Self-adaptive Strategy Research Articles

Self-Adaptation Management of Thailand Students in the Teacher Training Program at Islamic Universities in Indonesia

This research aims to understand the self-adaptation management of students from Thailand who are pursuing teacher training study programs in Indonesia. The focus of the research includes: 1) cultural differences between Thailand and Indonesia that affect students' self-adaptation, 2) the self-adaptation strategies used by Thai students, and 3) their expectations for self-adaptation management at the university. The method used was qualitative with a narrative inquiry approach through interviews and observations of 9 respondents. The results showed that Thai students at the Islamic University of Indonesia observed cultural differences in social values, norms, and religious aspects that influenced social interaction, responses to authority, and understanding of dominant cultures as well as lifestyles and perceptions of time. They develop adaptation strategies through Indonesian language courses, international communities, technology, lecturer guidance, and local friendships. Students expect the university to provide orientation programs, guidance, comfortable dormitories, cultural exchange, inclusive activities, and community support, as well as ensure the quality of higher education, competent lecturers, and relevant curriculum. This research provides insights for universities to develop more effective orientation and mentoring programs and shows the importance of technology support in the adaptation process. The limitation of this study is that its scope is limited to one university with a small number of respondents, so it is recommended for future research to include more universities and a wider range of respondents for a more comprehensive understanding. Keywords: Students, International, Environmental Adaptation, Universities, Indonesia.

Coupling Thiosulfate-Driven denitrification and anammox to remove nitrogen from actual wastewater.

A coupled thiosulfate-driven denitrification and anammox (TDDA) process was established to remove nitrogen from wastewater. It was optimized in an up-flow anaerobic sludge blanket reactor using synthetic wastewater, and its reliability was then verified with actual wastewater. The results demonstrated that nitrate, nitrite, and ammonium could be synergistically removed, and the highest total nitrogen removal efficiency reached 97.8% at a loading of 1.39 kgN/(m3·d). Anammox bacteria, primarily Candidatus_Brocadia, were the main contributors to nitrogen removal, while sulfur-oxidizing bacteria such as Thiobacillus and Rhodanobacter played a supportive role. By optimizing substrate conditions to enhance the anammox process, the coupled system attained higher abundances of functional genes such as napA, nirS, hzs, soxXA, and soxYZ, along with the corresponding microbial species. The data suggested that microbial cross-feeding and self-adaptation strategies were key to efficient nitrogen removal by TDDA.

An Inertial Subgradient Extragradient Method for Efficiently Solving Fixed-Point and Equilibrium Problems in Infinite Families of Demimetric Mappings

The primary objective of this article is to enhance the convergence rate of the extragradient method through the careful selection of inertial parameters and the design of a self-adaptive stepsize scheme. We propose an improved version of the extragradient method for approximating a common solution to pseudomonotone equilibrium and fixed-point problems that involve an infinite family of demimetric mappings in real Hilbert spaces. We establish that the iterative sequences generated by our proposed algorithms converge strongly under suitable conditions. These results substantiate the effectiveness of our approach in achieving convergence, marking a significant advancement in the extragradient method. Furthermore, we present several numerical tests to illustrate the practical efficiency of the proposed method, comparing these results with those from established methods to demonstrate the improved convergence rates and solution accuracy achieved through our approach.

On the Feasibility of a Self-adaptive Strategy for Hybrid RANS/LES Based on Physical Criteria and its Initial Testing on Low Reynolds Number Backward-Facing Step Flow

On the Feasibility of a Self-adaptive Strategy for Hybrid RANS/LES Based on Physical Criteria and its Initial Testing on Low Reynolds Number Backward-Facing Step Flow

Soft-Finite-Element-Method-Driven Multiphysics Airfoil Optimization

The soft finite element method (SoftFEM) was recently developed as an efficient black-box engineering optimization framework combining population-based evolutionary global optimization and a local search technique using a hybrid self-adaptive strategy. This framework simultaneously leverages exploration and exploitation without the need to know a priori which strategy is best fitted to the problem at hand. In this work, we first improve SoftFEM by replacing the original differential evolution by the so-called success-history-based adaptive differential evolution. The developed framework is then applied to optimize the aerodynamic performance of 1) an airfoil, 2) a wing, and 3) the fluid–structure interaction problem of an airfoil with a flexible trailing edge. In the first two cases, the open-source computational fluid dynamics code OpenFOAM is considered to evaluate the aerodynamic characteristics of the airfoil, while in the latter case, we combine OpenFOAM and MuPhiSim—our recently released open-source code for computational solids mechanics—to conduct fluid–structure interaction simulations. The results demonstrate that this approach can effectively find optimal solutions with improved aerodynamic performance in all three cases. The proposed framework has the potential to significantly impact the design and optimization of aerodynamic systems in complex design spaces. In particular, new systems involving soft components and soft–hard junctions would benefit from a seamless application of the developed framework to their optimization.

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification.

Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

FedADMM-InSa: An inexact and self-adaptive ADMM for federated learning

Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and systems, limited communication capacities, and constrained local computational resources. Recently developed FedADMM methods show great resilience to both data and system heterogeneity. However, they still suffer from performance deterioration if the hyperparameters are not carefully tuned. To address this issue, we propose an inexact and self-adaptive FedADMM algorithm, termed FedADMM-InSa. First, we design an inexactness criterion for the clients’ local updates to eliminate the need for empirically setting the local training accuracy. This inexactness criterion can be assessed by each client independently based on its unique condition, thereby reducing the local computational cost and mitigating the undesirable straggle effect. The convergence of the resulting inexact ADMM is proved under the assumption of strongly convex loss functions. Additionally, we present a self-adaptive scheme that dynamically adjusts each client’s penalty parameter, enhancing algorithm robustness by mitigating the need for empirical penalty parameter choices for each client. Extensive numerical experiments on both synthetic and real-world datasets have been conducted. As validated by some tests, our FedADMM-InSa algorithm improves model accuracy by 7.8% while reducing clients’ local workloads by 55.7% compared to benchmark algorithms.

Residual network with self-adaptive time step size

Residual Networks (ResNet) are pivotal in machine learning. The connection between ResNets and ordinary differential equations (ODEs) has inspired enhancements of ResNets using sophisticated numerical methods for ODE systems. Recent advancements in numerical self-adaptive schemes, which adjust time step sizes based on the feature maps or parameters of residual blocks, have demonstrated promising results in enhancing ResNet performance, surpassing those achieved with fixed time step methods. However, these self-adaptive time step constructions lack theoretical support and can limit performance improvements since the self-adaptive time steps should theoretically depend on both the feature maps and the parameters of the residual blocks. In this study, we conduct a rigorous theoretical analysis of the residual functions associated with fixed or self-adaptive time step methods to demonstrate the advantages and rational designs of the self-adaptive approach. Subsequently, we introduce a novel self-adaptive ResNet, AdaTS-ResNet, which effectively incorporates both feature maps and parameters in its time step adjustments. Experimental results reveal that AdaTS-ResNet surpasses TSCLSTM-ResNet (Yang et al., 2020) in prediction accuracy and computational efficiency, where TSCLSTM represents the latest advancements of the methods designed based on the self-adaptive scheme of ODE. Our findings highlight the potential of improving ResNet architectures through adaptive techniques of dynamical systems, offering insights for future enhancements in deep learning models.

Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory.

Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.

An efficient eigenvalue bounding method: CFL condition revisited

Direct and large-eddy simulations of turbulence are often solved using explicit temporal schemes. However, this imposes very small time-steps because the eigenvalues of the (linearized) dynamical system, re-scaled by the time-step, must lie inside the stability region. In practice, fast and accurate estimations of the spectral radii of both the discrete convective and diffusive terms are therefore needed. This is virtually always done using the so-called CFL condition. On the other hand, the large heterogeneity and complexity of modern supercomputing systems are nowadays hindering the efficient cross-platform portability of CFD codes. In this regard, our leitmotiv reads: relying on a minimal set of (algebraic) kernels is crucial for code portability and maintenance! In this context, this work focuses on the computation of eigenbounds for the above-mentioned convective and diffusive matrices which are needed to determine the time-step à la CFL. To do so, a new inexpensive method, that does not require to re-construct these time-dependent matrices, is proposed and tested. It just relies on a sparse-matrix vector product where only vectors change on time. Hence, both implementation in existing codes and cross-platform portability are straightforward. The effectiveness and robustness of the method are demonstrated for different test cases on both structured Cartesian and unstructured meshes. Finally, the method is combined with a self-adaptive temporal scheme, leading to significantly larger time-steps compared with other more conventional CFL-based approaches.

Enhancing self-adaptation for efficient decision-making at run-time in streaming applications on multicores

Parallel computing is very important to accelerate the performance of computing applications. Moreover, parallel applications are expected to continue executing in more dynamic environments and react to changing conditions. In this context, applying self-adaptation is a potential solution to achieve a higher level of autonomic abstractions and runtime responsiveness. In our research, we aim to explore and assess the possible abstractions attainable through the transparent management of parallel executions by self-adaptation. Our primary objectives are to expand the adaptation space to better reflect real-world applications and assess the potential for self-adaptation to enhance efficiency. We provide the following scientific contributions: (I) A conceptual framework to improve the designing of self-adaptation; (II) A new decision-making strategy for applications with multiple parallel stages; (III) A comprehensive evaluation of the proposed decision-making strategy compared to the state-of-the-art. The results demonstrate that the proposed conceptual framework can help design and implement self-adaptive strategies that are more modular and reusable. The proposed decision-making strategy provides significant gains in accuracy compared to the state-of-the-art, increasing the parallel applications’ performance and efficiency.

A pseudo resistor with temperature self-adaptive scheme

A temperature self-adaptive ultra-high-resistance pseudo-resistor (PR) circuit is proposed for a wide range of biomedical applications. It acts as a relatively constant resistor over a wide temperature range (−40 °C–85 °C) due to its potential to compensate for the impact of the temperature-induced current. Hence, the performance of many biomedical analog intellectual property (IP) circuits can be effectively improved with temperature variations. The proposed circuit consists of a gate-voltage-controlled pseudo-resistor and a proportional-to- absolute-temperature (PTAT) circuit. Besides, its analysis and proof of concept with the self-adaptive scheme are presented. The circuit is designed in standard 0.18 μm CMOS technology and occupies a silicon area of 18.5 × 43.7 μm2. It consumes 12 nW with a single power supply of 1.8 V. The post-layout simulation results demonstrate that the proposed pseudo-resistor could adequately improve the temperature-induced resistance variation by up to 18X while consuming ultra-low power and providing relatively high-temperature independence compared to the prior art.

A 2.3-5.7 μW Tri-Modal Self-Adaptive Photoplethysmography Sensor Interface IC for Heart Rate, SpO2, and Pulse Transit Time Co-Monitoring.

This paper presents a tri-modal self-adaptive photoplethysmography (PPG) sensor interface IC for concurrently monitoring heart rate, SpO2, and pulse transit time, which is a critical intermediate parameter to derive blood pressure. By implementing a highly-reconfigurable analog front-end (AFE) architecture, flexible signal chain timing control, and flexible dual-LED drivers, this sensor interface provides wide operating space to support various PPG-sensing use cases. A heart-beat-locked-loop (HBLL) scheme is further extended to achieve time-multiplexed dual-input pulse transit time extraction based on two PPG sensors placed at fingertip and chest. A self-adaptive calibration scheme is proposed to automatically match the chip's operating point with the current use case, guaranteeing a sufficient signal-to-noise ratio for the user while consuming minimum system power. This paper proposes a DC offset cancellation (DCOC) approach comprised by a logarithmic transimpedance amplifier and an 8-bit SAR ADC, achieving a measured 38 nA residue error and 8.84 μA maximum input current. Fabricated in a 65nm CMOS process, the proposed tri-modal PPG sensor interface consumes 2.3-5.7 μW AFE power and 1.52 mm2 die area with 102dB (SpO2 mode), 110-116 dB (HR & PTT mode) dynamic range. A SpO2 test case and a HR & PTT test case are both demonstrated in the paper, achieving 18.9 μW and 43.7 μW system power, respectively.

A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies.

An adaptive differential evolution algorithm with multi-strategy for solving complex optimization problem

In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms.

Self-adaptive piezoelectric vibration absorber with semi-passive tunable resonant shunts

This paper proposes a novel self-adaptive strategy to control piezoelectric vibration absorbers (PVA) using a semi-passive resonant shunt with tunable inductance for vibration attenuation of harmonically excited structures. The tunable inductor is realized using ferrite cores and its inductance is controlled by means of the air gap effect between the cores using piezoelectric stack actuators. This device allows the control of the resonance frequency of the shunt circuit. The adaptive resonant shunt leverages the effect of antiresonance resulting from the electromechanical coupling of the structure with a resonant shunt with low electrical damping to attenuate the vibration of the harmonically excited structure. A machine learning control method based on a Gaussian process regression model is used to drive the tunable inductance based on minimizing the time-averaged RMS response of the structure. The experimental application of the proposed strategy is illustrated in an application to attenuate a single-mode of a simplified aircraft fuselage structure. A reduction of about 30% in the maximum vibration amplitude is observed by comparing the self-adaptive resonant circuit and a traditional resonant circuit designed based on the equal-peaks method.

An inertial shrinking projection self-adaptive algorithm for solving split variational inclusion problems and fixed point problems in Banach spaces

Abstract In this article, we study the split variational inclusion and fixed point problems using Bregman weak relatively nonexpansive mappings in the p p -uniformly convex smooth Banach spaces. We introduce an inertial shrinking projection self-adaptive iterative scheme for the problem and prove a strong convergence theorem for the sequences generated by our iterative scheme under some mild conditions in real p p -uniformly convex smooth Banach spaces. The algorithm is designed to select its step size self-adaptively and does not require the prior estimate of the norm of the bounded linear operator. Finally, we provide some numerical examples to illustrate the performance of our proposed scheme and compare it with other methods in the literature.

Wave transmission in 2D nonlinear granular-solid composite systems

Wave propagation in granular media composed of contacting discrete elastic granules attracted considerable attention due to its highly tunable acoustic properties. While prior investigations focused on granular systems with natural boundaries (e.g., fixed or free), our study delves into two-dimensional nonlinear wave propagation within hybrid metamaterials composed of ordered granular media interacting with linearly elastic solids, employing discrete element modeling for the granular media and finite element analysis for the elastic solids. Challenges resulting from numerical instabilities arise from non-smooth contact nonlinearities and friction at granular and granular–solid interfaces. To tackle the numerical challenges, we developed an interrelated interpolation-iteration algorithm with a self-adaptive time scheme. Special consideration was given to the convergence of contact forces at the granular–solid interface. Specifically, to ensure robust computational modeling without numerical instabilities, we monitored the eigenvalues of appropriately defined local maps governing the iterative computations of the contact forces. We demonstrated the capacity of these hybrid metamaterials for shock mitigation and non-reciprocal acoustics with properties that are passively tuned (self-adaptive with) energy. Such results and computational methods contribute to the predictive modelling and design of 2D granular media with flexible interfaces, with diverse applications, e.g., in shock protectors and acoustic diodes.

A Self-Adaptive Memetic Algorithm for Distributed Job Shop Scheduling Problem

Distributed scheduling has become a common manufacturing mode, and the distributed job scheduling problem (DJSP) has attracted more manufacturers and researchers in the field of operation research. For the distributed scheduling problem, it emphasizes the flexibility of factory assignment and determines the sequence of operation related to each machine in related factories. In this paper, a mixed-integer linear programming model for the DJSP is formulated to be optimized by an SMA. Also in this paper, a self-adaptive memetic algorithm (SMA) is proposed to obtain a near-optimal solution in a limited time for the DJSP. To strengthen the effectiveness of the SMA, an independent encoding is designed with jobs assigned to factories and the sequence of operation. In the proposed algorithm, various local search strategies related to the critical path in the critical factory are designed to enhance the quality of the solution. Moreover, the self-adaptive scheme for solution improvement is formulated to reduce the search time and avoid prematurity effectively. To demonstrate the performance of the proposed algorithm, numerical experiments are carried out on 120 different instances extended from the well-known job shop scheduling benchmarks. The proposed SMA has updated 30 instance records in 120 instances and it has obtained the 91 best records in 120 instances. According to the comparison, an SMA is a more effective algorithm that could update several records of benchmarks.

A variant of improved discrete velocity method for efficient simulation of flows in entire Knudsen number regimes

In this paper, a variant of the improved discrete velocity method (VIDVM) is proposed for flows in the whole Knudsen number regimes. This method retains the advantage of the improved discrete velocity method (IDVM), which calculates numerical fluxes through a self-adaptive strategy by combining the microscopic reconstruction and the macroscopic reconstruction. Like the IDVM, the microscopic reconstruction for VIDVM is also based on the collisionless Boltzmann solver. However, different from IDVM, the macroscopic reconstruction for VIDVM is established on the Euler solver instead of the Navier–Stokes solver. Considering that the Euler solver merely computes the inviscid fluxes while the Navier–Stokes solver additionally calculates the viscous fluxes, the present method could be more efficient than IDVM. To validate the accuracy and efficiency of the present scheme, some benchmark cases from the continuum regime to the free molecular regime are conducted. Results reveal that the present scheme can predict the flow as well as IDVM, but the present solver is more efficient than IDVM.