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Background Currently, no universally accepted standardized VTE risk assessment model (RAM) is specifically designed for critically ill patients. Although the ICU-venous thromboembolism (ICU-VTE) RAM was initially developed in 2020, it lacks prospective external validation. Objectives To evaluate the predictive performance of the ICU-VTE RAM in terms of VTE occurrence in mixed medical-surgical ICU patients. Methods We prospectively enrolled adult patients in the ICU. The ICU-VTE score and Caprini or Padua score were calculated at admission, and the incidence of in-hospital VTE was investigated. The performance of the ICU-VTE RAM was evaluated and compared with that of Caprini or Padua RAM using the receiver operating curve. Results We included 269 patients (median age: 70 years; 62.5% male). Eighty-three (30.9%) patients experienced inpatient VTE. The AUC of the ICU-VTE RAM was 0.743 (95% CI, 0.682-0.804, P

ABSTRACT Objective: Tibial plateau fractures are common intra-articular fractures that pose classification and treatment challenges for orthopedic surgeons. Objective: This study examines the value of 3D printing for classifying and planning surgery for complex tibial plateau fractures. Methods: We reviewed 54 complex tibial plateau fractures treated at our hospital from January 2017 to January 2019. Patients underwent preoperative spiral CT scans, with DICOM data processed using Mimics software. 3D printing technology created accurate 1:1 scale models of the fractures. These models helped subdivide the fractures into seven types based on the tibial plateau's geometric planes. Surgical approaches and simulated operations, including fracture reduction and plate placement, were planned using these models. Results: The 3D models accurately depicted the direction and extent of fracture displacement and plateau collapse. They facilitated the preoperative planning, allowing for precise reconstruction strategies and matching intraoperative details with the pre-printed models. Post-surgery, the anatomical structure of the tibial plateau was significantly improved in all 54 cases. Conclusion: 3D printing effectively aids in the classification and preoperative planning of complex tibial plateau fractures, enhancing surgical outcomes and anatomical restoration. Level of Evidence IV, Prospective Study.

Abstract Achieving room-temperature high anisotropic magnetoresistance ratios is highly desirable for magnetic sensors with scaled supply voltages and high sensitivities. However, the ratios in heterojunction-free thin films are currently limited to only a few percent at room temperature. Here, we observe a high anisotropic magnetoresistance ratio of −39% and a giant planar Hall effect (520 μΩ⋅cm) at room temperature under 9 T in β-Ag2Te crystals grown by chemical vapor deposition. We propose a theoretical model of anisotropic scattering — induced by a Dirac cone tilt and modulated by intrinsic properties of effective mass and sound velocity — as a possible origin. Moreover, small-size angle sensors with a Wheatstone bridge configuration were fabricated using the synthesized β-Ag2Te crystals. The sensors exhibited high output response (240 mV/V), high angle sensitivity (4.2 mV/V/°) and small angle error (

Owing to the tolerance of circuit components in analogue circuits, feature overlap occurs between different component faults, especially in the case of early-stage faults. To address this issue, we present an improved model that utilizes the channel attention mechanism in conjunction with a fully convolutional network. The proposed model conducts end-to-end diagnosis by utilizing the response signals generated from the tested circuit. To diagnose different fault categories, we add a global average pooling layer to the last layer of the fully convolutional network to obtain the probabilities of various fault categories. Furthermore, we incorporating a channel attention, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones, within the network, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones. The proposed method is validated with four-opamp biquad high-pass filter circuit and leapfrog filter circuit. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 95.24% in Case 1,87.09% in Case 2. This method exhibits higher diagnostic accuracy and reliability than existing methods.

In Unmanned Aerial Vehicle (UAV) systems, packet loss during sensor data transmission causes data missing, which reduces fault features in sensor signals and causes the accuracy of state monitoring to decrease. This study proposes a state monitoring algorithm combining a convolutional neural network (CNN) with a random forest (RF) for data missing scenarios. CNN algorithm is designed to extract the distributed fault information from the available signals and acquire the state features of the system. Random forest algorithm processes the state features and judges the system state. The integrating strategy utilizes the automatic feature extraction capability of CNN and the superior discrimination capability of an RF classifier to improve the state monitoring accuracy. The experimental results show that the accuracy of state monitoring in data missing condition reaches 92.74%. The comparative experiments verify the validity of the proposed algorithm.

Because of the challenges associated with the difficulty of accurately labeling the remote sensing (RS) scene images and the need to identify new scene classes, few-shot learning has shown significant advantages in addressing the remote sensing scene classification (RSSC) tasks, leading to a growing interest. However, due to the scale variations of targets and irrelevant complex background in scene images, the current few-shot methods exist the following problems: the problem of the extraction capability of feature extractor in the few-shot mechanism; the problem of the separability of few-shot RS scene images classifier. To solve the above problems, an approach, called few-shot RSSC via subspace based on multiscale feature learning is introduced in this work. We first design a multiscale feature learning technique to address scale variations of the targets in the scene images. Concretely, different branches are utilized to learn scene features at various scales. The self-attention mechanism is embedded in each branch to incorporate the understanding of the global information in the different scale features. After that, a multiscale feature fusion operation, incorporating channel attention, will be devised to effectively merge the different scale features, so as to obtain a more precise feature representation of RS scene images. Furthermore, the subspace is utilized to capture the shared characteristics of each category, to reduce the impact of the complex irrelevant backgrounds in the scene images. The results of our experiments conducted on the public available RS scene datasets demonstrate the strong competitiveness of our approach.

In this paper, a hybrid nonlinear control scheme combining nonlinear dynamic inversion (NDI) and adaptive sliding mode (ASM) control is proposed for high angle of attack fighter system with center of gravity perturbation and aerodynamic parameter uncertainty. By introducing the affine nonlinear model of inner loop angular velocity with center of gravity perturbation, an adaptive sliding mode control scheme based on the online estimation of radial basis function (RBF) neural network is designed, which can reduce the dynamic inversion error of NDI control and rapidly compensate the multi-coupled channel oscillation. The aerodynamic force and moment coefficients are estimated via the iterative weighted least squares (IRLS) method. Then an outer loop integral sliding mode (ISM) NDI controller is designed, which can be robust to disturbances and mismatched uncertainties through adjusting adaptive gain, and the rudder yaw chattering caused by the uncertainty of aerodynamic parameters is reduced simultaneously. The Lyapunov stability theory and Barbarat’s lemma prove the stability of the designed control scheme, and simulation based on F-16 model verifies the effectiveness of the control scheme.

Aiming at the monitoring problem of the human climbing behaviour existing along the railway,a railway safety detection algorithm based on distributed optical fiber vibration sensors was proposed by combining the distributed optical fiber sensing technology and signal analysis technology.The surrounding vibration was sensed and transmitted through the optical cables laid along the fence network of the railway,and then the Internet of things (IoT) connection between the railway and monitoring algorithm was built to realize the intelligent monitoring of the climbing behavior.In view of the complicated surrounding environment of the railway and more interference,the Hamming window and wavelet threshold denoising method were used to filter the signal of each frame to improve the signal-to-noise ratio of the vibration signal.In the selection of features,the power spectrum and short-time over-level rate of the signal were extracted from the time domain and frequency domain respectively as a joint feature to determine whether there was climbing or creeping behavior.Since that the climbing behavior was spatially continuous,the minimum alarm range was set to filter out alarms with a too small range,which improved the accuracy of the monitoring system.

Due to oversaturated traffic condition, few models are capable of describing the complexity and control strategies are difficult to design. This article proposes an arterial coordination control for unsaturated and oversaturated traffic condition via discretization analysis model. The Robertson discrete formula is introduced to describe the discrete relationship between the upstream and downstream flow. The downstream vehicle arrival rate satisfies the cyclical relationship when the turning traffic flow is low. On this basis, the traffic parameters of the upstream and downstream can be calculated by the `look-up table' method instead of the `recursion' method in using the periodic array to save the intermediate variables in the model. As a result, the computation time significantly decreases. For unsaturated situation, delays are accumulated in each time period and reduced through adjusting offsets. For oversaturated traffic condition caused by lane occupancy accidents, green time is optimized by genetic algorithm after the road capacity decline is estimated. Simulation experimental results verify the effectiveness of the control strategies and the practicability of the proposed model.

ORAL squamous cell carcinoma (OSCC) is a malignant tumor with the highest incidence among tumors involving the oral cavity maxillofacial region, and is notorious for its high recurrence and metastasis potential. Long non-coding RNAs (lncRNAs), which regulate the genesis and evolution of cancers, are potential prognostic biomarkers. This study identified HOTAIRM1 as a novel significantly upregulated lncRNA in OSCC, which is strongly associated with unfavorable prognosis of OSCC. Systematic bioinformatics analyses demonstrated that HOTAIRM1 was closely related to tumor stage, overall survival, genome instability, the tumor cell stemness, the tumor microenvironment, and immunocyte infiltration. Using biological function prediction methods, including Weighted gene co-expression network analysis (WGCNA), Gene set enrichment analysis (GSEA), and Gene set variation analysis (GSVA), HOTAIRM1 plays a pivotal role in OSCC cell proliferation, and is mainly involved in the regulation of the cell cycle. In vitro, cell loss-functional experiments confirmed that HOTAIRM1 knockdown significantly inhibited the proliferation of OSCC cells, and arrested the cell cycle in G1 phase. At the molecular level, PCNA and CyclinD1 were obviously reduced after HOTAIRM1 knockdown. The expression of p53 and p21 was upregulated while CDK4 and CDK6 expression was decreased by HOTAIRM1 knockdown. In vivo, knocking down HOTAIRM1 significantly inhibited tumor growth, including the tumor size, weight, volume, angiogenesis, and hardness, monitored by ultrasonic imaging and magnetic resonance imaging In summary, our study reports that HOTAIRM1 is closely associated with tumorigenesis of OSCC and promotes cell proliferation by regulating cell cycle. HOTAIRM1 could be a potential prognostic biomarker and a therapeutic target for OSCC.

This study investigated minimum-entropy hybrid fault-tolerant control (FTC) theory for non-Gaussian stochastic systems with compound faults. After fuzzy linearization for the singular systems, the output probability density function (PDF) is generated by rational square root B-splines. To deal with the compound faults consisting of single sensor fault and intermittent multiple actuator faults, an active-passive hybrid adaptive FTC scheme is proposed: A passive compensation function can directly reconstruct the algorithm to mask the sensor fault; then, actuator fault estimation accurately tracks the multiple actuator faults. Hence, the hybrid FTC combines estimated information and passive compensation simultaneously implements active actuator fault repair and passive sensor fault shielding. A novel variable parameter algorithm that mimics animal predation behavior is designed and incorporated into learning rates, making the controller more sensitive to the incipient deviations in actuator faults. Finally, with the optimal indicators containing entropy and mean of non-Gaussian PDF, the minimum-entropy FTC is achieved. Lyapunov and indicator functions prove the stability, simulation verifies the effectiveness of the methods.

Aiming at the timing signal problem of single intersection,a timing method based on the improved Webster algorithm in transportation Internet of things was proposed by combining the micro level analysis method with the macro level analysis method.In order to optimize the saturation and signal control delay,the average delay model was established.The game model and the conflict model were established with non-signal control and signal control.And the conflict analysis was carried out to illustrate that the optimal signal timing could coordinate the conflicting traffic flow.Based on the improved Webster algorithm,the shortest time required for the motor vehicle to pass the single intersection was additionally considered and a three-step optimization method for timing method at single intersection was proposed to reduce the traffic delay to ensure that the vehicle could pass effectively.The simulation results illustrate the effectiveness of the proposed solution.

This study proposes a fault estimation and compensation scheme for hypersonic flight vehicle (HFV) attitude system with body flap fault. To achieve the fault compensation capability, an interval type-2 fuzzy estimator is designed to approximate the unknown nonlinear function caused by modeling uncertainties and the fault. The fault-tolerant controller via the terminal sliding mode and dynamic surface techniques is developed to ensure the tracking accuracy of attitude angles for HFV and the desired control torque can be achieved. Then, a novel control allocation scheme with a cuckoo search algorithm and linear programming method is proposed to distribute the desired torque to aerodynamic surfaces and reaction control system jets. The stability of the proposed scheme is analyzed using the Lyapunov stability theory. The validity of the method is verified by a series of comparisons on numerical simulation results.

This paper presents a nonlinear fault-tolerant control (FTC) and sensor fault diagnosis for longitudinal dynamics of hypersonic flight vehicle. A nominal sliding mode control is developed to track the reference command of velocity and altitude on the basis of an affine nonlinear dynamic model. A nonlinear adaptive observer is proposed to estimate the fault in velocity system. The fault in altitude system is estimated using the coupling item as an estimation item through a backstepping sliding-mode observer to achieve an estimation performance better than that of the original adaptive observer. The estimation results are used to design a nonlinear FTC to compensate the multi-sensor faults. The stability of the controller is analyzed through Lyapunov theory. Numerical simulation results demonstrate the validity and robustness of the proposed controller and observer.

This paper provides a mathematical model for a hypersonic flight vehicle system with actuator faults and then, proposes a fault-tolerant algorithm to increase the reliability of the attitude system. A terminal sliding mode controller for attitude angular loop is introduced to obtain the virtual control input. Thus, the required deflection angle of aerodynamic surfaces can be calculated via dynamic surface technique in inner angular rate loop. When the aero-surfaces are saturated and unable to supply the moment vector commanded by the fault tolerant controller, a reaction control system will be driven by quadratic programming allocator to provide the residual control torque. The system stability is analyzed utilizing the Lyapunov function. Several simulation results validate that the system output can track the desired command even under fault situations.

This article proposes a multiple-step fault estimation algorithm for hypersonic flight vehicles that uses an interval type-II Takagi–Sugeno fuzzy model. An interval type-II Takagi–Sugeno fuzzy model is developed to approximate the nonlinear dynamic system and handle the parameter uncertainties of hypersonic firstly. Then, a multiple-step time-varying additive fault estimation algorithm is designed to estimate time-varying additive elevator fault of hypersonic flight vehicles. Finally, the simulation is conducted in both aspects of modeling and fault estimation; the validity and availability of such method are verified by a series of the comparison of numerical simulation results.

In this paper, an improved reconfiguration control scheme via an H ∞ fault observer and adaptive control is studied for the quad-rotor helicopter with actuator faults. The bilinear problem is eliminated by constructing fault compensation and control law reconfiguration in the adaptive controller. Fault estimation is achieved by designing the fault observer with an H ∞ performance index, which is applied to evaluate the ‘locking in place’ fault of the actuator in a quad-rotor helicopter. By drawing the H ∞ performance index into the adaptive fault observer, an asymptotically convergent estimated error can be attained and the burden of the adaptive controller is alleviated. Some simulation and experimental results confirm the availability of the reconfiguration control scheme.

In this paper, an optimal reconfiguration control scheme is proposed for a quadrotor helicopter with actuator faults via adaptive control and combined multiple models. The combined models set contains several fixed models, an adaptive model and a reinitialized adaptive model. The fixed models and the adaptive model can describe the failure system under different fault conditions. Moreover, the proposed reinitialized adaptive model refers to the closest model of the current system and can improve the speed of convergence effectively. In addition, the reference model is designed in consideration of an optimal control performance index and the principle of the minimum cost to achieve perfect tracking performance. Finally, some simulation results demonstrate the effectiveness of the proposed reconfiguration control scheme for faulty cases.

In this paper, an adaptive controller is designed for a UAV flight control system against faults and parametric uncertainties based on quantum information technology and the Popov hyperstability theory. First, considering the bounded control input, the state feedback controller is designed to make the system stable. The model of adaptive control is introduced to eliminate the impact by the uncertainties of system parameters via quantum information technology. Then, according to the model reference adaptive principle, an adaptive control law based on the Popov hyperstability theory is designed. This law enable better robustness of the flight control system and tracking control performances. The closed-loop system's stability is guaranteed by the Popov hyperstability theory. The simulation results demonstrate that a better dynamic performance of the UAV flight control system with faults and parametric uncertainties can be maintained with the proposed method.