Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning-Based Artificial Intelligence Architecture.

In breast carcinoma, invasive ductal carcinoma (IDC) is the most common histopathologic subtype, and ductal carcinoma in situ (DCIS) is a precursor of IDC. They are often concomitant. The immunohistochemical staining of estrogen receptor (ER)/progesterone receptor (PR) in IDC/DCIS on whole slide histopathologic images (WSIs) can predict the prognosis of patients. However, the interobserver variability among pathologists in reading WSIs is inevitable. Thus, artificial intelligence (AI) technology is crucial. Herein, IDC/DCIS detection was conducted by a deep learning approach, including faster region-based convolutional neural network (Faster R-CNN), RetinaNet, single-shot multibox detector 300 (SSD300), you only look once (YOLO) v3, YOLOv5, YOLOv7, YOLOv8, and Swin transformer. Their performance was estimated by mean average precision (mAP) values. Cell recognition and counting were performed using AI technology to evaluate the intensity and proportion of ER/PR-immunostained cancer cells in IDC/DCIS. A three-round ring study (RS) was conducted to assess WSIs. A database for modelling the underlying probability distribution of a data set with labels was established. YOLOv8 exhibits the highest detection performance with an mAP at 0.5 of 0.944 and an mAP at 0.5 to 0.95 of 0.790. With the assistance of YOLOv8, the scoring concordance across all pathologists was boosted to excellent in RS3 (0.970) from moderate in RS1 (0.724) and good in RS2 (0.812). Deep learning detection can be applied in the clinicopathologic field. To facilitate the histopathologic diagnosis of IDC/DCIS and immunostaining scoring of ER/PR, a novel AI architecture and well-organized data set were developed.

Practical Event-Triggered Finite-Time Second-Order Sliding Mode Controller Design.

This article proposes a novel event-triggered second-order sliding mode (SOSM) control algorithm using the small-gain theorems. The developed algorithm has global event property in aspects of the triggering time intervals. First, an SOSM controller is designed related to the sampling error of states, and it is proved that the closed-loop system is finite-time input-to-state stable (FTISS) with the sampling error via utilizing the small-gain theorems. Second, combined with the constructed SOSM controller, a new triggering mechanism is proposed depending on the sampling error by designing the appropriate FTISS gain condition. Third, the practical finite-time stability of the closed-loop system is verified. It is shown that the minimum triggering time interval is always a positive value in the whole state space. Finally, the simulation results demonstrate the effectiveness of the developed control method.

Direct yaw-moment control of electric vehicles based on adaptive sliding mode.

The direct yaw-moment control (DYC) system consisting of an upper controller and a lower controller is developed on the basis of sliding mode theory and adaptive control technique. First, the two-degree of freedom (2-DOF) model is utilized to calculate the ideal yaw rate. Then, the seven-degree of freedom (7-DOF) electric vehicle model is given to design the upper controller by employing first-order sliding mode (FOSM) method, which is constructed to guarantee the actual yaw rate to approach the ideal value and gain the additional yaw moment. On this basis, an adaptive first-order sliding mode (AFOSM) controller is designed to enhance the system robustness against probable modelling error and parametric uncertainties. In order to mitigate the chattering issue present in the FOSM controller, a novel adaptive super-twisting sliding mode (ASTSM) controller is proposed for the design of DYC. Furthermore, the lower controller converting the additional yaw moment into driving or braking torque acting on each wheel is also developed. Finally, The simulation results indicate that the proposed DYC system can improve the electric vehicle driving stability effectively.

Adaptive NN Fixed-Time Fault-Tolerant Control for Uncertain Stochastic System With Deferred Output Constraint via Self-Triggered Mechanism.

For a class of nonstrict-feedback stochastic nonlinear systems with the injection and deception attacks, this article explores the problem of adaptive neural network (NN) fixed-time control ground on the self-triggered mechanism in a pioneering way. After developing the self-triggered mechanism and the delay-error-dependence function, a neural adaptive delay-constrained fault-tolerant controller is proposed by employing the backstepping technique. The self-triggered mechanism does not require an additional observer to determine the time of the data transmission, which reduces the consumption of the system resources more efficiently. In addition, the whole Lyapunov function with the delay-error-dependence term is developed to solve the deferred output constraint problem. Under the proposed controller, it can be proven that all the signals within the closed-loop system are semiglobally uniformly bounded in probability, while the convergence time is independent of the initial state and the deferred output constraint control performance is achieved. The feasibility and the superiority of the proposed control strategy are shown by some simulations.

A Generalized Supertwisting Algorithm.

The work proposes a generalized supertwisting algorithm (GSTA) and its constructive design strategy. In contrast with the conventional STA, the most remarkable characteristic of the proposed method is that the discontinuous term in the conventional STA is replaced with a fractional power term, which can fundamentally improve the performance of the conventional STA. It is shown that if the fractional power in the nonsmooth term becomes -1/2, the GSTA will reduce to the conventional STA. Under the GSTA, it will be rigorously verified by taking advantage of strict Lyapunov analysis that the sliding variables can finite-time converge to an arbitrarily small region in a neighborhood of the origin by tuning the gains and the fractional power. Finally, simulation studies are provided to demonstrate the superiority of the theoretically obtained results.

Composite SOSM controller for path tracking control of agricultural tractors subject to wheel slip.

In the work, the path tracking control methods are developed for autonomous agricultural tractors subject to wheel slip constraints by using second-order sliding mode (SOSM) and finite-time disturbance observer (FDOB) techniques. First of all, the path tracking error dynamics derived from the kinematic model is given and applied to controller design. In order to deal with the inevitable chattering problem existing in the conventional first-order sliding mode (FOSM) controller, a SOSM controller is constructed by regarding the controller derivative as the new control law, which means that the practical control law can be seen as an integration of the SOSM controller. Through a combination of the FDOB and the designed SOSM controller, the composite path tracking controller is further constructed to avoid high control gains in the designed SOSM controller. The strict Lyapunov stability analysis is carried out to ensure that the sliding variable can be finite-time stabilized to the origin under the proposed control algorithms. Finally, the comparative simulation results confirm that the developed guidance laws can achieve good tracking performance and strong robustness even in the presence of slipping effects.

Adaptive Fuzzy Control for Nontriangular Stochastic High-Order Nonlinear Systems Subject to Asymmetric Output Constraints.

In this article, an adaptive fuzzy control design strategy is presented for p -norm nontriangular stochastic high-order nonlinear systems with asymmetric output constraints and unknown nonlinearities. To prevent the violation of the asymmetric output constraint, a novel barrier Lyapunov function (BLF) is constructed. Then, combining the constructed BLF with adding a power integrator approach, the adaptive fuzzy control algorithm is developed by the backstepping technique. Simultaneously, the rigorous proof displays that the designed controller can ensure that all variables of the closed-loop system are bounded in probability with the achievement of the output constraint. Eventually, the theoretical result is further demonstrated via the simulation results.

HOSM controller design with an output constraint and its application.

This work has developed an approach to a high-order sliding mode (HOSM) controller design for nonlinear systems subject to output constraints. To handle the output constraints, a barrier Lyapunov function (BLF) has been adopted. By inserting the BLF into the revamped adding a power integrator (API) technique, a HOSM controller, which can handle the closed-loop sliding mode dynamics synchronously with and without output constraints, is constructively framed. The strictly mathematical proof shows that the presented strategy can assure the finite-time stability of the closed-loop system and the achievement of a pre-set output constraint. The differences between the proposed HOSM controller and the traditional HOSM controller are discussed via a numerical example. Finally, the theoretical results obtained are successfully applied to a series elastic actuator system. The simulation results clearly confirm the merit of the presented HOSM algorithm.

Recovery of immune activity by administration of polysaccharides of Toona sinensis and its characterization of major component.

The polysaccharides were acquired from the seeds of T. sinensis by hot water extraction, ethanol precipitation, decoloration with macroporous 900 resin, and deproteinization with sevag reagent, which possessed significantly immunomodulatory activity on the levels of IgA, IgG, and IgM, the relative indices of thymus and spleen, and the phagocytosis index of mice in vivo. T. sinensis seeds polysaccharides (TSP) were separated and purified using DEAE-52 cellulose and Sephacryl S-300 column chromatography to obtain TSP-3a characterized of molecular weight, monosaccharides composition, and other preliminary structural features. TSP-3a (758.6 kDa) mainly contained Man, GalN, GlcA, Rha, Glc, Gal, Ara, and Fuc in a molar ratio of 0.26:0.24:0.19:1.80:0.45:12.56:1.09:0.21. FT-IR analysis suggested TSP-3a was an acid polysaccharide and the presence of ß configuration within the TSP-3a was discovered. Combining 1H NMR, 13C NMR, and comparison with those reported in the literature, the backbone of TSP-3a might contain →6)-α-D-Glc-(1→, →3)-ß-D-GlcA-(1→, →6)-α-D-Gal-(1→, α-D-Glc-(1→, and →6)-ß-D-Gal-(1→.[Figure: see text].