Advances in iterative learning control: A recent five-year literature review.

Precision occupational lead exposure assessment through medical-informed machine learning.

A design of machine learning algorithms for Darcy-Forchheimer flow in magnetized Carreau nanofluid flow: An advanced competent Bayesian regularization framework

A generalization and lightweight recognition for citrus fruit harvesting based on improving YOLOv8

Application of fuzzy hybrid evolutionary algorithm for optimal design of grid connected renewable energy system

Application of multi-strategy integrated IVY optimization algorithms in diffuse reflective acoustic metasurface design

• Designs a bi-objective optimization model to solve the relocation problem involved in the double-ended outbound process of pallets in the palletized FWSBS/RS scenario. • Integrates Genetic Programming (GP) design rules to improve the A* algorithm for addressing the double-ended relocation problem in outbound operations of the palletized FWSBS/RS with multiple storage areas and pallets groups. • Proposes a dataset generation and algorithm configuration method for the relocation optimization problem in this specific scenario. • Rigorously validates the solution effectiveness of the innovative optimization algorithm through practical experiments. The Container Relocation Problem (CRP) in palletized FWSBS/RS(Four-Way-Shuttle-Based Storage and Retrieval System) presents a new challenge in automated warehousing research, emphasizing the need for effective strategy rules to optimize the movement of pallets within storage areas. This paper innovatively integrates the genetic programming (GP) design rule to improve the A* algorithm for the CRPMG (Container Relocation Problem with Multiple Groups), which involves a palletized FWSBS/RS with multiple storage areas and groups of palletized cargos on both ends. First, a bi-objective optimization model is designed to solve the CRP involved in the double-ended pallets outbound process. This model conforms to a realistic palletized FWSBS/RS scenario and minimizes the operating time of the four-way shuttle while reducing the number of CRP times. The optimal solution is obtained using a Gurobi solver firstly. Then, a set of construction criteria with a scenario-specific solution for the palletized FWSBS/RS is proposed to adapt the genetic programming and generate the relocation rules. This is the first time that genetic programming has been applied to the optimization model of the palletized FWSBS/RS. Numerical experiments demonstrate that the designed bi-objective optimization model and the improved A* algorithm significantly enhance problem-solving performance, offering excellent convergence speed and robustness, thereby proving their effectiveness and feasibility.

Towards understanding memory buffer based continual learning.

The convergence of optical transport and wireless access in next- and future-generation networks imposes strict QoS demands, particularly end-to-end reliability, which conventional redundancy approaches cannot meet. The paper presents an architectural framework integrating three aspects: a risk-diverse route-computation algorithm with shared-risk link group constraints that achieve polynomial complexity and overcome memory constraints. Secondly, it presents a self-optimising signal-control bus modelled as a closed-loop queueing system that maintains 95% throughput under an offered load of 400%, thereby representing a statistically significant improvement over static configurations. Lastly, it presents an adaptive multipath communication framework formalised as a multi-objective optimisation that enables application-specific trade-offs among reliability, latency, and bandwidth. Performance evaluation demonstrates polynomial versus exponential memory scaling, control-plane resilience under signalling storms, and sub-10 ms latency at 10% packet loss. As such, the discussed aspects establish design principles for reliable, resilient, and robust converged optical–wireless networks. In addition to formal architectural modelling and algorithm design, this study independently validates the proposed framework through original simulations conducted in OMNeT++ and ns-3.

Carbon-fiber-reinforced polymers (CFRPs) are essential for lightweight transport and energy systems, but most current forms-particulate, short-fiber, or laminated- break the continuity of reinforcing fibers, interrupting load transfer and limiting strength, safety, and design freedom. Architected lattice materials offer a route to higher strength-to-weight ratios, yet prior CFRP lattices are largely confined to the microscale or rely on joints and segmented fibers that compromise load transfer. Here we demonstrate fully continuous three-dimensional CFRP lattices fabricated at the mesoscale using a 3D node winding process guided by algorithmic design. By systematically controlling fiber continuity at the lattice unit-cell level, these structures achieve specific strengths of up to 782 MPa·cm³·g⁻¹ at foam-like densities, representing a considerable achievement in mesoscale CFRP lattice architectures. Unlike conventional CFRPs, the lattices fail progressively through pseudo-ductile, damage-tolerant mechanisms with partial height recovery under compression. System-level demonstrations, including a robotic drone with substantially reduced frame mass and extended endurance, confirm scalability and practical relevance. This work establishes continuity-engineered CFRP lattices as a promising class of lightweight architected materials for next-generation structural systems.

Unlike traditional model-based reinforcement learning (RL) approaches that estimate system parameters from data, nonmodel-based data-driven control learns the optimal policy directly from input-state data without any intermediate model identification. Although this direct RL approach offers increased adaptability and resilience to model misspecification, its reliance on raw data leaves it vulnerable to system noise and disturbances that may undermine convergence, robustness, and stability. In this article, we establish the convergence, robustness, and stability of value iteration (VI) for data-driven control of stochastic linear quadratic (LQ) systems in discrete time with entirely unknown dynamics and cost. Our contributions are threefold. First, we prove that VI is globally exponentially stable for any positive semidefinite initial value matrix in noise-free settings, thereby significantly relaxing restrictive assumptions on initial value functions in existing literature. Second, we extend our analysis to settings with external disturbances, proving that VI maintains small-disturbance input-to-state stability (ISS) and converges within a small neighborhood of the optimal solution when disturbances are sufficiently small. Third, we propose a new nonmodel-based robust adaptive dynamic programming (ADP) algorithm for adaptive optimal controller design, which, unlike existing procedures, requires no prior knowledge of an initial admissible control policy. Numerical experiments on a "data center cooling" problem demonstrate the convergence and stability of the algorithm compared to established methods, highlighting its robustness and adaptability for data-driven control in noisy environments. Finally, we apply the method to dynamic portfolio allocation, demonstrating its practical relevance outside traditional control tasks.

Abstract The Mølmer–Sørensen gate, a cornerstone entangling operation in trapped-ion systems, represents a promising alternative to standard entangling gates in superconducting quantum architectures. However, its performance on superconducting hardware has remained unverified. In this work, we present a hardware-efficient implementation of the Mølmer–Sørensen gate and characterize its performance using quantum process tomography (QPT) on IBM Quantum’s superconducting processors. Our implementation achieves a process fidelity of 92.47% on the real quantum hardware, a performance competitive with the 93.02% fidelity of the device’s native controlled-NOT (CX) gate. Furthermore, for the $$\vert {00} \rangle $$ | 00 ⟩ input state, the gate prepares the target Bell state with $$94.2\%$$ 94.2 % success probability, confirming its correct logical operation. These results demonstrate that non-native entangling gates can be optimized to perform on par with hardware-native operations. This work expands the effective gate set for algorithm design on fixed-architecture processors and provides a critical benchmark for cross-platform gate evaluation, underscoring the role of hardware-aware compilation in advancing noisy intermediate-scale quantum (NISQ) computing.

Identification of appropriate patients for hospital-in-the-home (HITH) remains a significant challenge. We hypothesise that a human-centred design (HCD) approach can assist in developing a computerised clinical decision support system (CDSS) to address this issue via leveraging data within the electronic medical record (EMR). We describe the design, development, validation and implementation outcomes of the HITH Eligibility Identifier (HEI), a CDSS for identifying inpatients suitable for HITH in Melbourne, Australia. Approaches include: (a) describing digital algorithmic design and workflow development via the iterative HCD process; (b) validation of the HEI algorithm, interface and performance using Message Understanding Conference and Miettinen-Nurminen analyses; and (c) evaluation of system impact and outcomes post-implementation. The HEI CDSS integrates algorithmic decision support with clinician judgement through an interactive user interface embedded within the EMR. It ranks hospitalised patients from most to least likely eligible for HITH using administrative data, clinical information and markers of illness severity. Validation of HEI performance found that high-ranking patients were twice as likely to be eligible for HITH (improvement in eligibility probability of 46%; 95% confidence interval: 38%-54%). Post-implementation metrics show improvements in HITH utilisation, service delivery and efficiency. The HEI CDSS illustrates the importance of an HCD approach in ensuring local relevancy and clinical adoption of digital healthcare projects. Principles from its design and implementation can improve patient identification for HITH programs and be adapted for other CDSSsrelevant to hospitalised patient cohorts.

Education is a strategic priority for the state, with youth development and civic upbringing being its integral component. Russia is currently undergoing parallel processes of reforming its entire education system and revitalizing the system of youth policy and educational work in universities. These large-scale transformations, accompanied by increased funding, a growing number of objectives, and a wider range of involved stakeholders, significantly complicate the management of youth policy at the institutional level. Existing approaches are often fragmented and fail to provide the necessary efficiency under these new conditions. This paper provides a comprehensive review of scholarly literature, demonstrating that existing research predominantly focuses on specific areas of youth policy implementation in higher education institutions, analysis of its content, or its management at the municipal, regional, or national levels. Simultaneously, there is a discernible lack of scientific developments in youth policy management at the level of an individual higher education institution that would enable the formation of universal approaches to building an effective management system for youth policy within a university. In this context, the development of universal management models capable of systematizing processes, harmonizing the interests of multiple stakeholders, and enhancing the effectiveness of educational activities is of particular relevance and novelty. The high-level model for managing youth policy and educational work in higher education institutions, developed by the author, holds both theoretical and practical significance. Given its generalized nature, this model can be used for further scientific research and the development of detailed approaches to managing youth policy in universities. It can also be applied in designing a management system for a specific HEI, serving as a foundation for such efforts. The article argues that a youth policy management model for a university must account for the specific features of youth policy as an object of management. Furthermore, it should incorporate a project selection mechanism, a classificatory framework defining the boundaries of youth policy as a managed object, a design algorithm, a set of youth policy management functions, a system of performance indicators, and a mechanism for adjusting subsequent decisions.

As the rapid development of AI (Artificial Intelligence), a series of profound ethical issues are illustrated to individuals. The most significant issues are the spread of algorithmic discrimination, and the encroachment of human dignity caused by instrumental rationality. In order to solve these issues, it is urgent for ethics to provide principled guidance. In this paper, I will display that Kantian Ethics, especially its core concept of categorical imperative, can provide irreplaceable formal foundation and critical lens for AI ethics. Through systematically explaining the key concepts of Kantian ethics, which are autonomy, self-legislation, and the principle that humanity is an end in itself. The current AI cannot be considered as a Kantian moral agent, because of its internal heteronomous nature and lack of free will. Based on this argument, this paper further discusses the programs of reconceptualized responsibility in the age of algorithms. Specifically, on the one hand, although AI is not a moral agent, its distributed feature may correspond to a distributed model of responsibility attribution. On the other hand, the formal features of algorithms provide a possibility for transforming the procedural cores of categorical imperative, such as the test of universalizability, into principles of ethical algorithm design. In conclusion, the dialogue between Kantian Ethics and AI is mutually enriching. Kantian ethics not only offers an essential ethical standard for evaluating and regulating AI systems, but also illustrates enduring vitality while facing contemporary technological challenges.

The rapid development of digital technology in education has encouraged the integration of innovative learning approaches to enhance 21st-century skills, particularly computational thinking. Augmented Reality (AR), as an interactive visual technology, is considered to have significant potential in supporting more contextual, engaging, and meaningful learning experiences. This study aims to analyze the effectiveness of Augmented Reality in improving students’ computational thinking skills through a systematic literature review approach. The method employed is a Systematic Literature Review (SLR) by examining scholarly articles published between 2018 and 2025 from various relevant academic databases. The literature selection process was conducted based on predefined inclusion and exclusion criteria to ensure the quality and relevance of the selected studies. The findings indicate that the use of AR in learning contributes positively to the development of computational thinking skills, particularly in terms of problem decomposition, pattern recognition, abstraction, and algorithm design. In addition, AR enhances student engagement and learning motivation by providing interactive and immersive learning experiences. However, several challenges remain, including limited technological infrastructure and teachers’ readiness in implementing AR-based learning. Therefore, further support is required to optimize the integration of AR technology in educational settings.

Design and performance analysis of a blind source separation algorithm for piano music signals

The increasing volume, temporal density, and diversity of satellite Earth observation (EO) data have fundamentally transformed quantitative vegetation remote sensing. Dense multi-sensor time series and computationally intensive modelling have rendered traditional download-and-process workflows increasingly impractical. Cloud-native computing—where data access, storage, and computation are co-located and analyses are executed in data-proximate environments—has therefore emerged as a key paradigm for scalable and reproducible vegetation EO analysis. This review provides a science-oriented synthesis of cloud-native EO for quantitative vegetation research. We examine architectural principles, data models, and compute patterns that shape how vegetation analyses are implemented, scaled, and scientifically interpreted. Particular attention is given to machine learning as a system component, including model lifecycle management, domain shift, and evaluation integrity in distributed environments. We analyse how cloud-native data abstractions influence algorithmic assumptions, validation design, and long-term product consistency, highlighting trade-offs between analytical complexity, computational cost, latency, and scientific robustness. We provide a forward-looking perspective on emerging imaging spectroscopy missions and the growing system-level requirements for reproducible, scalable, and uncertainty-aware vegetation analytics at continental-to-global scales. We also outline how cloud-native EO infrastructures are driving new scientific paradigms based on continuous monitoring, systematic reprocessing, and AI-driven modelling.

Selecting the best multi-objective algorithms for photovoltaic energy storage system (PV-ESS) design remains challenging due to limited benchmarking across renewable energy studies. This study addresses this gap through a systematic evaluation of four widely used multi-objective optimization algorithms: NSGA-II, Multi-Objective Particle Swarm Optimization (MOPSO), weighted-sum scalarization, and ε-constraint methods. Performance assessment utilized three Pareto front quality metrics: Inverted Generational Distance (IGD) for convergence quality, hypervolume (HV) for objective-space coverage, and spacing for solution distribution uniformity. The algorithms were tested on PV-ESS design problems in three developing economies (Nigeria, South Africa, India) under identical problem formulations and computational resources. NSGA-II achieved superior performance across all metrics in all three case studies. For convergence quality, NSGA-II attained a mean IGD of 0.0083, outperforming MOPSO by 29%, ε-constraint by 64%, and weighted-sum by 131%. For objective-space coverage, NSGA-II achieved a mean HV of 0. 700, representing 10–16% better coverage than other methods. For solution distribution, NSGA-II showed a mean spacing of 0.076, indicating 30–117% more uniform Pareto fronts. Computational efficiency analysis revealed that NSGA-II’s runtime is between 5.5 and 7.8 h per case, providing better quality–time ratios compared to ε-constraint methods (which are 18 times slower), while avoiding MOPSO’s premature convergence. Statistical validation confirmed NSGA-II’s superiority, with p < 0.01 across all quality metrics. These results establish NSGA-II as the best algorithm for lifecycle-aware PV-ESS optimization, offering quantitative, evidence-based guidance for practitioners selecting optimization tools for renewable energy system design. The demonstrated performance leads to $ 45,000–$ 60,000 lifecycle cost savings per MW/MWh of system capacity through improved Pareto front identification.

Industry batik fashion today undergoing a comprehensive transformation because of the convergence of digital technology and shifts in consumer behavior within the Creative Economy Ecosystem. These dynamics demand that industry players move beyond merely preserving traditional cultural identity and begin orchestrating design innovation and marketing strategies that resonate with digital markets. This research focuses on a qualitative analysis of the utilization of a generative design approach in the visual engineering of batik ornaments, as well as formulating a conceptual model of branding strategies based on consumer data to increase competitiveness in modern fashion. Through a qualitative approach that emphasizes exploratory design analysis and digital consumer behavior research, data are deeply examined through literature studies, observation of visual trends on social media, and narrative evaluation of audience preferences. The research findings indicate that algorithmic generative design systems can broaden the spectrum of motif innovation flexibly through modifications in pattern structures, repetition, and color composition. Furthermore, a qualitative understanding of digital data provides sharper empathetic insights into consumer visual preferences, guiding the design process to become more adaptive. This research culminates in the formulation of a conceptual branding model that closely integrates design exploration, consumer insight analysis, and digital communication strategies, thereby holistically strengthening the legitimacy of batik as a competitive cultural commodity in the global fashion arena.