Research on Rainfall Intensity Threshold of Occasional Debris Flow Based on Infiltration

The rainfall warning method for debris flows usually uses rainfall intensity and duration to establish an I-D relationship internationally and determine the rainfall warning threshold for debris flows. This method requires extensive rainfall data from debris flow events in the study area to establish the I-D relationship. However, some areas with occasional debris flows lack sufficient debris flow events to establish I-D relationships to determine rainfall warning thresholds. Therefore, this study uses the infiltration effect of water flow on gravel soil and establishes a rainfall intensity threshold judgment formula for debris flow initiation based on the limit equilibrium method. Taking the Taiqing debris flow that occurred in Laoshan, China, on June 13, 2018, as an example, the rainfall intensity and characteristics of the debris flow are analyzed. The maximum rainfall intensity during this rainfall process far exceeds the rainfall intensity threshold determined by the judgment formula. Using the judgment formula, it can be determined that the rainfall process will cause debris flow. The judgment result is consistent with the actual situation (where a debris flow occurred during the rainfall process). To further verify the accuracy of the judgment formula, the rainfall process of Typhoon Lichma on August 11, 2019, in the study area was analyzed. The rainfall process has a long history. Still, the rainfall intensity is much lower than the threshold of rainfall intensity for the initiation of debris flow, so this rainfall will not cause the occurrence of debris flow. The judgment result is consistent with the actual situation (no debris flow occurred during rains). Doi: 10.28991/CEJ-2023-09-09-02 Full Text: PD

Modular stem in total hip arthroplasty for patients with trochanter valgus deformity: surgical technique and case series.

BACKGROUND: Trochanter valgus deformity (TVD) is a rare condition of total hip arthroplasty (THA). Femoral osteotomy could be required in correcting the deformity to implant femoral stem in severe TVD. In this study, we described one unpublished technique of reverse sleeve of S-ROM to get through the complex situation. This study aimed to summarize and evaluate its technical challenges, safety and effectiveness. METHODS: From January 2006 to December 2014, we enrolled patients whose sleeves were implanted towards the great trochanter in THA with TVD. Their demographics, perioperative and postoperative information were recorded. To explore its indication, we measured and analyzed the ratio of greater trochanter/lesser trochanter (G/L ratio) and trochanter valgus angle (TVA). RESULTS: Twelve patients (1 male and 11 female, average age 42.30 ± 10.23) had mean follow-up of 6 years. Among them, only two patients had intraoperative femoral fracture. The survivorship of femoral prosthesis was 100%. The Harris hip score (HHS) increased from preoperative 34.31 ± 14.43 to postoperative 84.12 ± 11.33. All patients\u27 G/L ratio were larger than 1.50. CONCLUSIONS: The reverse sleeve of S-ROM was a reliable method for the patients with severe TVD, which brought satisfying clinical outcomes in mid-term follow-up

DGL: Dynamic Global-Local Prompt Tuning for Text-Video Retrieval

Text-video retrieval is a critical multi-modal task to find the most relevant video for a text query. Although pretrained models like CLIP have demonstrated impressive potential in this area, the rising cost of fully finetuning these models due to increasing model size continues to pose a problem. To address this challenge, prompt tuning has emerged as an alternative. However, existing works still face two problems when adapting pretrained image-text models to downstream video-text tasks: (1) The visual encoder could only encode frame-level features and failed to extract global-level general video information. (2) Equipping the visual and text encoder with separated prompts failed to mitigate the visual-text modality gap. To this end, we propose DGL, a cross-modal Dynamic prompt tuning method with Global-Local video attention. In contrast to previous prompt tuning methods, we employ the shared latent space to generate local-level text and frame prompts that encourage inter-modal interaction. Furthermore, we propose modeling video in a global-local attention mechanism to capture global video information from the perspective of prompt tuning. Extensive experiments reveal that when only 0.67% parameters are tuned, our cross-modal prompt tuning strategy DGL outperforms or is comparable to fully finetuning methods on MSR-VTT, VATEX, LSMDC, and ActivityNet datasets. Code will be available at https://github.com/knightyxp/DGLComment: AAAI2024, Code will be available at https://github.com/knightyxp/DG

Finite-Time Asynchronous Switching Control for Fuzzy Markov Jump Systems by Applying Polynomial Membership Functions

This article addresses the problem of finite-time asynchronous switching control for fuzzy Markov jump systems (FMJSs) using polynomial membership functions. Firstly, a Lyapunov-Krasovskii functional (LKF) is designed, incorporating both system modes and polynomial membership functions. This LKF contains more information about the membership functions, closely aligning with the characteristics of FMJSs, and effectively reducing conservatism. Based on the polynomial matrices switching rule, a practical asynchronous switching controller is introduced. The objective is to ensure finite-time boundedness of the closed-loop FMJSs while satisfying a H∞ performance index. Furthermore, the average dwell time method is applied to handle the switching signal, eliminating the predefined assumptions that switching numbers are finite or that system states are constrained to a specific region. Ultimately, the validity and practicability of the obtained results are verified through three examples

Double Asynchronous Switching Control for Takagi–Sugeno Fuzzy Markov Jump Systems via Adaptive Event-Triggered Mechanism

This article addresses the issue of adaptive event- triggered H∞ control for Markov jump systems based on Takagi-Sugeno (T-S) fuzzy model. Firstly, a new double asynchronous switching controller is presented to deal with the problem of the mismatch of premise variables and modes between the controller and the plant, which is widespread in real network environment. To further reduce the power consumption of communication, a switching adaptive event-triggered mechanism is adopted to relieve the network transmission pressure while ensuring the control effect. In addition, a new Lyapunov-Krasovskii functional (LKF) is constructed to reduce conservatism by introducing the membership functions (MFs) and time-varying delays informa- tion. Meanwhile, the invariant set is estimated to ensure the stability of the system. And the disturbance rejection ability is measured by the optimal H∞ performance index. Finally, two examples are presented to demonstrate the effectiveness of the proposed approach

Reward improves response inhibition by enhancing attentional capture

Reward plays a crucial role in enhancing response inhibition. While it is generally assumed that the process of response inhibition involves attentional capture and the stopping of action, it is unclear whether this reflects a direct impact of reward on response inhibition or rather an indirect mediation via attentional capture. Here, we employed a revised stop-signal task (SST) that separated these two cognitive elements, by including a continue signal that required the same motor response as in go trials, but also attention to a cue, as in stop trials. We first confirmed the engagement of the right inferior frontal gyrus (IFG) during stop and continue trials, both of which required the attentional capture of the task-relevant cue, but only one of which required motor inhibition. The pre-supplementary motor area (pre-SMA) was specifically activated by the contrast of the stop trials with the continue trials. The results indicated that the IFG played an important role in attentional capture by unexpected stimuli, while the pre-SMA was responsible for the direct control of motor inhibition. Behavioral performance of the SST was improved by reward, and moreover, reward induced an increase in IFG activity. In addition, this advantageous reward effect was associated with enhanced connectivity between the anterior cingulate cortex and the IFG. These results indicated that the reward facilitation effect on response inhibition was indirect, occurring via a change in attentional processing. The present data confirm the specific function of the IFG and pre-SMA in response inhibition and provide straightforward evidence that reward can increase attentional capture-related activation in the IFG, which in turn improves the performance of response inhibition

Asynchronous switching control for fuzzy Markov jump systems with periodically varying delay and its application to electronic circuits

This article focuses on addressing the issue of asynchronous H∞ control for Takagi-Sugeno (T-S) fuzzy Markov jump systems with generally incomplete transition probabilities (TPs). The delay is assumed to vary periodically, resulting in one monotonically increasing interval and one monotonically decreasing interval during each period. Meanwhile, a new Lyapunov-Krasovskii functional (LKF) is devised, which depends on membership functions (MFs) and two looped functions formulated for the monotonic intervals. Since the modes and TPs of the original system are assumed to be unavailable, an asynchronous switching fuzzy controller on the basis of hidden Markov model is proposed to stabilize the fuzzy Markov jump systems (FMJSs) with generally incomplete TPs. Consequently, a stability criterion with improved practicality and reduced conservatism is derived, ensuring the stochastic stability and H∞ performance of the closed-loop system. Finally, this technique is employed to the tunnel diode circuit system, and a comparison example is given, which verifies the practicality and superiority of the method

Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder

Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss. In this paper, we propose a novel unsupervised video hashing framework dubbed Self-Supervised Video Hashing (SSVH), that is able to capture the temporal nature of videos in an end-to-end learning-to-hash fashion. We specifically address two central problems: 1) how to design an encoder-decoder architecture to generate binary codes for videos; and 2) how to equip the binary codes with the ability of accurate video retrieval. We design a hierarchical binary autoencoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture. Then, we encourage the binary codes to simultaneously reconstruct the visual content and neighborhood structure of the videos. Experiments on two real-world datasets (FCVID and YFCC) show that our SSVH method can significantly outperform the state-of-the-art methods and achieve the currently best performance on the task of unsupervised video retrieval