Moisture content distribution model for the soil wetting body under moistube irrigation

This study investigated the moisture distribution characteristics of a soil wetting body under different influencing factors to inform the design and management of a moistube irrigation system. A mathematical model of soil moisture movement under moistube irrigation was established based on Hydrus-2D software. The suitability of the Hydrus-2D simulation model was verified by laboratory experiments. Numerical simulations were carried out with Hydrus-2D to investigate the influence of soil texture, initial moisture content, moistube specific discharge and irrigation time on the moisture distribution of a soil wetting body. The soil moisture content is highest at the moistube, and its value is related to the moistube-specific discharge and soil texture. The soil moisture content at any point in the wetting body decreased linearly with increasing distance from the wetting front to the moistube in the five set directions (vertical downward, 45° downward, horizontal, 45° upward and vertical upward). This trend is applicable to fine-textured and coarse-textured soil. An estimation model of soil moisture content including soil saturated hydraulic conductivity, initial soil moisture, the specific flow rate of the moistube and the maximum value of the wetting front distance in all directions is proposed. The model estimation is good (root mean square error = 0.008–0.018 cm3·cm−3, close to 0; Nash-Sutcliffe efficiency coefficient = 0.987, close to 1), and it can provide a practical tool for moistube irrigation design and agricultural water management

Case Report: Isolated facial and trigeminal nerve palsy without ataxia in anti-GQ1b antibody syndrome secondary to Mycoplasma pneumonia

The presence of anti-GQ1b antibodies in serum or cerebrospinal fluid is a diagnostic indicator of the Miller–Fisher variant of Guillain–Barré syndrome (GBS), whereas anti-GQ1b antibody syndrome is rarely presented as acute bilateral pain in the cheeks and masticatory muscle fatigue without ophthalmoplegia, ataxia, or limb weakness. Here, we report a case of a female patient diagnosed with GBS characterized only by the involvement of the facial and trigeminal nerves who was positive for serum anti-GQ1b antibodies secondary to Mycoplasma pneumoniae infection. The patient was treated with macrolide antibiotics and neurotrophic drugs, and her symptoms were significantly alleviated after 1 month. This case indicates a new clinical presentation of GBS and anti-GQ1b antibody syndrome with a differential diagnosis of multiple cranial nerve damage of which neurological physicians should be aware. Positive anti-GQ1b antibodies secondary to infection were observed in this case, and antibiotic treatment resulted in a favorable prognosis. The specific underlying mechanism requires further investigation

Lymphoplasmapheresis versus plasma exchange in severe myasthenia gravis: a retrospective cohort study

BackgroundLymphoplasmapheresis (LPE) is a new therapy developed on the basis of traditional plasma exchange (PE) in combination with leukapheresis. Currently, it remains unclear whether PE and LPE show differences in efficacy for severe MG.MethodsA retrospective analysis was conducted on 198 MG patients, 75 in the PE group and 123 in the LPE group, and the patients’ Myasthenia Gravis Foundation of America (MGFA) Clinical Classification was Class IV. The treatment outcome was the change in Quantitative Myasthenia Gravis Score (QMGS) from baseline to the end of treatment. Propensity score matching (PSM) was applied for the balance of confounders between the two groups.ResultsIn this study cohort, the safety profile of LPE and PE was good and no serious adverse events were observed. Based on PSM, 62 patients treated with LPE and 62 patients treated with PE were entered into a comparative efficacy analysis. In the PE group, patients underwent a total of 232 replacements, with a mean of 3.74. PE significantly improved the patients’ QMGS performance, with the mean QMGS decreasing from 22.98 ± 4.03 points at baseline to 18.34 ± 5.03 points after treatment, a decrease of 4.68 ± 4.04 points (p < 0.001). A decrease of ≥3 points in QMGS was considered a significant improvement, with a treatment response rate of 67.7% in the PE group. In the LPE group, patients received a total of 117 replacements, with a mean of 1.89. The patients’ mean QMGS was 23.19 ± 4.11 points at baseline and was 16.94 ± 5.78 points after treatment, a decrease of 6.26 ± 4.39 points (p < 0.001). The improvement in QMGS was more significant in patients treated with LPE compared to the PE group (p = 0.039). The treatment response rate in the LPE group was 79%, which was not significantly different compared to the PE group (p = 0.16). The LEP group had a shorter mean length of stay compared to the PE group (10.86 ± 3.96 vs. 12.14 ± 4.14 days), but the difference was not statistically significant (p = 0.13). During the 2-month follow-up period, LPE may be associated with better functional outcomes for patients, with lower QMG score and relapse rate. LPE and PE were both effective in reducing the levels of inflammatory cytokines (TNF-α, IL-1β, and IL-6) and AChR-Ab. Compared to PE, LPE was superior in the reduction of AChR-Ab titer.ConclusionIn severe MG, LPE may be a more preferred treatment option than PE. It achieves treatment outcomes that are not inferior to or even better than PE with fewer replacements. This study provides further evidence to support the application of LPE as a new treatment option for MG

Observer-Based Event-Triggered Predictive Control for Networked Control Systems under DoS Attacks

This paper studies the problem of DoS attack defense based on static observer-based event-triggered predictive control in networked control systems (NCSs). First, under the conditions of limited network bandwidth resources and the incomplete observability of the state of the system, we introduce the event-triggered function to provide a discrete event-triggered transmission scheme for the observer. Then, we analyze denial-of-service (DoS) attacks that occur on the network transmission channel. Using the above-mentioned event-triggered scheme, a novel class of predictive control algorithms is designed on the control node to proactively save network bandwidth and compensate for DoS attacks, which ensures the stability of NCSs. Meanwhile, a closed-loop system with an observer-based event-triggered predictive control scheme for analysis is created. Through linear matrix inequality (LMI) and the Lyapunov function method, the design of the controller, observer and event-triggered matrices is established, and the stability of the scheme is analyzed. The results show that the proposed solution can effectively compensate DoS attacks and save network bandwidth resources by combining event-triggered mechanisms. Finally, a smart grid simulation example is employed to verify the feasibility and effectiveness of the scheme’s defense against DoS attacks

Observer-Based Event-Triggered Predictive Control for Networked Control Systems under DoS Attacks

This paper studies the problem of DoS attack defense based on static observer-based event-triggered predictive control in networked control systems (NCSs). First, under the conditions of limited network bandwidth resources and the incomplete observability of the state of the system, we introduce the event-triggered function to provide a discrete event-triggered transmission scheme for the observer. Then, we analyze denial-of-service (DoS) attacks that occur on the network transmission channel. Using the above-mentioned event-triggered scheme, a novel class of predictive control algorithms is designed on the control node to proactively save network bandwidth and compensate for DoS attacks, which ensures the stability of NCSs. Meanwhile, a closed-loop system with an observer-based event-triggered predictive control scheme for analysis is created. Through linear matrix inequality (LMI) and the Lyapunov function method, the design of the controller, observer and event-triggered matrices is established, and the stability of the scheme is analyzed. The results show that the proposed solution can effectively compensate DoS attacks and save network bandwidth resources by combining event-triggered mechanisms. Finally, a smart grid simulation example is employed to verify the feasibility and effectiveness of the scheme’s defense against DoS attacks.</jats:p

Comparison of model precision rate after removing each module.

Comparison of model precision rate after removing each module.</p

CBAM structure diagram.

This article proposes an advanced classification algorithm for bronze drinking utensils, taking into account the complexity of their cultural characteristics and the challenges of dynasty classification. The SSA-CBAM-GNNs algorithm integrates the Sparrow Search Algorithm (SSA), Spatial and Spectral Attention (CBAM) modules, and Graph Neural Networks (GNNs). The CBAM module is essential for optimizing feature extraction weights in graph neural networks, while SSA enhances the weighted network and expedites the convergence process. Experimental results, validated through various performance evaluation indicators, illustrate the outstanding performance of the improved SSA-CBAM-GNNs algorithm in accurately identifying and classifying cultural features of bronze drinking utensils. Comparative experiments confirm the algorithm’s superiority over other methods. Overall, this study proposes a highly efficient identification and classification algorithm, and its effectiveness and excellence in extracting and identifying cultural features of bronze drinking utensils are experimentally demonstrated.</div

Bronze drinking vessel data example.

This article proposes an advanced classification algorithm for bronze drinking utensils, taking into account the complexity of their cultural characteristics and the challenges of dynasty classification. The SSA-CBAM-GNNs algorithm integrates the Sparrow Search Algorithm (SSA), Spatial and Spectral Attention (CBAM) modules, and Graph Neural Networks (GNNs). The CBAM module is essential for optimizing feature extraction weights in graph neural networks, while SSA enhances the weighted network and expedites the convergence process. Experimental results, validated through various performance evaluation indicators, illustrate the outstanding performance of the improved SSA-CBAM-GNNs algorithm in accurately identifying and classifying cultural features of bronze drinking utensils. Comparative experiments confirm the algorithm’s superiority over other methods. Overall, this study proposes a highly efficient identification and classification algorithm, and its effectiveness and excellence in extracting and identifying cultural features of bronze drinking utensils are experimentally demonstrated.</div

Comparison of model recall rate after removing each module.

Comparison of model recall rate after removing each module.</p