Enhancing preoperative diagnosis of microvascular invasion in hepatocellular carcinoma: domain-adaptation fusion of multi-phase CT images.

Objectives: In patients with hepatocellular carcinoma (HCC), accurately predicting the preoperative microvascular invasion (MVI) status is crucial for improving survival rates. This study proposes a multi-modal domain-adaptive fusion model based on deep learning methods to predict the preoperative MVI status in HCC. Materials and methods: From January 2008 to May 2022, we collected 163 cases of HCC from our institution and 42 cases from another medical facility, with each case including Computed Tomography (CT) images from the pre-contrast phase (PCP), arterial phase (AP), and portal venous phase (PVP). We divided our institution's dataset (n=163) into training (n=119) and test sets (n=44) in an approximate 7:3 ratio. Additionally, we included cases from another institution (n=42) as an external validation set (test1 set). We constructed three single-modality models, a simple concatenated multi-modal model, two current state-of-the-art image fusion model and a multi-modal domain-adaptive fusion model (M-DAFM) based on deep learning methods. We evaluated and analyzed the performance of these constructed models in predicting preoperative MVI using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI) methods. Results: In comparison with all models, M-DAFM achieved the highest AUC values across the three datasets (0.8013 for the training set, 0.7839 for the test set, and 0.7454 for the test1 set). Notably, in the test set, M-DAFM's Decision Curve Analysis (DCA) curves consistently demonstrated favorable or optimal net benefits within the 0-0.65 threshold probability range. Additionally, the Net Reclassification Improvement (NRI) values between M-DAFM and the three single-modal models, as well as the simple concatenation model, were all greater than 0 (all p < 0.05). Similarly, the NRI values between M-DAFM and the two current state-of-the-art image fusion models were also greater than 0. These findings collectively indicate that M-DAFM effectively integrates valuable information from multi-phase CT images, thereby enhancing the model's preoperative predictive performance for MVI. Conclusion: The M-DAFM proposed in this study presents an innovative approach to improve the preoperative predictive performance of MVI.

Sliding-mode fault-tolerant control of the six-rotor UAV with dead-zone-input under event-triggered mechanism.

This paper investigates the problem of event-triggered mechanism(ETM)-based sliding-mode fault-tolerant control (FTC) for a six-rotor Unmanned Aerial Vehicle (UAV) with dead zone input (DZI) cases, considering potential actuator and sensor faults. Initially, a dynamic ETM is designed, followed by the development of a non-fragile observer utilizing this designed ETM. An integral sliding surface (SS) is then designed in the observation space, and the system is augmented and treated as a variable time delay system. Subsequently, sufficient conditions to ensure the stability of the augmented system with an H∞ performance index γ are obtained using the Lyapunov-Krasovskii function. Next, a sliding mode control (SMC) law is formulated to guide the sliding variables to the SS in finite time. Furthermore, sufficient conditions for ensuring system stability with an H∞ performance index γ are decoupled, and the calculation methods for the non-fragile observer gain matrix and the sliding mode gain matrix are obtained. Finally, to validate the effectiveness of the proposed method in this paper, simulation experiments are conducted.

Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.

Introduction: Motor imagery electroencephalography (MI-EEG) has significant application value in the field of rehabilitation, and is a research hotspot in the brain-computer interface (BCI) field. Due to the small training sample size of MI-EEG of a single subject and the large individual differences among different subjects, existing classification models have low accuracy and poor generalization ability in MI classification tasks. Methods: To solve this problem, this paper proposes a electroencephalography (EEG) joint feature classification algorithm based on instance transfer and ensemble learning. Firstly, the source domain and target domain data are preprocessed, and then common space mode (CSP) and power spectral density (PSD) are used to extract spatial and frequency domain features respectively, which are combined into EEG joint features. Finally, an ensemble learning algorithm based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) is used to classify MI-EEG. Results: To validate the effectiveness of the algorithm, this paper compared and analyzed different algorithms on the BCI Competition IV Dataset 2a, and further verified the stability and effectiveness of the algorithm on the BCI Competition IV Dataset 2b. The experimental results show that the algorithm has an average accuracy of 91.5% and 83.7% on Dataset 2a and Dataset 2b, respectively, which is significantly better than other algorithms. Discussion: The statement explains that the algorithm fully exploits EEG signals and enriches EEG features, improves the recognition of the MI signals, and provides a new approach to solving the above problem.

Iterative learning approach for consensus tracking of partial difference multi-agent systems with control delay under switching topology.

In this paper, the consensus tracking problem for the linear and nonlinear partial difference multi-agent systems with switching communication topology and control delay is investigated. Based on relative local measurements of neighboring followers, while considering spatio-temporal discretization and initial state deviation, a discrete distributed consensus protocol with initial value learning is designed for each agent via D-type iterative learning approach. Through rigorous mathematical theoretical analysis, the necessary and sufficient conditions are obtained. Under the switching of the communication topology, these conditions ensure that the consensus tracking control of the MASs can be solved. After applying the designed protocol, in the sense of the L2 norm and along the positive direction of the iteration axis, the consensus tracking error between any two agents can converge to zero. Finally, some simulation examples are used to demonstrate the validity of the protocol and theoretical results.

Fault Estimation Method for Nonlinear Time-Delay System Based on Intermediate Observer-Application on Quadrotor Unmanned Aerial Vehicle.

In this paper, the problem of actuator and sensor faults of a quadrotor unmanned aerial vehicle (QUAV) system is studied. In the system fault model, time delay, nonlinear term, and disturbances of QUAV during the flight are considered. A fault estimation algorithm based on an intermediate observer is proposed. To deal with a single actuator fault, an intermediate variable is introduced, and the intermediate observer is designed for the system to estimate fault. For simultaneous actuator and sensor faults, the system is first augmented, and then two intermediate variables are introduced, and an intermediate observer is designed for the augmented system to estimate the system state, faults, and disturbances. The Lyapunov-Krasovskii functional is used to prove that the estimation error system is uniformly eventually bounded. The simulation results verify the feasibility and effectiveness of the proposed fault estimation method.