Adaptive Design Research Articles

DiscovEpi: automated whole proteome MHC-I-epitope prediction and visualization

BackgroundAntigen presentation is a central step in initiating and shaping the adaptive immune response. To activate CD8+ T cells, pathogen-derived peptides are presented on the cell surface of antigen-presenting cells bound to major histocompatibility complex (MHC) class I molecules. CD8+ T cells that recognize these complexes with their T cell receptor are activated and ideally eliminate infected cells. Prediction of putative peptides binding to MHC class I (MHC-I) is crucial for understanding pathogen recognition in specific immune responses and for supporting drug and vaccine design. There are reliable databases for epitope prediction algorithms available however they primarily focus on the prediction of epitopes in single immunogenic proteins.ResultsWe have developed the tool DiscovEpi to establish an interface between whole proteomes and epitope prediction. The tool allows the automated identification of all potential MHC-I-binding peptides within a proteome and calculates the epitope density and average binding score for every protein, a protein-centric approach. DiscovEpi provides a convenient interface between automated multiple sequence extraction by organism and cell compartment from the database UniProt for subsequent epitope prediction via NetMHCpan. Furthermore, it allows ranking of proteins by their predicted immunogenicity on the one hand and comparison of different proteomes on the other. By applying the tool, we predict a higher immunogenic potential of membrane-associated proteins of SARS-CoV-2 compared to those of influenza A based on the presented metrics epitope density and binding score. This could be confirmed visually by comparing the epitope maps of the influenza A strain and SARS-CoV-2.ConclusionAutomated prediction of whole proteomes and the subsequent visualization of the location of putative epitopes on sequence-level facilitate the search for putative immunogenic proteins or protein regions and support the study of adaptive immune responses and vaccine design.

Adaptive design of long-focal-length freeform off-axis reflective space cameras with lightweight and integration of primary/tertiary mirror

Adaptive design of long-focal-length freeform off-axis reflective space cameras with lightweight and integration of primary/tertiary mirror

Smart sustainable architecture: leveraging machine learning for adaptive digital design and resource optimization

Smart sustainable architecture: leveraging machine learning for adaptive digital design and resource optimization

Two-Stage Adaptive Enrichment Designs with Survival Outcomes and Adjustment for Misclassification in Predictive Biomarkers

Biomarker enrichment clinical trial designs are versatile tools to assess the treatment effect and increase the efficiency of clinical trials. In this article, we propose a two-stage enrichment clinical trial design with survival outcomes, and consider the situation where the biomarker assay and classification are possibly subject to errors. Specifically, the first stage is a randomized design, stratified by the biomarker appeared status. Depending on the result of the interim analysis and a pre-specified futility criterion, the second stage can be either enriched with only the biomarker appeared positive patients, or remain as the stratified design with both biomarker appeared positive and biomarker appeared negative patients. Compared to continuous and binary outcomes, test statistics for survival outcomes to account for biomarker misclassification are much more complicated and require special care. We develop log-rank statistics for the interim and final analyses, with an adjustment for the sensitivity and specificity of the biomarker assay. Control of Type I error rate is achieved by considering correlations between adjusted log-rank statistics from the same and/or different stages. R code is developed to calculate critical values, global/marginal power, and sample size. Our method is illustrated with examples of a recently successful development of immunotherapy in non-small-cell lung cancer.

Usability Optimization for Mobile Menu Design: An Empirical Study of Hand Grips and User Preferences

This paper investigates the relationship between menu design and hand positions in relation to the assessment of end users with main focus on usability, user preference, and potential adaptions to different hand positions. Sixteen (N=16) participants first participated in a co-design workshop, in which they proposed menu designs for different hand grips. Based on the design proposals, a selection of menu designs were derived and implemented in a mobile app prototype, on which a menu selection study was conducted to investigate performance and perceived usability of the menus in one-handed and two-handed interaction. The results include user ratings and performance, which highlight the need for mobile menus to be adapted for different hand positions. Based on that, we derive design recommendations for more adaptive, user-centric and ergonomic mobile menu designs to match the natural interactions of users.

Robust Design for Product Adaptation Considering Changes in Configurations and Parameters

Abstract Adaptable products are designed such that their configurations and parameters can be changed easily in the operation stage to satisfy changes in functional requirements. Design of adaptable products can extend lifespans of these products. A new robust adaptable product design method is introduced in this research to identify the optimal design including the product configurations and parameter values considering uncertainties in both product configurations and parameters. In this work, an AND-OR tree is used to model feasible design candidates and their configurations considering product adaptations, where each node represents a partial design solution. Different design candidates are created from the AND-OR tree through tree-based search, and a design candidate is defined by configurations of the original design and the adapted designs. Each configuration is further defined by parameters. A multi-level optimization method is used to obtain the optimal adaptable product design including its configurations and parameter values of these configurations. In this study, uncertainties of configurations are defined by probabilities for production adaptations, while uncertainties of parameters are defined by variations of parameter values. Both evaluation measures and their variations are considered in this robust adaptable product design method. A case study has been implemented to show how the developed method is used for the design of an adaptable mechanical system.

Finite-time SMC-based admittance controller design of macro-micro robotic system for complex surface polishing operations

In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.

Multi-objective parameter design and economic analysis of VSG-controlled hybrid energy systems in islanded grids

Photovoltaic (PV) systems offer cost-effective power solutions for outlying islands but often compromise system stability due to reduced inertia. This study introduces a Virtual Synchronous Generator (VSG) control strategy, integrated with Energy Storage Systems (ESS) and PV, to enhance system inertia. By optimizing coordination between these energy sources, the proposed method mitigates oscillations and improves grid stability. However, PV-VSG systems are generally not favored by energy providers due to the requirement for pre-curtailment of power output. To address this, the paper proposes a parameter design method for VSG control of ESS and PV, utilizing multi-objective genetic algorithm (MOGA) optimization to simultaneously increase the frequency nadir and minimize the settling time after disturbances. Additionally, an adaptive curtailment decision and parameter design method based on artificial neural networks is introduced to enhance the feasibility of PV-VSG systems by reducing PV pre-curtailment and prioritizing PV power release and ESS charging during frequency oscillations. Real data from the Penghu Archipelago in Taiwan are used to build a dynamic model in DIgSILENT, enabling interaction with MOGA. The Value at Risk (VaR) method with dual stochastic variables is employed to assess the allowable PV installed capacity. The results show that when VaR is set at 1%, the proposed PV-VSG method can increase PV penetration by 57.5% compared to scenarios without VSG. Furthermore, compared to traditional PV-VSG methods, the proposed approach achieves a 16.8% increase in PV penetration and reduces annual PV curtailment by 25 MWh. This study also evaluates the economic impact of planners choosing different risk levels, offering valuable insights for grid development in remote or island regions.

Adaptive finite time command filtered backstepping control scheme of uncertain strict-feedback nonlinear systems

This paper proposes an adaptive finite time command filtered backstepping control scheme for single‐input and single‐output (SISO) uncertain strict-feedback nonlinear systems. The problem of the explosion of complexity existing in the adaptive backstepping control design and the singularity problem are solved by using the proposed adaptive finite time control method. In addition, the compensating signals are introduced to deal with the effect of the known filtering errors caused by the command filters. By using the finite time Lyapunov stability theory, the proposed adaptive finite time control strategy guarantees that all the signals in the closed-loop system are practical finite time stable, and the tracking errors converge to an arbitrarily small neighbourhood of the origin in finite time. Simulation and experimental results of the DC-DC buck converter are given to illustrate the effectiveness of the proposed adaptive finite time control scheme.

Methodological aspects of the highly adaptive testing design for PISA.

This methods paper describes the methodological and statistical underpinnings of the highly adaptive testing design (HAT), which was developed for the Programme for International Student Assessment (PISA). The aim of HAT is to allow for a maximum of adaptivity in selecting items while taking the constraints of PISA into account with appropriate computer algorithms. HAT combines established methods from the area of computerized adaptive testing (a) to improve item selection when items are nested in units, (b) to make use of the correlation between the dimensions measured, (c) to efficiently accomplish constraint management, (d) to control for item position effects, and (e) to foster students' test-taking experience. The algorithm is implemented using the programming language R and readers are provided with the necessary code. This should facilitate future implementations of the HAT design and inspire other adaptive testing designs that aim to maximize adaptivity while meeting constraints.

Active Sampling: A Machine-Learning-Assisted Framework for Finite Population Inference with Optimal Subsamples

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods.

Group Response-Adaptive Randomization With Delayed and Missing Responses.

Response-adaptive randomization (RAR) procedures have been extensively studied in the literature, but most of the procedures rely on updating therandomization after each response, which is impractical in many clinical trials. In this article, we propose a new family of RAR procedures that dynamically update based on the responses of a group of individuals, either when available or at fixed time intervals (weekly or biweekly). We show that the proposed design retains the essential theoretical properties of Hu and Zhang's doubly adaptive biased coin designs (DBCD), and performs well in scenarios involving delayed and randomly missing responses. Numerical studies have been conducted to demonstrate that the new proposed group doubly adaptive biased coin design has similar properties to the Hu and Zhang's DBCDs in different situations. We also apply the new design to a real clinical trial, highlighting its advantages and practicality. Our findings open the door to studying the properties of other group response adaptive designs, such as urn models, and facilitate the application of response-adaptive randomized clinical trials in practice.

Datopotamab-deruxtecan plus durvalumab in early-stage breast cancer: the sequential multiple assignment randomized I-SPY2.2 phase 2 trial.

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer. The trial includes three blocks of treatment, with initial randomization to different experimental agent(s) (block A), followed by a taxane-based regimen tailored to tumor subtype (block B), followed by doxorubicin-cyclophosphamide (block C). Subtype-specific algorithms based on magnetic resonance imaging volume change and core biopsy guide treatment redirection after each block, including the option of early surgical resection in patients predicted to have a high likelihood of pathologic complete response, which is the primary endpoint assessed when resection occurs. There are two primary efficacy analyses: after block A and across all blocks for six prespecified HER2-negative subtypes (defined by hormone receptor status and/or response-predictive subtypes). In total, 106 patients were treated with Dato-DXd/durvalumab in block A. In the immune-positive subtype, Dato-DXd/durvalumab exceeded the prespecified threshold for success (graduated) after block A; and across all blocks, pathologic complete response rates were equivalent to the rate expected for the standard of care (79%), but 54% achieved that result after Dato-DXd/durvalumab alone (block A) and 92% without doxorubicin-cyclophosphamide (after blocks A + B). The treatment strategy across all blocks graduated in the hormone-negative/immune-negative subtype. No new toxicities were observed. Stomatitis was the most common side effect in block A. No patients receiving block A treatment alone had adrenal insufficiency. Dato-DXd/durvalumab is a promising therapy combination that can eliminate standard chemotherapy in many patients, particularly the immune-positive subtype.ClinicalTrials.gov registration: NCT01042379 .

White Paper on Adaptive Situational Awareness Enhancing Augmented Reality Interface Design on First Responders in Rescue Tasks

The advance in the development of augmented reality technologies has attracted interest in their applicability in rescue scenarios. The characteristics of the different missions covered by First Responders, as well as the different objectives they can cover in a rescue operation, condition the importance of the additional information they can receive in these rescue processes through technology. This white paper aims to analyze the difficulties encountered when converging on the design of an interface that is adaptable to the professional and contextual circumstances in a rescue task.

Design considerations for Factorial Adaptive Multi-Arm Multi-Stage (FAST) clinical trials

BackgroundMulti-Arm, Multi-Stage (MAMS) clinical trial designs allow for multiple therapies to be compared across a spectrum of clinical trial phases. MAMS designs fall under several overarching design groups, including adaptive designs (AD) and multi-arm (MA) designs. Factorial clinical trials designs represent a combination of factorial and MAMS trial designs and can provide increased efficiency relative to fixed, traditional designs. We explore design choices associated with Factorial Adaptive Multi-Arm Multi-Stage (FAST) designs, which represent the combination of factorial and MAMS designs.MethodsSimulation studies were conducted to assess the impact of the type of analyses, the timing of analyses, and the effect size observed across multiple outcomes on trial operating characteristics for a FAST design. Given multiple outcomes types assessed within the hypothetical trial, the primary analysis approach for each assessment varied depending on data type.ResultsThe simulation studies demonstrate that the proposed class of FAST trial designs can offer a framework to potentially provide improvements relative to other trial designs, such as a MAMS or factorial trial. Further, we note that the design implementation decisions, such as the timing and type of analyses conducted throughout trial, can have a great impact on trial operating characteristics.ConclusionsMotivated by a trial currently under design, our work shows that the FAST category of trial can potentially offer benefits similar to both MAMS and factorial designs; however, the chosen design aspects which can be included in a FAST trial need to be thoroughly explored during the planning phase.

Droplet-based triboelectric devices using liquid dielectrics for self-powered sensing applications

Triboelectric nanogenerators (TENGs) have attracted great interests by exploiting the complex interplay between contact electrification and electrostatic induction. The versatility of tribo-dielectric materials offers significant advantages for environmentally adaptive device design. In this regard, liquid dielectrics provide intimate contact between filled materials and high dielectric strength. Herein, a liquid dielectric-based TENGs system featuring a charge-transfer liquid layer is presented. A multifaceted summary is presented to illustrate the relationship between liquid dielectric materials and charge transfer mechanisms. New strategies for exploiting the dielectric properties of various liquids to control their electrical properties and monitor the parameters of liquid dielectric properties are highlighted. Additionally, it contributes to the advancement of self-powered sensing technology with increased flexibility, capacitance tuning, and sensor functionality through easy modularization. These achievements contribute to the understanding of charge transfer mechanisms in liquids and highlight their potential for self-powered sensing applications.

AdaMotif: Graph Simplification via Adaptive Motif Design.

With the increase of graph size, it becomes difficult or even impossible to visualize graph structures clearly within the limited screen space. Consequently, it is crucial to design effective visual representations for large graphs. In this paper, we propose AdaMotif, a novel approach that can capture the essential structure patterns of large graphs and effectively reveal the overall structures via adaptive motif designs. Specifically, our approach involves partitioning a given large graph into multiple subgraphs, then clustering similar subgraphs and extracting similar structural information within each cluster. Subsequently, adaptive motifs representing each cluster are generated and utilized to replace the corresponding subgraphs, leading to a simplified visualization. Our approach aims to preserve as much information as possible from the subgraphs while simplifying the graph efficiently. Notably, our approach successfully visualizes crucial community information within a large graph. We conduct case studies and a user study using real-world graphs to validate the effectiveness of our proposed approach. The results demonstrate the capability of our approach in simplifying graphs while retaining important structural and community information.

Census Bureau Household Panel

The Census Bureau Household Panel is a nationally-representative, address-based, probability-based internet panel (including non-internet households) designed to improve representativeness, significantly reduce burden on sampled households, and promote high-frequency data meeting the immediate needs of the government and public. The Panel facilitates near real-time analysis of national events that may impact social, economic, or demographic characteristics of the population. It supports research to improve surveys, including testing content changes, alternative methods for enhancing data with administrative and other data sources, and adaptive survey design procedures.

Immersion and Invariance Adaptive Control through Polynomial Adaptation

Abstract In conventional immersion and invariance (I&I) adaptive control design, control parameter adaptation is typically linear with respect to the parameter-error-induced perturbation, resulting in quadratic-rate dissipation of energy associated with the off-the-manifold variable. Departing from such a convention, this article contributes a novel strategy - polynomial adaptation. As the name suggests, control parameter adaptation in this approach takes the form of a general polynomial in relation to the perturbation. Accordingly, this new design induces polynomial-rate energy dissipation, which is faster than the quadratic one in the conventional scheme, thereby enhancing closed-loop control performance. The theoretical underpinnings of the new approach are demonstrated through the design of an I&I adaptive tracking control law for a general nth-order, single-input-single-output, parametrically uncertain, nonlinear system in the controllable canonical form. Additionally, a numerical study of the proposed method on the second-order forced Duffing oscillator showcases its improved transient performance in comparison to a baseline controller developed with the standard I&I adaptive control technique.

Adapting the Nurse Practitioner Primary Care Organizational Climate Questionnaire for Acute Care Nurse Practitioners.

Background and Purpose: This study adapted the Nurse Practitioner Primary Care Organizational Climate Questionnaire to measure the organizational climate of nurse practitioners in acute care settings and validated the Nurse Practitioner Acute Care Organizational Climate Questionnaire (NP-ACOCQ). Methods: We used a three-phase instrument adaptation design. In phase 1, experts assessed face and content validity. In phase 2, pilot testing enabled item analysis and preliminary reliability testing. In phase 3, field testing supported exploratory factor analysis. Results: The NP-ACOCQ had a three-factor structure representing subscales. The subscales had Cronbach's alphas of 0.87, 0.90, and 0.94. The subscales, Institutional Visibility, Interprofessional Relationships, and Organizational Autonomy each had between 5 and 13 items. Conclusions: The NP-ACOCQ is a valid and reliable measure to assess organizational factors determining the acute care nurse practitioner work environment.