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.

The user-value of rural electrification: An analysis and adoption of existing models and theories

User-value is a determining factor for product acceptance in product design. Research on rural electrification to date, however, does not draw sufficient attention to the importance of user-value with regard to the overall success of a project. This is evident from the analysis of project reports and applicable indicators from agencies active in the sector. Learning from the design, psychology and sociology literatures, it is important that rural electrification projects incorporate the value perception of the end-user and extend their success beyond the commonly used criteria of financial value, the appropriateness of the technology, capacity building and technology uptake. Creating value for the end-user is particularly important for project acceptance and the sustainability of a scheme once it has been handed over to the local community. In this research paper, existing theories and models of value-theory are transposed and applied to community operated rural electrification schemes and a user-value framework is developed. Furthermore, the importance of value to the end-user is clarified. Current literature on product design reveals that user-value has different properties, many of which are applicable to rural electrification. Five value pillars and their sub-categories important for the users of rural electrification projects are identified, namely: functional; social significance; epistemic; emotional; and cultural values. These pillars provide the main structure for the conceptual framework developed in this research paper. It is proposed that by targeting the values of the end-user, the key factors of user-value applicable to rural electrification projects will be identified and the sustainability of the project will be better ensured. © 2014 The Authors.

The mean value concept in mono-linear regression of multi-variables and its application to trace studies in geosciences

IEECAS SKLLQG

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

bridge the gap between software test process and business value: a case study

Chinese Academy of Sciences (ISCAS)