Improving the sustainability of an appropriate technology solution; bone char defluoridation filters in Arusha, Tanzania

High concentrations of fluoride naturally occurring in the ground water in the Arusha region of Tanzania cause dental, skeletal and non-skeletal fluorosis in up to 90% of the region’s population [1]. Symptoms of this incurable but completely preventable disease include brittle, discolored teeth, malformed bones and stiff and swollen joints. The consumption of high fluoride water has also been proven to cause headaches and insomnia [2] and adversely affect the development of children’s intelligence [3, 4]. Despite the fact that this array of symptoms may significantly impact a society’s development and the citizens’ ability to perform work and enjoy a reasonable quality of life, little is offered in the Arusha region in the form of solutions for the poor, those hardest hit by the problem. Multiple defluoridation technologies do exist, yet none are successfully reaching the Tanzanian public. This report takes a closer look at the efforts of one local organization, the Defluoridation Technology Project (DTP), to address the region’s fluorosis problem through the production and dissemination of bone char defluoridation filters, an appropriate technology solution that is proven to work. The goal of this research is to improve the sustainability of DTP’s operations and help them reach a wider range of clients so that they may reduce the occurrence of fluorosis more effectively. This was done first through laboratory testing of current products. Results of this testing show a wide range in uptake capacity across batches of bone char emphasizing the need to modify kiln design in order to produce a more consistent and high quality product. The issue of filter dissemination was addressed through the development of a multi-level, customerfunded business model promoting the availability of filters to Tanzanians of all socioeconomic levels. Central to this model is the recommendation to focus on community managed, institutional sized filters in order to make fluoride free water available to lower income clients and to increase Tanzanian involvement at the management level.

SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N >> n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup.