Self-adaptive Systems Research Articles

Methylisothiazolinone modulates community assembly and improves syntrophic cooperation via adaptive evolution during sludge anaerobic digestion

Various antimicrobials could pose multifaced threats to anaerobic digestion (AD) of waste-activated sludge (WAS), yet it is still unclear how antimicrobials would affect the metabolic activity and assembly process of anaerobic microbiome. In this study, a typical antimicrobial, methylisothiazolinone (MIT), was introduced in AD and induced an initial lag effect on methane production that was eventually recovered. Although MIT disintegrated WAS to promote the release of bioavailable substrates from extracellular polymeric substances (EPS), it also caused adverse stress to the microbiome. The initial microbial metabolic activities and syntrophic relationships associated with methane generation were distinctly inhibited. Because the self-adaptive systems of two-component systems (e.g., ddpF, and ddpD) and quorum sensing (e.g., cheR, and cydB) were activated to restore the syntrophic interaction among functional anaerobes, metabolic activities were gradually recovered for methane generation. Ultimately, the methane-generation species (e.g., Methanobacterium, and Methanomassiliicoccus) were driven as core species by deterministic processes, while non-functional species (e.g., Nitrospirota) were marginalized by stochastic processes under MIT stress. Overall, this finding indicated the intrinsic drive of evolutionary adaptation, unraveled the community assembly process at phylogenetic level under antimicrobial stress, and gave direction on the assessment and mitigation of external contaminant-related ecological risks in WAS treatment.

Physical-Matched Nanoplatelets Boost Heterogeneous Thrombi Targeting Through Self-Adaptive Deformation for Thrombolysis and Endothelial Repairing.

The heterogeneity of thrombi in terms of composition, structure, and blood rheology parameters presents a challenge for effective thrombus-targeting drug delivery. To address this, a self-adaptive nano-delivery system, termed D-PLT, is developed. It consists of platelet membrane-cloaked deformable mesoporous organic silicon dioxide nanocomposite, enabling it to respond to the challenge of the heterogeneity of thrombi in arteries and veins. The system exhibits progressive targeting, with the ability to target arterial and venous thrombosis and damaged blood vessels. D-PLT physically matches the pore structure of the thrombus by undergoing varied deformation, leading to advanced targeting and enrichment of arterial and venous thrombus. When co-loaded with the thrombolytic drug urokinase (UK) and the endothelium-protecting agent atorvastatin calcium (AT), the system improves rapid vascular opening of arterial and venous thrombosis in 90 min and provides up to 7 days of durable thrombolysis and recovery from endothelial dysfunction in vivo. This self-adaptive delivery system offers a promising strategy to overcome thrombus heterogeneity.

Software architecture-based self-adaptation in robotics

Context:Robotics software architecture-based self-adaptive systems (RSASSs) are robotics systems made robust to runtime uncertainty by adapting their software architectures. The research landscape of RSASS approaches is multidisciplinary and fragmented, with many aspects still unexplored or ineffectively shared among communities involved. Objective:We aim at identifying, classifying, and analyzing the state of the art of existing approaches for RSASSs from the following perspectives: (i) the key characteristics of approaches and (ii) the evaluation strategies applied by researchers. Method:We apply the systematic mapping research method. We selected 37 primary studies via automatic, manual, and snowballing-based search and selection procedures. We rigorously defined and applied a classification framework composed of 32 parameters and synthesize the obtained data to produce a comprehensive overview of the state of the art. Results:This work contributes (i) a rigorously defined classification framework for studies on RSASSs, (ii) a systematic map of the research efforts on RSASSs, (iii) a discussion of emerging findings and implications for future research, and (iv) a publicly available replication package. Conclusion:This study provides a solid evidence-based overview of the state of the art in RSASS approaches. Its results can benefit RSASS researchers at different levels of seniority and involvement in RSASS research.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Quantum-Resilient Self-Evolving Blockchains: AI- Driven Consensus and Autonomous Security Upgrades

The fragility of conventional blockchain systems is becoming more apparent in face of the relentless march forward in quantum computing. In this paper, we suggest the optimal architecture for quantum-resistant blockchains deploying AI regarding consensus self- evolving security protocols and mechanisms. An intuitive example of this integration is the use of machine learning algorithms for a self-adapting security system, which automatically adjusts the protection on its infrastructure capabilities to evolving quantum threats. In this paper, we address the consequences of quantum computing in blockchain security introduce a framework for AI-assisted consensus (Section 3) and cover the feasibility of self- evolving blockchains that guarantee their reliability as well as integrity in a post-quantum era.

Demonstration of an ultra-wideband wavelength self-adaptive and high-precision single-frequency laser linewidth measurement system

Demonstration of an ultra-wideband wavelength self-adaptive and high-precision single-frequency laser linewidth measurement system

Self-Adaptive Intelligent Metasurface Cloak System with Integrated Sensing Units.

Metasurfaces, which are ultrathin planar metamaterials arranged in certain global sequences, interact uniquely with the surrounding light field and exhibit unusual effects of light modulation. Many interesting applications have been discovered based on metasurfaces, particularly in invisibility cloaks. However, most invisibility cloaks are limited to working in specific directions. Achieving effectiveness in multiple directions requires the metasurface to be designed with both perception and modulation capabilities. Current multi-directional metasurface cloak systems are implemented with discrete components rather than an integrated sensing component. Here, we propose an intelligent metasurface cloak system that integrates sensing units, resulting in the cloaking effect with the help of a real-time direction sensor and an adaptive feedback control system. A reconfigurable reflective meta-atom based on phase modulation is presented. Sensing units replace parts of the meta-atoms in the designed tunable metasurface, integrating with an FPGA responsible for measuring the direction and frequency of the incident wave, constituting a closed-loop system together with the feedback parts. Experimental results demonstrate that the metasurface cloak system can recognize the different directions of the incoming wave, and can adaptively manipulate the reflected phase of EM waves to conceal objects without any human participation.

A Configurable Software Model of a Self-Adaptive Robotic System

Self-adaptation, meant to increase reliability, is a crucial feature of cyber-physical systems operating in uncertain physical environments. Ensuring safety properties of self-adaptive systems is of utter importance, especially when operating in remote environments where communication with a human operator is limited, like under water or in space. This paper presents a software model that allows the analysis of one such self-adaptive system, a configurable underwater robot used for pipeline inspection, by means of the probabilistic model checker ProFeat. Furthermore, it shows that the configurable software model is easily extensible to further, possibly more complex use cases and analyses.

An artificial intelligence based self-adaptive dynamic process control system for enhancing in-situ bioremediation of benzene-contaminated groundwater – Part I methods

This study develops a new AI-based self-adaptive dynamic process control (SADPC) system based on stepwise inference combined with genetic algorithm optimization technologies, including a filtered-clustering inference prediction model (FCI simulator), a stepwise inference controller (SI emulator), a model predictive control controller, and two optimizers. This system effectively reflects the dynamics and complexity of the bioremediation process and controls the remediation system based on the feedback information. To achieve this goal, a statistical model for simulating the enhanced bioremediation process through the FCI simulator is proposed, which can predict the resulting contamination situation based on the previous contamination situation and pumping/injecting flow rate. Then a bridge is established through the SI emulator, which can generate a control action based on a given contamination situation. This suggested decision makers that guidelines and policies on remediation-oriented SADPC systems could be tentatively investigated, developed, and applied in the future effort.

Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization

In recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods.

More efficient and precise: The innovation and future of probiotic delivery from the rise of intelligent self-adaptive systems

More efficient and precise: The innovation and future of probiotic delivery from the rise of intelligent self-adaptive systems

Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI’s within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies. †

Self-adapting Machine Learning-based Systems via a Probabilistic Model Checking Framework

This article focuses on the problem of optimizing the system utility of Machine Learning (ML)-based systems in the presence of ML mispredictions. This is achieved via the use of self-adaptive systems and through the execution of adaptation tactics, such asmodel retraining, which operate at the level of individual ML components.To address this problem, we propose a probabilistic modeling framework that reasons about the cost/benefit tradeoffs associated with adapting ML components. The key idea of the proposed approach is to decouple the problems of estimating (1) the expected performance improvement after adaptation and (2) the impact of ML adaptation on overall system utility.We apply the proposed framework to engineer a self-adaptive ML-based fraud detection system, which we evaluate using a publicly available, real fraud detection dataset. We initially consider a scenario in which information on the model’s quality is immediately available. Next, we relax this assumption by integrating (and extending) state-of-the-art techniques for estimating the model’s quality in the proposed framework. We show that by predicting the system utility stemming from retraining an ML component, the probabilistic model checker can generate adaptation strategies that are significantly closer to the optimal, as compared against baselines such as periodic or reactive retraining.

Towards artificial intelligence-based disease prediction algorithms that comprehensively leverage and continuously learn from real-world clinical tabular data systems.

This manuscript presents a proof-of-concept for a generalizable strategy, the full algorithm, designed to estimate disease risk using real-world clinical tabular data systems, such as electronic health records (EHR) or claims databases. By integrating classic statistical methods and modern artificial intelligence techniques, this strategy automates the production of a disease prediction model that comprehensively reflects the dynamics contained within the underlying data system. Specifically, the full algorithm parses through every facet of the data (e.g., encounters, diagnoses, procedures, medications, labs, chief complaints, flowsheets, vital signs, demographics, etc.), selects which factors to retain as predictor variables by evaluating the data empirically against statistical criteria, structures and formats the retained data into time-series, trains a neural network-based prediction model, then subsequently applies this model to current patients to generate risk estimates. A distinguishing feature of the proposed strategy is that it produces a self-adaptive prediction system, capable of evolving the prediction mechanism in response to changes within the data: as newly collected data expand/modify the dataset organically, the prediction mechanism automatically evolves to reflect these changes. Moreover, the full algorithm operates without the need for a-priori data curation and aims to harness all informative risk and protective factors within the real-world data. This stands in contrast to traditional approaches, which often rely on highly curated datasets and domain expertise to build static prediction models based solely on well-known risk factors. As a proof-of-concept, we codified the full algorithm and tasked it with estimating 12-month risk of initial stroke or myocardial infarction using our hospital's real-world EHR. A 66-month pseudo-prospective validation was conducted using records from 558,105 patients spanning April 2015 to September 2023, totalling 3,424,060 patient-months. Area under the receiver operating characteristic curve (AUROC) values ranged from .830 to .909, with an improving trend over time. Odds ratios describing model precision for patients 1-100 and 101-200 (when ranked by estimated risk) ranged from 15.3 to 48.1 and 7.2 to 45.0, respectively, with both groups showing improving trends over time. Findings suggest the feasibility of developing high-performing disease risk calculators in the proposed manner.

Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

Extending a MAPE-K loop-based framework for Dynamic Difficulty Adjustment in single-player games

Dynamic Difficulty Adjustment (DDA) has emerged as a notable solution to address the demand for adaptive gameplay in digital games. However, the DDA domain presents various research challenges that require careful consideration. In response, this study introduces an approach that merges insights from self-adaptive systems with the specific requirements of adaptive gameplay. Our contribution involves extending the DDA-MAPEKit framework, which was previously presented. Constructed on the modular MAPE-K loop foundation, this solution facilitates the assimilation of multiple DDA strategies. The primary objective is to provide tailored treatment for distinct game mechanics (or groups of mechanics) by constructing individual MAPE-K loops for each of them. Notable enhancements include the development of a descriptive player model involving continuous player profile assessment, performance calculation, and modifications to the rules’ system to better align with the player’s conditions. A proof of concept is executed by applying DDA-MAPEKit in a Space Shooter game to assess the viability of the proposed model. Simulations are conducted both with and without the DDA mechanisms. Encouraging results are obtained through a comparative analysis of the gathered data. Evidence points out that incorporating the DDA mechanism, implemented with DDA-MAPEKit, may effectively lead to adapting variables depicting the complexity of game mechanics following the player’s performance. This fact underscores the potential effectiveness of our approach in addressing the challenges within the realm of DDA.

Application of Adaptive Fuzzy Algorithm in Single-Chip Microcomputer Control System

Objective: In practical application, an SCM control system needs to respond to user instructions quickly and accurately, and at the same time needs to have robustness and adaptability to adapt to complex and changeable environments and working states. Therefore, how to improve the control accuracy and stability of SCM control systems, and how to adapt to the change of different working environments and working states, has become one of the important issues in the design and research of SCM control systems. Methods: Adaptive fuzzy algorithm, as a new control algorithm, has the advantages of simple structure, easy implementation and high precision, and has been widely used in many fields. On the basis of three kinds of fuzzy logic system framework, this paper analyzes the self-adaptive fuzzy algorithm single-chip microcomputer control system and carries on the corresponding performance test of the system by constructing an intelligent car. The testability test of single-chip microcomputer detection, hardware detection, algorithm recognition rate and system control rate is tested, respectively, to ensure the normal operation of the system. Conclusion: The microcontroller control system of fuzzy algorithm system is compared with that of traditional algorithm system in many aspects. The average error rate of the traditional algorithm system is 2%, while the average error rate of the fuzzy algorithm system is 0.8%. In other aspects, the fuzzy algorithm system is superior to the traditional system. For example, the fuzzy algorithm system has a 100% instruction recognition rate and a data processing speed of up to 1 second. According to the characteristics and requirements of the single-chip microcomputer control system, applying the adaptive fuzzy algorithm to the design of a single-chip microcomputer control system can effectively improve the control precision and stability of thesystem.

Investigating the Hierarchical Relationship Between Clause and Phrase Using the Menzerath-Altmann Law: Evidence from Academic Research Articles

ABSTRACT The study justifies phrases (instead of words) as the direct constituents of clauses using English academic research articles (RAs) by fitting the Menzerath-Altmann Law (MAL) to the correlations between sentence length (in clause numbers) and clause length (in phrase numbers), and between clause length and phrase length (in word numbers). The study was conducted under the framework of dependency grammar, with a self-built corpus of 104,499 tokens. The results indicate that 1) both correlations abide by the MAL, or abide when excluding the second MAL regime, regardless of part-genres, suggesting that phrases are the appropriate direct constituents of clauses. 2) The increasing MAL fitting curve is found in , which has the shortest mean dependency distance. 3) Clustering analysis on the MAL parameters in dependency structure can effectively differentiate the size-restricted from the other part-genres at the lower level, i.e., clause-phrase-word level. Furthermore, our findings have important implications: 1) The second regime may be closely related to text characteristics or linguistic unit levels. 2) The dependency distance minimization effect is possibly a counteractive force against the Menzerathian shortening effect. 3) The competing relationship between clausal and phrasal complexity in RAs is driven by the self-regulating and self-adapting language system.

Self-adaptive system state optimization based on nonlinear affine transformation for renewable energy volatility

The short-term power variations of renewable energy sources result in system state fluctuations and deviations from the scheduled operating point. Tracking short-term power variations and maintaining the system's optimality is challenging for traditional optimization methods due to their highly time-consuming nature. This paper proposes a short-timescale self-adaptive optimization strategy based on the nonlinear affine transformation to deal with this problem. Firstly, the multi-period optimization model is established and solved by considering static power-frequency characteristics to obtain an optimal operating point on a longer temporal scale, with the state security margins reserved using chance constraint programming. Next, analogously with the Taylor series, the nonlinear relationship between the system state variable and short-term power fluctuations is revealed through an analytic expression of the nonlinear affine transformation. Then, a self-adaptive optimization algorithm based on the nonlinear affine transformation is proposed to achieve frequency and voltage optimizations on a shorter temporal scale. With less communication, self-adaptive optimization is implemented at the local bus level to achieve more optimal states for short-term renewable power variations rapidly. Finally, simulations demonstrate that the proposed optimization strategy can effectively enhance frequency and voltage qualities, and decrease objective function, thereby improving the operation safety and economy.

Design and prediction simulation of an active-dispersing mechanism for magnetorheological damper with twin-tube configuration

Sedimentation immunity is one of the key features of magnetorheological (MR) dampers, which means the lifetime-long service without degradation under the sedimentation of MR fluid. In this paper, an active-dispersing mechanism is established with twin-tube configuration toward the sedimentation immunity, by adding a circulation channel powered by rotating blades between the tubes when the MR damper is not in operation. Finite element (FE) method is employed to reveal the re-dispersion process once the MR fluid settled to a specific degree, and the benefits of circulating channel and twin-tube sedimentation-immunity system for the MR fluid are discovered by the simulation. Ultimately, a self-adaptive system could be built to ensure the MR fluid in the damper keeping in a relative uniform and thus the sedimentation immunity is fulfilled.

Decision Making for Self-Adaptation Based on Partially Observable Satisfaction of Non-Functional Requirements

Approaches that support the decision-making of self-adaptive and autonomous systems (SAS) often consider an idealized situation where (i) the system’s state is treated as fully observable by the monitoring infrastructure, and (ii) adaptation actions are assumed to have known, deterministic effects over the system. However, in practice, the system’s state may not be fully observable, and the adaptation actions may produce unexpected effects due to uncertain factors. This article presents a novel probabilistic approach to quantify the uncertainty associated with the effects of adaptation actions on the state of a SAS. Supported by Bayesian inference and POMDPs (Partially-Observable Markov Decision Processes), these effects are translated into the satisfaction levels of the non-functional requirements (NFRs) to, therefore, drive the decision-making. The approach has been applied to two substantial case studies from the networking and Internet of Things (IoT) domains, using two different POMDP solvers. The results show that the approach delivers statistically significant improvements in supporting decision-making for SAS.