Adaptive Model Research Articles

Left Ventricular Unloading in Extracorporeal Membrane Oxygenation: A Clinical Perspective Derived from Basic Cardiovascular Physiology.

To present an abridged overview of the literature and pathophysiological background of adjunct interventional left ventricular unloading strategies during veno-arterial extracorporeal membrane oxygenation (V-A ECMO). From a clinical perspective, the mechanistic complexity of such combined mechanical circulatory support often requires in-depth physiological reasoning at the bedside, which remains a cornerstone of daily practice for optimal patient-specific V-A ECMO care. Recent conventional clinical trials have not convincingly shown the superiority of V-A ECMO in acute myocardial infarction complicated by cardiogenic shock as compared with medical therapy alone. Though, it has repeatedly been reported that the addition of interventional left ventricular unloading to V-A ECMO may improve clinical outcome. Novel approaches such as registry-based adaptive platform trials and computational physiological modeling are now introduced to inform clinicians by aiming to better account for patient-specific variation and complexity inherent to V-A ECMO and have raised a widespread interest. To provide modern high-quality V-A ECMO care, it remains essential to understand the patient's pathophysiology and the intricate interaction of an individual patient with extracorporeal circulatory support devices. Innovative clinical trial design and computational modeling approaches carry great potential towards advanced clinical decision support in ECMO and related critical care.

Smart traffic management of vehicles using faster R-CNN based deep learning method

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses adaptive background modeling. It also addresses shadow and illumination issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this framework with respect to other methods.

Robust Drone Delivery with Weather Information

Problem definition: Drone delivery has recently garnered significant attention due to its potential for faster delivery at a lower cost than other delivery options. When scheduling drones from a depot for delivery to various destinations, the dispatcher must take into account the uncertain wind conditions, which affect the delivery times of drones to their destinations, leading to late deliveries. Methodology/results: To mitigate the risk of delivery delays caused by wind uncertainty, we propose a two-period drone scheduling model to robustly optimize the delivery schedule. In this framework, the scheduling decisions are made in the morning, with the provision for different delivery schedules in the afternoon that adapt to updated weather information available by midday. Our approach minimizes the essential riskiness index, which can simultaneously account for the probability of tardy delivery and the magnitude of lateness. Using wind observation data, we characterize the uncertain flight times via a cluster-wise ambiguity set, which has the benefit of tractability while avoiding overfitting the empirical distribution. A branch-and-cut (B&C) algorithm is developed for this adaptive distributionally framework to improve its scalability. Our adaptive distributionally robust model can effectively reduce lateness in out-of-sample tests compared with other classical models. The proposed B&C algorithm can solve instances to optimality within a shorter time frame than a general modeling toolbox. Managerial implications: Decision makers can use the adaptive robust model together with the cluster-wise ambiguity set to effectively reduce service lateness at customers for drone delivery systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101049 and 72232001], the Natural Science Foundation of Liaoning Province [Grant 2023-BS-091], the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045], and the Major Project of the National Social Science Foundation [Grant 22&ZD151]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0339 .

New algorithm using an adaptive level set model applied to hippocampus segmentation and volume calculation in MRI images

New algorithm using an adaptive level set model applied to hippocampus segmentation and volume calculation in MRI images

A robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets

In order to better describe the uncertainty of renewable energy output, this paper proposed a novel robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets. First, an ellipsoidal uncertainty set was established using historical data on renewable energy output, and a data-driven convex hull polyhedral set was established by connecting high-dimensional ellipsoidal vertices; on this basis, an adaptive data-driven polyhedral set model was established to address the problem of high conservatism in the scaling process of convex hull polyhedral sets. Furthermore, a novel adaptive data-driven robust scheduling model for new distribution systems was established, and a column-and-constraint generation (C&CG) algorithm was used to solve the robust scheduling model. Finally, the improved IEEE-33 bus system simulation verification shows that the robust scheduling model for new distribution systems based on adaptive data-driven polyhedral sets can reduce conservatism and improve the robustness of optimization results.

Adaptive soft sensor modeling of chemical processes based on an improved just‐in‐time learning and random mapping partial least squares

AbstractThe just‐in‐time learning‐based partial least squares (JIT‐PLS) has been extensively applied to adaptive soft sensor modeling of complex nonlinear processes. However, it still has the problems of unreasonable relevant samples selection and unsatisfactory local modeling. Aiming at these problems, this paper proposes an improved just‐in‐time learning‐based random mapping partial least squares (IJIT‐RMPLS), including an improved relevant samples selection strategy and a random mapping PLS (RMPLS) model. On the one hand, considering the different correlation degrees between input variables and output variable, this method applies mutual information to evaluate the importance of each input variable and designs a variable‐weighted Euclidean distance to select relevant samples for local modeling. On the other hand, in order to prompt the prediction precision of local soft sensor models, this method combines the idea of nonlinear random mapping in extreme learning machines with PLS and builds a RMPLS with multiple activation functions. Applications on a numerical example and a real chemical process show that the proposed IJIT‐RMPLS has smaller prediction error compared with traditional JIT‐PLS.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions.

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

GMIM: Self-supervised pre-training for 3D medical image segmentation with adaptive and hierarchical masked image modeling

GMIM: Self-supervised pre-training for 3D medical image segmentation with adaptive and hierarchical masked image modeling

A qualitative risk assessment model for water reuse: Risks related to agricultural irrigation in Brazil.

Urbanization and industrialization are increasing extreme weather events, causing water quantity and quality reduction. Global water scarcity impacts 32.5 % of the urban population and is growing. Brazil has also witnessed water scarcity, notably in the southeast (2014-2015) and south (2019-2020), with reservoirs dropping below 20 % capacity. Water reuse is vital for mitigating scarcity, though it presents risks due to contaminants. Risk analysis studies are crucial for evaluating contamination sources, pathways, and exposure scenarios in water reuse practices. Various methodologies, including quantitative, semi-quantitative, and qualitative analyses, can be employed. Given the uncertainty and diverse factors, qualitative methods are recommended for non-potable water reuse risk analysis. This work presents a qualitative risk analysis methodology that allows to evaluate non-potable water reuse categories. It assesses factors affecting human health and the environment, considering exposure scenarios, characteristics of the receptors, and sources of reused water. The risk analysis of water reuse was carried out focusing on agricultural reuse, considering as alternatives the irrigation of soybean and sugarcane crops. By reviewing literature, the probability of occurrence and the magnitude of impact of the risk factors were identified and rated, using an increasing relative numeric scale. This process resulted in an overall risk value for comparing agricultural irrigation alternatives. The obtained results indicate a promising risk analysis model that can be adjusted and applied to various water reuse modalities and key factors. This adaptable risk analysis model is mainly related to water treatment methods, prompting the proposal of risk control measures.

Low-cycle fatigue crack growth in brittle materials: Adaptive phase-field modeling with variable-node elements

This work presents an adaptive phase field framework for the simulation of crack nucleation and propagation in brittle materials subject to low-cycle loading. We introduce a fatigue history strain parameter within the phase-field framework to capture the fatigue effect. The resulting coupled differential equations are solved using a staggered iteration scheme. In order to improve the computational efficiency of the phase-field model, an adaptive refinement scheme is introduced. This scheme utilizes an artificial threshold that takes into consideration of both phase-field variables and cumulative fatigue history field variables to determine the elements to be refined. The handling of the hanging nodes resulting from mesh refinement is accomplished through the application of the variable-node element technique, offering a flexible and efficient mesh integration technique. We validate our proposed numerical scheme through five representative numerical examples: single-edge cracked specimen, compact tension specimen, double-edge cracked panel, plate with a hole and plate with multi-cracks and hole. The results demonstrate that the adaptive mesh refinement scheme significantly reduces computational costs without compromising the accuracy of the numerical predictions.

The bumpy road to conservation: challenges and opportunities in updating the Galapagos zoning system

In 2014, the Galapagos National Park Directorate, sponsored by various international non-governmental organizations, initiated a comprehensive marine and terrestrial spatial planning process to improve the management effectiveness of Galapagos’ protected areas. This effort aimed to reconfigure management areas by expanding or redistributing no-take zones to protect Key Biodiversity Areas and ensure the conservation of at least 30% of terrestrial and marine ecosystems in the Galapagos. However, this process encountered significant challenges, notably the erosion of fishers' trust following the implementation of a "Marine Sanctuary". This top-down decision resulted in socio-political tension, compelling the Ecuadorian government to delay the implementation of the new marine zoning until there was conclusive scientific data on its socio-economic impact on local small-scale fishers’ livelihoods. This paper examines the achievements and shortcomings of the Galapagos zoning update process, highlighting the successful integration of scientific zoning criteria and the enhanced protection of critical biodiversity areas, together with the reduced support from fishers and legal disputes. To move forward, we recommended a bottom-up approach centered on rebuilding trust among the fishing community via innovative engagement strategies and the introduction of market incentives to promote sustainable practices. Assessing the long-term impact of no-take zones is essential for refining management approaches. Additionally, the application of cutting-edge technology within an adaptive co-management model is recommended to improve fisheries and ecological data gathering and analysis, facilitating proactive and well-informed management decisions. Our suggestions aim to reconcile conservation objectives with the needs and aspirations of the local communities in this iconic archipelago.

Personalized Federated Learning Incorporating Adaptive Model Pruning at the Edge

Personalized Federated Learning Incorporating Adaptive Model Pruning at the Edge

Nonlinear dynamic analysis of aircraft CFRP sandwich wings under explosive blast loading: Introducing SVM-DNN algorithm to predict dynamical information

The aircraft wings are a crucial component, particularly during mechanical shock excitation. Thus, for the first time, an inventive adaptable model to simulate the nonlinear dynamics of the wings under explosive blast loading at the moment of the collision is described in this article. It is crucial to increase the structure's stability because of explosive blast loading. The mentioned structure is made of two composite face sheets and a carbon fiber-reinforced polymer (CFRP). Functionally graded (FG) graphene origami (GOri)-enabled auxetic metallic metamaterial (GOEAM) is introduced to improve the stability of this kind of applicable structure, especially in air maneuvers. The analysis is conducted utilizing Reddy's third-order shear deformation of Reddy theory (TSDRT), incorporating Hamilton's principle and Von Kármán nonlinear geometric assumptions. The governing and boundary equations are accurately computed at the domain and boundary edges of the doubly curved structure. The coupled mesh-free radial point interpolation method (MRPIM) and Newton-Raphson method are applied for numerically simulating the current doubly curved structure by employing Newmark's temporal integration method with a constant-average acceleration approach. The present findings are cross-checked against the machine learning method and open-source results from the literature. Based on physical data, machine learning integrates supervised machine learning to predict the nonlinear vibration of the system. In this case, nonlinear transient bending properties are found using hybrid machine learning methods and solutions. The findings demonstrate the significance of GOEAM for nonlinear vibrations of the composite system. The current study's conclusions could be used as a benchmark and useful recommendations for future structural design techniques with enhanced features.

An Adaptive Trust Evaluation Model for Detecting Abnormal Nodes in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

Research on a machine learning-based adaptive and efficient screening model for psychological symptoms of community correctional prisoners.

Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, the SCL-90 Checklist faces issues such as complex item numbers, overall low cultural level of the subjects, and insufficient professional level of the administrators. The SF-12 health survey brief, as a preliminary screening tool, although only has 12 questions, to some extent simplifies the evaluation process and improves work efficiency, it is prone to missed screening. The research team collected 17-dimensional basic characteristic data and corresponding SCL-90 and SF-12 data from 25,480 samples of community correctional prisoners in Zhejiang Province, China. This study explored the application of multi-label multi-classification algorithms and oversampling techniques in building machine learning models to delve into the correlation between the psychological health risks of community correctional prisoners and their characteristic data. Inspired by computerized adaptive testing (CAT), we constructed an adaptive and efficient screening model for community correctional prisoners through experimental comparisons, based on the binary relevance algorithm with sample oversampling. This screening model personalize the assessment process by dynamically matching participants with the most relevant subset (s) of the nine dimensions of the SCL-90 based on their individual characteristics. Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale between question groups were achieved for the specific group of community correctional prisoners. As a screening tool for psychological symptoms of community correctional prisoners, this model significantly simplifies the number of questions compared to SCL-90, with a simplification rate of up to 65%. However, it achieves this simplification while maintaining excellent performance. The accuracy reached 0.66, with a sensitivity of 0.754, and an F1 score of 0.649. This innovation simplified the assessment process, reduced the assessment time, improved work efficiency, and enhanced the ability to judge the specificity of community correctional prisoners population. Compared to the SF-12, although the simplification rate and accuracy of the model are slightly lower than those of the SF-12, the sensitivity increased by 42.26%, and the F1 score improved by 15.28%. This means the model greatly reduces the possibility of missed screening, effectively preventing prisoners with abnormal psychological or mental states from losing control due to missed screening, and even committing suicide, self injury, or injuring others.

Adaptive Neuro-fuzzy Inference System-Based Data-Driven Model for Optimal Recharging of Electric Vehicles and Cost Prediction in Energy Hubs

Adaptive Neuro-fuzzy Inference System-Based Data-Driven Model for Optimal Recharging of Electric Vehicles and Cost Prediction in Energy Hubs

Theoretical perspectives on cybersecurity challenges in critical water infrastructure: Insights from Africa and the United States

This review paper examines the theoretical perspectives on cybersecurity challenges facing critical water infrastructure, with a focus on comparative insights from Africa and the United States. By exploring foundational theories of risk management, resilience, and deterrence, the paper delineates how these concepts are applied and adapted to address the cybersecurity needs of critical water systems within the distinct contexts of the two regions. The analysis identifies common challenges such as malware attacks, system vulnerabilities, human factors, and how regional differences influence technological infrastructure, regulatory environments, and cyber threat landscapes. Through a comparative analysis, the paper highlights lessons learned and best practices from both regions, emphasizing the importance of capacity building, comprehensive risk management, and the role of public-private partnerships. The paper concludes with a call for future research to develop adaptable theoretical models that address different regions' unique cybersecurity challenges, underlining theoretical understanding's critical role in enhancing the global resilience of critical water infrastructure against cyber threats.

Theoretical insights into telemedicine and healthcare ICT: lessons from implementation in Africa and the United States

This review paper explores the theoretical insights and practical lessons learned from the implementation of telemedicine and healthcare Information and Communication Technology (ICT) in Africa and the United States. By comparing the challenges, strategies, and outcomes of telemedicine initiatives in these diverse healthcare settings, the paper highlights the importance of context-specific solutions, supportive policy frameworks, and the role of technology in overcoming barriers to healthcare access. The analysis reveals key theoretical frameworks such as the adaptive model of telemedicine implementation and the ecosystem approach, emphasizing the necessity for flexibility, interoperability, and equity in telemedicine services. The paper concludes with implications for healthcare providers, policymakers, ICT developers, and recommendations for future research, aiming to enhance the global understanding and deployment of telemedicine.

RFG-TVIU: robust factor graph for tightly coupled vision/IMU/UWB integration

High precision navigation and positioning technology, as a fundamental function, is gradually occupying an indispensable position in the various fields. However, a single sensor cannot meet the navigation requirements in different scenarios. This paper proposes a “plug and play” Vision/IMU/UWB multi-sensor tightly-coupled system based on factor graph. The difference from traditional UWB-based tightly-coupled models is that the Vision/IMU/UWB tightly-coupled model in this study uses UWB base station coordinates as parameters for real-time estimation without pre-calibrating UWB base stations. Aiming at the dynamic change of sensor availability in multi-sensor integrated navigation system and the serious problem of traditional factor graph in the weight distribution of observation information, this study proposes an adaptive robust factor graph model. Based on redundant measurement information, we propose a novel adaptive estimation model for UWB ranging covariance, which does not rely on prior information of the system and can adaptively estimate real-time covariance changes of UWB ranging. The algorithm proposed in this study was extensively tested in real-world scenarios, and the results show that the proposed system is superior to the most advanced combination method in all cases. Compared with the visual-inertial odometer based on the factor graph (FG-VIO), the RMSE is improved by 62.83 and 64.26% in scene 1 and 82.15, 70.32, and 75.29% in scene 2 (non-line-of-sight environment).

Action-oriented entrepreneurial training and career resilience: a serial mediation model

PurposeThe combination of action and process has remained the attention of the psychology of entrepreneurship research. Moreover, special attention has also been paid to the whens and hows of entrepreneurial psychological training. Keeping this in view the current study has tested the impact of individual entrepreneurial orientation (IEO) training on small business entrepreneurial career resilience. Using action regulation theory (ART), a serial mediation model of IEO behaviour and entrepreneurial career adaptability is hypothesised.Design/methodology/approachThis study used a longitudinal randomised controlled field experiment in which the data were collected at four points. A total of 546 small business owners from Lahore, Pakistan, participated in this research (training group = 265, control group = 281). The data were analysed using ANCOVAs and PROCESS Model 6.FindingsThe research has found that IEO training impacts the entrepreneurial career resilience of small business owners through the development of IEO behaviour and career adaptability.Originality/valueThis experimental inquiry is a novel attempt to extend the career-related outcomes of IEO training (career resilience) based on ART through the mediating role of IEO behaviour and career adaptability. In addition, it has tested the IEO training in the developing country of Pakistan, which is a relevant context for enhancing its socio-economic growth.