In the context of cloud and edge computing for real-time fault diagnosis (FD) in autonomous ground vehicles (AGVs), this paper proposes a novel fault estimation method for AGV actuators. A two degrees-of-freedom nonlinear vehicle model is first used to characterize the AGV dynamics. Based on this model, a Takagi-Sugeno (TS) fuzzy observer is designed to estimate actuator fault signals. To handle unmeasurable premise variables, a nonlinear partitioning method reconstructs the TS fuzzy model into an N-TS fuzzy form. Unlike conventional TS fuzzy models with linear consequents, this reformulation incorporates the differential mean value theorem to explicitly address unmeasured nonlinearities–a common difficulty in TS fuzzy observer design. Using Lyapunov stability theory, the fault estimator is formulated as an optimization problem subject to strict linear matrix inequalities (LMIs), which can be solved efficiently with numerical tools. The proposed observer is validated through co-simulations in Simulink and CarSim, demonstrating its potential for deployment in cloud computing environments to enhance fault management in AGVs.

This paper demonstrates computational modeling's value as a tool for mapping the impact of hidden variables and evaluating the accuracy of statistical methods in bioarchaeology. As a working example, this paper presents an agent-based model of a 1,000-person cohort of individuals who can form an unspecified skeletal lesion at any age between birth and ten years and enter a simulated cemetery at the end of their lives. Skeletal lesions either have no effect on mortality risk (scenario 1) or are associated with doubled mortality risk (scenario 2). The agent-based model simulates data on individual age at death and lesion status. Kaplan-Meier survival analysis is run on each simulated dataset, comparing survival estimates for individuals with and without lesions. Survival analyses underestimate the true value of lesion-associated mortality risk in early life in scenario 2 and produce a false lesion-associated survival advantage under the null conditions of scenario 1. Researchers should account for the ages of a skeletal lesion's developmental window, where known, when assessing lesion-associated mortality. Survival analyses return accurate results when they exclude individuals in the ages of active lesion formation. Modeling experiments can identify which archaeologically unmeasurable variables have the greatest impact on estimates of population health and outline the ways in which they bias estimates of past health from the skeletal record. The only limits on modeling are limits of imagination and common sense. Many other archaeologically hidden variables remain to be explored with this approach.

A multiobjective optimization approach to data assimilation for complex biological systems with sparse data.

This paper presents a robust output-feedback fuzzy control scheme for autonomous bus–trailer systems, which can be formulated as a multi-input multi-output system. To ensure driving safety, the proposed design explicitly accounts for both input and output constraints. A core feature of this approach is the utilization of exponential dissipativity, which not only attenuates external disturbances but also serves as a unified framework encompassing exponential passivity and H∞ performance through the adjustment of weighting matrices. Additionally, a tunable decay rate is introduced to improve transient response characteristics. Recognizing that full state information is rarely available in practical scenarios, an observer is integrated to estimate unmeasurable state variables. Finally, the effectiveness and feasibility of the proposed control scheme are validated under various driving conditions through Simulink/dSPACE co-simulation.

ABSTRACT The rapid expansion of wind energy infrastructure over the past 20–30 years has led up to a situation where advanced non‐destructive testing (NDT) technologies are the need‐of‐the‐hour, not only for new wind turbine blades (WTBs) that are being installed, but also for older infrastructure which is reaching their designed lifetime. NDT technologies that improve both the quality as well as reduce the time required for the inspection are sought after, and one such example is passive infrared thermography (IRT). For passive IRT to provide significant information/insight into the integrity of the WTB, there needs to exist certain thermal contrast to both visualize and distinguish between features in WTB. These features could be surface features, subsurface structure or defects. The temperature variations due to air temperature fluctuations and the sun assist (passively) to obtain the necessary thermal contrast. To better understand the thermal response of composite structures such as WTBs, a validation study was conducted using a WTB section subjected to controlled temperature transients within a climate chamber, without external irradiation. Infrared measurements were recorded using a thermographic camera, and the same specimen was modeled using finite element methods (FEM) in COMSOL Multiphysics. While a direct validation of the simulation is limited due to transient and unmeasured variables in the experimental data, qualitative comparison provides valuable insight into the applicability of FEM for predicting thermal behavior in passive IRT scenarios. This article represents the first part of a two‐part study, focusing on the FEM modeling approach and associated challenges. The second part will address the experimental investigation in more detail, with an emphasis on thermal contrast behavior under varied transient conditions.

Ensuring safe driving requires continuous monitoring of both vehicle dynamics and external conditions such as road irregularities, which often act as unknown disturbances. Accurately estimating these unmeasured states and inputs is critical for advanced driver assistance systems (ADAS) and vehicle stability control. This study proposes a novel functional observer that simultaneously reconstructs unknown road disturbances and estimates unmeasured vehicle-state variables in real time using only standard onboard sensor measurements. The observer design is grounded in Lyapunov stability theory, with estimation conditions expressed as linear matrix inequalities (LMIs), whose solution guarantees robust convergence and stability. Validation is conducted through numerical simulations of a quarter-car vertical dynamics model under two scenarios. Results demonstrate that the proposed observer achieves accurate and reliable state estimation, outperforming conventional approaches such as the Kalman filter and full-order Luenberger observer, particularly in the presence of unknown inputs.

Black and Non-Hispanic White persons with multiple sclerosis: Social determinants of health and health inequities.

Abstract This paper presents an advanced control technique for a single‐stage grid‐tied photovoltaic (PV) system, including a three‐level neutral point clamped inverter with an LCL filter. The control objectives are threefold: (i) achieving power factor correction (PFC) by ensuring a sinusoidal grid current in phase with the grid voltage; (ii) transferring maximum PV power to the grid and tightly regulating the DC bus voltage; (iii) maintaining capacitor voltages balance to ensure proper power exchange. The control design entails several difficulties, namely: (i) the system's inherent nonlinearity and high dimensionality; (ii) the uncertainty in some system parameters; (iii) the numerous unmeasurable state variables and parameters. The considered control problem is dealt with by synthesizing a multi‐loop adaptive controller featuring three main cascaded loops. The fast inner loop utilizes an integral backstepping control alongside the formal Lyapunov technique for PFC achievement. The outer loop incorporates a filtered proportional‐integral (PI) regulator for DC bus voltage adjustment to its maximum power point reference. The auxiliary balancing loop implements a PI regulator for neutral‐point voltage stabilization. The control system is further enhanced with an adaptive observer for online estimation of network voltage and impedance parameters. The closed‐loop system stability is formally established through system averaging analysis. MATLAB/Simulink simulations demonstrate the performance of the suggested adaptive controller under varying operating conditions, and comparative studies confirm its supremacy over different other inner loop controllers.

Learning feedforward with unmeasured performance variables: With application to a wirebonder

Fuzzy observer-based adaptive fault tolerant control for uncertain underactuated nonlinear systems under TS fuzzy model with unmeasured premise variables

This study presents the design and analysis of a Luenberger observer with aggressively placed poles for a buck DC–DC converter. The objective is to estimate the inductor current and output voltage using only the measured output voltage, thereby enabling state feedback control in systems where current sensing is impractical or costly. A state-space model of the converter is derived, and the observer poles are placed sufficiently far in the left-half plane to ensure rapid estimation-error decay while maintaining numerical robustness. To validate the performance of the observer, MATLAB simulations are conducted under both nominal conditions and step changes in load and input voltage. Even when faced with abrupt disturbances, the results demonstrate that the proposed observer accurately reconstructs the unmeasured state variables with minimal delay, highlighting its robust performance. This makes the proposed observer a promising approach for advanced control schemes where full state feedback is essential.

Conventional survival analysis models typically assume that the hazard function depends solely on the baseline hazard and covariate values, overlooking unobserved factors that influence survival outcomes. In practice, however, unmeasured variables often contribute to heterogeneity among seemingly similar individuals. Frailty models offer an effective approach to account for such unobserved heterogeneity, providing a robust framework for analyzing naturally clustered survival data. This study applies frailty models to multistate event history data, emphasizing their ability to handle unobserved heterogeneity. We introduce individual-specific survival weights to adjust survival times, better reflecting the impact of unmeasured factors. These weighted survival times are critical when data exhibit bias or when standard models fail to fully capture the influence of investigated variables. Through a simulation study, we evaluate the effectiveness and performance of frailty models in a multistate framework, comparing mean, mean squared error (MSE), and bias of regression coefficients with and without frailty. For example, in the simulated dataset for age bias has reduced from -0.01 in unweighted survival time to -0.03 in weighted survival time for transition tau _{12}, similarly for tau _{23} bias has reduced from 0.01 to -0.05. Our findings underscore the importance of addressing unobserved heterogeneity in survival analysis, particularly in multistate models with weighted survival times.

The emergence of real-world evidence (RWE) has significantly broadened the scope of clinical research in internal medicine, providing insights that extend beyond the constraints of randomized controlled trials (RCTs). RWE is generated from real-world data (RWD)-information collected outside experimental settings, such as routine clinical practice. Observational studies, including cohort, case-control and cross-sectional designs, are fundamental to RWE generation, enabling the examination of patient outcomes, treatment patterns and disease epidemiology as they naturally occur. The expanded use of registries and electronic health records (EHRs) has revolutionized data collection, offering both depth and breadth for large-scale, longitudinal studies. However, drawing causal inferences from observational research presents substantial methodological challenges, as confounding and bias arising from non-randomized treatment allocation and unmeasured variables can distort results. Data quality issues-such as missing data, exposure and outcome misclassification, and inconsistent variable definitions-further complicate analysis. Advanced statistical techniques, including propensity score matching and instrumental variable analysis, have been developed to mitigate the limitations due to confounding, yet cannot fully substitute for randomization. Transparent reporting, pre-registration of protocols, and adherence to standardized guidelines (e.g. STROBE) are essential to enhance rigor and reproducibility. Despite their challenges, observational studies remain indispensable for addressing clinical questions that cannot be answered by RCTs, informing practice and guiding healthcare policy. Careful study design, rigorous analysis and transparent reporting are crucial for maximizing the reliability and impact of real-world evidence in internal medicine.

Postoperative surgical site infections (SSIs) following brain tumor surgery frequently necessitate wound revision and bone flap removal. However, data on subsequent cranial reconstruction in this context remain limited. The aim of this study was to characterize patients undergoing bone flap removal due to SSI, determine the proportion who proceed to cranioplasty, and evaluate surgical strategies, complication rates, and risk factors for implant failure. In this multicenter observational study, patients who underwent bone flap removal due to SSI following brain tumor surgery from 2008 to 2022 at four Swedish neurosurgical centers were included. Clinical, radiological, and surgical data were collected retrospectively. Risk factors for implant removal were evaluated with logistic regression and Kaplan-Meier survival analyses. Functional outcome was assessed using the modified Rankin Scale (mRS). Of 260 patients included in the analysis, 223 (86%, median age was 56 years) underwent cranioplasty and 37 (14%, median age 66 years) did not, primarily due to short life expectancy, poor medical condition, or wound concerns. Among patients who underwent cranioplasty, the most common tumor type was meningioma (75%) and the median cranial defect size was 35 cm2. Synthetic implants were used for all reconstructions. The overall implant removal rate was 21%, primarily due to wound dehiscence and infection. WHO grade 4 tumors and a cranial defect size > 64.5 cm2 were associated with an increased risk of implant removal (p < 0.05). Variables such as age, smoking, and diabetes did not predict complications. Functional outcome, as assessed by the mRS, remained unchanged postoperatively for most patients (87%). Cranioplasty after bone flap removal due to SSI following brain tumor surgery was associated with a substantial risk of implant failure despite reconstruction of relatively small cranial defects. Predictive factors for implant failure were limited, suggesting that unmeasured variables, such as soft tissue conditions, might play a significant role in these procedures. Given the high rate of implant removal and limited survival among patients with high-grade tumors, careful patient selection and individualized decision-making are essential.

Accessibility and observability are two properties of dynamic models that provide insights into the structural relationships between their input, output, and state variables. They are closely related to controllability and structural local identifiability, respectively. Observability and identifiability determine, respectively, the possibility of inferring the unmeasured state variables and parameters of a model from output measurements; accessibility and controllability describe the possibility of driving its state by changing its input. Analysing these structural properties in nonlinear models of ordinary differential equations can be challenging, particularly when dealing with large systems. Two main approaches are currently used for their study: one based on differential geometry, which uses symbolic computation, and another one based on sensitivity calculations that uses numerical integration. These approaches are implemented in two MATLAB (R2024b) software tools: the differential geometry approach in STRIKE-GOLDD, and the sensitivity-based method in StrucID. These toolboxes differ significantly in their features and capabilities. Until now, their performance had not been thoroughly compared. In this paper we present a comprehensive comparative study of them, elucidating their differences in applicability, computational efficiency, and robustness against computational issues. Our core finding is that StrucID has a substantially lower computational cost than STRIKE-GOLDD; however, it may occasionally yield inconsistent results due to numerical issues.

Relevance. Currently, the share of centrifugal electric pumps operating in intermittent modes is increasing, allowing for increased oil production from low- and medium-flow wells. This is achieved by switching the well to two-position control modes with separate pumping and fluid accumulation cycles. However, this significantly reduces the time it takes to bring the well up to operating mode, which can reach several minutes. This approach reduces the mean time between failures of submersible electric motors and components of the submersible section of the unit due to increased vibration amplitude and torsional oscillations compared to quasi-stationary processes during continuous well operation. The impact of starting currents and torques, which lead to excessive fatigue wear of the submersible section components, can be reduced by using closed-loop sensorless control systems for submersible electric motor drives. This necessitates the development of methods for identifying unmeasured state variables of submersible electric motors, such as the flux vector and rotor angular velocity, using full-order state observers. Aim. Development and research of a rotor speed and load torque full-order observer for the dynamic system «cable line – permanent magnet synchronous motor» based on explicit mathematical models in discrete time for the implementation of closed-loop sensorless control systems for the electric drive of centrifugal electric pump units. Methods. The work used methods of system analysis and structural-parametric synthesis of dynamic systems, methods of identifying unmeasured state variables of dynamic systems based on state observers, methods of numerical mathematical modeling of dynamic systems described by systems of stiff differential equations, and methods of solving systems of nonlinear algebraic equations. Results. The authors have developed the adaptive mathematical model of a rotor speed and load torque full-order observer for the dynamic system «cable line – permanent magnet synchronous motor». The observer performance was studied under typical operating conditions and when the parameters of its customizable model deviated from the reference values. For all operating conditions, the relative errors in estimating the rotor angular velocity and shaft drag torque were less than 1.5 and 1%, respectively.

We propose new observer-based fuzzy controllers for general Takagi-Sugeno fuzzy system with nonlinear output equations and unmeasurable premise variables. For Takagi-Sugeno fuzzy systems with the unmeasurable premise variables, the separation principle may not hold in general. To overcome this difficulty, we employ the differential mean value theorem and the sector nonlinearity approach to reformulate as an appropriate error system in which the errors between the actual states and its estimates follow. Then, with the state feedback controller and the error system, we have an augmented closed-loop system that can independently and simultaneously analyze the stability of the states and the errors. Since our designed conditions do not require the Lipschitz condition, our approach is more relaxed than the existing approach. Finally, an illustrative example is given to show the effectiveness of the proposed approach.

This paper studies the dual-mode control-based model predictive control algorithm for polyhedral uncertain systems under relay redundancy protocols. To address signal fading and packet loss caused by limited bandwidth in remote wireless signal transmission, the system employs amplify-and-forward (AF) relaying and redundant channel techniques to enhance transmission reliability. To further reduce the online computational burden of traditional model predictive control, a dual-mode robust model predictive control framework is proposed. The first mode rapidly drives the system state from the initial feasible (IF) region to the terminal constraint set (TeC) through coordinated design of a fixed control law and perturbation terms. The second mode asymptotically converges states within the TeC set to the origin using the fixed control law. For parameter stochasticity in polyhedral uncertain systems under relay redundancy protocols, an infinite-time quadratic function based on mathematical expectation is proposed to formulate the optimisation problem. A controller design optimisation framework integrating offline and online components is developed, where perturbation strategies and relaxation matrices decouple unmeasurable state variables to reduce online computational complexity. Theoretical proofs of recursive feasibility and system stability are provided, with simulations verifying the strategy's effectiveness.

Despite its significance, men’s fertility has been largely overlooked in demographic research. This study seeks to address this gap by conducting a systematic comparative analysis of men’s and women’s fertility using data from the Spanish ECEPOV–2021 survey, a large-scale data set (N = 424,493) from the Spanish national statistical office. Findings indicate that women generally exhibit slightly higher completed cohort fertility rates than men, with exceptions among remarried, college-educated, and immigrant men, who show higher fertility than their female counterparts. Childlessness emerges as a key factor underlying fertility differentials between the sexes, accounting for nearly half of the observed difference. After using matching techniques to control for compositional differences, the study concludes that adjusting for demographic and socio-economic factors significantly reduces, although does not entirely eliminate, the fertility differential. Residual differences may stem from measurement errors, selection biases, or unmeasured variables.

Real-World Effectiveness and Resource Utilization of Red Blood Cell Exchange in Adult Sickle Cell Disease: a Nationwide Robust Analysis from 2017-2022