Objectives
Respiratory rate (RR) measurement is critical to diagnosing pneumonia in resource‐constrained settings, but accurate RR measurement is challenging. The acute lower respiratory illness ...treatment and evaluation (ALRITE) mobile phone application (app), designed to help healthcare workers (HCWs) manage pediatric respiratory illnesses, includes a semiautomated RR counter. This study aimed to evaluate the accuracy and usability of the ALRITE RR counter and a commercially available RR counter app, RRate, with a reference standard.
Methods
This was a cross‐sectional observational study of HCWs. Participants used both apps to measure the RR of pediatric patients from standardized videos. The reference standard was determined by consensus of a manual 1‐min count by two providers. We assessed agreement using Spearman's rank correlation coefficient and constructed Bland–Altman plots to determine bias and limits of agreement. Participants completed a usability survey.
Results
Thirty‐nine HCWs participated. The agreement between the apps and reference standard (Spearman's coefficient) was 0.83 (95% confidence interval CI: 0.78–0.87) for ALRITE and 0.62 (95% CI: 0.52–0.70) for RRate. ALRITE had a bias of −2 breaths/min (lower limit of agreement LoA −16 to +12) and RRate had a bias of −0.4 breaths/min (LoA −24 to +23) compared to the reference standard. Both apps had a poorer agreement at higher RRs. Based on usability survey responses, 95% found ALRITE easy to use.
Conclusions
The ALRITE RR counter has acceptable accuracy for counting RR in infants with respiratory distress, appears to be more accurate than a commercially available option, and was user‐friendly. The ALRITE RR counter is a promising tool for meriting evaluation in real‐world settings.
ABSTRACT
Africa has over 2000 languages; however, those languages are not well represented in the existing natural language processing ecosystem. African languages lack essential digital resources to ...effectively engage in advancing language technologies. There is a need to generate high‐quality natural language processing resources for low‐resourced African languages. Obtaining high‐quality speech and text data is expensive and tedious because it can involve manual sourcing and verification of data sources. This paper discusses the process taken to curate and annotate text and speech datasets for five East African languages: Luganda, Runyankore‐Rukiga, Acholi, Lumasaba, and Swahili. We also present results obtained from baseline models for machine translation, topic modeling and classification, sentiment classification, and automatic speech recognition tasks. Finally, we discuss the experiences, challenges, and lessons learned in creating the text and speech datasets.
This paper discusses the process taken to create, curate and annotate text and speech corpus for low‐resourced languages in East Africa.
One of the perennial causes of food insecurity in Sub‐Saharan African countries is crop pests and diseases that affect agricultural production. To address this concern in Uganda, a mobile phone ...crowdsourcing Adhoc surveillance tool, Adsurv, was deployed and used by smallholder farmers. Adsurv is a crop health surveillance and monitoring mobile application that facilitates diagnosing and managing crop pests and diseases. Existing literature has examined the impacts of mobile technologies on food security or livelihoods in isolation, but has not conducted a holistic assessment of both. This study on the other hand aimed at assessing the contribution Adsurv made towards improved food security and livelihood assets of the smallholder farmers, which were not fully understood. The study contributes to applying the sustainable livelihood framework (SLF) as theory in evaluating Mobile for Development (M4D) initiatives. The Analytic Hierarchy Process (AHP) was further adopted to facilitate the development of a hierarchical assessment model of the multidimensional food security and livelihood aspects using data collected from 56 out of the targeted 70 participants. Results show that Adsurv mainly contributed towards food availability rather than utilization or access. These gains mostly enhanced the human assets of the smallholder farmers, empowering them with skills that positively impacted all other assets to support the attainment of sustainable livelihoods. The implication of these results in both research and practice is the need for further research trials, which will motivate farmers to recognize the importance of the nutritional value of food in their agronomic practices.
The atmospheric chemistry constituents of nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO) are associated with air pollution and climate change. In sub-Saharan Africa, a lack of ...sufficient ground-based and aircraft observations has, for a long time, limited the study of these species. This study thus utilized satellite observations as an alternative source of data to study the abundance of these species over the East African region. The instruments used included the Ozone Monitoring Instrument (OMI), the Atmospheric InfraRed Sounder (AIRS), and the TROPOspheric Monitoring Instrument (TROPOMI). An investigation of trends in the data series from 2005 to 2020 was carried out using the sequential Mann-Kendall test while the Pearson correlation coefficient was used to compare the data records of the instruments. The analysis revealed no trend in NO2 (p > 0.05), a decreasing trend in SO2 (p < 0.05), a decreasing trend (p < 0.05) in CO closer to the surface (850 hPa to 500 hPa) and an increasing trend (p < 0.05) in CO higher up in the atmosphere (400 hPa to 1 hPa). There is likely a vertical ascent of CO. The correlation between the instrument records was 0.54 and 0.77 for NO2 and CO, respectively. Furthermore, seasonal fires in the savanna woodlands were identified as the major source of NO2 and CO over the region, while cities such as Kampala, Nairobi, and Bujumbura and towns such as Dar es Salaam and Mombasa were identified as important NO2 hotspots. Similarly, the active volcano at Mt. Nyiragongo near Goma was identified as the most important SO2 hotspot.
What Makes Agile Software Development Agile? Kuhrmann, Marco; Tell, Paolo; Hebig, Regina ...
IEEE transactions on software engineering,
09/2022, Letnik:
48, Številka:
9
Journal Article
Recenzirano
Odprti dostop
Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the ...software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challenge the adoption of agile methods as prescribed by their creator(s), software processes in practice mutate into hybrids over time. Are these still agile? In this article, we investigate the question: what makes a software development method agile? We present an empirical study grounded in a large-scale international survey that aims to identify software development methods and practices that improve or tame agility. Based on 556 data points, we analyze the perceived degree of agility in the implementation of standard project disciplines and its relation to used development methods and practices. Our findings suggest that only a small number of participants operate their projects in a purely traditional or agile manner (under 15 percent). That said, most project disciplines and most practices show a clear trend towards increasing degrees of agility. Compared to the methods used to develop software, the selection of practices has a stronger effect on the degree of agility of a given discipline. Finally, there are no methods or practices that explicitly guarantee or prevent agility. We conclude that agility cannot be defined solely at the process level. Additional factors need to be taken into account when trying to implement or improve agility in a software company. Finally, we discuss the field of software process-related research in the light of our findings and present a roadmap for future research.
Automation improves the quality of fruits through quick and accurate detection of pest and disease infections thus contributing to the country's economic growth and productivity. Although humans can ...identify the fruit damage caused by pests and diseases, methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits, affect their market value. The issue of pest and disease infections further deteriorates fruits' quality, becoming a mounting stressor on farmers as they affect the potential income that could have been realised from production, processing and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews 32 Journal and Conference articles spanning 13 years obtained electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and general internet searches. This survey further presents a detailed discussion of related studies done in the past while emphasizing their strengths and limitations and presenting future research directions. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges.
In Uganda, cervical cancer (CaCx) is the commonest cancer, accounting for 35.7% of all cancer cases in women. The rates of human papillomavirus vaccination and CaCx screening remain low. Digital ...health tools and interventions have the potential to improve different aspects of CaCx screening and control in Uganda. This study aimed to describe stakeholders’ perceptions of the telemedicine system we developed to improve CaCx screening in Uganda. We developed and implemented a smartphone-based telemedicine system for capturing and sharing cervical images and other clinical data, as well as an artificial intelligence model for automatic analysis of images. We conducted focus group discussions with health workers at the screening clinics (n=27) and women undergoing screening (n=15) to explore their perceptions of the system. The focus group discussions were supplemented with field observations and an evaluation survey of the health workers on system usability and the overall project. In general, both patients and health workers had positive opinions about the system. Highlighted benefits included better cervical visualization, the ability to obtain a second opinion, improved communication between nurses and patients (to explain screening findings), improved clinical data management, performance monitoring and feedback, and modernization of screening service. However, there were also some negative perceptions. For example, some health workers felt the system is time-consuming, especially when it had just been introduced, while some patients were apprehensive about cervical image capture and sharing. Finally, commonplace challenges in digital health (eg, lack of interoperability and problems with sustainability) and challenges in cancer screening in general (eg, arduous referrals, inadequate monitoring and quality control) also resurfaced. This study demonstrates the feasibility and value of digital health tools in CaCx screening in Uganda, particularly with regard to improving patient experience and the quality of screening services. It also provides examples of potential limitations that must be addressed for successful implementation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
In East Africa, biomass burning in the savanna region emits nitrogen dioxide (NO
2
), carbon monoxide (CO), and aerosols among other species. These emissions are dangerous air pollutants which pose a ...health risk to the population. They also affect the radiation budget. Currently, limited academic research has been done to study their spatial and temporal distribution over this region by means of numerical modeling. This study therefore used the Weather Research and Forecasting model coupled with chemistry (WRF-chem) to simulate, for the first time, the distribution of NO
2
during the year 2012 and CO during the period June 2015 to May 2016 over this region. These periods had the highest atmospheric abundances of these species. The model’s performance was evaluated against satellite observations from the Ozone Monitoring Instrument (OMI) and the Measurement of Pollution in the Troposphere (MOPITT). Three evaluation metrics were used, these were, the normalized mean bias (NMB), the root mean square error (RMSE) and Pearson’s correlation coefficient (R). Further, an attempt was made to reduce the bias shown by WRF-chem by applying a deep convolutional autoencoder (WRF-DCA) algorithm and linear scaling (WRF-LS). The results showed that WRF-chem simulated the seasonality of the gases but made below adequate estimates of the gas abundances. It overestimated NO
2
and underestimated CO throughout all the seasons. Overall, for NO
2
, WRF-chem had an average NMB of 3.51, RMSE of 2 × 10
15
molecules/cm
2
and R of 0.44 while for CO, it had an average NMB of − 0.063, RMSE of 0.65 × 10
18
molecules/cm
2
and R of 0.13. Furthermore, even though both WRF-DCA and WRF-LS successfully reduced the bias in WRF-chem’s NO
2
estimates, WRF-DCA had a superior performance compared to WRF-LS. It reduced the NMB by an average of 3.2 (90.2%). Finally, this study has shown that deep learning has a strong ability to improve the estimates of numerical models, and this can be a cue to incorporate this approach along other stages of the numerical modeling process.
Climate change and air pollution are two interconnected daunting environmental challenges of the twenty-first century. Globally, stringent public health and environmental policies are set to mitigate ...the emissions of near-term climate forcers (NTCFs) because they double as air pollutants. While the global climate impact of NTCF mitigation has been investigated using coarse resolution climate models, the fine scale regional climate impacts over East Africa are not fully known. This study presents the first 2021–2055 downscaled model results of two future scenarios which both have increasing greenhouse gas emissions but with weak (SSP3-7.0) versus strong (SSP3-7.0_lowNTCF) levels of air quality control. NTCF mitigation is defined here as SSP3-7.0_lowNTCF–SSP3-7.0. The results reveal that NTCF mitigation could cause an increase in annual mean surface temperature ranging from 0.005 to 0.01 °C decade
−1
over parts of Kenya, Ethiopia and Somalia. It could also cause an increase in annual mean precipitation ranging from 0.1 to 1 mm month
−1
decade
−1
over parts of Uganda, Kenya, Tanzania, South Sudan and Ethiopia. Majority of the precipitation increase is projected to occur during the MAM season. On the other hand, Zambia, Malawi and southern Tanzania could also experience a decrease in annual mean precipitation by up to 0.5 mm month
−1
decade
−1
. Majority of this decrease is projected to occur during the DJF season. These findings suggest that pursuing NTCF mitigation alone while ignoring greenhouse gas emissions will cause additional climate change over East Africa. Mitigating both of them concurrently would be a better policy option.
In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to ...collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screenhouse and open field, respectively, until disease symptoms were visibly seen by the human eye.
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