Managing depression with complementary and alternative medicine therapies: a scientometric analysis and visualization of research activities.

Background: Complementary and Alternative Medicine (CAM) interventions may prove to be an attractive option for the treatment of depression. The aim of this scientometric analysis is to determine the global scientific output of research regarding managing depression with CAM and identify the hotspots and frontiers within this theme. Methods: Publications regarding the utilization of CAM for treating depression were collected from the Web of Science Core Collection from 1993 to 2022, and analyzed and visualized by Bibliometrix R-package, VOSviewer, and CiteSpace. Results: A total of 1,710 publications were acquired. The number of annual publications showed an overall rapid upward trend, with the figure peaking at 179 in 2021. The USA was the leading research center. Totally 2,323 distinct institutions involving 7,638 scholars contributed to the research theme. However, most of the cooperation was limited to within the same country, institution or research team. The Journal of Alternative and Complementary Medicine was the most productive periodical. The CAM therapies of most interest to researchers were acupuncture and body-mind techniques, such as yoga, meditation and mindfulness. Systematic review and meta-analysis are commonly used methods. "Inflammation," "rating scale" and "psychological stress" were identified as the most studied trend topics recently. Conclusion: Managing depression with evidence-based CAM treatment is gaining attention globally. Body-mind techniques and acupuncture are growing research hotspots or emerging trending topics. Future studies are predicted to potentially investigate the possible mechanisms of action underlying CAM treatments in reducing depression in terms of modulation of psychological stress and inflammation levels. Cross-countries/institutes/team research collaborations should be encouraged and further enhanced.

Event-Triggered Adaptive Fuzzy Asymptotic Tracking Control of Nonlinear Pure-Feedback Systems With Prescribed Performance.

This article considers the problem of fixed-time prescribed event-triggered adaptive asymptotic tracking control for nonlinear pure-feedback systems with uncertain disturbances. The fuzzy-logic system (FLS) is introduced to deal with the unknown nonlinear functions in the system. By constructing a new type of Lyapunov function, the restrictive requirement that the upper bounds of the partial derivative of the unknown system functions need to be known is relaxed during the controller design process. At the same time, by developing a novel fixed-time performance function (FPF), the fixed-time prescribed performance (FPP) can be achieved, that is, the tracking error can converge to the neighborhood of the origin in a fixed time and finally converges to zero asymptotically. In addition, the event-triggered strategy is developed to reduce the waste of communication resources. The proposed control law can ensure that all the signals of the system are bounded. Meanwhile, the Zeno behavior can be effectively avoided. Finally, an example is provided to prove the effectiveness of the proposed scheme.

Adaptive NN-Based Event-Triggered Containment Control for Unknown Nonlinear Networked Systems.

This article systematically addresses the distributed event-triggered containment control issues for multiagent systems subjected to unknown nonlinearities and external disturbances over a directed communication topology. Novel composite distributed adaptive neural network (NN) event-triggering conditions and event-triggered controller are raised meanwhile. Furthermore, the designed event-triggered controller is updated in an aperiodic way at the moment of event sampling, which saves the computation, resources, and transmission load. On the basis of the NN-based adaptive control techniques and event-triggered control strategies, the uniform ultimate bounded containment control can be achieved. In addition, the Zeno behavior is proven to be excluded. Simulation is presented to testify the effectiveness and advantages of the presented distributed containment control scheme.

Adaptive Neural Asymptotic Tracking of Uncertain Non-Strict Feedback Systems With Full-State Constraints via Command Filtered Technique.

This brief addresses the adaptive neural asymptotic tracking issue for uncertain non-strict feedback systems subject to full-state constraints. By introducing the significant nonlinear transformed function (NTF), the command filtered technology, and the boundary estimation method into control design, a novel command filtered backstepping adaptive controller is proposed. The proposed control scheme is able to not only deal with full-state constraints but also avoid the "explosion of complexity" issue. By means of a Lyapunov stability analysis, we prove that: 1) the tracking error asymptotically converges to zero; 2) all the variables in the controlled systems are bounded; and 3) all the states are constrained in the asymmetric predefined sets. Finally, a numerical simulation is used to demonstrate the validity of the proposed algorithm.

Nussbaum-Based Adaptive Fault-Tolerant Control for Nonlinear CPSs With Deception Attacks: A New Coordinate Transformation Technology.

In this article, two novel adaptive fault-tolerant control schemes for a class of nonlinear strict-feedback cyber-physical systems (CPSs) with deception attacks are presented. Deception attacks, such as false data-injection attacks, which destroy sensor networks, make the outputs and states of the CPSs unavailable. It is very difficult and challenging for a designer to achieve the tracking control under the circumstance of cyberattacks. To realize the tracking control for the studied CPSs, we propose a new coordinate transformation technology without precedent, where it takes the attack gains into account and uses the compromised states to design the corresponding controllers. In the backstepping design process, Nussbaum functions are presented to alleviate the influence of the unknown attack gains. Furthermore, we consider the actuator faults problem, which includes the loss of effectiveness and the bias fault. By skillfully designing the adaptive laws, the effect of actuator faults is completely eliminated. It is theoretically proved that the first proposed tracking control scheme can guarantee all signals in the closed-loop system are bounded and the output can track the desired reference signal. In addition, the second adaptive control scheme is also developed for the CPSs under the actuator faults and a more general assumption on the deception attacks is proposed simultaneously. Finally, the feasibility of the new proposed methods is verified by MATLAB simulation analysis.

Event-Triggered Adaptive Neural Control for Fractional-Order Nonlinear Systems Based on Finite-Time Scheme.

This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteria are introduced with the aim to ensure that the tracking error enters into a small region around the origin in finite time. Finally, the stability of the closed-loop system is ensured via a fractional Lyapunov function theory and two simulation examples were used to prove the validity of the designed control scheme.

A Bound Estimation Approach for Adaptive Fuzzy Asymptotic Tracking of Uncertain Stochastic Nonlinear Systems.

The adaptive fuzzy tracking control problems for a class of uncertain stochastic nonlinear systems are investigated in this article using the backstepping control approach. Different from the existing research, the crucial but highly restrictive hypothesis on the prior knowledge of unknown virtual control coefficients (UVCCs) is removed from this article. An asymptotic tracking control scheme is proposed by applying smooth functions and a bounded estimation method. By delicately constructing a specific composite Lyapunov function for the controlled system and several useful inequalities, the stability and asymptotic tracking performance with unknown nonlinear function and unknown UVCCs can be guaranteed almost surely. Finally, the method is illustrated with simulation examples.

Event-Triggered Fuzzy Adaptive Fixed-Time Tracking Control for Nonlinear Systems.

In this article, the problem of event-based adaptive fuzzy fixed-time tracking control for a class of uncertain nonlinear systems with unknown virtual control coefficients (UVCCs) is considered. The unknown nonlinear functions of the considered systems are approximated by fuzzy-logic systems (FLSs). Moreover, a novel Lyapunov function is designed to remove the requirement of lower bounds of the UVCC in control laws. In addition, an event-triggered control method is developed by using the backstepping technique to save the network resources. Through theoretical analysis, the event-based fixed-time controller was proposed, which can guarantee that all signals of the controlled system are bounded and the tracking error can converge to a small neighborhood of the origin in a fixed time. Meanwhile, the convergence time is independent of the initial states. Two numerical examples are presented to demonstrate the effectiveness of the proposed approach.

Event-Based Design of Finite-Time Adaptive Control of Uncertain Nonlinear Systems.

The problem of finite-time adaptive tracking control against event-trigger error is investigated in this article for a type of uncertain nonlinear systems. By fusing the techniques of command filter backstepping technical and event-triggered control (ETC), an adaptive event-triggered design method is proposed to construct the controller, under which the effect of event-triggered error can be compensated completely. Moreover, the proposed controller can increase robustness against uncertainties and event error in the backstepping design framework. In particular, we establish the finite-time convergence condition under which the tracking error asymptotically converges to zero in finite time with the aid of a scaling function. Detailed and rigorous stability proofs are given by making use of the improved finite time stability criterion. Two simulation examples are provided to exhibit the validity of the designed adaptive ETC approach.

Backstepping-Based Fuzzy Adaptive Stabilization of Reaction-Diffusion Equation With State Constraints.

This article proposes a stabilization scheme for a cascaded parabolic partial differential equation (PDE)-ordinary differential equation (ODE) system with state constraints. To begin, by employing the fuzzy-logic system (FLS) technique for the unknown nonlinear functions in the ODE subsystem, the influence of the nonlinearities is successfully eliminated. Then, the infinite and finite-dimensional backstepping methods are skillfully applied to the design of control schemes. Specifically, the infinite-dimensional backstepping transformation and its inverse are utilized to obtain a new PDE subsystem, which is convenient for control design. Next, based on the new target PDE-ODE cascade system, a novel first-order filter is subtly introduced into each step of the finite-dimensional backstepping-based controller design procedure to avoid the issue of "explosion of complexity." Furthermore, a novel barrier Lyapunov function (BLF) is constructed to guarantee that the states of the ODE subsystem do not violate the constraints. The controller designed in this article ensures that all signals in the closed-loop system are bounded and the states can converge to zero. Finally, a simulation example verifies that the proposed scheme can achieve satisfactory performance.

Adaptive Fuzzy Asymptotic Tracking for Nonlinear Systems With Nonstrict-Feedback Structure.

In this article, the tracking control problems of the nonlinear nonstrict-feedback systems are considered. By combining the backstepping technique and bound estimation method, a novel nonlinear adaptive asymptotical control law is proposed, which offsets the effect of the unknown virtual control parameters and the uncertain nonlinearities. Correspondingly, an improved Lyapunov function by introducing the lower bounds of control parameters has been devised in this article. Compared with the existing adaptive tracking control schemes, the controller designed in this article can guarantee that the tracking control error converges to zero asymptotically and all the signals which contain the state variables and the adaptive laws are bounded. Moreover, the asymptotic stability of the system is realized for the first time. Finally, simulation examples are applied to test and prove the availability of the presented methods and the performance of the controlled system.

Latest developments in chronic intestinal pseudo-obstruction.

Chronic intestinal pseudo-obstruction (CIPO) is a type of intestinal dysfunction presenting as symptoms of intestinal obstruction but without actual mechanical obstruction. An extremely low incidence, non-specific clinical symptoms, strong heterogeneity, and no definitive cause in some patients make CIPO very difficult to diagnose correctly. Imaging and gastrointestinal manometry are commonly used. Most patients have progressive worsening of their symptoms and require intervention, and nutritional assessment and treatment are very important to determine the prognosis. With improvements in surgical techniques, small bowel transplantation is a feasible treatment option for patients with advanced CIPO; however, the long-term prognosis for CIPO patients remains unsatisfactory. Generally, the disease is rare and difficult to diagnose, which leads to clinicians' lack of understanding of the disease and results in a high rate of misdiagnosis. This review describes the characteristics of CIPO and the latest developments in diagnosis and treatment, in detail. The goal of our review is to improve clinicians' understanding of CIPO so that the disease is identified quickly and accurately, and treated as early as possible to improve patients' quality of life.

Observer-Based Adaptive Fuzzy Decentralized Event-Triggered Control of Interconnected Nonlinear System.

This paper addresses the decentralized output feedback problem of an interconnected nonlinear system subject to uncertain interactions. A decentralized event-triggered control scheme is presented so that the decentralized output feedback problem is solved with only event-sampling states. With the proposed triggering mechanism, each subsystem only uses local signals to construct the decentralized controller at its own triggering times or the switching times. It is proved that both the tracking performance and the closed-loop stability can be preserved via the presented approach. Moreover, a uniform positive lower bound for the interevent time is guaranteed. Simulation results are presented to illustrate the effectiveness of the proposed control design.

Graph-Theory-Based Decentralized Adaptive Output-Feedback Control for a Class of Nonlinear Interconnected Systems.

This paper investigates the problem of decentralized output feedback control for a class of interconnected systems with unknown state-dependent interconnections. First, we design a new form of K -filters with extra design parameters to compensate the unmeasurable states. Then by introducing a smooth function, we can design a decentralized output feedback control law by integrating the well-known backstepping framework. Furthermore, from the graph theory and Lyapunov function method, we analyze the stability and tracking property of the closed-loop systems. As an illustrative example, the proposed control scheme is applied to the controller design of a mass-spring-damper system.

Event-Based Adaptive NN Tracking Control of Nonlinear Discrete-Time Systems.

This paper is concerned with the simultaneous design of a neural network (NN)-based adaptive control law and an event-triggering condition for a class of strict feedback nonlinear discrete-time systems. The stability and tracking performance of the closed-loop network control system under the event-triggering strategy is formally proven based on the Lyapunov theory in a hybrid framework. The proposed Lyapunov formulation yields an event-triggered algorithm to update the control input and NN weights based on conditions involving the closed-loop state. Different from the existing traditional NN control schemes where the feedback signals are transmitted and executed periodically, the feedback signals are transmitted and executed only when the event-trigger error exceeds the specified threshold, which can largely reduce the communication load. The effectiveness of the approach is evaluated through a simulation example.

Adaptive Neural Control of Pure-Feedback Nonlinear Systems With Event-Triggered Communications.

This paper is concerned with the adaptive event-triggered control problem for a class of pure-feedback nonlinear systems. Unlike the existing results where the control execution is periodic, the new proposed scheme updates the controller and the neural network weights only when desired control specifications cannot be guaranteed. Clearly, this can largely reduce the amount of transmission data. Besides, since the event-trigger error is discontinuous because of the event-triggering mechanism, the stability analysis in the classical sense may not be guaranteed. To solve this problem, we formulate the event-triggered network control systems as a nonlinear impulsive dynamical system, and a novel Lyapunov theorem is used to show the stability properties of the closed-loop systems. Finally, two simulation examples are given to illustrate the effectiveness of the theoretical results.

Hypoxia preconditioning protects Ca2+-ATPase activation of intestinal mucosal cells against R/I injury in a rat liver transplantation model.

AIM: To investigate the effect of ischaemia and reperfusion (I/R) injury on the Ca2+-ATPase activation in the intestinal tissue of a rat autologous orthotopic liver transplantation model and to determine if hypoxia preconditioning (HP) therapy induces HIF-1α to protect rat intestinal tissue against I/R injury. METHODS: Rats received non-lethal hypoxic preconditioning therapy to induce HIF-1α expression. We used an autologous orthotopic liver transplantation model to imitate the I/R injury in intestinal tissue. Then, we detected the microstructure changes in small intestinal tissues, Ca2+-ATPase activity, apoptosis, and inflammation within 48 h postoperatively. RESULTS: HIF-1α expression was significantly increased in intestinal tissue at 12 h postoperatively in rats that were exposed to a hypoxic environment for 90 min compared with a non-HP group (HP vs AT, P = 0.0177). Pathological analysis was performed on the intestinal mucosa cells, and the cells in the HP group appeared healthier than the cells in the AT group. The Ca2+-ATPase activity in the small intestinal cells in the AT group was significantly lower after the operation, and the Ca2+-ATPase activity in the HP group recovered faster than that in the AT group at 6 h postoperatively (HP vs AT, P = 0.0106). BCL-2 expression in the HP group was significantly higher than that in the AT group at 12 h postoperatively (HP vs AT P = 0.0010). The expression of the inflammatory factors NO, SOD, IL-6, and TNF-α was significantly lower in the HP group than in the AT group. CONCLUSION: Hypoxia-induced HIF-1α could protect intestinal mucosal cells against mitochondrial damage after I/R injury. HP could improve hypoxia tolerance in small intestinal mucosal cells and increase Ca2+-ATPase activity to reduce the apoptosis of and pathological damage to intestinal cells. HP could be a useful way to promote the earlier recovery of intestinal function after graft procedure.

Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems.

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.

[Nondestructive Measurement of RDX Grain's Internal Pressure by Using Raman Spectrum Method].

RDX is a most widely used military explosive. As the development of new warheads, there are more and more applications of RDX with abnormal grain shapes. To ensure the operational effectiveness of the new warheads, it is necessary to find an on-line nondestructive methods that can measure the RDX grain's internal pressure. In this paper, we use Raman spectrum and first-principles methods to determine the characteristic spectral line of the RDX samples and have studied the shift of the Raman spectral lines under various pressures. In particular, we measured the RDX samples under the pressures of 10, 20 and 30 MPa, respectively, and checked the fitting errors by measuring the RDX samples under the pressures of 15, 25 and 35 MPa, respectively. It is found that the shift of the characteristic Raman spectral line has a good linear dependence on the external pressures. Therefore, we can at first calibrate the linear relation between the shift of the Raman spectral line of regular RDX and the pressures in laboratory and then measure the Raman spectral line shift of the RDX with abnormal shapes. Based on the calibrated linear relation and the measurement of Raman spectra, one can determine the internal pressure in RDX. In this way, we can realize the nondestructive detection of the internal pressure of RDX. The experiment results show that this method has the advantage of good sensitivity and reproducibility. The Raman spectrum method has been successfully tested in the warhead production line.