Why Modern Open Source Projects Fail

Open source is experiencing a renaissance period, due to the appearance of modern platforms and workflows for developing and maintaining public code. As a result, developers are creating open source software at speeds never seen before. Consequently, these projects are also facing unprecedented mortality rates. To better understand the reasons for the failure of modern open source projects, this paper describes the results of a survey with the maintainers of 104 popular GitHub systems that have been deprecated. We provide a set of nine reasons for the failure of these open source projects. We also show that some maintenance practices -- specifically the adoption of contributing guidelines and continuous integration -- have an important association with a project failure or success. Finally, we discuss and reveal the principal strategies developers have tried to overcome the failure of the studied projects.Comment: Paper accepted at 25th International Symposium on the Foundations of Software Engineering (FSE), pages 1-11, 201

Iodine status in western Kenya: a community-based cross-sectional survey of urinary and drinking water iodine concentrations

Spot urinary iodine concentrations (UIC) are presented for 248 individuals from western Kenya with paired drinking water collected between 2016 and 2018. The median UIC was 271 µg L−1, ranging from 9 to 3146 µg L−1, unadjusted for hydration status/dilution. From these data, 12% were potentially iodine deficient ( 300 µg L−1). The application of hydration status/urinary dilution correction methods was evaluated for UICs, using creatinine, osmolality and specific gravity. The use of specific gravity correction for spot urine samples to account for hydration status/urinary dilution presents a practical approach for studies with limited budgets, rather than relying on unadjusted UICs, 24 h sampling, use of significantly large sample size in a cross-sectional study and other reported measures to smooth out the urinary dilution effect. Urinary corrections did influence boundary assessment for deficiency–sufficiency–excess for this group of participants, ranging from 31 to 44% having excess iodine intake, albeit for a study of this size. However, comparison of the correction methods did highlight that 22% of the variation in UICs was due to urinary dilution, highlighting the need for such correction, although creatinine performed poorly, yet specific gravity as a low-cost method was comparable to osmolality corrections as the often stated ‘gold standard’ metric for urinary concentration. Paired drinking water samples contained a median iodine concentration of 3.2 µg L−1 (0.2–304.1 µg L−1). A weak correlation was observed between UIC and water-I concentrations (R = 0.11)

Spatial distribution and loss of micronutrients in soils from two different land use management

Land use – land cover changes affect the ecosystems' status and integrity to support and supply the services. Agricultural activities and attendant soil erosion, leaching or depletion of nutrients may result in increased soil degradation. The study investigated micronutrient spatial distribution and concentration in soils within two different agricultural land use management. The study employed RUSLE equations to determine the erosion rate within the selected plots. Topsoils (5-10cm) from different points within the plots were collected and analyzed for micronutrients using ICPMS(QQQ). The plots are located in high potential soil erosion places with soil erodibility (K) factor OF 0.031-ton ha-1MJ-1mm-1 within the Ombeyi river catchment. The soil erosion was estimated to be > 50t ha-1 year-1 , implying the high loss of nutrients; hence, over 52 elements were analyzed. The two plots compared micronutrients iodine (I), calcium (Ca), copper (Cu), iron (Fe), magnesium (Mg), selenium (Se), zinc (Zn), and molybdenum (Mo). In Plot 1(no terraces), micronutrients were concentrated at the base of the plot, while in plot 2 ( terraces), some elements were evenly distributed. There is a significant difference in the concentration of elements between the plots; I, Se, Cu, Ca and Mg, depicting a p-Value of 0.05. Elements in plot one were mapped with high concentration at the lower part of the plot as related to plot two which most of the elements were evenly distributed hence reduced micronutrients in plot 2. This encourages educating farmers on the importance of good terrain soil management

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping is a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms such as random forest (RF) models have been developed for such tasks as they are capable of modelling non-linear relationships using a range of datasets and determining the importance of predictor variables, offering multiple benefits to traditional techniques such as kriging. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with auxiliary environmental covariates. The model was applied to predict the total concentration (mg kg-1 ) of 56 elements, soil pH and organic matter content, as well as to assess prediction uncertainty using 466 soil samples in western Kenya (Watts et al 2021). The results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work due to their contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH was assessed as an important parameter to define soil chemical reactions. Algorithm performance was evaluated to determine the importance of each predictor variable and the model’s response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating the out-of-bag predicted values. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of auxiliary values did improve model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agricommunity utilise the geochemical maps presented here, and the webtool, for assessing the relationship between environmental geochemistry and endemic diseases and preventable micronutrient deficiency

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping techniques represent a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms using random forest (RF) models have been developed for classification, pattern recognition and regression tasks, they are capable of modelling non-linear relationships using a range of datasets, identifying hierarchical relationships, and determining the importance of predictor variables. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with ancillary environmental covariates. The model was applied to predict the total concentration (mg kg−1) and assess the prediction uncertainty of 56 elements, soil pH and organic matter content using 466 soil samples in western Kenya; the results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work. These elements were selected due to contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH is an important parameter controlling soil chemical reactions. Algorithm performance was evaluated determining the relative importance of each predictor variable and the model's response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating out-of-bag predicted values. The models R2 values range from 0.31 to 0.64 whilst CCC values range from 0.51 to 0.77. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of ancillary environmental data improved model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agri-community utilise the geochemical maps presented here for assessing the relationship between environmental geochemistry, endemic diseases and preventable micronutrient deficiency

Online Microdialysis-High-Performance Liquid Chromatography-Inductively Coupled Plasma Mass Spectrometry (MD-HPLC-ICP-MS) as a Novel Tool for Sampling Hexavalent Chromium in Soil Solution

© 2020 American Chemical Society. All rights reserved. Conventional soil solution sampling of species-sensitive inorganic contaminants, such as hexavalent chromium (CrVI), may induce interconversions due to disruption of system equilibrium. The temporal resolution that these sampling methods afford may also be insufficient to capture dynamic interactions or require time-consuming and expensive analysis. Microdialysis (MD) is emerging as a minimally invasive passive sampling method in environmental science, permitting the determination of solute fluxes and concentrations at previously unobtainable spatial scales and time frames. This article presents the first use of MD coupled to high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS) for the continuous sampling and simultaneous detection of CrVI in soil solution. The performance criteria of the system were assessed using stirred solutions; good repeatability of measurement (RSD < 2.5%) was obtained for CrVI, with a detection limit of 0.2 μg L-1. The online MD-HPLC-ICP-MS setup was applied to the sampling of native CrVI in three soils with differing geochemical properties. The system sampled and analyzed fresh soil solution at 15 min intervals, offering improved temporal resolution and a significant reduction in analysis time over offline MD. Simple modifications to the chromatographic conditions could resolve additional analytes, offering a powerful tool for the study of solute fluxes in soil systems to inform research into nutrient availability or soil-to-plant transfer of potentially harmful elements

Short-term iodine dynamics in soil solution

Assessing the reactions of iodine (I) in soil is critical to evaluate radioiodine exposure and understand soil-to-crop transfer rates. Our mechanistic understanding has been constrained by method limitations in assessing the dynamic interactions of iodine between soil solution and soil solid phase over short periods (hours). We use microdialysis to passively extract soil solution spiked with radioiodine (129I– and 129IO3–) to monitor short-term (≤40 h) in situ fixation and speciation changes. We observed greater instantaneous adsorption of 129IO3– compared to 129I– in all soils and the complete reduction of 129IO3– to 129I– within 5 h of addition. Loss of 129I from solution was extremely rapid; the average half-lives of 129I– and 129IO3– in soil solution were 4.06 and 10.03 h, respectively. We detected the presence of soluble organically bound iodine (org-129I) with a low molecular weight (MW) range (0.5–5 kDa) in all soils and a slower (20–40 h) time-dependent formation of larger MW org-I compounds (12–18 kDa) in some samples. This study highlights the very short window of immediate availability in which I from rainfall or irrigation remains in soil solution and available to crops, thus presenting significant challenges to phytofortification strategies in soil-based production systems

Plutonium as a soil erosion tracer in east Africa

Subsistence farmers in Africa are often dependent on food grown within a limited area, and therefore, their health can often be associated with geochemical factors that influence the soil-tocrop transfer of micronutrients (MN) essential for health. Loss of essential MN because of soil erosion can affect both crop yields and the protection of crops against disease, which could dramatically increase the likelihood of food shortages worldwide. In addition to the effects on land, the associated downstream transport of sediments to water bodies associated with soil erosion can impact water security. A large proportion of the degradation caused by soil erosion processes is a direct result of poor land management practises as well as vegetation clearance, and so there is a need for reliable quantitative data detailing rates of soil erosion and sedimentation. This data can then help to reinforce sustainable soil conservation measures in areas where resources to manage soils sustainably can be limited. This research aims to investigate the potential of using plutonium as an alternative tracer of soil erosion in challenging environments such as tropical Africa. This will allow for further research into the extent of soil erosion across East Africa and inform future mitigation efforts to reduce further erosion in the future

Optimisation of plutonium separations using TEVA cartridges and ICP-MS/MS analysis for applicability to large-scale studies in tropical soils

The analysis of plutonium (Pu) in soil samples can inform the understanding of soil erosion processes globally. However, there are specific challenges associated for analysis in tropical soils and so an optimal analytical methodology ensuring best sensitivity is critical. This method aimed to demonstrate the feasibility of sample preparation and analysis of Pu isotopes in African soils, considering the environmental and cost implications applicable to low-resource laboratories. The separation procedure builds upon previous work using TEVA columns, further demonstrating their usefulness for the reduction of uranium (U) interference in ICP-MS analysis with enhanced selectivity for Pu. Here several steps were optimised to enhance Pu recovery, reducing method blank concentration, and improving the separation efficiency through the determination of the elution profiles of U and Pu. The elimination of the complexing agent in the eluent, increased the spike recovery by improving matrix tolerance of the plasma, and simplified the separation procedure, improving throughput by 20%. The subsequent method was validated through the analysis of Certified Reference Material IAEA-384, where high accuracy and improved precision of measurement were demonstrated (measured value 114 ± 12 versus certified value 108 ± 13 Bq kg−1). Optimisation of the column separation, along with the analysis of the samples using O2 gas in ICP-MS/MS mode to mass shift Pu isotopes away from interfering molecular U ions provided a simple, robust, and cost-effective method with low achievable method detection limits of 0.18 pg kg−1 239+240Pu, applicable to the detection of ultra-trace fallout Pu in African soils

Suitability of 210Pbex, 137Cs and 239+240Pu as soil erosion tracers in western Kenya

Land degradation resulting from soil erosion is a global concern, with the greatest risk in developing countries where food and land resources can be limited. The use of fallout radionuclides (FRNs) is a proven method for determining short and medium-term rates of soil erosion, to help improve our understanding of soil erosion processes. There has been limited use of these methods in tropical Africa due to the analytical challenges associated with 137Cs, where inventories are an order of magnitude lower than in the Europe. This research aimed to demonstrate the usability of 239+240Pu as a soil erosion tracer in western Kenya compared to conventional isotopes 210Pbex and 137Cs through the determination of FRN depth profiles at reference sites. Across six reference sites 239+240Pu showed the greatest potential, with the lowest coefficient of variation and the greatest peak-to-detection limit ratio of 640 compared to 5 and 1 for 210Pbex and 137Cs respectively. Additionally, 239+240Pu was the only radionuclide to meet the ‘allowable error’ threshold, demonstrating applicability to large scale studies in Western Kenya where the selection of suitable reference sites presents a significant challenge. The depth profile of 239+240Pu followed a polynomial function, with the maximum areal activities found between depths 3 and 12 cm, where thereafter areal activities decreased exponentially. As a result, 239+240Pu is presented as a robust tracer to evaluate soil erosion patterns and amounts in western Kenya, providing a powerful tool to inform and validate mitigation strategies with improved understanding of land degradation