Adaptive Design Research Articles

On the use of RWD in support of regulatory submission in drug development

ABSTRACT For the approval of a drug product, the United States Food and Drug Administration requires substantial evidence (SE) regarding effectiveness and safety of the test drug to be provided. In recent years, the use of real-world data in support of regulatory submission of pharmaceutical development has received much attention, and real-world evidence (RWE) is treated as complementary to SE by evaluating the real-world performance of the test treatment. In this article, we start by summarizing current regulatory perspectives on drug evaluation and some potential challenges in using RWE. To test for superiority in co-primary endpoints, a two-stage hybrid RCT/RWS adaptive design that combines randomized control trial for providing SE and real-world study for generating RWE is proposed. We use superiority in effectiveness and non-inferiority in safety as an example to illustrate how to implement this design. Numerical studies have shown that the proposed design has merits in reducing the required sample size compared with traditional co-primary endpoint tests while maintaining statistical power and controlling type I error inflation. The proposed design can be implemented in drug development considering co-primary endpoints, especially for oncology and rare disease drug development.

Exploration of Adaptive Environment Design Strategy Based on Reinforcement Learning in CAD Environment

Exploration of Adaptive Environment Design Strategy Based on Reinforcement Learning in CAD Environment

Adaptive critic design with weight allocation for intelligent learning control of wastewater treatment plants

With the deepening of modernization and industrialization, the issues of water pollution and scarcity have become more pressing. To address these issues, many wastewater treatment factories have been built to improve the reuse of water resources. However, the control of the wastewater treatment process (WWTP) is a complex task due to the highly nonlinear and strongly coupled nature. It is challenging to develop the accurate mechanism models of the wastewater treatment system. The improvement of the efficiency for the WWTP is crucial to safeguard the urban ecological environment. In this paper, adaptive critic with weight allocation (ACWA) is developed to address the optimal control problem in the WWTP. Different from the previous methods of the WWTP, system modeling is not adopted in this paper, which meets the actual physical background of the wastewater treatment system to a great extent. In addition, the actor-critic algorithm in reinforcement learning is used as the basic structure in the ACWA. It is worth noting that a novel weighted action-value function and the advantage function are introduced in the weight updating process of the action network and the critic network. The experimental results show that the control accuracy of the ACWA is greatly improved compared with the previous control methods.

Effectiveness of early pharmaceutical interventions in symptomatic COVID-19 patients: A randomized clinical trial

Objective: We assessed the effectiveness of oral Hydroxychloroquine (HC), Azithromycin (AZ) and Oseltamivir (OS), alone or combined, among patients hospitalized with mildly symptomatic coronavirus infectious disease (COVID-19).
 Methods: Following the approval of the National Bioethics Committee and prospective registration (clinicaltrials.gov NCT04338698), a multicenter randomized clinical trial of adaptive design was conducted at 10 multispecialty hospitals in Pakistan. Patients were randomized into seven treatment groups. Starting April 15, 2020, consenting, eligible, otherwise healthy adult patients or those with co-morbidities under control, were recruited if they presented with mildly symptomatic COVID-19 (scored 3 on a 7-point ordinal scale anchored between 1 = not hospitalized, able to undertake normal activities, to 7 = death) confirmed by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Two primary outcomes were assessed by day seven: Turning qRT-PCR negative; and clinical improvement of two points from the baseline. Outcome rates were compared using a chi-square test. Multiple imputations were applied to handle missing data. An interim data analysis was carried out on July 19, 2020, following which the study continued without treatment group changes. Data Safety and Monitoring Board advised to stop recruitment due to its futility on January 18, 2021.
 Results: Of 471 patients randomized, a total of 426 (90.4%) completed the follow-up for primary outcomes. Based on imputed data analyses at day seven: Total qRT-PCR negative cases were 137/471 (29%, 95% CI 25.0 - 33.4). By day seven, a total of 111/471 (23.5%, 95% CI 19.8 - 27.6) showed clinical improvement. No serious or non-serious adverse event was reported.
 Conclusions: Among patients with mild COVID-19, there was no statistically significant difference in the effectiveness of oral antimalarial, antiviral, or antibiotic treatments.
 Clinicaltrials.gov ID: NCT04338698.
 doi: https://doi.org/10.12669/pjms.40.5.8757
 How to cite this: Azhar S, Akram J, Latif W, Ibanez NC, Mumtaz S, Rafi A, et al. Effectiveness of early pharmaceutical interventions in symptomatic COVID-19 patients: A randomized clinical trial. Pak J Med Sci. 2024;40(5):---------. doi: https://doi.org/10.12669/pjms.40.5.8757
 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Towards the intentional multifunctionality of urban green infrastructure: a paradox of choice?

Decades of research on multifunctional Green Infrastructure (GI) has yet to translate into holistic implementation in the built environment. This oversight stems from assumptions that many ecosystem services occur passively and thus potential synergies are overlooked during planning and design. This study offers specific guidance for coordinating GI planning, design, and construction by examining the current state of academic literature on these aspects. It identifies 15 GI elements (e.g., green roofs) and 15 objectives (e.g., biodiversity) to collectively consider before implementation. The literature tends to isolate discussions of “engineered” GI elements with water-related objectives, while more “natural” GI are linked to biodiversity and human well-being. Coordinating across GI objectives and elements remains imperative, but evaluating too many options risks a paradox of choice. This study recommends short-term adherence to principles of adaptive design and, in the long-term, reemphasizes multifunctionality assessments, inter and transdisciplinary collaboration, and political will.

Enhancing building energy efficiency through a climate adaptive design: Considering upcoming extreme climate conditions

Known as one of the significant sources of impacting building energy efficiency, climate change can drastically alter the long-term energy consumption patterns of buildings. It is widely acknowledged in the literature that designs based on conventional pathways relying solely on historical weather data disregard the effects of the changing climate and lose their intended energy performance over time. Several endeavors have been made to generate weather files capable of capturing the essence of the changing climate and carrying its variability. In line with these efforts, the synthesized weather files have emerged as a promising alternative to the commonly used morphing method for incorporating climate change variability into building energy designs. However, a comprehensive framework with a potential to accommodate this variability in this context is yet to be investigated. Addressing this gap, the present study aims to propose a novel framework that can facilitate dynamic design upgrades in response to the changing climate including the extreme climate conditions. This framework has been applied to a case study building in Australia and around 31% annual thermal load reduction, on average, has been achieved. Furthermore, the results showed that conventional design pathways are likely to leave buildings inadequately prepared against the upcoming climate conditions. Therefore, the proposed framework is capable of generating a dynamic design that not only maintains the expected energy performance by incorporating the changing climate into its structure but also draws a more realistic picture of the building energy demand pattern over time.

A finite-time path-tracking control algorithm for nonholonomic mobile robots with unknown dynamics and subject to wheel slippage/skid disturbances

Path planning and tracking control are two performance-critical tasks for wheeled mobile robots, particularly when nonholonomic constraints are imposed on robots in dynamically uncertain conditions. Accomplishing certain performance and safety considerations related to path-tracking, such as global stability, transient performance, and smooth finite-time convergence, becomes more difficult for nonholonomic robots. This paper is concerned with proposing a new adaptive robust finite-time tracking control approach for a large class of differential drive autonomous nonholonomic wheeled mobile robots (NWMRs) that are subject to structured uncertainties and extraneous disturbances with fully unknown dynamics. For this purpose, nonlinear kinodynamics of a type of rear-wheel drive NWMRs are developed by incorporating the skid/slippage constituents of the wheel motion. Then, a path-tracking controller is proposed using a continuous finite-time adaptive integral sliding mode control coupled with an integral backstepping approach (FTAISM-IBC). For the adaptive controller design, the entire nonlinear dynamics of the robot, including nonlinear vector functions and control gain functions, together with extraneous disturbances, are estimated by leveraging the universal approximation capabilities of radial basis neural networks (RBFNNs). The finite-time stability proof is presented by utilizing the Lyapunov stability theorem. Furthermore, the adaptive gains are derived to ensure the finite-time stability of the system subject to unknown functions, parametric variations, and unknown but bounded disturbances. Finally, the effectiveness of the proposed controller is evaluated through simulations in terms of several key performance indicators against several reported studies.

Scoping Review of Workplace Mental Health and Well-Being Programs in Higher Education Institutions.

To conduct a scoping review of stressors in higher education institutions (HEIs), exacerbated by the COVID-19 pandemic, and identify successful interventions. We reviewed 79 studies published between January 2020 and January 2023. Stressors were organized into psychosocial, organizational, and environmental categories. They included high job demand/low control; work/personal life imbalance; pressure to publish; lack of resources; low perceived organizational support; and fear of infection. Interventions included providing back-up childcare; employee assistance programs; financial well-being resources; paid medical leave; flexible work arrangements; greater transparency in decision-making; leadership development; and adaptive physical space design. Mental health and well-being interventions directed at faculty and staff in HEIs are needed given the many stressors faced by this workforce. Several interventions are highlighted in the literature, but most are speculative regarding their impact, given the limited number of outcome studies.

Broadband transmissive polarization rotator by gradiently twisted α -MoO3

Polarization engineering has been proven to enhance the capabilities of light manipulation and thus facilitate the development of integrated photonic devices. In this study, we introduce a polarization rotator based on a gradiently twisted α-MoO3 thin film, which works in the mid-infrared range and functions in a transmission mode. To be specific, the proposed device is constructed by gradiently twisted α-MoO3 multilayers with a subwavelength thickness of only 5 μm, namely, one-third of the working wavelength. Our analytical calculation demonstrates the efficacy of this subwavelength thin film rotator in converting a linearly polarized wave into its orthogonal counterpart, thanks to its chiral nature. The twisted α-MoO3 multilayers exhibit the capability to significantly manipulate dispersion characteristics while maintaining low optical losses, thereby enabling a wide bandwidth exceeding 2.5 THz with a polarization ratio surpassing 17 dB. Moreover, the operational frequency can be adjusted across a 3.4 THz range by altering the incident angle of the incident waves. This adaptable design, characterized by its polarization versatility, can be customized to suit practical applications within wireless communication, radar systems, optical switching, and imaging technologies.

Multilayer adaptive critic design with digital twin for data-driven optimal tracking control and industrial applications

In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is constructed. The action-critic structure is employed by each artificial system to obtain an approximate optimal control policy. The model network (MN) is built by using the actual input and output data sets of the controlled system, which means the dependence on the dynamics of the system is overcame. Then, the weights of the trained action network (AN) and MN are passed to the real system to realize the optimal tracking control. The feasibility of the algorithm is proved by theoretical analysis. Finally, the algorithm is applied to a simple nonlinear torsional pendulum system and an industrial wastewater treatment system (WWTS), and the effectiveness of the algorithm is verified. The algorithm effectively realizes the tracking control of nonlinear systems.

Adaptive Cartesian space control of robotic manipulators: A concurrent learning based approach

This work introduces a concurrent learning-based adaptive control design for end-effector tracking and the corresponding stability analysis for robotic manipulators. The presented controller is developed directly in Cartesian space, thereby removing the necessity for inverse kinematics calculations at the position level. The designed adaptive controller ensures global exponential tracking of the end-effector in Cartesian space. Moreover, the developed controller assures globally exponential convergence of uncertain dynamical parameters to their actual values without demanding persistence excitation conditions via a combination of a standard gradient-based adaptation with a novel integral concurrent learning component. The developed integral concurrent learning part operates both real-time output data and the most informative historical data gathered by employing the singular-value maximization algorithm (SVMA) to reduce the size of memory allocation. Lyapunov-based arguments are applied to ensure the exponential stability of the closed-loop system. Numerical studies are performed to depict the feasibility and performance of the proposed design.

Current status of immunological therapies for rheumatoid arthritis with a focus on antigen-specific therapeutic vaccines.

Rheumatoid arthritis (RA) is recognized as an autoimmune joint disease driven by T cell responses to self (or modified self or microbial mimic) antigens that trigger and aggravate the inflammatory condition. Newer treatments of RA employ monoclonal antibodies or recombinant receptors against cytokines or immune cell receptors as well as small-molecule Janus kinase (JAK) inhibitors to systemically ablate the cytokine or cellular responses that fuel inflammation. Unlike these treatments, a therapeutic vaccine, such as CEL-4000, helps balance adaptive immune homeostasis by promoting antigen-specific regulatory rather than inflammatory responses, and hence modulates the immunopathological course of RA. In this review, we discuss the current and proposed therapeutic products for RA, with an emphasis on antigen-specific therapeutic vaccine approaches to the treatment of the disease. As an example, we describe published results of the beneficial effects of CEL-4000 vaccine on animal models of RA. We also make a recommendation for the design of appropriate clinical studies for these newest therapeutic approaches, using the CEL-4000 vaccine as an example. Unlike vaccines that create or boost a new immune response, the clinical success of an immunomodulatory therapeutic vaccine for RA lies in its ability to redirect autoreactive pro-inflammatory memory T cells towards rebalancing the "runaway" immune/inflammatory responses that characterize the disease. Human trials of such a therapy will require alternative approaches in clinical trial design and implementation for determining safety, toxicity, and efficacy. These approaches include adaptive design (such as the Bayesian optimal design (BOIN), currently employed in oncological clinical studies), and the use of disease-related biomarkers as indicators of treatment success.

Improving the Efficiency of Outbound CATI As a Nonresponse Follow-Up Mode in Address-Based Samples: A Quasi-Experimental Evaluation of a Dynamic Adaptive Design

Abstract This article evaluates the use of dynamic adaptive design methods to target outbound computer-assisted telephone interviewing (CATI) in the California Health Interview Survey (CHIS). CHIS is a large-scale, annual study that uses an address-based sample (ABS) with push-to-Web mailings, followed by outbound CATI follow-up for addresses with appended phone numbers. CHIS 2022 implemented a dynamic adaptive design in which predictive models were used to end dialing early for some cases. For addresses that received outbound CATI follow-up, dialing was paused after three calls. A response propensity (RP) model was applied to predict the probability that the address would respond to continued dialing, based on the outcomes of the first three calls. Low-RP addresses were permanently retired with no additional dialing, while the rest continued through six or more attempts. We use a difference-in-difference design to evaluate the effect of the adaptive design on calling effort, completion rates, and the demographic composition of respondents. We find that the adaptive design reduced the mean number of calls per sampled unit by about 14 percent (relative to a modeled no-adaptive-design counterfactual) with a minimal reduction in the completion rate and no strong evidence of changes in the prevalence of target demographics. This suggests that RP modeling can meaningfully distinguish between ABS sample units for which additional dialing is and is not productive, helping to control outbound dialing costs without compromising sample representativeness.

Adaptive robust control for fuzzy underactuated mechanical systems: A Stackelberg game-theoretic optimization approach

This article proposes a Stackelberg game-theoretic optimization approach for adaptive robust controller design of fuzzy underactuated mechanical systems (UMSs). As a commonly encountered challenge, several factors render it difficult to further improve the control performance of these UMSs, such as the coupling effects and nonlinearities resulting from the underactuated structure. Meanwhile, various sources of unexpected time-varying uncertainties can further add to the difficulty in the controller design procedure pipeline. To overcome the above-mentioned issues and further enhance the system performance, a dual-parameter optimization framework is presented here for adaptive robust controller design, leveraging fuzzy set theory and Stackelberg game. In this development, the time-varying uncertainty is characterized via suitable membership functions; and thus the appropriate fuzzy UMS is established. Then, an adaptive robust controller with a leakage-type adaptation law is presented to tackle the uncertainty appropriately. To further improve the system performance considering the trade-off among the transient performance; the steady-state performance; and the control cost; a Stackelberg game-based dual-parameter optimization method is proposed to determine the Stackelberg strategy. It is rigorously proved in the developments here that the proposed controller design and parameters optimization method guarantees the desired deterministic system performance, i.e., uniform boundedness (UB) and uniform ultimate boundedness (UUB). Moreover, a series of numerical simulations based on a flywheel inverted pendulum (FIP) are performed, which illustrate the effectiveness and superiority of the proposed method.

A new methodology to robustify an experimental design: Application to the Baranyi model

A robust experimental design is a desired object for practitioners when there is uncertainty about any of the assumptions necessary to compute the optimal design. For instance, when they use non-linear models, which requires having nominal values of the parameters. Several alternatives have been developed in the literature to obtain robust experimental designs such as adaptive or Bayesian designs, among others. Here a new methodology is proposed to robustify the optimal experimental design. Based on the maximin idea, the method adds support points to the optimal design, to obtain designs that are robust. It is applied to the Baranyi model, one of the most used mathematical models in predictive microbiology to describe the behaviour of microorganisms in food products, an essential issue for human health. Previously, D-optimal designs are provided for the model, considering 4 and 6 parameters to be estimated. A sensitivity analysis is carried out regarding the deviations in the nominal values of the parameters, which shows a greater loss of efficiency for two of them. Given these results, the new methodology is applied to the D-optimal design, checking the robustness of the augmented designs through the efficiency achieved. Finally, c-, A- and I-optimal designs are calculated to provide accurate estimation of the model parameters and the predictions.

Promoting sustainability in 3D printed sand casting through adaptive sand mold structures

The use of 3D printed sand molds offers a high degree of flexibility in structural design and satisfying customer requirements. The optimization and adjustment of the structure of sand mold module with the required functionality helps to maximize the application potential of binder jet 3D printing technology. In this study, an adaptive sand mold structure design method was proposed to meet the sustainable requirements of 3D printed sand casting. Firstly, the functional modular and module clustering of the sand mold is carried out and then each module is adaptive structure design, to reduce the carbon emission and cost of the 3D printed casting process. Therefore, taking the small casting furnace cover and medium-sized cast engine block as examples, the life cycle framework of the 3D printed sand casting process is established, and the sustainable advantages of the adaptive structure method are explored through the calculation and analysis of carbon emission and cost models. The results show that the adaptive structure method reduces the carbon emissions of the two castings by 9.2% and 11.9%, respectively, and the cost of two castings by 6.7% and 11.1%, respectively. This approach provides more sustainable design ideas for 3D printed sand casting and 3D printed sand mass production.

Implementing Circular Economy throughout the Construction Project Life Cycle: A Review on Potential Practices and Relationships

The linear economy model that is generally practised in the construction industry is one of the main reasons hindering the achievement of sustainability in construction. Alternatively, the Circular Economy (CE) model is becoming a promising approach to achieve sustainable construction, in which materials and products are circulated at their highest economic value and, thereby, contribute towards mitigating the negative economic, social, and environmental impacts of the construction industry. For a successful transition towards CE in the construction industry, it is important not only to understand CE practices that could be implemented across different stages of the life cycle of construction projects, but also to understand how a particular practice relates to another at those stages. Having limited studies identifying such relationships, this study aims to improve the successful implementation of CE practices in the construction industry by comprehending the practices, along with their relationships, throughout the project life cycle. A qualitative research method consisting of a systematic literature review was adopted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to identify CE practices. Amongst the identified highly cited CE practices applicable to construction projects, design for disassembly, design for adaptability and flexibility, and design for manufacture and assembly ended up being the most significant ones. Consequently, a relationship matrix was developed, highlighting the relationships among the highly cited practices. The matrix emphasises that practices applied in the design stage relate frequently to ones at the following stages, necessitating projects to start with proper planning. This study ultimately assists construction industry stakeholders in successfully and rationally implementing CE practices throughout the project life cycle while enhancing the value addition on the economy, society, and environment.

WaveFormer: transformer-based denoising method for gravitational-wave data

With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole events of LIGO we obtain a significant improvement of inverse false alarm rate. Our work highlights the potential of large neural networks in GW data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network in future observational runs.

Mapping using an adaptive sampling design

Interpolation is commonly used in the construction of maps and images when there is limited information for some of the sites. The accuracy of interpolation methods depends, in part, on the location of the sample sites where more complete information has been gathered. An initial survey design where the sample sites are spaced so there is wide-spread coverage is desirable. However, when there is considerable variation in the variable of interest, other design features may be preferable. Here we introduce an adaptive design where in the first stage of site selection gives wide-spread coverage, and in subsequent stages additional sites are selected adjacent to areas of high variability.

Design and testing of ultrasound probe adapters for a robotic imaging platform

Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapidly identify free fluid in abdominal and thoracic cavities which could necessitate immediate surgical intervention. However, proper ultrasound image capture requires a skilled ultrasonography technician who is likely unavailable at the point of injury where resources are limited. Instead, robotics and computer vision technology can simplify image acquisition. As a first step towards this larger goal, here, we focus on the development of prototypes for ultrasound probe securement using a robotics platform. The ability of four probe adapter technologies to precisely capture images at anatomical locations, repeatedly, and with different ultrasound transducer types were evaluated across more than five scoring criteria. Testing demonstrated two of the adapters outperformed the traditional robot gripper and manual image capture, with a compact, rotating design compatible with wireless imaging technology being most suitable for use at the point of injury. Next steps will integrate the robotic platform with computer vision and deep learning image interpretation models to automate image capture and diagnosis. This will lower the skill threshold needed for medical imaging-based triage, enabling this procedure to be available at or near the point of injury.