Systematic solution for multi-model control approaches in nonlinear dynamic systems based on the s-gap metric.

This paper proposes a systematic approach for optimizing the distribution of local models in multi-model control systems (MMCS) to enhance overall robustness. While existing literature discusses this method for linear parameter varying (LPV) and uncertain linear time-invariant (LTI) systems, significant limitations persist in addressing nonlinear dynamic systems. Robust control tools like the gap metric and generalized stability margin (GSM) have limited effectiveness in analyzing the robustness of nonlinear feedback systems. To address these challenges, novel concepts of the gap metric and GSM are introduced to determine central operating points (COPs) within local operating areas (LOAs) across the total operating area (TOA). These COPs guide the extraction of affine disturbance local models (ADLMs). Additionally, an optimization problem based on the s-gap metric and GSM is presented to optimize COPs placement and LOAs boundaries. Challenges such as non-monotonic behavior of the cost function and complexity arising from the s-gap metric formulation necessitate novel solution methods. To address these, constraints are applied to the cost function, and a novel discrete optimization approach is introduced. Finally, theoretical findings are applied to the Duffing system, pH neutralization process, and continuous stirred tank reactor (CSTR) plant to evaluate the proposed method's effectiveness. This comprehensive validation across different systems underscores the versatility and practical utility of the proposed approach.

A novel bounded loss framework for support vector machines.

This paper introduces a novel bounded loss framework for SVM and SVR. Specifically, using the Pinball loss as an illustration, we devise a novel bounded exponential quantile loss (Leq-loss) for both support vector machine classification and regression tasks. For Leq-loss, it not only enhances the robustness of SVM and SVR against outliers but also improves the robustness of SVM to resampling from a different perspective. Furthermore, EQSVM and EQSVR were constructed based on Leq-loss, and the influence functions and breakdown point lower bounds of their estimators are derived. It is proved that the influence functions are bounded, and the breakdown point lower bounds can reach the highest asymptotic breakdown point of 1/2. Additionally, we demonstrated the robustness of EQSVM to resampling and derived its generalization error bound based on Rademacher complexity. Due to the Leq-loss being non-convex, we can use the concave-convex procedure (CCCP) technique to transform the problem into a series of convex optimization problems and use the ClipDCD algorithm to solve these convex optimization problems. Numerous experiments have been conducted to confirm the effectiveness of the proposed EQSVM and EQSVR.

Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research.

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging offers unique insights into tumor biology and treatment response, it is imperative to elucidate the challenges and constraints inherent in this domain to facilitate their translation into clinical practice. This review examines the challenges and limitations of applying radiomics to PET imaging, synthesizing findings from the last five years (2019-2023) and highlights the significance of addressing these challenges to realize the full clinical potential of radiomics in oncology and molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, and Web of Science, using keywords relevant to radiomics issues in PET imaging. Only studies published in peer-reviewed journals were eligible for inclusion in this review. Although many studies have highlighted the potential of radiomics in predicting treatment response, assessing tumor heterogeneity, enabling risk stratification, and personalized therapy selection, various challenges regarding the practical implementation of the proposed models still need to be addressed. This review illustrates the challenges and limitations of radiomics in PET imaging across various cancer types, encompassing both phantom and clinical investigations. The analyzed studies highlight the importance of reproducible segmentation methods, standardized pre-processing and post-processing methodologies, and the need to create large multicenter studies registered in a centralized database to promote the continuous validation and clinical integration of radiomics into PET imaging.

Design principles of multi-map variation in biological systems.

Complexity in biology is often described using a multi-map hierarchical architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes driving evolution. Furthermore, natural selection, along with other neutral forces, can, in turn, modify these maps. At each level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and how they ultimately contribute to evolutionary dynamics.

Self-organization underlies developmental robustness in plants.

Development is a self-organized process that builds on cells and their interactions. Cells are heterogeneous in gene expression, growth, and division; yet how development is robust despite such heterogeneity is a fascinating question. Here, we review recent progress on this topic, highlighting how developmental robustness is achieved through self-organization. We will first discuss sources of heterogeneity, including stochastic gene expression, heterogeneity in growth rate and direction, and heterogeneity in division rate and precision. We then discuss cellular mechanisms that buffer against such noise, including Paf1C- and miRNA-mediated denoising, spatiotemporal growth averaging and compensation, mechanisms to improve cell division precision, and coordination of growth rate and developmental timing between different parts of an organ. We also discuss cases where such heterogeneity is not buffered but utilized for development. Finally, we highlight potential directions for future studies of noise and developmental robustness.

The multiverse of universes: A tutorial to plan, execute and interpret multiverses analyses using the R package multiverse.

Even when guided by strong theories and sound methods, researchers must often choose a singular course of action from multiple viable alternatives. Regardless of the choice, it, along with all other choices made during the research process, individually and collectively affects study results, often in unpredictable ways. The inability to disentangle how much of an observed effect is attributable to the phenomenon of interest, and how much is attributable to what have come to be known as researcher degrees of freedom (RDF), slows theoretical progress and stymies practical implementation. However, if one could examine the results from a particular set of RDF (known as a universe) against a systematically and comprehensively determined background of alternative viable universes (known as a multiverse), then the effects of RDF can be directly examined to provide greater context and clarity to future researchers, and greater confidence in the recommendations to practitioners. This tutorial demonstrates a means to map result variability directly and efficiently, and empirically investigate RDF impact on conclusions via multiverse analysis. Using the R package multiverse, we outline best practices in planning, executing and interpreting of multiverse analyses.

Technical Notes: Robustness of three-dimensional treatment and imaging isocenter testing using a new gel dosimeter and kilovoltage CBCT.

BACKGROUND: Coincidence of the treatment and imaging isocenter coordinates is required to safely perform small-margin treatments, such as stereotactic radiosurgery of multiple brain metastases. A comprehensive and direct methodology for verifying concordance of kilovoltage cone-beam computed tomography (kV-CBCT) and treatment coordinates using an x-ray CT-based polymer gel dosimeter (dGEL) and onboard kV-CBCT was previously reported. Using this methodology, we tested the ability of a new commercially available x-ray CT-based polymer dGEL with a rapid response to provide efficient quality assurance (QA). PURPOSE: The aim of this study was to evaluate the robustness of the three-dimensional geometric QA methodology using dGEL. METHODS: The dGEL were commercially manufactured. The prescribed dose for each field was determined by visually identifying the 5, 10, and 20 Gy isodose lines. A linear accelerator was used to irradiate the gels with seven non-coplanar beams. An in-house analysis program was used to identify the beam axes and treatment isocenter in kV-CBCT coordinates by processing the pre- and post-irradiation CBCT images. The impact of the radiation dose on the test reproducibility was examined, and the detectability of an intentional geometric error was assessed. RESULTS: The treatment isocenter was within 0.4 mm of the imaging isocenter for all radiation doses. The residual error of the test with the intentional error was within 0.2 mm. The analysis and image quality variations for a single dGEL introduced displacement errors less than 0.3 mm. CONCLUSIONS: The test assessed the coincidence of treatment and kV-CBCT isocenter coordinates and detected errors with high robustness. Even for a 10 Gy dose, the test yielded results comparable with those obtained using higher radiation doses owing to the rapid response of the dGEL dosimeter.

Sustainable Plastics with High Performance and Convenient Processibility.

Designing and making sustainable plastics is especially urgent to reduce their ecological and environmental impacts. However, it remains challenging to construct plastics with simultaneous high sustainability and outstanding comprehensive performance. Here, a composite strategy of in situ polymerizing a petroleum-based monomer with the presence of an industrialized bio-derived polymer in a quasi-solvent-free system is introduced, affording the plastic with excellent mechanical robustness, impressive thermal and solvent stability, as well as low energy, consumes during production, processing, and recycling. Particularly, the plastic can be easily processed into diverse shapes through 3D printing, injection molding, etc. during polymerization and further reprocessed into other complex structures via eco-friendly hydrosetting. In addition, the plastic is mechanically robust with Young's modulus of up to 3.7 GPa and tensile breaking strength of up to 150.2 MPa, superior to many commercially available plastics and other sustainable plastics. It is revealed that hierarchical hydrogen bonds in plastic predominate the well-balanced sustainability and performance. This work provides a new path for fabricating high-performance sustainable plastic toward practical applications, contributing to the circular economy.

Third-party arbitration and forgiving strategies increase cooperation when perception errors are common.

Humans cooperate in groups in which mutual monitoring is common, and this provides the possibility of third-party arbitration. Third-party arbitration stabilizes reciprocity in at least two ways: first, when it is accurate, it reduces the frequency of misunderstandings resulting from perception errors, and second, even when it is inaccurate, it provides a public signal that allows pairs to align their expectations about how to behave after errors occur. Here, we describe experiments that test for these two effects. We find that in an iterated, sequential Prisoner's Dilemma game with errors, players with the highest average payoffs are those who make use of third-party arbitration and who also employ forgiving strategies. The combination of these two behaviours reduces the detrimental effects of errors on reciprocity, resulting in more cooperation.

Body plan evolvability: The role of variability in gene regulatory networks.

Recent computational modeling of early fruit fly (Drosophila) development has characterized the degree to which gene regulation networks can be robust to natural variability. In the first few hours of development, broad spatial gradients of maternally derived transcription factors activate embryonic gap genes. These gap patterns determine the subsequent segmented insect body plan through pair-rule gene expression. Gap genes are expressed with greater spatial precision than the maternal patterns. Computational modeling of the gap-gap regulatory interactions provides a mechanistic understanding for this robustness to maternal variability in wild-type (WT) patterning. A long-standing question in evolutionary biology has been how a system which is robust, such as the developmental program creating any particular species' body plan, is also evolvable, i.e. how can a system evolve or speciate, if the WT form is strongly buffered and protected? In the present work, we use the WT model to explore the breakdown of such Waddington-type 'canalization'. What levels of variability will push the system out of the WT form; are there particular pathways in the gene regulatory mechanism which are more susceptible to losing the WT form; and when robustness is lost, what types of forms are most likely to occur (i.e. what forms lie near the WT)? Manipulating maternal effects in several different pathways, we find a common gap 'peak-to-step' pattern transition in the loss of WT. We discuss these results in terms of the evolvability of insect segmentation, and in terms of experimental perturbations and mutations which could test the model predictions. We conclude by discussing the prospects for using continuum models of pattern dynamics to investigate a wider range of evo-devo problems.

A Critical Candidate Node-Based Attack Model of Network Controllability.

The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closeness-based, pagerank-based, and hierarchical attacks. Results show that the model outperforms these methods in both disruption effectiveness and computational efficiency. Extensive experiments on both synthetic and real-world networks validate the superior performance of this approach. This study provides valuable insights for identifying key nodes crucial for maintaining network controllability. It also offers a solid framework for enhancing network resilience against malicious attacks.

Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.

Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions. The arm-based meta-analysis model is growing in importance due to its advantage of including single-arm studies and historical controls with estimation efficiency and its flexibility in drawing conclusions with both marginal and conditional effect measures. Despite its benefits, the inference may heavily depend on the heterogeneity parameters that reflect design and model assumptions. This article aims to evaluate the robustness of meta-analyses using the arm-based model within a Bayesian framework. Specifically, we develop a tipping point analysis of the between-arm correlation parameter to assess the robustness of meta-analysis results. Additionally, we introduce some visualization tools to intuitively display its impact on meta-analysis results. We demonstrate the application of these tools in three real-world meta-analyses, one of which includes single-arm studies.

Antireflective Superhydrophobic and Robust Coating Based on Chitin Nanofibers and Methylsilanized Silica for Outdoor Applications.

Antireflective coatings with superhydrophobicity have many outdoor applications, such as solar photovoltaic panels and windshields. In this study, we fabricated an omnidirectional antireflective and superhydrophobic coating with good mechanical robustness and environmental durability via the spin coating technique. The coating consisted of a layer of phytic acid (PA)/polyacrylamide (PAM)/calcium ions (Ca2+) (referred to as Binder), an antireflective layer composed of chitin nanofibers (ChNFs), and a hydrophobic layer composed of methylsilanized silica (referred to as Mosil). The transmittance of a glass slide with the Binder/ChNFs/Mosil coating had a 5.2% gain at a wavelength of 550 nm, and the antireflective coating showed a water contact angle as high as 160° and a water sliding angle of 8°. The mechanical robustness and environmental durability of the coating, including resistance to peeling, dynamic impact, chemical erosion, ultraviolet (UV) irradiation, and high temperature, were evaluated. The coating retained excellent antireflective capacity and self-cleaning performance in the harsh conditions. The increase in voltage per unit area of a solar panel with a Binder/ChNFs/Mosil coating reached 0.4 mV/cm2 compared to the solar panel exposed to sunlight with an intensity of 54.3 × 103 lx. This work not only demonstrates that ChNFs can be used as raw materials to fabricate antireflective superhydrophobic coatings for outdoor applications but also provides a feasible and efficient approach to do so.

A geometric approach to robust medical image segmentation.

Robustness of deep learning segmentation models is crucial for their safe incorporation into clinical practice. However, these models can falter when faced with distributional changes. This challenge is evident in magnetic resonance imaging (MRI) scans due to the diverse acquisition protocols across various domains, leading to differences in image characteristics such as textural appearances. We posit that the restricted anatomical differences between subjects could be harnessed to refine the latent space into a set of shape components. The learned set then aims to encompass the relevant anatomical shape variation found within the patient population. We explore this by utilising multiple MRI sequences to learn texture invariant and shape equivariant features which are used to construct a shape dictionary using vector quantisation. We investigate shape equivariance to a number of different types of groups. We hypothesise and prove that the greater the group order, i.e., the denser the constraint, the better becomes the model robustness. We achieve shape equivariance either with a contrastive based approach or by imposing equivariant constraints on the convolutional kernels. The resulting shape equivariant dictionary is then sampled to compose the segmentation output. Our method achieves state-of-the-art performance for the task of single domain generalisation for prostate and cardiac MRI segmentation. Code is available at https://github.com/AinkaranSanthi/A_Geometric_Perspective_For_Robust_Segmentation.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to patient setup errors.

PURPOSE: This study aimed to analyse correlations between planning factors including plan geometry and plan complexity with robustness to patient setup errors. METHODS: Multiple-target brain stereotactic radiosurgery (SRS) plans were obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets with a 20 Gy prescription. Setup error was simulated using an in-house tool. Dose to targets was assessed via dose covering 99 % (D99 %) of gross tumour volume (GTV) and 98 % of planning target volume (PTV). Dose to organs at risk was assessed using volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc of brainstem. Plan complexity was assessed via edge metric, modulation complexity score, mean multi-leaf collimator (MLC) gap, mean MLC speed and plan modulation. RESULTS: Even for small (0.5 mm/°) errors, GTV D99 % was reduced by up to 20 %. The strongest correlation was found between lower complexity plans (larger mean MLC gap and lower edge metric) and higher robustness to setup error. Lower complexity plans had 1 %-20 % fewer targets/scenarios with GTV D99 % falling below the specified tolerance threshold. These complexity metrics correlated with 100 % isodose volume sphericity and dose conformity, though similar conformity was achievable with a range of complexities. CONCLUSIONS: A higher level of importance should be directed towards plan complexity when considering plan robustness. It is recommended when planning multi-target SRS, larger MLC gaps and lower MLC aperture irregularity be considered during plan optimisation due to higher robustness should patient positioning errors occur.

Gradient-Robust Hybrid DG Discretizations for the Compressible Stokes Equations.

This paper studies two hybrid discontinuous Galerkin (HDG) discretizations for the velocity-density formulation of the compressible Stokes equations with respect to several desired structural properties, namely provable convergence, the preservation of non-negativity and mass constraints for the density, and gradient-robustness. The later property dramatically enhances the accuracy in well-balanced situations, such as the hydrostatic balance where the pressure gradient balances the gravity force. One of the studied schemes employs an H ( div ) -conforming velocity ansatz space which ensures all mentioned properties, while a fully discontinuous method is shown to satisfy all properties but the gradient-robustness. Also higher-order schemes for both variants are presented and compared in three numerical benchmark problems. The final example shows the importance also for non-hydrostatic well-balanced states for the compressible Navier-Stokes equations.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

Development of a Cell-Based Reporter Potency Assay for Live Virus Vaccines.

The rapid development of potency assays is critical in the development of life-saving vaccines. The traditional plaque assay or fifty percent tissue culture infectious dose (TCID50) assay used to measure the potency of live virus vaccines is time consuming, labor intensive, low throughput and with high variability. Described here is the development and qualification of a cell-based reporter potency assay for two vaccines for respiratory viral infection, one based on the recombinant vesicular stomatitis virus (rVSV) backbone, termed Vaccine 1 in this paper, and the other based on the measles virus vector, termed Vaccine 2. The reporter potency assay used a Vero E6 cell line engineered to constitutively express NanuLuc® luciferase, termed the VeroE6-NLuc or JM-1 cell line. Infection of JM-1 cells by a live virus, such as rVSV or measles virus, causes a cytopathic effect (CPE) and release of NanuLuc® from the cytoplasm into the supernatant, the amount of which reflects the intensity of the viral infection. The relative potency was calculated by comparison to a reference standard using parallel line analysis (PLA) in a log-log linear model. The reporter assay demonstrated good linearity, accuracy, and precision, and is therefore suitable for a vaccine potency assay. Further evaluation of the Vaccine 1 reporter assay demonstrated the robustness to a range of deliberate variation of the selected assay parameters and correlation with the plaque assay. In conclusion, we have demonstrated that the reporter assay using the JM-1 cell line could be used as a potency assay to support the manufacturing and release of multiple live virus vaccines.

Balancing the maintenance strategies to making decisions using Monte Carlo method.

This study aims to develop comprehensive maintenance strategies tailored to enhance the dependability, performance, and lifespan of critical assets within industrial and organizational settings. By integrating proactive, preventive, predictive, and corrective maintenance tactics, our strategy seeks to minimize downtime, reduce costs, and optimize asset performance. Drawing from extensive case studies across various industrial sectors, our research utilizes robust data analysis to inform strategy development. We employ mathematical cost models and simulations using the Monte Carlo Method in MATLAB to evaluate the performance and robustness of different maintenance strategies, including time-based and condition-based approaches. Our findings demonstrate that a holistic maintenance approach significantly improves operational efficiency and asset longevity. Specifically, our analysis reveals that integrated maintenance strategies lead to reduced downtime, lower maintenance costs, and enhanced asset reliability. Policy implications of our research suggest that organizations should adopt integrated maintenance strategies to enhance asset reliability and performance, ultimately achieving sustained operational excellence. By emphasizing the importance of proactive maintenance measures alongside traditional reactive approaches, organizations can effectively manage their critical assets, leading to improved operational outcomes and long-term success.-Integration of proactive, preventive, predictive, and corrective maintenance tactics-Evaluation of performance and robustness through mathematical cost models-Application of the Monte Carlo Method in MATLAB for comparative analysis.

A survey of ontology-enabled processes for dependable robot autonomy.

Autonomous robots are already present in a variety of domains performing complex tasks. Their deployment in open-ended environments offers endless possibilities. However, there are still risks due to unresolved issues in dependability and trust. Knowledge representation and reasoning provide tools for handling explicit information, endowing systems with a deeper understanding of the situations they face. This article explores the use of declarative knowledge for autonomous robots to represent and reason about their environment, their designs, and the complex missions they accomplish. This information can be exploited at runtime by the robots themselves to adapt their structure or re-plan their actions to finish their mission goals, even in the presence of unexpected events. The primary focus of this article is to provide an overview of popular and recent research that uses knowledge-based approaches to increase robot autonomy. Specifically, the ontologies surveyed are related to the selection and arrangement of actions, representing concepts such as autonomy, planning, or behavior. Additionally, they may be related to overcoming contingencies with concepts such as fault or adapt. A systematic exploration is carried out to analyze the use of ontologies in autonomous robots, with the objective of facilitating the development of complex missions. Special attention is dedicated to examining how ontologies are leveraged in real time to ensure the successful completion of missions while aligning with user and owner expectations. The motivation of this analysis is to examine the potential of knowledge-driven approaches as a means to improve flexibility, explainability, and efficacy in autonomous robotic systems.