Abstract: The adoption of digital twins has revolutionized industrial process simulation, monitoring, and control effectiveness. However, practical implementations of digital twins are hindered by substantial challenges, including extended development time, diminishing model accuracy, and restricted interactive capabilities. Addressing these critical issues, this paper proposes a comprehensive digital twin development framework that integrates digital twin identification, real-time model updating, and advanced process control. The proposed approach first identifies the offline digital twin model through the sparse identification of nonlinear dynamics algorithm, reducing the digital twin development time while maintaining model fidelity. Then, the identified model is updated by the extended Kalman filter to mitigate the problem of diminishing accuracy. Finally, incorporating the latest updated model into the model predictive control facilitates the control inputs optimization and enhances the interactive capacity of digital twins. Through one industrial case study and two simulation examples, the advantages of the proposed algorithm are demonstrated.

Abstract: This work presents a strategy for implementing advanced control in a real Linear Low-Density PolyEthylene (LLDPE) production unit (“Sclairtech” technology) followed by a systematic evaluation of economic benefits in accordance with best international practices. Melt Index (MI), density and conversion were considered as controlled variables. The analysis considered two different situations (with and without hydrogen as process input) and comprised groups/ families of polyethylene (rotational molding, low-density injection, octene film and high-density injection). The proposed control strategy is capable of efficiently addressing two of the main problems associated with “Sclairtech” technology, namely, the generation of out-of-specification product during grade transitions and wide specification ranges. The benefits analysis involved using real process data, a statistical analysis of key variables to identify the dispersion and percentage of out-of-specification products, and the calculation of the net present value of financial indicators capable of validating the investment. An annual gain of US$ 791,812 was estimated, with US$ 494,883 coming from the reduction in catalyst consumption and US$ 296,929 from other sources (reduction in out-of-specification product and production losses associated with grade transitions).

Abstract: To address the issues of detail loss and unstable segmentation quality in image segmentation, this paper proposes a muti-strategy improved dung beetle optimization algorithm and applied to muti-threshold image segmentation, thus, we have developed a Multi-strategy Improved Dung Beetle Optimizer Kapur entropy Multi-threshold Image segmentation Algorithm (MIDBO-KMIA). This algorithm enhanced global search capability and convergence stability, improved segmentation accuracy and algorithm robustness, and solved the problems of detail preservation and segmentation quality in complex scenarios. Firstly, Sobol sequences were adopted to initialize the population, enhancing its diversity. Secondly, a muti-stage perturbation update mechanism was introduced to prevent convergence to local optima and improved global exploration. Thirdly, the convergence precision was further improved by optimizing the hybrid dynamic switching mechanism and proposing dynamic mutation update and distance selection update strategies. Finally, the MIDBO algorithm was applied to Kapur entropy multi-threshold image segmentation, and experimental research was conducted using Peak Signal-to-Noise Ratio (PSNR), SIMilarity index (SSIM), and Feature SIMilarity index (FSIM) as evaluation metrics. The experimental results demonstrate that the performance of the multi-strategy improved dung beetle optimization Kapur entropy multi-threshold image segmentation algorithm is significantly better than other algorithms, which can more effectively solve the problems of detail preservation and segmentation quality in complex scenes, and enhance the ability to adapt to complex image scenes.

Abstract: High-quality RGB–thermal infrared (RGB-T) semantic segmentation datasets are crucial for search-and-rescue (SAR) applications, yet their development is hindered by the scarcity of annotated ground truth and by the challenges of thermal-camera calibration, which typically depends on heated targets with limited geometric definition. Recent approaches, such as MATT, focus on transferring SAM-based RGB masks to multi-spectral data, but they do not fully address the need for robust cross-modal alignment, quality control, or human-in-the-loop reliability assessment in RGB-T segmentation. To fill this gap, we propose a general annotation methodology that performs geometric alignment of RGB-T pairs, combines model-based proposals with interactive refinement, and incorporates annotation cost and systematic quality checks using inter-annotator agreement. In this methodology, multimodal alignment is ensured through feature-based matching and homography estimation. Annotation integrates automatic proposals and guided refinement, and final masks undergo quantitative cost and quality control before being used in downstream model training. The proposed methodology was evaluated on a SAR-oriented RGB-T dataset comprising 306 image pairs. Consistent cross-modal alignment was achieved via SuperGlue-based matching and homography estimation, enabling the implementation of a SAM2-based semi-automatic annotation pipeline in Label Studio. Results across two annotators show that the proposed approach reduces annotation time by 21% while achieving a high annotation quality mean IoU = 74.9%) and a high inter-annotator agreement (mean pixel accuracy = 88.4%, Cohen's kappa = 83%). The curated labels were then used to benchmark two representative RGB-T segmentation models. These findings demonstrate the practical value of the proposed methodology and establish a reproducible framework for generating reliable RGB-T semantic segmentation datasets, complementing and extending recent multispectral auto-labeling approaches.

Abstract: Simulator‑based digital twins are widely used in robotics education and industrial development to accelerate prototyping and enable safe experimentation. However, they often hide implementation details that are essential for understanding, diagnosing, and correcting system failures. This paper introduces a technology‑independent model‑based design framework that provides students with full visibility of the computational mechanisms underlying robotic controllers while remaining feasible within a 150‑hour undergraduate course. The approach relies on representing controller behavior using networks of Extended Finite State Machines (EFSMs) and their stacked extension (EFS2M), which unify all abstraction levels of the control architecture—from low‑level reactive behaviors to high‑level deliberation—under a single formal model. A structured programming template ensures traceable, optimization‑free software synthesis, facilitating debugging and enabling self‑diagnosis of design flaws. The framework includes real‑time synchronized simulation, transparent switching between virtual and physical robots, and a smart data logger that captures meaningful events for model updating and error detection. Integrated into the Intelligent Robots course, the system supports topics such as kinematics, control, perception, and SLAM while avoiding dependency on specific middleware such as ROS~2. Results over three academic years indicate that students gain a deeper understanding of controller internals, and demonstrate improved ability to reason about system errors and their causes. The proposed environment thus offers an effective methodology for teaching end‑to‑end robot controller design through transparent, simulation‑driven digital twins.

Abstract: Hydrogen is a versatile energy carrier essential for decarbonizing hard-to-abate sectors and long-duration storage. This study presents a unified techno-economic comparison of major production pathways—grey/blue steam methane reforming, biomass gasification, thermochemical cycles, biological methods, and solar-powered electrolysis—using 2025 benchmarks. Focus is on a 100 kW off-grid PV-electrolyzer system with realistic assumptions (PV performance ratio 0.85, electrolyzer efficiency 70% LHV). In Iran's high-insolation regions (PSH ≥ 5.15 kWh/kWp/day), annual yields reach 3.2–3.4 tonnes H₂—55–60% higher than northern Europe—with round-trip efficiency of 23.8%. Solar electrolysis offers zero direct emissions and 51–55 kWh/kg H₂ consumption. Scaling to multi-MW coastal hybrids with renewable desalination projects LCOH of 3.0–4.0 USD/kg by 2030, positioning Iran as a competitive exporter. A reproducible model and phased roadmap provide actionable insights.

Abstract:

To meet the demands of high-speed, high-precision execution of six-axis industrial robotic arms in complex manufacturing environments, this paper presents a real-time motion planning method incorporating multi-source error compensation based on production data and dynamic models. A self-developed control platform (EtherCAT bus, 0.25 ms cycle, <20 μs jitter) enables rapid command issuance and execution. The method first generates an initial trajectory using a calibrated model, then applies online corrections via a multi-source error estimation model to mitigate deviations from flexible structures, load changes, and installation offsets. A lightweight computation module ensures accuracy without increasing computational overhead. In 600 load variation experiments, trajectory error decreased from 0.41 mm to 0.24 mm (41.5% improvement), and path smoothness improved by 28.2%. Under typical assembly tasks, the success rate increased from 89.3% to 95.7%. Results confirm the method's effectiveness in real-time trajectory optimization and its strong engineering applicability across varied scenarios.

Abstract: A central goal of neuroeconomics is to understand how humans make decisions and how their neural processes interact during strategic situations. Game theory provides mathematical tools for modeling such interactions, with equilibrium concepts, most notably the Nash equilibrium, predicting stable patterns of behavior. Classical equilibrium analysis, however, treats cognition as a black box and assumes fully rational agents, whereas human decision making is shaped by bounded rationality, heuristics, and neural constraints. To bridge this gap, we investigate equilibrium behavior directly in the space of neurocognitive activity. Electroencephalogram (EEG) signals provide a high-resolution measurement of neural dynamics underlying attention, conflict monitoring, and evidence accumulation. In this work, we introduce a neuronic Nash equilibrium, an equilibrium concept defined not in the action space but in the EEG-derived neural representation space. We develop a framework for analyzing two-player turn-based games in EEG space by constructing DMD-based neural embeddings and associated directed network representations. Dynamic Mode Decomposition (DMD) reveals statistically significant differences between the neural dynamics associated with distinct strategic actions, demonstrating that EEG-derived features preserve behaviorally meaningful cognitive structure. The resulting neuronic network representation enables equilibrium analysis directly at the neural level and provides a principled method for linking strategic behavior with stable patterns of neural activity. Our findings suggest that neural-state equilibrium concepts can capture the cognitive foundations of strategic interaction and offer a pathway toward characterizing cognitive equilibrium outcomes in multi-agent settings.

Abstract: The determination of the actual series and sequence impedances, including the reduction factor of a certain HV or EHV distribution cable line, as well as the resulting screening factor of its sheaths and surrounding metal installations, including its inductive influence on any of the surrounding metal installations, is not possible by calculations alone. Considering the inductive influence of surrounding metal installations on the values of these quantities is possible only by the method that includes the test measurements during a simulated ground fault in the supplied substation. However, such measurements presuppose putting at least one HV substation and its feeding line out of service. That is why electricity distribution companies rarely allow such measurements, i.e., only immediately before the commissioning of a newly built HV substation or during a periodical overhaul. In this paper, it is demonstrated that these characteristics of cable lines can also be determined based on the results of synchronous measurements performed permanently in the substations at their ends. In this way, the need to perform a simulated ground fault and corresponding test measurements in HV distribution substations is practically disаpear, and the necessary characteristics can be obtained whenever a need for them appears.

Abstract: This review explores the link between engineering and creativity, challenging the perception gap between structured training and creative fields. It reframes human creativity insights from prominent scholars to inform the development of AI systems capable of creative problem-solving. The paper translates abstract and philosophical models into structured, computationally tractable frameworks to bridge human creativity research and machine learning applications. The review focuses on four core frameworks to guide AI design: Wallas’s Four-Stage Process, Rhodes’ Four Ps Model, Simonton’s Creativity-as-Influence Model, and Runco’s prevailing framework. It traces the historical progression of creativity research from early efforts by Guilford and Torrance to later dynamic frameworks by Amabile and Csikszentmihalyi. The document discusses how these models, which evolved from abstract theorizing to structured, multidimensional constructs, provide a foundation for examining and applying creativity within technical domains. It also addresses the growing integration of AI, distinguishing between human creativity and artificial creativity produced by machines. The forward-looking perspective suggests an augmentative role for AI within hybrid human-AI workflows. Ultimately, the review aims to provide a blueprint for developing AI systems that move beyond rote problem-solving to exhibit adaptive, context-sensitive, and generative capabilities, capitalizing on the synergy between creativity science and AI.

Abstract: New nuclear power plant (NPP) designs, particularly advanced reactors and small modular reactors (SMRs) are expected to be highly automated, changing job demands and shifting the roles and responsibilities of operators. The expanded capabilities of machines and their more prominent role in plant operation means that operators need new information to support effective human-automation teaming and the maintenance of situation awareness. To understand the impact of new automation and artificial intelligence (AI) technology in NPP control rooms, a systematic literature review (SLR) on function allocation (FA) methods was conducted. This SLR focused on four areas. (1) Identifying the prevalence of quantitative, qualitative, and mixed methodologies. (2) Developments in levels of automation frameworks. (3) Revisions to Fitts List. (4) Ena-bling factors for improved access to data-driven approaches. The review was limited to work occurring after 1983, when the U.S. Nuclear Regulatory Commission published research on FA [1]. The results of the review demonstrate that many of the post-1983 methods are qualitative and descriptive. The review also identified several themes for managing human out of the loop issues. The discussion closes with proposed future work leveraging large language models and simulator-based approaches to enhance existing FA methods.

Abstract: This paper describes the design and implementation of a laboratory system comprising customized hardware setups for each experiment and a dedicated software environment for interactive learning in the field of electronic devices. The hardware infrastructure is based on programmable equipment, including programmable power supplies and signal generators for input stimulus generation, programmable devices and circuits for signal routing, and data acquisition units for capturing output responses. The software framework is built using the LabVIEW graphical programming environment, enabling control of input signal generation, output signal acquisition, data processing, and result visualization in a user-friendly and comprehensible manner. All control functionalities are accessible via the virtual instrument’s front panel, allowing seamless remote operation through network-based control applications. By enabling remote control of physical equipment, the system provides access to laboratory resources without requiring the user’s physical presence at the laboratory site. The approach is demonstrated through experiments in analog electronics—such as plotting the static characteristics of diodes and transistors—as well as in digital electronics.

Abstract: Sulfate-reducing bacteria (SRB)-induced corrosion presents a considerable challenge to the integrity of shale gas pipelines. Conventional reliance on chemical biocides is limited by the potential for microbial resistance and environmental impact. As an alternative, the bio-competitive exclusion approach, utilizing microbes such as denitrifying bacteria (DNB), offers a promising strategy. This study investigates an integrated control method, combining the biocide glutaraldehyde with DNB to synergistically inhibit SRB activity and corrosion. The efficacy and mechanisms were systematically evaluated through electrochemical measurements, weight-loss analysis, surface characterization, and microbial community profiling. Following synergistic treatment with glutaraldehyde and DNB, the average corrosion rate was reduced by 44.2% and the maximum corrosion depth decreased by 84.3% compared to the SRB-inoculated system. Microbial community analysis revealed a substantial decline in SRB abundance from 62.7% on day 1 to 11.9% by day 14 under the synergistic treatment. The combined approach proves economically and environmentally viable, offering the advantages of reduced chemical dosage and the avoidance of additional corrosion typically associated with DNB. These results provide a novel strategy for developing microbial-influenced corrosion control measures in shale gas infrastructure.

Abstract: An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, we systematically studied the influence of experimental test pulse characteristics on the parameterized ECM accuracy. Test pulse duration and amplitude were varied along with the portion of the relaxation phase data used by the parameters fitting algorithm. That resulted in 168 parameter sets, each validated using 9 diverse current profiles including one based on a realistic drive cycle. The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point it can overshadow the use of a higher order Thevenin model. By choosing the optimal parameter set the simulated voltage root mean square error was reduced to as low as 1.2 mV.

Abstract:

The article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), Digital Twin (DT) technology, and PLC-based architecture aligned with the requirements of the NIS2 Directive. The proposed concept, denoted as Cyber-Physical Digital Twin with QFT & NIS2 Security (CPDTQN), unifies control, observability, synchronization, and traceability mechanisms within a single cyber-physical structure. The study employs the five-axis industrial manipulator FANUC M-430iA/4FH, modeled as a set of SISO servo-axis channels subject to parametric uncertainty and external disturbances. For each axis, QFT controllers and prefilters are synthesized, and the system performance is evaluated using joint-space and TCP-space metrics, including maximum error, RMS error, and 3D positional deviation. A CPDTQN architecture is proposed in which the QFT controllers are executed in MATLAB, a Siemens PLC (CPU 1215C, FW v4.5) provides deterministic communication via Modbus TCP, OPC UA, and NTP/PTP synchronization, and the digital twin implemented in FANUC ROBOGUIDE reproduces the robot’s kinematics and dynamics in real time. This represents one of the first architectures that simultaneously integrates QFT control, real PLC-in-the-loop execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. Simulation results using nominal and worst-case dynamic models, as well as scenarios with externally applied torque disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. Furthermore, the study analyzes how the proposed CPDTQN architecture supports key NIS2 principles, including command traceability, disturbance resilience, access control, and mechanisms for forensic reconstruction in robotic manufacturing systems.

Abstract: Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI long before electronics existed [1–9]. In this perspective, we treat insects as canonical edge-AI systems and translate their neurobiology and physiology into a concrete engineering stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries [1–9]. This architecture is realized through a neuromorphic Reflex Island built from spintronic and neuromorphic primitives, MRAM synapses for non-volatile, innate reflex memory, and spin-torque nano-oscillator (STNO) reservoirs for temporal processing—yielding instant-on, memory-centric reflexes compatible with emerging industrial roadmaps [10–16,56,62–67].We further formalize the thermoregulatory and respiratory strategies that allow insects to maintain nearly constant mechanical efficiency across a wide load range: active thoracic temperature control and Discontinuous Gas Exchange (DGC) [17–33]. These mechanisms motivate firmware-level “thermal-debt” and burst-budget controllers, contrasting sharply with the narrow best-efficiency islands of internal combustion engines and miniturbines [34–43]. We instantiate this integrated bio-inspired model in two concrete edge systems: an insect-like IFEVS thruster with nearly flat-band thermal efficiency over thrust, and a solar-assisted cargo e-bike equipped with an insect-inspired neuromorphic safety shell [6,11,14,28,58–61]. Across these examples we provide efficiency comparisons, latency and energy budgets, and safety-case hooks (fault taxonomies, WCET envelopes) aimed at guiding adoption in safety-critical domains.

Abstract: Accurately modeling and representing the collective dynamics of large-scale robotic systems remains one of the fundamental challenges in swarm robotics. Within the context of agricultural robotics, swarm-based coordination schemes enable scalable and adaptive control of multi-robot teams performing tasks such as crop monitoring, precision spraying, and autonomous field maintenance. For these applications, efficient modeling of both individual and collective robot dynamics is essential to achieve effective swarm coordination, ensuring an accurate representation of local interactions and emergent global behavior. This paper introduces a cohesive Potential Linked Nodes (PLN) framework, an adjustable formation structure that employs artificial potential fields (APFs) and virtual node-link interactions to regulate swarm cohesion and coordinated motion (CM). The proposed model governs swarm formation, modulates structural integrity, and enhances responsiveness to external perturbations such as uneven terrain and crop-induced obstacles. By interconnecting agricultural robots through a dynamically reconfigurable node-link topology, the PLN framework ensures decentralized stability while maintaining high cohesion and adaptability. The system’s tunable parameters enable online adjustment of inter-agent coupling strength and formation rigidity, allowing the swarm to adapt its configuration to varying environmental and operational constraints. Comprehensive simulation experiments were conducted to assess the performance of the cohesive PLN model under multiple swarm conditions, including static aggregation and dynamic flocking behavior using differential-drive mobile robots. Additional tests within a simulated cropping environment were performed to evaluate the framework’s stability and cohesiveness under realistic agricultural constraints. Swarm cohesion and formation stability were quantitatively analyzed using density-based and inter-robot distance metrics. The experimental results demonstrate that the PLN model effectively maintains formation integrity and cohesive stability throughout all scenarios, achieving coordinated coverage and motion adaptability suitable for precision agriculture applications.

Abstract: Financial fraud represents a growing challenge for financial institutions and e-commerce, requiring increasingly sophisticated detection methods. Traditional machine learning models, while effective, can reach limitations when facing complex fraud patterns and highly imbalanced datasets. This paper proposes a novel ensemble approach, KAN-XGBoost, which combines the power of Kolmogorov-Arnold Networks (KAN) for learning complex relationships with the robustness of the Extreme Gradient Boosting (XGBoost) algorithm for high-performance classification. Using the synthetic PaySim dataset, we demonstrate the effectiveness of our approach. To address the severe class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training data. Our experimental results show that the KAN-XGBoost ensemble model, in soft voting configuration, significantly outperforms the individual models, achieving a performance metrics of 99%. This high performance suggests that the hybridization of KANs with established boosting algorithms constitutes a promising avenue for enhancing the security of financial transactions.

Abstract: An extended operational space kinematics and dynamics formulation is presented for control of redundant non-serial compound robotic manipulators. A broad spectrum of high load capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely on Jacobian pseudoinverses and local null-space projections, a globally valid, differential geometry-based, multi-valued inverse kinematic mapping is defined at the configuration level, with explicit self-motion parameterization of manipulator redundancy. The formulation yields coupled second-order ordinary differential equations of manipulator dynamics on the product space of task variables and self-motion coordinates. This enables direct integration of system dynamics with control strategies, such as model predictive control or feedback design, while maintaining task constraint compliance. The methods presented are validated through simulation and control of a multi-degree of redundancy non-serial compound material loader manipulator, demonstrating advantages in generality, numerical accuracy, and trajectory smoothness.

Abstract: Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for single UAV guidance and flight attitude control systems to meet the growing demand for landing assistance. In this work, we present a shipborne manipulator arm designed for grasping drones that utilize low-altitude visual servo to land on the water surface. The shipborne manipulator arm is fabricated as a key component of a seaplane drone dock comprising a ship-type embedded drone storage, a packaged helistop for power transfer and UAV recovery, and a multi-degree-of-freedom arm integrated multi-source information sensors for the treatment of air to a water-related airplane crash. Dynamics model tests have demonstrated that the end-effector of the shipborne manipulator arm stabilizes and performs optimally for water surface disturbances. A down-to-top grasp docking paradigm for a UAV-assisted perching on shipborne helistop that enables the charging components of the station system to be equipped automatically to ensure that the drone performs its mission in the best condition is also presented. The efficacy of this grasp paradigm when compared with a previous top-to-down model without power recovery has been verified by retrieving vessels in the military fields.