Modified quasilinearization method for solving nonlinear equations

Modified quasilinearization algorithm for solving nonlinear equation

Biodegradation of rocket propellant waste, ammonium perchlorate

The short term effects of ammonium perchlorate on selected organisms were studied. A long term experiment was also designed to assess the changes incurred by ammonium perchlorate on the nitrogen and chloride contents of soil within a period of 3 years. In addition, an attempt was made to produce methane gas from anaerobic fermentation of the aquatic weed, Alternanthera philoxeroides

Deep Impact: Geo-Simulations as a Policy Toolkit for Natural Disasters

Adverse post-natural disaster outcomes in low-income regions, like elevated internal migration levels and low consumption levels, are the result of market failures, poor mechanisms for stabilizing income, and missing insurance markets, which force the affected population to respond, and adapt to the shock they face. In a spatial environment, with multiple locations with independent but inter-connected markets, these transitions quickly become complex and highly non-linear due to the feedback loops between the micro individual-level decisions and the meso location-wise market decisions. To capture these continuously evolving micro–meso interactions, this paper presents a spatially explicit bottom-up agent-based model to analyze natural disaster-like shocks to low-income regions. The aim of the model is to temporally and spatially track how population distributions, income, and consumption levels evolve, in order to identify low-income workers that are “food insecure”. The model is applied to the 2005 earthquake in northern Pakistan, which faced catastrophic losses and high levels of displacement in a short time span, and with market disruptions, resulted in high levels of food insecurity. The model is calibrated to pre-crisis trends, and shocked using distance-based output and labor loss functions to replicate the earthquake impact. Model results show, how various factors like existing income and saving levels, distance from the fault line, and connectivity to other locations, can give insights into the spatial and temporal emergence of vulnerabilities. The simulation framework presented here, leaps beyond existing modeling efforts, which usually deals with macro long-term loss estimates, and allows policy makers to come up with informed short-term policies in an environment where data is non-existent, policy response is time dependent, and resources are limited

COVID-19: Visualizing regional socioeconomic indicators for Europe

The COVID-19 pandemic struck the world out of the blue and displayed unprecedented transmission rates. Part of the reason for its rapid worldwide spread, was the nature of the virus itself, its presentation (symptoms were visible well after a person was infected) and the highly complex, interconnected world we live in today. An equally important contributing factor is our, now apparent, collective inability to deal with a rapidly emerging global threat in a coherent and integrated manner across countries and continents. Our existing multilateral systems are simply not yet geared to respond to such an emerging global challenge in an adequate and timely manner. The plethora of national responses have also been shown to be inadequate. The extent of our inter-connectedness has led us to recognize that we live in a global village, and this pandemic has removed any remaining doubts. However, the underlying global order of pervasive tourism, trade, business, education has the potential to create vulnerabilities while also generating critical sector specific information that could be systematically harnessed in order to allow rapid and effective global and national responses to risks. Undoubtedly this data is being collected in some form. At a more operational level the world is still struggling to bring together the necessary data from across different sectors of society, across scales and a sufficiently integrated manner to fully enable a rapid analysis and a comprehensive disaster risk mitigation response. The looming era of machine learning and artificial intelligence too has the potential to fast-track our capabilities and responsiveness to such epidemics, but presently, we are still struggling to access relevant information and timely data at appropriate scales and resolutions. Early information from the COVID-19 pandemic suggested a greater vulnerability of older citizens to the virus. Current mortality patterns support the notion that the elderly, especially those with underlying medical conditions, are more vulnerable. However, clearly, all segments of the population are vulnerable. In the absence of an available cure for the virus, key measures deployed to limit transmission include to curb mobility, social distancing, to strengthen the medical infrastructure to improve the palliative treatment for vulnerable patients (ventilators) and protect key medical practitioners (protective clothing and masks). A fully functional coordinated system of international cooperation would help with the effective execution of many of these measures. Given that the pandemic has now reached nearly all countries around the world and is still spreading, countries are responding by shutting borders and are competing for scarce resources - medical, technical, and/or financial. The developing geography of the pandemic illustrates how different countries become more vulnerable at different times during the development of the pandemic - not all countries are necessarily equally vulnerable at the same time. The same principle applies to different parts within a country (Northern Italy, New York). In understanding these differences, mortality rates are probably a more robust indicator, albeit delayed, considering the different modes and extents of COVID-19 testing implemented around the world. Clearly COVID-19 has caught all of us o_ guard, yet we need to respond to the emerging crisis to the best of our ability. While numerous epidemiological analyses and models are currently informing and assisting global decision makers to respond to the virus, we at IIASA can assist by making available critical socioeconomic and demographic data that may be of use to policymakers and the health community to allocate scarce resources more strategically between countries and even within countries. This IIASA mapbook is made available to rapidly and urgently disseminate key demographic and population information in a visible form to assist health professionals, disaster response operations, governments and policymakers from across the European Union. This IIASA mapbook publishes and will continue to expand on a list of key indicators that can be used to better understand the socioeconomic and demographic contexts under which the current COVID-19 crisis is unfolding. Accessible data is presently limited to the EU

COVID-19: Visualizing regional socioeconomic indicators for Europe

The COVID-19 pandemic struck the world out of the blue and displayed unprecedented transmission rates. Part of the reason for its rapid worldwide spread, was the nature of the virus itself, its presentation (symptoms were visible well after a person was infected) and the highly complex, interconnected world we live in today. An equally important contributing factor is our, now apparent, collective inability to deal with a rapidly emerging global threat in a coherent and integrated manner across countries and continents. Our existing multilateral systems are simply not yet geared to respond to such an emerging global challenge in an adequate and timely manner. The plethora of national responses have also been shown to be inadequate. The extent of our inter-connectedness has led us to recognize that we live in a global village, and this pandemic has removed any remaining doubts. However, the underlying global order of pervasive tourism, trade, business, education has the potential to create vulnerabilities while also generating critical sector specific information that could be systematically harnessed in order to allow rapid and effective global and national responses to risks. Undoubtedly this data is being collected in some form. At a more operational level the world is still struggling to bring together the necessary data from across different sectors of society, across scales and a sufficiently integrated manner to fully enable a rapid analysis and a comprehensive disaster risk mitigation response. The looming era of machine learning and artificial intelligence too has the potential to fast-track our capabilities and responsiveness to such epidemics, but presently, we are still struggling to access relevant information and timely data at appropriate scales and resolutions. Early information from the COVID-19 pandemic suggested a greater vulnerability of older citizens to the virus. Current mortality patterns support the notion that the elderly, especially those with underlying medical conditions, are more vulnerable. However, clearly, all segments of the population are vulnerable. In the absence of an available cure for the virus, key measures deployed to limit transmission include to curb mobility, social distancing, to strengthen the medical infrastructure to improve the palliative treatment for vulnerable patients (ventilators) and protect key medical practitioners (protective clothing and masks). A fully functional coordinated system of international cooperation would help with the effective execution of many of these measures. Given that the pandemic has now reached nearly all countries around the world and is still spreading, countries are responding by shutting borders and are competing for scarce resources - medical, technical, and/or financial. The developing geography of the pandemic illustrates how different countries become more vulnerable at different times during the development of the pandemic - not all countries are necessarily equally vulnerable at the same time. The same principle applies to different parts within a country (Northern Italy, New York). In understanding these differences, mortality rates are probably a more robust indicator, albeit delayed, considering the different modes and extents of COVID-19 testing implemented around the world. Clearly COVID-19 has caught all of us o_ guard, yet we need to respond to the emerging crisis to the best of our ability. While numerous epidemiological analyses and models are currently informing and assisting global decision makers to respond to the virus, we at IIASA can assist by making available critical socioeconomic and demographic data that may be of use to policymakers and the health community to allocate scarce resources more strategically between countries and even within countries. This IIASA mapbook is made available to rapidly and urgently disseminate key demographic and population information in a visible form to assist health professionals, disaster response operations, governments and policymakers from across the European Union. This IIASA mapbook publishes and will continue to expand on a list of key indicators that can be used to better understand the socioeconomic and demographic contexts under which the current COVID-19 crisis is unfolding. Accessible data is presently limited to the EU

Students’ Research: Tradition Ahead of Its Time

This view point describes the experience of introducing research at an undergraduate level during clinical rotation in psychiatry. Objective of this initiative was to encourage critical thinking, self directed learning and sensitization to mental health issues. This contributed to student learning besides galvanizing their interest in the subject. The opinion piece aims to expose various issues to students’ research in the context of medical education in Pakistan

Schizophrenia: a concept

Much of the current research-work into biological basis of mental disorders is predicted on implicit concept of disease that is less critical and sophisticated as it should be. It is remarkable, how the fundamental conceptual frame work of schizophrenia, as proposed by Professor Emil Kraepelin has stayed the same, since its inception almost 100-years ago. This review explores these issues besides highlighting alternative disease classification that suits behavioural neuroscience research