Adaptive Process Model Research Articles

Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

AbstractOptimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

Adaptive change: Emerging economy enterprises respond to the international business environment challenge

The technological and market containment of emerging economy enterprises (EEEs) from developed countries is becoming a great challenge for EEE development. In this context, how do EEEs respond to international business challenges and achieve continuous organizational innovation? This study proposes a process model of EEEs’ organizational adaptive change to achieve innovation using the case of Hikvision, a leading Chinese artificial intelligence (AI) enterprise. The results show that the adaptive change in EEEs is a dynamic process. The negative impact of international business environment challenges on enterprise development is the starting point for enterprises to adopt change behaviors. Through adaptive change in innovation strategy, innovation architecture and innovation systems, EEEs can break through technology blockades, overcome market crises and further weaken the negative impact of such challenges. This study constructs an adaptive change process model for EEEs and proposes strategic initiatives for EEEs with the help of a national innovation system (NIS), which has policy implications for EEE strategies and industrial policy design in emerging economies.

Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting

Concept drift (CD) refers to a phenomenon where the data distribution within datasets changes over time, and this can have adverse effects on the performance of prediction models in software engineering (SE), including those used for tasks like cost estimation and defect prediction. Detecting CD in SE datasets is difficult, but important, because it identifies the need for retraining prediction models and in turn improves their performance. If the concept drift is caused by symmetric changes in the data distribution, the model adaptation process might need to account for this symmetry to maintain accurate predictions. This paper explores the impact of CD within the context of cross-version defect prediction (CVDP), aiming to enhance the reliability of prediction performance and to make the data more symmetric. A concept drift detection (CDD) approach is further proposed to identify data distributions that change over software versions. The proposed CDD framework consists of three stages: (i) data pre-processing for CD detection; (ii) notification of CD by triggering one of the three flags (i.e., CD, warning, and control); and (iii) providing guidance on when to update an existing model. Several experiments on 30 versions of seven software projects reveal the value of the proposed CDD. Some of the key findings of the proposed work include: (i) An exponential increase in the error-rate across different software versions is associated with CD. (ii) A moving-window approach to train defect prediction models on chronologically ordered defect data results in better CD detection than using all historical data with a large effect size (δ≥0.427).

Adaptive design of bus chassis using a design structure matrix to facilitate integration of new power sources

To meet the various demands for bus platforms tailored for the emerging new power sources, an adaptive design method is proposed for a bus chassis development using a design structure matrix. The structural feature parameters of the product modules are extracted and the corresponding design structure matrix is established to analyze structural correlations between modules. An adaptive design method process model is constructed following quantification of the design structure matrix. A digital matrix of the module interface is then built to characterize integration and interchangeability of functional modules. Reconfiguration of modules is conducted using the parent-child relationship premise so that the interface matrixes are well integrated. The adaptability of these products is subsequently improved by sharing dominant modules and replacement of local modules. Finally, the implementation of the proposed methodology is illustrated for a current bus chassis.

Model updating of rotor system based on the adaptive Gaussian process model using unbalance response

For predicting the dynamic responses of rotor system with high accuracy, an adaptive Gaussian process model-based model updating method for rotor system using the unbalance responses is proposed. To utilize the data from multiple sensors, multi-channel data fusion is adopted to construct the objective function of the model updating. The fusion feature is the mean of the cumulative distribution function of the response errors of each channel. The Gaussian process model is used to describe the mapping relationship between the selected parameters and fusion features. An adaptive sampling strategy is developed to reduce the computation cost of repeated finite element calculation. Based on Sobol sequence sampling, new sampling parameters are added near the sample parameter with a larger error in the predicted value. The performance of the proposed method is validated by a gas-generator rotor and a dual-disks rotor. Results show that the unbalance responses of the updated model show good agreement with the exact responses and measured responses. The maximum error of the support parameters for the gas-generator rotor is 3.56%. For the dual-disks rotor, the discrepancies of the first three modal frequencies between the updated model and the measured model are below 1%, 2.5% and 5%. The proposed method is effective for model updating of the rotor system.

The attachment adaptation process model: Developing attachment security after experiencing addiction in the family

AbstractObjectiveThe purpose of this study is to explore the adaptation processes of building attachment security later in life when one's attachment style has been affected by growing up in a household with a parent who uses substances.BackgroundParental substance use may have negative effects on children who grow up in those homes, including developing an insecure attachment style. Limited studies have focused on adult children of alcoholics/addicts' (ACOAs) attachment adaptation processes into adulthood.MethodUsing a constructivist grounded theory approach, we explored the process ACOA's use to build attachment security. The first author interviewed 14 participants using a semistructured interview format through the video conferencing platform Zoom.ResultsThrough analysis of the data, we identified a four‐phase process: (a) confusion, (b) transition, (c) restoration, and (d) dedication. This information provides a framework to show clients the attachment adaptation process model (AAPM), developed in this study, and helps them to visualize the process by which they may learn to develop more attachment security.ConclusionACOAs may incur adverse experiences in childhood that influence their attachment development later in life. The researchers in this study built a model of how ACOAs build attachment security later in life.ImplicationsPractitioners can help clients connect with community support groups, places of worship, and other small groups that the participants identified as instrumental in the healing process. Overall, helping clients find a place for safe and healthy connections is crucial during their journey to attachment security.

Adaptation process of a culturally congruent parenting intervention for parents of Hispanic adolescents to an online synchronous format.

Adaptations to interventions for specific settings or communities are critical for facilitating successful implementation. The Dynamic Adaptation Process model was applied to systematically assess the adaptation process made to an in-person parenting intervention (FPNG+) prior to its implementation online. This qualitative case study design included meeting notes and interviews completed with project team members. Meeting notes were analyzed using content analysis. Semi-structured interviews regarding project team members' roles on FPNG+ and processes and activities they identified as critical for the adaptation of FPNG+ to an online intervention were analyzed using a deductive-inductive approach. In the formative phase, three primary processes were identified: information gathering to determine if the environment existed for the implementation of an online program; considerations to support the facilitation process of FPNG+ to an online environment, and decision-making to support modifications to FPNG+. The pre-implementation phase consisted of three processes: information shared by the instructional designer to facilitate the delivery of the FPNG+ content online; modifications made to the format of the intervention to meet the cultural needs of Hispanic families and address contextual issues; and iterative efforts to tailor the intervention by the project team. This study used implementation science to distinguish the dynamic processes that occurred across different systems and multiple levels in the project team's effort to modify FPNG+ to an online intervention. The contribution of this study underscores the importance of identifying the processes that facilitate the modifications made to an intervention and the capacity to implement the modified intervention with Hispanic families.

Cultural adaptation in television technology for older adults with dementia in care settings.

There is a lack of literature on the cultural adaptation of technologies for dementia care. This paper presents an example of the cultural adaptation of a television video about drinking water for older Chinese adults with dementia in care settings, in Vancouver, Canada. We refer to the cultural adaptation process (CAP) model to guide the cultural adaptation process by collecting and incorporating feedback from different parties into the culturally adapted video, including Phase 1 local consultations and information gathering, phase 2 iterative testing and adaptation, and phase 3 finalizing adaptation. We also referred to the Ecological Validity Model (EVM) to present the adjustments we made to the video from the cultural adaptation. We adjusted the video on seven domains suggested by the EVM: goal, context, content, language, people, concept, and method. We draw attention to the opportunities and challenges of the cultural adaptation of technology into a new community. Based on our lessons, we outline concrete suggestions about what aspects of, and how, cultural adaptation can be made to promote cultural inclusivity in technology development and implementation.

With the New Technology in Hands and New Audiences in Minds: A Review of Creative Adaptation Process Model

With the New Technology in Hands and New Audiences in Minds: A Review of Creative Adaptation Process Model

CULTURAL ADAPTATION OF THE ADULT DAY SERVICE PLUS PROGRAM FOR HISPANIC/LATINO DEMENTIA CAREGIVERS

Abstract Although Hispanic/Latinos are at disproportionate and increased risk for Alzheimer’s disease and related dementias, few evidence-based supportive care interventions have been specifically developed for or adapted for this population. Adapting a supportive care intervention requires more than Spanish language translation and necessitates an understanding of cultural nuances and care preferences of Hispanic/Latino families and staff who implement the intervention. This paper reports on the cultural adaptation of the Adult Day Service Plus (ADS Plus) intervention for delivery by staff to Hispanic/Latino caregivers which was guided by the Cultural Adaptation Process Model. Also, using the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME),we discuss: 1) when modifications were made, 2) who determined modifications needed, 3) what aspects of the intervention were modified, 4) the relationship to fidelity and how fidelity was maintained, and 5) reasons for modifications. Modifications to the delivery and content were changed to reflect values and norms of both the Hispanic/Latino staff and the caregivers they serve. As supportive interventions for dementia caregivers are developed and implemented into real world settings, inclusion of cultural elements may enhance research participation from Hispanic/Latino provider sites and caregivers. We suggest in this paper that cultural adaptation is an essential consideration in developing an intervention as well as adapting evidence-based previously tested interventions, and in implementation science. Cultural adaptation offers an important lens by which to identify contextual factors impacting intervention adoption interventions and needed adaptations to assure equity in the reach of evidence-based programs.

Remaining useful life estimation based on a nonlinear Wiener process model with CSN random effects

Remaining useful life (RUL) prediction of rolling bearings is crucial to equipment operation and maintenance. The data-driven Wiener-based methods have aroused widespread concern. The commonly adopted distribution to represent inter-product variation in the Wiener degradation model is the normal distribution. However, this assumption is not appropriate for some practical applications. Aiming at this problem, an adaptive nonlinear Wiener process model with degradation drift satisfying the closed skew-normal (CSN) distribution to model the degradation characteristic exhibiting non-normality and asymmetry in inter-product variation is proposed in this paper. An online recursive algorithm based on Bayes’ theorem is derived to update the hidden drift distribution. A Bayesian smoother based on CSN distribution is developed, which is employed to estimate model parameters involving the expectation–maximization algorithm. Furthermore, an analytical expression of the RUL distribution is derived. Finally, a numerical case and a practical bearing dataset are provided to illustrate the proposed method.

Sensing Data-Based Degradation Estimation of Electromechanical Actuator Under Dynamic Operating Conditions

In flight control actuation systems, electromechanical actuators (EMAs) as energy-efficient components play critical roles in next-generation aircraft. Reliable degradation estimation models based on sensing data from sensors installed in EMA can help to improve its availability and safety, which need to be studied. However, the dynamic operating conditions, which will lead to the mismatch issues of the sensing data-based degradation estimation models, reduce the overall accuracy of the degradation estimation. To address this issue, an improved adaptive Wiener process model for EMA degradation estimation based on sensing data with dynamic operating conditions is proposed. First, a health indicator (HI) is extracted from sensing data through physical analysis or extracted using data processing techniques. Then, a novel model for EMA degradation estimation with the improved strong tracking filter and the Wiener process model (i.e., ISTF-WPM) is formulated based on the obtained HI. By introducing a saturation function to the improved strong tracking filter (STF), the performance of sensing data-based degradation estimation with dynamic operating conditions can be improved. The proposed model presents a novel routine for EMA degradation estimation based on sensing data with dynamic operating conditions. In the experiments of this study, the effectiveness of the ISTF-WPM is validated using simulation data as well as practical data collected by sensors of EMA. Experimental results indicate that EMA sensing data-based degradation estimation with the proposed model has a high overall accuracy.

It's More Than Language: Cultural Adaptation of a Proven Dementia Care Intervention for Hispanic/Latino Caregivers.

Although Hispanic/Latino older adults are at disproportionate and increased risk for Alzheimer's disease and related dementias, few evidence-based supportive care interventions are specifically developed for or adapted for this population. Adapting a supportive care intervention requires more than Spanish language translation; it necessitates an understanding of cultural nuances and care preferences of Hispanic/Latino families and staff who implement the intervention. This article describes the cultural adaptation of the Adult Day Service Plus intervention for delivery by staff to Hispanic/Latino caregivers, which was guided by the cultural adaptation process model. Also, using the Framework for Reporting Adaptations and Modifications-Enhanced, we discuss (a) when modifications were made, (b) who determined the modifications needed, (c) what aspects of the intervention were modified, (d) the relationship to fidelity and how fidelity was maintained, and (e) reasons for modifications. Modifications to the delivery and content were changed to reflect the values and norms of both the Hispanic/Latino staff and the caregivers they serve. As supportive interventions for caregivers are developed and implemented into real-world settings, inclusion of cultural elements may enhance research participation among Hispanic/Latino provider sites, people living with dementia, and their caregivers. Cultural adaptation is an essential consideration when developing, adapting, and implementing previously tested evidence-based interventions. Cultural adaptation offers an important lens by which to identify contextual factors that influence successful adoption to assure equity in the reach of evidence-based programs.

Is Daily Workplace Anxiety Adaptive?A Regulatory Model of Email Activity, Goal Progress, Performance

Despite workplace anxiety being a common experience of daily work life, past research has primarily examined its effects at the between-person level of analysis, documenting detrimental effects of state or trait anxiety. This research considers the experience of day-to-day workplace anxiety, and how employees manage their work performance on days they experience workplace anxiety. Drawing on workplace anxiety and affect-as-information frameworks, we consider an adaptive regulatory process model of daily workplace anxiety, where workplace anxiety initiates work e-mail activity, a self-regulatory behavior underlying an engagement function of workplace anxiety. Work e-mail activity facilitates work goal progress, which enhances performance. Utilizing a multi-level, time-lagged experience sampling field study across 10 workdays (Level 1 N = 809; Level 2 N = 96), we demonstrate support for our theorized model. On days when employees experience workplace anxiety, their performance (in-role and extra-role) is enhanced through behavioral regulatory activity manifested in work e-mail activity, which promotes work goal progress. This serial indirect effect is strengthened for employees with higher (vs. lower) work e-mail centrality. Further, these results hold alongside tests of a maladaptive response to workplace anxiety via non-work e-mail activity that disengages employees from work. This research points to the adaptive function of day-to-day workplace anxiety, suggesting that employees are active, rather than passive, drivers of their experiences of daily workplace anxiety.

SECPON: Analysis of Engineering Change Processes for Manufacturing Companies

The problem of process models in engineering change management is their static approach, which hinders enterprises from using them in daily business. The SECPON process is a standardized implementation process for engineering changes with the additional benefit of enabling users to adjust this process according to the specific requirements of their company and product. The indication that there are influencing factors on change processes is innovative for the theory of engineering change management. Additionally, special process steps for certain branches, such as the food industry, the pharmaceutical industry, the aeronautical industry, and the railway industry, are integrated. Due to the wide variety of literature and survey participants, the reference process is universally applicable. The deductive–inductive research results enable business users to apply more accurate and structured engineering change processes. Researchers profit from the successful new approach to engineering change management with the adaptive process model.

Automated Plankton Classification With a Dynamic Optimization and Adaptation Cycle

With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis.

Cultural adaptation of meaning-centered psychotherapy for latino families: a protocol

IntroductionLiterature suggests couple-based interventions that target quality of life and communication can lead to positive outcomes for patients with cancer and their partners. Nevertheless, to date, an intervention to address...

Adaptive Gaussian Process with PCA for prediction of complex dispersion relations for periodic structures

The complex dispersion relation is important for understanding the wave propagation in periodic structures. However, its calculation is much more expensive compared to calculation of the real dispersion relation, due to the need to solve the dispersion equation at large number of frequencies. For uncertainty quantification in the dispersion relations, the computational challenge is even higher. This paper proposes an adaptive Gaussian process (AGP) model to efficiently predict the complex dispersion relations for periodic structures with different properties. Instead of directly building GP for the dispersion relation, which is challenging due to discontinuity in the dispersion relation, we first reformulate the problem as predicting the coefficients of the dispersion equation at selected frequencies, where these coefficients are continuous and smooth functions of the properties of periodic structures. Then GP model is trained to predict these coefficients, based on which the dispersion equation is then analytically solved to establish the complex dispersion relation. Second, Principal component analysis (PCA) is used to reduce the dimension of these coefficients to facilitate efficient training of GP model. Third, an adaptive procedure is integrated to iteratively add training data that can most effectively improve the GP model accuracy. Results verify the great efficiency and accuracy of the proposed approach for both undamped and damped periodic structures. The proposed approach has great promise in improving efficiency for uncertainty quantification, sensitivity analysis, and design optimization of periodic structures.

Strategic Decision-Making and Performance in Social Enterprises: Process Dimensions and the Influence of Entrepreneurs’ Proactive Personality

Abstract Different organizational perspectives surrounding social enterprises (SEs) have burgeoned over the past few years. However, integrating financial sustainability with social value remains a “black box” with respect to entrepreneurial strategic decision-making (SDM). Drawing from decision theories and the proactivity perspective of personality-based SDM literature, we investigate the impact of synoptic (rationalistic) and incremental (adaptive) process models, and moderate these approaches with the entrepreneur’s proactive personality traits on SEs’ financial and social performance. Our results show that when a rational and intuitive SDM develops in conjunction, financial performance improves. In contrast, a departure from rationality in favor of incremental decision-making processes advances only the social performance of SEs. A proactive entrepreneurial personality positively moderates strategic cognitions in improving SEs’ both financial and social objectives. On the other hand, when proactivity moderates rationality, the financial performance of SEs declines.

Plastic Extrusion Process Optimization by Digital Twins

AbstractContinuous plastics compounding processes are highly complex from a physicochemical point of view and correspondingly difficult to optimize. Model‐based digital twins suitable for this purpose require complex simulation approaches, trained personnel and input variables that are difficult to determine. However, data‐based digital twins, which are in principle suitable for this purpose, often fail because of the enormous trial effort which is required to generate a sufficiently large database. To overcome this problem this paper describes a hybrid approach for generating the necessary database for a data‐based digital twin. By intelligent combination of real experiments, adaptation of the physical‐chemical process model to these few experimental data and subsequent data cloud generation with the adapted process model results in a sufficient data base for the digital twin.