Adaptive Iteration Research Articles

Multi-grained Guaranteeable Requirement Analysis for Iterative Adaptation

Multi-grained Guaranteeable Requirement Analysis for Iterative Adaptation

Joint Iterative Adaptive Approach for Sidelobe Suppression and Migration Correction of Migrating Targets

Joint Iterative Adaptive Approach for Sidelobe Suppression and Migration Correction of Migrating Targets

Assessing Climate Change Adaptation Strategies in Agriculture using Agent-Based Modeling

This project seeks to analyze the complex dynamics of climate change adaptation techniques in agriculture by investigating the synergistic link between diverse agricultural practices and technology intervention s via agent-based modeling. The experimental data, consisting of synthetic datasets, clarifies the iterative adaptation process by illustrating examples such as “Drought Resistant Crop-alpha” and “Water Conservation System-beta,” which demonstrate the effectiveness of swift modifications to evolving climatic circumstances. The revolutionary influence of technology adoption is shown by developments such as “Smart Irrigation-Enhanced,” which underscores its ability to improve resilience and production. The seamless integration of these methods across all agricultural phases, from initial acceptance to extensive application, highlights their interrelated and mutually reinforcing connection. Confronting difficulties, especially those associated with socio-economic dynamics and resource allocation, requires the integration of ethical and policy considerations. This study offers critical insights into the essential comprehension of climate adaptation techniques, which will inform future research and underscore the need for ethical frameworks in enhancing the efficacy of agent-based models for agricultural resilience.

An extended Kalman filter-based versatile adaptive iteration learning control for heavy-haul train trajectory tracking control

An extended Kalman filter-based versatile adaptive iteration learning control for heavy-haul train trajectory tracking control

Uncertainty Compensated High-Order Adaptive Iteration Learning Control for Robot-Assisted Upper Limb Rehabilitation

Uncertainty Compensated High-Order Adaptive Iteration Learning Control for Robot-Assisted Upper Limb Rehabilitation

Implementation science for cancer control: One center's experience addressing context, adaptation, equity, and sustainment.

Implementation science (IS) has great potential to enhance the frequency, speed, and quality of the translation of evidence-based programs, policies, products, and guidelines into practice. Progress has been made, but with some notable exceptions, this promise has not been achieved for cancer prevention and control. We discuss five interrelated but conceptually distinct, crosscutting issues important to accelerate IS for cancer prevention and control and how our Colorado Implementation Science Center in Cancer Control (COISC3) addressed these issues. These needs and opportunities include more fully addressing changing, multi-level context; guiding rapid, iterative adaptations; evaluating innovative approaches to engagement and health equity; greater attention to costs and economic issues; and sustainability. We summarize conceptual issues; evaluation needs and capacity building activities and then provide examples of how our IS center addressed these five needs for cancer prevention and control. We discuss changes made to address priorities of (i) guiding adaptations of implementation strategies to address changing context and (ii) working on issues identified and prioritized by our primary care partners rather than the research team. We conclude with discussion of lessons learned, limitations, and directions for future research and practice in IS to enhance cancer prevention and control as well as translational behavioral medicine more generally.

LESSONS LEARNED DEVELOPING A NUTRITIONAL ADHERENCE INTERVENTION FOR DEMENTIA PREVENTION: A QUALITATIVE ANALYSIS

Abstract Lifestyle interventions show considerable promise in delaying the onset and reducing risk for Alzheimer’s disease and related dementias (ADRD). Unfortunately, scalable interventions that address issues of long-term adherence have not been adequately developed or disseminated. Especially important for this effort is iterative evaluation and adaptation of interventions using participant feedback throughout the development process. Using the NIH ORBIT model for behavioral intervention development, our team has established the proof-of-concept (N=9) and pilot tested (N=58) a Mediterranean ketogenic nutrition (MKN) adherence program for older adults at risk for ADRD. This program incorporates motivational interviewing strategies and cognitive behavioral skills training to enhance uptake and long-term adherence to MKN. For each of these pilots, participants completed exit interviews at the conclusion of the 6-week, group intervention and 3-months post-intervention. Qualitative and quantitative data on the acceptability of the program, as well as detailed participant feedback about their experience and recommendations for improvements were collected. While quantitative data demonstrate high acceptability, thematic analysis of qualitative interviews demonstrate a range of recommendations for enhancing and refining important components of the intervention that will be necessary for future scaling and dissemination. We discuss important themes, future directions of this work, and implications for lifestyle intervention development more broadly.

CMAIS-WOA: An Improved WOA with Chaotic Mapping and Adaptive Iterative Strategy

This paper proposes an improved whale optimization algorithm with chaotic mapping and adaptive iteration strategy (CMAIS-WOA). This algorithm addresses the issues of the WOA algorithm that is prone to local optimal solutions with low stability. CMAIS-WOA utilizes chaotic mapping to enhance the diversity and coverage of the initial population. Also, it adaptively adjusts the weight values based on the current distribution of whale populations and the fitness of search agents. In addition, CMAIS-WOA uses an improved nonlinear convergence factor to adjust the breadth-first and depth-first search during the optimization process. The performance of the proposed CMAIS-WOA is evaluated by using 13 classical benchmark functions and IEEE CEC2014 test suite. The experimental results show that CMAIS-WOA effectively improves the stability of the optimal solution and helps the algorithm to approach the global optimal solution. The method proposed in this paper contributes to the field of optimization which solves problems more powerfully and efficiently.

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.

Two-Dimensional Angular Super-Resolution for Airborne Real Aperture Radar by Fast Conjugate Gradient Iterative Adaptive Approach

Two-Dimensional Angular Super-Resolution for Airborne Real Aperture Radar by Fast Conjugate Gradient Iterative Adaptive Approach

Design Research to Embed mHealth into a Community-Led Blood Pressure Management System in Uganda: Protocol for a Mixed Methods Study.

Uncontrolled hypertension is a leading risk factor for cardiovascular diseases. In Uganda, such diseases account for approximately 10% of all deaths, with 1 in 5 adults having hypertension (>90% of the hypertensive cases are uncontrolled). Although basic health care in the country is available free of cost at government facilities, regularly accessing medication to control hypertension is difficult because supply chain challenges impede availability. Clients therefore frequently suspend treatment or buy medication individually at private facilities or pharmacies (incurring significant costs). In recent years, mobile health (mHealth) interventions have shown increasing potential in addressing health system challenges in sub-Saharan Africa, but the acceptability, feasibility, and uptake conditions of mobile money approaches to chronic disease management remain understudied. This study aims to design and pilot-test a mobile money-based intervention to increase the availability of antihypertensive medication and lower clients' out-of-pocket payments. We will build on existing local approaches and assess the acceptability, feasibility, and uptake of the designed intervention. Furthermore, rather than entering the study setting with a ready-made intervention, this research will place emphasis on gathering applied ethnographic insights early, which can then inform the parameters of the intervention prototype and concurrent trial. We will conduct a mixed methods study following a human-centered design approach. We will begin by conducting extensive qualitative research with a range of stakeholders (clients; health care providers; religious, cultural, and community leaders; academics; and policy makers at district and national levels) on their perceptions of hypertension management, money-saving systems, and mobile money in the context of health care. Our results will inform the design, iterative adaptation, and implementation of an mHealth-facilitated pooled financing intervention prototype. At study conclusion, the finalized prototype will be evaluated quantitatively via a randomized controlled trial. As of August 2023, qualitative data collection, which started in November 2022, is ongoing, with data analysis of the first qualitative interviews underway to inform platform and implementation design. Recruitment for the quantitative part of this study began in August 2023. Our results aim to inform the ongoing discourse on novel and sustainable pathways to facilitate access to medication for the management of hypertension in resource-constrained settings. German registry of clinical trials DRKS00030922; https://drks.de/search/en/trial/DRKS00030922. DERR1-10.2196/46614.

Has the NHS national extended access scheme delivered its policy aims? A case study of two large scale extended access providers.

In 2018, NHS England mandated that all patients in England should be able to access general practice services outside of ordinary hours. While some patients would access additional hours at their own practice, others would need supra-practice level provision - that is, they would be seen in a different location and by a different care team. The policy aim was to enhance patient access to care, with a particular focus on those who work during the day. This study examines (a) how supra-practice level provision of extended access appointments for general medical problems are operationalised and (b) whether the aims of the policy are being met. This study presents qualitative comparative case studies of two contrasting service providers offering extended access. The data collected included 30hours of clinician-patient observations, 25 interviews with staff, managers, and commissioners, 20 interviews with patients, organisational protocols/documentation, and routinely collected appointment data. Thematic analysis ran concurrently with data gathering and facilitated the iterative adaptation of data collection. Three cross-cutting themes were identified: extended access is being used to bolster a struggling primary care system, extended access provides a different service to in-hours general practice, and it is difficult for extended access to provide seamless care. Supra-practice access models can provide effective care for most patients with straightforward issues. When ongoing management of complex problems is required, this model of patient care can be problematic.

Mapping evidence-based interventions to the care of unaccompanied minor refugees using a group formulation approach.

How we adapt treatment algorithms to complex, clinically untested, difficult-to-engage patient groups without losing evidence base in everyday practice is a clinical challenge. Here we describe process and reasoning for fast, pragmatic, context-relevant and service-based adaptations of a group intervention for unaccompanied minor asylum seekers (UASC) arriving in Europe. We employed a distillation-matching model and deployment-focused process in a mixed-method, top-down (theory-driven) and bottom-up (participant-informed) approach. Prevalence of mental disorders amongst UASC is extremely high. They also represent a marginalised and hard-to-engage group with limited evidence for effective treatments. Content and process adaptations followed four steps: (1) descriptive local group characterisation and theoretical formulation of problems; (2) initial adaptation of evidenced treatment, based on problem-to-component grid; (3) iterative adaptation using triangulated feedback; and (4) small-scale pilot evaluation. Based on evidence and participant feedback, adaptations included minimising verbal demands, facilitating in-session inductive learning, fostering social connectedness via games, enhancing problem-solving skills, accounting for multi-traumatisation, uncertainty and deportation. Quantitative evaluation suggested improved feasibility, with increased attendance, low drop-out and symptom improvement on depression and trauma scores. By describing the principles under-pinning development of a group intervention for severely traumatised UASC, we contribute to the literature supporting dynamic adaptations of psychological interventions, without losing reference to evidence base. Complex and difficult-to-reach clinical groups are often those in most need of care, yet least researched and most affected by inequality of care. Pragmatic adaptations of proven programs are often necessary to increase feasibility.

Exploring iterative dual domain adaptation for neural machine translation

Domain adaptation for neural machine translation (NMT) has always been a hot research topic in the community of machine translation. Generally, previous studies focus on the one-pass translation knowledge transfer from single source domain to a target domain, which, however, is unable to fully exploit domain-shared translation knowledge. In this paper, we propose several iterative dual domain adaptation frameworks for NMT, which iteratively perform distillation-based bidirectional translation knowledge transfer (from one domain to another and then vice versa). Specifically, we consider three scenarios of NMT domain adaptation: (1) One-to-one domain adaptation for NMT; (2) Many-to-one domain adaptation for NMT, where the above-mentioned transfer is performed sequentially between the target domain and each source domain in the ascending order of their domain similarities; and (3) Many-to-many domain adaptation for NMT. In this setting, each domain is considered as the target domain, and the translation knowledge of other domains are synchronously exploited to enhance the target-domain NMT model via distillation-based knowledge transfer. Particularly, during this process, impacts of the translation knowledge from other domains mainly depend on their similarities to the target domain. Experimental results and in-depth analyses show that the proposed frameworks significantly outperform commonly-used domain adaptation approaches across several language pairs. We release our code and at Github https://github.com/DeepLearnXMU/JIDDANMT.

Designing and Implementing the Adaptive Learning Meeting Cycle: The (re)solve Project Experience in Burkina Faso.

Through the collection and utilization of timely data, program implementers can review feedback and make rapid adjustments and adaptations to interventions while they are in progress.Responsive feedback mechanisms (RFMs), which emphasize flexibility and iterative adaptation, provide opportunities for improving the fit and feasibility of a program, as well as its effectiveness. The (re)solve project-a 5-year, multicountry project testing new, context-specific solutions to address unmet need for family planning-used responsive feedback to ensure products and services designed and implemented were responsive to the context and preferences of health care workers, women, and girls. The adaptive learning meeting (ALM) cycle was designed as an RFM during implementation. This frequent series of rapid, actionable, cross-team meetings builds on several existing frameworks and practices. The ALM cycle used rapid, close to real-time data and observations from a wide range of stakeholders; routine monitoring data; and a structured, facilitated process to examine and act on feedback. Each cycle was repeated every 2 weeks for 3 months in Burkina Faso. During each cycle, the team interpreted data and feedback from multiple sources; assessed pragmatic, actionable options to address the feedback; and identified and agreed upon short- and long-loop adaptations that could improve the implementation process, coordination, efficiency, and outputs of the project. The ALM cycle proved helpful in engendering practices of surfacing and checking assumptions, careful interpretation of data, options analysis, and decision-making. The emphasis was on actionable feedback for improvement of the intervention rather than rigor of results and findings. The (re)solve project's experience with designing, structuring, and implementing the ALM cycle to address challenges and gaps in implementation can be informative for similar programs seeking to implement RFMs in complex and dynamic settings, especially where technology-based RFMs are not an option.

Target Detection Method Based on Adaptive Step-Size SAMP Combining Off-Grid Correction for Coherent Frequency-Agile Radar

Coherent frequency-agile radar (FAR) has a low probability of intercept (LPI) and excellent performance of electronic counter-countermeasures (ECCM) and electromagnetic compatibility, which can improve radar cooperation and survivability in complex electromagnetic environments. However, due to the nonlinearity of radar carrier frequency and the limitation of the Doppler tolerance of high-resolution range cells, the undesirable blind-speed sidelobes are generated in the two-dimensional (2D) range–velocity plane after coherent integration (CI) using the traditional methods based on a matching filter, which may degrade the target detection performance. To solve this problem, an adaptive step-size sparsity adaptive matching pursuit (SAMP) algorithm combining off-grid correction (ASSAMP-OC) is proposed in this paper, which seeks to achieve a better trade-off between recovery efficiency and detection performance. Firstly, an adaptive iteration step size based on the Spearman correlation coefficients (SCCS) is devised, which solves the problem of the traditional SAMP algorithm being insensitive to the change in iteration step size when the residuals vary slightly, and improves the recovery speed. Secondly, the off-grid correction method by combining a regularized stagewise backtracking idea and gradient descent optimization (GDO) is adopted to improve the recovery accuracy and suppress the blind-speed sidelobe energy (BSSE), which helps to reduce CI gain loss and improve the target detection performance without the prior information of the sparsity lever. Finally, simulation and experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of target detection probability, target signal energy ratio after recovery, and computational cost, compared to several existing methods.

Event-Triggered Cooperative Model-Free Adaptive Iterative Learning Control for Multiple Subway Trains With Actuator Faults.

This article investigates the issue of speed tracking and dynamic adjustment of headway for the repeatable multiple subway trains (MSTs) system in the case of actuator faults. First, the repeatable nonlinear subway train system is transformed into an iteration-related full-form dynamic linearization (IFFDL) data model. Then, the event-triggered cooperative model-free adaptive iterative learning control (ET-CMFAILC) scheme based on the IFFDL data model for MSTs is designed. The control scheme includes the following four parts: 1) the cooperative control algorithm is derived by the cost function to realize cooperation of MSTs; 2) the radial basis function neural network (RBFNN) algorithm along the iteration axis is constructed to compensate the effects of iteration-time-varying actuator faults; 3) the projection algorithm is employed to estimate unknown complex nonlinear terms; and 4) the asynchronous event-triggered mechanism operated along the time domain and iteration domain is applied to lessen the communication and computational burden. Theoretical analysis and simulation results show that the effectiveness of the proposed ET-CMFAILC scheme, which can ensure that the speed tracking errors of MSTs are bounded and the distances of adjacent subway trains are stabilized in the safe range.

Composite iterative learning adaptive fuzzy control of fractional-order chaotic systems using robust differentiators

In conventional adaptive fuzzy control, to improve the approximation ability of fuzzy logic systems (FLSs), more fuzzy rules should be employed, which will greatly increase the computational burden. This paper investigates the adaptive fuzzy backstepping control of a specific category of incommensurate fractional-order chaotic systems afflicted by functional uncertainties and actuator faults. To address the challenging “explosion of complexity” issue, a novel modified fractional-order robust differentiator is proposed, capable of effectively suppressing noise. Importantly, an iterative learning adaptation law including parameter errors between adjacent periods and prediction errors derived from a series–parallel model is developed to improve the approximation accuracy of FLSs without using abundant fuzzy rules. Utilizing the frequency distribution model and the Lyapunov stability criterion, this approach guarantees the semi-global uniform boundedness of the closed-loop system and facilitates the convergence of tracking errors to a small region. Finally, the effectiveness of theoretical results is demonstrated through numerical simulation examples.

Is the problem driven iterative adaptation approach (PDIA) a panacea for public financial management reform? Evidence from six African countries

This article assesses the application of the problem driven iterative adaptation (PDIA) approach to public financial management reform in six African countries. It draws on primary data collected using a mix of interviews, overt participation observations and a short survey. PDIA responds to shortcomings in orthodox approaches to reform and technical assistance in developing countries. It stresses local solutions to local problems, achieved through experimentation and adaptation. The principles of PDIA are appealing, but its empirical assessment is in its infancy. This study aims to fill part of this gap. Findings show that PDIA delivers results in the short-term, particularly in cases where there is an influential authorising agent and dedicated team. Progress was less forthcoming for reforms that required high level political buy-in from senior officials. The approach does exceptionally well to develop staff capability, transferable skills, and local empowerment to solve local problems, thus potentially benefitting future reforms.

The care of critically ill adults with COVID-19 in Ontario pediatric intensive care units.

To describe and review the experience of two pediatric intensive care units (PICUs) in Ontario, Canada, adapting and providing care to critically ill adults during the COVID-19 pandemic. At a time of extreme pressure to adult intensive care unit (ICU) capacity, two PICUs provided care to critically ill adults with COVID-19 pneumonia. Substantial yet rapid planning was required to facilitate safe delivery of critical care to adult patients while maintaining PICU services, including thoughtful development of care pathways and patient selection. To prepare clinical staff, several communication strategies, knowledge translation, skill consolidation, andsystem-adaptation mechanisms were developed. There was iterative adaptation of operational processes, including staffing models, specialist consultation, and the pharmacy. Care provided by the interprofessional teams was reoriented as appropriate to the needs of critically ill adults in close collaboration with adult ICU teams. Forty-one adults were admitted to the two PICUs over a 12-week period. In total, 36 patients (88%) received invasive ventilation, eight patients (20%) were supported with venovenous extracorporeal membrane oxygenation, and six patients (15%) received continuous renal replacement therapy. Four died in the PICU during this period. Feedback from staff included anxiety around reorienting practice to the care of critically ill adults, physical exhaustion, and psychological distress. Importantly, staff also reported a renewed sense of purpose with participation in the program. Though challenging, the experience has provided opportunity to enhance collaboration with partner institutions and improve the care of older children and adolescents in the PICU.