Search Results (2,561)

Chatter is a self-excited vibration that limits productivity, accelerates roll wear and compromises strip surface quality in high-speed tandem cold-rolling. This work presents a predictive hybrid model that couples the strip-deformation physics to the structural dynamics of a five-stand, 4-high mill, providing a fast decision tool for process optimization and real-time control. The model represents each stand as a four-degree-of-freedom mass–spring–damper system whose parameters are extracted from manufacturing automation datasheets and roll-gap sensing. Linearization about the nominal point yields analytical sensitivity matrices that close the electromechanical loop; the delay between stands is also included in the model. Implemented in MATLAB/Simulink, the computational model, based on data provided by Danieli & C. Officine Meccaniche S.p.A., reproduces the onset of chatter for two types of steel. The framework therefore supports automation-ready scheduling, active vibration mitigation and design-space exploration for next-generation mechatronic cold-rolling systems. Full article

This study presents a first-of-its-kind investigation into retrofitting domestic vessels with a novel hybrid system integrating a Solid Oxide Fuel Cell (SOFC) and an Internal Combustion Engine (ICE). Using a Lake Ferry and an Island Ferry as case studies, three power-sharing scenarios (10–20% SOFC contribution) were examined for cruise and port operations. The results show that increasing the SOFC power share enhances overall system efficiency, reducing daily fuel energy consumption by up to 9% while achieving SOFC efficiencies of 58–60% in port. The design analysis confirms the physical retrofit feasibility for both vessels, with all scenarios occupying 72–92% of available machinery space. However, increasing the SOFC share from 10% to 15–20% raised total system weight by 10–20% and volume by 12–27%. Economically, the system demonstrates strong viability for high-utilization vessels, with Levelized Cost of Energy (LCOE) values of 236–248 EUR/MWh, while the sensitivity analysis highlights the SOFC capital cost as the dominant economic driver. Environmentally, the hybrid system achieves annual CO₂ reductions of 46–51% and NOx reductions of 51–62% compared to conventional diesel systems, with zero NOx emissions in port. The SOFC-ICE hybrid system proves to be a robust transitional pathway for maritime decarbonization, particularly for vessels with significant port-side operating hours. Full article

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images. Full article

The growing demand for electric vehicles, renewable energy systems, and portable electronics has led to the widespread adoption of power conversion systems. Although advanced structures like the superjunction MOSFET have prolonged the viability of silicon in power applications, maintaining its dominance through cost efficiency, Si-based technology is ultimately constrained by its intrinsic limitations in critical electric fields. To address these constraints, research into wide bandgap semiconductors aims to minimize system footprint while maximizing efficiency. This study reviews the semiconductor landscape, demonstrating why Gallium Nitride (GaN) has emerged as the most promising technology for next-generation power applications. With a critical electric field of $3.75\mathrm{MV}/\mathrm{cm}$ ($12.5\times$ higher than Si), GaN facilitates power devices with lower conduction loss and higher frequency capability when compared to their Si counterpart. Furthermore, this paper surveys the GaN ecosystem, ranging from device modeling and packaging to monolithic ICs and switching converter implementations based on discrete transistors. While existing literature primarily focuses on discrete devices, this work addresses the critical gap regarding GaN monolithic integration. It synthesizes key challenges and achievements in the design of GaN integrated circuits, providing a comprehensive review that spans semiconductor technology, monolithic circuit architectures, and system-level applications. Reported data demonstrate monolithic stages reaching $30\mathrm{m}\Omega$ and $25\mathrm{MHz}$, exceeding Si performance limits. Additionally, the study reports on high-density hybrid implementations, such as a space-grade POL converter achieving $123.3\mathrm{kW}/\mathrm{L}$ with $90.9\%$ efficiency. Full article

Mamba, based on the state space model (SSM), offers an efficient alternative to the quadratic complexity of attention, showing promise for long-sequence data processing and global modeling in 3D object detection. However, applying it to this domain presents specific challenges: traditional serialization methods can compromise the spatial structure of 3D data, and the standard single-layer SSM design may limit cross-layer feature extraction. To address these issues, this paper proposes MSHI-Mamba, a Mamba-based multi-stage hierarchical interaction architecture for 3D backbone networks. We introduce a cross-layer complementary cross-attention module (C3AM) to mitigate feature redundancy in cross-layer encoding, as well as a bi-shift scanning strategy (BSS) that uses hybrid space-filling curves with shift scanning to better preserve spatial continuity and expand the receptive field during serialization. We also develop a voxel densifying downsampling module (VD-DS) to enhance local spatial information and foreground feature density. Experimental results obtained on the KITTI and nuScenes datasets demonstrate that our approach achieves competitive performance, with a 4.2% improvement in the mAP on KITTI, validating the effectiveness of the proposed components. Full article

Proteoglycans are macromolecules consisting of a core protein and one or more glycosaminoglycan side chains. Proteoglycans synthesized by vascular endothelial cells modulate various functions such as anticoagulant activity and vascular permeability. We previously reported that some heavy metals interfere with proteoglycan expression, and that organic–inorganic hybrid molecules, such as metal complexes and organometallic compounds, serve as useful tools to analyze proteoglycan synthesis mechanisms. However, the effects of metal compounds lacking electrophilicity on proteoglycan synthesis remain unclear. Au₂₅(SG)₁₈, a nanoscale gold cluster consisting of a metal core protected by gold–glutathione complexes, exhibits extremely low intramolecular polarity. In this study, we investigated the effect of Au₂₅(SG)₁₈ on proteoglycan synthesis in vascular endothelial cells. Au₂₅(SG)₁₈ accumulated significantly in vascular endothelial cells at low cell density and suppressed the expression of perlecan, a major heparan sulfate proteoglycan in cells, by inactivating ADP-ribosylation factor 6 (Arf6). Additionally, Au₂₅(SG)₁₈ reduced the expression of biglycan, a small dermatan sulfate proteoglycan, in vascular endothelial cells at low cell density; however, the underlying mechanisms remain unclear. Overall, our findings suggest that organic–inorganic hybrid molecules regulate the activity of Arf6-mediated protein transport to the extracellular space and that perlecan is regulated through this mechanism, highlighting the importance of Arf6-mediated extracellular transport for maintaining vascular homeostasis. Full article

This work presents a hybrid chaotic–cryptographic image encryption method that integrates a physical two-dimensional delta-kicked oscillator with a PBKDF2-HMAC-SHA256 key derivation function (KDF). The user-provided key material—a 12-character, human-readable key and four salt words—is transformed by the KDF into 256 bits of high-entropy data, which is then converted into 96 balanced decimal digits to seed the chaotic system. Encryption operates in the real number domain through a chaotic partition–permutation stage followed by modular diffusion. Experimental results confirm perfect reversibility, high randomness (Shannon entropy $\approx 7.9981$), and negligible adjacent-pixel correlation. The method resists known- and chosen-plaintext attacks, showing no statistical dependence between plain and cipher images. Differential analysis yields $\mathrm{NPCR}\approx 99.6\%$ and $\mathrm{UACI}\approx 33.9\%$, demonstrating complete diffusion. The PBKDF2-based key derivation expands the effective key space to $2^{256}$, eliminates weak-key conditions, and ensures full reproducibility. The proposed approach bridges deterministic chaos and modern cryptography, offering a secure, verifiable framework for protecting sensitive images. Full article

The 3D reconstruction of vessel hulls is crucial for enhancing safety, efficiency, and knowledge in the maritime industry. Neural Radiance Fields (NeRFs) are an alternative to 3D reconstruction and rendering from multi-view images; particularly, tensor-based methods have proven effective in improving efficiency. However, existing tensor-based methods typically suffer from a lack of spatial coherence, resulting in gaps in the reconstruction of fine-grained geometric structures. This paper proposes a spatial multi-scale weighted NeRF (MDW-NeRF) for accurate and efficient surface reconstruction of vessel hulls. The proposed method develops a novel multi-scale feature decomposition mechanism that models 3D space by leveraging multi-resolution features, facilitating the integration of high-resolution details with low-resolution regional information. We designed separate color and density weighting, using a coarse-to-fine strategy, for density and a weighted matrix for color to decouple feature vectors from appearance attributes. To boost the efficiency of 3D reconstruction and rendering, we implement a hybrid sampling point strategy for volume rendering, selecting sample points based on volumetric density. Extensive experiments on the SVH dataset confirm MDW-NeRF’s superiority: quantitatively, it outperforms TensoRF by 1.5 dB in PSNR and 6.1% in CD, and shrinks the model size by 9%, with comparable training times; qualitatively, it resolves tensor-based methods’ inherent spatial incoherence and fine-grained gaps, enabling accurate restoration of hull cavities and realistic surface texture rendering. These results validate our method’s effectiveness in achieving excellent rendering quality, high reconstruction accuracy, and timeliness. Full article

With the increasing integration of renewable energy into power grids, voltage source converter-based high-voltage direct current (VSC-HVDC) stations often adopt hybrid grid-following (GFL) and grid-forming (GFM) control strategies to improve adaptability to varying grid strengths. In many existing schemes, the hybrid coefficient changes abruptly, which may produce large transient current overshoots and compromise the safe and stable operation of converters. An adaptive hybrid GFL-GFM control framework equipped with a hybrid coefficient transition regulation is proposed. Small-signal state–space models are established and eigenvalue analysis confirms stability over the considered short-circuit ratio (SCR) range. The regulating method is activated only during coefficient transitions and is inactive in steady-state, thereby preserving the operating-point eigenvalue properties. Dynamic equations of the converter current change rate are derived to reveal the key role of the hybrid-coefficient change rate in driving transient current overshoots, based on which a real-time hybrid coefficient regulating method is developed to shape coefficient transitions. Simulations on a 500 kV/2100 MW VSC-HVDC project demonstrate reduced transient current overshoot and power oscillations during SCR variations, with robustness under moderate parameter deviations as well as representative SCR assessment error and update delay. Full article

Autonomous UAV/UGV swarms increasingly operate in contested environments where purely digital control architectures are vulnerable to cyber compromise, communication denial, and timing faults. This paper presents Guarded Swarms, a hybrid framework that combines digital coordination with hardware-level analog safety enforcement. The architecture builds on Topic-Based Communication Space Petri Nets (TB-CSPN) for structured multi-agent coordination, extending this digital foundation with independent analog guard channels—thrust clamps, attitude limiters, proximity sensors, and emergency stops—that operate in parallel at the actuator interface. Each channel can unilaterally veto unsafe commands within microseconds, independently of software state. The digital–analog interface is formalized via timing contracts that specify sensor-consistency windows and actuation latency bounds. A two-robot case study demonstrates token-based arbitration at the digital level and OR-style inhibition at the analog level. The framework ensures local safety deterministically while maintaining global coordination as a best-effort property. This paper presents an architectural contribution establishing design principles and interface contracts. Empirical validation remains future work. Full article

This article presents a hybrid optimization model designed to determine the optimal location and operation of capacitor banks in medium-voltage distribution networks, aiming to reduce energy losses and enhance the system’s economic efficiency. The use of reactive power compensation through fixed-step capacitor banks is highlighted as an effective and cost-efficient solution; however, their optimal placement and sizing pose a mixed-integer nonlinear programming optimization challenge of a combinatorial nature. To address this issue, a multi-objective optimization methodology based on the Sine Cosine Algorithm (SCA) is proposed to identify the ideal location and capacity of capacitor banks within distribution networks. This model simultaneously focuses on minimizing technical losses while reducing both investment and operational costs, thereby producing a Pareto front that facilitates the analysis of trade-offs between technical performance and economic viability. The methodology is validated through comprehensive testing on the 33- and 69-bus reference systems. The results demonstrate that the proposed SCA-based approach is computationally efficient, easy to implement, and capable of effectively exploring the search space to identify high-quality Pareto-optimal solutions. These characteristics render the approach a valuable tool for the planning and operation of efficient and resilient distribution networks. Full article

This study investigates peak daylight exposure in zenithally lit museum rooms at 51° latitude through an experimental campaign using a 1:20 physical mock-up of a 12 × 12 × 6 m exhibition gallery space. Nine configurations of shading and light-transmitting elements (CSaLTE) were tested under real clear-sky conditions between June and October. To ensure a valid comparative analysis, indoor vertical illuminance (E_v) was measured at 15 min intervals and subsequently interpolated and normalised to a unified equinox-day solar geometry (06:00–18:00). This hybrid empirical-computational methodology allows for a direct performance comparison across different geometric arrangements regardless of their specific measurement dates. The results demonstrate that while traditional annual metrics are the standard, short-term illuminance peaks pose a severe and underexplored threat to conservation safety. Even the most light-attenuating diffusing-roof configurations produced short-term illuminance peaks and cumulative clear-sky exposures that are comparable in magnitude to commonly cited annual limits for highly light-sensitive materials, with several configurations recording extreme spikes surpassing the sensor’s 20,000 lx saturation limit. Stable, low-illuminance distributions were observed only in selected diffusing-roof arrangements (M05–M07), whereas direct-glazing systems (M01–M04) produced unsafe exposure patterns with high temporal variability and poor visual adaptation conditions. The study concludes that passive roof geometries alone are insufficient to ensure conservation-level safety without additional active filtering or adaptive control strategies, providing an experimentally grounded framework for designing zenithal daylighting systems in museum environments. The results are intended for relative peak-risk comparison under controlled clear-sky conditions rather than direct generalisation to whole-room annual conservation safety. Full article

Rapid urbanization and climate change have intensified flood risk and ecological degradation along urban riverfronts. Recent literature suggests that combining green and grey infrastructure can enhance resilience while delivering ecological and social co-benefits. This study analyzes and compares five riverfront projects in China and Spain, assessing how their tactic mixes operationalize three urban flood-resilience strategies—Resist, Delay, and Store/reuse—and how these mixes translate into ecological, social, and urban impacts. A six-phase framework was applied: (1) literature review; (2) case selection; (3) categorization of resilience strategies; (4) systematization and typification of tactics into green vs. grey infrastructure; (5) percentage analysis and qualitative matrices; and (6) comparative synthesis supported by an alluvial diagram. Across cases, Delay emerges as the structural backbone—via wetlands, terraces, vegetated buffers, and floodable spaces—while Resist is used selectively where exposure and erodibility require it. Store/reuse appears in targeted settings where operational capacity and water-quality standards enable circular use. The comparison highlights hybrid, safe-to-fail configurations that integrate public space, ecological restoration, and hydraulic performance. Effective urban riverfront resilience does not replace grey infrastructure but hybridizes it with nature-based solutions. Planning should prioritize Delay with green systems, add Resist where necessary, and enable Store/reuse when governance, operation and maintenance, and water quality permit, using iterative monitoring to adapt the green–grey mix over time. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

The increasing core counts and architectural heterogeneity of modern processors make performance optimization insufficient if energy consumption is not simultaneously considered. By providing a novel characterization of how the interaction between hybrid architectures and system software disrupts the traditional correlation between execution speed and energy efficiency, this research study analyzes the performance–energy trade-offs of parallel LU matrix decomposition algorithms implemented in Java, focusing on the Crout and Doolittle variants. This study is conducted on four different platforms, including ARM-based, Hybrid x86, and many-core accelerators. Execution time and speedup are evaluated for varying thread counts, while energy consumption is measured externally to capture whole-system energy usage. Experimental results show that the configuration yielding the maximum speedup does not necessarily minimize energy consumption. While x86 systems showed energy savings exceeding 80% under optimal parallel configurations, the ARM-based platform required distinct thread counts to minimize energy consumption compared with maximizing speed. These findings demonstrate that energy-efficient configurations represent a distinct optimization space that often contradicts traditional performance metrics. In the era of hybrid computing, green software optimization must transition from a simplistic “race-to-sleep” paradigm toward sophisticated, architecture-aware strategies that account for the specific power profiles of heterogeneous cores to achieve truly sustainable high-performance computing. Full article