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Highly accurate traffic flow prediction is essential for effectively managing traffic congestion, providing real-time travel advice, and reducing travel costs. However, traditional traffic flow prediction models often fail to fully consider the correlation and periodicity among traffic state data and rely on static network topology graphs. To solve this problem, this paper proposes a expressway traffic flow prediction model based on multi-feature spatial-temporal adaptive periodic fused graph convolutional network (MFSTAPFGCN). First, we make fine preprocessing of the raw data to construct a complete and accurate dataset. Second, by deeply investigating the correlation properties among section speed, traffic flow, and section saturation rate, we incorporate these features into a multi-feature temporal attention mechanism in order to dynamically model the correlation of traffic flow in different time periods. Next, we adopt a spatial-temporal adaptive fusion graph convolutional network to capture the daily cycle similarity and potential spatial-temporal dependence of traffic flow data. Finally, the superiority of the proposed MFSTAPFGCN model over the traditional baseline model is verified through comparative experiments on real Electronic Toll Collection (ETC) gantry transaction data, and the effectiveness of each module is demonstrated through ablation experiments.

Intrusion detection aims to effectively detect abnormal attacks in Internet of Things (IoT) networks, which is crucial for cybersecurity. However, it is difficult for traditional intrusion detection methods to effectively extract data features from traffic data, and most existing models are too complex to be deployed on edge servers. Addressing this need, this paper proposes a hybrid feature selection method and a lightweight deep learning intrusion detection model. Firstly, the data feature space is reduced using variance filtering, mutual information, and the Pearson Correlation Coefficient, thereby reducing the computational cost of subsequent model training. Then, an intrusion detection model based on a Temporal Convolutional Network (TCN) is constructed. This model utilizes dilated causal convolutions to effectively capture long-term temporal dependencies in network traffic. Simultaneously, the residual connections are used to mitigate the vanishing gradient problem, making the model easier to train and converge. Finally, experiments are conducted on the newly released Edge-IIoTset dataset. The results show that the proposed feature selection algorithm maintains good detection performance despite a significant reduction in feature dimensionality. Furthermore, compared with other models, the proposed TCN-based approach achieves higher classification accuracy with lower computational overhead, demonstrating its suitability for deployment in resource-constrained edge computing environments.

The production of “Laba” garlic is limited to the homemade method with long processing time and non-uniform color quality. Innovative food processing technologies including high hydrostatic pressure (HHP) and high pressure carbon dioxide (HPCD) were applied to the processing of “Laba” garlic. Products prepared at different treatment pressures (200, 350 and 500 MPa of HHP; 4, 7 and 10 MPa of HPCD) were compared by evaluating the texture, color, flavor and sensory qualities. The results indicated that HHP treatment at 200 MPa was optimal for retaining the textural quality of “Laba” garlic, which was mainly attributed to the compacted cells and the increased Ca 2+ -cross linked cell-cell adhesion. HHP had greater effect on facilitating the formation of the attractive green color of “Laba” garlic than HPCD. The flavor profiles of “Laba” garlic were modified after treatments, with pungent compounds decreased to non-detectable. The results from sensory study confirmed that “Laba” garlic treated by HHP at 200 MPa was most acceptable to consumers. Moreover, considering the treatment capacity and feasibility of commercialization, HHP would be a promising technology in production of “Laba” garlic with improved quality and efficiency.

Intrusion detection is essential to cybersecurity. However, the curse of dimensionality and class imbalance limit detection accuracy and impede the identification of rare attacks. To address these challenges, this paper proposes the high-dimensional feature sequence temporal convolutional network (HiSeq-TCN) for intrusion detection. The proposed HiSeq-TCN transforms high-dimensional feature vectors into pseudo-temporal sequences, enabling the network to capture contextual dependencies across feature dimensions. This enhances feature representation and detection robustness. In addition, a few-shot reinforcement strategy adaptively assigns larger loss weights to minority classes, mitigating class imbalance and improving the recognition of rare attacks. Experiments on the NSL-KDD dataset show that HiSeq-TCN achieves an overall accuracy of 99.44%, outperforming support vector machines, deep neural networks, and long short-term memory models. More importantly, it significantly improves the detection of rare attack types such as remote-to-local and user-to-root attacks. These results highlight the potential of HiSeq-TCN for robust and reliable intrusion detection in practical cybersecurity environments.

Background It has been found that up-regulation of histone deacetylases 1 (HDAC1) is involved in the development of pulmonary arterial hypertension (PAH). However, it is still unclear whether inhibition of HDAC1 suppresses the development of PAH via restoring miR-34a level in monocrotaline (MCT)-induced PAH rats. Methods PAH rat models were induced by intraperitoneal injection of MCT. HDAC1 was suppressed by intraperitoneal injection of the class I HDAC inhibitor MS-275, and miR-34a was over-expressed via tail vein injection of miR-34a agomiR. Results HDAC1 protein was significantly increased in MCT-induced PAH rats; this was accompanied with down-regulation of miR-34a and subsequent up-regulation of matrix metalloproteinase 9 (MMP-9)/tissue inhibitor of metalloproteinase 1 (TIMP-1) and MMP-2/TIMP-2. Administration of PAH rats with MS-275 or miR-34a agomiR dramatically abolished MCT-induced reduction of miR-34a and subsequent up-regulation of MMP-9/TIMP-1 and MMP-2/TIMP-2, finally reduced extracellular matrix (ECM) accumulation, pulmonary arterial remodeling, right ventricular systolic pressure (RVSP) and right ventricle hypertrophy index (RVHI) in PAH rats. Conclusions HDAC1 contributes to the development of MCT-induced rat PAH by suppressing miR-34a level and subsequently up-regulating the ratio of MMP-9/TIMP-1 and MMP-2/TIMP-2. Inhibition of HDAC1 alleviates pulmonary arterial remodeling and PAH through up-regulation of miR-34a level and subsequent reduction of MMP-9/TIMP-1 and MMP-2/TIMP-2, suggesting that inhibition of HDAC1 might have potential value in the management of PAH.

This paper considers a wireless energy harvesting two-way relay (TWR) network where the relay has energy-harvesting abilities and the effects of practical hardware impairments are taken into consideration. In particular, power splitting (PS) receiver is adopted at relay to harvests the power it needs for relaying the information between the source nodes from the signals transmitted by the source nodes, and hardware impairments is assumed suffered by each node. We analyze the effect of hardware impairments on both decode-and-forward (DF) relaying and amplify-and-forward (AF) relaying networks. By utilizing the obtained new expressions of signal-to-noise-plus-distortion ratios, the exact analytical expressions of the achievable sum rate and ergodic capacities for both DF and AF relaying protocols are derived. Additionally, the optimal power splitting (OPS) ratio that maximizes the instantaneous achievable sum rate is formulated and solved for both protocols. The performances of DF and AF protocols are evaluated via numerical results, which also show the effects of various network parameters on the system performance and on the OPS ratio design.

In this study we investigate how β-catenin-dependent WNT signalling impacts midbrain dopaminergic neuron (mDA) specification. mDA cultures at day 65 of differentiation responded to 25 days of the tankyrase inhibitor XAV969 (XAV, 100nM) with reduced expression of markers of an A9 mDA phenotype ( KCNJ6 , ALDH1A1 and TH ) but increased expression of the transcriptional repressors NR0B1 and NR0B2 . Overexpression of NR0B1 and or NR0B2 promoted a loss of A9 dopaminergic neuron phenotype markers ( KCNJ6 , ALDH1A1 and TH ). Overexpression of NR0B1 , but not NR0B2 promoted a reduction in expression of the β-catenin-dependent WNT signalling pathway activator RSPO2 . Analysis of Parkinson’s disease (PD) transcriptomic databases shows a profound PD-associated elevation of NR0B1 as well as reduced transcript for RSPO2 . We conclude that reduced β-catenin-dependent WNT signalling impacts dopaminergic neuron identity, in vitro , through increased expression of the transcriptional repressor, NR0B1 . We also speculate that dopaminergic neuron regulatory mechanisms may be perturbed in PD and that this may have an impact upon both existing nigral neurons and also neural progenitors transplanted as PD therapy.

This paper considers simultaneous wireless information and power transfer (SWIPT) in a decode-and-forward two-way relay (DF-TWR) network, where a power splitting protocol is employed at the relay for energy harvesting. The goal is to jointly optimize power allocation (PA) at the source nodes, power splitting (PS) at the relay node, and time allocation (TA) of each duration to minimize the system outage probability. In particular, we propose a static joint resource allocation (JRA) scheme and a dynamic JRA scheme with statistical channel properties and instantaneous channel characteristics, respectively. With the derived closed-form expression of the outage probability, a successive alternating optimization algorithm is proposed to tackle the static JRA problem. For the dynamic JRA scheme, a suboptimal closed-form solution is derived based on a multistep optimization and relaxation method. We present a comprehensive set of simulation results to evaluate the proposed schemes and compare their performances with those of existing resource allocation schemes.

A joint resource allocation algorithm to minimize the system outage probability is proposed for a decode-and-forward (DF) two-way relay network with simultaneous wireless information and power transfer (SWIPT) under a total power constraint. In this network, the two sources nodes exchange information with the help of a passive relay, which is assumed to help the two source nodes’ communication without consuming its own energy by exploiting an energy-harvesting protocol, the power splitting (PS) protocol. An optimization framework to jointly optimize power allocation (PA) at the source nodes and PS at the relay is developed. Since the formulated joint optimization problem is non-convex, the solution is developed in two steps. First, the conditionally optimal PS ratio at the relay node for a given PA ratio is explored; then, the closed-form of the optimal PA in the sense of minimizing the system outage probability with instantaneous channel state information (CSI) is derived. Analysis shows that the optimal design depends on the channel condition and the rate threshold. Simulation results are obtained to validate the analytical results. Comparison with three existing schemes shows that the proposed optimized scheme has the minimum system outage probability.

We aim to reveal the clinical features of transluminal broncholiths and to evaluate the efficacy and safety of bronchoscopy for treating transluminal broncholiths. Patients with transluminal broncholiths were enrolled in this retrospective study in Lanzhou University Second Hospital between January 2010 and December 2018. Then age, gender, symptoms, and signs, imaging characteristics, treatment methods, outcomes as well as complications were retrospectively analyzed. Twenty-eight patients with 36 pieces of transluminal broncholiths were diagnosed using chest CT and bronchoscopy, of which two patients underwent broncholiths removal via an elective surgical procedure and six patients were treated with one-time removal of broncholiths by bronchoscopy. Among the six patients who received one-time removal of stones by bronchoscopy, two underwent massive hemorrhage and one suffered from bronchial wall laceration in the process of broncholiths removal, all of the three patients received surgical treatment eventually. No serious complications occurred in the other 20 patients who underwent broncholiths removal via repeated bronchoscopy. Removal of transluminal broncholiths by bronchoscopy are effective and safe with less complications. When it is difficult to remove the transluminal broncholith completely at one time, repeated bronchoscopy could be chosen: First, to remove the portion which causes airway obstruction; and then to remove the remaining part by repeated bronchoscopy during the follow-up period. In case that severe distal lung tissue injury, massive hemoptysis or bronchial wall laceration occurs or the diagnosis of broncholiths is unclear, surgical treatment is required.