Adaptive Model Research Articles

Infection Prevention Elements Streptococcus suis using One Health System Health Zone 2, Thailand

This study assesses the effectiveness of the One Health System in preventing Streptococcus suis infections in Health Zone 2. Utilizing an exploratory sequential mixed-method approach, the research integrates in-depth interviews with affected individuals and specialists with a survey of 375 disease control practitioners. Analysis revealed a comprehensive set of factors critical to infection prevention, broadly categorized under human, swine, and environmental domains. Significant human-related factors include hygiene practices among swine workers and the influence of behaviors and personal beliefs on disease risk. In the swine domain, management practices before slaughter and during raising significantly impact safety measures against Streptococcus suis. Environmental factors such as sanitation and local regulations also play crucial roles in disease prevention. These findings suggest that targeted improvements across these domains can considerably enhance prevention strategies. The study underscores the need for a dynamic, adaptable prevention model and proposes longitudinal research to further investigate these strategies across more diverse geographic areas.

New Fault Diagnosis Method for Rolling Bearings Based on Improved Residual Shrinkage Network Combined with Transfer Learning.

The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shrinkage network model is combined with transfer learning to solve the above problems. The model is trained on the Case Western Reserve dataset, and then the trained model is migrated to a small-sample dataset with a scaled-down sample size and the Jiangnan University bearing dataset to conduct the experiments. The experimental results show that the proposed method can efficiently learn from small-sample datasets, improving the accuracy of the fault diagnosis of bearings under variable loads and variable speeds. The adaptive parameter-rectified linear unit is utilized to adapt the nonlinear transformation. When rolling bearings are in operation, noise production is inevitable. In this paper, soft thresholding and an attention mechanism are added to the model, which can effectively process vibration signals with strong noise. In this paper, the real noise is simulated by adding Gaussian white noise in migration task experiments on small-sample datasets. The experimental results show that the algorithm has noise resistance.

Modeling and supporting adaptive Complex Data-Intensive Web Systems via XML and the O-O paradigm: The OO-XAHM model

The data model is a critical component of an Adaptive Web System (AWS). The major goals of such a data model are describing the application domain of the AWS and capturing data about the user in order to support the “adaptation effect”. There have been many proposals for data models, principally based on knowledge representation, machine learning, logic and reasoning, and, recently, ontologies. These models are focused on the implementation of the core layer of AWS, that is realizing the adaptation of contents and presentations of the system, but sometimes they are poor with respect to the application domain design. In this paper, we present an extension of the state-of-the-art XML Adaptive Hypermedia Model (XAHM), Object-Oriented XAHM (OO-XAHM) that supports the application domain modeling using an object-oriented approach. We also provide the formal definition of the model, its description via Unified Modeling Language (UML), and its implementation using XML Schema. Finally, we provide a complete case study that focuses the attention on the well-known Italian archaeological site Pompeii.

An improved immersed boundary method for turbulent flow simulations on Cartesian grids: extension of a global geometric approach for thin boundary layers and strong flow incidence

This paper presents further improvements to the immersed boundary method introduced in [1]. The proposed developments take place during the pre- and post-processing stages of the simulation workflow and aim to further increase the accuracy of the approach for steady-state simulations of high Reynolds number turbulent flows around aerodynamic geometries facing strong incidence. To this end, the location of the forcing points is further optimized prior to simulation, with an adaptive and local modeling height that accounts for the evolution of the turbulent boundary layer thickness, especially at the leading edge. In addition, the direct extrapolation of the pressure solution at the wall is replaced by first- and second-order reconstructions using the normal pressure gradients interpolated at a new set of image points. This second approach is used only in post-processing, after the simulation, and prevents the degradation of the wall pressure in the presence of strong curvatures or thin boundary layers. These developments have been validated by simulating subsonic turbulent flows around the NACA0012 profile, 2D multi-element airfoil (2DMEA), and HL-CRM half-plane at significant angles of attack. Smooth and accurate skin pressure and friction coefficients are observed, in excellent agreement with body fitted wall-resolved solutions, even for coarser Cartesian meshes. Better drag and lift coefficients calculated by direct near-field integrations are obtained, along with accurate predictions of the near-wall flow physics throughout the turbulent boundary layer.

Hippocampal Cognitive Function Based on Deep Learning

Abstract This research focuses on the study of agent behavior decision-making based on hippocampal cognitive functions, aiming to enhance the decision-making capabilities of agents in complex task environments by deeply exploring the crucial role of the hippocampus in learning, memory, and cognitive processes. By drawing inspiration from the biological structure and functional characteristics of the hippocampus, researchers are dedicated to designing and developing more intelligent and adaptive decision-making models to enhance agents' behavioral performance, problem-solving abilities, and adaptability to new situations. To achieve this goal, the research integrates advanced artificial intelligence technologies such as reinforcement learning and deep learning to simulate the complex functions of the hippocampus in memory encoding, storage, retrieval, and cognitive reasoning. This research not only contributes to advancing intelligent systems towards higher levels of intelligence and personalization but also plays a significant role in improving the interaction between intelligent agents and humans, providing intelligent services that better meet user needs. We found that the neural network trained in multi-task learning benefits from a loss term that promotes relevant and irrelevant representations. Therefore, the complementary coding we found in CA3 can provide extensive computational advantages for solving complex tasks. Furthermore, the study emphasizes the importance of further elucidating the functional mechanisms of the hippocampus, with the expectation of providing a more solid theoretical foundation for the optimization and refinement of agent decision-making models in the future.

When Play Reveals the Ache: Introducing Co-constructive Patient Simulation for Narrative Practitioners in Medical Education.

Despite the ubiquity of healthcare simulation and the humanities in medical education, the two domains of learning remain unintegrated. The stories suffused within healthcare simulation have thus remained unshaped by the developments of narrative medicine and the health humanities. Healthcare simulation, in turn, has yet to utilize concepts like co-construction and narrative competence to enrich learners' understanding of patient experience alongside their clinical competencies. To create a conceptual bridge between these two fields (including narrative-based inquiry more broadly), we redescribe narrative competence via Ronald Heifetz's distinction of "technical" and "adaptive" challenges outlined in his adaptive leadership model. Heifetz, we argue, enriches learners' self-understanding of the unique demands of cultivating narrative competence, which can be both elucidated on the page and tested within the charged yet supportive simulation environment. We introduce Co-constructive Patient Simulation (CCPS) to demonstrate how working with simulated patients can support narrative work by drawing on the clinical vicissitudes of learners in the formulation and enactment of case studies. The three movements of CCPS-resensing, retelling, and retooling-told through learner experiences, describe the affinities and divergences between narrative medicine's sequence of attention, representation, and affiliation; Montello's three forms of narrative competence (departure, performance, change), and Heifetz's three steps (observe, interpret, and intervene) of adaptive leadership.

Robustness Analysis of a Distributed Adaptive Model Predictive Control for Connected and Automated Vehicles Against Delays

Robustness Analysis of a Distributed Adaptive Model Predictive Control for Connected and Automated Vehicles Against Delays

Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification

ABSTRACTEnsuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. This study introduces an advanced approach for automated rice plant disease detection and classification by integrating deep learning and metaheuristic optimization techniques. Specifically, a deep dense neural network (DNN) is employed for its capacity to capture intricate patterns in images and extreme learning machine (ELM) for classification. To enhance the optimization process, an innovative variant of the Shuffled Shepherd Optimization (SSO) algorithm, known as Enhanced Artificial Shuffled Shepherd Optimization (EASSO), is introduced. EASSO incorporates adaptive strategies and enhanced exploration–exploitation mechanisms, enabling more efficient convergence during the training of the DNN. The proposed system operates by processing high‐resolution images of rice plants through the DNN, extracting nuanced features indicative of various diseases, including blast, bacterial blight, and brown spots. EASSO optimizes the DNN's parameters, maximizing its accuracy in disease classification. The synergy between DNN and EASSO ensures a robust and adaptive model capable of handling diverse and complex disease patterns. This automated approach significantly reduces the reliance on manual inspection, enabling timely intervention and improving overall agricultural productivity. Experimental results demonstrate the superiority of the DNN‐EASSO framework over traditional methods, showcasing higher accuracy rates and faster convergence. The incorporation of Enhanced Artificial Shuffled Shepherd Optimization enhances the precision and reliability of disease classification, making this integrated system a valuable tool for farmers and agricultural practitioners. This research represents a significant stride toward sustainable agriculture, showcasing the potential of advanced technologies in ensuring food security worldwide.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Computational Modeling of Ganglion Cell Bicolor Opponent Receptive Fields and FPGA Adaptation for Parallel Arrays.

The biological system is not a perfect system, but it is a relatively complete system. It is difficult to realize the lower power consumption and high parallelism that characterize biological systems if lower-level information pathways are ignored. In this paper, we focus on the K, M and P pathways of visual signal processing from the retina to the lateral geniculate nucleus (LGN). We model the visual system at a fine-grained level to ensure efficient information transmission while minimizing energy use. We also implement a circuit-level distributed parallel computing model on FPGAs. The results show that we are able to transfer information with low energy consumption and high parallelism. The Artix-7 family of xc7a200tsbv484-1 FPGAs can reach a maximum frequency of 200 MHz and a maximum parallelism of 600, and a single receptive field model consumes only 0.142 W of power. This can be useful for building assistive vision systems for small and light devices.

Stress Response Profiles and Physical Aggression in Early Childhood

This study examined the applicability of the four stress response patterns proposed by the Adaptive Calibration Model (ACM; Del Giudice et al., 2011) in 12-month-old children (N = 214; 116 boys) and their associations with physical aggression at 12, 20, and 30 months of age. Salivary alpha-amylase (sAA) and pre-ejection period (PEP) served as indices of the sympathetic nervous system, respiratory sinus arrhythmia as an index of the parasympathetic nervous system, salivary cortisol as an index of the hypothalamic-pituitary-adrenal axis, and heart rate as a measure of overall autonomic nervous system activity. A fear challenge was administered at 12 months to measure baseline, response, and recovery levels of the stress systems, while maternal reports were used to evaluate physical aggression at the various time points. Through latent profile analysis, three stress response profiles were identified: (1) ANS Responders, (2) Moderate Responders (across all stress systems), and (3) Hormonal Responders. Despite some deviations, each profile could be associated with a corresponding ACM stress response pattern, primarily due to the inclusion of both sAA and PEP as measures of sympathetic activity. At 12 months, ANS Responders showed higher levels of physical aggression, whereas Hormonal Responders exhibited higher levels of physical aggression at 20 months. The findings confirm that differences in stress response patterns can be observed as early as 12 months of age. Moreover, this study emphasizes the importance of using multiple indices of stress responsivity to identify such distinct patterns. Finally, stress response profiles that differ from the most common (moderate) profile, are associated (albeit not consistently) with higher levels of physical aggression at different ages during early childhood.

Generative AI in Fintech: Advancing Risk Assessment and Fraud Detection in Digital Payment Technologies

This article explores the transformative potential of artificial intelligence (AI), particularly generative AI, in enhancing risk assessment and fraud detection within digital payment technologies. As financial fraud evolves in sophistication, traditional detection methods often fall short. We propose a novel approach leveraging generative AI to create synthetic data that mimics real-world scenarios, thereby training more comprehensive and adaptive fraud detection models. This article addresses limitations in historical datasets, improves the accuracy of predictive models, and reduces false positives. Furthermore, we demonstrate how this approach enables proactive detection of emerging fraud trends, allowing fintech firms to stay ahead in the ongoing arms race against fraudsters. Our findings suggest that integrating generative AI into fraud detection systems can significantly enhance the security and reliability of digital payment platforms, potentially revolutionizing risk management in the fintech industry. This article contributes to the growing body of knowledge on AI applications in financial security and provides a foundation for future developments in this critical area.

Adaptive Model Predictive Control of Yaw Rate and Lateral Acceleration for an Active Steering Vehicle Based on Tire Stiffness Estimation

Dynamic systems for vehicles are commonly established based on physical rules that are simplified and are not accurately reflective of their dynamic characteristics under certain operating conditions, affecting their accuracy and safety. This paper presents an adaptive model predictive controller (AMPC) with an estimator that controls the yaw rate and lateral acceleration of a realistic ground vehicle. A number of vehicle parameters are estimated using different advanced estimators, of which recursive least squares is one that is widely used. AMPCs and estimators are used to manage the driving process in order for vehicles to remain stable and controllable. In this research, experiments were conducted on a realistic vehicle based on a nonlinear brush tire model. In this estimator, lateral force measurements are analyzed to estimate the tire cornering stiffnesses that are used in the AMPC from a linear bicycle model (lateral force model), where each parameter describes the nonlinearity of the vehicle model. The results demonstrate that the controlled vehicle's performance is improved by combining a recursive least squares estimator with an AMPC in the simulation process. As tire stiffness estimates become more accurate, AMPC performance improves. Yet, AMPC controllers are described in terms of a table of design parameters. As different steering inputs are applied to different vehicles, the yaw rate and lateral acceleration are varied, while tire stiffness is determined. According to the results of this paper, the proposed method for estimating tire-cornering stiffness exhibits high estimation accuracy, robustness, computational efficiency, stability, comfort, and accuracy and reliability under a variety of steering input maneuvers using an AMPC controller.

Adaptive network modeling for joint action and memory recall for elderly by detecting interpersonal synchrony

This paper explores the potential of adaptive network modeling for joint action and memory recall among elderly through detecting interpersonal synchrony. With the aging population increasing, there is a crucial need to focus on the health and social interaction of older adults. Based on research of the significance of social interaction and memory use for the elderly, as well as the role of interpersonal synchrony in joint action, this paper aims to analyse computationally how to enhance positive effects of social interactions among older individuals by applying an adaptive network model. The research examines the concept of interpersonal synchrony and its impact on joint action, memory, and emotional well-being in elderly populations. Through simulation experiments and analysis, the study demonstrates the potential benefits for music in memory recall for older adults with cognitive decline, highlighting the importance of social interaction and emotional resonance. This study offers a valuable contribution to understanding and improving social interactions and memory recall among the elderly.

Dual Stage Bionic Spatial Convolutional Restoration Model for Image De-noising and De-mosaicking

The first two important steps in the pipeline for processing picture signals are de-noising and de-mosaicking. The joint solution of the highly uncertain inverse problem of de-noising and de-mosaicking has garnered increased attention in research today. It is difficult to restore high-quality images from raw data in low light because of a variety of disturbances brought on by a low photon count and a complex image signal processing scheme. Even if some restoration and improvement techniques have been used, they might not work in harsh situations, including raw data imaging with brief exposure. Therefore, this research focuses on developing a de-mosaicking and de-noising model with effective end to end manner outcomes. Initially, the pre-processing is conducted using Gaussian filtering to eliminate artifacts from the input image, thereby enhancing the image quality. Then, the proposed method incorporates an Enhanced Spatial Convolutional Residual Net (EnConvResNet) for image de-mosaicking and an Adaptive U-net restoration model for image de-noising. An enhanced gazelle optimization (EnGa) algorithm is used to fine-tune the hyper-parameters of the model in order to maximize its performance and improve its generalization capacity. The proposed method accomplished peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of 46.65 and 98.89, respectively.

Cultural Transformation through Digitalization: A Bibliometric Approach in Responding to Changes in Social Values, Norms, and Cultural Practices in Society

This study employs VOSviewer to conduct a bibliometric analysis and visualize the intricate research landscape surrounding the themes of digitalization, cultural transformation, organizational practices, and the impact of global crises like the COVID-19 pandemic. Through a series of network density maps, the research identifies key thematic concentrations and academic collaborations over recent years. It reveals how digitalization intersects with various societal dimensions, influencing education, cultural heritage, sustainability, and business practices. The analysis emphasizes the role of digital technologies in facilitating significant social and organizational changes, advocating for policies that enhance digital literacy and inclusivity. Additionally, the examination of authorship networks sheds light on influential scholars and collaborative dynamics, providing insights that can guide future research directions. The study underscores the necessity for adaptive business models that integrate sustainability and resilience, crucial for navigating contemporary and future challenges.

Identification of anomaly of tunnel segment strain using an adaptive machine learning model

ABSTRACT Anomaly identification is a crucial issue for preventing diseases in tunnel structures. However, distinguishing whether the structure is abnormal from outliers is challenging, as the monitoring dataset composed of time series may contain non-structural abnormal data caused by operational environmental pollution. Therefore, this study aims to propose a structural anomaly identification model to distinguish the structural anomaly from the polluted dataset based on an improved autoencoder model with adaptive loss function modification (AAE). To solve the problem of insufficient abnormal samples in practice, the proposed model is applied to a time series dataset obtained from numerical simulation, where extreme conditions are introduced to generate structural abnormal data, and Gaussian noises are overlaid to pollute the raw data and generate non-structural abnormal data. Then, the anomaly identification capability of the AAE model under different intensities of noise pollution conditions is discussed, along with its reliability compared to other common methods. Experimental results demonstrate that the AAE model can effectively identify structural anomaly information from heavily polluted datasets. The accuracy of the AAE model improved by at least 4% to other models even on a serious polluted dataset. Therefore, the structural anomaly identification model presented in this study is reliable.

Dynamics of productivity in pigeonpea [Cajanus cajan (L.) Millsp.] in subtropical Australia

AbstractPigeonpea productivity can be enhanced by optimally matching the physiology of genotypes to environmental conditions. Information on crop responses to the environment has been lacking for the short‐duration pigeonpea genotypes, which are being trialed to develop the Australian pigeonpea industry. The objective of this study was to examine the dynamics of productivity in relation to radiation use efficiency (RUE) and its influence on yield partitioning. Seven field trials, employing three pigeonpea [Cajanus cajan (L.). Millsp.] genotypes, were established at the Gatton Campus, the University of Queensland, Australia, in 2017/2018 and 2018/2019 summer seasons. The study reveals that leaf area development, influenced by growing environment, genotypes, and their interactions, were the key factors for the differences in leaf area duration and RUE. Pigeonpea planted in December had higher seasonal (1.11 g MJ−1) as well as reproductive (0.71 g MJ−1) RUE, resulting in significant differences in total dry matter (TDM) and grain yield (GY). GY was positively associated with seasonal RUE (R2 = 0.62), and the relationship was stronger (R2 = 0.83) for the reproductive phase (RUE(R)). The positive association between GY and RUE(R) suggested that maintaining optimum leaf area during the grain filling period is crucial to achieve higher productivity. Variations in GY were related to amount and rate of TDM accumulation before flowering (R2 = 0.51 and R2 = 0.53, respectively). Hence, achieving greater TDM before flowering was determinant for achieving higher productivity. The present study provided updated information on dynamics of productivity that will enable more comprehensive modelling of pigeonpea adaptation under subtropical conditions.

Composite adaptive neural control for automatic carrier landing system with input saturation and output constraints

This paper investigates the automatic carrier landing control problem in the presence of model uncertainty, airwake disturbances, input saturation, and output constraints. Considering the performance requirements of the carrier-based aircraft, a composite adaptive neural controller is proposed based on the time-varying barrier Lyapunov function and backstepping control techniques. The radial basis function neural network is used to approximate the model uncertainty, where the neural network weight update law incorporating prediction and tracking errors further improves the convergence rate of the neural network and mitigates high-frequency oscillations. Furthermore, an adaptive disturbance compensation model is established to mitigate the adverse effects of airwake disturbances and estimation errors in the neural network. Based on the Lyapunov stability theory, it is proven that the proposed controller maintains the aircraft trajectory within the prescribed constraints and also ensures that all signals in the closed-loop control system are semiglobally uniformly ultimately bounded. Finally, comparative simulations are performed to demonstrate the effectiveness and superiority of the proposed composite adaptive neural control method.

Performance optimization of a neon turboexpander based on the Kriging model and genetic algorithm

The rapid development of high-temperature superconducting (HTS) technology has increased demand for stable and reliable cooling sources, with the neon refrigerator emerging as a favorable solution for HTS applications, among which neon turboexpander is the core component. This paper proposes an optimal design method tailored for the neon turboexpander, which combines a one-dimensional mean-line design, three-dimensional CFD analysis, adaptive Kriging surrogate model, and the genetic algorithm. The meridional contour of the turboexpander impeller is geometrically parameterized, and the three most critical structural parameters are selected through Sobol sensitivity analysis, and then the response surface analysis is carried out to analyze the coupling relationship between the structural parameters. After optimization, the total-to-static efficiency and output power of the neon turboexpander increased by 3.98% and 6.12%, respectively. Notably, the flow separation phenomenon within the optimized impeller is significantly improved, resulting in reduced flow loss. Furthermore, the optimized impeller demonstrates robust performance in a wide range of variable operating conditions. Therefore, the optimization design method has been proven effective, and the optimal turboexpander impeller structure can be obtained quickly and accurately.