka though Sri Lanka is a small recipient of Singapore’s total overseas investment. The bulk of Singapore’s investment in Sri Lanka has been in service industries. As usual these investments have created many employment opportunities. However, because of high import dependence the Singapore firms in Sri Lanka have begun to generate trade surpluses only recently. Revealed comparative advantage indices combined with attractive fiscal incentives and low-cost factors of production indicate that there are large investment opportunities in the manufacturing sector that remain to be exploited. The ongoing war obviously has deterred the expansion of Sri Lanka’s FDI base to its full extent.Economic and social indicators, foreign direct investment, employment, trade balance, revealed comparative advantage, future investment opportunities.

Singapore’s Recurrent Budget Surplus The Role of Conservative Growth Forecasts

Aided by strong economic growth the Singapore government has been able to keep both the tax rate and the government expenditure rate low and yet generate healthy budget surpluses year after year. Although the gap between the tax rate and the government expenditure rate is the obvious source of the surplus, this paper shows the presence of another subtle source, a surplus generated by conservative growth forecasts that lay the base for revenue projections. An omitted variable bias in a model based on the tax smoothing hypothesis led us to consider the role played by the growth forecast error in predicting the budget surplus. Our computations show that on average the underprediction of the tax base (GDP) must have contributed about $376 million per year to the realized budget surplus over the period 1990-2005. This appears to be simply a byproduct of the Government’s philosophy of “fiscal prudence”.Tax smoothing model, Reported and adjusted budget surplus, GDP forecast errors.

Study and Design of Kynurenine Aminotransferase-II Inhibitors for the Treatment of Neurological Conditions

The majority of tryptophan metabolism passes through the kynurenine pathway. Metabolic imbalances in this pathway are implicated disease. KYNA, transaminated by the kynurenine aminotransferase (KAT) enzymes, is elevated in patients with schizophrenia. Schizophrenia is a neuropsychiatric disease with limited treatment options and debilitating symptoms. Glutamatergic systems are thought to have a significant role in its pathogenesis, providing a basis by which KYNA, an endogenous glutamate antagonist, is implicated in the disease. Four pyridoxal 5’-phosphate-dependent homologues of KAT are reported. KAT-II is primarily responsible for KYNA production in the human brain. KAT-II inhibitors reduce KYNA production, increase neurotransmitter release and elicit pro-cognitive effects, indicative of their potential as novel therapies in treating schizophrenia. In this work, surface plasmon resonance has been employed to screen a fragment library, from which two fragments, F6037-0164 and F0037-7280 were pursued (IC50 of 524.5 μM and 115.2 μM, respectively). Another strategy was to consider estrogen compounds as schizophrenia is a sexually dimorphic condition, in which female patients have reduced estrogen levels. Enzyme inhibitory assays displayed estradiol disulfate as a strong inhibitor of KAT-I and KAT-II (IC50: 291.5 μM and 26.3 μM, respectively), with estradiol, estradiol 3-sulfate and estrone sulfate inhibiting weakly. Molecular modelling suggests that the 17-sulfate moiety in estradiol disulfate improves its potency by 10-100 fold compared to estradiol. This 17-sulfate moiety was mimicked on existing KAT-II inhibitor scaffolds to develop two novel inhibitors, JN-01 and JN-02, with improved potencies (IC50: 73.8 μM and 112.8 μM, respectively). Co-crystallisation studies resulted in the determination of a human KAT-II crystal structure (PDB ID: 6D0A) with 1.47 Å resolution, the highest resolution structure provided for KAT-II, with the least structural inconsistencies

The Implication of Hyogo Framework for Action for Disaster Resilience Education

The Hyogo Framework for Action (HFA) is the first global strategy to provide a detailed work plan for different sectors and actors to work on disaster risk reduction. Although, the Priority Action 3 of the HFA demand for a global call to governments and others to use knowledge, innovation and education to build a culture of safety and resilience at all levels. Evidence suggests that there are only very few effective initiatives that have been implemented by stakeholders and especially in the higher education sector where the future policy makers and practitioners are trained. This creates a significant challenge as the ten year plan of the HFA is coming to an end in 2015. This paper attempts to view the world in post-HFA and suggested a framework on mapping and integrating disaster risk reduction into formal, informal and non-formal education at policy, practice and community levels. A case study approach was used to examine how the HFA has been embraced into disaster resilience related higher education programme. The study argues that integrating disaster resilience into education is a key factor for reducing the adverse impact of future disasters. The suggested framework provides an insight into current gaps in knowledge, innovation and education and proposes solutions for effective integration of disaster resilience education at all levels

Detecting trash and valuables with machine vision in passenger vehicles

The research conducted here will determine the possibility of implementing a machine vision based detection system to identify the presence of trash or valuables in passenger vehicles using a custom designed in-car camera module. The detection system was implemented to capture images of the rear seating compartment of a car intended to be used in shared vehicle fleets. Onboard processing of the image was done by a Raspberry Pi computer while the image classification was done by a remote server. Two vision based algorithmic models were created for the purpose of classifying the images: a convolutional neural network (CNN) and a background subtraction model. The CNN was a fine-tuned VGG16 model and it produced a final prediction accuracy of 91.43% on a batch of 140 test images. For the output analysis, a confusion matrix was used to identify the correlation between correct and false predictions, and the certainties of the three classes for each classified image were examined as well. The estimated execution time of the system from image capture to displaying the results ranged between 5.7 seconds and 11.5 seconds. The background subtraction model failed for the application here due to its inability to form a stable background estimate. The incorrect classifications of the CNN were evident due to the external sources of variation in the images such as extreme shadows and lack of contrast between the objects and its neighbouring background. Improvements in changing the camera location and expanding the training image set were proposed as possible future research