Adaptive Design Research Articles

Accelerating development in wood‐plastic composites: Presenting key material properties and demonstrating statistical methods

AbstractIn a time when global initiatives to lower CO2 emissions are accelerating the shift towards bio‐based products, various efforts are being made to develop wood plastic composites (WPCs) and similar bio‐composites using a variety of raw materials. This paper first showcases benchmark material properties serving as standards and models the composition, for polypropylene‐based WPCs integrating wood flour and maleic anhydride‐modified polypropylene. Subsequently, by applying statistical modeling techniques, we demonstrate an adaptive experimental design that efficiently and rapidly adjusts formulations. Beginning with an empirical dataset (24 samples), we established high‐performing models that articulate the individual contributions of the constituents on ten vital properties of WPCs. Our adaptive experimental design, leveraging nonlinear partial least squares regression and the Bayesian optimization, successfully refined formulations to enhance bending and impact strengths. Notably, we proposed optimal conditions starting from just eight samples with minimal iterations. This study shows that statistical techniques can quickly optimize WPC formulations, making the overall development process faster. By addressing the multiple conflicting properties, these techniques can greatly reduce the time and effort needed for development.Highlights Applied several regression methods to analyze ten properties of PP‐based WPCs. Showcased distinct impacts of the formulations of WF and MAPP on WPCs. Implemented the adaptive experimental design for formulation optimization. Enhanced bending and impact strengths in the formulations of WPCs. Advanced sustainable material development with data‐driven approach.

Addressing the Evidence Gap in Aneurysmal Subarachnoid Hemorrhage: The Need for a Pragmatic Randomized Trial Platform.

Aneurysmal subarachnoid hemorrhage (aSAH) occurs less often than other stroke types but affects younger patients, imposing a disproportionately high burden of long-term disability. Although management advances have improved outcomes over time, relatively few aSAH treatments have been tested in randomized clinical trials (RCTs). One lesson learned from COVID-19 is that trial platforms can facilitate the efficient execution of multicenter RCTs even in complex diseases during challenging conditions. An aSAH trial platform with standardized eligibility criteria, randomization procedures, and end point definitions would enable the study of multiple targeted interventions in a perpetual manner, with treatments entering and leaving the platform based on predefined decision algorithms. An umbrella institutional review board protocol and clinical trial agreement would allow individual arms to be efficiently added as amendments rather than stand-alone protocols. Standardized case report forms using the National Institutes of Health/National Institute of Neurological Disorders and Stroke common data elements and general protocol standardization across arms would create synergies for data management and monitoring. A Bayesian analysis framework would emphasize frequent interim looks to enable early termination of trial arms for futility, common controls, borrowing of information across arms, and adaptive designs. A protocol development committee would assist investigators and encourage pragmatic designs to maximize generalizability, reduce site burden, and execute trials efficiently and cost-effectively. Despite decades of steady clinical progress in the management of aSAH, poor patient outcomes remain common, and despite the increasing availability of RCT data in other fields, it remains difficult to perform RCTs to guide more effective care for aSAH. The development of a platform for pragmatic RCTs in aSAH would help close the evidence gap between aSAH and other stroke types and improve outcomes for this important disease with its disproportionate public health burden.

Transformative Role of Artificial Intelligence in the Pharmaceutical Sector

Background: The pharmaceutical sector is a critical component of healthcare, driving innovation in drug discovery, development, and delivery. With the increasing integration of artificial intelligence (AI), digital health technologies, and biotechnology, the industry is transforming rapidly. This review examines the key areas of the pharmaceutical industry and highlights the growing impact of AI in enhancing various processes, from drug discovery to clinical trials. To explore the applications of AI in drug discovery, development, manufacturing, clinical trials, personalized medicine, and regulatory compliance. This review also addresses the challenges, such as data privacy and interoperability, that accompany the adoption of AI in the pharmaceutical sector. Methods: A comprehensive review of existing literature and case studies on the application of AI in pharmaceutical research and operations was conducted. Key areas of focus include AI's role in predictive analytics, target identification, manufacturing, supply chain management, clinical trial optimization, and pharmacovigilance. Results: AI significantly enhances drug discovery by improving target identification, predictive modeling, and high-throughput screening. It optimizes manufacturing through real-time quality control and process automation. In clinical trials, AI facilitates patient recruitment and adaptive trial designs, while in personalized medicine, it enables biomarker discovery and treatment optimization. AI also supports regulatory compliance through automated monitoring and risk assessment. Conclusion: AI is transforming the pharmaceutical sector, making processes more efficient, precise, and tailored to individual patients. However, challenges such as data privacy, ethical considerations, and interoperability must be addressed to fully harness AI's potential. Standardization and collaboration will be essential in driving the next phase of innovation in pharmaceutical development and healthcare delivery.

Irregular IRS-aided SWIPT secure communication system with imperfect CSI

The performance of traditional regular Intelligent Reflecting Surface (IRS) improves as the number of IRS elements increases, but more reflecting elements lead to higher IRS power consumption and greater overhead of channel estimation. The Irregular Intelligent Reflecting Surface (IIRS) can enhance the performance of the IRS as well as boost the system performance when the number of reflecting elements is limited. However, due to the lack of radio frequency chain in IRS, it is challenging for the Base Station (BS) to gather perfect Channel State Information (CSI), especially in the presence of Eavesdroppers (Eves). Therefore, in this paper we investigate the minimum transmit power problem of IIRS-aided Simultaneous Wireless Information and Power Transfer (SWIPT) secure communication system with imperfect CSI of BS-IIRS-Eves links, which is subject to the rate outage probability constraints of the Eves, the minimum rate constraints of the Information Receivers (IRs), the energy harvesting constraints of the Energy Receivers (ERs), and the topology matrix constraints. Afterward, the formulated non-convex problem can be efficiently tackled by employing joint optimization algorithm combined with successive refinement method and adaptive topology design method. Simulation results demonstrate the effectiveness of the proposed scheme and the superiority of IIRS.

Drug-resistant tuberculosis treatments, the case for a phase III platform trial.

Most phase III trials in drug-resistant tuberculosis have either been underpowered to quantify differences in microbiological endpoints or have taken up to a decade to complete. Composite primary endpoints, dominated by differences in treatment discontinuation and regimen changes, may mask important differences in treatment failure and relapse. Although new regimens for drug-resistant tuberculosis appear very effective, resistance to new drugs is emerging rapidly. There is a need for shorter, safer and more tolerable regimens, including those active against bedaquiline-resistant tuberculosis. Transitioning from multiple regimen A versus regimen B trials to a single large phase III platform trial would accelerate the acquisition of robust estimates of relative efficacy and safety. Further efficiencies could be achieved by adopting modern adaptive platform designs. Collaboration among trialists, affected community representatives, funders and regulators is essential for developing such a phase III platform trial for drug-resistant tuberculosis treatment regimens.

Adaptive critic design for safety-optimal FTC of unknown nonlinear systems with asymmetric constrained-input

Safe fault tolerant control is one of the key technologies to improve the reliability of dynamic complex nonlinear systems with limited inputs, which is hard to solve and definitely a great challenge to tackle. Thus the paper presents a novel safety-optimal FTC (Fault Tolerant Control) approach for a category of completely unknown nonlinear systems incorporating actuator fault and asymmetric constrained-input, which can guarantee the system’s operation within a safe range while showcasing optimal performance. Firstly, a CBF (Control Barrier Function) is incorporated into the cost function to penalize unsafe behaviors, and then we translate the intractable safety-optimal FTC problem into a differential ZSG (Zero-Sum Game) problem by defining the control input and the actuator fault as two opposing sides. Secondly, a neural-network-based identifier is employed to reconstruct system dynamics using system data, and the resolution of handling asymmetric constrained-input with the introduced non-quadratic cost function is achieved through the design of an adaptive critic scheme, aiming to reduce computational expenses accordingly. Finally, through the theoretical stability analysis, it is demonstrated that all signals in the closed-loop system are consistently UUB (Uniformly Ultimately Bounded). Furthermore, the proposed method’s effectiveness is also verified in the simulation experiments conducted on a model of a single-link robotic arm system with actuator failure. The result shows that the algorithm can fulfill the safety-optimal demand of fault tolerant control in fault system with asymmetric constrained-input.

Adaptive Current Observer Design for Single Current Sensor Control in PMSM Drives

Adaptive Current Observer Design for Single Current Sensor Control in PMSM Drives

Energy retrofit and climate adaptive design of dwellings in the hot arid climate: Trends and future challenges

As long as buildings face various climate change impacts in recent decades, such as increased heat, particularly in the residential sector. Therefore, energy retrofit, and climate-adaptive designs may contribute significantly to climate change mitigation and adaptation strategies. At the same time, these strategies do not only improve the sustainability of dwellings/houses/residences but also contribute to broader goals of increasing energy efficiency, reducing environmental impact, offering economic benefits, and enhancing the community's resilience in the challenges they face impact from the effects of climate change. This study aims to examine and present the development of energy retrofitting, energy efficiency and climate-adaptive design for dwellings/ residential buildings in hot weather publications through bibliometric research. The research has been examined within the Web of Science™ Core Collection (W.O.S.) online database spanning from 2012 to November 2023 by using the "Title/Abstract/Keywords" category, and a comprehensive data visualisation has been conducted utilising the VOSviewer and CiteSpace programmes. The findings indicate the research trends in the literature and future challenges, and the results from these findings demonstrate the need for sustainable and energy-efficient buildings to preserve the environment and climate. These sustainable developments focus on improvements in energy retrofitting technologies, energy efficiency and saving targets, indoor thermal comfort, optimising passive design and minimising energy demand. This study will probably be a source to provide valuable insights for researchers, practitioners, experts, and policymakers to understand the implications of energy retrofitting and climate-adaptive design in hot arid climates. As well as it would offer theoretical and practical initiatives to be applied in this field.

Dosing Strategies and Quantitative Clinical Pharmacology for Bispecific T-Cell Engagers Development in Oncology.

Bispecific T-cell Engagers (TCEs) are promising anti-cancer treatments that bind to both the CD3 receptors on T cells and an antigen on the surface of tumor cells, creating an immune synapse, leading to killing of malignant tumor cells. These novel therapies have unique development challenges, with specific safety risks of cytokine release syndrome. These on-target adverse events fortunately can be mitigated and deconvoluted from efficacy via innovative dosing strategies, making clinical pharmacology key in the development of these therapies. This review assesses dose selection and the role of quantitative clinical pharmacology in the development of the first eight approved TCEs. Model informed drug development (MIDD) strategies can be used at every stage to guide TCE development. Mechanistic modeling approaches allow for (1) efficacious yet safe first-in-human dose selection as compared with in vitro minimum anticipated biological effect level (MABEL) approach; (2) rapid escalation and reducing number of patients with subtherapeutic doses through model-based adaptive design; (3) virtual testing of different step-up dosing regimens that may not be feasible to be evaluated in the clinic; and (4) selection and justification of the optimal clinical step-up and full treatment doses. As the knowledge base around TCEs continues to grow, the relevance and utilization of MIDD strategies for supporting the development and dose optimization of these molecules are expected to advance, optimizing the benefit-risk profile for cancer patients.

Adaptive Fuzzy Filter-based L 1 Adaptive Controller Design for Electromechanical Actuator Containing Uncertainties and Disturbances

Conventional L 1 adaptive controller utilizes high adaptation gain to quickly adapt time varying uncertainties and disturbances. However, it contaminates the control signal with high frequency components. To eliminate this high frequency signal, it uses a low pass filter with fixed structure. Yet, the main problem with this fixed structured filter is that it may cancel the estimated values of uncertainties and disturbances along with the high frequencies. To overcome this problem, in this research work, an adaptive fuzzy low pass filter is proposed in place of the fixed structured filter of the L 1 adaptive controller. In this proposed research work, the filter changes its structure according to the time varying uncertainties and disturbances. Hence, the contributory part of this work is that the proposed adaptive fuzzy filter efficiently cancels out high frequency components without nullifying the estimated values of uncertainties and disturbances from the control signal. The stability of the proposed adaptive fuzzy filter-based L 1 adaptive controller is guaranteed analytically. The proposed method is employed on an electromechanical actuator, viz., DC motor experimental setup to validate its effectiveness. Adequate cancellation of high frequency signal incurs smooth control signal which in turn provides lower control energy.

Novel dissolution methods for drug release testing of Long-Acting injectables

Long-acting parenteral drug products are a popular choice for therapeutic areas requiring long term treatment. These products range from dispersed systems such as drug suspensions and polymeric microspheres to in situ forming polymeric implants. The lack of reliable drug release testing methods for these drug products not only impedes the development of new drug products but also affects generic drug development. Current release methods suffer from a range of problems such as high variability, poor reproducibility, poor discriminatory ability, lack of depot-like structure formation (that could mimic the in vivo situation). Moreover, shorter duration (less than a week) of release renders them unsuitable for in vitro-in vivo correlations (IVIVCs). To overcome these issues, novel adapters were developed for both USP-type-II & IV apparatus. These adapters were validated and assessed using the long-acting injectable (LAI) suspension drug product Depo Provera 150® as well as its Q1/Q2 equivalents. For USP-type-IV apparatus, two open adapter designs (conical and ellipsoidal shaped cavity with volume capacities of 50 µl and 1 ml, respectively) were developed. A closed conical adapter design with a volume capacity of 0.05 ml was developed for USP apparatus type-II. All three novel adapter designs effectively retained the suspensions, achieved release durations of 3–6 weeks with good reproducibility, minimal variability (RSD≤5%) and had good discriminatory ability. Based on this, the adapter-based dissolution methods were deemed suitable for IVIVC development of long-acting injectables. A successful Level A IVIVC was developed for Depo SubQ Provera 104® and its Q1Q2 equivalents using USP apparatus type IV with a conical adapter design. The closed adapter design for apparatus type-II was also investigated for suitability with risperidone in situ forming implants. The adapter was able to securely retain and maintain the shape of the in situ forming implants and resulted in release profiles of up to one month with good discriminatory ability and low standard error (RSD≤5%). These novel adapters hold promise of wide use for in vitro release testing of different long-acting parenteral drug products.

Multi-state Markovian-random walk adaptive filter for time-varying block sparse system identification

This paper presents a higher order multi-state Markov model for block sparsity model of a system impulse response. To capture the time-varying behavior of the system, a random walk model for temporal evolution is incorporated; resulting in a two-dimensional multi-state Markov-Random walk model across spatial and temporal domains. Additionally, a block batch data structure is utilized for the adaptive filter design. The proposed adaptive filter is aimed to find the Maximum A Posteriori (MAP) estimator of the batch time-varying impulse response. The associated optimization problem is demonstrated to be convex and efficiently solvable through the Steepest-Descent (SD) approach. Furthermore, the final recursion of the proposed adaptive filter is derived and a mean convergence analysis of the algorithm is provided. Simulation results show the effectiveness of the proposed algorithm under severe time-varying conditions of the system impulse response, where alternative algorithms may exhibit divergence or poor convergence. Synthetic experiments and real-world experiments using acoustic channel estimation further validate the superiority of the proposed algorithm.

Adaptive Preference Measurement with Unstructured Data

Many products are most meaningfully described using unstructured data such as text or images. Unstructured data are also common in e-commerce, in which products are often described by photos and text but not with standardized sets of attributes. Whereas much is known about how to efficiently measure consumer preferences when products can be meaningfully described by structured attributes, there is scant research on doing the same for unstructured data. This paper introduces a real-time, adaptive survey design framework for measuring preferences over unstructured data, leveraging Bayesian optimization. By adaptively choosing items to display based on uncertainty around a nonparametric utility model, the proposed method maximizes information gain per question, enabling quick estimation of individual-level preferences. The approach operates on embeddings of the unstructured data, thereby eliminating the requirement for manual coding of product attributes. We apply the method to measuring preferences over clothing and highlight its potential for both the general task of marketing research and the specific task of designing customer onboarding surveys to mitigate the cold-start recommendation problem. We also develop methods for interpreting the nonparametric utility functions, which allow us to reconstruct consumer valuations of discrete attributes, even for attributes that were not considered or available a priori. This paper was accepted by Duncan Simester, marketing. Fundings: Funding for this project was provided by Analytics at Wharton, the Wharton Behavioral Lab, and the Wharton Dean’s Fund. The author also thanks the Govil Family for financial support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03775 .

Rationale and Design of PURE: A Randomized Controlled Trial to Evaluate Peritoneal Ultrafiltration with PolyCore™ in Refractory Congestive Heart Failure

Introduction: Peritoneal ultrafiltration (PUF) has been proposed as an additional therapeutic option for refractory congestive heart failure (RCHF) patients. Despite promising observational studies and/or case report results, limited clinical trial data exist, and so far, PUF solutions remain only indicated for chronic kidney diseases. In this article, we described a multicenter, randomized, controlled, unblinded, adaptive design clinical trial, about to start, investigating the effects of PolyCore™, an innovative PUF solution, in the treatment of RCHF patients. Methods: The Peritoneal Ultrafiltration in Cardiorenal Syndrome (PURE) study is a phase II, multicenter, randomized, controlled, unblinded, adaptive design clinical trial that aims to evaluate the safety and efficacy of PUF, using PolyCore™ as the investigational solution, in the treatment of RCHF patients who present with prominent right ventricular failure due to afterload mismatch, functional tricuspid regurgitation and enlarged cava vein consequent to intravascular fluid overload. Approximately 84 patients will be randomized 1:1 either to continue with their prescribed guideline-directed medical therapy or to add the PUF treatment on top of it. The primary objective is to evaluate if PUF treatment has an impact on the composite endpoint of the patient’s mortality or worsening of the patient’s condition such as hospitalization for cardiovascular causes, increasing the initial daily dose of loop diuretic or worsening of renal function. Statistical analysis for the primary endpoint will be standard survival analysis to estimate the failure rate at month 7 for each group via Kaplan-Meier curves. Sensitivity analysis and various secondary analyses, including a multiple events analysis, will be conducted to evaluate the robustness of the primary endpoint results. Safety will be evaluated for up to 12 months. Conclusion: The PURE study was designed to evaluate the safety and efficacy of peritoneal ultrafiltration with PolyCore™ on top of guideline-directed medical therapy in patients with RCHF, assuming a combined clinical endpoint of mortality or worsening patients’ condition. If successful, the treatment should allow for an improvement of the RCHF symptoms, decreasing hospitalization rate of patients.

A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions

Background and Objective:One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based on psychophysiological state, based on success rate, or by modeling the user’s skills. However, all studies propose solutions for a single session, requiring a calibration process before using it in each session. We propose a dynamic adaptation method that can be used during different rehabilitation sessions, without the need for recalibration between sessions. Methods:The adaptation architecture is based on a genetic algorithm that aims to maintain a certain score level and to motivate the user to move. The method has been evaluated with two serious games for five sessions using a rehabilitation robot. A common initial evaluation was made for all the users involved in the study, and the game parameters that best suited each user from the previous session were introduced as the starting point of the next session. In addition, the desired score rate was lowered between sessions to increase the difficulty level. The psychophysiological state of the users was measured based on the Self-Assessment Manikin test, as well as different cardiorespiratory and galvanic skin response signals were analyzed. Results:The adaptation architecture proposed can find those game parameters that maximize the user movement for both games. In one of the games, the score rate set for each session is followed with high fidelity. The degree of personalization in the games increases between sessions as the dispersion of the game parameters grows. The Self-Assessment Manikin test and the physiological signals results would indicate that the psychophysiological state remains equal between sessions despite an increase in game difficulty. Conclusions:The genetic algorithm-based game adaptation has proven efficacy in maximizing the therapy performance through the sessions without needing recalibration. It also can be concluded that the design of the game influences the adaptation performance. Additionally, adaptive game design facilitated by our method does not significantly impact players’ emotional or physiological states.

Adaptive Designs for Clinical Trials in Nephrology.

Adaptive Designs for Clinical Trials in Nephrology.

Adaptive distributed fault estimation observer design for nonlinear time-delay multi-agent systems with directed graphs

This paper focuses the development of an adaptive observer-based distributed fault estimation observer (DFEO) for multi-agent nonlinear time-delay systems under a directed communication topology. The process involves constructing a fault estimation observer for each agent based on their relative output estimation errors. An adaptive DFEO design is proposed for multi-agent systems with a directed communication topology. Furthermore, investigating the effect of faulty agents on the criteria of fault detection on healthy agents. The proposed design is represented with Linear Matrix Inequalities (LMIs) to establish a systematic design procedure. The study also includes simulation results to demonstrate the effectiveness of the proposed design techniques.

Nonlinear Adaptive Control Design for Quadrotor UAV Transportation System

In response to the non-linear and underactuated characteristics of quadrotor UAV suspension transportation system, this paper proposes a novel control strategy aimed at achieving precise position control, attitude control, and anti-swing capabilities. Firstly, a dynamical model required for controller design is established through the Newton-Euler method. In the controller design process, the paper employs the energy method and barrier Lyapunov function to design a double-closed-loop nonlinear controller. This controller is capable of not only accurately controlling the position and attitude angles of the quadrotor UAV suspension transportation system but also effectively suppressing the swing of the payload. Building on this, considering the elastic deformation of the lifting cable, and by analyzing the forces in the Newton-Euler equations, this paper proposes an adaptive control design for the case where the length of the cable connecting the UAV and the payload is unknown. To validate the effectiveness of the proposed control scheme, comparative experiments were conducted in the MATLAB simulation environment, and the results indicate that the method proposed in this paper exhibits superior control performance compared to traditional controllers.

Finite time adaptive recoil controller design for deepwater drilling riser systems

In this paper, we propose a finite-time model reference adaptive controller to attenuate the recoil response of deepwater drilling riser systems following an emergency disconnection. Firstly, a coupled model that characterizes the recoil movements of the riser, wave-excited heave motion, and the frictional resistance due to drilling fluid discharge is developed. Secondly, an adaptive control law is presented, with a portion of the adaptive gain determined through linear matrix inequalities. Thirdly, the stability criteria and updating rules that ensure finite settling time for the recoil control system is established. Then, the effectiveness and robustness of the controller against nonlinear systematic perturbations are verified. It is worth pointing out that: (1) The proposed adaptive controller significantly attenuates the recoil responses of the riser in finite time. (2) Compared to other controllers, the finite-time adaptive controller provides faster convergence for both error states and adaptive gains. (3) Perturbation cases involving nonlinear and unmodeled dynamics are examined, demonstrating the controller’s effectiveness and robustness, which ensures the safety of the drilling riser system.

Innovative design for enhancing transient stability with an ATFOPID controller in hybrid power systems

In order to make multi-area power systems more efficient and stable, this study aims to create a new intelligent controller design based on adaptive property. The integrated power grid becomes more unstable as renewable energy sources become more prevalent, and system inertia decreases. Resultant, the penetration of renewable and load variance causes frequency changes to be more pronounced. Keeping frequency stable is essential for the electrical energy transmission system to work reliably and efficiently. This paper presents an adaptive design based on the tilt fractional order proportional integral derivative (TFOPID) controller by using an optimized property of bio inspired tunicate search algorithm (TSA). The gains of the TFOPID controller are continuously updated by utilizing the ancillary TPID controller, which is dependent on changes in area frequency. The suggested control system is implemented in the northern and southern regions, which consist of photovoltaic (PV), wind, redox flow battery (RFB), hydro, and thermal power plants. Therefore, this study contributes in the creation of more resilient and efficient power grids, which are critical for incorporating renewable energy sources and satisfying the increasing need for environmentally friendly power solutions.