Adaptive Information Research Articles

The Implementation of a Primary Care-Based Integrated Mobile Health Intervention for Stroke Management in Rural China: Mixed-Methods Process Evaluation.

Background: There is a lack of evidence concerning the effective implementation of strategies for stroke prevention and management, particularly in resource-limited settings. A primary-care-based integrated mobile health intervention (SINEMA intervention) has been implemented and evaluated via a 1-year-long cluster-randomized controlled trial. This study reports the findings from the trial implementation and process evaluation that investigate the implementation of the intervention and inform factors that may influence the wider implementation of the intervention in the future.Methods: We developed an evaluation framework by employing both the RE-AIM framework and the MRC process evaluation framework to describe the implementation indicators, related enablers and barriers, and illustrate some potential impact pathways that may influence the effectiveness of the intervention in the trial. Quantitative data were collected from surveys and extracted from digital health monitoring systems. In addition, we conducted quarterly in-depth interviews with stakeholders in order to understand barriers and enablers of program implementation and effectiveness. Quantitative data analysis and thematic qualitative data analysis were applied, and the findings were synthesized based on the evaluation framework.Results: The SINEMA intervention was successfully implemented in 25 rural villages, reached 637 patients with stroke in rural Northern China during the 12 months of the trial. Almost 90% of the participants received all follow-up visits per protocol, and about half of the participants received daily voice messages. The majority of the intervention components were adopted by village doctors with some adaptation made. The interaction between human-delivered and technology-enabled components reinforced the program implementation and effectiveness. However, characteristics of the participants, doctor-patient relationships, and the healthcare system context attributed to the variation of program implementation and effectiveness.Conclusion: A comprehensive evaluation of program implementation demonstrates that the SINEMA program was well implemented in rural China. Findings from this research provide additional information for program adaptation, which shed light on the future program scale-up. The study also demonstrates the feasibility of combining RE-AIM and MRC process evaluation frameworks in process and implementation evaluation in trials.Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03185858.

Heterogeneous graph driven unsupervised domain adaptation of person re-identification

How to incrementally optimize a pre-trained classifier in an unlabeled target domain is a core challenging problem of domain adaptation (DA) for many visual tasks, such as Person Re-identification (re-ID). Most of the existing methods optimize the model based on pseudo labels or similarity of instance pairs, but ignoring the diverse manifold structures of unlabeled instances in the whole dataset. In this paper, we address the importance of such structural information in domain adaptation, and propose a Heterogeneous Graph driven Optimization scheme, namely H-GO, for structure based unsupervised learning. In particular, H-GO builds a heterogeneous graph of unlabeled images to consider the heterogeneous properties of images from various cameras with varied visual styles. A heterogeneous affinity propagation method is further applied to explore the graph based affinity between the instances which share similar manifold structures. Finally, a heterogeneous affinity learning procedure is taken to optimize the visual models by using the graph based affinity of instances. Comprehensive experiments are conducted on three large-scale re-ID datasets, and the results demonstrate the flexibility and the superior performance of H-GO than state-of-the-art unsupervised domain adaptation algorithms.

Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers.

The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM-LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets.

A New Type of 5G-Oriented Integrated BDS/SON High-Precision Positioning

China is promoting the construction of an integrated positioning, navigation, and timing (PNT) systems with the BeiDou Navigation Satellite System (BDS) as its core. To expand the positioning coverage area and improve the positioning performance by taking advantage of device-to-device (D2D) and self-organizing network (SON) technology, a BDS/SON integrated positioning system is proposed for the fifth-generation (5G) networking environment. This system relies on a combination of time-of-arrival (TOA) and BeiDou pseudo-range measurements to effectively supplement BeiDou signal blind spots, expand the positioning coverage area, and realize higher precision in continuous navigation and positioning. By establishing the system state model, and addressing the single-system positioning divergence and insufficient accuracy, a robust adaptive fading filtering (RAF) algorithm based on the prediction residual is proposed to suppress gross errors and filtering divergence in order to improve the stability and accuracy of the positioning results. Subsequently, a federated Kalman filtering (FKF) algorithm operating in fusion-feedback mode is developed to centrally process the positioning information of the combined system. Considering that the prediction error can reflect the magnitude of the model error, an adaptive information distribution coefficient is introduced to further improve the filtering performance. Actual measurement and significance test results show that by integrating BDS and SON positioning data, the proposed algorithm realizes robust, reliable, and continuous high precision location services with anti-interference capabilities and good universality. It is applicable in scenarios involving unmanned aerial vehicles (UAVs), autonomous driving, military, public safety and other contexts and can even realize indoor positioning and other regional positioning tasks.

Smart screen-printed photochromic fabrics with fast color switching performance and high fatigue resistance for energy storage applications

Photochromic materials have great potential in various novel optoelectronic devices such as adaptive regulation, smart detection, information storage, and camouflage anti-counterfeiting displays. However, the problems of high sensitivity and not to be influenced by external factors are urgent to be solved. Here, we have successfully prepared smart photochromic fabrics for stimulus-responsive applications with high-sensitive spiropyran (SP) and a phase change material of butyl stearate as core materials and encapsulated by a highly translucent poly(methyl methacrylate) (PMMA) shell. And then, they are coated on the fabrics with water-based polyurethane (WPU) by a facile method of screen printing. The preparation of photochromic materials as the form of microcapusles aims at reducing external factors’ disturb and achieving a rapid response when in use. The phase change material of microcapusles acts as a thermal regulation effect on the whole system, providing energy storage and temperature regulation. The resultant reversible photochromic fabrics prepared by this method has the potential applications of tangible information storage (QR codes), anti-counterfeit printing and rewritable patterns due to their fast response and reusability under UV light.

Weighted adaptive Kalman filtering-based diverse information fusion for hole edge crack monitoring

The information fusion of different sensors is an effective means of improving the damage quantification of aircraft structures operating in a complex environment. A weighted adaptive Kalman filtering-based information fusion method (WAKFIFM) is proposed to achieve the feature-level fusion of diverse signals of the hybrid piezoelectric-fiber optic sensor network. The damage identification results obtained from guided wave signals and strain signals are described by the quantification curves of damage index (DI) and crack length. A new fusion curve is obtained by fitting the above two quantification curves using the quadratic polynomial method. The mean square error and the root mean square error of the fusion curve and the actual curve are used as the error and the covariance of the proposed WAKFIFM, respectively. The weighted value in WAKFIFM is the reciprocal of the square of the actual measured maximum DI. The hole edge crack propagation experiments are conducted on both an aluminum plate and a carbon fiber reinforced polymer plate in a load-temperature changing environment.A new damage location method integrated with DI information is developed to obtain a more accurate result. The feature-level fusion results show that the proposed WAKFIFM can improve the accuracy of damage quantification, compared against that obtained from the single guided wave signals or fiber optic sensor signals.

The influences of college life adaptation and career decision self-efficacy on career achievement stress: Focus on students majoring in piano performance in South Korea

The purpose of this research was to investigate the relationships among college life adaptation, career decision self-efficacy, and career achievement stress regarding students majoring in piano performance. A multiple regression analysis was used to investigate the relationships between college life adaptation and career achievement stress, and career decision self-efficacy and career achievement stress. The findings of the study indicated that personal and emotional adaptation, goal selection, and job information variables had a significant impact on career achievement stress. The findings of this study can be theoretically and practically meaningful, providing opportunities for the development of efficient retention strategies for students majoring in piano performance in a competitive college market. Through this research, the result should be utilized for development of job-seeking, career-related programs, and curriculum.

Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes.

Observed and projected trends in climate extremes in a tropical highland region: An agroecosystem perspective

AbstractTropical highland environments present substantial challenges for climate projections due to sparse observations, significant local heterogeneity and inconsistent performance of global climate models (GCMs). Moreover, these areas are often densely populated, with agriculture‐based livelihoods sensitive to transient climate extremes not always included in available climate projections. In this context, we present an analysis of observed and projected trends in temperature and precipitation extremes across agroecosystems (AESs) in the northwest Ethiopian Highlands, to provide more relevant information for adaptation. Limited observational networks are supplemented with a satellite‐station hybrid product, and trends are calculated locally and summarized at the adaptation‐relevant unit of the AES. Projections are then presented from GCM realizations with divergent climate projections, and results are interpreted in the context of agricultural climate sensitivities. Trends in temperature extremes (1981–2016) are typically consistent across sites and AES, but with different implications for agricultural activities in the other AES. Trends in temperature extremes from GCM projected data also generally have the same sign as the observed trends. For precipitation extremes, there is greater site‐to‐site variability. Summarized by AES, however, there is a clear tendency towards reduced precipitation, associated with decreases in wet extremes and a tendency towards temporally clustered wet and dry days. Over the retrospective analysis period, neither of the two analysed GCMs captures these trends. Future projections from both GCMs include significant wetting and an increase in precipitation extremes across AES. However, given the lack of agreement between GCMs and observations with respect to trends in recent decades, the reliability of these projections is questionable. The present study is consistent with the “East Africa Paradox” that observations show drying in summer season rainfall while GCMs project wetting. This has an expression in summertime Ethiopian rain that has not received significant attention in previous studies.

Multi-modal approach to evaluate adaptive visual stimuli of remote operation system using gaze behavior

A multi-modal approach is proposed to evaluate the usability of Adaptive Visual Stimuli for User Interface (AVS4UI) of remote operation systems. This study focuses on the evaluation of AVS4UI for forklift work because the operation complexity includes driving and cargo handling, which typically requires multiple salient attention. Presenting this amount of information simultaneously on a User Interface (UI) tends to cause confusions to operators and reduces operation efficiency. AVS4UI can therefore be one of the promising solutions where the optimal visual stimuli are autonomously presented for different work conditions. However, evaluation of AVS4UI is challenging because operators may be disoriented by adaptive information and worked without safety considerations. Therefore, novel gaze metrics are proposed to evaluate responses of forklift operators to AVS4UI so that undesired behavior can be evaluated. The proposed metrics implicitly represent gaze pattern in terms of transition and distribution between UI elements, operation safety, and familiarity with the adaptive system. The ideal AVS4UI is expected to minimize the proposed gaze metrics and outperform the non-adaptive UI. More importantly, the results of these metrics are consistent with those of perceived workload defined by NASA-Task Load Index. We also propose a correlation model using stepwise linear regression that provides reasonable estimation of perceived workload. Such novel metrics and correlation model enable objective and online evaluation to minimize biases of subjective response. Therefore, online work support system can be developed to support workers.

Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

Remote sensing image scene classification acts as an important task in remote sensing image applications, which benefits from the pleasing performance brought by deep convolution neural networks (CNNs). When applying deep models in this task, the challenges are, on one hand, that the targets with highly different scales may exist in the image simultaneously and the small targets could be lost in the deep feature maps of CNNs; and on the other hand, the remote sensing image data exhibits the properties of high inter-class similarity and high intra-class variance. Both factors could limit the performance of the deep models, which motivates us to develop an adaptive decision-level information fusion framework that can incorporate with any CNN backbones. Specifically, given a CNN backbone that predicts multiple classification scores based on the feature maps of different layers, we develop a pluginable importance factor generator that aims at predicting a factor for each score. The factors measure how confident the scores in different layers are with respect to the final output. Formally, the final score is obtained by a class-wise and weighted summation based on the scores and the corresponding factors. To reduce the co-adaptation effect among the scores of different layers, we propose a stochastic decision-level fusion training strategy that enables each classification score to randomly participate in the decision-level fusion. Experiments on four popular datasets including the UC Merced Land-Use dataset, the RSSCN 7 dataset, the AID dataset, and the NWPU-RESISC 45 dataset demonstrate the superiority of the proposed method over other state-of-the-art methods.

Cultural transmission based multi-objective evolution strategy for evolutionary multitasking

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller population size and fewer computing resources. As a result, the proposed algorithm can effectively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative transfer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms.

Accelerated basis adaptation in homogeneous chaos spaces

Polynomial chaos expansions (PCE) provide an efficient approach to uncertainty quantification (UQ) and have been adapted to diverse applications across the spectrum of science and engineering. For situations involving large stochastic parameterizations, the curse of dimensionality renders PCE-based methods computationally prohibitive. A basis adaptation approach for PCE was proposed by Tipireddy and Ghanem (2014) which transforms the input random variables through an isometry such that sufficient probabilistic characterization of specific quantities of interest (QoI) is concentrated in an algebraic manifold embedded in the linear span of the dominant transformed variables. While quite versatile, that original version of the PCE basis adaptation exhibited slow convergence for a number of problems of practical significance. In the present paper, we propose two novel methods to accelerate the convergence of the original basis adaptation approach, thus expanding its reach while also providing insight into its performance. In the first method, information gained from a pilot PCE representation is used to correct the mean and Gaussian coefficients in the adapted space. By taking advantages of probabilistic information in higher dimensional adaptation gleaned from an initial adaptation, the second method updates the rotation matrix used to identify the dominant transformed variables. In this manner, the new rotation matrix concentrates even more probabilistic information in its first few dimensions. These two method can be combined to achieve even better performance, the combined method is referred to as sequentially optimized adaptation method. The methods are demonstrated on an analytical test function and a model of a space structure with several sub-components and a non-smooth quantity of interest representing the maximum acceleration over time. Both methods achieve accelerated convergence of the basis adaptation approach with negligible additional costs.

EFFECT OF METABOLIC ENZYMES ON ACCELERATION ABILITY IN EXERCISE FATIGUE

ABSTRACT Introduction Study the relationship between the metabolic enzyme and the biological image, filtered by an adaptive filtering algorithm. Objective The research aims to In this study, human metabolic enzymes were evaluated by electrocardiogram and electromyogram images, and an adaptive filtering algorithm removed the noises in the images. Methods The electrocardiogram and electromyogram images at different periods were obtained, and the calculation method and application scope of the adaptive filtering algorithm were analysed. Results Adaptive filter was designed by the combination of adaptive filtering algorithm and dynamic information. Therefore, the artefact of the image was removed. Conclusions The adaptive filtering algorithm can effectively remove the noise or artefact in electrocardiogram and electromyogram signals. The optimal image information can be obtained. Level of evidence II; Therapeutic studies - investigation of treatment results.

Climate change impacts and adaptation for dryland farming systems in Zimbabwe: a stakeholder-driven integrated multi-model assessment

Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multi-model approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity-enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture.

Monitoring intra-urban temperature with dense sensor networks: Fixed or mobile? An empirical study in Baltimore, MD

The combination of heat waves and urban heat “island” (UHI) and “archipelago” (UHA) effects can adversely impact the health of city residents. These impacts could be amplified in the future, with low-income communities most affected, and neighborhood-scale temperature data provide essential information for adaptation. In recent years, fixed and mobile sensor approaches are widely used for intra-urban temperature monitoring. However, empirical studies comparing these two approaches and investigating how network designs can affect the information collected remain limited. This study analyzes temperature data collected by the two monitoring approaches in Baltimore, MD to address representation of spatio-temporal temperature variability within a city and to investigate the sensor distribution strategy that most efficiently capture the UHA. We find that: (1) vehicle-based mobile monitoring with only on-street data does not capture the full range of urban thermal environments; (2) the sampling bias of vehicle-based monitoring results in lower predictive accuracy for unmonitored locations under hot afternoon conditions; (3) single-event measurement campaigns on “hot” days do not capture spatial temperature variations observed for non-hot days; (4) the efficiency of a fixed sensor network can be significantly increased through appropriate designs. Each of these findings is important for improving future intra-urban temperature monitoring activities.

Positive side effects in trauma-focusing PTSD treatment: Reduction of attendant symptoms and enhancement of affective and structural regulation.

Trauma-focusing treatments such as eye movement desensitization and reprocessing (EMDR) are highly effective in reducing the core symptoms of posttraumatic stress disorder (PTSD), for example, intrusive memories and flashbacks, hyperarousal, and avoidance. Additionally, suffering from PTSD is often accompanied by a broader set of mental comorbidities and complaints such as depression, anxiety disorders or somatization, and disturbed self-regulation abilities. According to the Adaptive Information Processing model (Shapiro, 2001), the processing of pathogenic memories can help not only to reduce the PTSD symptoms but also accompanying complaints additionally. In an eye movement desensitization and reprocessing treatment study of 116 patients suffering from PTSD, we targeted the course of additional symptoms and structural skills using the Symptom Checklist-90 SCL-90, Beck Depression Inventory, Toronto Alexithymia Scale-20, and Hannover Selbstregulationsinventar in a pre-post design. The results showed that apart from alleviating the PTSD symptoms, exposure-based treatment of pathogenic memories led to a significant decrease in accompanying symptoms such as depression, anxiety, and somatization. Furthermore, patients improved their structural abilities with regard to emotional perception and differentiation, controlling impulses, tolerating frustration, and regulating self-esteem. PTSD core symptoms and comorbid complaints are closely interlinked and can be seen as a traumatic-stress cluster, which is accompanied by significant impairments in self- and emotion regulation. Therefore, treatment concepts should explicitly foster emotional processing and structural abilities to target the posttraumatic stress responses entirely. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Role of Gender and Institutional Dynamics in Adapting Seed Systems to Climate Change: Case Studies from Kenya, Tanzania and Uganda

We explore how seed systems enhance access to seeds, and information for climate-change adaptation in farming communities in Kenya, Tanzania and Uganda, as well as how gender-driven roles and institutional dynamics influence the process. Men and women farmers equally experience climate-change related effects, including drought, short rainy seasons and increased pest and disease incidence. Our study relies on exploratory data analysis of 1001 households surveyed in four sites in 2016. Farmers surveyed preferred early-maturing, heat-tolerant, high-yielding, and pest- and disease-resistant varieties, all important climate-adaptive traits. Seed systems of the focus crops studied are largely informal—overall, 68% women and 62% men use their own seed, indicating women’s higher reliance on ‘informal’ seed and information sources. Only 21% of respondents reported interacting with seed experts who are affiliated with formal organizations. Both formal and informal organizations play a key role in providing access to climate-adapted seed/information, with access for men and women varying across the countries studied. There is a need to support further development of those connections, building on existing social networks. We conclude that inclusive and gender-responsive context- and country-specific seed interventions will ensure equitable outcomes, increase women’s empowerment and strengthen both formal and informal seed systems for more effective climate-change adaptation.

A Textual Deconstruction of the RNA World.

RNAs can do many things. They can store information, act in the world, and respond to the world. Because of these capabilities biologists have proposed a primordial ‘RNA world’ in which RNA, rather than DNA, performed the central role of replicator and repository of adaptive information. Deacon dismisses this hypothesis because replication is not about anything and because the structure of replicating molecules cannot contain information about the environment. I dispute both claims. An RNA and its opposite-sense complement represent each other and, by two rounds of complementation, represent themselves. Although (with some exceptions) nucleic acid sequences do not change in response to their present environment, these sequences embody information about ancestral environments via the selective filtering of alternative sequences in those environments. Nucleic acid sequences are the textual record of what has worked in the past.

Robustness of Adaptive Measurement of Change to Item Parameter Estimation Error.

Adaptive measurement of change (AMC) is a psychometric method for measuring intra-individual change on one or more latent traits across testing occasions. Three hypothesis tests-a Z test, likelihood ratio test, and score ratio index-have demonstrated desirable statistical properties in this context, including low false positive rates and high true positive rates. However, the extant AMC research has assumed that the item parameter values in the simulated item banks were devoid of estimation error. This assumption is unrealistic for applied testing settings, where item parameters are estimated from a calibration sample before test administration. Using Monte Carlo simulation, this study evaluated the robustness of the common AMC hypothesis tests to the presence of item parameter estimation error when measuring omnibus change across four testing occasions. Results indicated that item parameter estimation error had at most a small effect on false positive rates and latent trait change recovery, and these effects were largely explained by the computerized adaptive testing item bank information functions. Differences in AMC performance as a function of item parameter estimation error and choice of hypothesis test were generally limited to simulees with particularly low or high latent trait values, where the item bank provided relatively lower information. These simulations highlight how AMC can accurately measure intra-individual change in the presence of item parameter estimation error when paired with an informative item bank. Limitations and future directions for AMC research are discussed.