Model Discrepancy Research Articles

Silicon Isotopic Composition of Mainstream Presolar SiC Grains Revisited: The Impact of Nuclear Reaction Rate Uncertainties

Abstract Presolar grains are stardust particles that condensed in the ejecta or in the outflows of dying stars and can today be extracted from meteorites. They recorded the nucleosynthetic fingerprint of their parent stars and thus serve as valuable probes of these astrophysical sites. The most common types of presolar silicon carbide grains (called mainstream SiC grains) condensed in the outflows of asymptotic giant branch stars. Their measured silicon isotopic abundances are not significantly influenced by nucleosynthesis within the parent star but rather represent the pristine stellar composition. Silicon isotopes can thus be used as a proxy for galactic chemical evolution (GCE). However, the measured correlation of 29Si/28Si versus 30Si/28Si does not agree with any current chemical evolution model. Here, we use a Monte Carlo model to vary nuclear reaction rates within their theoretical or experimental uncertainties and process them through stellar nucleosynthesis and GCE models to study the variation of silicon isotope abundances based on these nuclear reaction rate uncertainties. We find that these uncertainties can indeed be responsible for the discrepancy between measurements and models and that the slope of the silicon isotope correlation line measured in mainstream SiC grains agrees with chemical evolution models within the nuclear reaction rate uncertainties. Our result highlights the importance of future precision reaction rate measurements for resolving the apparent data–model discrepancy.

Optimal gait design for a soft quadruped robot via multi-fidelity Bayesian optimization

This study focuses on the locomotion capability improvement in a tendon-driven soft quadruped robot through an online adaptive learning approach. Leveraging the inverse kinematics model of the soft quadruped robot, we employ a central pattern generator to design a parametric gait pattern, and use Bayesian optimization (BO) to find the optimal parameters. Further, to address the challenges of modeling discrepancies, we implement a multi-fidelity BO approach, combining data from both simulation and physical experiments throughout training and optimization. This strategy enables the adaptive refinement of the gait pattern and ensures a smooth transition from simulation to real-world deployment for the controller. Compared to previous result using a fixed gait pattern, the multi-fidelity BO approach improves the robot’s average walking speed from 0.14 m/s to 0.214 m/s, an increase of 52.7%. Moreover, we integrate a computational task off-loading architecture by edge computing, which reduces the onboard computational and memory overhead, to improve real-time control performance and facilitate an effective online learning process. The proposed approach successfully achieves optimal walking gait design for physical deployment with high efficiency, effectively addressing challenges related to the reality gap in soft robotics.

Bipedal Stepping Controller Design Considering Model Uncertainty: A Data-Driven Perspective.

This article introduces a novel perspective on designing a stepping controller for bipedal robots. Typically, designing a state-feedback controller to stabilize a bipedal robot to a periodic orbit of step-to-step (S2S) dynamics based on a reduced-order model (ROM) can achieve stable walking. However, the model discrepancies between the ROM and the full-order dynamic system are often ignored. We introduce the latest results from behavioral systems theory by directly constructing a robust stepping controller using input-state data collected during flat-ground walking with a nominal controller in the simulation. The model uncertainty discrepancies are equivalently represented as bounded noise and over-approximated by bounded energy ellipsoids. We conducted extensive walking experiments in a simulation on a 22-degrees-of-freedom small humanoid robot, verifying that it demonstrates superior robustness in handling uncertain loads, various sloped terrains, and push recovery compared to the nominal S2S controller.

Addressing mathematical discrepancies in the adsorption model of Hashem et al. (2024): A technical commentary

This letter provides a critical technical analysis of the application of the Freundlich isotherm in the study by Hashem et al. (2024), entitled "Chromium adsorption capacity from tannery wastewater on thermally activated adsorbent derived from kitchen waste biomass." While the study presents an innovative approach by utilizing pumpkin peel as an adsorbent for chromium removal, a critical issue was identified in the formulation and application of the Freundlich isotherm. The authors presented an incorrect mathematical modification of the isotherm, which significantly impacts the validity of the adsorption parameters derived, such as KF and 1/n, and ultimately affects the conclusions drawn about the system's adsorption capacity. Additionally, the assertion that the adsorption process can continue indefinitely is fundamentally flawed based on the principles of adsorption thermodynamics. This letter aims to rectify these discrepancies by revisiting the correct formulation of the Freundlich isotherm and highlighting the importance of accurate mathematical modeling in ensuring scientifically sound and replicable results. Despite these issues, the study remains a valuable contribution to the field, and the suggested revisions would further strengthen its impact.

Bayesian Optimization Using Simulation-Based Multiple Information Sources over Combinatorial Structures

Bayesian optimization due to its flexibility and sample efficiency has become a standard approach for simulation optimization. To reduce this problem, one can resort to cheaper surrogates of the objective function. Examples are ubiquitous, from protein engineering or material science to tuning machine learning algorithms, where one could use a subset of the full training set or even a smaller related dataset. Cheap information sources in the optimization scheme have been studied in the literature as the multi-fidelity optimization problem. Of course, cheaper sources may hold some promise toward tractability, but cheaper models offer an incomplete model inducing unknown bias and epistemic uncertainty. In this manuscript, we are concerned with the discrete case, where fx,wi is the value of the performance measure associated with the environmental condition wi and p(wi) represents the relevance of the condition wi (i.e., the probability of occurrence or the fraction of time this condition occurs). The main contribution of this paper is the proposal of a Gaussian-based framework, called augmented Gaussian process (AGP), based on sparsification, originally proposed for continuous functions and its generalization in this paper to stochastic optimization using different risk profiles for combinatorial optimization. The AGP leverages sample and cost-efficient Bayesian optimization (BO) of multiple information sources and supports a new acquisition function to select the new source–location pair considering the cost of the source and the (location-dependent) model discrepancy. An extensive set of computational results supports risk-aware optimization based on CVaR (conditional value-at-risk). Computational experiments confirm the actual performance of the MISO-AGP method and the hyperparameter optimization on benchmark functions and real-world problems.

Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation.

Federated learning (FL) commonly encourages the clients to perform multiple local updates before the global aggregation, thus avoiding frequent model exchanges and relieving the communication bottleneck between the server and clients. Though empirically effective, the negative impact of multiple local updates on the stability of FL is not thoroughly studied, which may result in a globally unstable and slow convergence. Based on sensitivity analysis, we define in this paper a local-update stability index for the general FL, as measured by the maximum inter-client model discrepancy after the multiple local updates that mainly stems from the data heterogeneity. It enables to determine how much the variation of client's models with multiple local updates may influence the global model, and can also be linked with the convergence and generalization. We theoretically derive the proposed local-update stability for current state-of-the-art FL methods, providing possible insight to understanding their motivation and limitation from a new perspective of stability. For example, naively executing the parallel acceleration locally at clients would harm the local-update stability. Motivated by this, we then propose a novel accelerated yet stabilized FL algorithm (named FedANAG) based on the server- and client-level Nesterov accelerated gradient (NAG). In FedANAG, the global and local momenta are elaborately designed and alternatively updated, while the stability of local update is enhanced with help of the global momentum. We prove the convergence of FedANAG for strongly convex, general convex and non-convex settings. We then conduct evaluations on both the synthetic and real-world datasets to first validate our proposed local-update stability. The results further show that across various data heterogeneity and client participation ratios, FedANAG not only accelerates the global convergence by reducing the required number of communication rounds to a target accuracy, but converges to an eventually higher accuracy.

Updated ultraviolet fluorescence efficiencies of CS: Evidence for model discrepancies in the enhancement of NUV-derived CS abundances in comets

Observations of carbon monosulfide (CS) have a long history serving as a remote proxy for atomic sulfur, and more broadly, one of the sulfur reservoirs in cometary bodies. Recently, systematic discrepancies between NUV- and radio-derived CS abundances have been found to exceed a factor of 2–5, with NUV-derived abundances appearing enhanced for a wide array of comets. Interpretation of cometary CS emission in the ultraviolet has relied on a murky and ill-documented lineage of calculations whose accuracy can be difficult to assess. We report new fluorescence efficiencies of the CS radical, utilizing a rovibrational structure with vibrational states up to v=8 and rotational states up to N=100. The models utilize a new set of band transition rates derived from laboratory electron impact experiments. Benchmark comparisons to IUE observations of C/1979 Y1 (Bradfield) show favorable agreement with the fluorescence models. The present results affirm the accuracy of the historical record of CS abundances derived via ultraviolet CS emission in comets with IUE and HST, but do not explain the consistent enhancement of NUV-derived CS abundances relative to the radio measurements during the same apparitions. Alternative explanations of the factor of 2–5 discrepancy between NUV- and radio-derived CS abundances are discussed, as well as possible connections to sulfur reservoirs in protoplanetary disks. The model code and computed fluorescence efficiencies are made publicly available on the Zenodo service.

Model experimental studies on active heave compensation control strategy for electric-driven offshore cranes

Ship-mounted heave compensation offshore cranes are indispensable for isolating connected payloads from the support vessel during lifting operations under harsh sea conditions. In this paper, an innovative adaptive robust control strategy is presented for the electric-driven active heave compensation (EDAHC) system, combining an equivalent model predictive control (EMPC) method with a bias proportional integral derivative (BPID) framework to effectively mitigate the adverse effects of wave-induced heave motions from the support vessel on the suspended payload. Building upon the inherent field-oriented control in the permanent magnet synchronous motor (PMSM), the BPID-based control structure is introduced, motivated by its prompt responsiveness and robust resistance against model discrepancies and irregular disturbances. Facilitated by a torque compensation mechanism, the EMPC-based control scheme, synthesized with an autoregressive integrated moving average (ARIMA)-based heave motion prediction algorithm, is subsequently developed to achieve nonlinear friction deadzone correction and adaptive regulation of BPID parameters, thereby ensuring optimal performance of the EDAHC system within specified state and input constraints. Comparative experimental tests conducted on a scaled EDAHC testbed validate the superior capabilities of the proposal in station-keeping, position tracking, constraint satisfaction, and robustness against parametric uncertainties.

Adaptive sliding mode control with nonlinear MPC-based obstacle avoidance using LiDAR for an autonomous surface vehicle under disturbances

This manuscript proposes a path-following and collision avoidance guidance and control system for an autonomous surface vehicle operating in cluttered environments under perturbations. A nonlinear model predictive control strategy handles the state and control constraints for obtaining a guidance law. The approach considers the vessel sideslip angle to provide the desired heading angle, satisfying path-following and obstacle avoidance. Additionally, to solve the nonlinear optimization problem for obtaining the guidance law in real-time, the acados software package is used. Furthermore, in order to guarantee that the vehicle tracks the guidance in presence of model uncertainties and external disturbances, a robust adaptive sliding mode low-level controller is adopted. This controller is finite time convergent, and dynamically adapts its control gain to use only the necessary control inputs. Furthermore, a low-memory and computationally-light obstacle detection strategy employing a LiDAR sensor is presented and proven to run in real-time. Simulation and experimental tests conducted using a customized prototype validate the advantages of the proposed strategy and demonstrate the effectiveness of the path following while evading multiple obstacles in the presence of external disturbances and model discrepancies.

Disagreement on the North Atlantic Cold Blob Formation Mechanisms among Climate Models

Abstract Despite global warming, the sea surface temperature (SST) in the subpolar North Atlantic has decreased since the 1900s. This local cooling, known as the North Atlantic cold blob, signifies a unique role of the subpolar North Atlantic in uptaking heat and hence impacts downstream weather and climate. However, a lack of observational records and their constraints on climate models leave the North Atlantic cold blob formation mechanism inconclusive. Using simulations from phase 6 of Coupled Model Intercomparison Project, we assess the primary processes driving the North Atlantic cold blob within individual models and whether the mechanisms are consistent across models. We show that 11 out of 32 models, which we call “Cold Blob” models, simulate the subpolar North Atlantic cooling over 1900–2014. Further analyzing the heat budget of the subpolar North Atlantic SST shows that models have distinct mechanisms of cold blob formation. While 4 of the 11 Cold Blob models indicate decreased oceanic heat transport convergence (OHTC) as the key mechanism, another four models suggest changes in radiative processes making predominant contributions. The contribution of OHTC and radiative processes is comparable in the remaining three models. Such a model disagreement on the mechanism of cold blob formation may be associated with simulated base-state Atlantic meridional overturning circulation (AMOC) strength, which explains 39% of the intermodel spread in the contribution of OHTC to the simulated cold blob. Models with a stronger base-state AMOC suggest a greater role of OHTC, whereas those with a weaker base-state AMOC indicate that radiative processes are more responsible. This model discrepancy suggests that the cold blob formation mechanism diagnosed from single model should be interpreted with caution. Significance Statement The mechanisms driving sea surface temperatures over the subpolar North Atlantic to cool since the 1900s remain uncertain due to the lack of direct observations. Here, we use a temperature change decomposition framework to dissect the historical trend of surface temperature simulated in multiple global climate models. The models diverge on whether the subpolar North Atlantic cooling is induced by reduced ocean heat transport convergence or altered radiative processes. Notably, the importance of ocean heat transport convergence is influenced by the simulated base-state strength of Atlantic meridional overturning circulation and the Irminger Sea’s mixed layer depth. This finding cautions against concluding the cooling mechanism from a single model and highlights a need for ongoing observations to constrain AMOC-related climate projection in the subpolar North Atlantic.

Investigating the sign of stratocumulus adjustments to aerosols in the ICON global storm-resolving model

Abstract. Aerosols can cause brightening of stratocumulus clouds, thereby cooling the climate. Observations and models disagree on the magnitude of this cooling, partly because of the aerosol-induced liquid water path (LWP) adjustment, with climate models predicting an increase in the LWP and satellites observing a weak decrease in response to increasing aerosols. With higher-resolution global climate models, which allow the simulation of mesoscale circulations in which stratocumulus clouds are embedded, there is hope to start bridging this gap. In this study, we present boreal summertime simulations conducted with the ICOsahedral Non-hydrostatic (ICON) global storm-resolving model (GSRM). Compared to geostationary satellite data, ICON produces realistic cloud coverage in the stratocumulus regions; however, these clouds look cumuliform, and the sign of LWP adjustments disagrees with observations. We investigate this disagreement with a causal approach, which combines time series with knowledge of cloud processes, allowing us to diagnose the sources of observation–model discrepancies. The positive ICON LWP adjustment results from a superposition of processes, with an overestimated positive response due to (1) precipitation suppression, (2) a lack of wet scavenging, and (3) cloud deepening under a weak inversion, despite (4) small negative influences from cloud-top entrainment enhancement. We also find that precipitation suppression and entrainment enhancement occur at different intensities during the day and the night, implying that daytime satellite studies suffer from selection bias. This causal methodology can guide modelers on how to modify model parameterizations and setups to reconcile conflicting studies concerning the sign and magnitude of LWP adjustments across different data sources.

Unraveling Subseasonal Drought Dynamics in India: Insights from NCMRWF Extended Range Prediction System

Abstract Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.

Dwindling Effective Radiative Forcing of Large Volcanic Eruption: The Compensation Role of Ocean Latent Heat Flux

AbstractClimatic impacts of historical volcanism are principally tied to the eruption size, while observation versus model discrepancies have been commonly attributed to the uncertainties in paleo‐reconstruction or malpresentation of volcanic aerosols in models. Here we present convergent evidence for significant compensation effect of ocean latent heat (LH) in balancing the tropical volcanic‐induced heat loss, by introducing an effective perturbation ratio which is found to decrease with increasing eruption magnitude. Four LH compensation hot spots overlapping with the trade wind regions are identified, together with three western boundary currents regions with intensified LH loss. Comparison between the 1258 Samalas and 1452 Unidentified eruptions suggests considerable modulation of the concurring El Nino‐Southern Oscillation on LH anomaly, which is further verified by CESM large ensemble sensitivity experiments. This study depicts how the interplay between the ocean and the atmosphere could contribute to the overall resilience of the climate system in the face of volcanic disturbances.

A Bayesian framework for in-flight calibration and discrepancy reduction of spacecraft operational simulation models

Modeling and Simulation (M&S) tools have become indispensable for the comprehensive design, operations, and maintenance of products in the space industry. An example is the European Space Agency (ESA), which relies heavily on M&S throughout the entire lifecycle of a spacecraft. However, their use in operational settings poses significant challenges, mainly attributable to (i) the harsh, uncontrollable, and often unforeseen environmental conditions; (ii) the dramatic changes in operating conditions throughout a spacecraft’s lifespan, often beyond the intended designed-for lifetime; and (iii) the presence of epistemic and aleatoric uncertainty. This results in unavoidable discrepancies between the numerical simulations and real measurements, limiting their use for delicate operational tasks. To address those challenges, we present a Bayesian framework for simultaneous calibration of M&S tools, reduction of the model discrepancy, and quantification of the process and model uncertainties. The approach leverages the Kennedy and O’Hagan (KOH) calibration, tailored for a multi-objective problem. Its effectiveness is shown by its application to flying Earth observation spacecraft data and the operational simulation models.

Efficient and lightweight layer-wise in-situ defect detection in laser powder bed fusion via knowledge distillation and structural re-parameterization

Powder bed quality is critical in Laser Powder Bed Fusion (LPBF) as defects in the powder bed will affect the quality of the forming parts. Recently, numerous powder bed defect detection methods based on semantic segmentation algorithms have been developed. However, due to the computational resource constraints and real-time requirements in industry scenarios, these heavy models face challenges in deploying into the LPBF process monitoring system. Meanwhile, existing lightweight models struggle to achieve sufficient performance on industrial data due to their limited feature extraction capabilities. To address the challenges in industrial deployment, this paper proposes a lightweight and efficient LPBF layer-wise defect detection paradigm to achieve a balance between performance and efficiency. Aiming at improving the performance of the lightweight model, this paper proposes a structured Knowledge Distillation framework, in which a pre-trained high-performance segmentation model (UNet++) is used to guide the training process of a lightweight model. To address the impact of model discrepancies on the effectiveness of Knowledge Distillation and further improve the inference speed, this paper constructs a specific lightweight model and applies Structural Re-parameterization to deeply compress the model. Moreover, we created a dataset comprising 406 images of powder-bed defects, with each image annotated at the pixel level. Massive experiments based on the dataset demonstrate the effectiveness of the proposed paradigm, with an excellent trade-off in performance and efficiency. The speed of the proposed model is approximately 5 times faster than that of high-performance models while maintaining comparable performance.

The effects of supplemental instruction derived from peer leaders on student outcomes in undergraduate human anatomy.

Supplemental instruction (SI) confers student success, as represented by grades, knowledge retention, and student engagement. However, studies often report professional, not undergraduate, program findings. To measure these effects, students studying human anatomy at a university in Ontario, Canada, attended structured (peer-assisted) or unstructured (nonpeer-assisted) SI sessions and completed a pre-/post-survey. Fifty-eight learners (39 systems (SYS) and 19 musculoskeletal (MSK) anatomy) completed both surveys and had responses analyzed. Both cohorts, maintained initial perceptions across pre-/post-analyses (MSK p = 0.1376 and SYS p = 0.3521). Resource usage was similar across both cohorts with discrepancies in skeletal model and textbook use. No MSK learner ranked any labresourcesas "not at all useful." MSK learners felt more prepared to write a graded assessment (p = 0.0269), whereas SYS learners did not (p = 0.0680). Stratification of learners in MSK and SYS revealed learners spending between 30 and 60 min in SI sessions during the study period had the highest grades compared to students who spent less than 30 (p = 0.0286) or more than 60 (p = 0.0286) min attending SI sessions, respectively. Most learners in MSK (89.4%) and SYS (66%) concluded that they preferred structured over unstructured SI. Sentiment/thematic analysis using a generative AI-driven large language model revealed learners held positive perceptions of SI, emphasizing structured learning, resources, personalized learning, and support offered as the most prevalent themes surrounding SI. Ultimately, this study provides evidence that supports SI for improving student outcomes related to perceived preparedness for completing assessments and preferred teaching/learning styles in undergraduate human anatomy.

Prediction of slug length distribution in horizontal large-diameter gas/liquid pipeline systems

Large-diameter (ID ≥ 0.1524 m) pipeline network systems are widely used in the food industry for grain transportation, in the chemical and nuclear industries for steam/water thermal distribution systems, in the oil and gas industry for crude oil transportation and exploitation, and in urban water supply and drainage systems. Due to the favorable operational and geometrical conditions in surface gas/liquid pipelines, slug flow often prevails, dominating the horizontal and slightly inclined flow pattern map. Formed liquid slugs at large-diameter pipeline entrances and dip can grow, accelerate, and overtake other slugs, resulting in high-momentum and long slugs ranging in length from a few meters to a few hundred meters. The discrepancy of current mechanistic models to predict slug length in large diameter pipes, and the scarcity of large diameter slug length data motivated this work to propose a combined mechanistic and empirical approach to predict full slug length distribution in large diameter surface pipeline systems. A first-principle mechanistic mean slug length model is improved by optimizing its closure relationships using a large-diameter lab and field slug length database. Furthermore, an empirical large-diameter slug length standard deviation dimensionless model is proposed and statistically validated. The proposed mean and standard deviation slug length models are combined to construct a probability distribution and maximum slug length models. Validation of the proposed model revealed an absolute average error of 27% and 31% for the mean and maximum slug lengths, respectively, vastly outperforming all existing models.

A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements

This article presents a method within a Bayesian framework for quantifying uncertainty in satellite aerosol remote sensing when retrieving aerosol optical depth (AOD). By using a Bayesian model averaging technique, we take into account uncertainty in aerosol optical model selection and also obtain a shared inference about AOD based on the best-fitting optical models. In particular, uncertainty caused by forward-model approximations has been taken into account in the AOD retrieval process to obtain a more realistic uncertainty estimate. We evaluated a model discrepancy, i.e., forward-model uncertainty, empirically by exploiting the residuals of model fits and using a Gaussian process to characterise the discrepancy. We illustrate the method with examples using observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite. We evaluated the results against ground-based remote sensing aerosol data from the Aerosol Robotic Network (AERONET).

Practical experience with initial quality assurance of a high dose rate brachytherapy grid-based Boltzmann solver algorithm.

The purpose of this study was to validate the use of a model-based dose calculation algorithm (MBDCA), Acuros BV, for high dose rate brachytherapy treatment planning for a community-based hospital with a Bravos afterloader. Based on published AAPM recommendations, this work details a practical approach for community-based clinics to complete initial validation of Acuros BV, in order to add a MBDCA to a TG-43 based brachytherapy treatment planning program. Source dimensions and materials used in Acuros BV and TG-43 source models were compared to the physical source. TG-186 testing was completed with standardized test cases externally calculated with Monte Carlo compared to locally calculated with Acuros BV. Point doses calculated using TG-43 were compared to those calculated with Acuros BV in water at various dose grid settings. Secondary dose check software was used to evaluate dose distributions resembling clinical patient plans, both in water and on CT datasets representative of patient anatomy. The major source of discrepancy of source models was the length of modeled steel cable. TG-186 testing showed that the largest differences between Monte Carlo and Acuros BV dose distributions were located along the source axis for cases calculated in water, as well as located in regions of high dose gradients and within the applicator for the case calculated with a generic shielded applicator. An audit of point doses calculated with both TG-43 and Acuros BV in water found that dose grid settings significantly affected agreement. Secondary dose check software indicated that Acuros BV functioned satisfactorily, and a 5% threshold was adopted for secondary dose checks on gynecologic plans. This validation process indicated that Acuros BV met expected standards and affirmed its suitability for integration into this clinical practice's brachytherapy treatment planning.

Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems

In the dynamic realm of Unmanned Aerial Vehicles (UAVs), and, more specifically, Quadrotor drones, this study heralds a ground-breaking integrated optimal control methodology that synergizes a distributed framework, predictive control, H-infinity control techniques, and the incorporation of a Kalman filter for enhanced noise reduction. This cutting-edge strategy is ingeniously formulated to bolster the precision of Quadrotor trajectory tracking and provide a robust countermeasure to disturbances. Our comprehensive engineering of the optimal control system places a premium on the accuracy of orbital navigation while steadfastly ensuring UAV stability and diminishing error margins. The integration of the Kalman filter is pivotal in refining the noise filtration process, thereby significantly enhancing the UAV’s performance under uncertain conditions. A meticulous examination has disclosed that, within miniature Quadrotors, intrinsic forces are trivial when set against the formidable influence of control signals, thus allowing for a streamlined system dynamic by judiciously minimizing non-holonomic behaviors without degrading system performance. The proposed control schema, accentuated by the Kalman filter’s presence, excels in dynamic efficiency and is ingeniously crafted to rectify any in-flight model discrepancies. Through exhaustive Matlab/Simulink simulations, our findings validate the exceptional efficiency and dependability of the advanced controller. This study advances Quadrotor UAV technology by leaps and bounds, signaling a pivotal evolution for applications that demand high-precision orbital tracking and enhanced noise mitigation through sophisticated nonlinear control mechanisms.