FORCE Technology (Norway)

Ultrafast events, from the rapid propagation of shock waves to molecular vibrations and electron dynamics, can unfold on timescales as short as femtoseconds. Limited by the imaging speed of charge‐coupled device and complementary metal‐oxide‐semiconductor sensors, conventional imaging systems cannot capture such high‐speed phenomena. Researchers have developed various ultrafast photography techniques to address this challenge, incorporating electronic, mechanical, and optical methods to achieve imaging speed in the trillion frames per second. This review categorizes ultrafast photography into four types: multishot passive technique, multishot active technique, single‐shot passive technique, and single‐shot active technique. It examines the evolution from early mechanical and electronic high‐speed cameras to modern optical and computational imaging. These innovations have broad applications in physics, chemistry, biology, and engineering, providing new insights into ultrafast dynamics. This review discusses the latest developments, compares key techniques, and explores future directions in ultrafast photography research.

Abstract Existing 3D point cloud analysis methods prioritize translation and permutation invariance but generally lack systematic rotation-invariant encoding, degrading their performance on arbitrarily rotated 3D shapes. In this paper, we propose RiDBNet, an innovative rotation-invariant dual-branch network that incorporates complementary global and local rotation-invariant information through novel designs. Specifically, the global branch leverages singular value decomposition (SVD) for canonical alignment and establishes rotation-robust representations. Unlike methods relying on simple MLP for global encoding, we introduce a self-attention unit to enhance topological modeling and integrate a novel Graph Feature Extraction module for hierarchical feature encoding. In the global branch, we design a rotation-invariant geometric descriptor based on composite triangular structures, capturing 22-dimensional features (i.e., Euclidean distances, surface geometric angles, normal-derived angles) to overcome limitations of prior descriptors. An adaptive dual-attention fusion mechanism dynamically integrates features from both branches at each network stage, effectively preventing feature dilution. RiDBNet achieves accuracy of 93.3% and 80.5% on ModelNet40 and ScanObjectNN for the classification task, and 82.2% mIOU on ShapeNet for the segmentation task, and remains highly competitive against state-of-the-art methods.

During the braking process, temperature of brake disc in high-speed light-load vehicles sharply increases, causing the thermal fatigue crack. Therefore, it is necessary to design new brake disc and study its thermomechanical and friction properties. Here, authors designed new brake discs by way of constructing biomimetic nonsmooth structures in contact surface, then conducted simulation and experiment to quantify their effect on thermomechanical and friction properties. Compared with the smooth brake disc and under the initial velocity 120/160/200 km/h, the brake disc with straight grooves reduces the friction temperature that exhibits the reduction rate of 27.16%, 36.25% and 31.20%. Equivalent stress shows the increase rate of 61.64%, 46.92% and 71.58%, but these values are within the material yield strength. Experiment results showed the biomimetic brake discs have slightly smaller friction coefficient (0.32–0.48) but significantly lower friction temperature (41.7°C–77.9°C) than the smooth brake disc (0.56, 96.7°C). The biomimetic brake disc with straight grooves and air inlets exhibit relatively better performance, resulting from the superior capability in dissipating heat. This study demonstrates biomimetic nonsmooth structures can improve brake disc’s thermomechanical properties, which provides an available design approach for brake discs in high-speed light-load vehicles.

Abstract This work aims to develop and study the behavior of epoxy resin composites with two different microfillers. Epoxy composites are fabricated using the magnetic stirring technique by varying weight percentages of tungsten disulfide (WS2), a solid lubricant with a fixed weight percentage of wollastonite (CaSiO3), a ceramic reinforcement. The study investigates the effect of CaSiO3 and WS2 on the physical, mechanical, and thermal properties of epoxy composites. The experimental findings indicate that the inclusion of fillers significantly improves the flexural strength, thermal stability, and hardness of the epoxy composites. The Coats Redfern method was also considered for activation energy analysis of epoxy composites. Furthermore, the physical, mechanical, and thermal properties of filler-based epoxy composites are optimized using a hybrid of the analytic hierarchy process and Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS). The order of performance attributes according to AHP is density > hardness > tensile strength > flexural strength > impact strength, whereas as per TOPSIS, it is revealed that composite C3 (94 wt. % epoxy + 2 wt. % CaSiO3 + 4 wt. % WS2) emerged as the best material.

<strong class="journal-contentHeaderColor">Abstract.</strong> Hydrological modeling plays a key role in water resource management and flood forecasting. However, in China with diverse geography and complex climate types, a systematic evaluation of different modeling schemes for large-sample hydrological datasets is still lacking. This study preliminarily constructed a dataset of catchment attributes and meteorology covering 544 basins in China, and systematically evaluated the applicability of process-based models (PBMs), long short-term memory (LSTM) models, and hybrid modeling methods. The results demonstrated: (1) The accuracy of meteorological data critically impacts the prediction performance of hydrological models. High-quality precipitation data enables the model to better simulate the runoff generation process in the basin, thereby improving prediction accuracy. (2) The hybrid modeling method possesses regional modeling capabilities comparable to those of LSTM model. It also demonstrates strong generalization capabilities. In predicting ungauged basins, the hybrid model exhibits greater stability than the LSTM model. (3) Among the two hybrid modeling methods, the differentiable hybrid modeling scheme offers a deeper understanding and simulation of hydrological processes, along with the ability to output unobserved intermediate hydrological variables, compared to the alternative hybrid modeling schemes. Its prediction results are more consistent with the water balance of the basin. The research results provide a systematic analysis for evaluating the applicability of different hydrological modeling methods in 544 basins in China, offering important guidance for the selection and optimization of future hydrological models.

<strong class="journal-contentHeaderColor">Abstract.</strong> Hydrological modeling plays a key role in water resource management and flood forecasting. However, in China with diverse geography and complex climate types, a systematic evaluation of different modeling schemes for large-sample hydrological datasets is still lacking. This study preliminarily constructed a dataset of catchment attributes and meteorology covering 544 basins in China, and systematically evaluated the applicability of process-based models (PBMs), long short-term memory (LSTM) models, and hybrid modeling methods. The results demonstrated: (1) The accuracy of meteorological data critically impacts the prediction performance of hydrological models. High-quality precipitation data enables the model to better simulate the runoff generation process in the basin, thereby improving prediction accuracy. (2) The hybrid modeling method possesses regional modeling capabilities comparable to those of LSTM model. It also demonstrates strong generalization capabilities. In predicting ungauged basins, the hybrid model exhibits greater stability than the LSTM model. (3) Among the two hybrid modeling methods, the differentiable hybrid modeling scheme offers a deeper understanding and simulation of hydrological processes, along with the ability to output unobserved intermediate hydrological variables, compared to the alternative hybrid modeling schemes. Its prediction results are more consistent with the water balance of the basin. The research results provide a systematic analysis for evaluating the applicability of different hydrological modeling methods in 544 basins in China, offering important guidance for the selection and optimization of future hydrological models.