Development Engineering最新文献

Data collected in Mobile Health Data Collections Systems (MHDCS) are diverse, both in terms of type and value. This calls for different data protection measures to meet security goals of confidentiality, integrity, and availability. The majority of commonly used open-source MHDCS track and monitor individuals over a while. It is therefore important to have sensitive data defined and proper security measures identified. We propose a data classification model as a basis for secure design and implementation. Our method combines interviews with case studies. The case studies focused on three of the widely used MHDCS platforms in low-resource settings; that is Muzima, Open Data Kit (ODK), and District Health Information Software (DHIS) 2 Tracker Capture. Interviews with domain experts helped define the sensitivity of data in MHDCS. The proposed data classification model provides for three sensitivity levels: public, confidential, and critical. The model uses context information and multiple parameters as inputs to a classification scheme that maps data to sensitivity levels. The generated data classifications are intended to guide developers and users to build security into MHDCS starting from the early stages of the software development life cycle.

In 2009, fibre-optic cables landed on the East coast of Africa, the last major area of the world to be connected to the Internet triggering a decade of Internet development (Graham et al., 2015). During the same period, there has been a general transformation of the Internet from static content to video streaming. Technologies such as Software Defined Networking (SDN) and Network Functions Virtualisation (NFV) are about to reshape the Internet once again. Globally Internet eXchange Points (IXP) have been a key node on the Internet and a central location for Content Delivery Networks (CDN), though in East Africa they have generally been confined to large cities. There is an understanding that if technology hubs are to develop in other cities, the Internet ecosystem, including IXPs, must extend outwards.

This research uses a Proof of Concept (PoC) system design methodology to investigate solutions that containerise IXP functions and develops affordable models for IXPs of various sizes and configurations based on both traditional and software-defined switching paradigms as well as automate the IXP build function. The research argues that it is necessary to develop a national IXP ecosystem by supplementing the national IXP with local IXPs to support economic development outside of the major economic cities of the region. The technology solutions must be used in conjunction with research on the political economy landscape plus optimum deployment to ensure success. This research demonstrates that systems can be designed which are achievable and affordable by exploiting the most suitable model and switching technology for each site. It also determines that software-defined models offer the potential for application development across the IXP.

This research concludes that with a combination of function containerisation and astute model selection it is possible to build an affordable set of IXPs to support multiple technology hubs across a national Internet ecosystem. Proposed systems are discussed in the context of East Africa and testbed results discussed in relation to the optimum system design which can be deployed in any IXP setting.

The cost-effectiveness and reliability of waste collection services in informal settlements can be difficult to optimize given the geospatial and temporal variability of latrine use. Daily servicing to avoid overflow events is inefficient, but dynamic scheduling of latrine servicing could reduce costs by providing just-in-time servicing for latrines. This study used cellular-connected motion sensors and machine learning to dynamically predict when daily latrine servicing could be skipped with a low risk of overflow. Sensors monitored daily latrine activity, and enumerators collected solid and liquid waste weight data. Given the complex relationship between latrine use and the need for servicing, an ensemble machine learning algorithm (Super Learner) was used to estimate waste weights and predict overflow events to facilitate dynamic scheduling. Accuracy of waste weight predictions based on sensor and historical weight data was adequate for estimating latrine fill levels (mean error of 20% and 22% for solid and liquid wastes), but there was greater accuracy in predicting overflow events (area under the receiver operating characteristic curve of 0.90). Although our simulations indicate that dynamic scheduling could substantially reduce costs for lower use latrines, we found that cost reduction was more modest for higher use latrines and that there was a significant gap between the simulated and implemented results.

Empirical social sciences rely heavily on surveys to measure human behavior. Previous studies show that such data are prone to random errors and systematic biases caused by social desirability, recall challenges, and the Hawthorne effect. Moreover, collecting high frequency survey data is often impossible, which is important for outcomes that fluctuate. Innovation in sensor technology might address these challenges. In this study, we use sensors to describe solar light adoption in Kenya and analyze the extent to which survey data are limited by systematic and random error. Sensor data reveal that households used lights for about 4 h per day. Frequent surveyor visits for a random sub-sample increased light use in the short term, but had no long-term effects. Despite large measurement errors in survey data, self-reported use does not differ from sensor measurements on average and differences are not correlated with household characteristics. However, mean-reverting measurement error stands out: households that used the light a lot tend to underreport, while households that used it little tend to overreport use. Last, general usage questions provide more accurate information than asking about each hour of the day. Sensor data can serve as a benchmark to test survey questions and seem especially useful for small-sample analyses.

Quantifying the impact of improved stoves and fuels designed to combat the health and environmental burdens of traditional cooking is necessary to ensure sustainable outcomes but remains challenging for practitioners. The current standard method to determine household fuel consumption, the Kitchen Performance Test, is costly, time intensive, and subject to error. To address these challenges, the Fuel Use Electronic Logger (FUEL), a sensor-based system that monitors fuel consumption in households was developed. In this study, the accuracy, granularity, and cost of FUEL were compared to that of the standard Kitchen Performance Test through simultaneous testing. Monitoring was conducted over four and five consecutive days in 10 households in Burkina Faso that were each stacking LPG, charcoal, and wood stoves; and in 20 households in Uganda stacking multiple wood stoves, respectively. Results show good agreement between the two methods on an aggregate level, with an overall R² value of 0.81, and more varied agreement when comparing fuel consumption on a day-to-day basis. The sample variation was found to generally decrease with increasing monitoring length, pointing to value in monitoring over longer durations afforded by the FUEL. There was no systematic over- or under-prediction of fuel consumption between FUEL and the KPT, suggesting that the FUEL method does not have significant bias relative to the KPT, but the accuracy of the methods relative to the true, “ground truth” household fuel consumption value was not known. There was no agreement between either method with self-reported survey data, further illustrating the unreliability of quantitative survey data. Moisture content and Standard Adult Equivalence measurements were found to be similar whether measurements were taken only on the first and last days of the study period as compared to each day, although this should be evaluated over a longer time period for future studies. Potential errors in each method are discussed and resulting suggestions for developing an effective study with the FUEL system are presented. An economic analysis shows that the FUEL system becomes increasingly economical as monitoring duration increases or new studies are conducted, with a breakeven point at 40 days in this case. Overall, these results point to the viability of the FUEL system to quantify long-term, in-situ fuel consumption with similar accuracy to current methods and the capability for more granular data over longer time periods with less intrusion into households.

Smallholder farmers provide the foundation for food security in South Asia. However, increasing seasonal labor scarcity caused by rural out-migration has resulted in growing agricultural labor costs, presenting challenges to cash-constrained smallholder farmers that hire manual labor for land preparation, sowing, harvest and post-harvest operations. Technological innovations in small-scale agricultural machinery appropriate for the small field sizes and limited resource endowments of South Asia's farmers have been proposed as a potential solution to this problem. An increasing number of development initiatives also promote rural entrepreneurial approaches to mechanization, whereby smallholder farmers can access and use machinery in their own fields on an affordable fee-for-service basis offered by machinery owners. This approach reduces capital constraints for smallholder farmers while enabling entrepreneurs who can afford equipment to enter into business serving stallholder farmers as clients. This approach is now widely practiced in Bangladesh, where machinery entrepreneurs play a crucial role in providing access to productive technologies for smallholder farmers who could not otherwise afford direct purchase of labor- and cost-saving machinery. In order to maintain low machinery purchase costs for emerging yet capital constrained rural entrepreneurs, while also assuring high quality standards, cost-effective domestic production of agricultural machinery is increasingly championed as an important long-term national development objective. With no safety standards or guidelines for best production practices, the few manufacturing workshops that exist within Bangladesh operate inefficiently and without clear rationalization of manufacturing processes. Haphazard copying of prototypes or imported available machinery is common. This leads to inefficient production and poor product quality in an emerging but potentially highly beneficial industry. This paper addresses these problems and presents a case study to increase machinery manufacturers' capacity while improving manufacturing operations and workplace safety through equipment selection, workshop layout, and usability.

Janata Engineering (JE) is a small-scale machinery manufacturing enterprise in Bangladesh, specializing in two-wheel tractor attachments such as bed planters, local derivations of power-tiller operated seeders, and other equipment for planting, irrigating, and processing crops. JE was expanding and setting up a second factory for which the authors provided assistance on its design. Our research question was whether participatory action research (PAR) supported by empirical data could provide improved factory design in terms of functionality, safety and human interactions, when compared with conventional approaches driven by technical efficiency concerns alone. Using PAR, we developed a number of alternative process and layout recommendations for JE to increase the eff