Abstract: Cavitation in rotating hydraulic machinery -- such as industrial pumps and hydropower turbines -- can cause blade and casing erosion, excessive vibration, noise and efficiency loss, posing significant operational and economic risks across industrial sectors. Reliable and scalable monitoring strategies are therefore essential, particularly under variable operating conditions in real-world environments. Recent advances in machine learning (ML) and deep learning (DL) have enabled data-driven approaches for cavitation detection based on operational sensor signals, yet a structured synthesis of these developments is lacking. This scoping review systematically analyzes measurement-based ML and DL approaches for cavitation monitoring, with the aim of identifying key trends, challenges and future research directions. Following PRISMA-ScR and JBI guidelines, 52 peer-reviewed studies published between 1996 and 2025 were evaluated, covering laboratory and field investigations across pumps and turbines and a wide range of model architectures. The analysis reveals that most studies are laboratory-based (∼ 80%), focus on pumps (∼ 70%) and rely on single-machine datasets (> 80%), limiting generalization across machines and operating conditions. Classical ML approaches remain relevant due to interpretability and robustness with limited data, while DL enables end-to-end learning from raw or time-frequency transformed signals, frequently achieving diagnostic accuracy above 95%. Hybrid frameworks combining DL-based feature extraction with classical classifiers are increasingly adopted. Key limitations across the literature include domain shifts between laboratory and field data, scarce or inconsistent labeling and a predominant focus on categorical cavitation severity levels.

Abstract: Hydrodynamic cavitation is attracting increasing interest in food processing as a non-thermal approach for preserving product quality and supporting the recovery of valuable bioactive compounds. Conventional Venturi devices are usually designed for fixed operating conditions, whereas real process streams may vary in temperature, viscosity, and gas or solid content. This can make it difficult to maintain stable and effective operating conditions when a fixed geometry is used. In this work, an adjustable circular Venturi is presented as a simple conceptual device for hydrodynamic cavitation in food applications. The external body and pipeline connections remain unchanged, while the throat section can be adjusted to adapt the device to different process requirements. In this sense, the proposed concept may also serve as an adjustable platform for exploring different operating conditions and identifying suitable throat configurations for specific food matrices and process targets. Once identified, such conditions may support the definition of a dedicated final Venturi configuration for the intended application. The proposed concept may be of interest for applications such as green extraction, food by-product valorization, and mild processing strategies aimed at preserving or enhancing bioactive compounds. This study is presented as a conceptual design contribution for food applications.

Abstract: Digital Twin (DT) systems combining physics-based simulation with hardware execution are critical for Industry 4.0 manufacturing, yet proprietary software solutions remain expensive and platform-dependent. This work addresses three technical challenges: maintaining geometric and kinematic fidelity across CAD-to-simulation conversion pipelines, synchronizing dual physics engines (Unity and ROS middleware) under hardware latency constraints, and optimizing motion planning while preserving trajectory quality and interactive responsiveness. We developed an integrated framework for a 7‑Degree of Freedom manipulator using CAD modeling, URDF/SRDF semantic representation, and bidirectional Unity-ROS (Robot Operating System) communication via WebSocket connectors. Motion planning uses RRTConnect from OMPL with collision-aware optimization through the Flexible Collision Library. Validation across 12 manipulation trials demonstrated positional synchronization accuracy of ±2.0 degrees, motion planning performance of 0.064 ± 0.020 seconds. Latency analysis reveals that hardware execution to be the dominant system bottleneck, significantly exceeding network communication delays. The system achieves performance metrics comparable to proprietary industrial solutions. This work establishes a replicable, cost-effective Industry 4.0 framework, demonstrating that modern game engine technology combined with open-source robotics middleware can deliver DT systems matching proprietary solutions. The architecture and validated implementation enable adaptation to alternative robotic platforms and support broader adoption of simulation-validated automation in manufacturing contexts.

Abstract: This paper presents a certification-oriented, system-level argument that Linux is fundamentally unsuitable for safety-critical avionics. Because Linux is a feature-rich, high-performance general-purpose OS, it exhibits open and dynamic execution semantics that cannot be finitely bounded or frozen at integration time. Two consequences follow. First, airworthiness infeasibility: an oversized TCB, prohibitive DO-330 toolchain qualification burden, and continuous patch churn that prevents stable, certifiable baselines. Second, semantic complexity: temporal non-isolation and spatial non-isolation, materializing as mutable logical-to-physical mappings, driver-induced contamination of global kernel state, and lack of fault containment. We consolidate these observations into an avionics-oriented OS evaluation framework that makes certification implications explicit—closed-world timing analysis at the partition level, provable spatial and fault isolation, TCB minimization, and lifecycle-stable evidence under DO-178C/DO-330 and ARINC 653. The framework turns architectural properties into concrete certification risks and provides actionable guidance for OS selection and governance in integrated modular avionics.

Abstract: Abstract To assess the electromagnetic effect on the children’s sensitive organs when they using the smartwatches, this study analyzes the electromagnetic dose absorbed by the children, especially calculate the combind electromagnetic exposure, We propose a multi-frequency smartwatch antenna (GPS L1, LTE FDD band1, 2.4G Wi-Fi/Bluetooth) and construct a 6-year-old human model including ten tissues from three sensitive organs (brain, eyes, heart). The Specific Absorption Rate (SAR) distributions are simulated under speaking and listening postures at different frequencies using CST Studio Suite. Furthermore, the SAR distributions under multi-frequency combined electromagnetic exposure are evaluated to investigate the superposition effects of electromagnetic fields. The results show that, when the child uses the smartwatch for 4G communication in the listening posture, the maximum local SAR reaches 1.683 W/kg, which is nearly half of the ICNIRP exposure limit. Electromagnetic radiation in the child’s brain is mainly concentrated on the surface of the cerebrum and cerebellum, while the radiation in the eyes is mainly distributed in the outer and anterior tissues. Under combined electromagnetic exposure, the SAR values of tissues increase by 1.17 to 3.53 times compared with single-frequency conditions but remain within safety limits. Considering the long-term use of smartwatches, the potential health risks to developing children still deserve attention.

Abstract: Background/Objectives: Rapid Eye Movement (REM) Sleep Behavior Disorder (RBD) is characterized by dream enactment due to reduced physiological muscle atonia during REM sleep and is clinically relevant as a potential prodromal marker for neurodegenerative disorders. This study aims to evaluate whether normalized beta-band power extracted from poly-somnographic signals can differentiate RBD subjects from healthy controls, and to compare the discriminative behavior of C4–A1 EEG versus EMG1–EMG2 channels during REM sleep. Methods: Polysomnographic recordings were obtained from the PhysioNet CAP Sleep Data-base. One-minute epochs were analyzed across sleep stages, with emphasis on REM. Signals were preprocessed to remove DC offset and were windowed with overlap prior to spectral estimation. Short time–frequency analysis of power spectral density (PSD) was applied to compute band-limited power in standard EEG frequency ranges (delta, theta, alpha, beta). Band power values were normalized by total spectral power to derive nor-malized indices. Comparative feature analysis was performed for C4–A1 and EMG1–EMG2 channels. Results: Normalized beta-band power during REM sleep showed clear separation between healthy subjects and RBD patients. In the C4–A1 channel, normalized beta power was higher in RBD than controls (controls: 0.0010–0.0049; RBD: 0.0076–0.014). In the EMG1–EMG2 channel, the difference was more pronounced (controls: 0.0020–0.0089; RBD: 0.053–0.0791). Conclusions: Normalized beta-band power, particularly during REM sleep, is a promising, low-complexity marker for RBD detection. The stronger separation in EMG1–EMG2 sug-gests that targeted channel selection may enhance practical screening pipelines for sleep disorder assessment.

Abstract: In this article we analyze a dynamical system known as the FitzHugh-Nagumo model, which offers many characteristics of nonlinear systems, such as bifurcation, excitability or limit cycle. The dynamics associated with sets of values of the parameters associated with this model, called excitable, oscillatory and bistable, are analyzed. Then adding a perturbing or diffusive term to the system through the Laplacian, it is studied how these dynamics propagate in a one-dimensional or two-dimensional extended medium, through the definition of a cellular automaton with periodic initial conditions.

Abstract: Finite-size suppression of the Curie temperature (T_c) in ferroelectric perovskite nanostructures remains an important yet insufficiently resolved problem, with reported scaling exponents varying considerably across experimental and theoretical studies. Although density functional theory provides atomistic insight into size-dependent behaviour, its high computational cost limits systematic exploration across broad size ranges. Conversely, purely empirical fitting approaches often lack physical interpretability and formal uncertainty quantification. In this work, a physics-informed surrogate modelling framework is developed to investigate finite-size scaling in BaTiO₃ and KNbO₃ nanostructures using a structured dataset compiled from the literature. The model is based on thermodynamically motivated scaling behaviour, enabling extraction of physically meaningful size-dependent parameters. Bootstrap resampling is employed to quantify statistical robustness, yielding scaling exponents of 1.59 (95% confidence interval: 1.43–1.72) for BaTiO₃ and 1.40 (95% confidence interval: 1.31–1.52) for KNbO₃. Gaussian Process regression is further integrated to provide uncertainty-aware predictions across the nanoscale domain. In addition to forward prediction, the framework enables inverse estimation of the minimum particle size required to preserve ferroelectric stability at a specified operating temperature. For a threshold of 300 K, the predicted critical sizes are approximately 4.96 nm for BaTiO₃ and 2.89 nm for KNbO₃. Extension to a coupled size–strain formulation produces a two-dimensional stability map, demonstrating tunable interactions between confinement and strain. Overall, the proposed methodology provides a transparent, statistically rigorous, and computationally efficient framework for predictive analysis and rational design of nanoscale ferroelectric materials.

Abstract: Detecting small objects in drone imagery remains challenging because of extreme object scale variations, dense scenes, and limited pixel information. Although recent YOLOv8 variants provide multiple model scales and architectural options, systematic guidance on their practical use in UAV-based detection remains limited. Accordingly, this study conducted a comprehensive empirical evaluation of the complete YOLOv8 family on the VisDrone dataset to assess the effects of the model capacity, input resolution, and architectural modifications on the small-object detection performance. The results showed that increasing the model capacity exhibited diminishing returns: YOLOv8l achieved the best overall accuracy (15.9% mAP50), while the larger YOLOv8x model exhibited a substantial performance degradation (7.32% mAP50) owing to training instability under data-constrained conditions. Scaling the input resolution from 640 to 1280 yielded a 25% improvement in the detection performance, substantially exceeding the gains obtained through architectural modifications, such as adding a P2 detection layer (+6%). The optimal configuration (YOLOv8l @ 1280) achieved a 488% improvement compared to the YOLOv5 baseline. These findings demonstrate that, for UAV-based small-object detection, prioritizing an appropriate model capacity and input resolution is more effective than increasing the architectural complexity.

Abstract: Built heritage is increasingly affected by climate-driven processes, yet its capacity to inform broader understandings of urban environmental change remains insufficiently explored. Here, we synthesize recent literature (2020–2024) on the application of the Historic Urban Landscape (HUL) approach to the integrated management of cultural heritage under climate risk, reframing the historic built environment as a multiscale diagnostic medium for climate–urban interactions. We analyze the steps and tools employed to support decision-making across territorial planning, risk assessment, and heritage governance in the papers selected from Web of Science, Science Direct, and Scopus databases. Results show that the approach is a flexible analytical framework that allows the integration of heterogeneous data, multi-criteria evaluations, and diverse stakeholder perspectives across spatial and temporal scales. Information modelling tools are shown to play a central role in structuring territorial knowledge, identifying patterns of vulnerability, and supporting comparative analyses across urban contexts. Nonetheless, significant challenges persist, including limited quantification of climate-induced degradation mechanisms, uncertainties in linking vulnerability assessments to predictive models, structural constraints on participatory implementation, and a tendency to apply the approach as a checklist due to inadequate understanding of its holistic dimensions. Overall, the HUL approach emerges as a scalable and transferable framework for embedding cultural heritage within climate research, advancing the conceptual integration of built heritage into resilience science and sustainability-oriented urban systems.

Abstract: We pose the Typhoon Engulfment Grand Challenge: determine whether any physically realizable feedback control law can guarantee the safe flight and landing of a commercial aircraft under all extreme, physically admissible typhoon wind fields—or prove that such a guarantee is mathematically impossible. The problem is formulated as a two-player zero-sum differential game between an autopilot (the minimizer, seeking survival) and an adversarial but physics-constrained typhoon (the maximizer, seeking to force the aircraft outside its safe operating envelope). We detail the coupled nonlinear dynamics, define the safe operating set in the airspeed–load-factor plane, and identify four interlocking barriers—high-dimensional Hamilton–Jacobi–Isaacs equations, PDE-constrained adversarial disturbances, hybrid structural failure dynamics, and imperfect observations—that place this problem beyond the reach of any existing mathematical framework. This article serves as a formal statement of the challenge, provides accessible explanations for researchers across disciplines, and charts concrete research directions for communities spanning control theory, aerospace engineering, applied mathematics, machine learning, fluid dynamics, structural mechanics, formal verification, operations research, signal processing, meteorology, and independent researchers worldwide.

Abstract: Purpose: To quantify second-law performance in a vertically oriented helically coiled tube heat exchanger (HCTHEX) and to develop predictive correlations for the dimensionless exergy-destruction fraction ϕ_D=E ̇_D/E ̇_(in,total)across a matrix of operating conditions and coil pitches. Methodology: A steady, real fluid-to-fluid CFD model was used with water on both shell and coil sides and laminar (or weakly transitional) treatment over the stated Reynolds-number range. Exergy rates at shell/coil inlets and outlets were computed for a heat-exchanger control volume and used to evaluate E ̇_Dand ϕ_D. Predictability was assessed via (i) global (non-pitch-specific) regressions including pitch as an explicit predictor, and (ii) pitch-specific regressions trained separately at each pitch; all models were trained in log space and evaluated using five-fold cross-validation. Findings: The global baseline power-law regression ϕ_D=A" " Re_shell^a Re_coil^b p^cyields statistically significant dependence on pitch and Reynolds numbers (e.g., for the D_h-based case: A=0.2238, a=0.04885, b=0.04982, c=0.7507). However, cross-validation shows limited predictive fidelity for the baseline (for D_h: R_(CV,log⁡)^2=0.1687, RMSE_(CV,log⁡)=0.1402). Among advanced surrogates, LogLog–GPR–ARDSE provides the best global performance for both characteristic-length definitions (for D_h: R_(CV,log⁡)^2=0.7171, RMSE_(CV,log⁡)=0.08181), representing a substantial reduction in prediction error relative to the baseline. Pitch-specific analysis demonstrates that the best advanced model depends on pitch: GPR–ARDSE is selected at p=1.80, 1.85, and 2.00, while bagged trees slightly outperform GPR at p=1.90and 1.95 under the minimum RMSE_(CV,log⁡)criterion. limitations: The reported correlations are calibrated to the simulated geometry family and operating ranges examined (including the pitch range studied) and should not be extrapolated beyond these conditions without additional verification. Practical implications: The resulting correlations enable rapid estimation of ϕ_Dfrom readily available nondimensional inputs (Re_shellⓜ,Re_coilⓜ,p), reducing the need for repeated full exergy accounting during design screening and operating-map exploration. Originality: This work couples a full fluid-to-fluid CFD exergy framework with systematic, cross-validated benchmarking of baseline power-law, advanced surrogate, and pitch-conditioned predictive models for ϕ_Din HCTHEX geometries, explicitly quantifying how model form and pitch conditioning affect predictive accuracy.

Abstract: Most of the technological processes in modern industrial production are realized with the help of mechanical energy, which is most conveniently obtained by means of an electric drive. In the metallurgical, machine-tool and other industries, it is advisable to use a multi-motor asynchronous electric drive for general industrial mechanisms such as overhead cranes (trolley movement mechanism), conveyors, rolling mills, taking into account technological requirements and operating modes. This requires the use of more sophisticated control methods for electromechanical systems, since two or more electric motors must work in concert for a single load. This, in turn, entails the use of a new element base, power and control, which makes it possible to implement these technological work cycles. The constant development of technology places increased demands on the electric drive regarding the accuracy of movement, both in statics and dynamics, speed and reliability. At the present stage, all these requirements can be achieved using specialized high-speed microprocessors as the basis of a control system for a twin-motor asynchronous electric drive, which opens up wide opportunities for creating technically advanced adjustable drives. At the same time, due to the intensive development of electronics and semiconductor technology, it is necessary to reduce the cost of electric drive control systems.

Abstract: Radio signals carry information in three natural ways: by changing how strong the signal is (amplitude), how high or low its tone is (frequency), and how its timing shifts within the wave (phase). In most communication systems, engineers use only one of these features at a time. As a result, much of the signal’s potential to carry information remains unused. This paper explores a simple but powerful idea: using all three features of a radio wave simultaneously to transmit information on a single carrier signal. By combining amplitude, frequency, and phase modulation together, a single radio wave can carry far more information without requiring additional bandwidth.To explain and analyze this concept, the work introduces an intuitive geometric framework inspired by a four-dimensional shape called a \emph{tesseract}, often described as a “four-dimensional cube.” In this framework, three directions represent the three information channels—amplitude, frequency, and phase—while the fourth represents time. This geometric picture provides a clear way to visualize how the three channels coexist without interfering with each other.As a simple demonstration, the phrase “I Love You” is encoded by assigning each word to a different feature of the signal: “I” is carried by amplitude changes, “Love” by frequency variations, and “You” by phase shifts. Colourful waveform plots, three-dimensional visualizations, and a novel “tesseract slicing” illustration help make the four-dimensional behaviour easier to understand.The proposed framework has potential applications in satellite communication, future 5G/6G networks, radar systems, and signal-processing education. By using all three dimensions of a signal at once, this approach reveals previously unused communication capacity and shows how a single radio wave could deliver substantially more information without consuming extra spectrum.

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: In recent years we have seen Large Language Models (LLMs) demonstrating robust reasoning capabilities comparable to human performance. This makes them increasingly appealing for driver assistance, where adaptation to dynamic human context is essential. Yet, research in this area remains fragmented, often focusing on isolated applications, lacking utilization of LLM's full potential to deliver integrated, context-specific support and action. This survey synthesizes recent advancements in LLM-driven occupant monitoring systems, focusing on their capabilities for interpreting driver states and acting appropriately, enabling a new generation of intelligent driver assistance. We critically examine pioneering frameworks, benchmarks, and foundational datasets that employ techniques like reasoning chains, multimodality, and human-in-the-loop feedback to create personalized and safe driving experiences. We lay out the current trends, limitations, emerging patterns, in addition to a novel human-centered evaluation of the field, providing researchers with a roadmap towards transparent and trustworthy in-cabin systems, that bridge safety with driver experience.

Abstract: Despite advances in engineering, fire safety improvements have plateaued in developed nations, necessitating a reassessment of resource allocation. This study develops a comprehensive fire safety assessment model for the Polish context using the Analytic Hierarchy Process (AHP). A panel of ten experts—comprising fire safety inspectors, State Fire Service officers, and architects—evaluated safety through a two-dimensional framework: the Fire Hazard Index (FHI) and Fire Safety Index (FSI). The results reveal a critical asymmetry: human factors (0.228) and combustible materials dominate the hazard landscape, whereas intelligent AI/IoT systems (0.4133) and passive protection (0.2113) offer the highest potential for safety enhancement. A novel "convergence-divergence" phenomenon was identified: hazard-focused assessments produce convergent priorities across building types (span 0.116), implying universal mitigation needs (e.g., education), while protection-focused assessments yield divergent priorities (span 0.250), justifying targeted investment. Specifically, healthcare facilities (ZL II) require disproportionate protection investment (priority 0.310). The study concludes that sustainable fire safety strategies must combine universal hazard mitigation with targeted technological interventions, offering a data-driven framework for policy optimization in Poland.

Abstract: Raising the turbine inlet gas temperature is an effective strategy for improving turbomachinery efficiency; however, it imposes severe thermal loads on turbine blades. To enhance blade cooling performance, this study employs computational fluid dynamics (CFD) to investigate the influence of sinusoidal ribs on turbulent flow and heat transfer in rectangular internal cooling channels. Numerical simulations demonstrate that sinusoidal rib configurations achieve superior heat transfer enhancement with reduced pressure losses across a wide Reynolds number range (Re = 20,000–90,000) compared to conventional transverse rib geometries. This improvement is quantified by higher normalized Nusselt numbers (N_u/N_u0) and lower normalized friction factors (f/f₀). Through systematic parametric analysis, the study elucidates how key geometric parameters—amplitude, wave number, and rib height—regulate flow and heat transfer performance. The study ranks nine pre-specified sinusoidal rib configurations under uniform heat flux conditions and identifies SR-I and SR-C as top performers for different design criteria, providing quantitative guidance for the preliminary design of turbine blade cooling channels.

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&D, standard and platform strategies, and policy and regulatory design.

Abstract: To address the accuracy divergence problem of the integrated navigation system caused by drilling slippage and mismatch between the tail cable encoder and the robot's motion when a seafloor drilling robot operates in deep-sea soft sedimentary layers, this paper proposes a robust navigation method based on robust square root ductile Kalman filter (RSRCKF). Considering the large deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model including the time-varying odometer scaling factor error is first established. To solve the numerical instability of the nonlinear system under non-Gaussian noise interference, the square root ductile Kalman filter (SRCKF) framework is introduced, and the positive definiteness of the error covariance matrix is dynamically maintained using QR decomposition. Based on this, an online fault detection mechanism based on the novel chi-square test is designed, and an adaptive variance expansion factor is constructed by combining a two-segment IGG weight function to realize the real-time identification and weight reduction processing of abnormal observations caused by slippage. Field drilling and turning tests on the mudflats off the coast of Zhoushan show that, under typical soft clay slippage conditions, this method can effectively identify "false displacement" interference. Compared with the traditional EKF and standard SRCKF, the position error is reduced by approximately 82.4%, and the heading angle error is controlled within±0.5∘Within a certain range, the high robustness and engineering practicality of the algorithm under complex seabed topography were verified.