Currently, supervised person re-identification (Re-ID) models trained on labeled datasets can achieve high recognition performance in the same data domain. However, accuracy drops dramatically when ...these models are directly applied to other unlabeled datasets or natural environments, due to a significant sample distribution gap between the two domains. Unsupervised Domain Adaptation (UDA) methods can solve this problem by fine-tuning the model on the target dataset with pseudo-labels generated by the clustering method. Yet, these methods are primarily aimed at the image-based person Re-ID domain. This is because the background noise and interference information are complex and changeable in the video scenarios, resulting in large intra-class distances and small inter-class spaces, which easily lead to noisy labels. Huge domain gap and noisy labels hinder clustering and training processes heavily in the video-based person Re-ID. To address the problem, we propose a novel UDA method via Dynamic Clustering and Co-segment Attentive Learning (DCCAL) for it. DCCAL includes a Dynamic Clustering (DC) module and a Co-segment Attentive Learning (CAL) module. The DC module is responsible for adaptively clustering pedestrians within different generation processes to alleviate noisy labels. On the other hand, the CAL module reduces the domain gap using a co-segmentation-based attention mechanism. Additionally, we introduce Kullback-Leibler (KL) divergence loss to reduce the distribution of features between two domains for better performance. Experimental results on two large-scale video-based person Re-ID datasets, MARS and DukeMTMC-VideoReID (DukeV), demonstrate exceptional precision performance. Our method outperforms state-of-the-art semi-supervised and unsupervised approaches by 1.1% in Rank-1 and 1.5% in mAP on DukeV, as well as 3.1% and 2.1% in Rank-1 and mAP on MARS, respectively.
Designing routing protocols in Low power and Lossy Networks (LLNs) imposes great challenges. In emergency scenarios, the large and rapid data traffic caused by emergencies will lead to network ...congestion and bring about significant packet loss and delay. Routing protocol for LLNs (RPL) is the IETF standard for IPv6 routing in LLNs. The basic version of RPL uses Expected Transmission Count (ETX) as the default routing metric; it cannot solve the problem of sudden large data traffic. In this paper, we propose a congestion avoidance multipath routing protocol which uses composite routing metrics based on RPL, named CA-RPL. A routing metric for RPL that minimized the average delay towards the DAG root is proposed, and the weight of each path is computed by four metrics. The mechanism is explained and its performance is evaluated through simulation experiments based on Contiki. Simulation results show that the proposed CA-RPL reduces the average time delay by about 30% compared to original RPL when the interpacket interval is short and has almost 20% reduction in packet loss ratio. The CA-RPL can effectively alleviate the network congestion in the network with poor link quality and large data traffic and significantly improve the performance of LLNs.
Chronic periodontitis is an inflammatory disease that represents a major public health issue nowadays. Here, we investigated the protective role of nuclear factor kappa B (NF-κB) inducing kinase ...(NIK)-inhibitor on chronic periodontitis and revealed the underlying molecular mechanism. NIK-inhibitor was synthesized, and its functions were examined in primary osteoclasts and wild-type (WT) and NIK-/- chronic periodontitis mouse model. Lipopolysaccharides (LPS) or activator of NF-κB was applied to stimulate inflammatory response of osteoclasts. The qRT-PCR, ELISA and Western blot were used to measure the expression of pro-inflammatory and osteoclast-related genes, and the activation of NF-κB signaling. Osteoclastogenesis and bone damage were detected by TRAP staining and micro-CT. NIK knockdown mice had lower expression of osteoclast-related genes and improved CEJ-ABC damage. Similarly, NIK-inhibitor administration inhibited inflammatory responses and CEJ-ABC damage in chronic periodontitis models. NIK-inhibitor suppressed osteoclastogenesis and osteoclast-related genes expression through inhibiting the non-canonical NF-κB signaling. NIK plays important role in bone destruction of chronic periodontitis and NIK-inhibitor represents a promising therapeutic strategy for this disease.
•Rutting resistance of DCLR modified asphalt mixture under variable loads over a wide temperature range was studied.•Temperature affects the rutting resistance of mixture more significantly than the ...load for DCLR modified asphalt mixture.•The DCLR modified asphalt mixture presents a good and stable ability to resist rutting.
This research aims to investigate the rutting resistance of Direct Coal Liquefaction Residue (DCLR) modified asphalt mixture under variable loads over a wide temperature range. The SK-90 asphalt mixture and Styrene-Butadiene-Styrene (SBS) modified asphalt mixture were selected as comparisons. The rutting resistance of these three mixtures was studied by the rutting test with temperature of 30–70 °C under variable loads of 0.7–1.0 MPa. Meanwhile, the double factor variance analysis was performed to study the effect of temperature and load on the rutting resistance of mixtures, and the radar analysis was applied for comprehensive evaluation on the rutting resistance as well. The results demonstrate that the rutting resistance of mixtures presents a negative linear correlation with the load or the temperature. Comparing load, the temperature shows a more significant influence on the resistance to rutting of mixtures. Under the coupling effect of temperature and load, the DCLR modified asphalt mixture had higher values of Va, Vb, WL and WT than the other two mixtures. Therefore, among these three mixtures, the DCLR modified asphalt mixture owns the best stable rutting resistance, which gives the least sensitivity and dependence on the temperature and load.
Automatic computer security inspection of X-ray scanned images has an irresistible trend in modern life. Aiming to address the inconvenience of recognizing small-sized prohibited item objects, and ...the potential class imbalance within multi-label object classification of X-ray scanned images, this paper proposes a deep feature fusion model-based dual branch network architecture. Firstly, deep feature fusion is a method to fuse features extracted from several model layers. Specifically, it operates these features by upsampling and dimension reduction to match identical sizes, then fuses them by element-wise sum. In addition, this paper introduces focal loss to handle class imbalance. For balancing importance on samples of minority and majority class, it assigns weights to class predictions. Additionally, for distinguishing difficult samples from easy samples, it introduces modulating factor. Dual branch network adopts the two components above and integrates them in final loss calculation through the weighted sum. Experimental results illustrate that the proposed method outperforms baseline and state-of-art by a large margin on various positive/negative ratios of datasets. These demonstrate the competitivity of the proposed method in classification performance and its potential application under actual circumstances.
Flexure hinges are susceptible to fatigue damage under cyclic loading, resulting in performance degradation. This paper investigates the stiffness degradation of the right circular flexure hinges ...(RCFHs) under cyclic loading. Fatigue damage experiments are conducted to obtain the stiffness degradation curves, which can be divided into several stages by feature points. A relationship between feature lives and alternating stress amplitudes is established. A fatigue damage stiffness degradation piecewise curve model for RCFHs is proposed. The effect of notch stress concentration on fatigue damage is analyzed. Fatigue damage experiments under non-zero mean stress are conducted, and an equivalent fatigue stress equation is obtained. Finally, a generalized fatigue damage stiffness degradation model for RCFHs is developed, which establishes a relationship between residual stiffness and cycle number. On this basis, a fatigue damage performance modeling method for flexure hinge mechanisms is proposed. The fatigue damage performance of a compliant bridge mechanism was modeled and tested. The experimental results of input stiffness degradation are generally in agreement with the predicted results, which verify the validity of the method.
The study of anthropogenic carbon monoxide (CO) emissions is crucial to investigate anthropogenic activities. Assuming the anthropogenic CO emissions accounted for the super majority of the winter CO ...fluxes in western Europe, they could be roughly estimated by the inversion approach. The CO fluxes and concentrations of four consecutive winter seasons (i.e., December–February) in western Europe since 2017 were estimated by a regional CO flux inversion system based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART). The CO retrievals from the Measurements Of Pollution In The Troposphere instrument (MOPITT) version 8 level 2 multi-spectral Thermal InfraRed (TIR)/Near-InfraRed (NIR) CO retrieval data products were assimilated by the inversion system. The analyses of the MOPITT data used by the inversion system indicated that the mean averaging kernel row sums of the surface level was about 0.25, and the difference percentage of the surface-level retrievals relative to a priori CO-mixing ratios was 14.79%, which was similar to that of the other levels. These results suggested the MOPITT’s surface-level observations contained roughly the same amount of information as the other levels. The inverted CO fluxes of the four winter seasons were 6198.15 kilotons, 4939.72 kilotons, 4697.80 kilotons, and 5456.19 kilotons, respectively. Based on the assumption, the United Nations Framework Convention on Climate Change (UNFCCC) inventories were used to evaluate the accuracy of the inverted CO fluxes. The evaluation results indicated that the differences between the inverted CO fluxes and UNFCCC inventories of the three winter seasons of 2017–2019 were 13.36%, −4.59%, and −4.76%, respectively. Detailed surface-CO concentrations and XCO comparative analyses between the experimental results and the external Community Atmosphere Model with Chemistry (CAM-Chem) results and the MOPITT data were conducted. The comparative analysis results indicated that the experimental results of the winter season of 2017 were obviously affected by high boundary conditions. The CO concentrations results of the experiments were also evaluated by the CO observation data from Integrated Carbon Observation System (ICOS), the average Mean Bias Error (MBE), and the Root Mean Square Error (RMSE) between the CO concentrations results of the inversion system, and the ICOS observations were −22.43 ppb and 57.59 ppb, respectively. The MBE and RMSE of the inversion system were 17.53-ppb and 4.17-ppb better than those of the simulation-only parallel experiments, respectively.
NBN is a bioflavonoid that counteracts the harmful effects of UVA radiation-induced cytotoxicity, reducing ROS production, DNA damage, and inhibiting apoptosis. NBN mitigates the signs of photoaging ...in HaCaT cells by downregulating the expression of MAPKs, AP-1, and MMPs while promoting collagen production. NBN enhances the Prdx-1 expression during the UVA-irradiation, thereby impeding the expression MAPKs, MMPs, AP-1, and downregulation of collagens, and Smad-3 in HaCaT cells.
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•UVA radiation induces MMPs and collagen degradation leads to photoaging.•NBN, a dietary flavonoid, enhances Prdx-I, thereby inhibiting antioxidant depletions.•NBN impedes UVA-mediated Photoaging responses by Prdx-I expressions.
This study investigates the role of nobiletin (NBN), a dietary flavonoid preventing ultraviolet-A (UVA) radiation-induced oxidative damage and photoaging responses in skin epidermal keratinocytes (HaCaT). NBN treatment prevents cell toxicity, ROS, DNA damage, and apoptosis in UVA-exposed HaCaT cells. Furthermore, NBN prevents UVA radiation treatment-mediated overexpression of p-Erk-1, p-Jnk, p-p38, and AP-1 in HaCaT cells. Peroxiredoxin I (Prdx-1) is an antioxidant protein and enhanced activities of Prdx-1 have been considered a major target for inhibition of UVA radiation-mediated oxidative damage and photoaging responses. In this study, we noticed that NBN enhanced Prdx-1 expression, thereby enhancing antioxidants and inhibiting matrix metalloproteinase (MMP-1, MMP-2, MMP-9) and collagen degradation (Col-I and Col-III) in UVA-exposed HaCaT cells. Moreover, NBN treatment prevents UVA irradiation-induced downregulations of p-Smad and TGF-β1 expressions in HaCaT cells. In conclusion, our present results stated that NBN effectively prevents UVA-induced skin oxidative damage and photoaging responses through enhanced Prdx-1 expression.
In order to address the problem of incomplete data set of real seismic impact of cultural relics in collections, we propose a small sample set oriented center-of-mass positioning method to solve the ...center-of-mass attribute of cultural relics ontology. First, the method combines the cultural relics image data to construct a deep feature fusion cultural relics ontology recognition model, which extracts and learns the cultural relics image features through multi-layer feature extraction, global information perception and ontology fusion recognition, so as to realize the efficient and accurate recognition of the cultural relic's ontology. Then, the parameter synchronous migration learning method is designed to train and initialize the model, and the data migration strategy of semi-supervised learning is used to retrain the model using the public dataset and the cultural relic image data, to achieve the fine-tuning of the model parameters to improve the accuracy of the cultural relic ontology recognition. Finally, the center-of-mass positioning method is designed to integrate the attention mechanism with the quality of relics and other ontological attributes, and the segmentation of the relic's ontology region is achieved by rectangular stereo fitting and the calculation of the center-of-mass points. At the same time, the fusion self-attention mechanism is used to adjust the regional center-of-mass weights to achieve weighted positioning of the center-of-mass position of the cultural relics. The experimental results show that the present method achieves the best Dice, Acc and MIoU metrics in comparison with various classical models, with improvements of 8.3%, 5.1% and 3.4% respectively. The overall center-of-mass offset of cultural relics is less than 2%, which can achieve accurate identification and center-of-mass positioning of the cultural relics body, fully improve the seismic impact dataset of cultural relics, and enhance the ability of preventive protection of cultural relics against earthquakes.
This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation ...method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect.
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