Rakib, Sakhawat Hossen · Rakib, Sakhawat Hossen · Imtiaz Bin Hamid, Nafiz · et al.

1.7 MB

This dataset contains 5,625 finite element simulation records generated using COMSOL Multiphysics 6.3 (RF Module, Electromagnetic Waves Frequency Domain interface) for a modified microstrip patch antenna (MPA) sensor designed for non-invasive electrolyte monitoring from sweat at 2.4 GHz. The sensor incorporates a circular analyte well and 45-degree chamfered stubs to enhance dielectric sensitivity. Simulations were conducted across three substrate materials (Rogers RO4003C, FR4, and Cellulose Paper/Whatman Grade 3) and five physiologically relevant NaCl concentrations (20, 40, 60, 80, and 100 mmol/L). Each record corresponds to a unique combination of five geometric and electrolyte input parameters: patch width (WP), patch length (LP), analyte well diameter (d_well), feed inset offset (xf), and NaCl concentration (c), sampled on a systematic grid of 5 x 5 x 5 x 3 levels per substrate and 5 concentration levels, yielding 1,875 records per substrate. The two output (target) variables per record are the minimum reflection coefficient (S11_min, in dB) and the resonant frequency at the S11 minimum (fr, in GHz), both extracted from the Asymptotic Waveform Evaluation (AWE) frequency sweep. The permittivity of the NaCl analyte complex at each concentration is modeled using the Cole-Cole dispersion parameters established by Peyman, Gabriel, and Grant (Bioelectromagnetics, 2007). The dataset was used to train and validate three machine learning regression models — Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) — for surrogate-based optimization of sensor dimensions. XGBoost achieved the lowest prediction error: S11 RMSE of 0.81-1.14 dB and concentration RMSE of 1.31-1.82 mmol/L across the three substrates. The dataset is provided as a structured Excel workbook (XLSX) containing the full 5,625-record dataset, per-substrate subsets, summary statistics, a stratified 80/20 train-test split, XGBoost feature importance results, and model performance comparisons. The research targets clinical patient populations where continuous sweat electrolyte monitoring is medically significant, including palmar hyperhidrosis, chronic kidney disease (hemodialysis patients), motor paralysis, and congestive heart failure.

M.G. Daoud

This dataset contains the forcing files required to run the STEMMUS-SCOPE-MODFLOW 6 model in three Dutch sites (Loobos, Cabauw and Veenkampen). The dataset also contains the input and output files that are written within the models, in addition to the in-situ measurements, used to validate the model's simulations. Soil-plant-atmosphere continuum (SPAC) models are commonly used to investigate various components’ role(s) in ecosystem functioning. Yet, in most SPAC models, groundwater is ignored or at best represented in an over-simplified manner, leading to misunderstanding of its critical role in simulating soil-vegetation dynamics. This study investigates the groundwater’s role in soil-plant-atmosphere processes. To this end, an integrated ecohydrological modelling (IEM) framework is developed by coupling the STEMMUS-SCOPE SPAC model to the MODFLOW 6 integrated hydrological model. The standalone STEMMUS-SCOPE (ST-SC) and coupled STEMMUS-SCOPE-MODFLOW 6 (ST-SC-MF6) models were applied over an 8-year period (1 April 2016 – 31 March 2024) to three sites in the Netherlands (Loobos, Cabauw and Veenkampen). Simulated various essential variables, including soil moisture (θ), soil temperature (Ts), groundwater level, groundwater temperature, evapotranspiration (ET), gross primary productivity, net ecosystem exchange (NEE), and sun-induced chlorophyll fluorescence (SIF) were then compared to corresponding in-situ observations to evaluate the ST-SC and ST-SC-MF6 setups. Results indicated that the groundwater contribution is spatially and temporally variable. ST-SC-MF6 showed better agreement with observations than ST-SC for: a) Ts, and ET at Loobos, b) θ, ET, NEE, and SIF at Cabauw, and c) θ, and ET at Veenkampen. Notably, benefits of ST-SC-MF6 simulation were particularly prominent during dry periods, when shallow groundwater mitigated vegetative water stress. Overall, the proposed ST-SC-MF6 IEM helped to: (1) incorporate groundwater as a key component in the water, energy and carbon cycles, and (2) define the important role groundwater dynamics play in soil-plant-atmosphere continuum for deepening our understanding of ecosystem functioning.

Ye, Zhengyang · Aitken, Sally · Rieseberg, Loren · et al.

Abstract Understanding how tree species adapt to climate is crucial for forest management under climate change. This study employs landscape genomics to investigate climate adaptation in the interior spruce complex (Picea glauca, P. engelmannii, and their hybrids) across western Canada. Using gradient forest modeling with 41,253 SNPs genotyped in 1692 natural interior spruce individuals from 252 populations, we identified winter temperature and moisture-related variables as key drivers of genomic variation. Both adaptive and neutral genetic variation showed similar patterns along climatic gradients, suggesting that population structure largely follows environmental clines. We delineated 11 seed zones based on genomic variation and climate relationships, achieving 88.2% concordance with previous genetic studies in defining species boundaries. Analysis of genetic offsets under future climate scenarios revealed potential risks of maladaptation, particularly in northern and eastern British Columbia. These predictions were validated using fitness-related measurements from common-garden experiments, which showed negative correlations between genetic offsets and traits such as height and diameter at breast height (DBH). Maladaptation predictions based on genetic offsets showed some differences compared with common-garden-based assessments in several regions, offering new perspectives on population vulnerability. Our results demonstrate the effectiveness of landscape genomics as a complementary approach to traditional methods for assessing climate adaptation in tree species. Our findings provide a scientific foundation for climate-smart reforestation strategies and offer practical guidance for forest management in the context of climate change.

Loureiro, Alexandre M. M. C. · Hallwachs, Winnie · Janzen, Daniel H. · et al.

These are the data, phylogenetic trees, and code for our analysis of the change in thermal tolerance with elevation for the Staphylinidae community on Volcan Cacao, in Área de Conservación Guanacaste, Costa Rica. We set out to determine how heat and cold tolerance, as well as thermal tolerance breadth (via a statistical proxy) changed with elevation for this beetle community, and to contextualize the heat tolerance values against environmental temperatures and against other insect heat tolerance data from one of the most popular thermal tolerance databases in the literature. We found that heat tolerance and thermal breadth decreased with elevation while cold tolerances slightly increased, all of which were according to theory. These results (particularly heat tolerances), however, went against the patterns previously described for ectotherms using this popular thermal tolerance database. This is a replication dataset for the second chapter of my PhD thesis. The chapter is under review for publication, so this dataset will be associated as a supplementary material for the paper. Here you will find thermal tolerance data for Costa Rican beetles, and all associated supporting data files, code, and prose that make up the manuscript.

Riffe, William · Zare, Saman · Caratenuto, Andrew · et al.

This data repository contains crystal structure data in the form of crystallographic information files (CIFs), data from density functional theory calculations, and data from running the Phonopy code. The material of interest is the Yb2Si2O7. Using the data given in this repository, the phonon dispersion curves, phonon density of states, and phonon dielectric functions can be reproduced.

Arrowood, Anthony · Frazier, Jackson · Ciccotti, Matteo · et al.

Many soft, tough materials have emerged in recent years, paving the way for advances in wearable electronics, soft robotics, and flexible displays. However, understanding their interfacial fracture behavior remains a significant challenge, owing to the difficulty of quantifying the respective contributions from viscoelasticity and damage to energy dissipation ahead of cracks. This work aims to address this challenge by labeling a series of polymer networks with fluorogenic mechanophores, subjecting them to T-peel tests at various rates and temperatures, and quantifying their force-induced damage using a confocal microscope. The results challenge longstanding assumptions underlying linear viscoelastic fracture theories, revealing a complex interplay between viscoelasticity and damage governed by the Weissenberg number, Wi. Specifically, they suggest a molecular picture in which the interfacial toughness increases due to polymer chain breakage and enlarged strains when Wi 0.3, with the damage being negligible in the limits of Wi > 0.3 either due to insufficient strains at the peel front or because of excessive stress at the weak interfacial bonds. Overall, these results illustrate the molecular and mesoscopic mechanisms underpinning interfacial fracture, aiding to refine current viscoelastic fracture theories and accelerating the development of advanced polymer networks for increasingly demanding applications.

Piotr Szychowiak · Jonathan Wong-So · Hélène Messet · et al.

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Developing objective structured clinical examination (OSCE) stations is time-consuming for medical teachers. We aimed to evaluate the ability of a large language model (LLM) to generate ready-to-use OSCE stations. Five OSCE stations generated by the LLM GPT-4o were evaluated by 7 expert assessors using a 5-point Likert scale and compared with 5 teacher-written stations targeting similar learning objectives. A station was considered to be of good quality if most assessors responded “agree” or “strongly agree” to the statement “The station is good enough to be used by students.” All teacher-written stations were rated as being of good quality, compared with only one GPT-4o-generated station. The LLM produced adequate clinical scenarios when reference knowledge was provided and tasks were clearly ordered, but it failed to generate reliable assessment grids. Careful review by teachers remained essential. GPT-4o failed to consistently produce fully ready-to-use OSCE stations.

Carroll, Anne

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6 numeric · 4 text

Data used for analysis in Pools of rain and risk: How forecast skill affects enrollment in rainfall-indexed insurance.

Kooy, Michael

Stemphylium leaf blight (SLB), caused by Stemphylium vesicarium (Wallr.) E.G. Simmons, is an important foliar disease of onion in Ontario. Field trials were conducted in 2021 and 2022 at the Ontario Crops Research Centre in Bradford, Ontario (Holland Marsh) to evaluate disease forecasting programs for their ability to reduce fungicide applications while maintaining effective disease management. Trials were arranged in a randomized complete block design with four replicates using yellow onion cv. Traverse, a variety susceptible to SLB. Forecasting programs were compared against a calendar-based spray program and untreated controls (with and without EverGol penflufen seed treatment). Disease severity was assessed throughout the growing season and at final destructive harvest on 16 August in both years. Yield was assessed on 15 September 2021 and 8 September 2022. Airborne spore data were collected using a Burkard 7-day volumetric spore sampler and a Rotorod spore sampler in both years. A controlled growth chamber study was conducted in 2024 to evaluate the effect of temperature and leaf wetness duration on infection of onion by S. vesicarium.