Temporal Evolution Research Articles

Elucidating the Transport of Electrons and Molecules in a Solid Electrolyte Interphase Close to Battery Operation Potentials Using a Four-Electrode-Based Generator-Collector Setup.

In lithium-ion batteries, the solid electrolyte interphase (SEI) passivates the anode against reductive decomposition of the electrolyte but allows for electron transfer reactions between anode and redox shuttle molecules, which are added to the electrolyte as an internal overcharge protection. In order to elucidate the origin of these poorly understood passivation properties of the SEI with regard to different molecules, we used a four-electrode-based generator-collector setup to distinguish between electrolyte reduction current and the redox molecule (ferrocenium ion Fc+) reduction current at an SEI-covered glassy carbon electrode. The experiments were carried out in situ during potentiostatic SEI formation close to battery operation potentials. The measured generator and collector currents were used to calculate passivation factors of the SEI with regard to electrolyte reduction and with regard to Fc+ reduction. These passivation factors show huge differences in their absolute values and in their temporal evolution. By making simple assumptions about molecule transport, electron transport, and charge transfer reaction rates in the SEI, distinct passivation mechanisms are identified, strong indication is found for a transition during SEI growth from redox molecule reduction at the electrode | SEI interface to reduction at the SEI | electrolyte interface, and good estimates for the transport coefficients of both electrons and redox molecules are derived. The approach presented here is applicable to any type of electrochemical interphase and should thus also be of interest for interphase characterization in the fields of electrocatalysis and corrosion.

The Future of Neurodiagnosis: Deep Learning for Earlier Intervention

This study presents an innovative deep learning framework for improved early detection of a debilitating neurodegenerative condition marked by cognitive decline and memory impairment. Timely diagnosis is crucial for effective interventions and improved patient outcomes. Our framework integrates diverse data sources, including structural and functional neuroimaging (MRI and PET) alongside clinical information, to enhance detection precision. Convolutional Neural Networks (CNNs) analyze structural MRI scans, extracting subtle changes in brain structure indicative of early disease progression. Functional insights are gleaned from PET scans, contributing to increased sensitivity. Additionally, longitudinal data is incorporated through Recurrent Neural Networks (RNNs) to capture the disease's temporal evolution. Training on a diverse dataset utilizes transfer learning, optimizing performance even with limited labeled data. Rigorous validation consistently demonstrates the model's effectiveness, achieving a 92% accuracy rate.

Coexistence of BCR∷ABL1 translocation and JAK2V617F mutations in indian patients with myeloproliferative neoplasms: A case series.

BCR∷ABL1 translocation and JAK2V617F mutations are canonical variants of myeloproliferative neoplasms (MPNs). Traditionally considered mutually exclusive, they may rarely coexist. We report the clinicopathological profile and treatment outcomes of four MPN patients with coexistence of these disease-defining genetic variants. Both mutations were detected simultaneously in three patients who did not harbor tell-tale signs of CML and were evaluated for both BCR∷ABL1 and JAK2V617F based on clues from hemogram, peripheral-blood and bone-marrow examination. All were treated with imatinib and hydroxyurea and attained major molecular response after 2-7 months. In another patient, JAK2V617F was detected 15 years after the diagnosis of CML at the time of evaluation of loss of hematological and molecular response. She was treated with dasatinib but no hematologic or molecular response was attained after 6 months despite good compliance. In conclusion, BCR∷ABL1 and JAK2V617F may rarely coexist in MPN with variable temporal evolution, clinicopathological profile, and treatment response.

Evolution and Drivers of Production Patterns of Major Crops in Jilin Province, China

Exploring the evolution of the production patterns of major crops and their driving factors can provide new ideas to ensure the security of supply of important agricultural products. This study selected four major crop types in Jilin Province—grain, oil crop, vegetable, and fruit—as research subjects. Using the center of gravity migration model, spatial autocorrelation analysis, and comparative advantage index, the spatial and temporal evolution of these crops’ production patterns from 2000 to 2020 was examined. A geographically weighted regression model was employed to analyze driving factors. The results showed that the planting area of grain was the most extensive; the percentage of production of grain, oil crop, and fruit increased to different degrees; the unit area yield of the four crops increased. The centers of gravity of all four crops’ productions were located in the central-northwestern region of Jilin Province. The global Moran’s I was positive for all four crops’ productions. The comparative advantage characteristics of the four crops were closely related to the natural geographical environment. Grain, vegetable, and fruit production were significantly and positively correlated with fertilizer application, and the most significant factor affecting oil crop production was land input intensity.

Evolution and Drivers of Production Patterns of Major Crops in Jilin Province, China

Exploring the evolution of the production patterns of major crops and their driving factors can provide new ideas to ensure the security of supply of important agricultural products. This study selected four major crop types in Jilin Province—grain, oil crop, vegetable, and fruit—as research subjects. Using the center of gravity migration model, spatial autocorrelation analysis, and comparative advantage index, the spatial and temporal evolution of these crops’ production patterns from 2000 to 2020 was examined. A geographically weighted regression model was employed to analyze driving factors. The results showed that the planting area of grain was the most extensive; the percentage of production of grain, oil crop, and fruit increased to different degrees; the unit area yield of the four crops increased. The centers of gravity of all four crops’ productions were located in the central-northwestern region of Jilin Province. The global Moran’s I was positive for all four crops’ productions. The comparative advantage characteristics of the four crops were closely related to the natural geographical environment. Grain, vegetable, and fruit production were significantly and positively correlated with fertilizer application, and the most significant factor affecting oil crop production was land input intensity.

A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models.

Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.

Error estimation of Bernardi heat evaluation of Li-ion cells under drive cycle operation

Accurate estimation of heat generation is critical for EV batteries. Conventional means of heat estimation (i.e. Bernardi equation) works only for constant current discharge. However, fluctuating current condition is the normal mode of operation for cells in EV applications. The current study attempts a comprehensive investigation of heat generation and temperature evolution for a cylindrical lithium iron phosphate cell under standard drive cycles (FUDS, US06). An experimentally parameterized electrochemical-heat transfer is developed. Experimental voltage curve of FUDS drive cycle is used to obtain unknown electrochemical parameters while the fidelity is tested against US06. It is observed that Bernardi method overpredicts heat generation rate by approximately 25% under drive cycle operation. It is noted that Bernardi method is an indirect method of heat estimation based majorly on electrical parameters. It is also observed that the reversible part of Bernardi heat is negligible under drive cycle operation. Moreover, SOC and temperature evolution are accurately estimated by the electrochemical-heat transfer model – errors lie within 7% and 2° C, respectively. Thus, the present study demonstrates the capability of the electrochemical-heat transfer model for estimating heat generation rate of Li-ion cells under drive cycle operation where Bernardi equation is shown to be an inadequate method.

China's groundwater situation in 2011–2020: Dynamic evolution, Influencing Factors, and sustainable development

Groundwater plays important roles in resource security and ecological maintenance and is sensitive to changes in the natural environment and human activities. It is of paramount importance to investigate the dynamic evolution trend of groundwater and its affecting factors and sustainable development. This paper takes mainland China as the study area, using data sourced from the Groundwater Dynamic Monthly Report, the China Water Resources Bulletin, and the Provincial Water Resources Bulletin. The temporal and space-time evolution trend of groundwater depth in 2011–2020 is determined, along with the correlation between variations in groundwater resources and precipitation, the factors affecting these changes, and the sustainability of groundwater use. The results are as follows: (1) The northern and western regions of mainland China had a greater depth of groundwater compared to the southern and eastern regions. The largest groundwater depth is in the Northwest Rivers Basin (Nw RB), which can reach 17.61–21.10 m, and the shallowest groundwater depth is in the Southeast Rivers Basin (Se RB), only 1.61–5.19 m. (2) Regarding the factors affecting the changes in groundwater resources, precipitation, land use pattern, human activities, and industrial and agricultural water use are highlighted. (3) Overall, the percentage of groundwater in the total water supply has declined. The optimization of groundwater resource allocation and the adjustment of industrial structure have resulted in the coordinated utilization of groundwater resources. The research establishes a scientific foundation for ensuring national water security and promoting sustainable economic and social development.

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Assessing drought in Turkish basins through satellite observations

AbstractDrought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins.

Temporal evolution of permeability for porous rock during mineral dissolution and precipitation process based on fractal theory

Temporal evolution of permeability for porous rock during mineral dissolution and precipitation process based on fractal theory

Spatiotemporal evolution and driving factors of vegetation net ecosystem productivity in Henan Province over the past 20 years

Net ecosystem productivity (NEP) is a crucial parameter for assessing the carbon cycle dynamics in terrestrial ecosystems. This study analyzed the spatial and temporal evolution characteristics and future trends of NEP in Henan Province over the past 20 years based on MOD17A3HGF, meteorological, and land-use data, employing the frequency counting method, trend test, Hurst index, and the center of gravity model. Various areas of changes in vegetation carbon sequestration were explored, and the driving factors were quantitatively assessed through correlation analysis, Sankey diagrams, and Geodetector. The results demonstrate that: 1) Continuous temporal changes in NEP in Henan, with annual average values fluctuating between 272.84 and 451.39 gC·m-2·a1, exhibiting an overall upward trend. 2) Spatially, there is a distinct distribution of NEP, concentrating more in the south and less in the north. While the study area generally experiences a dominant gradual enhancement of vegetation carbon sequestration capacity, the middle and north of Zhengzhou City exhibit a significant decline, which is expected to persist in the future. The migration of the centers of gravity of NEP over the past 20 years is characterized by stage-specific differentiation. 3) Among the various land cover types, forests have the strongest carbon sequestration capacity; however, cropland emerges as the province’s main source of NEP due to its extensive size. 4) The driving factors for spatial differentiation in NEP exhibit some temporal variability. Overall, climate factors and atmospheric pollution exert stronger influences, with the interactive explanatory power of the two-factor interaction being higher than that of the single factor. The results of this study can serve as a scientific theoretical basis for ecological policy-making and sustainable development in Henan Province.

Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data

Abstract. Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air–pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements and the importance of such high-resolution data for new satellite retrievals, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea ice surface emerging from the increasingly available visual image data recorded from uncrewed aerial vehicles (UAVs) or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models.

Neotectonics of the Barents Sea shelf eastern part: seismicity, faults and impact of the Atlantic-Arctic Rift System

distribution with the fault network defined from seismic data, and kinematic characteristics of seismic activity spatial migration are obtained. It is shown that seismic events recorded by the Norwegian NORSAR regional network within the Russian part of the Barents Sea shelf are grouped into linear clusters along shear kinematics faults. The fault network displaces Mesozoic seismic complexes and reaches the bottom surface, displacing quaternary deposits, which clearly indicates the modern age of displacements along which linear clusters of weak seismicity are grouped. The calculation of the total seismic moment in the space-time dimension showed the presence of seismic activity migration along short fragments of faults on the shelf with velocities from 10.5 to 25.7 km/year. There has been a surge in general activity in the shelf area since 2012. A comparison of the temporal evolution of seismic activity on the shelf with fragments of the Atlantic-Arctic Rift system suggests that it is the effect of tectonic deformation waves triggered along the geodynamically active intraplate boundary and propagating to the shelf at a speed of 20–22 km/year. Migration rates with speeds up to 77 km/year are less likely. There is a possibility that the increase in the intensity of seismic activity on the shelf after 2012 is not an emission from the effects of a slow deformation wave, but the result of a direct trigger effect on the shelf from the structures of the Knipovich and Gakkel ridges.

Temporal evolution of suicide by levels of rurality and deprivation among Japanese adults aged 20years or over between 2009 and 2022.

Previous studies have reported that levels of rurality and deprivation are factors associated with suicide risk. Reports on the association between rurality, deprivation and suicide incidence during the COVID-19 pandemic are scarce. The study aims to investigate how suicide rates evolved in areas with different levels of rurality and deprivation among Japanese adults aged 20years or older between 2009 and 2022. This study used population density in 2020 as an indicator of rurality and per capita prefectural income in 2019 as a proxy for deprivation in Japan's 47 prefectures. Joinpoint regression analysis was performed to analyze secular trends in suicide rates by rurality and deprivation. Suicide rates for both men and women at different levels of rurality and deprivation remained roughly parallel during the research period. Suicide rates for men and women at all levels of rurality and deprivation were on a downward trend until around 2019, just before the onset of the pandemic. Following this, suicide rates in women showed a clear upward trend, while the trend in suicide rates for men also changed around 2019, with a slightly increasing or flat trend thereafter. Changes in suicide rates were greater among women and those aged 20-59years. In Japan, time trends in suicide rates for both men and women have changed before and after the pandemic, but levels of rurality and deprivation across the 47 prefectures do not appear to have contributed much to these changes.

Spatial–Temporal Evolution and Driving Factors of Industrial Land Marketization in Chengdu–Chongqing Economic Circle

Industrial land is essential for supply-side structural reforms, particularly in the Chengdu–Chongqing area, Western China’s most densely populated and industrially robust region. This area, a pivotal hub linking Southwest China with South Asia and Southeast Asia, is critical for the national strategic layout and regional economic restructuring. Despite its substantial industrial foundation as an old industrial base, internal developmental stagnation has led to an irrational industrial land use structure. This paper analyzed land transaction data from the China Land Market Network (2010–2021) using methods such as kernel density estimation, the standard deviation ellipse method, and Global Moran’s I index. The analysis focuses on the spatiotemporal evolution of industrial land marketization and its driving factors in 44 cities within the Chengdu–Chongqing economic circle. The findings aim to enhance the strategic implementation of national policies and regional economic optimization, suggesting intensified development efforts in key cities and promoting integrated growth in potential areas like Suining and Ziyang to foster a conducive environment for high-quality regional development.

Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine: A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels.

Generic modelling to develop thermal yield nomograms for coaxial deep borehole heat exchangers (DBHEs)

Numerical modelling of coaxial deep borehole heat exchangers (DBHEs) can be resource-intensive. Simpler, transparent analytical models and nomograms would be valuable to developers and geologists for evaluating thermal output. In this paper, Beier’s published analytical computational model was used to produce nomograms of geothermal heat yield by systematically varying the DBHE depth and rock thermal conductivity, while assuming two generic simplified DBHE designs, a geothermal gradient of 25°C km −1 and a fluid circulation rate of 5 l s −1 . Continuous 25 year heat yields from a 1000 m DBHE range from 27.3 to 54.8 kW for thermal conductivities of 1.6–3.6 W m −1 K −1 . For a 3000 m DBHE, they range from 165 to 281 kW. Effective borehole thermal resistance ( R b,eff ) increases strongly as DBHE depth increases due to internal heat transfer between the upflow and downflow elements. Simulations correspond well with results from industry-standard Earth Energy Designer software for a shallow 200 m coaxial BHE. They modestly underestimate the OpenGeoSys numerical modelled thermal yields by 2–4% for DBHE in the depth range 1000–3000 m. Modelled temperature evolution closely approximates an analytical ‘line heat source’ approach, implying that simpler analytical approaches are plausible for DBHE simulation. Future research should focus on methods for forward quantification of R b,eff .

Time-dependent Stellar Flare Models of Deep Atmospheric Heating

Optical flares have been observed from magnetically active stars for many decades; unsurprisingly, the spectra and temporal evolution are complicated. For example, the shortcomings of optically thin, static slab models have long been recognized when confronted with the observations. A less incorrect—but equally simple—phenomenological T ≈ 9000 K blackbody model has instead been widely adopted in the absence of realistic (i.e., observationally tested) time-dependent, atmospheric models that are readily available. We use the RADYN code to calculate a grid of 1D radiative-hydrodynamic stellar flare models that are driven by short pulses of electron-beam heating. The flare heating rates in the low atmosphere vary over many orders of magnitude in the grid, and we show that the models with high-energy electron beams compare well to the global trends in flux ratios from impulsive-phase stellar flare, optical spectra. The models also match detailed spectral line-shape properties. We find that the pressure broadening and optical depths account for the broad components of the hydrogen Balmer γ lines in a powerful flare with echelle spectra. The self-consistent formation of the wings and nearby continuum level provides insight into how high-energy electron-beam heating evolves from the impulsive to the gradual decay phase in white-light stellar flares. The grid is publicly available, and we discuss possible applications.

Time-dependent Stellar Flare Models of Deep Atmospheric Heating

Optical flares have been observed from magnetically active stars for many decades; unsurprisingly, the spectra and temporal evolution are complicated. For example, the shortcomings of optically thin, static slab models have long been recognized when confronted with the observations. A less incorrect—but equally simple—phenomenological T ≈ 9000 K blackbody model has instead been widely adopted in the absence of realistic (i.e., observationally tested) time-dependent, atmospheric models that are readily available. We use the RADYN code to calculate a grid of 1D radiative-hydrodynamic stellar flare models that are driven by short pulses of electron-beam heating. The flare heating rates in the low atmosphere vary over many orders of magnitude in the grid, and we show that the models with high-energy electron beams compare well to the global trends in flux ratios from impulsive-phase stellar flare, optical spectra. The models also match detailed spectral line-shape properties. We find that the pressure broadening and optical depths account for the broad components of the hydrogen Balmer γ lines in a powerful flare with echelle spectra. The self-consistent formation of the wings and nearby continuum level provides insight into how high-energy electron-beam heating evolves from the impulsive to the gradual decay phase in white-light stellar flares. The grid is publicly available, and we discuss possible applications.