Robust Design Research Articles

Risk minimization using robust experimental or sampling designs and mixture of designs

For both experimental and sampling designs, the efficiency or balance of designs has been extensively studied. There are many ways to incorporate auxiliary information into designs. However, when we use balanced designs to decrease the variance due to an auxiliary variable, the variance may increase due to an effect which we define as lack of robustness. This robustness can be written as the largest eigenvalue of the variance operator of a sampling or experimental design. If this eigenvalue is large, then it might induce a large variance in the Horvitz–Thompson estimator of the total. We calculate or estimate the largest eigenvalue of the most common designs. We determine lower, upper bounds and approximations of this eigenvalue for different designs. Then, we compare these results with simulations that show the trade-off between efficiency and robustness. Those results can be used to determine the proper choice of designs for experiments such as clinical trials or surveys. We also propose a new and simple method for mixing two sampling designs, which allows to use a tuning parameter between two sampling designs. This method is then compared to the Gram–Schmidt walk design, which also governs the trade-off between robustness and efficiency. A set of simulation studies shows that our method of mixture gives similar results to the Gram–Schmidt walk design while having an interpretable variance matrix.

Five decades' experience of long‐term soil monitoring, and key design principles, to assist the EU soil health mission

AbstractThe European Union has a long‐term objective to achieve healthy soils by 2050. The European Commission has proposed a Directive of the European Parliament and of the Council on Soil Monitoring and Resilience (Soil Monitoring Law, SML), the first stage of which is to focus on setting up a soil monitoring framework and assessing soils throughout the EU. Situated in NW Europe, the UK has substantial experience in soil monitoring over the last half century which may usefully contribute to this wider EU effort. A set of overarching principles have and continue to guide design of national soil monitoring and may prove helpful as other European countries embark on similar monitoring programmes. Therefore, we present the principles of design from five decades of national soil monitoring. The monitoring discussed is based on a stratified‐random design, has matured in support of policy questions, and operates over space and time scales relevant to the SML. The UK Centre for Ecology & Hydrology (UKCEH) Countryside Surveys (CS) of Great Britain and Northern Ireland, Welsh Government, Environment and Rural Affairs Monitoring and Modelling Programme (ERAMMP) and the England Ecosystem Survey (EES) monitoring programme are national programmes currently operating in the UK. Some important lessons learnt include: adopting a question‐based approach; having a clear robust statistical design for the purpose; selecting indicators that address policy and underlying scientific questions; and selecting indicators that can detect change and use robust and well‐tested methodologies across a wide range of soil and land use types, remaining valid over long time scales, supporting thinking long‐term. Technical lessons learned include the proven cost effectiveness of a stratified‐random design including replication, while adopting a common stratification layer of stable environmental attributes aids comparability between monitoring programmes. Common protocols are vital for future intercomparisons, but a full ecosystem approach that includes co‐located soil and vegetation samples for interpreting a co‐evolving system has proved hugely advantageous. UK monitoring programmes offer a range of experience that may prove valuable to future soil monitoring design to address the major societal challenges of our time, such as maintaining food production and addressing climate change and biodiversity loss.

Association Between Alpha-1 Adrenoreceptor Antagonist Use and Cognitive Impairment: A Systematic Review.

Alpha-1 adrenergic receptor (α1-AR) antagonists are commonly used for management of benign prostatic hyperplasia or hypertension. Some studies have shown a potential link between α1-AR antagonist use and cognitive impairment. Given the conflicting data surrounding α1-AR antagonists association with cognitive dysfunction, we aim to systematically review the association of cognitive dysfunction with α1-AR antagonist use to aid clinician decision both with medication initiation and continuation. A systematic review was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched Ovid Cochrane, Ovid Embase, Ovid MEDLINE, Scopus, and Web of Science on March 7, 2023, with an update run on January 22, 2024. The primary outcome was cognitive dysfunction. We used Cochrane risk of bias for randomized controlled trials (RCTs) and MINORS (Methodological Index for Non-Randomized Studies) criteria for non-RCTs to evaluate study quality. This study was registered with PROSPERO (CRD42024505751). We identified 7 studies for our systematic review (3 RCTs, 4 non-RCTs). Tamsulosin was the most studied medication (6 of 7 studies). Tamsulosin was associated with no cognitive dysfunction in 2 RCTs, increased risk for dementia in 2 non-RCTs, no change in cognition in 1 non-RCT, and decreased risk for dementia in 1 non-RCT. Among 3 non-RCTs analyzing alfuzosin, it was associated with decreased risk of or no association with dementia in 2 studies and increased risk for dementia in 1 study. Doxazosin, prazosin, and terazosin were neutral or showed a negative risk for dementia. Our systematic review did not show a convincing causal association between α1-AR antagonists, including tamsulosin, and cognitive dysfunction. Considering the existing literature, it is appropriate to use α1-AR antagonists without concern for cognitive dysfunction. Future research, through robust study designs considering the multifactorial nature of cognitive dysfunction, is required to further evaluate this association.

Spin-dependent electrocatalysis.

The shift towards sustainable energy requires efficient electrochemical conversion technologies, emphasizing the crucial need for robust electrocatalyst design. Recent findings reveal that the efficiency of some electrocatalytic reactions is spin-dependent, with spin configuration dictating performance. Consequently, understanding the spin's role and controlling it in electrocatalysts is important. This review succinctly outlines recent investigations into spin-dependent electrocatalysis, stressing its importance in energy conversion. It begins with an introduction to spin-related features, discusses characterization techniques for identifying spin configurations, and explores strategies for fine-tuning them. At the end, the article provides insights into future research directions, aiming to reveal more unknown fundamentals of spin-dependent electrocatalysis and encourage further exploration in spin-related research and applications.

Robust secure beamforming design in cognitive satellite communication for coexistence with terrestrial networks

This paper examines the scope of physical layer security (PLS) enabled in cognitive satellite communication for co-existence with terrestrial networks. The primary concern is to ensure secure information transmission from a Satellite Earth Station to a GEO Satellite while safeguarding against potential eavesdropping from a Spacecraft. To this end, an optimization problem is formulated, considering the inherent imperfections in the channel state information (CSI). The aim is to maximize the achievable secrecy rate while adhering to specific constraints on transmit power and interference at the nearby Fixed Service transmitter. This optimization task involves fine-tuning the beamforming vectors at the Satellite Earth Station transmitter. Due to the mathematical complexity of the formulated problem, the Semidefinite Relaxation (SDR) technique is employed. This conversion technique transforms the initial non-convex problem into a tractable one, allowing the derivation of beamforming weight vectors. Finally, simulation results are presented to confirm the effectiveness of the robust beamforming scheme. To the best of our knowledge, this study potentially marks the first attempt at developing a robust, secure beamforming strategy for uplink transmission in the forthcoming era of satellite communications. Its aim is to enable spectral coexistence with nearby terrestrial networks.

Robust Design of the Security Scheme in IRS-Assisted MISO Systems With Imperfect Eavesdropping CSI

Robust Design of the Security Scheme in IRS-Assisted MISO Systems With Imperfect Eavesdropping CSI

Robust Design Optimization for a Hairpin Winding Permanent Magnet Motor Considering Permanent Magnet Inconsistency and Manufacturing Uncertainty

Robust Design Optimization for a Hairpin Winding Permanent Magnet Motor Considering Permanent Magnet Inconsistency and Manufacturing Uncertainty

Anti-Spectral Interference Waveform Design Based on High-Order Norm Optimized Autocorrelation Sidelobes Properties

This paper introduces a robust waveform design method aimed at reducing the impact of electromagnetic interference in radar systems, thereby enhancing target detection accuracy. We propose utilizing a high-order p-norm to characterize the peak sidelobe level (PSL) of the waveform. Additionally, the method incorporates spectral zero-trapping within known interfering frequency bands to mitigate interference effects. A unified optimization objective function is developed to ensure optimal correlation properties of waveforms for dual-use in radar and communication systems. By employing the AdamW algorithm for dynamic adjustment of the iteration factor, combined with a gradient descent search, this method refines both the autocorrelation of the waveform and its resilience to known disturbances. Experimental results demonstrate that our approach significantly improves autocorrelation performance over randomly generated initial waveforms. Moreover, the introduction of spectral zero-trapping notably enhances interference suppression in targeted frequency bands, thereby boosting overall signal performance. Our method effectively balances interference rejection with the minimization of sidelobe levels, offering a pragmatic waveform solution for complex radar environments.

Lyapunov-based robust model predictive tracking controller design for discrete-time linear systems with ellipsoidal terminal constraint

In this article, a robust model predictive control (MPC) method is introduced based on Lyapunov-theory to control the discrete-time uncertain linear systems due to external disturbances. The structure of the proposed controller consists of two parts designed based on the offline/online schemes. In the offline-scheme, an H ∞ -based robust tracking controller is provided to satisfy the robust and tracking performance. Based on the H ∞ performance, a new linear matrix inequality problem is developed to calculate the offline-controller gains. By considering the Lyapunov-function of the offline-controller as the terminal cost of the MPC and the designed offline-controller, the online optimisation problem (OOP) is structured. This means, the MPC is designed based on the stabilised system to satisfy the physical limitations of the overall controller and the system states. Moreover, to improve the feasibility problem of the OOP, an ellipsoidal terminal constraint is added to the proposed MPC problem. Therefore, compared with the existing min–max MPC and tube MPC, the advantages of the overall controller are feasibility improvement and reducing the computational complexity with less conservatism while the robustness against parametric uncertainties and disturbances is achieved. Two simulation examples are employed to show the superiorities of the proposed robust MPC.

Surrogate based design space exploration and exploitation for an efficient airfoil optimization under uncertainties using transition models

The pursuit of sustainable, zero-emission air travel is heavily dependent on the creation of energy-efficient aircraft. Key strategies for achieving this sustainability in aviation include reducing fuel consumption through low-drag designs harnessing laminar flow. However, designing aircraft with laminar flow characteristics is complex due to their sensitivity to environmental and operational factors. This study tackles the challenge of developing energy-efficient aircraft by using computational fluid dynamics models and sophisticated optimization techniques that account for uncertainty. Our approach demonstrates the effectiveness of surrogate-based optimization and uncertainty quantification in optimizing airfoil drag for a natural laminar airfoil (NLF) design. We use surrogate models, trained with data from detailed airfoil simulations, which include a boundary layer code coupled with a linear stability method and a newly developed transition transport model. Transition location predicted using transition models facilitate an accurate drag prediction used in the optimization process. The accuracy of these surrogate models is enhanced through active sampling strategies. Our robust optimization method considers uncertainties in environmental and operational conditions, offering a deeper insight into their effects on crucial design parameters. Unlike traditional deterministic aerodynamic design optimization, our findings highlight the efficacy and precision of uncertainty-based optimization in achieving robust NLF airfoil designs over large (exploration mode) and small (exploitation mode) design spaces. Investigating design space parameterization based on the size of design variables reveals significant differences in optimal airfoil configurations. The optimized designs we propose favor delayed transition, in contrast to deterministic designs which often result in significant loss of laminarity when facing uncertainties. This study represents a significant advancement in aerospace engineering, providing a practical and effective methodology for creating energy-efficient airfoil designs. The application of these advanced optimization and uncertainty quantification techniques shows great potential for the wider field of aerospace engineering, paving the way for more resilient and robust aircraft designs.

Robust stabilisation of uncertain nonlinear systems with matched and unmatched uncertainties

This paper considers the problem of robust stabilisation of uncertain nonlinear systems with both matched and unmatched uncertainties. A new robust controller is proposed to guarantee practical or asymptotic stability under non-vanishing or vanishing unmatched uncertainties. First, the system uncertainties are decomposed optimally into the matched and unmatched portions with an objective of the minimum norm for the unmatched part. Next, instead of a robust control design based on only the matched part of uncertainties commonly used in the previous works, a robust controller is synthesised based on both matched and mismatched portions of the uncertainties. It is shown that in uncertain systems whose state cannot simultaneously stay in a so-called null set and diverge to infinity, the proposed controller guarantees stability no matter how large the input-unrelated unmatched uncertainties are. Numerical examples illustrate that the proposed controller is more robust against uncertainties, and offers improved damping and stabilisation features.

De Novo Design and Characterization of Amphiphilic Peptides with Basic Side Chains for Tailored Interfacial Chemistries.

Lysine-leucine (LK) peptides have been used as model systems and platforms for 2D material design for decades. LK peptides are amphiphilic sequences designed to bind and fold at hydrophobic surfaces through hydrophobic leucine side chains and hydrophilic lysine side chains extending into the aqueous subphase. The hydrophobic periodicity of the sequence dictates the secondary structure at the interface. This robust design makes them ideal candidates for controlling interfacial chemistry. This study presents the de novo design and characterization of two novel peptides: LRα14 and LHα14, which substitute lysine with arginine and histidine, respectively, in the helical LKα14 sequence. This modification is intended to expand the LK peptide platform to a new basic interfacial chemistry. We explore the stability of the new LRα14 and LHα14 designs with respect to changes in pH and salt concentration in bulk solution and at the interface using circular dichroism (UV-CD) and vibrational sum-frequency generation spectroscopy, respectively. Notably, the structural stability of the peptides remains unaffected across a wide range of pH and ionic strength values. At the same time, the variation of side-chain chemistry leads to a wide spectrum of interfacial water structures. By extension of the LK platform to include arginine and histidine, this study broadens the toolbox for designing tailored interfacial chemistries with applications in material and biomedical sciences.

Robust control design of uncertain nonlinear systems: a self-compensating and self-tuning approach

ABSTRACT The problem of robust stabilisation is considered for a class of uncertain nonlinear systems with linear control term. It is not required that the unforced nominal (nonlinear) system has to be stable and/or has to be known, which means that its Lyapunov function need not be known and/or found for designing a stabilising control scheme to compensate the effect of uncertainty on the systems like some traditional control design approaches. For such a class of uncertain nonlinear systems, a design approach, called a self-compensating and self-tuning one, is presented whereby some robust state feedback control schemes can be easily synthesised. The proposed design approach can result in a linear state feedback control scheme with a time-varying control gain for such a class of uncertain nonlinear systems, which implies the simplicity of the design approach and control results. In addition, the system uncertainty is also not required to have to satisfy the matching conditions.

Multi-Objective Robust Design Optimization for Crashworthiness Enhancement of Hybrid 2D Triaxially Braided Composite Tube Using Evolutionary Algorithms.

An innovative optimal design framework is developed aiming at enhancing the crashworthiness while ensuring the lightweight design of a hybrid two-dimensional triaxial braided composite (2DTBC) tube, drawing insights from the mesostructure of the composite material. To achieve these goals, we first compile the essential mechanical properties of the 2DTBC using a concentric cylinder model (CCM) and an analytical laminate model. Subsequently, a kriging surrogate model to elucidate the intricate relationship between design variables and macroscopic crashworthiness is developed and validated. Finally, employing multi-objective evolutionary optimization, we identify Pareto optimal solutions, highlighting that reducing the total fiber volume and increasing the glass fiber content in the total fiber volume are crucial for optimal crashworthiness and the lightweight design of the hybrid 2DTBC tube. By integrating advanced predictive modeling techniques with multi-objective evolutionary optimization, the proposed approach not only sheds light on the fundamental principles governing the crashworthiness of hybrid 2DTBC but also provides valuable insights for the design of robust and lightweight composite structures.

Taguchi‐based robust design for minimising torque ripple in 6‐slot/2‐pole modular high‐speed permanent magnet motor with manufacturing tolerances

Taguchi‐based robust design for minimising torque ripple in 6‐slot/2‐pole modular high‐speed permanent magnet motor with manufacturing tolerances

Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches. To empower a language model for a wider range of molecular design tasks, we introduce an unordered simplified molecular-input line-entry system based on fragments (FU-SMILES). Building upon this foundation, we propose FragGPT, a universal fragment-based molecular generation model. Initially pretrained on extensive molecular datasets, FragGPT utilizes FU-SMILES to facilitate efficient generation across various practical applications, such as de novo molecule design, linker design, R-group exploration, scaffold hopping, and side chain optimization. Furthermore, we integrate conditional generation and reinforcement learning (RL) methodologies to ensure that the generated molecules possess multiple desired biological and physicochemical properties. Experimental results across diverse scenarios validate FragGPT's superiority in generating molecules with enhanced properties and novel structures, outperforming existing state-of-the-art models. Moreover, its robust drug design capability is further corroborated through real-world drug design cases.

A robust design of time-varying internal model principle-based control for ultra-precision tracking in a direct-drive servo stage

This paper proposes a robust design of the time-varying internal model principle-based control (TV-IMPC) for tracking sophisticated references generated by linear time-varying (LTV) autonomous systems. The existing TV-IMPC design usually requires a complete knowledge of the plant I/O (input/output) model, leading to the lack of structural robustness. To tackle this issue, we, in this paper, design a gray-box extended state observer (ESO) to estimate and compensate unknown model uncertainties and external disturbances. By means of the ESO feedback, the plant model is kept as nominal, and hence the structural robustness is achieved for the time-varying internal model. It is shown that the proposed design has bounded ESO estimation errors, which can be further adjusted by modifying the corresponding control gains. To stabilize the ESO-based TV-IMPC, a time-varying stabilizer is developed by employing Linear Matrix Inequalities (LMIs). Extensive simulation and experimental studies are conducted on a direct-drive servo stage to validate the proposed robust TV-IMPC with ultra-precision tracking performance (∼60nm RMSE out of ±80mm stroke).

Regenerative Aesthetics: A Genuine Frontier or Just a Facet of Regenerative Medicine: A Systematic Review.

Regenerative aesthetics claims to enhance cosmetic outcomes through advanced biological interventions like Stem cell and Exosome therapy, Polydeoxyribonucleotide (PDRN), Photobiomodulation, bioactive peptides and treatment for cellular senescence yet lacks substantial scientific and regulatory validation. To evaluate the scientific and clinical foundations of regenerative medicine techniques in non-surgical aesthetics and assess the legitimacy of regenerative aesthetics as a medical specialty. A systematic review was conducted according to PRISMA guidelines, searching databases including PubMed, Scopus, and Web of Science for studies published in the last ten years. We included 19 studies, comprising 14 randomized controlled trials (RCTs) and 5 prospective studies, focusing on interventions that purportedly use regenerative medicine principles in aesthetic applications. The review highlights a prevalent gap in molecular and clinical evidence supporting the efficacy and safety of regenerative aesthetics. Despite the robust design of the included RCTs and prospective studies, there remains a significant lack of consistent, high-quality evidence proving the effectiveness of these interventions. Issues such as inadequate reporting, unclear molecular mechanisms, and absence of long-term safety data were common. The field of regenerative aesthetics lacks the necessary scientific rigour and regulatory compliance to be recognized as a legitimate medical specialty. This review underscores the need for stringent scientific validation and regulatory oversight to ensure patient safety and treatment efficacy before these techniques can be recommended for clinical use. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Prediction method for re-damage behavior of the repaired CFRP bolted joint

Composite materials are widely used in various aircraft due to their high specific strength, stiffness, corrosion resistance, and robust design flexibility. However, when subjected to complex loading conditions, composite structures are susceptible to damage, necessitating prompt repair upon aircraft return. Analyzing the re-damage process of repaired structures is crucial for assessing aircraft structural reliability, lifespan, and determining whether a repeat flight is warranted. In this study, a prediction method was proposed for re-damage behavior through meso-modeling and energy analysis of the repaired composite bolted joint. First, this article established a meso-mechanical model for composite laminates, analyzed force transfer characteristics at the fiber − matrix interface, and derived equations for stress field. Additionally, a strain energy distribution model was developed for composite bolted repaired joint near fibers and repaired area, proposing a damage prediction method based on the strain energy criterion. This method could anticipate the location, type, sequence, and magnitude of damage. Finally, simulation calculations yielded mechanical characteristics of the repaired area under typical load conditions, while loading tests verified the feasibility of the prediction method. This study offered an effective means of predicting performance degradation in composite structures and provided a vital direction for optimal spacecraft structure repairing methods and parameters.

Multiwavelength Achromatic Deflector in the Visible Using a Single-Layer Freeform Metasurface.

Deflectors are essential for modulating beam direction in optical systems but often face form factor issues or chromatic aberration with conventional optical elements, such as prisms, mirrors, and diffractive/holographic optical elements. Despite recent efforts to address such issues using metasurfaces, their practicality remains limited due to operation wavelengths in the near-infrared or the fabrication difficulties inherent in the multilayer scheme. Here, we propose a novel single-layer metasurface achieving multiwavelength chromatic aberration-free deflection across the visible spectrum by employing the robust freeform design strategy to simplify the fabrication process. By properly selecting diffraction orders for red, green, and blue wavelengths to achieve identical wavelength-diffraction-order products, the metasurface deflects light at a consistent angle of 41.3° with a high efficiency. The coupled Bloch mode analysis explains the physical properties, and experimental fabrication and characterization confirm its effectiveness. This approach holds potential for various applications such as AR/VR, digital cameras, and high-quality optical systems.