5-({[(E)-Benzyl­idene­amino]­oxy}meth­yl)-1,3,4-thia­diazol-2-amine

In the mol­ecule of the title compound, C10H10N4OS, the configuration about the C=N double bond is E. The dihedral angle between the thia­diazole and benzene rings is 81.1 (1)°. In the crystal, mol­ecules are linked by N—H⋯N and C—H⋯O hydrogen bonds to form a two-dimensional network parallel with the bc plane

(E)-(2-Chloro­benzyl­idene)amino 2-amino-4-chloro­benzoate

In the title compound, C14H10Cl2N2O2, the configuration about the C=N double bond is E and the dihedral angle between the benzene rings is 1.75 (5)°. An intra­molecular N—H⋯O inter­action generates an S(6) ring. In the crystal, mol­ecules are linked by N—H⋯O hydrogen bonds, resulting in [101] chains

Pedestrian Counting Based on Piezoelectric Vibration Sensor

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods.Peer reviewe

Hydrodeoxygenation of p-cresol on unsupported Ni–P catalysts prepared by thermal decomposition method

AbstractUnsupported Ni–P catalysts were prepared from the mixed precursor of NiCl2 and NaH2PO2 by thermal decomposition method, and their catalytic activities were measured using the hydrodeoxygenation (HDO) of p-cresol as probe. The effects of the H2PO2−/Ni2+ molar ratio in the precursor and the thermal decomposition temperature on the catalyst purity, crystallite size and HDO activity were studied. The HDO of p-cresol on these Ni–P catalysts proceeded with two parallel pathways yielding methylbenzene and methylcyclohexane as final products. The higher HDO catalytic activity of the catalyst was attributed to its bigger crystallite size and purer phase of Ni2P

Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation

Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts: The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach