Robust Criteria Research Articles

Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions

This paper analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and globally robustly exponentially stable equilibrium point. This method not only avoids the difficult to set up any Lyapunov–Krasovskii functional, but also derives simpler global robust exponential stability criteria. Compared with the data from other literature, the robust exponential stability criteria obtained in this paper have been presented to have more merits theoretically and numerically.

Comparative vertical quality analysis using different methods for MBES data

Vertical quality evaluation in hydrographic surveys, particularly utilizing Multibeam Echosounders (MBES), presents challenges due to limited bathymetric information in obtaining homologous points and the absence of robust statistical criteria in depth estimation. This study addresses these challenges by proposing and validating the Point-to-Point (P2P) method, which prevents geostatistical interpolation while enhancing accuracy using a limited distance to search for the nearest neighbor and to find a probable homologous point. Three distinct methods were applied to compare depths of sounding lines and check line, namely Surface-to-Surface, Surface-to-Point, and Point-to-Point. The efficacy of the P2P method was established through a comprehensive evaluation of RMSE and ϕRobust, besides the discrepancies. This research underscores the significance of the P2P method, showcasing its superiority over conventional approaches and its potential to rectify the absence of statistical rigor in vertical quality assessment.

Do risk adjustment calculations used by governmental agencies adequately assess risk for infection after colon surgery?

Surgical site infections (SSIs) following colon surgery are associated with clinical and financial consequences. The Centers for Medicare and Medicaid Services (CMS) and the New York State Department of Health (NYSDOH) use risk adjustment variables to determine quality measure scores. Among patients in a large public system, surgical risk variables were compared between patients with and without SSIs. Propensity score matching, using CMS and NYSDOH risk variables, created control groups. Receiver Operating Characteristics (ROC) curves were created using current and augmented risk adjustment variables. When matched using CMS risk variables, more patients with SSIs had contaminated/dirty wounds, longer duration of surgery, and emergency surgery. The addition of these variables significantly improved the CMS ROC curve. When matching NYSDOH variables, more SSI patients were male, had contaminated/dirty wounds, and tended to be younger. The addition of these variables to the current NYSDOH adjustment criteria did not significantly improve the ROC curve. The CMS adjustment criteria for colon SSIs do not adequately account for complicated surgeries. The inclusion of additional variables significantly improved the performance of CMS risk adjustment. Until more robust risk adjustment criteria are developed, the reporting of SSIs following colon surgery as a quality measure should be suspended.

Overlapping Physiologic Signs of Sepsis and Paroxysmal Sympathetic Hyperactivity After Traumatic Brain Injury: Exploring A Clinical Conundrum.

Paroxysmal sympathetic hyperactivity (PSH) occurs in a subset of patients with traumatic brain injury (TBI) and is associated with worse outcomes. Sepsis is also associated with worse outcomes after TBI and shares several physiologic features with PSH, potentially creating diagnostic confusion and suboptimal management of each. This is the first study to directly investigate the interaction between PSH and infection using robust diagnostic criteria. We performed a retrospective cohort study of patients with TBI admitted to a level I trauma center intensive care unit with hospital length of stay of at least 2weeks. From January 2016 to July 2018, 77 patients diagnosed with PSH were 1:1 matched by age and Glasgow Coma Scale to 77 patients without PSH. Trauma infectious diseases subspecialists prospectively documented assessments corroborating diagnoses of infection. Extracted data including incidence, timing, classification, and anatomical source of infections were compared according to PSH diagnosis. We also evaluated daily PSH clinical feature severity scores and systemic inflammatory response syndrome (SIRS) criteria and compared values for patients with and without confirmed infection, stratified by PSH diagnosis. During the first 2weeks of hospitalization, there were no differences in rates of suspected (62%) nor confirmed (48%) infection between patients with PSH and controls. Specific treatments for PSH were initiated on median hospital day 7 and for confirmed infections on median hospital day 8. SIRS criteria could identify infection only in patients who were not diagnosed with PSH. In the presence of brain injury-induced autonomic nervous system dysregulation, the initiation and continuation of antimicrobial therapy is a challenging clinical decision, as standard physiologic markers of sepsis do not distinguish infected from noninfected patients with PSH, and these entities often present around the same time. Clinicians should be aware that PSH is a potential driver of SIRS, and familiarity with its diagnostic criteria as proposed by the PSH assessment measure is important. Management by a multidisciplinary team attentive to these issues may reduce rates of inappropriate antibiotic usage and misdiagnoses.

An adaptive solution strategy for Richards' equation

Flow in variably saturated porous media is typically modeled by the Richards equation, a nonlinear elliptic-parabolic equation which is notoriously challenging to solve numerically. In this paper, we propose a robust and fast iterative solver for Richards' equation. The solver relies on an adaptive switching algorithm, based on rigorously derived a posteriori indicators, between two linearization methods: L-scheme and Newton. Although a combined L-scheme/Newton strategy was introduced previously in [1], here, for the first time we propose a reliable and robust criteria for switching between these schemes. The performance of the solver, which can be in principle applied to any spatial discretization and linearization methods, is illustrated through several numerical examples.

Robust function-on-function interaction regression

A function-on-function regression model with quadratic and interaction effects of the covariates provides a more flexible model. Despite several attempts to estimate the model’s parameters, almost all existing estimation strategies are non-robust against outliers. Outliers in the quadratic and interaction effects may deteriorate the model structure more severely than their effects in the main effect. We propose a robust estimation strategy based on the robust functional principal component decomposition of the function-valued variables and [Formula: see text]-estimator. The performance of the proposed method relies on the truncation parameters in the robust functional principal component decomposition of the function-valued variables. A robust Bayesian information criterion is used to determine the optimum truncation constants. A forward stepwise variable selection procedure is employed to determine relevant main, quadratic, and interaction effects to address a possible model misspecification. The finite-sample performance of the proposed method is investigated via a series of Monte-Carlo experiments. The proposed method’s asymptotic consistency and influence function are also studied in the supplement, and its empirical performance is further investigated using a U.S. COVID-19 dataset.

Fractional Order Weighted Mixed Sensitivity-Based Robust Controller Design and Application for a Nonlinear System

This paper focuses on fractional-order modeling and the design of a robust speed controller for a nonlinear system. An induction motor (IM), widely used in Electrical Vehicles (EVs), is preferred in this study as a well-known nonlinear system. The major challenge in designing a robust speed controller for IM is the insufficiency of the machine model due to inherent machine dynamics. Fractional calculus is employed to model the IM using the small-signal method, accounting for model uncertainties. In this context, experimental data is approximated using a fractional-order small-signal transfer function. Consequently, a mixed sensitivity problem is formulated with fractional-order weighting functions. The primary advantage of these weighting functions is their greater flexibility in solving the mixed sensitivity problem by involving more coefficients. Hereby, three robust speed controllers are designed using the PID toolkit of the Matlab program and solving the H∞ mixed sensitivity problem, respectively. The novelty and contribution of the proposed method lie in maintaining the closed-loop response within a secure margin determined by fractional weighting functions while addressing the controller design. After evaluating the robust speed controllers with Bode diagrams, it is proven that all the designed controllers meet the desired nominal performance and robustness criteria. Subsequently, real-time implementations of the designed controllers are performed using the dsPIC microcontroller unit. Experimental results confirm that the designed H∞-based fractional-order proportional-integral-derivative (FOPID) controller performs well in terms of tracking dynamics, exhibits robustness against load disturbances, and effectively suppresses sensor noise compared to the robust PID and fixed-structured H∞ controller.

Contribution to the Objective Evaluation of Combined Longitudinal and Lateral Vehicle Dynamics in Nonlinear Driving Range

<div>Since the complexity of modern vehicles is increasing continuously, car manufacturers are forced to improve the efficiency of their development process to remain profitable. A frequently mentioned measure is the consequent integration of virtual methods. In this regard, objective evaluation criteria are essential for the virtual design of driving dynamics. Therefore, this article aims to identify robust objective evaluation criteria for the nonlinear combined longitudinal and lateral dynamics of a vehicle. The article focuses on the acceleration in a turn maneuver since available objective criteria do not consider all relevant characteristics of vehicle dynamics. For the identification of the objective criteria, a generic method is developed and applied. First, an open-loop test procedure and a set of potential robust objective criteria are defined. Subsequently, suitable criteria are selected for different vehicle dynamics characteristics based on an investigation of Pearson correlations between the objective criteria and established subjective rating criteria. For this purpose, a subjective evaluation study with six specifically selected vehicle variants is conducted. Finally, the applicability of the selected objective criteria for vehicles of different segments is assessed through a benchmark of current vehicles. The results are objective criteria for the vehicle characteristics driving stability, oversteer/understeer, and traction. In contrast to existing objective criteria, the identified criteria shows a high robustness to measurement noise. Furthermore, there is a comprehensible correlation to established subjective rating criteria for each objective criterion. Lastly, the benchmark of current vehicles proves the applicability of the identified criteria.</div>

Model Selection for independent not identically distributed observations based on Rényi’s pseudodistances

Model selection criteria are rules used to select the best statistical model among a set of candidate models, striking a trade-off between goodness of fit and model complexity. Most popular model selection criteria measures the goodness of fit trough the model log-likelihood function, yielding to non-robust criteria. This paper presents a new family of robust model selection criteria for independent but not identically distributed observations (i.n.i.d.o.) based on the Rényi’s pseudodistance (RP). The RP-based model selection criterion is indexed with a tuning parameter α controlling the trade-off between efficiency and robustness. Some theoretical results about the RP criterion are derived and the theory is applied to the multiple linear regression model, obtaining explicit expressions of the model selection criterion. Moreover, restricted models are considered and explicit expressions under the multiple linear regression model with nested models are accordingly derived. Finally, a simulation study empirically illustrates the robustness advantage of the method.

Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region revealed by attentive deep learning model

The Hindu Kush-Pamir region (HKPR) is characterized by complex ongoing deformation, unique slab geometry, and intermediate seismic activity. The availability of extensive seismological data in recent decades has prompted the use of deep learning algorithms to extract valuable insights. In this study, we present a fully automated approach for augmenting earthquake catalogue within the HKPR. Our method leverages an attention mechanism-based deep learning architecture to simultaneously detect events, perform phase picking, and estimate magnitudes. We applied this model to a ten-month dataset (January 2013–October 2013) from 83 stations in the region. Utilizing a robust criterion to evaluate the model's probabilities, we associated phases at different stations and pinpointed earthquake locations in the region. Our results demonstrate a significant enhancement, revealing nearly four and a half times more earthquakes than previously documented in the International Seismological Center (ISC) catalogue. A notable portion of these newly detected events falls within the category of very low-magnitude earthquakes (<3), which were absent in the ISC catalogue. Notably, our spatiotemporal analysis reveals a concentration of crustal seismicity along poorly mapped neotectonic north and northeast-oriented faults in the western Pamir, as well as the Vakhsh Thrust System and the Darvaz Karakul Fault. These findings underscore potential sources of future seismic hazards. Furthermore, our expanded earthquake catalogue facilitates a deeper understanding of the interplay between crustal and intermediate seismic activity in the HKPR, shedding light on the deformation and active faulting resulting from Eurasian-Indian plate interactions.

Fundamentals and practice of the application of nasal packing in sinonasal surgery

This paper presents an overview on nasal packing materials which are available in Germany. The current literature is analyzed whether there are robust criteria regarding use nasal packing after sinonasal surgery, whether there are fundamental and proven advantages or disadvantages of products, and what this means in clinical practice. Selective literature analysis using the PubMed database (key words "nasal packing", "nasal tamponade", "nasal surgery", "sinonasal surgery", or "sinus surgery"), corresponding text books and resulting secondary literature. Because of systematic methodological shortcomings, the literature does not help in the decision-making about which nasal packing should be used after which kind of sinonasal surgery. In fact, individual approaches for the many different clinical scenarios are recommended. In principle, nasal packing aims in hemostasis, should promote wound healing, and should not result in secondary morbidity. Nasal packing materials should be smooth (non-absorbable materials), inert (absorbable materials), and should not exert excessive pressure. Using non-absorbable packing entails the risk of potentially lethal aspiration and ingestion. For safety reasons inpatient control is recommended as long as this packing is in situ. With other, uncritical packing materials and in patients with special conditions, outpatient control could be justified.

Enhancement of Backward Feature Elimination as a Pre-Processing Method for K Nearest Neighbor Algorithm Applied to Insurance Fraud Detection

This study presents novel enhancements to the Backward Feature Elimination (BFE) method for improved insurance fraud detection using the K-Nearest Neighbor (KNN) algorithm. The research addresses issues inherent in the baseline BFE process, such as the over-reliance on p-values, the potential for misleading results, and suboptimal feature selection leading to overfitting. To address these, the study integrates confidence intervals and feature importance into the BFE process, establishing a more robust and reliable criterion for feature selection. Moreover, feature engineering techniques are introduced during preprocessing to enhance model performance. The modified BFE method demonstrates superior performance over the baseline model regarding the recall, precision, and F1 score. Stratified K-Fold Cross-Validation, ROC-AUC Score, and Coefficient of Variation (CV) confirm the consistency and robustness of the enhanced model across varying data subsets. These innovations offer a comprehensive and reliable solution to feature selection in the BFE method, applied to the KNN model for effective insurance fraud detection. The study mitigates the issues related to p-value dependence and boosts model performance, paving the way for more accurate and robust fraud detection systems.

Proton Therapy for Ocular Melanoma: A Dosimetric Comparison of Three Different Delivery Systems

The purpose of this study is to compare the treatment plans for intraocular tumors with two types of Pencil Beam Scanning (PBS) machine configurations and compare them with the widely used uniform scanning (US)/scattering technique. Two virtual PBS machines were constructed for ocular planning. The first machine (P1) consisted of a standard spot size and a 7.5cm WET range shifter (RS) positioned 50cm upstream from the aperture. The second machine (P2) had a smaller spot size and used a 4cm WET RS but had no aperture support. These PBS delivery systems resemble currently available models. Ten patients with different locations and sizes of intraocular tumors were planned with US using 2-3 conformal fields following our institution's intraocular criteria. PBS spot patterns for each machine were optimized using the same beam geometry as the US plan to achieve identical tumor coverage. PBS distribution was calculated using Monte Carlo. All air gaps were minimized as small as clinically achievable. PBS plans passed robustness criteria with all scenarios meeting CTV D95>95% with 2mm translational offsets and 3.5% range uncertainty. The average conformity index (CI) of 95% reference isodose was better for both PBS techniques (P1: 1.43±0.22, P2:1.5±0.32) versus US (1.72±0.56) for all plans (p = 0.025, p = 0.034). The average CI of 50% reference isodose was worse for the P2 plans (10.4±3.3) compared to US (9.34±2.93). However, CI 50% for P1 plans was better than US (5.21±1.24, p<0.003). There were no significant improvements to OARs using P2. However, the mean dose to anterior structures ciliary body, lacrimal gland, and D0.03cc lens were significantly lower in the P1 plans than in the US plans (p<0.016). Small spot size PBS systems without apertures can achieve similar coverage to US plans but have higher OAR dose. PBS systems with a standard spot size and reconfigured RS and apertures are better for anterior OAR sparing and is non-inferior to US planning of intraocular targets.

Is Robust Optimization Essential When Planning Pencil Beam Scanned Proton Therapy for Intracranial Lesions?

Creating intensity modulated proton therapy (IMPT) plans usually involves a robust optimization step to account for uncertainties in proton range and positioning instead of using the PTV margins typically seen in photon IMRT planning. Because robust optimization adds significant planning time per iteration, and proton planning typically involves many iterations to obtain an optimal plan, this project evaluates whether a PTV approach can be used to more efficiently create plans for intracranial lesions by comparing plan quality from both approaches to determine if they produce equivalent plans. Five patients with intracranial lesions treated with IMPT at our center were randomly chosen for this study. 3-4 beams were used to treat CTVs ranging between 46 and 246 cc. Patients were treated on a Mevion S250i single-vault proton machine with Hyperscan. Static blocks (7mm conformed to the CTV) were used for all cases and plans were created in a treatment planning system using robustness criteria of 3mm (position uncertainty) and 3.5% (range uncertainty). For each patient, the CTV was uniformly grown by 3 mm to create a PTV. The optimization criteria used in the clinical plan were used as baseline to create two plans - one using robust optimization for CTV coverage and one without robust optimization for PTV coverage. A script was used to time two otherwise identical optimization runs. All plans were normalized so that the prescription was delivered to 95% of the CTV. The plan was robustly evaluated under conditions of 3.5% range uncertainty and 2mm positional uncertainty. For each nominal plan, the CTV dose heterogeneity and conformity as well as the following dose metrics were collected: CTV D99% and D98%, Normal Brain V100%, V90%, V80%, V50% as well as overall plan Dmax (to 0.03cc). For each plan, the minimum CTV D99 and maximum Brain Dmax were also collected for the robust evaluation scenarios. All collected metrics were compared between the robust CTV-based and non-robust PTV-based plans using paired t-tests with 5% significance. For the five cases investigated here, all dosimetric metrics investigated were not significantly different between the CTV-based and PTV-based plans except for the plan maximum dose (CTV-based: 104.6% Rx - 110.0% Rx, PTV-based: 105.6% Rx-110.6% Rx, p = 0.029). The optimization times were also significantly different, averaging 1532 s for CTV-based plans versus 252 s for the PTV-based plans (p = 0.004). For the plans investigated here, non-robust PTV planning approach creates plans of very similar quality to a robust CTV-based plan, while having significantly shorter planning times.

Variational Bayesian and generalized maximum-likelihood based adaptive robust nonlinear filtering framework

An adaptive robust nonlinear filtering framework is proposed based on the variational Bayesian (VB) method and generalized maximum likelihood estimation (GM estimation) to simultaneously handle the outliers and uncertain measurement noise covariance matrix (MNCM) for state estimation. This framework utilizes the cubature criterion to solve nonlinear integration problems. By embedding GM estimation in the VB method, the uncertain MNCM is modified while robust estimation is performed. The modified MNCM also provides more accurate model parameters for GM estimation, achieving the unity of adaptability and robustness. The VBCEECKF algorithm was obtained by applying the strong robust centered error entropy criterion to the adaptive robust nonlinear filtering framework. The simulation results of target tracking under different noise conditions verify the superiority of the adaptive robust filter compared to existing methods.

On a robust stability criterion in the subdiffusion equation with Caputo–Dzherbashian fractional derivative

On a robust stability criterion in the subdiffusion equation with Caputo–Dzherbashian fractional derivative

Robust job-sequencing with an uncertain flexible maintenance activity

In this study, the problem of scheduling a set of jobs and one uncertain maintenance activity on a single machine, with the objective of minimizing the makespan is addressed. The maintenance activity has a given duration and must be executed within a given time window. Furthermore, duration and time window of the maintenance are uncertain, and can take different values which can be described by different scenarios. The problem is to determine a job sequence which performs well, in terms of makespan, independently on the possible variation of the data concerning the maintenance. A robust scheduling approach is used for the problem, in which four different measures of robustness are considered, namely, maximum absolute regret, maximum relative regret, worst-case scenario, and ordered weighted averaging. Complexity and approximation results are presented. In particular, we show that, for all the four robustness criteria, the problem is strongly NP-hard. A number of special cases are explored, and an exact pseudopolynomial algorithm based on dynamic programming is devised when the number of scenarios is fixed. Two Mixed Integer Programming (MIP) models are also presented for the general problem. Several computational experiments have been conducted to evaluate the efficiency and effectiveness of the MIP models and of the dynamic programming approach.

Robust optimization of a marine current turbine using a novel robustness criterion

The present paper aims to establish a systematic robust optimization framework for the hydrodynamic performance of marine current turbines against uncertain conditions. To this end, we developed a novel robustness criterion based on the cumulative distribution function and employed an XFOIL-based blade element momentum method to predict turbine performance. The non-intrusive polynomial chaos expansion was also applied for uncertainty propagation and probabilistic computations. Initially, a set of sensitivity analysis studies was conducted on a model turbine to determine the most influential parameters. The results indicated that uncertainties have a significant impact on turbine performance, even under controlled experimental conditions. In the next stage, we applied deterministic and robust optimization approaches to optimize a real-world marine turbine design under practical conditions. We utilized a hybrid evolutionary optimization algorithm based on the Genetic Algorithm and Particle Swarm Optimization. Additionally, the novel robustness criterion was introduced to address the issues associated with conventional criteria. The robust optimization was performed independently using three criteria: the worst-case scenario, the mean value penalty, and the novel criterion. By investigating the probabilistic characteristics of the performance in the deterministic and robust optimum turbines, we demonstrated that the power generation capacity of the deterministic optimum is expectedly higher than that of the robust optimum in deterministic hypothetical conditions. However, during turbine operation in real conditions, which are accompanied by large uncertainties, the optimum design achieved using the new robustness criterion is more likely to produce greater power. Furthermore, the power variations of the turbine are more limited, indicating increased reliability and stability of the optimized design.

NGDEEP Epoch 1: The Faint End of the Luminosity Function at z ∼ 9–12 from Ultradeep JWST Imaging

We present a robust sample of very high redshift galaxy candidates from the first epoch of JWST/NIRCam imaging from the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) survey. The NGDEEP NIRCam imaging, spanning 9.7 arcmin2 in the Hubble Ultra Deep Field Parallel Field 2, reaches m = 30.4 (5σ, point-source, 2″ diameter apertures corrected to total) in F277W, making it the deepest public JWST GO imaging data set to date. We describe our detailed data reduction process of the six-filter broadband JWST/NIRCam imaging, incorporating custom corrections for systematic effects to produce high-quality calibrated images. Using robust photometric redshift selection criteria, we identify a sample of 38 z ≳ 9 galaxy candidates. These objects span a redshift range of z = 8.5–15.8 and apparent magnitudes of m F277W = 27–30.5 AB mag, reaching ∼1.5 mag deeper than previous public JWST imaging surveys. We calculate the rest-frame ultraviolet luminosity function at z ∼ 9 and 11 and present a new measurement of the luminosity function faint-end slope at z ∼ 11. We find a faint-end slope of α = −2.5 ± 0.4 and −2.2 ± 0.2 at z ∼ 9 and 11, respectively. This is consistent with no significant evolution in the faint-end slope and number density from z = 9 to 11. Comparing our results with theoretical predictions, we find that some models produce better agreement at the faint end than the bright end. These results will help to constrain how stellar feedback impacts star formation at these early epochs.

Robust stability analysis for uncertain neutral type descriptor systems with mixed delays

AbstractThe issue of robust stability here is carefully thought about for uncertain neutral type descriptor systems (NTDSs) with mixed delays. An augmented Lyapunov–Krasovskii functional considering the cross information of the singular matrix and state delay derivative is constructed. With the help of the zero‐value equation technique and an improved matrix inequality, a novel delay‐dependent admissible criterion is developed to make sure of the regularity, non‐impulsiveness, and stability of the NTDSs. On this basis, the robust stability of the uncertain NTDSs is further explored, and a novel robust stability criterion is developed. These criteria have less conservatism. Through three number‐based examples, some comparisons are provided to show the superiority and effectiveness of our method.