Mapping research gaps for sustainable forest management based on the nominal group technique

Managing a complex social-ecological system requires data about the many social and ecological variables characterizing it and about their interactions. While the selection of research topics has its own, mostly unpredictable dynamics and contingencies, there has been a recent surge of interest regarding the involvement of non-academic stakeholders in suggesting research topics and identifying perceived knowledge gaps regarding the management of complex social-ecological systems. Decision-makers will invariably be confronted with limitations regarding resources to be allocated to the study of various systems components, and regarding the processing capacity of scientists and other stakeholders alike. Matang forest is one of the longest-managed mangroves in the world and provides a widely cited example of silvicultural management for charcoal and pole production, while providing a range of other ecosystem services. We applied the nominal group technique (NGT) to identify research priorities for Matang, as it provides a systematic and participatory approach to identify collective priorities while also reducing bias. The method consists of two rounds, during which participants were asked to reflect first individually, and then collectively, about key characteristics of mangrove management and about research priorities in Matang. The results were compared to the recommendations of the scientific literature. NGT provides a rapid, robust and systematic approach to identify research priorities for mangrove management and can hence be a timely method to support decision-makers across South-East Asia in guiding resource allocation toward research needs in times of increasing mangrove degradation. This is the first time that the application of NGT has been documented in a mangrove context. Moreover, NGT is not yet being used frequently in natural resources management, hence in documenting our NGT application, we aim to contribute to the development of a the NGT body of knowledge beyond mere mangrove forest settings. Rapid methods (such as NGT) to identify pressing research priorities are needed to guide resource allocation and investment of time and scientific capacity based on a systematic and pluralistic assessment.Fonds De La Recherche Scientifique—FNRS

Microwave assisted solvent free synthesis of 1,3-diphenylpropenones

<p>Abstract</p> <p>Background</p> <p>1,3-Diphenylpropenones (chalcones) are well known for their diverse array of bioactivities. Hydroxyl group substituted chalcones are the main precursor in the synthesis of flavonoids. Till date various methods have been developed for the synthesis of these very interesting molecules. Continuing our efforts for the development of simple, eco-friendly and cost-effective methodologies, we report here a solvent free condensation of aryl ketones and aldehydes using iodine impregnated alumina under microwave activation. This new protocol has been applied to a variety of substituted aryl carbonyls with excellent yield of substituted 1,3-diphenylpropenones.</p> <p>Results</p> <p>Differently substituted chalcones were synthesized using iodine impregnated neutral alumina as catalyst in 79-95% yield in less than 2 minutes time under microwave activation without using any solvent. The reaction was studied under different catalytic conditions and it was found that molecular iodine supported over neutral alumina gives the best yield. The otherwise difficult single step condensation of hydroxy substituted aryl carbonyls is an attractive feature of this protocol to obtain polyhydroxychalcones in excellent yields. In order to find out the general applicability of this new endeavor it was successfully applied for the synthesis of 15 different chalcones including highly bioactive prenylated hydroxychalcone xanthohumol.</p> <p>Conclusion</p> <p>A new, simple and solvent free method was developed for the synthesis of substituted chalcones in environmentally benign way. The mild reaction conditions, easy work-up, clean reaction profiles render this approach as an interesting alternative to the existing methods.</p

Application of artificial intelligence for the assessment of the status of power transformers

The Dissolved Gas Analysis (DGA) a non destructive test procedure, has been in vogue for a long time now, for assessing the status of power and related transformers in service. An early indication of likely internal faults that may exist in Transformers has been seen to be revealed, to a reasonable degree of accuracy by the DGA. The data acquisition and subsequent analysis needs an expert in the concerned area to accurately assess the condition of the equipment. Since the presence of the expert is not always guaranteed, it is incumbent on the part of the power utilities to requisition a well planned and reliable artificial expert system to replace, at least in part, an expert. This paper presents the application of Ordered Ant Mner (OAM) classifier for the prediction of involved fault. Secondly, the paper also attempts to estimate the remaining life of the power transformer as an extension to the elapsed life estimation method suggested in the literature

Uptake of HIV testing and HIV positivity among presumptive tuberculosis patients at Puducherry, South India.

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Tuberculosis patients with diabetes and diabetes control.

<p>Figure shows flow of Tuberculosis patients registered under the Revised National Tuberculosis Control Programme (RNTCP) in Malappuram District, Kerala, India, from April 2010 to September 2011, by diabetes and diabetes control status. Out of 3116 TB patients, 2794(90%) know their diabetic status and of this 667(24%) are diabetic. Out of the diabetic TB patients 240(36%) know their diabetic control status during treatment and of this 103(43%) are under diabetic control during treatment.</p

Smear conversion at three months of treatment of sputum positive Tuberculosis patients in Malappuram district, Kerala state,India from April 2010 to September 2011, by status of Diabetes Mellitus (n = 1727).

<p>RR = relative risk; CI = confidence interval.</p