Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps.

Broken water pumps continue to impede efforts to deliver clean and economically-viable water to the global poor. The literature has demonstrated that customers' health benefits and willingness to pay for clean water are best realized when clean water infrastructure performs extremely well (>99% uptime). In this paper, we used sensor data from 42 Afridev-brand handpumps observed for 14 months in western Kenya to demonstrate how sensors and supervised ensemble machine learning could be used to increase total fleet uptime from a best-practices baseline of about 70% to >99%. We accomplish this increase in uptime by forecasting pump failures and identifying existing failures very quickly. Comparing the costs of operating the pump per functional year over a lifetime of 10 years, we estimate that implementing this algorithm would save 7% on the levelized cost of water relative to a sensor-less scheduled maintenance program. Combined with a rigorous system for dispatching maintenance personnel, implementing this algorithm in a real-world program could significantly improve health outcomes and customers' willingness to pay for water services.

Measuring and Increasing Adoption Rates of Cookstoves in a Humanitarian Crisis.

Traditional smoky cooking fires are one of today's greatest environmental threats to human life. These fires, used by 40% of the global population, cause 3.9 million annual premature deaths. "Clean cookstoves" have potential to improve this situation; however, most cookstove programs do not employ objective measurement of adoption to inform design, marketing, subsidies, finance, or dissemination practices. Lack of data prevents insights and may contribute to consistently low adoption rates. In this study, we used sensors and surveys to measure objective versus self-reported adoption of freely-distributed cookstoves in an internally displaced persons camp in Darfur, Sudan. Our data insights demonstrate how to effectively measure and promote adoption, especially in a humanitarian crisis. With sensors, we measured that 71% of participants were cookstove "users" compared to 95% of respondents reporting the improved cookstove was their "primary cookstove." No line of survey questioning, whether direct or indirect, predicted sensor-measured usage. For participants who rarely or never used their cookstoves after initial dissemination ("non-users"), we found significant increases in adoption after a simple followup survey (p = 0.001). The followup converted 83% of prior "non-users" to "users" with average daily adoption of 1.7 cooking hours over 2.2 meals. This increased adoption, which we posit resulted from cookstove familiarization and social conformity, was sustained for a 2-week observation period post intervention.

Reducing Ultrafine Particle Emissions Using Air Injection in Wood-Burning Cookstoves.

In order to address the health risks and climate impacts associated with pollution from cooking on biomass fires, researchers have focused on designing new cookstoves that improve cooking performance and reduce harmful emissions, specifically particulate matter (PM). One method for improving cooking performance and reducing emissions is using air injection to increase turbulence of unburned gases in the combustion zone. Although air injection reduces total PM mass emissions, the effect on PM size distribution and number concentration has not been thoroughly investigated. Using two new wood-burning cookstove designs from Lawrence Berkeley National Laboratory, this research explores the effect of air injection on cooking performance, PM and gaseous emissions, and PM size distribution and number concentration. Both cookstoves were created using the Berkeley-Darfur Stove as the base platform to isolate the effects of air injection. The thermal performance, gaseous emissions, PM mass emissions, and particle concentrations (ranging from 5 nm to 10 µm in diameter) of the cookstoves were measured during multiple high-power cooking tests. The results indicate that air injection improves cookstove performance and reduces total PM mass but increases total ultrafine (less than 100 nm in diameter) PM concentration over the course of high-power cooking.

Bending characteristics of polymethylmethacrylate columns, connecting bars of carbon fiber, titanium, and stainless steel used in external skeletal fixation and an acrylic interface.

OBJECTIVE: To (1) mechanically evaluate polymethylmethacrylate (PMMA) columns of various sizes and compare them to connecting bar materials (carbon fiber composite, titanium, stainless steel) and (2) compare the properties of an intact PMMA column to those of an acrylic interface. STUDY DESIGN: Experimental mechanical study. SAMPLE POPULATION: Experiment 1: 6 groups of 6 specimens each; experiment 2: 2 groups of 12 specimens each. METHODS: All specimens were tested in 3-point bending. Stiffness, yield strength, and ultimate strength values were calculated for each specimen. RESULTS: PMMA 1 columns (23.25 mm) compared favorably to titanium or stainless-steel bars. PMMA 3 columns (30.15 mm) and carbon fiber bars had similar yield strength but PMMA 3 columns were less stiff than carbon fiber bars. PMMA 3 columns had lower bending modulus and a higher variability in their mechanical properties than PMMA 1 or PMMA 2 (25.64 mm) columns. Acrylic interface specimens were less strong but as stiff as intact specimens. CONCLUSION: An acrylic interface was easily created and had acceptable biomechanical characteristics. CLINICAL RELEVANCE: PMMA 2 and PMMA 3 columns are not recommended for clinical use at this time. Further tests are needed to analyze an acrylic patch in a more clinically relevant loading model.