Self-stabilizing Numerical Iterative Computation

Many challenging tasks in sensor networks, including sensor calibration, ranking of nodes, monitoring, event region detection, collaborative filtering, collaborative signal processing, {\em etc.}, can be formulated as a problem of solving a linear system of equations. Several recent works propose different distributed algorithms for solving these problems, usually by using linear iterative numerical methods. In this work, we extend the settings of the above approaches, by adding another dimension to the problem. Specifically, we are interested in {\em self-stabilizing} algorithms, that continuously run and converge to a solution from any initial state. This aspect of the problem is highly important due to the dynamic nature of the network and the frequent changes in the measured environment. In this paper, we link together algorithms from two different domains. On the one hand, we use the rich linear algebra literature of linear iterative methods for solving systems of linear equations, which are naturally distributed with rapid convergence properties. On the other hand, we are interested in self-stabilizing algorithms, where the input to the computation is constantly changing, and we would like the algorithms to converge from any initial state. We propose a simple novel method called \syncAlg as a self-stabilizing variant of the linear iterative methods. We prove that under mild conditions the self-stabilizing algorithm converges to a desired result. We further extend these results to handle the asynchronous case. As a case study, we discuss the sensor calibration problem and provide simulation results to support the applicability of our approach

Arresting gully formation in the Ethiopian highlands

Over the past five decades, gullying has been widespread and has become more severe in the Ethiopian highlands. Only in very few cases, rehabilitation of gullies has been successful in Ethiopia due to the high costs. The objective of this paper is to introduce cost effective measures to arrest gully formation. The research was conducted in the Debre-Mewi watershed located at 30 km south of Bahir Dar, Ethiopia. Gullying started in the 1980s following the clearance of indigenous vegetation and intensive agricultural cultivation, leading to an increase of surface and subsurface runoff from the hillside to the valley bottoms. Gully erosion rates were 10–20 times the measured upland soil losses. Water levels, measured with piezometers, showed that in the actively eroding sections, the water table was in general above the gully bottom and below it in the stabilized sections. In order to develop effective gully stabilizing measures, we tested and then applied the BSTEM and CONCEPT models for their applicability for Ethiopian conditions where active gully formation has been occurring. We found that the model predicted the location of slips and slumps well with the observed groundwater depth and vegetation characteristics. The validated models indicated that any gully rehabilitation project should first stabilize the head cuts. This can be achieved by regrading these head cuts to slope of 40 degrees and armoring it with rock. Head cuts will otherwise move uphill in time and destroy any improvements. To stabilize side walls in areas with seeps, grass will be effective in shallow gullies, while deeper gullies require reshaping of the gullies walls, then planting the gully with grasses, eucalyptus or fruit trees that can be used for income generation. Only then there is an incentive for local farmers to maintain the structures

Standards for associations and alliances of the U.S. National Vegetation Classification

This article provides guidelines for the description, documentation, and review of proposals for new or revised plant associations and alliances to be recognized as units of vegetation within the U.S. National Vegetation Classification (NVC). By setting forth standards for field records, analysis, description, peer review, and archiving, the Ecological Society of America's Vegetation Classification Panel, in collaboration with the U.S. Federal Geographic Data Committee, NatureServe, and others, seeks to advance our common understanding of vegetation and improve our capability to sustain and restore natural systems. We provide definitions for the two floristic levels of the NVC hierarchy: associations and alliances. This is followed by a description of standards for field plot records and the identification and classification of vegetation types. Procedures for review and evaluation of proposed additions and revisions of types are provided, as is a structure for data archiving and dissemination. These procedures provide a dynamic and practical way to publish new or revised descriptions of vegetation types while maintaining a current, authoritative list of types for multiple users to access and apply

Changes in waterbird occurrence and abundance at their northern range boundaries in response to climate warming: importance of site area and protection status

Funding: Research was funded through the TCSMT (grant EG), Kone Foundation (grant LJ, 202103360), the 2017-2018 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND program (Academy of Finland [University of Turku: 326327, University of Helsinki: 326338], Swedish Research Council [Swedish University of Agricultural Sciences: 2018-02440, Lund University: 2018-02441], Research Council of Norway [Norwegian Institute for Nature Research, 295767], National Science Foundation [Cornell University, ICER-1927646] and through Biodiversa+, under the 2021–2022 BiodivProtect Program (Ministry of Environment of Finland [VN/7162/2023], Swedish Research Council [Swedish Univ. Agric. Sci.: 2022-01752], Research Council of Norway [Norwegian Instit. for Nature Res.: 3000593], Innovation Fund Denmark [Aarhus Univ.: 1159-00033B], Swiss National Science Foundation [Swiss Ornith. Intit.: 20BD21_209665], and Ministerio de Ciencia e Innovacion; Agencia Estatal de Investigacion [Ecol. Fores. Appl. Res. Centr.: PCI2022-135056-2]).Climate warming is driving changes in species distribution, but habitat characteristics can interact with warming temperatures to affect populations in unexpected ways. We investigated wintering waterbird responses to climate warming depending on habitat characteristics, with a focus on the northern boundary of their non‐breeding distributions where winter climatic conditions are more extreme. At these Nordic latitudes, climate warming is expected to drive positive changes in species occurrence and abundance, with likely differences in species‐specific responses. We analyzed the occurrence and abundance of 18 species of waterbirds monitored over 2,982 surveys at 245 inland wetlands over a 25‐year period in Sweden. We used hierarchical modeling of species communities (HMSC) which enabled us to relate species‐specific changes to both functional traits and phylogenetic relatedness. We investigated occurrence and abundance changes in response to average temperature, temperature anomalies, site area, site protection status (Natura 2000), and land use in agricultural and urban surfaces. Unsurprisingly, both average temperatures and temperature anomalies were the most important variables influencing positively waterbird occurrence and abundance. For 60% of the species, the effect of temperature anomalies was even stronger in large or protected wetlands. Geese and mallard occurred more often at sites surrounded by agricultural and urban surfaces, respectively, but their occurrence in these habitats was not affected by interactive effects with climate warming. Species abundance was greater inside protected areas only for 11% of the species, but occurrence probability was higher inside protected areas for 44% of the species. Overall, we observed that species thermal affinity was a strong predictor for positive species response to temperature anomalies, and that species sharing similar phylogenetic history had similar relationships with environmental variables. Protection of large wetlands and restoration of the surrounding habitats are two targets for climate change adaptation strategies to facilitate future responses of waterbirds to climate warming.Peer reviewe

Performance of prediction models for nephropathy in people with type 2 diabetes:systematic review and external validation study

OBJECTIVES To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. DESIGN Systematic review and external validation. DATA SOURCES PubMed and Embase. ELIGIBILITY CRITERIA Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. METHODS Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell's C statistic). RESULTS 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. CONCLUSIONS This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42020192831.Molecular Epidemiolog