Abstract: A numerical model is presented for heat-coupled continuous-wave second harmonic generation in a double-pass type-II potassium titanyl phosphate (KTP) cavity. The model solves eight coupled partial differential equations governing forward and backward ordinary and extraordinary fundamental fields at 1064 nm, forward and backward second-harmonic fields at 532 nm, three-dimensional transient heat diffusion, and thermally induced phase mismatching (TIPM). Given crystal geometry, beam parameters, pump power, and cooling boundary conditions, the solver produces spatiotemporal temperature distributions, phase-mismatch profiles, and electric-field amplitudes along the propagation axis. The implementation requires less than 8 GB of memory and runs on standard desktop hardware. Comparison with published experimental data yields agreement within 4 % in predicted conversion efficiency. The source code is available under the MIT License (v1.0.2, DOI 10.5281/zenodo.17362470).

Abstract: Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human-robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Multiple Correlation Coefficient (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurements protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices.

Abstract: Vision-Language-Action (VLA) models unify visual perception, natural-language understanding, and action generation within a single foundation model, allowing a robot to follow instructions such as “fold the towel” or “fly to the red building” directly from camera images. Because VLAs inherit world knowledge from internet-scale pre-training, they have become the dominant framework for learning-based manipulation, with bimanual coordination serving as the most demanding testbed: two arms with 7+ degrees of freedom each must move in concert to fold, assemble, and reorient objects. Unmanned aerial robotics faces a structurally similar challenge: a drone must coordinate thrust, attitude, and increasingly gripper commands from visual observations under strict latency and payload constraints. This review covers 186 contributions spanning 2017–2026 and organized along seven dimensions: VLA architectures, training recipes, action representations, bimanual coordination (2022–2026), unmanned aerial vehicle (UAV) navigation and control (2017–2026), language grounding, and cross-cutting concerns including memory and world models. We show that the coordination strategies, training recipes, and action representations developed for bimanual VLAs transfer to unmanned aerial systems, and identify fourteen research directions across both domains.

Abstract: Peer-to-peer (P2P) systems have emerged as a fundamental paradigm for decentralized resource sharing, communication, and computation across diverse application domains such as energy trading, distributed storage, social networking, and federated learning. Unlike traditional client–server architectures, P2P networks operate without centralized control, enabling nodes to act as both resource providers and consumers, thereby improving scalability, robustness, and system efficiency . However, the decentralized and dynamic nature of P2P systems introduces significant challenges in ensuring reliability, performance, and security, making testing a critical research area.This paper presents a comprehensive survey of peer-to-peer network testing techniques, integrating insights from existing literature on P2P architectures, simulation frameworks, and application-specific implementations. Early research primarily relied on analytical models and simulations to evaluate P2P systems, as real-world experimentation is often impractical due to large-scale deployment requirements. Simulation-based testing remains a widely adopted approach, though it suffers from limitations such as scalability constraints and lack of reproducibility in complex environments.Modern testing approaches for P2P systems include model-based testing, fault injection, stress testing, fuzzing, differential testing, and concurrency testing. These techniques address key challenges such as validating global system properties, handling peer churn, ensuring consistency, and managing heterogeneous network conditions . Additionally, load testing and performance evaluation methods have evolved to leverage decentralized testing frameworks, eliminating bottlenecks associated with centralized testing systems. Measurement-based techniques and sampling methods are also used to analyze large-scale P2P networks efficiently, though issues such as bias and incomplete data collection persist.The emerging application domains such as P2P energy trading and federated learning introduce new testing requirements, including real-time system validation, privacy preservation, and integration with physical systems. Advanced approaches like hardware-in-the-loop testing and AI-driven evaluation mechanisms are being explored to bridge the gap between simulation and real-world deployment. This survey consolidates key testing methodologies, identifies open challenges, and highlights future research directions in P2P network testing, emphasizing its critical role in enabling reliable, scalable, and intelligent decentralized systems.

Abstract: The Bhatnagar-Gross-Krook (BGK) model in the lattice Boltzmann method (LBM) is being widely used for simulating fluid flow and heat transfer due to its simplicity and parallelization capability. However, improving its computational efficiency and accuracy remains an ongoing challenge. This work introduces a time-averaged equilibrium distribution function (TAE) within the BGK model of collision operator in LBM, aiming to explore its performance in heat diffusion and compressible flows, such as the Sod shock tube. The TAE-LBM is tested on the heat diffusion problem using different values of the numerical relaxation coefficient, and the results show that convergence is achieved with significantly fewer time steps compared to standard LBM and the finite difference method (FDM), while maintaining reasonable accuracy. Higher coefficient values improve accuracy but reduce convergence speed. In Sod shock tube simulation, the TAE approach achieves the lowest root mean square error (RMSE) compared to the finite volume method using the Harten-Lax-van Leer scheme with monotonic upstream-centred for conservation laws reconstruction (FVM-HLL-MUSCL) both first-order Runge-Kutta and second-order Runge-Kutta.

Abstract: Reliable detection of internal defects in pressure vessel structures remains essential for structural safety and condition based maintenance. This study presents a low-complexity structural health monitoring framework based on fiber Bragg grating (FBG) sensing and multiresolution wavelet analysis for void detection in curved pressure vessel structures under guided-wave excitation. Guided waves are introduced using piezoelectric actuators, while the FBG sensors capture the resulting strain-induced wavelength variations. Due to the limited bandwidth of the optical interrogator, the recorded signals represent the strain envelope response associated with guided-wave interaction rather than the resolved ultrasonic carrier waveform. To characterize defect-induced changes, the acquired signals are analyzed using continuous wavelet transform (CWT) for time frequency interpretation, and discrete wavelet transform (DWT) and wavelet packet transform (WPT) for energy-based multiresolution feature extraction. Experimental results show that void defects lead to consistent redistribution of wavelet-domain energy and increased non-stationarity in the measured strain responses. These trends are further supported by finite element simulations, which reproduce similar energy redistribution patterns between intact and damaged cases. The proposed framework provides a physically interpretable and computationally efficient approach for defect detection using low-bandwidth FBG sensing, without reliance on high-speed acquisition or data-intensive learning models. The results demonstrate the feasibility of using energy-based multiresolution analysis of FBG strain signals for practical and scalable structural health monitoring of pressure vessel systems.

Abstract: In this work, we present an integrated hybrid approach of multi-objective evolutionary decomposition algorithm for a microgrid-connected smart grid test-bench. The optimization technique was tested in a power hardware-in-the-loop setup, using different distributed energy resources. During evaluation, the optimization ran in real time. To reduce computational time, we added a surrogate-assistance to help find the feasible operational region and detect knee points for decision-oriented prioritization of candidate solutions. We focused more on human decision preferences instead of only choosing the best solution from the Pareto set, giving priority to decision-based knee points for dynamic constraints. The research integrates an OPAL-RT microgrid power hardware-in-the-loop platform with a Lucas-Nülle smart-grid laboratory system to investigate energy management system optimization under various operational modes. The proposed hybrid framework also incorporates a stochastic survival strategy to maintain both convergence and diversity of the search process. The method was evaluated in both grid-connected and off-grid modes, where the microgrid operates as the primary energy source for the smart-grid unit during off-grid mode. In off-grid operation, grid-forming objectives were applied, while grid-following objectives were used in grid-connected scenarios. In the grid-following mode, a selective physical replay mechanism was employed to reduce computational complexity while maintaining stable decision quality. In the results for grid-connected mode, it observed constrained feasible regions because of the limited renewable utilization and reliance on battery and grid support which shows the influence of measure system conditions. In the grid-forming scenario, the results reveal stable convergence behavior with consistent Pareto set quality across independent runs. Experimental validation demonstrates objective-dependent modeling capability for nonlinear system behavior such as battery stress, efficiently reduce thousands of candidate solutions to a small and robust Pareto set, and provide a practical decision-intelligence layer linking evolutionary optimization with real-time energy management systems.

Abstract: As regional industrial hubs face structural decline, the "Regional industrial resurrection (RIR)" model—utilizing Open Labs to integrate high-end infrastructure with customized corporate support—emerges as a critical solution to bridge the systemic "triple burden" of capital, talent, and infrastructure deficits. This study evaluates the Busan maritime Open Lab's performance (2021–2022) focusing on representative SMEs, with outcomes strictly verified by national authorities. Technical KPIs improved significantly: design lead times were reduced by 50–60%, and technology readiness advanced from TRL 5 to 9. High-fidelity results included 0.027% spatial precision and 91 FPS rendering stability, facilitating industrial-grade digital twin commercialization. Economically, these firms realized over 3.3 million USD in consolidated revenue, achieving a +66.4% organic growth rate based on a constant exchange rate to isolate macroeconomic volatility. Socially, the Open Lab’s contribution resulted in 11.5 verified new employees, proving the model’s capacity for regional job creation. Qualitative surveys revealed a 100% satisfaction rate for technical advisory, confirming the Open Lab’s role as an essential "Innovation intermediary". Finally, the study proposes tailored expansion strategies for Taebaek/Samcheok, Gumi, and Gunsan across South Korea. These findings underscore the vital importance of public-led shared infrastructure and specialized human capital support in responding to declining regional industries.

Abstract: Automated decision systems are increasingly deployed in high-stakes domains such as credit allocation, hiring, and healthcare screening. Although sensitive demographic attributes are often excluded from model training, concerns remain regarding unequal predictive behavior across population groups. This study presents an empirical evaluation of subgroup-level predictive performance, error disparities, and calibration reliability using the Adult Income benchmark dataset. Logistic Regression and Random Forest classifiers are evaluated using a leakage-free nested cross-validation framework. Beyond aggregate performance metrics, we analyze false negative rates across demographic groups, statistically test observed disparities using bootstrap resampling, and examine probability calibration behavior. The results indicate that false negative rates differ systematically across sex and race groups, with several disparities remaining statistically significant. Furthermore, improvements in overall discrimination achieved by the Random Forest model do not uniformly translate into improved probability calibration across demographic groups. These findings demonstrate that evaluating machine learning systems solely through aggregate accuracy may obscure important subgroup-level differences and highlight the importance of comprehensive evaluation practices when deploying automated decision systems.

Abstract: Mycelium-based composites (MBC) have already been applied in various fields like construction, architecture, packaging, waste management and many others, as a sustainable replacement material. The composites created from such materials are lightweight, biodegradable and can take many different geometrical shapes. As there are many different combinations of fungal mycelium and organic substrates it is not only important to investigate and determine which of these combinations perform best from an acoustic perspective but also from an environmental point of view. The sound absorption qualities of these biocomposites have been investigated. It was found that the sound absorption coefficients range from 0,33 to 0,49 for the four different mixtures of substrate and oyster mushroom (Pleurotus ostreatus). The results from the acoustic testing are promising, but the environmental impact of these mycelium-based composites also needs to be determined. The impact from water and especially from energy, used during the growth and preparation cycles are the main contributors to the environmental impact of MBCs, which is also confirmed by relevant literature. A cradle-to-grave life cycle assessment (LCA) was conducted, utilizing the ReCiPe method, with selected environmental impact categories, based on real world production data and scientific literature. The obtained results were also compared with a commercially produced acoustical stone wool panel. The influence on environmental impact of the different substrates is also analyzed, determining which MBC is the most environmentally friendly and has the best acoustical properties.

Abstract: Strain energy density-based algorithms are widely applied in modelling bone healing, yet their use under patient-specific conditions remains underdeveloped. This study aims not only to perform patient-specific bone healing simulations, but specifically to identify which postoperative loading condition provides the most favourable mechanical environment for callus remodeling and thus supports optimal fracture healing. Using postoperative radiographic data of a 63-year-old male patient with a distal diaphyseal tibial fracture and concomitant proximal and distal fibular fractures, a three-dimensional finite element model of the tibia was reconstructed, imported into a multiphysics simulation environment, and coupled with an iterative numerical algorithm. An initial uniform callus density of 750 kg/m³ was assigned to represent the later stage of secondary healing. The effects of different mechanical loading conditions (partial weight-bearing, physiological loading, and supraphysiological loading) on the mechanical response and density evolution of the callus were evaluated. Partial weight-bearing resulted in insufficient mechanical stimulation and progressive density loss within the callus. Physiological loading generated strain energy density levels consistent with known osteogenic ranges and promoted continuous cortical shell formation and overall density increase. Supraphysiological loading led to overload-related resorption and spatial heterogeneity, ultimately compromising callus stability. The findings demonstrate that loading magnitude significantly influences bone healing. Depending on the healing stage, an optimal load can be determined to minimize the risk of non-union formation and enhance bone remodeling via this methodology. Furthermore, by additionally evaluating unphysiological overloading, this study provides a more robust validation of the model’s behaviour outside the optimal mechanobiological window.

Abstract: The effects of thermal and non-thermal pretreatments combined with different drying methods on the drying kinetics, physicochemical properties, and bioactive compounds of the Chilean wild mushroom Morchella conica were investigated. Fresh samples were subjected to hot-air drying (HAD, 60 °C), freeze-drying (FD), and a hybrid process (FD–HAD), applied directly or after pretreatments including thermal pre-drying (55 and 75 °C), ultrasound (US, 10 and 20 min), and high hydrostatic pressure (HPP, 600 MPa). Drying curves were successfully fitted using the Weibull model (R² > 0.987). The highest drying rates were observed in HAD combined with thermal and ultrasound pretreatments, whereas the FD–HAD process significantly reduced the total drying time. Freeze-drying preserved color (ΔE < 2) and minimized shrinkage (< 8%), while HAD resulted in darker samples and greater structural deformation. Water activity decreased below 0.30 in most treatments, ensuring microbiological stability, with the lowest values observed for HPP–FD and US 20 min–FD (0.10–0.11). Thermal pretreatments enhanced total phenolic content, whereas FD preserved antioxidant capacity. Principal component analysis explained 68.9% of the total variance and revealed distinct quality profiles among drying methods. These findings indicate that combining moderate pretreatments with freeze-drying or hybrid drying processes improves the technological efficiency and functional quality of Morchella conica.

Abstract: Kerf bending, a technique for curving material through repeated cuts, has evolved into a powerful tool in design and construction. Following experimental methodology, aiming at the design and construction of flexible and functional surfaces at real scale, this paper presents a creative Kerf Bending technique using a laser-cutting CNC machine. This research reviews the relevant techniques related to geometries, bending methods, and parameters that affect the behaviour of the material, wood. A systematic experimental process was undertaken, involving several birch plywood test specimens, on which different patterns were cut. The mechanical properties of the samples are tested to accurately record the performance under deformation, elasticity, and flexibility of each specimen. Comparative analysis showed that kerf geometry is the primary factor influencing the bending performance of plywood panels, having a greater impact than thickness or material removal percentage. Thinner specimens consistently demonstrated improved curvature capacity and higher load resistance, indicating more efficient stress distribution during deformation. Flexibility and strength were not directly proportional to the amount of removed material; instead, geometrically optimised layouts achieved favourable deformation while retaining mechanical integrity, whereas excessive removal reduced structural performance. The findings confirm that carefully designed kerf patterns can balance flexibility, strength, and aesthetic quality, supporting their use in structurally functional bent plywood components.

Abstract: Automatic speech recognition has advanced rapidly for high-resource languages, yet performance remains limited for low-resource languages such as Bangla, particularly in telehealth settings. Most systems rely on a standardized 16 kHz sampling rate, a design choice despite evidence that Bangla contains sibilant fricatives and other phonetic cues with substantial high-frequency energy that may be suppressed under bandwidth and latency constraints. This study evaluates audio sampling rate as a controllable signal-level parameter for Bangla telehealth ASR to identify an empirically grounded operating range balancing transcription accuracy, execution time, and network bandwidth. Twenty real-world Bangla doctor–patient consultations recorded at 32 kHz were deterministically resampled to 55 configurations between 8 kHz and 32 kHz and transcribed using a fixed cloud-based ASR system. Session-level Word Error Rate, execution latency, payload bandwidth, and high-frequency phonetic content were analyzed using a composite sibilant-likelihood score. WER decreased from 0.338 at 8 kHz to a local minimum of 0.232 at 18.75 kHz, with gains plateauing beyond this range despite substantial bandwidth increases. Elbow-point, Pareto frontier, weighted scoring, and Minimum Acceptable Trade-off analyses converged on an optimal region between 17.25 and 18.75 kHz, demonstrating that sampling-rate optimization improves ASR accuracy without proportional resource costs in telehealth settings.

Abstract: Engineer-to-Order (ETO) manufacturers face persistent cost and complexity challenges driven by product variety, including duplicate components, redundant variants, and inconsistent procurement setups. Although enterprise resource planning (ERP) and product lifecycle management (PLM) systems contain detailed Bills of Materials (BOMs) and procurement records, they typically lack portfolio-wide support for systematic cross-product commonality analysis without substantial manual effort. Prior approaches are either conceptual (e.g., indices and modularity frameworks) or ad hoc in practice, often relying on one-off spreadsheet analyses. This paper introduces the concept of Product Commonality Analysis Tools (PCATs) and develops and evaluates a lightweight PCAT in an action-research collaboration with a European ETO laser manufacturer. The PCAT operates on exported enterprise data to provide interactive portfolio-level views of component reuse and cross-product consistency. Its usefulness is evaluated through scenario-based think-aloud usability sessions and a functional comparison against Excel workarounds, standard ERP/PLM reporting, and vendor customizations. The results indicate that a lightweight PCAT can be integrated into existing ERP/PLM workflows with minimal disruption and can reduce the effort required to prepare reusable portfolio views for engineering and procurement reviews.

Abstract: Strong light interference severely degrades imaging system performance. This paper presents a novel Digital Micromirror Device (DMD)-based imaging system for robust strong light suppression and long-distance detection. Our design strategically places the DMD at the primary image plane, utilizing a large F-number objective for extended depth of field. The relay imaging system employs a tilted image plane in a near-symmetric configuration to effectively balance DMD-induced aberrations, simplifying alignment and achieving a compact, high-performance layout. The DMD's regional flipping capability enables precise, dynamic suppression of strong light. Experimental results from a fabricated prototype demonstrate superior imaging quality (MTF > 0.3 at 167.3 lp/mm) and exceptional suppression of intense laser interference, ensuring clear image acquisition in challenging lighting. This system offers an efficient solution for high-quality, long-range imaging in strong light environments.

Abstract: The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through meaning-based analysis. Using abstracts from 86,674 mobile-industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise of policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based change-point detection with topic-lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&amp;D, standard and platform strategies, and policy and regulatory design.

Abstract: Background: Sleep disorders like insomnia, obstructive sleep apnea (OSA), REM behavior disorder etc. are nowadays diagnosed through the Internet of Things (IoT)-enabled sys-tems that monitor and analyze the subject's sleep data. Health IoT networks are rife with communications of sensitive physiological data from wearable EEG, ECG, SpO₂ and res-piratory sensors. However, these networks face threats from anomalous traffic flows, sig-nal sabotage and data integrity violation. In this paper, an AI-based hybrid detection and classification framework is proposed for secure Sleep Health IoT (S-HIoT) networks. The integrated CNN, BiLSTM and RF model provides a proposed framework for joint sleep-stage classification and network anomaly detection. To this end, a multi-objective loss function is proposed for jointly optimizing the physiological state prediction and se-cure traffic monitoring. Experimental validation using the Sleep-EDF and CICIoMT2024 datasets demonstrate a classification accuracy of 97.8% for sleep staging, and 98.6% for network detection with low inference latency (

Abstract: Differential Liberation Expansion (DLE) and viscosity tests are core elements of the Pressure–Volume–Temperature (PVT) laboratory suite used to characterize reservoir oils under depletion and to support compositional modeling and reservoir simulation. Nevertheless, both DLE and viscosity testing remain expensive and time-consuming due to specialized equipment, strict operating procedures, and the need for experienced laboratory personnel.Building on our prior work that introduced the proximity-informed Local Interpolation Model (LIM) framework for Constant Composition Expansion (CCE), this study demonstrates how the same end-to-end, neighborhood-based workflow is applied to DLE and viscosity test data. A target fluid is embedded in a compositional–thermodynamic descriptor space and paired with a small set of thermodynamically similar fluids drawn from a PVT data archive. Within this locality, LIM is used to infer DLE behavior by combining local interpolation for key scalar quantities (e.g., saturation-point and endpoint PVT values) with shape-preserving reconstruction of pressure-dependent curves. For viscosity, the same approach reconstructs the oil-viscosity curve across the undersaturated and saturated regions. Evaluation on a proprietary database of DLE and viscosity tests shows strong agreement across diverse fluids for both DLE and oil viscosity trends. This supports reducing reliance on new DLE and viscosity measurements while maintaining engineering-grade fidelity in reservoir-engineering and simulation workflows. This approach has been fully automated through software so it can be set up and directly utilized by the field operators on their own databases to significantly reduce their fluid sampling and laboratory analysis costs. Moreover, the proposed AI model does not use others’ data while respecting data privacy and data ownership.

Abstract: This paper introduces a novel theoretical framework for classifying Autonomous Mobile Robots (AMRs) into three hierarchical layers: Perception, Cognition, and Operation. Unlike prior hardware-centric taxonomies, our approach, grounded in a structured review of seminal works, foundational methodologies, and state-of-the-art advances, explicitly integrates locomotion mechanisms (wheeled, legged), application domains (industrial, agricultural), and autonomy levels with navigation strategies. The framework unifies terrestrial navigation techniques into a cohesive taxonomy, clarifying modular boundaries and interdependencies. Serving as both a conceptual guide and educational tool, it empowers researchers to evaluate trade-offs in sensor configurations, decision-making algorithms, and trajectory execution under real-world constraints. A comparative analysis positions this framework against established navigation architectures, highlighting its role as a high-level reference design for modular implementations. By bridging theoretical principles with system optimization, the framework enhances interoperability across robotic platforms. Ultimately, this work delivers a practical design atlas, structuring the end-to-end pipeline of autonomous navigation to guide researchers and practitioners in selecting algorithms suited to their specific robotic platforms and mission requirements.