Abstract: The autonomous grasping of flexible slings is a pivotal challenge for unmanned crane systems, primarily stemming from the slings' geometric indeterminacy, material compliance under load,and stochastic initial pose relative to the hook.To address this challenge,we propose an intelligent hook system featuring a novel compound mechanical architecture.This architecture integrates a horizontal slewing mechanism for in-plane alignment with a self-locking worm-gear drive for secure grasping.A coordinated control strategy,employing a Fuzzy PID algorithm,ensures robust dynamic performance under variable loading conditions.Finite element analysis confirms structural integrity under a rated load of 500 kg,with a maximum stress of 344.34 MPa.Experimental results demonstrate that the hook completes a full pick-and-release cycle in approximately 2 seconds for parallel slings, with a success rate exceeding 95%.This represents an approximately 60% improvement in operational efficiency over manual operation.This work provides a practical and efficient solution for automating flexible sling handling.

Abstract: This paper describes the step-by-step processes towards the formalization of a core ontology for missions and capabilities in a system of systems, and the development of a specific SoS domain ontology from the formalized ontology. The study adopts the SABIOx methodology to trace the different aspects of the ontology development process from requirements, setup, capture, design, and implementation phases. This study also demonstrates the core ontology’s usability and reusability. Usability refers to its adequacy for specific use as a reference point for SoS knowledge exploration for development and operational purposes. Reusability refers to adequacy for several uses, such as facilitating the understanding of different domain-specific systems of systems. This is done in three steps: formalization of the core ontology, exploration of the usefulness of this formalization, and development of a domain ontology from the core ontology. These result in: the application of ontology tools to demonstrate the machine readability, inferences, and possibilities to querying the ontology knowledge base; the incorporation of systematic ontology development processes; and testing of the core knowledge with a domain prompt. An alignment of these aspects provides different points of view of how an SoS can be formulated, how the concepts collectively describe the development of an SoS emergent behavior, and how the ontology knowledge base can be explored to support decision frameworks guiding an SoS.

Abstract: Sustainable agriculture is under increasing pressure due to climate variability, resource scarcity, and the need to reduce environmental impacts without compromising productivity. This study aimed to systematically analyze recent advances in emerging digital technologies applied to sustainable agriculture. The PRISMA protocol was applied to Scopus and Web of Science, considering publications from 2020 to 2025, which were analyzed using RStudio 4.5.10 and VOSviewer 1.6.20, resulting in 101 relevant articles. The findings indicate that multisensor monitoring and precision agriculture enable high-resolution characterization of soil–crop variability, supporting site-specific irrigation, fertilization, and phytosanitary management. Likewise, machine learning-based predictive models improve decision-making by forecasting yield, water stress, nutrient deficiencies, and disease outbreaks. In addition, edge computing and autonomous systems enhance operational efficiency and reduce labor dependency. Blockchain strengthens transparency and sustainability certification through secure traceability, while digital twins optimize management strategies through prior simulation. Despite these advances, limitations remain, including platform fragmentation, limited interoperability, uneven adoption among smallholders, and challenges in model generalization across heterogeneous agroecosystems. Therefore, further progress toward integrated and interoperable digital ecosystems is recommended

Abstract: This study develops an adaptive optimal tracking control law using neural network (NN)-based reinforcement learning (RL) for high-order partially unknown nonlinear systems. By designing a cost function associated with the sliding mode surface (SMS), the original tracking control problem is equivalently transformed into solving the optimal control problem related to the tracking Hamilton-Jacobi-Bellman (HJB) equation. Since the analytical solution of the HJB equation is generally intractable, we employ a policy iteration algorithm derived from the HJB equation, where both the partial derivative of the optimal tracking cost function and the optimal control law are approximated by NNs. The proposed RL framework achieves simplification through actor-critic training laws derived under the condition that a simple function is zero. Finally, two simulative examples are provided to demonstrate the effectiveness and advantages of the proposed adaptive optimal tracking control method.

Abstract: This work addresses the design of a predefined-time adaptive fuzzy control scheme for high-order nonlinear systems with nonstrict-feedback structures, subject to unmodeled dynamics and input time delay. To mitigate the influence of unmodeled dynamics, a predefined-time auxiliary dynamic signal is incorporated into the controller design. Meanwhile, the adverse effects caused by input delay are handled by integrating a Padé approximation with the introduction of an intermediate state variable. Fuzzy logic systems are utilized to approximate the unknown nonlinear terms present in the system dynamics. Based on a recursive backstepping framework and a power-type Lyapunov function formulation, an adaptive fuzzy tracking controller with predefined-time convergence char acteristics is constructed. A detailed stability analysis demonstrates that the closed-loop system achieves practical predefined-time convergence, and appropriate selection of design parameters guarantees that the tracking errors remain confined within a small bounded region around the origin. Finally, the effectiveness and advantages of the proposed control strategy are validated through a numerical example and a practical example.

Abstract: The management of specialty coffee production represents a complex dynamical process characterized by highly nonlinear interconnections between environmental variables, agronomic practices, and chemical compositions. Traditionally, the classification of specialty coffee relies on sensory evaluations conducted by highly certified coffee experts Q-Graders, using a strict, standardized Specialty Coffee Association (SCA) protocol. However, scientific methods that generate spectral fingerprints provide a more reliable guarantee of quality while also ensuring traceability to the farm of origin. Panamanian Geisha coffee is one of the world's most expensive, award-winning microlots frequently exceeding $1,000 per pound, with a 2025 record-breaking price of over 30,000 American dollars per kilogram. This research introduces an integrated framework that combines Precision Agriculture Management Systems (PAMS) to support the identification of the spectral fingerprint using Near-Infrared (NIR) and Fourier Transform Infrared (FTIR) spectroscopy, enabling the objective characterization of chemical processes. A mathematical model is introduced to formally characterize the mobile application's behavior, distributed structure, and inherent constraints. Serving as a mathematical blueprint, this model identifies critical influencing factors and establishes strategic assumptions to distill complex real-world variables into a rigorous, manageable framework. Large-scale experiments conducted across more than 820 coffee farms in Chiriquí, Panama, demonstrate that the proposed decentralized architecture effectively coordinates the acquisition and synchronization of georeferenced chemical data. The decentralized architecture of the application utilizes private blockchain technology to facilitate autonomous operations, effectively decoupling the system from central authorities to ensure functional continuity in environments characterized by intermittent connectivity.

Abstract: Disability inclusion in the engineering profession remains an underexplored but essential dimension of workforce transformation, professional equity, and sustainable development. Globally, engineering regulators and professional bodies are increasingly expected to address systemic barriers that limit the participation and progression of engineers with disabilities. In South Africa, the Engineering Council of South Africa (ECSA) holds a statutory mandate to protect the public, promote professional competence, and advance transformation within the engineering sector. This article examines the role of ECSA in supporting disability inclusion and evaluates the extent to which its policies, accreditation criteria, stakeholder engagements, and regulatory instruments contribute to creating an inclusive engineering environment. Using a qualitative document analysis of regulatory frameworks, disability legislation, and engineering education and workplace literature, the study identifies persistent structural barriers related to accessibility, professional registration, workplace accommodation, data availability, and institutional culture. While ECSA has made progress through its Transformation Framework, education accreditation standards, and stakeholder engagement mechanisms, disability inclusion remains insufficiently institutionalised. The article proposes a disability inclusion model tailored for ECSA, centred on accessibility, universal design, disability-responsive regulation, data-driven transformation strategies, and strengthened partnerships with disability rights organisations. The study contributes to debates on inclusive professional regulation and offers practical pathways for advancing disability-inclusive engineering in South Africa.

Abstract: The cascaded liquid crystal polarization grating (CLCPG), a core non-mechanical beam scanning device, suffers from insufficient pointing accuracy due to inherent manu-facturing/assembly errors, which requires compensation by the liquid crystal optical phased array (LCOPA). Yet LCOPA is vulnerable to internal/external disturbance cou-pling and inherent time delay in practical conditions, hindering accurate compensation and limiting integrated system performance. This paper proposes a fractional-order com-posite control strategy: a fractional-order dynamic model of LCOPA is established first to characterize its response and viscoelastic memory effect; then a fractional-order mod-el-assisted extended state observer is designed for total disturbance estimation, combined with an improved Smith predictor for time-delay compensation and a phase margin method for fractional-order PID parameter tuning. Comparative experiments on a CLCPG-LCOPA experimental platform validate the strategy’s effectiveness: it suppresses disturbances, compensates CLCPG errors, reduces the overall pointing error by over 30%, improves dynamic response speed by 25%, and exhibits excellent robustness and stability, providing theoretical and technical support for high-precision CLCPG scanning system engineering.

Abstract: The paper introduces a novel control approach that integrates Proportional-Integral-Derivative (PID) control with a Linear Quadratic Regulator (LQR) for a Full-Bridge Boost Converter (FBBC). To enable effective linear control design, the FBBC system is linearized around its steady-state operating. The control architecture is structured into four cases: Case 1: PI-LQR Output Feedback, Case 2: PI-LQR State Feedback, Case 3: PID-LQR Output Feedback, and Case 4: PID-LQR State Feedback. Each case is evaluated for system responsiveness by examining output voltage stability, inductor current dynamics, and control signal characteristics. The analysis aims to identify the optimal and most reliable system performance under input voltage disturbance and load variation. The simulation results indicate that Case 3 (PID-LQR Output Feedback) consistently delivers the best performance, characterized by the shortest settling time, optimal control signal, and fast response to disturbances.

Abstract: Optimal path tracking is a fundamental requirement for autonomous electric vehicles to ensure safety, stability, and driving comfort. This paper introduces a Prediction–Preview Cooperative Steering Control (MPC–PEM) method, which is a prediction-based steering control strategy designed to minimize lateral and heading errors. The approach utilizes a technique referred to as Preview Error Minimization (PEM). The controller predicts vehicle dynamics within a limited preview horizon to generate anticipative steering actions that adapt to road curvature and speed variations. A bicycle model is employed to represent the lateral–yaw dynamics, while the control law is formulated by considering system constraints and stability margins. Simulation results demonstrate that the proposed prediction–preview approach significantly improves tracking accuracy compared to conventional LQR and MPC methods, as evidenced by smaller heading and lateral errors, smoother steering angles, and more stable and realistic dynamic responses. This method offers an efficient, adaptive, and reliable steering control solution for the next generation of autonomous electric vehicles.

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.