Non-Farm Rural Activities (NFRA) in a Peasant Economy: The Case of the North Peruvian Sierra

Is it feasible to increase income and generate employment in the context of a traditional labour intensive rural industry with strong linkages to an agriculturally backward economy? In order to address this issue, primary data from four villages of Peruvian North Sierra was used. The case of the hat making activity, employing exclusively family labour, purchasing the main input (straw, paja de palma) from Ecuador, and with consumers concentrated on villages and small towns, was investigated. The analysis was made at the market level. Considering the context of a self-employment activity, a theoretical framework was developed to explain the determinants of labour demand, input demand, hat output and labour return. Demand and supply constraints to the expansion of hat making activity were found. Important differences in the value of labour marginal product across the sample were identified. These were mainly associated with the use of varied input quality. Growth based on local demand would not be viable given falls in consumer incomes - mainly farmers - and expected changes in consumer preferences; therefore the growth motor would rest more in market expansion and product diversification to urban consumers.Non-farm rural activities, self-employment activity, peasant economy, Peru, Community/Rural/Urban Development, D12, D21, Q12.,

3D Digital Modeling for Urban Design + Planning

Cities throughout the country face a number of challenges in dealing with the changing development needs of the 21st century. In urban planning, the fusion of social, environmental, political, economic, and functional considerations are required. Today, local, state, and federal governments are experiencing an economic downturn, making resources scarce to plan for smarter development, attain greater public interest, and economic revival. The primary objective of this project is to provide innovative solutions in city and regional planning using software applications, such as Google SketchUp, that enable assessment of various urban design projects. A second objective was to create a 3D model of an existing city in which the model could be updated, corrected, and augmented. Modeling of the existing city enable designers and planners to study the impact of planned urban development. The Pismo Beach model can function in multiple ways. It can be used to gather data not otherwise available, such as the alignment of built features in the city, with respect to each other or natural features and allow the public greater access to, and understanding of, the development of the city. Additionally, the model can: \u3e Communicate/display to the public, who may be impacted about the decisions made by the Planning Department and City Council, \u3e Manipulate design and demonstrate impacts on site lines, views, aesthetics, etc. and \u3eAct as a tool for decision makers to - Visually foresee opportunities - Arbitrate or decide better compelling designs - Request option

Constraining sleptons at the LHC in a supersymmetric low-scale seesaw scenario

We consider a scenario inspired by natural supersymmetry, where neutrino data is explained within a low-scale seesaw scenario. We extend the Minimal Supersymmetric Standard Model by adding light right-handed neutrinos and their superpartners, the R-sneutrinos, and consider the lightest neutralinos to be higgsino-like. We consider the possibilities of having either an R-sneutrino or a higgsino as lightest supersymmetric particle. Assuming that squarks and gauginos are heavy, we systematically evaluate the bounds on slepton masses due to existing LHC data.Comment: 26 pages, 8 figures, 2 appendices; v2: Minor changes, version accepted for publication in EPJ

Improved Search of Relevant Points for Nearest-Neighbor Classification

Given a training set $P \subset \mathbb{R}^d$, the nearest-neighbor classifier assigns any query point $q \in \mathbb{R}^d$ to the class of its closest point in $P$. To answer these classification queries, some training points are more relevant than others. We say a training point is relevant if its omission from the training set could induce the misclassification of some query point in $\mathbb{R}^d$. These relevant points are commonly known as border points, as they define the boundaries of the Voronoi diagram of $P$ that separate points of different classes. Being able to compute this set of points efficiently is crucial to reduce the size of the training set without affecting the accuracy of the nearest-neighbor classifier. Improving over a decades-long result by Clarkson, in a recent paper by Eppstein an output-sensitive algorithm was proposed to find the set of border points of $P$ in $O( n^2 + nk^2 )$ time, where $k$ is the size of such set. In this paper, we improve this algorithm to have time complexity equal to $O( nk^2 )$ by proving that the first steps of their algorithm, which require $O( n^2 )$ time, are unnecessary