Adaptive Approach Research Articles

A Cheminformatics Workflow to Select Representative TSCA Chemicals for New Approach Methodology (NAM) Screening.

The Toxic Substances Control Act (TSCA) requires the US EPA to evaluate the hazard and exposure of new and existing chemicals. New chemical notifications are typically data-poor and EPA has historically relied upon approaches including chemical categories to fill data gaps. As part of a multi-year Research Program, opportunities are being explored to leverage New Approach Methods (NAMs) in hazard and exposure assessments. Data from a battery of in vitro NAMs will be generated to form a case study for an adaptable approach to inform new chemical assessments. Herein, a cheminformatics workflow was developed to identify a set of ∼300 representative candidate chemicals for in vitro screening from the TSCA non-confidential active inventory. The freely available web application ClassyFire was used to categorize all discrete organic structures from the TSCA inventory into one of 68 primary structural categories. Large primary categories were subcategorized into smaller categories using hierarchical agglomerative clustering, ultimately yielding 180 structural terminal categories. The inventory was filtered to substances that lacked previous ToxCast bioactivity screening, were associated with physicochemical property predictions indicating non-volatile solids or liquids, and had a higher chance of procurement. Amenability predictions for liquid chromatography-mass spectrometry were also generated to provide an indication of which chemicals lent themselves to aqueous-based screening and analytical verification in solvated samples. Structures associated with transformation in solvent, potentially explosive or highly reactive, were excluded. Potential candidate substances were selected on the basis of being structurally representative of the terminal category and meeting other screenability conditions. A final set of 318 candidate chemicals were proposed to undergo analytical quality control and screening in a range of broad and targeted biological technologies for human health-relevant end points. Finally, in silico tools were applied to explore predicted hazard profiles of these candidate substances relative to the full inventory.

Human-in-the-loop active learning for goal-oriented molecule generation

Machine learning (ML) systems have enabled the modelling of quantitative structure–property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules.Scientific contributionWe present an adaptable framework that integrates AL and human expertise to refine property predictors for goal-oriented molecule generation. This approach is robust to noise in human feedback and ensures that navigating chemical space with human-refined predictors leverages human insights to identify molecules that not only satisfy predicted property profiles but also score highly on oracle models. Additionally, it prioritizes practical characteristics such as drug-likeness, synthetic accessibility, and a favorable balance between exploring diverse chemical space and exploiting similarity to existing training data.

Energy-based approach to the assessment of traffic flow

This article focuses on modeling vehicle acceleration noise in different road conditions, emphasizing urban, highway, and rural roads in Ukraine. Acceleration noise, which refers to the fluctuations in a vehicle's acceleration, is a critical factor in vehicle safety, fuel efficiency, and driving comfort. The research aims to improve current vehicle dynamics models by integrating multi-body dynamics and machine learning algorithms, allowing for more precise predictions of acceleration variability in real-time. The study is based on the existing literature, showing that road surface quality significantly affects acceleration noise. With frequent stop-and-go traffic, urban roads produce moderate but irregular noise patterns. Highways show stable acceleration noise at moderate speeds, but noise increases sharply as vehicles approach higher speeds due to aerodynamic forces. Rural roads, especially those in poor condition, exhibit the highest variability in acceleration noise, even at low speeds. The proposed model has been validated using real-world data. It demonstrates a strong correlation between the predictions and actual vehicle behavior on various road types. One of the key innovations in this research is the use of machine learning to adjust model parameters in real-time dynamically. This adaptive approach enhances the model’s accuracy and applicability, especially in intelligent transport systems. The model can inform traffic management strategies, allowing for real-time adjustments to speed limits, traffic signals, and routing decisions based on road conditions. This contributes to safer, more efficient, and sustainable transport systems, particularly in regions with inconsistent road infrastructure. The research concludes that integrating acceleration noise modeling into intelligent transport systems can significantly improve traffic flow and vehicle safety. Future research will expand the dataset to include a broader range of vehicle types and road conditions, further refining the model's predictive capabilities.

Role of AI, Automation & Robotics in Pharmaceutical Industry

The pharmaceutical industry is undergoing a massive transformation driven by technological advancements and leapfrogging further due to the integration of Artificial intelligence (AI), automation, and robotics. These technologies are being deployed to cover various aspects, including drug discovery, manufacturing, supply chain, and patient care. AI's ability to process and analyze massive data sets allows researchers to identify new drug candidates faster or improve on current ones through various strategies. Automation transforms repetitive tasks and increases accuracy, but most importantly, it frees people up from work that they need not do and concentrates on the jobs that still require human involvement. Conversely, when integrated with AI, robots enhance the production process by enabling speedy, accurate, and scalable manufacturing. Robotics are now being used in pharmacies for medication dispensing and are very efficient. Furthermore, all these innovations driven by AI, Automation, and robotics also contribute to personalized medicine by extending tailored treatments based on individual patient data. This paper explores the role of AI, Automation, and Robotics in the Pharmaceutical industry and its benefits. The ongoing evolution of such technologies holds immense potential to address the growing needs and handle industry challenges such as increasing demand, regulatory compliance, and global health needs to ultimately lead the pharmaceutical industry towards a more efficient, adaptive, and patient-centered approach.

Probabilistic Robust Damping Controller Optimization for Mitigating Subsynchronous Control Interaction in DFIG and PMSG‐Based Wind Farms

ABSTRACTAdvanced control and mitigation solutions are required due to the enormous challenges posed by sub‐synchronous control interaction in wind farms. These concerns include system instability, potential damage to turbines, and grid stability issues. This research presents the probabilistic robust damping controller, an adaptive control approach, to address the issues in grid‐connected wind farms. The innovation employs the balanced remora optimization algorithm to optimize controller parameters, ensuring system stability under various conditions. For permanent magnet synchronous generator‐based wind farms with weak grid circumstances, critical elements such as the DC‐link voltage, the d‐q axis current reference, and the actual values in the grid‐side converter are essential for problem mitigation. The controller generates controlled output using these input signals. The MATLAB‐based implementation demonstrates the controller effectiveness in damping issues within a series‐compensated doubly fed induction generator. Comparative analysis with conventional controllers under different grid conditions and disturbances validates its superior performance. Time‐domain simulations, both with and without time delay, further confirm the controller's ability to enhance system stability and dampen the issues. Four schemes are used to evaluate the doubly fed induction generator‐based wind farms: wind speed at 13 m/s with 75% series compensation, 7 m/s with 75% compensation, 9 m/s with 45% compensation, and 9 m/s with 60% compensation. Under short circuit ratio, the wind farms based on permanent magnet synchronous generators are analyzed in three scenarios: normal, strong, and weak cases.

Personalized College English Learning Experience Assisted by Artificial Intelligence: An Algorithm-Driven Adaptive Learning Approach

Personalized College English Learning offers customized education based on individual necessities and aims. Students enhance their linguistic ability efficiently over engaging sessions, specific curricula, and professional teachers. This study aims to progress a smart Artificial Intelligence (AI)-based adaptive learning method for enhancing personalized college English learning experience. Our proposed model provides an intelligent detecting device-enabled setting for learning English over big data analysis and machine learning based on a DM approach. Intelligent sensing strategies detention significant statistics from college English students and big data analysis examines the data to produce beneficial insights that increase real-time personalizing of English learning experiences. We suggest an innovative Starling Murmuration fine-tuned Dynamic Weighted Random Forest (SM-DWRF) system for classifying the data. Our model influences perceptions from collective behavior in starling flocks to dynamically alter feature weights in DWRF arrangement. It integrates fine-tuning mechanisms to adaptively weight features, developing classification accurateness and strength in adapted college English learning settings. We implemented our suggested model in Python software. In the assessment stage, we accurately measure the effectiveness of our proposed SM-DWRF model in identifying diverse features of English learning across various parameters. Our experimental findings incontestably showcase the greater performance of our model associated with conventional approaches in classifying content from multimodal statistics. Significantly, we perceive prominent enhancements in reliability and robustness, when acclimating to dynamic learning settings.

Digital Transformation in Civil Service Management: Implementing the SmartASN Platform

Digital transformation is a critical need for modern governments to address the growing demands for fast, transparent, and efficient public services. This study examines the implementation of the SmartASN platform in driving the digital transformation of State Civil Apparatus (ASN) management. Employing a qualitative research approach with a narrative review methodology, the study systematically reviews and synthesizes secondary data collected from 2016 to 2022 on the digital transformation of ASN management. The Agile Governance theory guides the analysis. It focuses on three key aspects: 1) SmartASN Platform Governance, 2) Systematic and Adaptive Approaches in ASN Management, and 3) ASN Management in the Digital Age. The findings demonstrate that the SmartASN platform is instrumental in advancing ASN’s digital transformation, supporting Indonesia’s broader goals of creating a world-class bureaucracy. This is evidenced by Indonesia’s significant improvement in the E-Government Development Index (EGDI), which rose from 116 in 2016 to 77 in 2022. The study contributes to the academic understanding of digital governance by providing actionable recommendations to optimize the SmartASN platform and develop policies that support agile digital transformation. These insights highlight the potential of digital platforms in fostering innovation and enhancing adaptability in the public sector.

A temporally adaptive schlieren approach by fusing frame- and event-based cameras

A temporally adaptive schlieren approach by fusing frame- and event-based cameras

Gamification in Flipped Classrooms for Sustainable Digital Education: The Influence of Competitive and Cooperative Gamification on Learning Outcomes

This study examines the effects of competitive versus cooperative gamification mechanics on the learning achievement of female first-year middle school students in flipped classrooms, highlighting its implications for designing sustainable learning environments. Employing a quasi-experimental design, 60 students were randomly assigned to two experimental groups, each engaged in a flipped classroom environment with either competitive or cooperative gamified elements. While both groups showed significant improvement in post-test scores, no significant differences were observed in cognitive achievement or skills acquisition between the groups. These findings emphasize that competitive and cooperative gamification mechanics can be equally effective in enhancing learning, suggesting that the choice of mechanic does not critically impact learning outcomes. The study provides practical guidance for educators and instructional designers in developing balanced gamified learning environments that optimize competitive and cooperative strategies, thus fostering a more versatile and adaptable approach to student motivation and engagement in sustainable technology-enhanced education.

The First Fifth-Generation District Heating and Cooling System in Kazakhstan: Planning and Design

This paper focuses on the potential for integrating Geographic Information System (GIS) software into Fifth-Generation District Heating and Cooling (5GDHC) systems to promote efficient and sustainable energy management, particularly in Kazakhstan. By reviewing the key literature, we identify three main areas where GIS software enhances the planning of 5GDHC systems: decision-making in the context of energy market regulations, operational data utilization, and modeling/simulations for technical design. The technical design emphasizes the role of heat pumps, ultra-low temperature district heating (ULTDH) networks, and end-user buildings. Previous research has explored various methodologies for integrating network and demand-side strategies, developing sustainable district heating and cooling (DHC) systems, and mapping urban areas suitable for DHC deployment. However, none has presented an open-source model incorporating GIS-based decision-making in designing 5GDHC systems. This study, for the first time, addresses this gap through a case study conducted in the Northern Industrial Zone of Karaganda, Kazakhstan, demonstrating how GIS-enhanced modeling can be effectively applied in a developing, industry-driven economy. We outline the mathematical framework for comparing existing simulation tools and developing a custom model suited to the region’s needs. Additionally, we discuss validation and calibration methods, which remain underexplored in the current literature. The proposed model incorporates waste heat recovery from local sources, including nearby wastewater treatment plants, showcasing a sustainable energy solution for the industrial park. The results indicate that a well-structured 5GDHC system, supported by GIS tools, can markedly enhance energy efficiency and sustainability, presenting a scalable and adaptable approach for other regions in Kazakhstan and beyond.

Securing the Future: Strategies for Global Polio Vaccine Security Amid Eradication Efforts

Background/Objectives: As we commemorate 50 years of the Expanded Programme on Immunization (EPI), the global mission to eradicate polio stands at a critical juncture. While remarkable progress has been made over the past decades, ensuring a steady supply of polio vaccines remains a significant challenge that could undermine these achievements. This manuscript aims to address the complexities of polio vaccine security within the context of the Immunization Agenda 2030 (IA2030) and the Global Polio Eradication Strategy 2022–2029, proposing actionable strategies to strengthen the vaccine supply. Methods: This manuscript analyzes obstacles to vaccine security, including supply disruptions and market uncertainties. It presents the Polio Vaccine Security Framework as a key strategy for addressing these challenges. Data were gathered from Global Polio Eradication Initiative (GPEI) reports, consultations with key stakeholders, and analyses of past vaccine shortages. Results: The findings indicate that the primary risks to vaccine security include the lack of a coherent long-term policy framework on polio vaccination, the absence of a clear polio vaccine development roadmap, and insufficient long-term, predictable forecasting. Additionally, stronger coordination is needed between stakeholders involved in vaccine supply, polio containment, and research, as well as addressing challenges related to financing and access to resources. Conclusions: A robust, adaptable, and sustainable approach to vaccine security, proposed in the Polio Vaccine Security Framework, is critical to achieving and sustaining polio eradication. Collaboration among policymakers, manufacturers, and stakeholders to implement it is essential to ensure the uninterrupted supply of polio vaccines, protecting the progress made over the past half century, and preventing a resurgence of poliovirus in the future.

Output feedback control for non-strict feedback nonlinear systems: an adaptive fuzzy backstepping scheme

The adaptive fuzzy tracking control design issue is considered in this paper for a class of non-strict feedback nonlinear systems subject to external disturbances. A fuzzy high-gain state observer is constructed to reconstruct the unmeasurable states of the system by leveraging the universal approximation property of fuzzy logic systems for arbitrary unknown nonlinear functions. Within the framework of backstepping control design and in conjunction with adaptive techniques, an adaptive fuzzy control approach is presented. Subsequently, to work out the inherent ‘complexity explosion’ problem of the proposed control approach, dynamic surface control technique is introduced, leading to a simplified adaptive fuzzy output feedback control scheme. Stability analysis shows that the two proposed control schemes can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, meanwhile the system tracking error can converge to a small neighborhood around the origin. Ultimately, a numerical simulation example is provided to validate the feasibility of the proposed control scheme.

An adaptive thresholding approach for open set fault diagnosis

Abstract Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation methods that align source and target domains often overlook the spatial distribution of each class in the feature space, leading to potential negative transfer and misclassification of unknown faults. To address these challenges, this paper proposes a k-nearest neighbors based adaptive thresholding (KNNAT) method, which dynamically adjusts classification thresholds based on the spatial distribution of each class in the feature space. This approach effectively isolates unknown faults, reducing their impact on domain adaptation and improving the reliability of the diagnostic process. Extensive experiments on the publicly available CWRU bearing and PHM09 datasets demonstrate that the proposed KNNAT method outperforms other state-of-the-art methods, achieving higher accuracy and robustness in identifying known faults while successfully isolating unknown faults. These results highlight the potential of using the KNNAT method to enhance the reliability of mechanical systems in cross-domain fault diagnosis applications.

Penerapan Teknologi Pendidikan: Menghadapi Kendala, Menciptakan Solusi

This research aims to identify obstacles and solutions in the application of educational technology through literature review. Technology has great potential in improving the quality of learning, but its application in the field still faces various obstacles. This research uses a qualitative approach with a literature review method. The sources used include academic journals, scientific articles, government reports, and publications from relevant national and international educational institutions. The data obtained is analyzed thematically by identifying the main categories that emerge from the literature. This literature review reveals four main obstacles in the application of educational technology, namely the lack of adequate infrastructure, limited technological competence among educators, resistance to change, and lack of policy support from the government and educational institutions. To overcome these obstacles, several solutions are recommended, including increasing equitable access to technology infrastructure, providing sustainable technology training for educators, implementing an adaptive approach in the technology integration process, and strengthening policies that support technology adoption in education

An effective and open source interactive 3D medical image segmentation solution

3D medical image segmentation is a key step in numerous clinical applications. Even though many automatic segmentation solutions have been proposed, it is arguably that medical image segmentation is more of a preference than a reference as inter- and intra-variability are widely observed in final segmentation output. Therefore, designing a user oriented and open-source solution for interactive annotation is of great value for the community. In this paper, we present an effective interactive segmentation method that employs an adaptive dynamic programming approach to incorporates users’ interactions efficiently. The method first initializes an segmentation through a feature-based geodesic computation. Then, the segmentation is further refined by using an efficient updating scheme requiring only local computations when new user inputs are available, making it applicable to high resolution images and very complex structures. The proposed method is implemented as a user-oriented software module in 3D Slicer. Our approach demonstrates several strengths and contributions. First, we proposed an efficient and effective 3D interactive algorithm with the adaptive dynamic programming method. Second, this is not just a presented algorithm, but also a software with well-designed GUI for users. Third, its open-source nature allows users to make customized modifications according to their specific requirements.

Self-adaptive weighted physics-informed neural networks for inferring bubble motion in two-phase flow

Modeling complex fluid flow using machine learning is increasingly recognized as a valuable approach for revealing multiphase fluid phenomena. Bubble dynamics represent a classical two-phase flow problem that plays a crucial role in various engineering domains. In this paper, physics-informed neural networks (PINNs) are applied to facilitate incompressible two-phase bubble motion modeling by integrating governing equations and interface evolution equations. The loss function of PINNs consists of multiple loss terms, including initial and boundary conditions constraints, partial differential equations residuals, and volume fraction constraints. The performance of PINNs is influenced by the competing effects of these loss terms. Therefore, we introduce a heuristic adaptive weights approach to automatically adjust loss weights for each training point, avoiding manual tuning and improving the accuracy of PINNs. We investigate typical bubble motion cases, specifically focusing on bubble rising and breakup, to showcase the capabilities of the proposed method. We explore the impact of weights and present the results in comparison to the baselines. Through the bubble breakup case, we illustrate that our model shows superior performance even with more complex scenarios. Then we further discuss the generalization and robustness of our model, showing their indispensability over traditional solvers in gas–liquid two-phase systems. Specifically, we accelerate computation speed in transfer learning without the need to modify the original model. We also show that our method effectively solves ill-posed problems, such as those without initial data or with incomplete or noisy boundary conditions.

An On-Board Adaptive Modeling Approach Based on MIRLS-MA Extended Kalman Filter for Aeroengines

An On-Board Adaptive Modeling Approach Based on MIRLS-MA Extended Kalman Filter for Aeroengines

Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

Load forecasting using fuzzy logic, artificial neural network, and adaptive neuro-fuzzy inference system approaches: application to South-Western Morocco

Load forecasting using fuzzy logic, artificial neural network, and adaptive neuro-fuzzy inference system approaches: application to South-Western Morocco

Optimal gait design for a soft quadruped robot via multi-fidelity Bayesian optimization

This study focuses on the locomotion capability improvement in a tendon-driven soft quadruped robot through an online adaptive learning approach. Leveraging the inverse kinematics model of the soft quadruped robot, we employ a central pattern generator to design a parametric gait pattern, and use Bayesian optimization (BO) to find the optimal parameters. Further, to address the challenges of modeling discrepancies, we implement a multi-fidelity BO approach, combining data from both simulation and physical experiments throughout training and optimization. This strategy enables the adaptive refinement of the gait pattern and ensures a smooth transition from simulation to real-world deployment for the controller. Compared to previous result using a fixed gait pattern, the multi-fidelity BO approach improves the robot’s average walking speed from 0.14 m/s to 0.214 m/s, an increase of 52.7%. Moreover, we integrate a computational task off-loading architecture by edge computing, which reduces the onboard computational and memory overhead, to improve real-time control performance and facilitate an effective online learning process. The proposed approach successfully achieves optimal walking gait design for physical deployment with high efficiency, effectively addressing challenges related to the reality gap in soft robotics.