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Interior soft x-ray tomography with sparse global sampling
Full text linkTo investigate the feasibility of interior imaging reconstruction in soft X-ray tomography for higher-resolution cellular imaging, including whole-cell imaging, we develop an alignment and reconstruction algorithm that combines a small number of sparse whole-cell images with a high-resolution local interior scan. Based on numerical simulations, we demonstrate that combined reconstructions mitigate the depth-of-field limitation in high-resolution scans, enable radiation dose optimization, and yield quantitative X-ray absorption values with sparse sampling. We further validate our numerical approach using experimental data from two different cell types and show that the combined reconstruction reliably provides high spatial resolution within an interior region of interest of a whole cell. The resulting sparse reconstruction framework offers robust, faithful visualization of cellular organelles in soft X-ray tomography. This mesoscale imaging strategy allows one to ‘scout’ and zoom into selected subcellular volumes of interest, enabling increased spatial resolution without sacrificing larger-volume imaging and providing information on the relative positions of all organelles within a cell
Quantifying the spatial extent and attenuation of lake thermal regulation at diurnal scales under extreme heat
Full text linkLakes worldwide are experiencing intensifying extreme heat, with escalating ecological impacts. Despite lakes\u27 role as thermal buffers to modulate air temperature is well-documented, the spatial propagation dynamics of lake effects remain poorly understood due to complex interactions of lake-atmosphere. This study proposes a synergistic WRF modeling and directional buffer analysis framework to investigate the spatial propagation dynamics and underlying physical mechanisms of lake-induced thermal regulation during extreme heat, focusing on Poyang Lake, China\u27s largest freshwater lake. The results demonstrate a pronounced diurnal asymmetry in lake-induced thermal effects, with distinct spatial propagation characteristics between daytime and nighttime periods. Daytime cooling exhibits an intensity of −1.16 °C, with its influence confined within a 40 km radius, showing a relatively rapid attenuation rate of 0.28 °C per 10 km. In contrast, nighttime warming (+0.97 °C) propagates 1.75 times farther than its daytime counterpart, extending up to 70 km downwind while maintaining a slower attenuation rate of 0.13 °C per 10 km. Directional analysis reveals north-oriented propagation of lake thermal effects, influenced by prevailing southerly winds and lake-land breeze. Vertical profile analysis reveals distinct altitudinal penetration of lake-induced thermal effects, with daytime influences confined below 900 hPa while nighttime impacts extend up to 700 hPa. Daytime cooling extent is limited by turbulent mixing, whereas nighttime warming is enhanced by stable air conditioning and advective transport. The study underscores the role of lake-atmosphere interactions in mitigating regional climate extremes, providing critical insights for nature-based heat adaptation strategies in lake-rich regions. These findings advance the understanding of inland water bodies as active climate regulators under anthropogenic warming
Towards improved accounting and mitigation of greenhouse gas emissions from ditches and canals
Full text linkDynamics and model representation of two contrasting extreme precipitation events in the Sahel
Full text linkTwo extreme flood-inducing precipitation events in two cities in Mali, on August 8, 2012 in San (127 mm) and on August 25, 2019 in Kenieba (126 mm), are investigated with respect to rainfall structures, dynamical forcings, and the ability of the ICOsahedral Nonhydrostatic model (ICON) to represent their evolution. Two sets of experiments with convective parameterization enabled (PARAM) and disabled (EXPLC), both at 6.5-km grid spacing, are conducted for each case. While the (thermo)dynamical fields of the simulations are compared with fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data, the rainfall fields are tested against the satellite-based precipitation dataset Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) by applying spatial verification methods based on the fractions skill score (FSS) and structure–amplitude–location (SAL) score. In addition, a spectral filtering of tropical waves is applied to investigate their impact on extreme events. The most prominent results are as follows. (1) Both cases were caused by organized convective systems associated with a westward-propagating cyclonic vortex, but differ in their environmental setting. Although both cases featured an African easterly wave, the San case involved convective enhancement along dry Saharan airmasses, whereas the Kenieba case occurred within an unusual widespread wet environment extending deep into the Sahel. (2) Although EXPLC captures the rainfall distribution in the San case better than PARAM, it fails to organize convection in the moisture-laden Kenieba case, which PARAM is capable of simulating. (3) The FSS confirms the case dependence of the ICON skill. The SAL method hints at a systematic deficiency of EXPLC in representing convective organization by producing too many scattered and weak rainfall systems, while PARAM is more effective in converting abundant moisture into excessive rainfall. The results stress the continued need for more research into capturing the complex convective dynamics to forecast the extremes of Sahelian rainfall better
Fuzzy-Logic and Deep Learning for Environmental Condition-Aware Road Surface Classification
Full text linkMonitoring states of road surfaces provides valuable information for the planning and controlling vehicles and active vehicle control systems. Classical road monitoring methods are expensive and unsystematic because they require time for measurements. This article proposes an real time system based on weather conditional data and road surface condition data. For this purpose, we collected data with a mobile phone camera on the roads around the campus of the Karlsruhe Institute of Technology. We tested a large number of different image-based deep learning algorithms for road classification. In addition, we used road acceleration data along with road image data for training by using them as images. We compared the performances of acceleration-based and camera image-based approaches. The performances of the simple Alexnet, LeNet, VGG, and Resnet algorithms were compared as deep learning algorithms. For road condition classification, 5 classes were considered: asphalt, damaged asphalt, gravel road, damaged gravel road, pavement road and over 95% accuracy performance was achieved. It is also proposed to use the acceleration or the camera image to classify the road surface according to the weather and the time of day using fuzzy logic
HARNode: A Time-Synchronised, Open-Source, Multi-Device, Wearable System for Ad Hoc Field Studies
Full text linkHuman activity recognition (HAR) research often lacks accessi-
ble, comprehensive field data. Commercial systems are rarely open
source, hard to expand, and limited by issues like node synchro-
nisation, data throughput, unclear sensor placement, complexity,
and high cost. As a result, researchers typically use only a few
intuitively placed sensors and conduct limited field trials. HARN-
ode overcomes these challenges with a fully open-source hardware
and software platform. Each node includes an ESP32-S3 module
(AtomS3), a 9-axis IMU (Bosch BMX160), pressure and tempera-
ture sensors (Bosch BMP388), a display, and an I2C port. Data is
streamed via Wi-Fi, with NTP-based time synchronisation achiev-
ing 1 ms accuracy. The system runs for up to 8 hours and is built
using off-the-shelf parts, a simple online PCB service, and a com-
pact 3D-printed housing with Velcro straps, enabling flexible and
scalable body placement while requiring little hardware knowledge.
In a study with ten subjects wearing eleven HARNodes each, setup
took under five minutes per person. A random forest classifier dis-
tinguished walking from stair-climbing transitions, showing the
benefits of sensor-overprovisioning: Seven nodes achieved≈98%
accuracy, matching the performance of all eleven. These findings
confirm HARNode’s value as a fast-deploying, scalable tool for
field-based HAR research and optimised sensor placement
The Berry curvature in the framework of current density functional theory for molecules in external magnetic fields
No full textIn this work, we investigate the quantum geometry framework for molecules in external magnetic fields. For electronic ground states, the linear response formalism through which the quantum geometric tensor can be computed was described by Culpitt et al. [J. Chem. Phys. 156, 044121 (2022)], and this work expands their framework to current density functional theory. We show that for nuclear displacements, the Fubini–Study metric can be connected to the diagonal Born–Oppenheimer correction. Furthermore, we examine the effects of external magnetic fields on the molecular Berry curvature. For selected systems, we investigate how different density functional approximations compare to both full configuration interaction and Hartree–Fock theory. Finally, the convergence of the Berry curvature with respect to the numerical grid is estimated for different functionals, highlighting some known deficiencies of modern density functional approximations
The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal–Organic Framework Networks
No full textUnderstanding the reaction-induced restructuring of CoOₓ species in silicalite-1 to control selectivity in non-oxidative dehydrogenation of propane
No full textPb corrosion of ferritic/martensitic steels at 600–700 °C
No full textThis research investigates the corrosion behavior of three different ferritic/martensitic steels (P91, 1.4748, 1.4136) in liquid lead containing 2 × 10 7 wt% dissolved oxygen. Additionally, one of the steels (P91) is surface-aluminized by pack cementation prior to Pb exposure to explore the efficacy of an aluminide coating. The aim of this study is to explore the possibility to extend the use from a corrosion point of view of the selected F/M steels in liquid Pb up to 700 ◦C. To gain a better understanding of the corrosion behavior over time, exposure tests with three different durations up to 5000 h are performed. Post-exposure analysis demonstrates oxidation on all materials in the initial stage, followed by various differing scenarios specific for each material and exposure temperature. These range from oxidation with protective properties and selective Cr dissolution with inter-granular Pb penetration to total dissolution