Promoting Entrepreneurship through a Community Learning Model – Case Study: Green Businesses

This article presents a community learning model formulated by Engineers Without Borders Colombia with the aim of providing communities with tools to create sustainable productive solutions which have relevancy for members and for potential customers. The goal of this formulation is to promote learning processes that are guided by decisions made by community members to propose sustainable and replicable initiatives. The model applicability is evidenced through a case study devoted to strengthening community-led green businesses in the Guavio Province, Colombia by collecting lessons and conclusions. Ultimately, this collection will prove useful in replicating the learning model in other similar rural communities.

     

Systems Thinking and Scenario Planning: Application in the Clothing Sector

Industrial sectors that operate in uncertain environments - with demand variability, product seasonality and different industrialisation structures - need studies that enable identification and forecast trends. Therefore, the development of competitiveness extends beyond a company’s individual performance. Collective action, whether toward consumer markets, supplier markets, competitors and substitutes, can reinforce or help reformulate the current practices of an organisation, besides providing better results in the development of strategies and competitive positioning. Thus, clothing, the sector addressed in this work, is characterised by a long, fragmented, heterogeneous production chain, the competitiveness of which is linked to product differentiation. Therefore, the use of systemic approaches to study this sector is effective. In this sense, this research aims at adapting Systems Thinking and Scenario Planning (STSP) so that it supports the development and planning process in a given sector. Thus, this research applies STSP adapted to an analysis of the clothing sector in the northern region of Rio Grande do Sul State, Brazil. As a result, in academic terms, this research proposed and validated a method for analysing industrial sectors of the clothing industry. In the sectoral context, this research identified elements that leverage the sector’s competitiveness, besides generating knowledge and learning aimed at strengthening the sectoral structure identified, and fostering the formation of a new clothing cluster.     

Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite

From the standpoints of both basic research and biotechnology, there is considerable interest in reaching a clearer understanding of the diversity of biological mechanisms employed during lignocellulose degradation. Globally, termites are an extremely successful group of wood-degrading organisms and are therefore important both for their roles in carbon turnover in the environment and as potential sources of biochemical catalysts for efforts aimed at converting wood into biofuels. Only recently have data supported any direct role for the symbiotic bacteria in the gut of the termite in cellulose and xylan hydrolysis. Here we use a metagenomic analysis of the bacterial community resident in the hindgut paunch of a wood-feeding 'higher' Nasutitermes species (which do not contain cellulose-fermenting protozoa) to show the presence of a large, diverse set of bacterial genes for cellulose and xylan hydrolysis. Many of these genes were expressed in vivo or had cellulase activity in vitro, and further analyses implicate spirochete and fibrobacter species in gut lignocellulose degradation. New insights into other important symbiotic functions including H2 metabolism, CO2-reductive acetogenesis and N2 fixation are also provided by this first system-wide gene analysis of a microbial community specialized towards plant lignocellulose degradation. Our results underscore how complex even a 1-microl environment can be.     

The role of dyking and fault control in the rapid onset of eruption at Chaitén volcano, Chile

Rhyolite is the most viscous of liquid magmas, so it was surprising that on 2?May 2008 at Chaitén Volcano, located in Chile's southern Andean volcanic zone, rhyolitic magma migrated from more than 5?km depth in less than 4?hours (ref.?1) and erupted explosively with only two days of detected precursory seismic activity. The last major rhyolite eruption before that at Chaitén was the largest volcanic eruption in the twentieth century, at Novarupta volcano, Alaska, in 1912. Because of the historically rare and explosive nature of rhyolite eruptions and because of the surprisingly short warning before the eruption of the Chaitén volcano, any information about the workings of the magmatic system at Chaitén, and rhyolitic systems in general, is important from both the scientific and hazard perspectives. Here we present surface deformation data related to the Chaitén eruption based on radar interferometry observations from the Japan Aerospace Exploration Agency (JAXA) DAICHI (ALOS) satellite. The data on this explosive rhyolite eruption indicate that the rapid ascent of rhyolite occurred through dyking and that melt segregation and magma storage were controlled by existing faults.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

Palaeoclimatic distribution models predict Pleistocene refuges for the Neotropical harvestman Geraeocormobius sylvarum (Arachnida: Opiliones: Gonyleptidae)

This paper primarily aims to test a Pleistocene refuge-type scenario, as previously proposed for the gonyleptid Geraeocormobius sylvarum, a semi-deciduous forests dweller in subtropical Argentina, Brazil and Paraguay. Palaeodistributional models of this species were built using MaxEnt for two Last Glacial Maximum (LGM = 21,000 years ago) simulations – Community Climate System Model (CCSM) and Model for Interdisciplinary Research on Climate (MIROC) – and for 6000 years ago (?6k = HCO, the Holocene climatic optimum). Both LGM models retrieved a fragmented pattern. For CCSM, range was split into multiple, scattered fragments. MIROC resulted in very few patches, with a decided range reduction because of a strong humidity drop. Models for ?6k recovered a moderate range expansion. No past connection between the core area and the yungas was predicted. Analysis of variables importance showed that two precipitation predictors (bc18, precipitation warmest quarter; bc14, precipitation driest month) and two temperature predictors (bc7, temperature annual range; bc9, mean temperature driest quarter) scored as the most influencing overall. The Limiting Factor analysis recognized them as limiting too, in different parts of the species range. LGM palaeomodels of G. sylvarum are compatible with the refuge hypothesis invoked in previous molecular analyses, to explain the high genetic diversity found in the core area. Additionally, the results reinforced the hypothesis of the recent anthropogenic origin of the yungas disjunct populations.     

Active transport of Ca2+ by an artificial photosynthetic membrane

Transport of calcium ions across membranes and against a thermodynamic gradient is essential to many biological processes, including muscle contraction, the citric acid cycle, glycogen metabolism, release of neurotransmitters, vision, biological signal transduction and immune response. Synthetic systems that transport metal ions across lipid or liquid membranes are well known, and in some cases light has been used to facilitate transport. Typically, a carrier molecule located in a symmetric membrane binds the ion from aqueous solution on one side and releases it on the other. The thermodynamic driving force is provided by an ion concentration difference between the two aqueous solutions, coupling to such a gradient in an auxiliary species, or photomodulation of the carrier by an asymmetric photon flux. Here we report a different approach, in which active transport is driven not by concentration gradients, but by light-induced electron transfer in a photoactive molecule that is asymmetrically disposed across a lipid bilayer. The system comprises a synthetic, light-driven transmembrane Ca2+ pump based on a redox-sensitive, lipophilic Ca2+-binding shuttle molecule whose function is powered by an intramembrane artificial photosynthetic reaction centre. The resulting structure transports calcium ions across the bilayer of a liposome to develop both a calcium ion concentration gradient and a membrane potential, expanding Mitchell's concept of a redox loop mechanism for protons to include divalent cations. Although the quantum yield is relatively low (approximately 1 per cent), the Ca2+ electrochemical potential developed is significant.