Technological-forecasting and research inputs in area of birth-control technology

Technological forecasting, defined as quantified probabilistic prediction of timings and degree of change in the technological parameters, capabilities desirability or needs at different times in the future, is applied to birth control technology (BCT) as a means of revealing the paths of most promising research through identifying the necessary points for breakthroughs. The present status of BCT in the areas of pills and the IUD, male contraceptives, immumological approaches, post-coital pills, abortion, sterilization, luteolytic agents, laser technologies, and control of the sex of the child, are each summarized and evaluated in turn. Fine mapping is done to identify the most potentially promising areas of BCT. These include efforts to make oral contraception easier, improvement of the design of the IUD, clinical evaluation of the male contraceptive danazol, the effecting of biochemical changes in the seminal fluid, and researching of immunological approaches and the effects of other new drugs such as prostaglandins. The areas that require immediate and large research inputs are oral contraception and the IUD. On the basis of population and technological forecasts, it is deduced that research efforts could most effectively aid countries like India through the immediate production of an oral contraceptive pill or IUD with long-lasting effects. Development of a pill for males or an immunization against pre gnancy would also have a significant impact. However, the major impediment to birth control programs to date is attitudes, which must be changed through education.

A systems approach to rural housing

An attempt is made in this paper to arrive at a methodology for generating building technologies appropriate to rural housing. An evaluation of traditional modern' technologies currently in use reveals the need for alternatives. The lacunae in the presently available technologies also lead to a definition of rural housing needs. It is emphasised that contending technologies must establish a 'goodness of fit' between the house form and the pattern of needs. A systems viewpoint which looks at the dynamic process of building construction and the static structure of the building is then suggested as a means to match the technologies to the needs. The process viewpoint emphasises the role of building materials production and transportation in achieving desired building performances. A couple of examples of technological alternatives like the compacted soil block and the polythene-stabilised soil roof covering are then discussed. The static structural system viewpoint is then studied to arrive at methodologies of cost reduction. An illustrative analysis is carried out using the dynamic programming technique, to arrive at combinations of alternatives for the building components which lead to cost reduction. Some of the technological options are then evaluated against the need patterns. Finally, a guideline for developments in building technology is suggested

Forecasting rain for groundnut farmers�how good is good enough

: We illustrate how climatological information about adverse weather events and meteorological forecasts (when available) can be used to decide between alternative strategies so as to maximize the long-term average returns for rainfed groundnut in semi-arid parts of Karnataka, We show that until the skill of the forecast, i.e. probability of an adverse event occurring when it is forecast, is above a certain threshold, the forecast has no impact on the optimum strategy, This threshold is determined by the loss in yield due to the adverse weather event and the cost of the mitigatory measures, For the specific case of groundnut, it is found that while for combating some pests/diseases, climatological information is adequate, for others a forecast of sufficient skill would have a significant impact on the productivity.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Probabilistic Models for Forecasting Earthquakes in the Northeast Region of India

Northeast India and its adjoining areas are characterized by very high seismic activity. According to the Indian seismic code, the region falls under seismic zone V, which represents the highest seismic-hazard level in the country. This region has experienced a number of great earthquakes, such as the Assam (1950) and Shillong (1897) earthquakes, that caused huge devastation in the entire northeast and adjacent areas by flooding, landslides, liquefaction, and damage to roads and buildings. In this study, an attempt has been made to find the probability of occurrence of a major earthquake (M-w > 6) in this region using an updated earthquake catalog collected from different sources. Thereafter, dividing the catalog into six different seismic regions based on different tectonic features and seismogenic factors, the probability of occurrences was estimated using three models: the lognormal, Weibull, and gamma distributions. We calculated the logarithmic probability of the likelihood function (ln L) for all six regions and the entire northeast for all three stochastic models. A higher value of ln L suggests a better model, and a lower value shows a worse model. The results show different model suits for different seismic zones, but the majority follows lognormal, which is better for forecasting magnitude size. According to the results, Weibull shows the highest conditional probabilities among the three models for small as well as large elapsed time T and time intervals t, whereas the lognormal model shows the lowest and the gamma model shows intermediate probabilities. Only for elapsed time T = 0, the lognormal model shows the highest conditional probabilities among the three models at a smaller time interval (t = 3-15 yrs). The opposite result is observed at larger time intervals (t = 15-25 yrs), which show the highest probabilities for the Weibull model. However, based on this study, the IndoBurma Range and Eastern Himalaya show a high probability of occurrence in the 5 yr period 2012-2017 with >90% probability.