Dataset and analysis of automated and manual methods to differentiate wide QRS complex tachycardiasDataverse by Sarah LoCoco, Anthony H. Kashou, Abhishek J. Deshmukh, Samuel J. Asirvatham, Christopher V. DeSimone, Krasimira M. Mikhova, Sandeep S. Sodhi, Phillip S. Cuculich, Rugheed Ghadban, Daniel H. Cooper, Thomas M. Maddox, Peter A. Noseworthy, Adam M. May

“…Brugada, Vereckei aVR, and VT Score) to novel automated wide QRS complex tachycardia differentiation algorithms (i.e. WCT Formula I, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). …”
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Identifying unrecognised risks to life from debris flows by M. Bloomberg, T. Davies, E. Moltchanova, T. Robinson, D. Palmer

“…We show that given these credible intervals and precautionary but realistic assumptions about debris-flow behaviour and the vulnerability of dwellings and their occupants, catchments with no history of debris-flow activity can pose an unrecognised and unacceptable annual risk to life (<span class="inline-formula"><i>P</i>=0.256</span> that the annual risk-to-life threshold of 1 in 1000 is exceeded).…”
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A predefined-time radial basis function (RBF) neural network tracking control method considering actuator faults for a new type of spraying robot by J. Zhao, J. Zhao, Y. Li, Y. Li, Y. Li, B. Pei, Z. Yu, Z. Dong

“…Firstly, the dynamics equations of the <span class="inline-formula"><i>n</i></span>-joint manipulator are derived using the Euler–Lagrange equation. …”
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CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India by N. K. Mangukiya, K. B. Kumar, P. Dey, S. Sharma, V. Bejagam, P. P. Mujumdar, P. P. Mujumdar, A. Sharma, A. Sharma

“…Notably, CAMELS-IND includes available observed streamflow and catchment mean time series of 19 meteorological forcings, including precipitation, maximum, minimum, average temperature, long-wave and short-wave radiation flux, <span class="inline-formula"><i>U</i></span> and <span class="inline-formula"><i>V</i></span> components of wind, relative humidity, evaporation rates from canopy and soil surface, actual and potential evapotranspiration, and soil moisture of four layers (covering depth up to 3 m below ground) for detailed hydrometeorological studies. …”
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Status and influential factors of soil nutrients and acidification in Chinese tea plantations: a meta-analysis by D. Wang, D. Wang, D. Wang, B. Liu, B. Liu, B. Liu, F. Li, F. Li, F. Li, Z. Wang, Z. Wang, Z. Wang, J. Hou, J. Hou, J. Hou, R. Cao, R. Cao, R. Cao, Y. Zheng, Y. Zheng, Y. Zheng, W. Yang, W. Yang, W. Yang

“…Specifically, tea plantations in the southern zone showed the lowest concentrations of available N, available K, and total K but the highest stoichiometric ratios of soil nutrients (<span class="inline-formula"><i>P</i></span> <span class="inline-formula">&lt;</span> 0.05). …”
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An Arctic sea ice concentration data record on a 6.25&thinsp;km polar stereographic grid from 3 years of Landsat-8 imagery by H.-S. Jung, S.-M. Lee, S.-M. Lee, J.-H. Kim, K. Lee

“…In order to collect relatively bright and clear scenes, thresholds of solar elevation <span class="inline-formula"><i>&gt;</i>15</span>° and cloud cover <span class="inline-formula"><i>&lt;</i>10</span> % were applied in this study. …”
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Ultra-high-resolution Mass Spectra of Dissolved Organic Matter in the Absorption Mode by Rong QIN, Lun YANG, Qing-long FU

“…The absorption mode identifies fewer peaks but has more assigned formulas (1.21-1.39 folds). Moreover, the absorption mode is mainly favorable to the number of identified peaks for DBPs but has limited effects on decreasing the peak intensity deviation of DBPs peaks due to their low intensity at the environmental levels of chlorination reagents. …”
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Long-term variations in pH in coastal waters along the Korean Peninsula by Y.-W. Lee, M.-O. Park, S.-G. Kim, T.-H. Kim, Y. H. Oh, S. H. Lee, D. Joung, D. Joung

“…Analysis of environmental data including temperature, salinity, chlorophyll <span class="inline-formula"><i>a</i></span>, and dissolved oxygen (DO) revealed a strong correlation between pH and DO. …”
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EARice10: a 10&thinsp;m resolution annual rice distribution map of East Asia for 2023 by M. Song, M. Song, M. Song, L. Xu, L. Xu, J. Ge, J. Ge, J. Ge, H. Zhang, H. Zhang, H. Zhang, L. Zuo, L. Zuo, J. Jiang, J. Jiang, J. Jiang, Y. Ding, Y. Ding, Y. Ding, Y. Xie, Y. Xie, Y. Xie, F. Wu, F. Wu

“…The reliability of the product was verified by <span class="inline-formula"><i>R</i>²</span> values ranging between 0.94 and 0.98 with respect to official statistics and between 0.79 and 0.98 with respect to previous rice mapping products. …”
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Reactive nitrogen in and around the northeastern and mid-Atlantic US: sources, sinks, and connections with ozone by M. Huang, M. Huang, G. R. Carmichael, K. W. Bowman, I. De Smedt, A. Colliander, M. H. Cosh, S. V. Kumar, A. B. Guenther, S. J. Janz, R. M. Stauffer, A. M. Thompson, N. M. Fedkin, R. J. Swap, J. D. Bolten, A. T. Joseph

“…Model-based surface O<span class="inline-formula">₃</span>–NO<span class="inline-formula">₂</span> column correlation, which shows a dependency on column formaldehyde <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M9" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="880d1b22cfae9b4167ff115d05c6894c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-1449-2025-ie00001.svg" width="8pt" height="14pt" src="acp-25-1449-2025-ie00001.png"/></svg:svg></span></span> NO<span class="inline-formula">₂</span>, is higher in 2020 (<span class="inline-formula"><i>r</i>=0.62</span>) than in other years (<span class="inline-formula"><i>r</i>=0.47</span>–0.56). …”
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Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…Twenty-eight effective features were preferred, based on which the SVR model had <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula>, <i>RMSEP</i> and <i>RPD</i> scores of 0.9533, 0.4186, and 4.4186, respectively; the pH prediction performance was substantially improved. …”
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A hyperspectral and multi-angular synthetic dataset for algorithm development in waters of varying trophic levels and optical complexity by J. Pitarch, V. E. Brando, V. E. Brando

“…Derived AOPs encompass an array of above- and below-surface reflectances, diffuse attenuation coefficients, average cosines, and <span class="inline-formula"><i>Q</i></span> factor. The dataset is distributed in 5000 files, each encapsulating a specific IOP scenario, ensuring sufficient data volume for each represented water type. …”
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Regularizing Effects for a Singular Elliptic Problem by Ida de Bonis, Maria Michaela Porzio

“…Worth notice is that this result fails if <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>b</mi><mo>≡</mo><mn>0</mn></mrow></semantics></math></inline-formula>, i.e., in absence of the lower order term. …”
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Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach by T. H. Malik, C. Bak

“…Refined simulations using further virtual sensors quantified the effect size of these sensors' output under different turbulence levels and blade states, employing Cohen's <span class="inline-formula"><i>d</i></span> – a dimensionless metric measuring the standardised difference between two means. …”
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Electron-spin decoherence in trityl radicals in the absence and presence of microwave irradiation by G. Jeschke, N. Wili, N. Wili, Y. Wu, S. Kuzin, H. Karas, H. Hintz, A. Godt

“…Cluster correlation expansion (CCE) becomes borderline numerically unstable already at order 3 at times comparable to the decoherence time <span class="inline-formula"><i>T</i>_2<i>ρ</i></span> and cannot be applied at order 4. …”
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External particle mixing influences hygroscopicity in a sub-urban area by S. Deshmukh, L. Poulain, B. Wehner, S. Henning, J.-E. Petit, P. Fombelle, P. Fombelle, O. Favez, H. Herrmann, M. Pöhlker, M. Pöhlker

“…The prevalence of the hygroscopic mode increased with particle size, with mean hygroscopicity (<span class="inline-formula"><i>κ</i></span>) values of 0.23 and 0.38 for 100 and 200 nm particles, respectively. …”
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Automatic detection of floating instream large wood in videos using deep learning by J. Aarnink, J. Aarnink, T. Beucler, T. Beucler, M. Vuaridel, V. Ruiz-Villanueva, V. Ruiz-Villanueva

“…We analysed the contribution of each scenario to the effectiveness of the model using <span class="inline-formula"><i>k</i></span>-fold cross-validation. The mean average precision of the model varies between 35 % and 93 % and is influenced by the quality of the data that the model detects. …”
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Field heterogeneity of soil texture controls leaf water potential spatial distribution predicted from UAS-based vegetation indices in non-irrigated vineyards by L. Delval, J. Bates, J. Bates, F. Jonard, F. Jonard, M. Javaux, M. Javaux

“…Our results demonstrated that in non-irrigated vineyards, the spatial distribution of <span class="inline-formula">Ψ_leaf</span> was mainly governed by the within-vineyard soil hydraulic conductivity heterogeneity (<span class="inline-formula"><i>R</i>²</span> up to 0.81) and was particularly marked when the evaporative demand and the soil water deficit increased, since the range of <span class="inline-formula">Ψ_leaf</span> was greater, up to 0.73 MPa, in these conditions. …”
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Multiple eco-regions contribute to the seasonal cycle of Antarctic aerosol size distributions by J. Brean, D. C. S. Beddows, E. Asmi, A. Virkkula, A. Virkkula, L. L. J. Quéléver, M. Sipilä, F. Van Den Heuvel, T. Lachlan-Cope, A. Jones, M. Frey, A. Lupi, J. Park, Y. J. Yoon, R. Weller, G. L. Marincovich, G. L. Marincovich, G. C. Mulena, G. C. Mulena, R. M. Harrison, R. M. Harrison, M. Dall'Osto

“…We utilise <span class="inline-formula"><i>k</i></span>-means cluster analysis to separate the PNSD data into six main categories. …”
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Spatial identification of regions exposed to multi-hazards at the pan-European level by T.-E. Antofie, T.-E. Antofie, S. Luoni, A. Tilloy, A. Sibilia, S. Salari, G. Eklund, D. Rodomonti, C. Bountzouklis, C. Corbane

“…We further validate our results by comparing them with empirical data on fatalities and disaster events, revealing a relatively high correlation between statistically significant multi-hazard hotspots and fatalities (<span class="inline-formula"><i>r</i>=0.59</span>). By providing a detailed assessment of multi-hazard exposure at the pan-European scale, this study contributes to a better integration of multi-hazard risks in European disaster risk management plans.…”
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