Search

It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly focus on local geographic correlations, ignoring cross-region interdependencies in a global context, which is insufficient to extract comprehensive semantic relationships, thereby limiting prediction accuracy. Additionally, most GCN-based models rely on pre-defined graphs and unchanging adjacency matrices to reflect the spatial relationships among node features, neglecting the dynamics of spatio-temporal features and leading to challenges in capturing the complexity and dynamic spatial dependencies in traffic data. To tackle these issues, this paper puts forward a fresh approach: a new self-supervised dynamic spatio-temporal graph convolutional network (SDSC) for traffic flow forecasting. The proposed SDSC model is a hierarchically structured graph–neural architecture that is intended to augment the representation of dynamic traffic patterns through a self-supervised learning paradigm. Specifically, a dynamic graph is created using a combination of temporal, spatial, and traffic data; then, a regional graph is constructed based on geographic correlation using clustering to capture cross-regional interdependencies. In the feature learning module, spatio-temporal correlations in traffic data are subjected to recursive extraction using dynamic graph convolution facilitated by Recurrent Neural Networks (RNNs). Furthermore, self-supervised learning is embedded within the network training process as an auxiliary task, with the objective of enhancing the prediction task by optimising the mutual information of the learned features across the two graph networks. The superior performance of the proposed SDSC model in comparison with SOTA approaches was confirmed by comprehensive experiments conducted on real road datasets, PeMSD4 and PeMSD8. These findings validate the efficacy of dynamic graph modelling and self-supervision tasks in improving the precision of traffic flow prediction. [ABSTRACT FROM AUTHOR]

Accurate traffic flow forecasting is a crucial component of intelligent transportation systems, playing a pivotal role in enhancing transportation intelligence. The integration of Graph Neural Networks (GNNs) and Transformers in traffic flow forecasting has gained significant adoption for enhancing prediction accuracy. Yet, the complex spatial and temporal dependencies present in traffic data continue to pose substantial challenges: (1) Most GNN-based methods assume that the graph structure reflects the actual dependencies between nodes, overlooking the complex dependencies present in the real-world context. (2) Standard time-series models are unable to effectively model complex temporal dependencies, hindering prediction accuracy. To tackle these challenges, the authors propose a novel Transformer-based Spatiotemporal Graph Diffusion Convolution Network (TSGDC) for Traffic Flow Forecasting, which leverages graph diffusion and transformer to capture the complexity and dynamics of spatial and temporal patterns, thereby enhancing prediction performance. The authors designed an Efficient Channel Attention (ECA) that learns separately from the feature dimensions collected by traffic sensors and the temporal dimensions of traffic data, aiding in spatiotemporal modeling. Chebyshev Graph Diffusion Convolution (GDC) is used to capture the complex dependencies within the spatial distribution. Sequence decomposition blocks, as internal operations of transformers, are employed to gradually extract long-term stable trends from hidden complex variables. Additionally, by integrating multi-scale dependencies, including recent, daily, and weekly patterns, accurate traffic flow predictions are achieved. Experimental results on various public datasets show that TSGDC outperforms conventional traffic forecasting models, particularly in accuracy and robustness. [ABSTRACT FROM AUTHOR]

Multilevel thresholding image segmentation is one of the most widely used segmentation methods in the field of image segmentation. This paper proposes a multilevel thresholding image segmentation technique based on an improved whale optimization algorithm. The WOA has been applied to many complex optimization problems because of its excellent performance; however, it easily falls into local optimization. Therefore, firstly, a mixed-strategy-based improved whale optimization algorithm (MSWOA) is proposed using the k-point initialization algorithm, the nonlinear convergence factor, and the adaptive weight coefficient to improve the optimization ability of the algorithm. Then, the MSWOA is combined with the Otsu method and Kapur entropy to search for the optimal thresholds for multilevel thresholding gray image segmentation, respectively. The results of algorithm performance evaluation experiments on benchmark functions demonstrate that the MSWOA has higher search accuracy and faster convergence speed than other comparative algorithms and that it can effectively jump out of the local optimum. In addition, the image segmentation experimental results on benchmark images show that the MSWOA–Kapur image segmentation technique can effectively and accurately search multilevel thresholds. [ABSTRACT FROM AUTHOR]

This work outperforms the previous literatures by proposing a delay-cell-controlled voltage control oscillator (VCO) design for common unipolar, single-gate, and enhancement-mode thin-film transistor (TFT) technologies. A design example with InZnO TFTs is simulated to verify the proposed design. The design example has a 500 μW power consumption, 0.7 mm2 area, 3.8 kHz–8 kHz output frequency range, 600 Hz/V tuning sensitivity, and 4% maximum linear error. This design may have the potential to be used for flexible, low cost, and moderate speed sensor readout interfaces. [ABSTRACT FROM AUTHOR]

Copyright of Technical Electrodynamics / Tekhnichna Elektrodynamika is the property of National Academy of Sciences of Ukraine, Institute of Electrodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Acute kidney injury (AKI) frequently occurs as a complication of sepsis. PANoptosis refers to a type of inflammatory programmed cell death that exhibits key characteristics of apoptosis, necroptosis, and pyroptosis. Here, we evaluated the role of absent in melanoma 2 (AIM2) and eukaryotic translation initiation factor 2 alpha kinase 2 (EIF2AK2) in septic AKI., Methods: A septic AKI model was created through cecal ligation and puncture (CLP), while an in vitro model was developed using lipopolysaccharide (LPS)-stimulated HK2 cells. Hematoxylin and eosin (HE), Periodic acid-Schiff (PAS), and TUNEL staining were conducted to assess kidney injury in mice. Levels of serum creatinine (Scr) and blood urea nitrogen (BUN) were detected by kits. Gene expression was detected utilizing RT-qPCR, and Western blot was used to test protein levels. Immunofluorescence was employed to measure EIF2AK2 and AIM2 expression in mouse kidney tissue. Lactate dehydrogenase (LDH) activity assay was conducted to evaluate cytotoxicity. Co-immunoprecipitation (Co-IP) was performed to verify the binding relationship between EIF2AK2 and AIM2., Results: AIM2 expression was increased in the renal tissue of mice subjected to CLP. Activation of the inflammasome and PANoptosis were observed in the renal tissue of CLP mice. AIM2 depletion attenuated PANoptosis in LPS-treated HK-2 cells. Additionally, EIF2AK2 could directly target AIM2, leading to a positive regulation of AIM2 expression. Notably, EIF2AK2 induced PANoptosis through upregulating AIM2 in HK-2 cells stimulated by LPS., Conclusions: Our results revealed the important role of EIF2AK2-induced AIM2 upregulation in the activation of PANoptosis during septic AKI.

Necroptosis, a form of regulated necrosis, has been reported to be involved in numerous pathologies, including sepsis. However, a protective effect of the selective inhibitor of necroptosis, necrostatin-1 (Nec-1), against sepsis remains to be confirmed. Animals (rats and mice) were subjected to cecal ligation and puncture (CLP) to mimic clinical sepsis. Nec-1 or its vehicle (control) was administered 20 min before CLP. Survival time was observed up to 72 h after CLP. Specimens of liver tissue and serum were obtained at 6 h, 12 h, and 18 h. Expression of necroptosis-related proteins [receptor-interacting protein kinase (RIP)1, RIP3, and mixed lineage kinase domain-like (MLKL)] was determined by Western blot analysis. The RIP1/RIP3 interaction and the recruitment of MLKL to RIP3 were also analyzed. Liver function, histopathological changes, serum inflammation cytokines, TUNEL staining, and the expression of apoptosis-related protein, including caspase-3, B-cell lymphoma 2 (Bcl-2), and Bcl-2-associated X (Bax), was determined. As expected, Nec-1 administration reduced the expression of necroptosis-related proteins and the RIP1/RIP3 interaction, indicating inhibited necroptosis. Surprisingly, Nec-1 treatment exacerbated the liver injury and shortened survival time of septic rats with increased TUNEL-positive cells, cleaved caspase-3 protein content, and Bax/Bcl-2 ratio. Collectively, these findings show that Nec-1 administration inhibited the hepatocyte necroptosis pathway but accelerated apoptosis via the apoptotic pathway in CLP-induced sepsis rat. NEW & NOTEWORTHY The present study demonstrated that a chemical inhibitor necrostatin-1 (Nec-1) or receptor-interacting protein kinase(RIP1) knock down targeted at necroptosis inhibition accelerated liver injury of following sepsis. For fundamental research, these results warrant further investigation of the potential link between Nec-1 administration and the cellular apoptosis following sepsis induced liver injury. For applied research, these results suggest the potential harmful effect of Nec-1 on future sepsis treatment.

Sepsis is the leading cause of death in the intensive care unit and continues to lack effective treatment. It is widely accepted that high-mobility group box 1 (HMGB1) is a key inflammatory mediator in the pathogenesis of sepsis. Moreover, some studies indicate that the functions of HMGB1 depend on its molecular localization and posttranslational modifications. Our previous study confirms that sirtuin 1, silent information regulator 2-related enzyme 1 (SIRT1), a type III deacetylase, can ameliorate sepsis-associated acute kidney injury (SA-AKI). We explored the effect and mechanism of SIRT1 on HMGB1 using a mouse model of cecal ligation and puncture-induced sepsis and LPS-treated human kidney (HK-2) cell line. We found that HMGB1 is elevated in the serum but is gradually reduced in kidney cells in the later stages of septic mice. The acetylation modification of HMGB1 is a key process before its nucleus-to-cytoplasm translocation and extracellular secretion in kidney cells, accelerating the development of SA-AKI. Moreover, SIRT1 can physically interact with HMGB1 at the deacetylated lysine sites K28, K29, and K30, subsequently suppressing downstream inflammatory signaling. Thus the SIRT1-HMGB1 signaling pathway is a crucial mechanism in the development of SA-AKI and presents a novel experimental perspective for future SA-AKI research.