Corruption and the Smallholder: A Review of Current Literature and Research

WorldAgInfo Project Literature ReviewCorruption is a hot topic of academic and public policy research. Specifically, corruption is often characterized as directly impacting the economies of developing countries: “Corruption undermines governance, economic growth, and, ultimately, the stability of countries and regions” (Spector, 2005). Of particular concern are areas of the world, such as Sub-Saharan Africa, where corruption is perceived to be rampant and where smallholders form the backbone of the economy. This paper addresses the current research and literature on corruption with a specific focus on the impact of corruption on the smallholder

Agile Development Methods for Space Operations

Main stream industry software development practice has gone from a traditional waterfall process to agile iterative development that allows for fast response to customer inputs and produces higher quality software at lower cost. How can we, the space ops community, adopt state of the art software development practice, achieve greater productivity at lower cost, and maintain safe and effective space flight operations? At NASA Ames, we are developing Mission Control Technologies Software, in collaboration with Johnson Space Center (JSC) and, more recently, the Jet Propulsion Laboratory (JPL)

An investigation of machine learning based prediction systems

Traditionally, researchers have used either o�f-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software eff�ort estimates. More recently, attention has turned to a variety of machine learning methods such as artifcial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software e�ort prediction systems. We briefly describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the prediction systems in terms of three factors: accuracy, explanatory value and configurability. We show that ANN methods have superior accuracy and that RI methods are least accurate. However, this view is somewhat counteracted by problems with explanatory value and configurability. For example, we found that considerable eff�ort was required to configure the ANN and that this compared very unfavourably with the other techniques, particularly CBR and least squares regression (LSR). We suggest that further work be carried out, both to further explore interaction between the enduser and the prediction system, and also to facilitate configuration, particularly of ANNs

Informality and the Development and Demolition of Urban Villages in the Chinese Peri-urban Area

The fate of Chinese urban villages (chengzhongcun) has recently attracted both research and policy attention. Two important unaddressed questions are: what are the sources of informality in otherwise orderly Chinese cities; and, will village redevelopment policy eliminate informality in the Chinese city? Reflecting on the long-established study of informal settlements and recent research on informality, it is argued that the informality in China has been created by the dual urban–rural land market and land management system and by an underprovision of migrant housing. The redevelopment of chengzhongcun is an attempt to eliminate this informality and to create more governable spaces through formal land development; but since it fails to tackle the root demand for unregulated living and working space, village redevelopment only leads to the replication of informality in more remote rural villages, in other urban neighbourhoods and, to some extent, in the redeveloped neighbourhoods

Urban built environment configuration and psychological distress in older men: Results from the Caerphilly study

Background: Few studies have examined the impact of the built environment configuration upon mental health. The study examines the impact of objectively assessed land use and street network configuration upon psychological distress and whether this association is moderated by the natural environment and area-level deprivation. Methods. In a community sample of 687 older men from the Caerphilly Prospective Study, built environment morphological metrics (morphometrics) were related to differences in psychological distress as measured by the General Health Questionnaire. Cross-sectional data were taken from the most recent (5th) phase. A multi-level analysis with individuals nested within census-defined neighbourhoods was conducted. Environmental measures comprised GIS-constructed land use and street network metrics, slope variability and a satellite derived measure of greenness. Results: Reduced psychological distress was associated with residing in a terraced dwelling (OR = 0.48, p = 0.03), higher degrees of land-use mix (OR = 0.42, p = 0.03 for the high tertile) and having higher local-level street-network accessibility ('movement potential') (OR = 0.54, p = 0.03). Hillier topography with higher slope variability was associated with increased risks of psychological distress (OR = 1.38, p = 0.05). Conclusions: The findings support our hypothesis that built environment configuration is independently associated with psychological distress. The study underscores the need for effective intervention in the planning and design of residential built environment to achieve the goal of health-sustaining communities. © 2013 Sarkar et al.; licensee BioMed Central Ltd.published_or_final_versio

The Application of Integrated Knowledge-based Systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through the Biomedical Risk Assessment Intelligent Network (BRAIN), an integrated network of both human and computer elements. The BRAIN will function as an advisor to flight surgeons by assessing the risk of in-flight biomedical problems and recommending appropriate countermeasures. This paper describes the joint effort among various NASA elements to develop BRAIN and an Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of the following: (1) knowledge acquisition; (2) integration of IDRA components; (3) use of expert systems to automate the biomedical prediction process; (4) development of a user-friendly interface; and (5) integration of the IDRA prototype and Exercise Countermeasures Intelligent System (ExerCISys). Because the C Language, CLIPS (the C Language Integrated Production System), and the X-Window System were portable and easily integrated, they were chosen as the tools for the initial IDRA prototype. The feasibility was tested by developing an IDRA prototype that predicts the individual risk of influenza. The application of knowledge-based systems to risk assessment is of great market value to the medical technology industry

High Quality Research Environments

A major challenge facing all research communities is creating and sustaining high quality research environments. A model describing strategic social structures that constrain knowledge production suggests that targeting these structures will have greater impact than addressing issues surrounding individual lab cultures, as important as these are. A literature search identified five common themes underlying bioscience research environments comprising collaboration, data processing, confidence in data and scientists, trust, user-led development, and a deep commitment to public benefit. Club theory was used to develop a model describing the social structures that constrain and contextualise research environments. It is argued that collaboration underlies impactful science and that this is hindered by high transaction costs, and the benefits associated with competition. These combined with poorly defined property rights surrounding publicly funded data limit the ability of data markets to operate efficiently. Although the science community is best placed to provide solutions for these issues, incentivisation by funding agencies to increase the benefits of collaboration will be an accelerator. Given the complexity of emerging datasets and the collaborations need to exploit them, trust-by-design solutions are suggested. The underlying ‘glue’ that holds this activity together is the aesthetic and ethical value-base underlying good science

High Quality Research Environments

A major challenge facing all research communities is creating and sustaining high quality research environments. A model describing strategic social structures that constrain knowledge production suggests that targeting these structures will have greater impact than addressing issues surrounding individual lab cultures, as important as these are. A literature search identified five common themes underlying bioscience research environments comprising collaboration, data processing, confidence in data and scientists, trust, user-led development, and a deep commitment to public benefit. Club theory was used to develop a model describing the social structures that constrain and contextualise research environments. It is argued that collaboration underlies impactful science and that this is hindered by high transaction costs, and the benefits associated with competition. These combined with poorly defined property rights surrounding publicly funded data limit the ability of data markets to operate efficiently. Although the science community is best placed to provide solutions for these issues, incentivisation by funding agencies to increase the benefits of collaboration will be an accelerator. Given the complexity of emerging datasets and the collaborations need to exploit them, trust-by-design solutions are suggested. The underlying ‘glue’ that holds this activity together is the aesthetic and ethical value-base underlying good science

High Quality Research Environments

A major challenge facing all research communities is creating and sustaining high quality research environments. A model describing strategic social structures that constrain knowledge production suggests that targeting these structures will have greater impact than addressing issues surrounding individual lab cultures, as important as these are. A literature search identified five common themes underlying bioscience research environments comprising collaboration, data processing, confidence in data and scientists, trust, user-led development, and a deep commitment to public benefit. Club theory was used to develop a model describing the social structures that constrain and contextualise research environments. It is argued that collaboration underlies impactful science and that this is hindered by high transaction costs, and the benefits associated with competition. These combined with poorly defined property rights surrounding publicly funded data limit the ability of data markets to operate efficiently. Although the science community is best placed to provide solutions for these issues, incentivisation by funding agencies to increase the benefits of collaboration will be an accelerator. Given the complexity of emerging datasets and the collaborations need to exploit them, trust-by-design solutions are suggested. The underlying ‘glue’ that holds this activity together is the aesthetic and ethical value-base underlying good science