Adaptive System Research Articles

Insights on usability testing: The effectiveness of an adaptive e-learning system for secondary school mathematics

The study evaluates the Mythematix prototype, an innovative adaptive e-learning system designed specifically to enhance secondary school students’ understanding and proficiency in mathematics. Utilizing a mixed-methods approach, it combines qualitative and quantitative research methodologies to thoroughly assess students’ perceptions and experiences with the Mythematix prototype. The study involved a group of 30 students participating in a comprehensive usability survey, which included a range of Likert scale questions alongside an open-ended response. This survey was designed to evaluate the usability of the prototype, pinpointing its strengths and weaknesses from the students’ perspectives. The analysis of the survey results provided detailed insights into the most and least effective aspects of the prototype. Overall, the study contributes valuable insights into the effectiveness of adaptive e-learning platforms in enhancing and enriching students’ learning experiences in mathematics, and it lays the groundwork for future research in this evolving educational field.

Therapeutic Avenues to Modulate B-Cell Function in Patients With Cardiovascular Disease.

The adaptive immune system plays an important role in the development and progression of atherosclerotic cardiovascular disease. B cells can have both proatherogenic and atheroprotective roles, making treatments aimed at modulating B cells important therapeutic targets. The innate-like B-cell response is generally considered atheroprotective, while the adaptive response is associated with mixed consequences for atherosclerosis. Additionally, interactions of B cells with components of the adaptive and innate immune system, including T cells and complement, also represent key points for therapeutic regulation. In this review, we discuss therapeutic approaches based on B-cell depletion, modulation of B-cell survival, manipulation of both the antibody-dependent and antibody-independent B-cell response, and emerging immunization techniques.

A Crop Adaptive Irrigation System for Improving Farm Yield in Rural Communities

A Crop Adaptive Irrigation System for Improving Farm Yield in Rural Communities

ACM Transactions on Autonomous and Adaptive Systems (ACM TAAS): Editorial Welcome and Update on State of the Journal, Vision and Ongoing Developments

<i>ACM Transactions on Autonomous and Adaptive Systems</i> (ACM TAAS): Editorial Welcome and Update on State of the Journal, Vision and Ongoing Developments

Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G–4G spectrums. Evaluation reveals SVM’s 98.8% accuracy in detecting spectrum holes and ANFIS’s 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications.

Microneedle-Mediated Delivery of Immunomodulators Restores Immune Privilege in Hair Follicles and Reverses Immune-Mediated Alopecia.

Disorders in the regulatory arm of the adaptive immune system result in autoimmune-mediated diseases. While systemic immunosuppression is the prevailing approach to manage them, it fails to achieve long-lasting remission due to concomitant suppression of the regulatory arm and carries the risk of heightened susceptibility to infections and malignancies. Alopecia areata is a condition characterized by localized hair loss due to autoimmunity. The accessibility of the skin allowslocal rather than systemic intervention to avoid broad immunosuppression. It is hypothesized that the expansion of endogenous regulatory T cells (Tregs) at the site of antigen encounter can restore the immune balance and generate a long-lasting tolerogenic response. A hydrogel microneedle (MN) patch is therefore utilized for delivery of CCL22, a Treg-chemoattractant, and IL-2, a Tregsurvival factor to amplify them. In an immune-mediated murine model of alopecia, local bolstering of Tregnumbers is shown, leading to sustained hair regrowth and attenuation of inflammatory pathways. In a humanized skin transplant mouse model, expansion of Tregs within human skin is confirmed without engendering peripheral immunosuppression. The patch offers high-loading capacity and shelf-life stability for prospective clinical translation. By harmonizing immune responses locally, the aim is to reshape the landscape of autoimmune skin disease management.

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system’s robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model’s robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise’s standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data.

Prevalence of the CRISPR-cas system and its association with antibiotic resistance in clinical Klebsiella pneumoniae isolates

Background and objective(s)CRISPR-Cas is a prokaryotic adaptive immune system that protects bacteria and archaea against mobile genetic elements (MGEs) such as bacteriophages plasmids, and transposons. In this study, we aimed to assess the prevalence of the CRISPR-Cas systems and their association with antibiotic resistance in one of the most challenging bacterial pathogens, Klebsiella pneumoniae.Materials and methodsA total of 105 K. pneumoniae isolates were collected from various clinical infections. Extended-spectrum β-lactamases (ESBLs) phenotypically were detected and the presence of ESBL, aminoglycoside-modifying enzymes (AME), and CRISPR-Cas system subtype genes were identified using PCR. Moreover, the diversity of the isolates was determined by enterobacterial repetitive intergenic consensus (ERIC)-PCR.ResultsPhenotypically, 41.9% (44/105) of the isolates were found to be ESBL producers. A significant inverse correlation existed between the subtype I-E CRISPR-Cas system’s presence and ESBL production in K. pneumoniae isolates. Additionally, the frequency of the ESBL genes blaCTX−M1 (3%), blaCTX−M9 (12.1%), blaSHV (51.5%), and blaTEM (33.3%), as well as some AME genes such as aac(3)-Iva (21.2%) and ant(2’’)-Ia (3%) was significantly lower in the isolates with the subtype I-E CRISPR-Cas system in comparison to CRISPR-negative isolates. There was a significant inverse correlation between the presence of ESBL and some AME genes with subtype I-E CRISPR-Cas system.ConclusionThe presence of the subtype I-E CRISPR-Cas system was correlated with the antibiotic-resistant gene (ARGs). The isolates with subtype I-E CRISPR-Cas system had a lower frequency of ESBL genes and some AME genes than CRISPR-negative isolates.

Scikit-ANFIS: A Scikit-Learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System

AbstractThe Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software.

Predicting problem-solving success in an office simulation applying N-grams and a random forest to behavioral process data

Predicting students' problem-solving success in computer-based simulations at an early stage allows adaptive educational systems to provide learners with personalized support. In this paper, we predict students' problem-solving success by applying a machine-learning model, the random forest, to produce a binary classification (more vs. less successful students). During a business-related problem scenario that lasted 55 min, early behavioral data (during the first 5, 10, and 20 min) such as mouse clicks and keyboard strokes (approx. 29,800 early-window clickstreams and keystrokes during the first 20 min) of 234 trainees were recorded, mirroring the students' problem-solving behavior. We used the n-gram sequence mining technique, which was originally introduced within the emerging disciplines of natural language processing, text mining, and machine learning and has proven to be effective, particularly in the examination of online behavior. We trained the random forest model with training datasets that included all features (bigrams), as well as selected features (the most predictable bigrams explaining inter-group differences). Our results show that early predictions based on the first 10 and 20 min contained sufficient information to accurately predict problem-solving success, while predictions that are too early (based on the first 5 min) do not. As the size of the initial time window expanded, the classification performance improved. Moreover, the selection of the most predictable features improved the models' performance for all three time intervals. The model that was trained with only selected robust features that occurred in the first 20 min achieved the highest ROC AUC score of almost 0.70. This result falls within the range of accuracy scores observed in similar studies. From the instructor's perspective, predictions help in the early identification of weak students and can provide them with personalized learning prompts. For more successful students, tasks can be enriched adaptively.

Horizontal sodium density variations for laser guide star tip-tilt measurements

ABSTRACT Adaptive optics, particularly with laser guide stars, has revolutionized ground-based astronomy. However, one known limitation is the requirement of a natural guide star to retrieve tip-tilt information. This is the limiting factor in the overall achievable sky coverage with adaptive optics systems as natural guide stars are not available for all science targets. There have been numerous different techniques proposed to overcome this limitation; however, many are restricted by current technology. This work investigates the use of density perturbations in the sodium layer to retrieve the tip-tilt information. In this work, we identify atmospheric gravity waves as a mechanism that could cause perturbations in the sodium layer density. Archival hydroxyl airglow data was analysed to assess whether the density perturbations caused by atmospheric gravity waves are on the correct scales and have enough contrast to be used for laser guide star tip-tilt retrieval. This data showed a median contrast of 1.9 per cent for a laser guide star with a full width half-maximum of 25 arcsec. Then to determine if these perturbations would be visible with a laser guide star at a high enough signal-to-noise ratio, the Mathematica package LGSBloch was utilized. The CaNaPy laser guide star R&amp;D facility was chosen to model as it has a state-of-the-art 70 W sodium laser. Promising results were found when modelling this method with the CaNaPy facility as a framework. This demonstrates that if these same perturbations were present in the sodium layer, sodium density variations are a potential mechanism to retrieve tip-tilt using laser guide stars.

Using complex adaptive systems (CAS) framework to assess success factors that lead to successful organizational change: a new way to understand change implementation for success

Using complex adaptive systems (CAS) framework to assess success factors that lead to successful organizational change: a new way to understand change implementation for success

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.

Model for motivating learners with personalized learning objects in a hypermedia adaptive learning system

&lt;span lang="EN-US"&gt;A number of weaknesses were demonstrated in the E-learning platforms during the Covid-19 pandemic despite the efforts invested. This has negatively influenced learners' motivation and consequently their performance. With the proliferation of technology and the revolution of information and communication technologies (ICT), learning objects have become new epitomes widely used, accessible, and implemented with educational resources and technological support. The integration of learning objects into E-learning has enhanced educational progress, but during critical periods, it is crucial to ensure pedagogical continuity and learner motivation. Based on this observation, we will propose architecture of a personalized learning object model in the context of an adaptive hypermedia learning system (AHS). The objective of our model is to increase the motivation factor which is a determining element in the success of E-learning, our model aims to improve the performance of the learners in order to avoid the abounding of learning and to promote the attendance of the learners. This will be useful later for any design or development of learning objects in hypermedia learning systems that are adaptive to the needs of the learners and in line with their preferences and profiles throughout the learning process offered by the system. &lt;/span&gt;

Enhancing aircraft crack repair efficiency through novel optimization of piezoelectric actuator parameters: A design of experiments and adaptive neuro-fuzzy inference system approach

This study addressed the critical problem of repairing cracks in aging aircraft structures, a safety concern of paramount importance given the extended service life of modern fleets. Utilized a finite element (FE) method enhanced by the design of experiments (DOE) and adaptive neuro-fuzzy inference system (ANFIS) approaches to analyze the efficacy of piezoelectric actuators in mitigating stress intensity factors (SIF) at crack tips—a novel integration in structural repair strategies. Through simulations, we examined the impact of various factors on the repair process, including the plate, actuator, and adhesive bond size and characteristics. In this work, initially, the SIF estimation used the FE approach at crack tips in aluminum 2024-T3 plate under the uniform uniaxial tensile load. Next, numerous simulations have been performed by changing the parameters and their levels to collect the data information for the analysis of the DOE and ANFIS approach. The FE simulation results have shown that changing the parameters and their levels will result in changing of SIF. Several DOE and ANFIS optimization cases have been performed for the depth analysis of parameters. The current results indicated that optimal placement, size, and voltage applied to the piezoelectric actuators are crucial for maximizing crack repair efficiency, with the ability to significantly reduce the SIF by a quantified percentage under specific conditions. This research surpasses previous efforts by providing a comprehensive parameter optimization of piezoelectric actuator application, offering a methodologically advanced and practically relevant pathway to enhance aircraft structural integrity and maintenance practices. The study innovation lies in its methodological fusion, which holistically examines the parameters influencing SIF reduction in aircraft crack repair, marking a significant leap in applying intelligent materials in aerospace engineering.

Smart traffic forecasting: leveraging adaptive machine learning and big data analytics for traffic flow prediction

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models.

Modeling and prediction of flame retardant performance of alkyl phosphinate using artificial neural network and adaptive neural fuzzy inference system

AbstractThe flame retardancy of polymer materials is closely linked to the types and compositions of flame retardants used. However, due to the complex relationships involved, it is difficult to quantify and predict flame retardancy using linear equations and models. Alkyl phosphinate flame retardants have emerged as promising candidates for their environmentally friendly, low toxicity, and excellent flame retardant properties. In this study, we aimed to develop a novel, low‐cost alkyl phosphinate flame retardant by predicting the quantitative impact of flame retardant composition on flame retardancy. To establish prediction models for the limiting oxygen index (LOI) value of alkyl phosphinate flame retardants, we utilized artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) programs, namely ANN, ANFIS‐grid partition (GP), and ANFIS‐subtractive clustering (SC). The prediction models were trained and validated, and their reliability was assessed based on the root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE) criteria. Our results indicate that the ANFIS‐SC prediction model exhibits the highest reliability, with high accuracy and ideal prediction results.

Learner’s attention detection in connected smart classroom using internet of things and convolutional neural networks

Detecting learner attention is an essential part of learning assessment. Consequently, it becomes an essential requirement for adaptive intelligent teaching systems, to identify specific needs and anticipate orientations. In this article, we propose a new model of a connected smart classroom, based on the internet of things, artificial intelligence and machine learning to detect in real time learners' attention and marking their presence during the execution of a teacher-assisted pedagogical activity, as well as to adapt the most suitable learning objects to these learners. The proposed model is based on head position, gaze direction, yawning and eye-state analysis as facial landmarks detected by cameras connected via the Bluetooth low energy network and transmitted to a developed convolutional neural network. In addition, a series of experiments have been conducted to evaluate the performance and efficiency of the model developed. The findings demonstrate that the model developed can be used to precisely capture the status of learners in the classroom in terms of attention and identification. In this way, these interesting findings can be used to adapt teaching activities to the individual needs of learners, and to identify areas where they have difficulties and needs extra help.