Breeding biology of pond heron in Kerala, South India

Regular intensive investigations to locate nesting colonies were carried out early monsoon of 1999 up to 2002. For detailed study a few breeding colonies were selected. The nesting trees, nest characteristics, eggs characteristics, incubation, hatching and hatchlings were investigated.

Altogether 17 plant species were used for nesting. Nest materials were collected from 24 plant species for building nest. The mean maximum size of the nest material used was 29.05 cm and the mean minimum size was 13.46 cm. The clutch size varied from 2-5 and clutches of three were very common. The maximum and minimum length and breadth of eggs were 48.0 x 32.0 and 33.3 x 24.1 respectively. The weight of eggs varied between 17.8 and 11.2 gm. Both sexes take part in incubation that extended for 18-24 days. Hatching success reached 82%. Their food consisted mainly of fishes and both parents took part in feeding the nestlings

Lifetimes of Machinery and Equipment. Evidence from Dutch Manufacturing

This paper estimates service lifetimes for capital assets in Dutch manufacturing industries, using information on asset retirement patterns. A Weibull distribution function is estimated using a nonlinear regression technique to derive service lifetimes for three selected asset types: transport equipment, machinery and computers. For this purpose the benchmark capital stock surveys for different two digit industries are linked to annual discard surveys. On average the estimated lifetimes are respectively 6, 9 and 26 years for transport equipments, computers and machinery. However, these estimates vary across industries. A comparison of our estimates with Canadian, US and Japanese estimates shows notable differences in the lifetimes of all the asset types, with machinery showing the largest difference

The Falling Productivity in West Asian Arab Countries Since the 1980s:Causes, Consequences, and Cures

This article analyzes the macro trends in real per capita GDP and productivity in12 West Asian Arab countries, distinguishing between the oil-rich GCC economies and the non-GCC West Asian Arab economies. We use a panel data econometric analysis to understand the trade-off between productivity and job creation in the region. Further, we examine the sources of aggregate labour productivity growth in terms of a) structural change and within-industry productivity improvements and b) capital deepening and total factor productivity growth. Although the nature of productivity problems in the two groups of countries - the GCC and non-GCC West Asian Arab economies - differ, the challenges in addressing those are substantial for both. Developing a vibrant private sector that can foster productivity growth is a common challenge for both groups of countries. The inability to embrace innovation and technology and to translate investment in capital to productivity are important impediments to boosting productivity growth. Focusing on technology and innovation, continuing the efforts to diversify away from oil, and upskilling the local workforce are essential to creating more productive jobs for the native population

The Falling Productivity in West Asian Arab Countries Since the 1980s:Causes, Consequences, and Cures

This article analyzes the macro trends in real per capita GDP and productivity in12 West Asian Arab countries, distinguishing between the oil-rich GCC economies and the non-GCC West Asian Arab economies. We use a panel data econometric analysis to understand the trade-off between productivity and job creation in the region. Further, we examine the sources of aggregate labour productivity growth in terms of a) structural change and within-industry productivity improvements and b) capital deepening and total factor productivity growth. Although the nature of productivity problems in the two groups of countries - the GCC and non-GCC West Asian Arab economies - differ, the challenges in addressing those are substantial for both. Developing a vibrant private sector that can foster productivity growth is a common challenge for both groups of countries. The inability to embrace innovation and technology and to translate investment in capital to productivity are important impediments to boosting productivity growth. Focusing on technology and innovation, continuing the efforts to diversify away from oil, and upskilling the local workforce are essential to creating more productive jobs for the native population

Economic reforms and industrial performance : an analysis of capacity utilisation in Indian manufacturing

This paper examines the performance of Indian manufacturing sector in terms of economic capacity utilization (CU), over 1974-1998. An attempt is also made to understand the impact of policy changes, inter alia, on the observed movements of CU. The economic CU, defined as the realization of output at which the short run average total cost is minimized, is estimated using a translog cost function. We observe cyclical movements in CU over the period. Three distinct phases have been identified with regard to the movements in CU. While phase one (1974-1984) is characterized by relatively wide fluctuations, phase two (1985-1990) witnessed a roughly stable level of utilization. In the third phase (1991-1998), a variant of the fluctuations witnessed in the first phase is seen to have resurfaced. Interestingly, there has not been any significant correspondence between the observed phases of CU with the corresponding policy environment. While supply and demand side factors are significant in determining CU in Indian manufacturing, the impact of economic reforms per se is not remarkable. Key words: India, Manufacturing, Capacity Utilization, Economic reforms JEL Classifications: D24, L5, L60, O4

Developing sustainable university campus index

Tertiary education is central to economic and political development and it is a powerful potential for growth which is very vital in an increasingly globalizing society. Universities play very important roles in the development of tertiary education and the society. It is probably in view of this fact, that the state of universities generally, is attracting global attention and the universities are currently ranked in order of performances. Although human development and economic growth depend largely on the environmental setting, however, the existing ranking system fail to consider environmental issues, despite current trend in green campus movement and sustainable development of universities. It is in the light of this, that this paper considers the prospects of developing a sustainable development rating system as an addendum to the method of ranking university campuses. Existing tools of measuring sustainable development in buildings were reviewed and BREEAM was considered most suitable for the development of university Campus Sustainable Index (CSI). It is also believed that the rating system will not only create environmental awareness and stimulate competition among universities but effect improvement in environmental conditions and environmental management systems of university campuses as well as provide a melting point of understanding sustainable development in university campus.Keywords: Tertiary Education; Sustainable Campus Index; University Campus; Environmental Managemen

Modeling the correlations of crude oil properties based on sensitivity based linear learning method

This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crudeoil systems using sensitivity based linear learning method (SBLLM). PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties, such as bubble-point pressure and oil formation volume factor, is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially their instability and inconsistency during predictions. In this paper, a sensitivitybasedlinearlearningmethod (SBLLM) prediction model for PVT properties is presented using three distinct databases while comparing forecasting performance, using several kinds of evaluation criteria and quality measures, with neural network and the three common empirical correlations. In the formulation used, sensitivity analysis coupled with a linear training algorithm for each of the two layers is employed which ensures that the learning curve stabilizes soon and behaves homogenously throughout the entire process operation. In this way, the model will be able to adequately model PVT properties faster with high stability and consistency. Empirical results from simulations demonstrated that the proposed SBLLM model produced good generalization performance, with high stability and consistency, which are requisites of good prediction models in reservoir characterization and modeling