Adaptive Model Research Articles

Integrating Paths: Enhancing Deliberative Democracy Through Collective Intelligence Insights

This literature review critically examines the potential of collective intelligence (CI) to enhance theories of deliberative democracy and participatory governance through academic discourse. We employed PRISMA guidelines for systematic article selection, complemented by a narrative approach for in-depth thematic analysis and supplemented by quantitative methodologies such as Sankey diagrams and keyness analysis. Reviewing 61 scholarly articles focusing on CI within the public sector, this study identifies theoretical insights that could significantly impact the field of democratic innovations and participatory governance. Our analysis reveals that CI methodologies can make governance more inclusive and dynamic by integrating advanced digital tools that foster broader and more effective citizen participation. We conclude that integrating CI with deliberative democracy and participatory governance theories holds substantial promise for developing more responsive and adaptive governance models. Future research should focus on measuring deliberative quality in real time, deploying CI tools to empower underrepresented groups and address specific governance challenges, and examining CI’s ethical and social implications, especially concerning privacy, security, and power dynamics in technology-driven public decision-making.

Method for establishing the multipath adaptive mitigation model considering deformation displacement of monitoring stations

ABSTRACT Multipath effects are an unavoidable error in Global Navigation Satellite System (GNSS) deformation monitoring. Existing multipath models are predominantly established under stationary conditions and are not applicable to scenarios involving slope ruptures or rapid displacements. To address this issue, we propose the Multipath Adaptive Mitigation Model (MAMM) and validate its precision. The study demonstrates that traditional multipath models are ineffective in suppressing multipath errors under the specified conditions. The comparison between MAMM and the Multipath Hemispherical Model (MHM) indicates that, in the simulated displacement experiments, MHM’s effectiveness in mitigating multipath errors significantly decreases as the actual displacement distance increases, whereas MAMM consistently maintains an improvement rate in positioning precision of over 100%, markedly surpassing that of MHM. In real displacement scenarios, MAMM demonstrates similar advantages to those observed in the simulated experiments under instantaneous deformation conditions, while in slow displacement scenarios, MAMM offers a limited precision advantage over MHM.

Machine learning-based constitutive modelling for material non-linearity: A review

Machine learning (ML) models are widely used across numerous scientific and engineering disciplines due to their exceptional performance, flexibility, prediction quality, and ability to handle highly complex problems if appropriate data are available. One example of such areas which has attracted a lot of attentions in the last couple of years is the integration of data-driven approaches in material modeling. There has been several successful researches in implementing ML-based constitutive models instead of classical phenomenological models for various materials, particularly those with non-linear mechanical behaviors. This review paper aims to systematically investigate the literature on ML-based constitutive models for materials and classify these models based on their suitability for material non-linearity including Non-linear elasticity (hyperelasticity), plasticity, visco-elasticity, and visco-plasticity. Furthermore, we also reviewed and compared the ML-based approaches that have been applied for architectured materials as these groups of materials are designed to represent specific material behaviors that might not exist in classical and conventional material categories. The other goal of this review paper is to provide initial steps in understanding of various ML-based approaches for material modeling, including artificial neural networks (ANN), Gaussian processes, random forests (RF), generated adversarial networks (GANs), support vector machines (SVM), different regression models and physics-informed neural networks (PINN). This paper also outlines different data collection methods, types of data, data processing approaches, the theoretical background of the ML models, advantage and limitations of various models, and potential future research directions. This comprehensive review will provide researchers with the knowledge necessary to develop high-fidelity, robust, adaptable, flexible, and accurate data-driven constitutive models for advanced materials.

Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity—A Review

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with fewer annotated data. This review investigates how machine learning addresses data heterogeneity in HAR by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.

Economic impacts of floods in China and adaptation strategies under climate change

Abstract Climate change has intensified the frequency and severity of heavy rainfall and flooding in China, posing serious threats to economic and social stability. Over the past 40 years, the increasing trend of these events has led to significant economic losses, highlighting the urgent need to develop effective adaptation strategies in both research and policy. This study used the Adaptive Regional Input-Output model to comprehensively assess the economic impacts of the July 2021 Henan flood, with a main focus on indirect economic losses that are often underestimated in traditional assessments. The findings revealed that the indirect economic losses were up to $37.9 billion, nearly twice the direct economic losses. Additionally, a sensitivity analysis showed that increasing production capacity and the timing required to achieve it were two key factors to reduce indirect economic losses. Based on these insights, this study proposes an adaptation strategy framework tailored to China’s current development context, where systemic governance, strategic investments, and technological innovation are accounted for. This framework offers practical strategies to reduce the impacts caused by floods, thereby providing valuable guidance for sustainable disaster risk reduction and long-term economic stability in China.

Advancing a biomechanical framework for cellular interaction and mechanotransduction analysis using data mining techniques

As biomechanics advances, understanding the structural and functional dynamics of biological systems at molecular and cellular levels becomes increasingly critical. This study investigates a biomechanical framework for analyzing cellular interaction forces and information flow in mechanotransduction processes. Leveraging Data Mining (DM) technology, we evaluate cellular system efficiency and adaptation mechanisms under varying biomechanical stimuli, simulating stress conditions akin to high parallel processing loads. The proposed algorithm achieves an accuracy of 95.27% in predicting cellular response behaviors, with system stability maintained above 90% under simulated mechanical stress. These findings highlight the potential of computational methods to enhance our understanding of cellular resilience and adaptation in biomechanical contexts. Ultimately, this work contributes to the development of robust, adaptive models for studying the mechanobiology of cells and tissues, advancing diagnostic and therapeutic approaches in molecular and cellular biomechanics.

Crowd-CapsNet: capsule network based adaptive crowdsourcing task recommendation

Purpose The crowdsourcing software development platforms organize geographically distributed developers to complete various developing tasks, bringing convenience and efficiency to users. However, with the increasing number of both developers and tasks, it becomes more and more challenging to match tasks and suitable developers, especially for imbalanced data. The purpose of this paper is to propose an accurate and diverse recommendation model for crowdsourcing tasks. Design/methodology/approach A revised circle loss function is applied to achieve a certain adaptive ability, which is critical for imbalanced data, it guarantees diversity by maximizing the target label score and leveraging mathematical approximation to automatically balance the weights. Besides, the authors leverage the capsule network to obtain the semantic feature of tasks’ descriptions, modify the dynamic routing mechanism to better learn users’ preferences and improve the recommendation accuracy. Findings The comprehensive experiments conducted on real crowdsourcing platform data demonstrate that the proposed Crowd-CapsNet model can achieve high recommendation accuracy with a certain diversity. It improves around 1% accuracy with only 37% training time of the LSFA approach. Originality/value This paper proposes Crowd-CapsNet, an adaptive crowdsourcing task recommendation model. A relatively general feature pre-processing method describes crowd-sourcing tasks and the modified capsule network further obtains the semantic features to improve the recommendation accuracy and diversity.

AI-boosted and motion-corrected, wireless near-infrared sensing system for continuously monitoring laryngeal muscles

Neuromuscular diseases pose significant health and economic challenges, necessitating innovative monitoring technologies for personalizable treatment. Existing devices detect muscular motions either indirectly from mechanoacoustic signatures on skin surface or via ultrasound waves that demand specialized skin adhesion. Here, we report a wireless wearable system, Laryngeal Health Monitor (LaHMo), designed to be conformally placed on the neck for continuously measuring movements of underlying muscles. The system uses near-infrared (NIR) light that features deep-tissue penetration and strong interaction with myoglobin to capture muscular locomotion. The incorporated inertial measurement unit sensor further decouples the superposition of signals from NIR recordings. Integrating a multimodal AI-boosted algorithm based on recurrent neural network, the system accurately classifies activities of physiological events. An adaptive model enables fast individualization without enormous data sources from the target user, facilitating its broad applicability. Long-term tests and simulations suggest the potential efficacy of the LaHMo platform for real-world applications, such as monitoring disease progression in neuromuscular disorders, evaluating treatment efficacy, and providing biofeedback for rehabilitation exercises. The LaHMo platform may serve as a general noninvasive, user-friendly solution for assessing neuromuscular function beyond the anterior neck, potentially improving diagnostics and treatment of various neuromuscular disorders.

Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction

Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts.

Integrating RFPA and DEM in Adaptive RDFA Modeling of Rock Fracturing Process

Integrating RFPA and DEM in Adaptive RDFA Modeling of Rock Fracturing Process

Synergizing Federated AI Systems with Circular Economy Principles: A Framework for Sustainable and Resilient Data Science

The convergence of Federated Artificial Intelligence (AI) systems and Circular Economy (CE) principles marks a transformative approach to sustainable and resilient data science. Federated AI, with its decentralized architecture and emphasis on data privacy, seamlessly integrates with CE's objectives of maximizing resource efficiency, reducing waste, and fostering closed-loop systems. This paper proposes a novel framework that synergizes CE principles with Federated AI to address persistent challenges, including data siloing, scalability, energy efficiency, and environmental sustainability. The framework leverages adaptive learning models and resource-aware algorithms to optimize data- driven decision-making across applications such as smart city development, sustainable supply chain management, and renewable energy optimization. Through rigorous simulations and real-world case studies, the study demonstrates measurable improvements: a 20–30% increase in resource efficiency and a marked reduction in computational energy consumption compared to traditional centralized AI systems. These findings highlight the transformative potential of Federated AI in driving circular and sustainable ecosystems. The research also contributes to ethical AI discourse, providing actionable insights for policymakers, academics, and industry leaders to harmonize AI advancements with global sustainability imperatives.

Adaptive Learning in AI Agents for the Metaverse: The ALMAA Framework

This study investigates the adaptability of Artificial Intelligence (AI) agents in the Metaverse, focusing on their ability to enhance responsiveness, decision-making, and engagement through the proposed Adaptive Learning Model for AI Agents (ALMAA) framework. The research does not introduce new interventions to existing platforms like Epic Games or AltspaceVR but instead analyzes how their operations align with adaptive learning principles. By examining these platforms, the study demonstrates the alignment between real-world practices and theoretical constructs, offering insights into how adaptive AI systems operate in dynamic virtual environments. Case observations highlight key metrics such as user interaction efficiency, contextual decision accuracy, and predictive engagement strategies. The data, derived from detailed user interaction logs and feedback reports, underscore the practical application of adaptive learning in optimizing user satisfaction and system performance. Statistical analyses reveal notable gains in response speed, predictive precision, and user engagement, validating the theoretical framework’s relevance. This paper positions the ALMAA framework as a critical lens for understanding and analyzing adaptive AI in virtual settings. It emphasizes theoretical exploration rather than experimental application, providing a foundation for future research into scalable, user-centered AI systems tailored for the Metaverse’s evolving demands.

Advancing Information Governance in AI-Driven Cloud Ecosystem: Strategies for Enhancing Data Security and Meeting Regulatory Compliance

This study explores adaptive information governance models to address critical challenges in AI-driven cloud environments, focusing on enhancing data security and achieving regulatory compliance. Existing frameworks often fail to account for the complexities introduced by AI and cloud integration, leaving significant gaps in incident response, privacy protection, and governance practices. To bridge these gaps, this research evaluates governance components—Privacy-Enhancing Technologies (PETs), ethical oversight, and incident response metrics—through advanced quantitative methods, including Structural Equation Modeling (SEM), Cox Proportional Hazards Modeling, and Difference-in-Differences (DiD) analysis. Key findings highlight the significant influence of incident response metrics (β = 0.51, p < 0.001) and PET integration (β = 0.25, p = 0.001) on governance effectiveness, with model fit indices (RMSEA = 0.04, CFI = 0.96) confirming the robustness of the proposed framework. Industry-specific vulnerabilities were identified, with retail and technology sectors experiencing a 25% increased risk of incidents due to minimal security controls. The adoption of PETs, such as federated learning and homomorphic encryption, significantly improved privacy compliance and data utility, particularly in high-risk sectors. The study recommends the integration of advanced security controls and PETs to mitigate risks and improve compliance, especially in vulnerable industries like retail and technology. It also emphasizes the continuous optimization of AI-driven incident response protocols to reduce the impact of emerging threats. Furthermore, ethical oversight should be prioritized to ensure fairness, accountability, and public trust in AI applications within cloud ecosystems. These actionable strategies provide a roadmap for organizations to achieve secure and ethically governed cloud environments.

From classroom to industry: Developing universal competencies and AI integration in engineering education for technological advancements

The purpose of this study is to develop strategies for enhancing higher and postgraduate education in Kazakhstan, with a focus on improving graduate readiness for professional roles and building predictive models to analyze and assess doctoral graduation rates in technical fields for 2023–2025. This research emphasizes a flexible framework that integrates universal competencies into engineering programs, adaptable to various institutional and national contexts. Active learning methods, including project-based tasks, problem-solving exercises, internships, and lab work, are employed to cultivate practical skills aligned with real-world engineering demands. The study also highlights the need for comprehensive assessment systems that extend beyond theoretical testing, incorporating continuous feedback and curriculum adjustments to align with labor market needs and scientific developments. Results reveal key areas for higher education reform, as outlined in Kazakhstan’s State Program for Education and Science Development (2020–2025). These include implementing a competency-based approach, optimizing interdisciplinary and practice-oriented teaching methods, and enhancing the global competitiveness of Kazakhstani universities. Additionally, through time-series analysis of doctoral graduation data, a trend model was created, showing a steady increase in graduates, tested for anomalies and trend adequacy, making it reliable for forecasting graduate numbers. The scientific significance of this work lies in its contributions to models that improve graduate training, fostering high-quality education to enhance Kazakhstani universities’ global competitiveness. By integrating AI-driven strategies and emerging technologies, the research provides an adaptive educational model to equip students with essential professional and universal competencies, essential for navigating the evolving labor market and supporting sustainable development.

Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems

Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value.

FGRL: Federated Growing Reinforcement Learning for Resilient Mapless Navigation in Unfamiliar Environments

In this paper, we propose a federated growing reinforcement learning (FGRL) approach for solving the mapless navigation problem of unmanned ground vehicles (UGVs) facing cluttered unfamiliar obstacles. Deep reinforcement learning (DRL) has the potential to provide adaptive behaviors for autonomous agents through interactive learning, but standard episodic DRL algorithms often struggle with out-of-distribution observations. For navigation tasks, UGVs often encounter unfamiliar situations where novel obstacles differ from prior experience. To address this problem, the proposed FGRL approach enables multiple agents to obtain their individual navigation models in diverse scenarios, and achieves online knowledge aggregation to obtain an adaptive and resilient model that copes with unfamiliar uncertain obstacles. Specifically, during the learning process of navigation tasks, we introduce the growth rate of each agent’s local model based on the performance of consecutive learning rounds. Then, we weight the local model of each agent based on the growth rate to achieve knowledge aggregation in a shared model. We also consider a growth threshold to eliminate the interference of low-quality local models. We carry out extensive simulations to validate the proposed solution, and the results show that our approach can learn resilient behaviors of collision avoidance for UGVs to cope with never encountered and cluttered unfamiliar obstacles.

Rockin’ the Subsurface: Learning Geophysics with ‘Electromagnetic Odyssey’

In the education of applied geophysics, innovative learning tools are essential to bridge the gap between theory and practical field experience. Fieldwork is critical for reinforcing classroom knowledge and fostering peer interactions. However, it faces financial costs, physical accessibility, and logistical challenges, which can deter student participation and create equity concerns. To address these issues, adaptable educational models and digitalization are crucial. Integrating Information and Communication Technologies (ICT) has significantly enhanced learning processes, with alternatives like recorded fieldwork videos, high-resolution imagery, and virtual outcrop models gaining prominence, particularly during the COVID-19 pandemic. Through thoughtful design, video games offer educational potential by enhancing motivation and engagement. This paper introduces Electromagnetic Odyssey, an immersive learning tool to overcome traditional field trips’ financial and logistical constraints. The video game considers the application of Electromagnetic methods for groundwater exploration. Electromagnetic Odyssey aims to promote equitable access to quality education and motivate students through well-designed, technology-enhanced activities, aligning with educational principles emphasizing motivation’s importance in learning.

A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation.

The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient's death or incapacity to move their muscles, if it is not appropriately identified in the early stages. A Rapid Diagnostic Test (RDT) is a frequently used approach to find malaria cells in red blood cells. However, it might not be able to identify infections with small amounts of samples. In the microscopic detection model, blood stains are placed under a microscope for diagnosing malaria. But accurate diagnosis is hard in this method, particularly in developing nations where the disease is most common. The microscopic detection processes are expensive and time-consuming due to the usage of microscopes. The quality of the blood smears and the availability of a qualified specialist, who is skilled in recognizing the disease, impact the accuracy of malaria detection results. The traditional deep learning-based malaria identification models need more processing power. Therefore, a deep learning-based adaptive method is designed to detect malaria cells through the medical image. Hence, the images are gathered from the standard sites and then fed to the segmentation process. Here, the abnormality segmentation is carried out with the help of a developed Trans-MobileUNet + + (T-MUnet + +) network. Trans-MobileUNet + + captures global context, so it is well-suited for segmentation tasks. The segmented image is applied to the adaptive detection phase where the Adaptive and Atrous Convolution-based Recurrent MobilenetV2 (AA-CRMV2) model is designed for the effective recognition of malaria cells. The efficiency of the designed approach is elevated by optimizing the parameters from the AA-CRMV2 network with the help of the Updated Random Parameter-based Fennec Fox Optimization (URP-FFO) algorithm. Several experimental analyses are evaluated in the implemented model over classical techniques to display their effectualness rate.

Chosen-Ciphertext Secure Unidirectional Proxy Re-Encryption Based on Asymmetric Pairings

Proxy re-encryption (PRE) is a cryptographic primitive that extends public key encryption by allowing ciphertexts to be re-encrypted from one user to another without revealing information about the underlying plaintext. This makes it an essential privacy-enhancing technology, as only the intended recipient is able to decrypt sensitive personal information. Previous PRE schemes were commonly based on symmetric bilinear pairings. However, these have been found to be slower and less secure than the more modern asymmetric pairings. To address this, we propose two new PRE scheme variants, based on the unidirectional symmetric pairing-based scheme by Weng et al. and adapted to utilize asymmetric pairings. We employ a known automated black-box reduction technique to transform the base scheme to the asymmetric setting, identify its shortcomings, and subsequently present an alternative manual transformation that fixes these flaws. The adapted schemes retain the properties of the base scheme and are therefore CCA-secure in the adaptive corruption model without the use of random oracles, while being faster, practical, and more secure overall than the base scheme.

A Conceptual Model of Learning Adaptability of Higher Vocational Students in China

Abstract Vocational education plays a crucial role in China’s education system, with higher vocational education being a pivotal and dynamic aspect of the country’s educational reform. Despite this, higher vocational students often struggle with lethargy and inadequate adaptation to their studies. This paper constructs a conceptual model of learning adaptability by systematically coding and analyzing textual materials, such as literature, research reports, news, and interviews, pertaining to learning adaptability using grounded theory. The aim is to address the diverse factors influencing learning adaptability. The research revealed that the fundamental categories shaping learning adaptability encompass students’ psychological, academic, and personal growth states. To enhance learning adaptability, proactive intervention is necessary within these core categories outlined in the model, guiding students to anticipate and understand the challenges they may encounter. Notably, there remains a dearth of theoretical research on the learning adaptability of higher vocational students. This study aims to significantly contribute to improving teaching effectiveness in this domain.