During takeoff and landing, aircraft operate in a variety of situations, posing significant challenges to landing gear systems. Passive hydraulic–pneumatic dampers are commonly used in conventional landing gear to absorb impact energy and reduce vibration. However, due to their fixed damping characteristics and inability to adjust to changing operating conditions, these passive systems have several limitations. Recent research has focused on creating intelligent landing gear systems with magnetic dampers (MR) to overcome these limitations. By changing the magnetic field acting on the MR fluid, MR dampers provide semi-active control of the landing gear dynamics and adjust the damping force in real time. This flexibility reduces structural load during landing, increases riding comfort, and improves energy absorption efficiency. This study examines the current state of MR damper application for aircraft landing gear. The review categorizes current control techniques and highlights the structural integration of MR dampers in landing gear assemblies. Purpose: The magnetorheological (MR) damper has become a promising semiactive system to replace the conventional passive damper in aircraft landing gear. However, the mechanical structure and control strategy of the MR damper must be designed to be suitable for aircraft landing gear applications. Methods: Researchers have explored the potential structure designed, the mathematical model of the MR landing gear system, and the control algorithm that was developed for aircraft landing gear applications. Results: According to the mathematical model of the MR damper, three types of models, which are pseudo-static models, parametric models, and unparameterized models, are detailed with their application. Based on these mathematical models, many control algorithms were studied, from classical control, such as PID and skyhook control, to modern control, such as intelligent control and SMC control.

A clear understanding of how visual information affects postural sway is crucial for assessing normal balance control and developing diagnostic and rehabilitation methods for balance disorders. However, a quantitative model of sway responses to visual perturbations with improved accuracy is still needed. We used virtual reality to apply rotational visual perturbations (0.04-1 Hz, 2.5°-15°) to fourteen healthy adults. Participants were splinted at the knee and hip to ensure the ankle strategy was used. Postural responses, including body angles and ankle torques, were recorded. Initial analysis demonstrated that right-eye dominant subjects showed more coherent body sway responses, possibly related to the higher magnitude of the optical flow in the right half-plane of the visual field. Detailed analysis was therefore focused on eight subjects with large, coherent responses. A detrending method was applied to angles and torques based on inverse Fourier transform to remove frequencies below the smallest stimuli frequency. Our methodology yielded a model with improved accuracy between the visual input and body angle output, i.e., coherence values close to 1. Frequency response analysis revealed a low-pass gain characteristic and a linear phase decrease showing a consistent delay in the system across all amplitudes. A parametric model fitted to the frequency response yielded a delayed, second-order, low-pass transfer function. The transfer function gain decreased with increasing stimulus amplitude, demonstrating a nonlinear response reflecting reduced responsiveness to larger visual amplitudes. In conclusion, this paper provides an experimental and analytical framework to accurately quantify the nonlinear dynamics of postural responses to visual stimuli.

In both human and wildlife disease systems, temporal shifts in host immunity may shape the timing and severity of epidemics. Yet, immune responses, as well as seasonal patterns in their expression, are difficult to measure. Rather, field studies collect phenomenological data on infection outcomes. Pairing epidemic data of multiple outbreaks with models that directly parameterize immune metrics can be a powerful approach for exploring the role of time-varying immunity on disease. Field data can be used to determine how well a parameterized model can reproduce trends and differences observed among outbreaks.Previous work in the Daphnia dentifera-Metschnikowia bicuspidata focal host-fungal pathogen disease system has not taken full advantage of coupling patterns in nature with mechanisms predicted by theory. Here, we study a mathematical model accounting for host immunity in the form of resistance to and recovery from M. bicuspidata infections and temporal variation in key aspects of the system's epidemiology and ecology. Specifically, host population birth, predation and transmission rates, the fraction of recovering hosts, as well as the fungal spore yield were allowed to vary within the epidemic season. Modifying the system's carrying capacity produces good correspondence between observed and model-estimated densities. Adjusting the transmission rate, spore yield, and the fraction of recovering hosts, captures the timing of disease outbreaks, as well as other qualitative features of outbreaks, such as the disparity between the prevalence of early- and late-stage infections. Our findings suggest that host immunological parameters are an important within-host constraint on disease dynamics.

In parametric statistics, it is well established that the canonical measures of estimator performance—such as bias, variance, and mean squared error—are inherently dependent on the parameterization of the model. Consequently, these quantities describe the behavior of an estimator only relative to a particular parameterization, rather than representing intrinsic properties of either the estimator itself or the underlying probability distribution it seeks to estimate. Some years ago, the authors introduced a framework, termed the intrinsic analysis of point estimation, in which tools from information geometry were employed to construct analogues of classical statistical notions that are intrinsic to both the estimator and the associated probability measure. Within this framework, a contravariant vector field was introduced to define the intrinsic bias, while the squared Riemannian distance naturally emerged as the intrinsic analogue of the classical squared distance. Intrinsic counterparts of the Cramér–Rao inequalities, as well as the Rao–Blackwell and Lehmann–Scheffé theorems, were also established. The present work extends the intrinsic analysis—originally founded on the concept of intrinsic risk, a fundamentally local measure of estimator performance—to an approach that characterizes the estimator over an entire region of the parameter space, thereby yielding an intrinsically global perspective. Building upon intrinsic risk, two indices are proposed to evaluate estimator performance within a bounded region: (i) the integral of the intrinsic risk with respect to the Riemannian volume over the specified region, and (ii) the maximum intrinsic risk attained within that region. The Riemannian volume induced by the Fisher information metric on the manifold associated with the parametric model provides a natural means of averaging the intrinsic risk. Using variational methods, integral inequalities of the Cramér–Rao type are derived for the mean squared integrated Rao distance of the estimators, thereby extending previous contributions by several authors. Furthermore, lower bounds for the maximum intrinsic risk are obtained through corresponding integral formulations.

To address the challenges of high computational cost and lengthy design cycles in the high-precision optimization of ray-like underwater gliders, this study proposes a high-accuracy, low-cost parametric modeling and optimization method. The proposed framework begins by extracting the characteristic contours of the manta ray and reconstructing the airfoil sections using the Class-Shape Transformation (CST) method, resulting in a flexible parametric geometry capable of smooth deformation. High-fidelity Computational Fluid Dynamics (CFD) simulations are employed to evaluate the hydrodynamic characteristics, and detailed flow field analyses are conducted to identify the most influential geometric features affecting lift and drag performance. On this basis, a Kriging-based sequential optimization framework is developed. The surrogate model is adaptively refined through dynamic infilling of sample points based on combined Mean Squared Prediction (MSP) and Expected Improvement (EI) criteria, thus improving optimization efficiency while maintaining predictive accuracy. Comparative case studies demonstrate that the proposed method achieves a 116% improvement in lift-to-drag ratio and a more uniform flow distribution, confirming its effectiveness in enhancing both design accuracy and computational efficiency. The results indicate that this approach provides a practical and efficient tool for the parametric design and hydrodynamic optimization of bio-inspired underwater vehicles.

On the uncertainty associated with using a signal detection theory model to analyze data from forensic black-box studies.

Phenomenological modeling of gene transcription by approximating cooperativity of transcription factors improves prediction and reduces complexity in gene regulatory network models.

Abstract We developed a Python package \textsc{GEHONG} to mock the three-dimensional spectral data cube under the observation of an ideal telescope for the Integral Field Spectrograph of the Chinese Space Station Telescope (CSST-IFS). This package can generate one-dimensional spectra corresponding to local physical properties at specific positions according to a series of two-dimensional distributions of physical parameters of target sources. In this way, it can produce a spatially resolved spectral cube of the target source. Two-dimensional distributions of physical parameters, including surface brightness, stellar population, and line-of-sight velocity, can be modeled using the parametric model or based on real observational data and numerical simulation data. For the generation of one-dimensional spectra, we have considered four types of spectra, including the stellar continuum spectra, ionized gas emission lines, AGN spectra, and stellar spectra. That makes \textsc{GEHONG} able to mock various types of targets, including galaxies, AGNs, star clusters, and HII regions.

The FDA plans to gradually replace animal testing with organoid and organ-on-a-chip technologies for drug safety assessment, driving surging demand for gut-on-a-chip in food and drug safety evaluation and highlighting the need for efficient, precise chip designs. Oxygen gradients are central to these devices because they shape epithelial metabolism, microbial co-culture, and overall gut homeostasis. We coupled machine learning with finite element analysis to build a parametric COMSOL Multiphysics model linking channel geometry, transport coefficients, and cellular oxygen uptake to the resulting oxygen field. For numerical prediction, three models—Random Forest (RF), XGBoost, and MLP—were employed, with XGBoost achieving the highest accuracy (RMSE = 1.68%). SHAP analysis revealed that medium flow rate (39.7%), external flux (26.9%), and cellular oxygen consumption rate (24.8%) contributed most importantly to the prediction. For oxygen distribution mapping, an innovative Boundary-Guided Generative Network (BG-Net) model was employed, yielding an average concentration error of 0.012 mol/m3 (~4.8%), PSNR of 33.71 dB, and SSIM of 0.9220, demonstrating excellent image quality. Ablation experiment verified the necessity of each architectural component of BG-Net. This pipeline offers quantitative, data-driven guidance for tuning oxygen gradients in gut-on-a-chip. Future work will explore extensions including real experimental data integration, real-time prediction, and multi-task scenarios.

Identifying dynamic treatment regimes (DTRs) is a key objective in precision medicine. Value search approaches, including (Bayesian) dynamic marginal structural models offer an attractive approach to estimation by mapping candidate regimes to their expected outcome. As parametric models for the expected outcomes may be mis-specified and lead to incorrect conclusions, a grid search over candidate DTRs has been proposed, but this may be computationally prohibitive and also subject to high uncertainty in the estimated value function. These inferential challenges can be addressed using Gaussian process ($\mathcal {GP}$) optimization methods with estimators for the causal effect of adherence to a specified DTR. We demonstrate how to identify optimal DTRs using this approach in a variety of settings, including when the value function is multi-modal and show that the $\mathcal {GP}$ modeling approach that recognizes noise in the estimated response surface leads to improved results as compared to a grid search approach. Further, we show that a grid search may not yield a robust solution and that it often utilizes information less efficiently than a $\mathcal {GP}$ approach. The proposed approach is used to understand tailoring of HIV therapy to optimize CD4 cell counts.

HighlightsWhat are the main findings?We experimentally characterized all operating states (startup, transmission, reception, and sleep) of the SemtechSX1276 LoRa transceiver and built a parametric power model validated againstmeasurements.The model captures the dependence ontransmission power (RFO vs. PA_BOOST), sleep strategy (VCC ON/OFF) andpacketization effects, and it remains configurable for the number of receptionevents.What are the implications of the mainfindings?The model provides design guidelines forultra-low power, harvested or battery-less IoT nodes, where minimizing the RFenergy budget is critical.A distributable Python simulator based onthe model allows other researchers to estimate system consumption and adapt theconfiguration to their own needs.Energy efficiency is a key requirement for Internet of Things (IoT) nodes, particularly in applications powered by energy harvesting that operate without batteries. In this work, we present a parametric power model of a LoRa transceiver (Semtech SX1276) aimed at ultra-low power remote sensing scenarios. The transceiver was characterized in all relevant states (startup, transmission, reception, and sleep), and the results were used to build a state-based model that predicts average power consumption as a function of transmission power, sleep strategy, packetization, and input data rate. Experimental validation confirmed that the cubic fit for transmission peaks achieves a determination coefficient of 0.99, while reception is added as a constant consumption. The model was implemented in a Python simulator that provides mean, best-case, and worst-case estimates of system power consumption, and it was validated in an ASIC-based sensor node demonstration, with predictions within 10% of measured values. The framework highlights the trade-offs between energy efficiency and robustness (e.g., minimal SF and no CRC vs. higher spreading factors and error-control) and supports the design of custom controllers for ultra-low power IoT nodes as well as more energy-permissive applications.

BackgroundVirtual clinical trials provide an efficient alternative to clinical imaging trials for evaluating imaging technologies. In x‐ray simulations, however, modeling material‐specific attenuation becomes computationally intensive as anatomical complexity and material heterogeneity in digital phantoms increase. Parameterization models offer a potential solution by representing material properties with a compact set of coefficients.PurposeTo develop and validate an x‐ray simulation framework that models material attenuation using parameterization models, reducing computational cost while maintaining accuracy.MethodsMaterial attenuation was modeled with a five‐coefficient parameterization derived from physical cross‐section data. Unlike conventional ray‐tracing, which projects each material separately, the proposed method projects only the five parameter maps, making computational cost independent of phantom complexity. This framework was evaluated in two scenarios: breast imaging with 10 compressed breast phantoms with varying fibro‐glandular content, and whole‐body imaging with head and abdomen phantoms. Accuracy was assessed by computing percent errors in attenuation coefficients, sinograms, and reconstructed images relative to the conventional approach. For whole‐body imaging only, additional analyses included the impact of resolution loss and noise, the comparison with errors introduced by different projector models to place results in the context of standard simulation variability, and computational time measurements.ResultsAcross all materials and both applications, the maximum attenuation coefficient error was 0.007% (breast skin tissue), far below reported biological variability. Projection and reconstruction errors remained within ± 0.006% for all cases. In whole‐body imaging, these errors were well below those from projector model differences (± 0.5%), and image modification routines further concentrated the error distribution around zero. Simulation times decreased significantly, with acceleration factors scaling linearly with the number of materials within the phantoms.ConclusionsThe proposed framework achieves accurate and efficient simulation of material attenuation in x‐ray imaging, especially in anatomically complex scenarios. Validated in both breast and whole‐body imaging, it offers a robust and efficient alternative to conventional methods, supporting the development of advanced virtual clinical trials and spectral imaging research.

Research on the optimization design of low-noise valve cores in steam turbine control valves based on parametric modeling and flow field analysis

Dispersal of marine plastic litter during tropical cyclones.

A multi-fidelity deep operator network for parametric transonic flow modeling with shock discontinuity

Blind identification of state transitions and latent neural dynamics from electrophysiological recordings.

Exposure to nighttime blackout regulations during the in-utero and early-life periods is associated with health outcomes in adulthood.

Artificial intelligence-enabled electrocardiography for risk prediction in chronic liver disease: A systematic review.

Human beings have the ability to continuously analyze a video and immediately extract the motion components. We want to adopt this paradigm to provide a coherent and stable motion segmentation over the video sequence. In this perspective, we propose a novel long-term spatio-temporal model operating in a totally unsupervised way. It takes as input the volume of consecutive optical flow (OF) fields, and delivers a volume of segments of coherent motion over the video. More specifically, we have designed a transformer-based network, where we leverage a mathematically well-founded framework, the Evidence Lower Bound (ELBO), to derive the loss function. The loss function combines a flow reconstruction term involving spatio-temporal parametric motion models combining, in a novel way, polynomial (quadratic) motion models for the spatial dimensions and B-splines for the time dimension of the video sequence, and a regularization term enforcing temporal consistency on the segments. We report experiments on four VOS benchmarks, demonstrating competitive quantitative results while performing motion segmentation on a sequence in one go. We also highlight through visual results the key contributions on temporal consistency brought by our method.

We analyze the observational features of hot-spots orbiting parametrized black hole (BH) spacetimes. We select a total of four BH spacetimes, two of them adapted from the Johanssen-Psaltis (JP) parametrization, and two from the Konoplya-Rezzolla-Zhidenko (KRZ) parametrization, corresponding to the most extreme configurations whose shadow sizes are within the 2σ-constraints of the Event Horizon Telescope (EHT). We use the ray-tracing software GYOTO to simulate the orbit of a spherically symmetric hot-spot emitting synchrotron radiation close to a central parametrized BH object, in a vertical magnetic field configuration, and we extract the corresponding astrometric and polarimetric observables for the Stokes parameters I, Q and U, namely the time integrated fluxes, temporal fluxes and magnitudes, temporal centroid, temporal QU-loops, and temporal Electric Field Position Angle (EVPA). Our results indicate that at low inclination the astrometric observables extracted from the parametrized BH spacetimes considered are qualitatively similar to those extracted from the Schwarzschild one, with minor quantitative deviations caused by differences in the size and position of the secondary images. On the other hand, the polarimetric observables at high inclination present qualitative differences, but these are only visible for a short portion of the whole hot-spot orbit. Furthermore, the observables extracted from the JP parametrized BH models deviate more prominently from those of the Schwarzschild model than the ones extracted from the KRZ parametrized BH models, with the JP model with a positive free parameter deviating the most among all models tested. Given the strong similarity among the observables extracted from all models tested, we point out that more precise observations are needed to successfully impose constraints on parametrized BH models via this method.