Managing public lands for equitable and sustainable development in Cambodia

Public lands accounted for 80% of the country area until a decade ago. As Cambodia emerged from three decades of civil war and internal strife, the Royal Government of Cambodia (RGC) has granted more than 10% of the country area or 50% of the cultivatable land as large scale “Economic Land Concessions” (ELCs) to private companies, mostly foreign owned, in a mostly rigged process. Land disputes have become a permanent fixture in the press and a hot issue on human rights reports. There is a need for detailed public review of ELCs and to re-approach land management processes both before and after the concessions

Management of economic land concessions

The Cambodian government redistributed 1.2 million hectares, some revoked from economic land concessions (ELC), to more than 710,000 smallholders as private ownerships (2013-2014). The paper outlines key steps for granting new land concessions and improving the efficiency of existing ELCs (or similar large-scale state land licences). Cambodia’s excessive large-scale state land concessions have adversely affected the livelihoods and land tenure rights of local people, threatening the country’s rich biodiversity and restricting access to land especially for new farmer households

Technical report : the 34th Annual Conference of the Federation of the ASEAN Economic Associations (FAEA) on “The Impact of the Global Economic Downturn on the ASEAN Countries and How to Mitigate the Impact on Poor People” on 15-16 December 2009, Phnom Penh

The Federation of ASEAN Economic Associations (FAEA) was founded in 1975. The objective of this Federation is to promote the study of economic science and economic research in ASEAN countries. It comprises of economic societies and associations from Indonesia, Malaysia, the Philippines, Singapore, Thailand, Vietnam and Cambodia. Each year the FAEA organize an annual conference brining together all the member associations to discuss pressing economic issues facing countries in the region. The 34th FAEA annual conference was organized under the title, “The Impact of the Global Economic Downturn on the ASEAN countries and How to Mitigate the Impact on Poor People”. It was successfully co-hosted by CDRI and CEA. This high profile event brought together key members from the respective countries of FAEA to present and discuss the impact of the global economic downturn on the respective economies and how to mitigate the impact on poor people. A number of policymakers from Cambodia and the region benefited not only from the conference exchanges but also from the comparative synthesis/summary and proceedings published after the conference. FAEA continued to be strong as a regional network. The Co-host, CEA, became a stronger body in networking economists and professionals in Cambodia to influence policy making in the country

Early Detection of Osteoarthritis Stage by Applying Classification Technique to Human Joint imagery

Thesis (M.Sc., Information Technology)--Prince of Songkla University, 201

Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

Knee Osteoarthritis (OA) is one of the most prominent diseases in an ageing society and has affected over 10 million people in Thailand. When people suffer from OA, it is very difficult to recover. Therefore, early detection and prevention are important. The typical way to detect OA is by using X-ray imaging. This research study is focused on early detection of OA by applying image processing and classification techniques to knee X-ray imagery. The fundamental concept is to find a region of interest, use feature extraction techniques and build a classifier that can classify between OA or non-OA imageries. There are four regions of interest obtained from each image: (i) Medial Femur (MF), (ii) Lateral Femur (LF), (iii) Medial Tibia (MT), and (iv) Lateral Tibia (LT). The ten texture analysis techniques are then adopted to generate the embedded properties of the bone surface. Once the feature vector has been generated the variety of techniques of machine learning mechanisms are applied to generate the desired classifiers, which can be used to distinguish between OA and non-OA images. From the conducted experiments, a total of 131 images (68 OA cases and 63 non-OA cases) was used, the results obtained show that LF region with Local Binary Pattern descriptor produced the most appropriate classifier with an AUC value of 0.912

Epilepsy in Asia: a Cambodian experience.

International audienceEpilepsy is particularly challenging for resource-poor countries and in turn for Asia which is likely to have greater challenges in terms of treatment cost and deficit, premature mortality, health transitions, population and poverty size, etc. Here we present an example of working in one of the resource-poor 'least-talked-about' populations to demonstrate that finding financial means and achieving cross-country cooperation over a long period of time is possible even in countries with currently limited resources. Conducting such cooperation could be a model for other initiatives. Scientific, capacity-building, and political tools should be employed to generate local representative data and influence government policies. These measures can be of immediate benefit for patients in these countries

Flood Hazard and Management in Cambodia: A Review of Activities, Knowledge Gaps, and Research Direction

Cambodia is located in one of the most severe flood-vulnerable zones in mainland Southeast Asia. Flooding is the country’s most recurrent and impactful hazard among other natural hazards. This hazard alone, observed in many river basins, has been inflicting huge damages on livelihoods, social infrastructure, and the country’s economy. This study aims to review the current status of flood hazards, impacts, driving factors, management capacity, and future research directions on floods in Cambodia. The findings of this study suggested that there is still a lack of flood-related studies on flood hazard mapping, risk and damage assessment, and future flood analysis in Cambodia. The existing related studies mainly focused on the Tonle Sap Basin and its tributaries, the Lower Mekong Basin, the whole Mekong River Basin, and some of the tributaries of the Mekong River in Cambodia. The fundamental driving factors of the current flooding in Cambodia are impacts of climate change, land-use change, water infrastructure development, and weather extremes. The applications of mathematical and statistical tests and indices, conceptual and physically-based modeling, artificial intelligence and machine learning, and remote sensing are recommended to focus on future research directions on flood in Cambodia in the areas of land-use change, existing and planned operation of water infrastructure, flood hazard and damage assessment, and flood forecasting. The outcomes from these studies and applications would improve the understanding of flood hazard characteristics, reinforce flood management, and achieve flood damage reduction