Making the deployment of pico-PV more sustainable along the value chain

Pico-PV is an excellent technology for bringing electric light to rural areas in the developing world and replacing kerosene lanterns and candles. However, as pico-PV is a comparatively new technology, relatively little is known about appropriate methods for sustainable product development and deployment. For this reason current dissemination methods are often ineffective and unsustainable. This research aims to help project developers deploy pico-PV technologies successfully and in a sustainable manner. To achieve this, a conceptual framework of key sustainability criteria along the value chain was developed and tested. The analysis revealed that the most important criteria for the sustainable deployment of pico-PV systems are: (a) easy and safe operation of the product; (b) that a system for product return is established; (c) the retailer understands the target market and (d) the end-user is aware of the product's existence and its benefits. This research reveals that criteria (b) and (c) are of greatest concern. In light of these findings, the authors propose to focus on the following five factors; namely: (a) raising awareness for certification and creating market reassurance; (b) introducing support mechanisms to facilitate local repair; (c) using existing supply channels and establishing in-country (dis)assembly; (d) introducing financial support mechanisms at product supply stages and; (e) undertaking marketing campaigns. © 2013 Elsevier Ltd. All rights reserved.

Decision making at the front end of innovation: The hidden influence of knowledge and decision criteria

Decision making at the front end of innovation is critical for the success of companies. This paper presents a method, called decision making based on knowledge (DeBK), which was created to analyze the decision-making process at the front end. The method evaluates the knowledge of project information and the importance of decision criteria, compiling a measure that indicates whether decisions are founded on available knowledge and what criteria are in fact being considered to delineate them. The potential contribution of DeBK is corroborated through two projects that faced decision-making issues at the front end of innovation. © 2014 RADMA and John Wiley & Sons Ltd.

Motor effort alters changes of mind in sensorimotor decision making.

After committing to an action, a decision-maker can change their mind to revise the action. Such changes of mind can even occur when the stream of information that led to the action is curtailed at movement onset. This is explained by the time delays in sensory processing and motor planning which lead to a component at the end of the sensory stream that can only be processed after initiation. Such post-initiation processing can explain the pattern of changes of mind by asserting an accumulation of additional evidence to a criterion level, termed change-of-mind bound. Here we test the hypothesis that physical effort associated with the movement required to change one's mind affects the level of the change-of-mind bound and the time for post-initiation deliberation. We varied the effort required to change from one choice target to another in a reaching movement by varying the geometry of the choice targets or by applying a force field between the targets. We show that there is a reduction in the frequency of change of mind when the separation of the choice targets would require a larger excursion of the hand from the initial to the opposite choice. The reduction is best explained by an increase in the evidence required for changes of mind and a reduced time period of integration after the initial decision. Thus the criteria to revise an initial choice is sensitive to energetic costs.

Is there a preferred classifier for operational thematic mapping?

The importance of properly exploiting a classifier's inherent geometric characteristics when developing a classification methodology is emphasized as a prerequisite to achieving near optimal performance when carrying out thematic mapping. When used properly, it is argued that the long-standing maximum likelihood approach and the more recent support vector machine can perform comparably. Both contain the flexibility to segment the spectral domain in such a manner as to match inherent class separations in the data, as do most reasonable classifiers. The choice of which classifier to use in practice is determined largely by preference and related considerations, such as ease of training, multiclass capabilities, and classification cost. © 1980-2012 IEEE.