Reliability of cross-regional applications of global fire danger models: a Peruvian case study

Background: Fire danger indexes (FDIs) are used as proxies for fire potential and are often developed for specific locations. For practical purposes, the extrapolation of the underlying calculations into novel locations is common, but it is generally uncertain if the relationships between FDIs and fire potential observed in the environment in which the index was developed are equally relevant in others. For example, although a topographically, ecologically, and climatologically complex country, f ire danger forecasts in Peru use a standard set of nationwide thresholds applied to the Fire Weather Index. In this study, we validate the underlying assumption that weather-fire relationships are spatially uniform within Peru by (1) making cross-regional comparisons of the statistical distributions of four FDIs—Burning Index, Energy Release Component, Fire Weather Index, and Keetch-Byram Drought Index, and (2) making cross-regional comparisons of the expected daily MODIS hotspot count percentiles conditioned on FDI values. Results: Significant regional differences in the distributions of daily FDI values were observed in every pair of regions within Peru, and with the exception of a pair of regions within the Amazon, little data (< 90 days) were necessary to detect these differences. After controlling for FDI values and seasonal and annual effects with regressions, differences in predicted hotspot percentiles were common, differing by as much as 47 percentage points. Across the pairs of regions, the magnitude of these differences tended to decrease as climatic similarity increased, but some counterexamples were also apparent. Conclusions: The noticeable differences in the distributions of daily FDI values suggest that a standard set of breakpoints may produce unreliable inferences regarding fire potential. We also find that even if the climatic conditions were similar across Peru, the same FDI values in two locations can produce substantially differing predictions of wildfire activity. This suggests that other factors besides FDI values can strongly mediate wildfire activity and that better fire potential predictions could be produced if these factors are accounted for

Climate models predict a divergent future for the medicinal tree Boswellia serrata Roxb. in India

Predicting the distribution of future climatically suitable habitat areas is crucial for the long-term success of species conservation and management plans. However, generating accurate predictions may be difficult as the assumptions and variables used in the construction of different climate scenarios may result in divergent trajectories of change. Nevertheless, generating species distribution models under multiple scenarios is helpful in selecting an optimal solution for practical applications. In this study, we compare the current distribution of climatically suitable areas of a threatened medicinally important tree, Boswellia serrata Roxb. in India with its distribution in the year 2050 modeled using two climate change scenarios - IPSL-CM5A-LR and NIMR-HADGEM2-AO - each represented by four representative concentration pathways (RCPs). Maximum entropy modeling with 19 bioclimatic variables was used to construct the climatic niche of B. serrata for predictions of present and future climatically suitable areas within India. The study revealed that annual mean temperature, mean temperature of wettest quarter and driest quarter, precipitation seasonality, and precipitation of wettest quarter potentially influence the distribution of the species. After thresholding, the model showed that ∼21.95% of the geographical area in India is presently climatically suitable for the species. The IPSL-CM5A-LR and NIMR-HADGEM2-AO climate models revealed contrasting distribution scenarios of climatically suitable areas in India. However, irrespective of these climate models, the four RCPs predict a consistent decrease in suitable area with increases in climatic harshness. Substantial area in peninsular India is expected to lose climatic suitability in 2050, though new areas are also predicted to become climatically suitable. We suggest long-term conservation strategies for B. serrata be prioritized within future areas that are projected to retain climatic suitability

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Evolution of Texture during Hot Rolling of a New Magnesium Alloy

High-strength wrought magnesium alloys are one of the sought-after materials in the automotive sector owing to the demands for weight reduction in the automobiles due to fuel economy and CO2 emission. However, because of low room temperature strength and formability of Mg alloys, only a few applications in wrought form have been explored with these materials. In the present investigation, a high strength, good ductility and low cost wrought magnesium alloy with Mg-Sn-Zn composition have been developed and subjected to conventional wrought processing. Hot rolling was carried out at 350 degrees C without homogenization and after homogenization at 300 degrees C and 330 degrees C. The phase stability, microstructure and texture of the alloy has been investigated for as-cast, homogenized and hot rolled conditions. The compositional and microstructural characterization was carried out by Electron Probe Micro-analysis (EPMA) and optical microscopy respectively. Texture evolution was investigated by X-ray diffraction method. A strong (0002) basal texture develops after hot rolling without homogenization. The (0002) basal texture has been weekend by splitting of poles and double peak distribution when hot rolling was carried out after homogenization

Comparative Study on the Effect of Recesses on Conical Hybrid Journal Bearing Compensated with CFV under Micropolar Fluid Lubrication

This paper theoretically presents the comparative performances of conical hybrid journal bearing with 4-pockets and 6-pockets compensated with constant flow valve (CFV) under micropolar fluid lubrication for various semi-cone angles. The numerical solution of the modified Reynolds equation for micropolar fluid flow on the conical surface has been done by using finite element method using Galerkin’s technique along with the necessary boundary conditions and iterative scheme. The bearing performances have been presented for two different semi-cone angles, radial load and the restrictor design parameter. It is observed that bearing performances improve for 6-pockets CFV than 4-pockets. The bearing performances also increase with increase in semi-cone angle, constant flow valve restrictor and micropolar parameters significantly. With increase in semi-cone angle of bearing the direct stiffness, damping coefficients and the stability have been observed to increase under micropolar lubrication for 6-pockets CFV restrictors

Effect of forest fire on tree diversity and regeneration potential in a tropical dry deciduous forest of Mudumalai Tiger Reserve, Western Ghats, India

Abstract Introduction The study was conducted in Mudumalai Tiger Reserve, in the Western Ghats to understand the effect of a single fire event on tree diversity and regeneration status. Four forest patches were selected which were unburned, 2-year-old burn, 5-year-old burn, and 15-year-old burn. Three 0.1 ha square plots were laid randomly in all four patches and analyzed for tree diversity, stand structure, and regeneration of tree species. Results A total of 4129 individuals of tree species were recorded in field surveys, comprising 3474 seedlings, 121 saplings, and 534 trees. Totally, 40 tree species were recorded in study plots, from which 28 species were seedlings, 16 species were saplings, and 37 species were at tree stages. Conclusions Tree diversity decreased in 2-year-old and 5-year-old burnt plots and was reached to the level of unburnt plots in 15 years of interval. Stems of small size classes started increasing after the fire. Seedling density increased linearly in subsequent years after fire but sapling and tree density recorded less than control in B2 but was higher in B5 and B15. The overall fire affected diversity, but regeneration showed a positive trend

MODIS-Derived Fire Characteristics and Greenhouse Gas Emissions from Cropland Residue Burning in Central India

Cropland residue burning is one of the major causes of the emission of greenhouse gases and pollutants into the atmosphere, and is a major global environmental problem. This study analyzes the spatiotemporal changes in greenhouse gas emissions from cropland residue burning in Chhattisgarh, India. The Moderate Resolution Imaging Spectroradiometer (MODIS) active fire data was analyzed over a 21-year (2001–2021) period, and associated greenhouse gas emissions were estimated. A total of 64,370 fire points were recorded for all land cover types. The number of cropland fires increased from 49 to 1368 between 2001 and 2021, with a burning peak observed between December and March. Fires in cropland areas contributed to 32.4% (19,878) of the total fire counts in the last 21 years. The total estimated emissions of greenhouse gases between 2001 and 2021 ranged from 421.5 to 37,233 Gg, with an annual rate of emission of 8972 Gg from wheat residue burning, and from 435.45 to 64,108.1 Gg, with an annual emission of 15,448.16 Gg from rice residue burning. The Chhattisgarh plain region was the cropland fire hotspot of the state. The present study indicates increased cropland residue-burning activity in Chhattisgarh. Therefore, there is an immediate need to develop sustainable alternative methods for agricultural residue management and eco-friendly methods for the disposal of crop residues