Gut microbiota changes associated with frailty in older adults:A systematic review of observational studies

BACKGROUND Frailty is a complex aging-related syndrome characterized by a cumulative loss of physiological reserve and increased vulnerability to adverse clinical outcomes,including falls,disability,incapacity and death.While an increasing number of studies suggest that the gut microbiota may play a key role in the pathophy-siology of frailty,direct evaluation of the association between gut microbiome alterations and frailty in older adults remains limited.AIM Seven electronic databases(China National Knowledge Infrastructure,VIP,SinoMed,Wanfang,PubMed,Web of Science and EMBASE)were searched for articles published before October 31,2023 to identify observational studies that compared the microbiomes of older adults with and without frailty.The diversity and composition of the gut microbiota were the main outcomes used to analyze the associations of changes in the gut microbiota with frailty in older adults.The quality of the included studies was assessed via the Newcastle-Ottawa Scale and the Agency for Healthcare Research and Quality.RESULTS Eleven observational studies with 912 older adults were included in this review.Consistent results revealed a significant difference in the gut microbiota composition between frail and non-frail older adults,with a significant decrease inαdiversity and a significant increase inβdiversity in frail older adults.The pooled results revealed that at the phylum level,four microbiota(Actinobacteria,Proteo-bacteria,Verrucomicrobia and Synergistetes)were significantly enriched,and two microbiota(Firmicutes and Fusobacteria)were significantly depleted in frail older adults.At the family level,the results consistently revealed that the abundances of 6 families,most of which belong to the Actinobacteria or Proteo-bacteria phylum,were greater in frail than in non-frail older adults.At the genus or species level,consistent results from more than two studies revealed that the abundances of the genera Prevotella,Faecalibacterium,and Roseburia were significantly lower in frail older adults;individual studies revealed that the abundances of some genera or species(e.g.,Megamonas,Blautia,and Megasphaera)were significantly lower,whereas those of other genera or species(e.g.,Bifidobacterium,Oscillospira,Ruminococcus and Pyramidobacter)were significantly greater in frail older adults.CONCLUSION This systematic review suggests that changes in the gut microbiota are associated with frailty in older adults,which is commonly reflected by a reduction in beneficial species and an increase in pathogenic species.However,further studies are needed to confirm these findings.

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

Vibration Control of A Flexible Marine Riser System Subject to Input Dead Zone and Extraneous Disturbances

An observer-based adaptive backstepping boundary control is proposed for vibration control of flexible offshore riser systems with unknown nonlinear input dead zone and uncertain environmental disturbances.The control algorithm can update the control law online through real-time data to make the controller adapt to the environment and improve the control precision.Specifically,based on the adaptive backstepping framework,virtual control laws and Lyapunov functions are designed for each subsystem.Three direction interference observers are designed to track the timevarying boundary disturbance.On this basis,the inverse of the dead zone and linear state transformation are used to compensate for the original system and eliminate the adverse effects of the dead zone.In addition,the stability of the closed-loop system is proven by Lyapunov stability theory.All the system states are bounded,and the vibration offset of the riser converges to a small area of the initial position.Finally,four examples of flexible marine risers are simulated in MATLAB to verify the effectiveness of the proposed controller.

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

Disturbances rejection optimization based on improved two-degree-of-freedom LADRC for permanent magnet synchronous motor systems

Permanent magnet synchronous motor(PMSM)speed control systems with conventional linear active disturbance rejection control(CLADRC)strategy encounter issues regarding the coupling between dynamic response and disturbance suppression and have poor performance in suppressing complex nonlinear disturbances.In order to address these issues,this paper proposes an improved two-degree-of-freedom LADRC(TDOF-LADRC)strategy,which can enhance the disturbance rejection performance of the system while decoupling entirely the system's dynamic and anti-disturbance performance to boost the system robustness and simplify controller parameter tuning.PMSM models that consider total disturbances are developed to design the TDOF-LADRC speed controller accurately.Moreover,to evaluate the control performance of the TDOF-LADRC strategy,its stability is proven,and the influence of each controller parameter on the system control performance is analyzed.Based on it,a comparison is made between the disturbance observation ability and anti-disturbance performance of TDOF-LADRC and CLADRC to prove the superiority of TDOF-LADRC in rejecting disturbances.Finally,experiments are performed on a 750 W PMSM experimental platform,and the results demonstrate that the proposed TDOF-LADRC exhibits the properties of two degrees of freedom and improves the disturbance rejection performance of the PMSM system.

A Systematic Review of Observational Studies, Demonstrating Smoking among School Going Adolescents

Objective: To characterize the methods of design and analysis currently adopted in survey research of school-based observational studies for smoking, and to identify the common pitfalls made by researchers. Methods: The systematic review was conducted in 2009 and consisted of observational studies in school settings published between January 2005 and January 2009. Smoking status was the main outcome of interest. Following Cochrane style, five steps were followed: setting selection criteria for studies and conducting a literature search;review of abstracts;review of complete articles;data extraction and quality assessment of included studies;and, finally, synthesis of studies. Results: Of the 292 abstracts retrieved, 45 (15.4%) articles were selected for the final review. Inconsistencies were found in the definition of smoking behaviour which impeded generalisability. Individual-level factors had importance, but environmental level factors were also important in studying the aetiology of smoking. Results showed that studies inappropriately reported sample size estimation and important confounding factors. Hierarchical linear modelling, random effects modelling and structural equation modelling were employed in comparatively few studies. Conclusions: There were concerns regarding data analysis of complex surveys. Fifty five percent of reviewed studies ignored environmental effects which may have produced unreliable inferences. Multi-level analysis assisted in understanding school-level effects.

Design and analysis of an advanced thermal management system for the solar close observations and proximity experiments spacecraft

In this paper,the mission and the thermal environment of the Solar Close Observations and Proximity Experiments(SCOPE)spacecraft are analyzed,and an advanced thermal management system(ATMS)is designed for it.The relationship and functions of the integrated database,the intelligent thermal control system and the efficient liquid cooling system in the ATMS are elaborated upon.For the complex thermal field regulation system and extreme space thermal environment,a modular simulation and thermal field planning method are proposed,and the feasibility of the planning algorithm is verified by numerical simulation.A solar array liquid cooling system is developed,and the system simulation results indicate that the temperatures of the solar arrays meet the requirements as the spacecraft flies by perihelion and aphelion.The advanced thermal management study supports the development of the SCOPE program and provides a reference for the thermal management in other deep-space exploration programs.

Current observation and analysis based on mooring systems in the Andaman Sea

The basic structure and intraseasonal evolution of currents in the southeastern Andaman Sea was analyzed based on data collected in 2017 from two subsurface moorings(C1 and C5).Periodic variation in the upper ocean currents of the Andaman Sea was investigated by combining observational and satellite data.Mooring observations show that rapid changes of current speed and direction occurred in May and June,with a significant increase in current velocity at the C1 mooring.In the second half of the year,southward flow dominated at the C1 mooring,and alternating northward and southward flows were evident at the C5 mooring during the same period but the northward flow prevailed in boreal winter.In addition,analysis of the power spectra of the upper currents revealed that the tidal period at both moorings is primarily semidiurnal with weaker energy than that of the low-frequency currents.The upper ocean currents at the C1 and C5 moorings exhibited intraseasonal variation of 30-60 d and 120 d,while the zonal current at the C1 mooring exhibited a notable period of approximately 180 d.Further analysis indicated that the variability of currents in the Andaman Sea is influenced primarily by equatorial Kelvin waves and Rossby wave packets.Moreover,our results suggest that equatorial Kelvin waves from the eastern Indian Ocean entered the Andaman Sea in the form of Wyrtki Jets and propagated primarily along two distinct pathways during the observation period.In addition to coastal boundary Kelvin waves,it was found that a branch of the Wyrtki Jet that directly enters the Andaman Sea and flows northward along the slope of the continental shelf,and reflected Rossby wave packets by topography.

Disturbance Observer-Based Predictive Tracking Control of Uncertain HOFA Cyber-Physical Systems

Dear Editor,In this letter,an output tracking control problem of uncertain cyber-physical systems(CPSs)is considered in the perspective of high-order fully actuated(HOFA)system theory,where a lumped disturbance is used to denote the total uncertainties containing parameters perturbations and external disturbances.

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle(UAV)in the presence of parameter uncertainties,unmodeled uncertainties and wind disturbances.First,a fixed-time disturbance observer(FXDO)based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model.Then,a fixed-time integral sliding mode control(FXISMC)is combined with the FXDO to achieve strong robustness and chattering reduction.Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability.Finally,numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances.In addition,the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

A Mean-Field Game for a Forward-Backward Stochastic System With Partial Observation and Common Noise

This paper considers a linear-quadratic(LQ) meanfield game governed by a forward-backward stochastic system with partial observation and common noise,where a coupling structure enters state equations,cost functionals and observation equations.Firstly,to reduce the complexity of solving the meanfield game,a limiting control problem is introduced.By virtue of the decomposition approach,an admissible control set is proposed.Applying a filter technique and dimensional-expansion technique,a decentralized control strategy and a consistency condition system are derived,and the related solvability is also addressed.Secondly,we discuss an approximate Nash equilibrium property of the decentralized control strategy.Finally,we work out a financial problem with some numerical simulations.

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

Research on fixed-time time-varying formation of heterogeneous multi-agent systems based on tracking error observer under DoS attacks

In this paper,the fixed-time time-varying formation of heterogeneous multi-agent systems(MASs) based on tracking error observer under denial-of-service(DoS) attacks is investigated.Firstly,the dynamic pinning strategy is used to reconstruct the communication channel for the system that suffers from DoS attacks to prevent the discontinuous transmission information of the communication network from affecting MASs formation.Then,considering that the leader state is not available to each follower under DoS attacks,a fixed-time distributed observer without velocity information is constructed to estimate the tracking error between followers and the leader.Finally,adaptive radial basis function neural network(RBFNN) is used to approximate the unknown ensemble disturbances in the system,and the fixed-time time-varying formation scheme is designed with the constructed observer.The effectiveness of the proposed control algorithm is demonstrated by the numerical simulation.

BSN:Photometric Light Curve Analysis of Two Contact Binary Systems LS Del and V997 Cyg

The light curve analyses and orbital period variations for two contact binary stars,LS Del and V997 Cyg,are presented in this work which was conducted in the frame of the Binary Systems of South and North project Ground-based photometric observations were performed at two observatories in France.We used the Transiting Exoplanet Survey Satellite(TESS)data for extracting times of minima and light curve analysis of the targe systems.The O-C diagram for both systems displays a parabolic trend.LS Del and V997 Cyg’s orbital periods are increasing at rates of dP/dt=7.20×10^(-08)days yr^(-1)and dP/dt=2.54×10^(-08)days yr^(-1),respectively Therefore,it can be concluded that mass is being transferred from the less massive star to the more massive component with a rate of dM/dt=-1.96×10^(-7)M_(⊙)yr^(-1)for the LS Del system,and dM/dt=-3.83×10^(-7)M_(⊙)yr-1for V997 Cyg.The parameters of a third possible object in the system are also considered.The PHysics Of Eclipsing BinariEs Python code was used to analyze the light curves.The light curve solutions needed a cold starspot due to the asymmetry in the LS Del system’s light curve maxima.The mass ratio fill-out factor,and star temperature all indicate that both systems are contact binary types in this investigation.Two methods were applied to estimate the absolute parameters of the systems:one method relied on the parallax of Gaia DR3,and the other used a P-M relationship.The positions of the systems are also depicted on the M-L,M-R q-L_(ratio),and logM_(tot)-logJ_(0)diagrams.We recommend that further observations and investigations be done on the existence of a fourth body in this system.

Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

Data-Driven Modeling of Partially Observed Biological Systems

We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at all.We demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden parameters.The learned DNN model serves as an accurate predictive tool for system analysis.Through a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction process.The learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.

Force Compensation Control for Electro-Hydraulic Servo System with Pump-Valve Compound Drive via QFT-DTOC

Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit(HDU),which features a high power-weight ratio.However,most HDUs are throttling-valve-controlled cylinder systems,which exhibit high energy losses.By contrast,pump control systems offer a high efficiency.Nevertheless,their response ability is unsatisfactory.To fully utilize the advantages of pump and valve control systems,in this study,a new type of pump-valve compound drive system(PCDS)is designed,which can not only effectively reduce the energy loss,but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of robots.Herein,considering the force control requirements of energy conservation,high precision,and fast response of the robot joint HDU,a nonlinear mathematical model of the PCDS force control system is first introduced.In addition,pressure-flow nonlinearity,friction nonlinearity,load complexity and variability,and other factors affecting the system are considered,and a novel force control method based on quantitative feedback theory(QFT)and a disturbance torque observer(DTO)is designed,which is denoted as QFT-DTOC herein.This method improves the control accuracy and robustness of the force control system,reduces the effect of the disturbance torque on the control performance of the servo motor,and improves the overall force control performance of the system.Finally,experimental verification is performed using the PCDS performance test platform.The experimental results and quantitative data show that the QFT-DTOC proposed herein can significantly improve the force control performance of the PCDS.The relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

An observation control system for radio telescopes based on Python and C++languages

An observation control system is the foundation to support automatic observations by any radio telescope.Traditional observation control systems are usually coded using a compiled language,which is of higher efficiency compared with interpreted languages.Indeed,observation control systems are usually programmed using the C or C++languages.However,the high execution efficiency of C/C++is at the cost of a long development cycle,which is not only time consuming but also requires considerable skills for the developers.The development of computer hardware performance,as well as the optimization of the just-in-time compiler for new interpreted languages such as Python,provides a good balance between execution and development efficiency.In this paper,we introduce the observation control system developed for the Kunming 40-meter radio telescope run by Yunnan Observatories,Chinese Academy of Sciences.The system is developed mainly with the Python language,and we have optimized computationally intensive components with C++.We demonstrate that it is possible to achieve the required functionality and control precision with such a Python-C++programming paradigm.The performance of the control system is also assessed in this paper,demonstrating that satisfactory pointing accuracy and user experience can be attained.

Solar adaptive optics systems for the New Vacuum Solar Telescope at the Fuxian Lake Solar Observatory

Adaptive optics(AO)is essential for high-quality ground-based observations with large telescopes because it counters the impact of wavefront aberrations caused by atmospheric turbulence.The new vacuum solar telescope(NVST)is one of the most important high-resolution solar observation instruments in the world.Three sets of solar adaptive optics systems have been developed and installed on this telescope:conventional adaptive optics,ground layer adaptive optics,and multi-conjugate adaptive optics.These have been in operation from 2018 to 2023.This paper details the development and application of solar adaptive optics on the NVST and discusses the newest instrumentation.