Search Results (1,043)

Ensuring the visibility of road markings is critical for traffic safety, yet current inspection methods remain either prohibitively expensive (retroreflectivity) or subjective (manual assessment). This article introduces the Random Generated Reference (RGR) method, a novel automated solution for quantifying marking degradation using a standard on-board camera. The proposed pipeline is a complete protocol from video acquisition to road marking inspection and validation of the inspection that combines deep learning with computer vision: YOLOv8 is employed for robust detection, while a unique algorithm generates a “perfect virtual reference” that dynamically replicates the real scene’s geometry and illumination conditions, including shadows. By computing pixel-level deviations between the observed marking and this ideal reference, the system assigns a continuous degradation score aligned with the UK CS126 standard. Experimental validation was conducted on a real-world circuit yielding over 20,000 detections. Verification via Cochran sampling demonstrates that 68% of the automated assessments fall within one class of human inspection. This proof-of-concept confirms the viability of an approach based on generating the ground truth and scene conditions—such as illumination, shadows, rain, traffic, etc.—for road marking inspection. Full article

We study a 1D model of a diffraction-mediated self-structuring instability which can occur when a Bose–Einstein condensate is illuminated by a pump laser and its reflection from a single feedback mirror. We carry out a linear stability analysis and, using numerical simulations, investigate the dynamics of the self-structuring process. Two dynamical regimes are identified: one in which the system behaves as a continuous space-time crystal oscillating between two states (one spatially uniform and one spatially periodic) and another where many condensate momentum states are involved and the condensate density develops chevrons which form and disperse quasi-periodically. We show the dependence of the pattern modulation depth and pattern formation time on pump saturation parameter and compare the simulation results with analytical expressions derived from a quantum Hamiltonian Mean Field model. The results show that this system offers a route to the first experimental realisation of the quantum Hamiltonian Mean Field model and of a continuous space-time crystal with a tunable spatial period. Full article

The neuropeptides vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) have emerged as potent modulators of immune responses during sepsis, yet their roles remain complex, alternating between protective and permissive depending on timing, tissue compartment, and inflammatory context. This review presents a historical assessment of VIP and PACAP in sepsis research, highlighting the evolution of conceptual advances across five decades. Starting in the 1980s, early studies revealed that VIP levels rise during endotoxemia and correlated with hypotension and mortality, suggesting a deleterious role. By the 1990s, research pivoted toward understanding gut-derived VIP and its interaction with nitric oxide, culminating in the classification of VIP and PACAP as “macrophage deactivating factors” that downregulate TNFα and IL-6. The 2000s further clarified their cell-specific actions through VPAC1/2 and PAC1 receptors, showing anti-inflammatory effects on both innate and adaptive immune cells, while illuminating delivery challenges overcome by liposomal encapsulation. The 2010s expanded this narrative by dissecting receptor dynamics, gut barrier regulation, and VIP’s role in neuroimmune crosstalk and thrombo-inflammation. Most recently, studies in the 2020s provide a nuanced view of how VIP suppresses inflammatory damage but also enables pathogen persistence during live bacterial infection, implicating VIP signaling in trade-offs between tolerance and clearance. Across this chronological framework, VIP and PACAP have oscillated between friend, foe, and frenemy, underscoring the importance of context in leveraging their therapeutic potential in sepsis. Full article

Light-responsive hydrogel-based oscillators typically exhibit small oscillation amplitudes because solvent diffusion is intrinsically slow, and their dependence on external periodic light modulation further results in limited amplitude, poor stability, and insufficient autonomy. Inspired by the trigger and sliding mechanism of the ancient crossbow, this study introduces an innovative system that integrates a sliding-block mechanism with time-delay feedback, breaking from conventional approaches that rely on hydrogel inertia or external modulation, within a purely theoretical and simulation-based framework. By establishing a nonlinear dynamic model coupling solvent diffusion, photoisomerization, and optical attenuation, this research shows through numerical simulations that the system can exhibit two distinct modes under constant illumination: a stable state and a self-sustained oscillatory state. The model predicts that the oscillation frequency can be flexibly tuned by varying key parameters, including the crosslinking density, Flory–Huggins interaction parameters of the spiropyran and hydrophilic polymer, ring-opening reaction rate, light intensity, fraction of light-sensitive molecules, and sliding displacement, whereas the initial absorption coefficient has only a minor influence. The slider displacement is also identified as an effective means to regulate the oscillation amplitude. Furthermore, the expansion force at the container bottom is predicted to oscillate synchronously with the hydrogel’s volume change. This theoretical framework represents a paradigm shift from “static small deformation” to “dynamic large-amplitude oscillation”, significantly enhancing the mechanical responsiveness of the material. This work provides a novel and controllable strategy for the conceptual design of autonomous light-driven micromechanical systems. Full article

Signals of opportunity (SoO) enable emission-free passive sensing, but low Earth orbit (LEO) satellite illumination with unmanned aerial vehicle (UAV) array receivers exhibits rapid geometry variation. As a result, the received phase evolves in a space–time coupled manner, and the array snapshots become nonstationary even within one coherent processing interval (CPI), degrading conventional stationary-snapshot direction-of-arrival (DOA) estimators. This paper proposes a decomposition-based sparse reconstruction with successive interference cancellation (D-SR-SIC) framework for dynamic DOA estimation in LEO SoO UAV passive sensing. The proposed estimator leverages a sparse-reconstruction signal model and is implemented via a computationally efficient decomposition-based search-and-cancel procedure. A short-CPI parameterized space–time phase model captures the common motion-induced phase history and the time-varying steering drift; the coupled multi-parameter estimation is decomposed into two low-dimensional correlation searches followed by least-squares amplitude estimation and multi-target peeling. Optional local refinement and multi-beam pre-screening improve robustness to off-grid mismatch, near–far interference, and wide field-of-view operation. Simulations show that the proposed method achieves about 0.11${}^{\circ }$ DOA root-mean-square error (RMSE) at $-20$ dB signal-to-noise ratio (SNR) in a representative highly dynamic setting. Full article

Coastal nurseries are critical for the early stages of many elasmobranchs, and understanding spatial ecology during these periods is essential for effective population management. Here, we investigated the environmental drivers shaping shark presence and spatial distribution in an open coastal nursery used by young-of-the-year Sphyrna lewini along the southern Pacific Coast of Mexico. Using acoustic telemetry data collected over three consecutive seasons, we combined Random Forest models with an interpretable machine learning framework, including permutation-based variable importance, accumulated local effects, and a Rashomon set approach. Shark presence was primarily driven by seasonal patterns and lunar illumination, whereas spatial distribution within the nursery area was structured by tide level, shark length, accumulated precipitation, and sea surface temperature. Tide level emerged as the most influential and stable predictor of spatial preference, while size-dependent responses revealed ontogenetic spatial segregation among zones. These results demonstrate that open-coast nurseries can operate through dynamic environmental processes rather than static habitat features, with river-influenced areas playing a key role for smaller individuals. By integrating telemetry data with interpretable machine learning methods, this study provides a mechanistic understanding of nursery habitat use and offers a transferable framework for assessing spatial ecology and conservation priorities in threatened coastal shark populations. Full article

This study presents a reproducible simulation-based framework for visual-comfort and energy-optimised lighting design in UK residential buildings using DIALux Evo. Circadian and biophilic principles inform the conceptual approach, specifically colour temperature selection aligned with occupant comfort—but the study measures only photopic illuminance (lux) and electrical energy consumption (kWh), explicitly excluding biological circadian metrics, dynamic controls, and daylight harvesting. A controlled comparative design evaluates twenty matched lighting scenes in one-bedroom flats, compliant with EN 12464-1 and CIBSE LG9. The DIALux-optimised designs, incorporating LED luminaires in place of CFL luminaires used in existing manual designs, reduced mean energy consumption from 10.25 kWh to 8.68 kWh—a statistically significant reduction of 15.3% (t = 5.12, p = 1.2 × 10⁻⁵, d = 1.61)—while increasing mean illuminance from 165.86 lux to 205.14 lux (t = 3.084, p = 1.0 × 10⁻⁶, d = 0.81), improving CIBSE LG9 compliance across scenes. The framework offers a standards-aligned reproducible methodology with direct relevance to UK Net Zero objectives, Part L compliance, and residential retrofit policy, providing actionable guidance for architects, engineers, and policymakers. It is acknowledged that the observed gains reflect the combined benefit of an integrated LED-plus-simulation workflow; the absence of a same-technology comparison condition is identified as the primary design limitation. Full article

Water surface object detection in shipborne remote sensing is challenged by unstable wave-induced backgrounds, illumination variations, extreme scale changes with tiny objects, and limited annotations. Multimodal RGB–infrared (RGB–IR) sensing leverages complementary visible and infrared cues to enhance robustness. However, most existing RGB–IR methods rely on backbones pretrained on limited-scale data, which constrain their performance for complex water surface scenes. In this work, we propose the WS-R-IR Adapter, a parameter-efficient vision foundation model (VFM)-based framework for shipborne RGB–IR object detection. Instead of full fine-tuning, it adapts frozen VFM representations via lightweight task-specific designs. the WS-R-IR Adapter includes (1) a water scene domain-aware modal adapter that progressively guides frozen backbone features with evolving semantic cues, (2) a parallel multi-scale structural perception module for fine-grained, scale-sensitive modeling, (3) an adaptive RGB–IR feature modulation fusion strategy, and (4) a resolution-aligned context semantic and structural detail fusion module. Moreover, we introduce an object-guided global-to-local registration framework to address dynamic cross-modal misalignment, and construct modality-aligned PoLaRIS-DET and ASV-RI-DET datasets that cover diverse water surface scenes. On the two datasets, the proposed method achieves mAP@0.5:0.95 scores of 74.2% and 50.2%, respectively, significantly outperforming existing methods with only 11.9M additional parameters. These results demonstrate the effectiveness of parameter-efficient VFM adaptation for multimodal water surface remote sensing. Full article

Achieving a high frame rate and high dynamic range (HDR) under complex illumination remains a significant challenge for airborne push-broom visible-near-infrared (VNIR) hyperspectral cameras. Problematic scenarios typically include high-contrast scenes, such as ocean whitecaps alongside deep water or concurrently sunlit and shadowed urban surfaces. To address this, a real-time HDR acquisition system based on a dual-gain complementary metal–oxide–semiconductor (CMOS) image sensor is proposed. Specifically, a four-pixel HDR fusion method is developed, utilizing an optical calibration setup to accurately determine the fusion parameters and configure the spectral region of interest (ROI) for reduced data volume. The complete workflow, encompassing spectral–spatial four-pixel binning and piecewise dual-gain fusion, is implemented on a field-programmable gate array (FPGA) using a dual-port RAM-based buffering strategy and a low-latency five-stage pipeline. Experimental results demonstrate a minimal processing latency of 0.0183 ms and a maximum frame rate of 290 frames/s. By extending the output bit depth from 11 to 15 bits, the system achieves a digital dynamic range of the final output of 2.03 × 10⁴:1, representing a 9.58-fold improvement over the original low-gain data. The fused HDR data maintain high linearity and good spectral fidelity, with spectral angle mapper (SAM) values at the 10⁻³ level. Featuring a compact and low-power design, this system provides a practical engineering solution for efficient airborne VNIR hyperspectral acquisition. Full article

Remote photoplethysmography (rPPG) estimates heart rate by analyzing subtle blood-flow-induced color variations from camera videos; however, its performance is highly sensitive to illumination changes caused by variations in light intensity, position, and environmental conditions. To address this limitation, this study proposes a lightweight, training-free brightness normalization method that suppresses illumination-induced luminance fluctuations while preserving physiologically relevant color variations associated with blood perfusion. The proposed approach separates luminance and chrominance components from the frame-mean RGB vector and applies normalization only to the brightness component, thereby maintaining the intrinsic color direction essential for rPPG signal extraction and stabilizing temporal brightness without distorting chrominance relationships. Experimental evaluations show that channel-wise mean values vary only within $\pm 6$–$12\%$ with negligible changes in standard deviation, while dynamic range and temporal stability are significantly improved. Furthermore, when combined with an SNR-based signal selection strategy, the proposed method reduces the mean absolute error (MAE) of the CHROM algorithm on the DLCN dataset from approximately 18–19 BPM to 4.87 BPM under complex illumination scenarios, with consistent improvements also observed on the MR-NIRP dataset. These results suggest that the proposed preprocessing method helps preserve blood-flow-induced temporal color variations and improves the robustness of rPPG measurement under diverse illumination conditions. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

Conventional Image Signal Processors (ISPs) employ manually crafted designs with limited adaptability, resulting in suboptimal performance in dynamic environments for both visual quality and machine vision applications. While deep learning facilitates adaptive AI-ISPs, supervised approaches encounter domain shift limitations and substantial computational demands that impede edge deployment. This work introduces an adaptive zero-shot AI-ISP that dynamically optimizes processing pipelines without requiring paired training data. The proposed architecture implements dual specialized subnetworks for illumination estimation and denoising enhancement, operating collaboratively under Retinex theory principles to achieve boundary-aware illumination mapping and noise-resilient image restoration. Additionally, a physically constrained loss function is introduced to enhance color fidelity and noise suppression. For practical implementation, an FPGA-accelerated computing engine replaces transposed convolution with optimized bilinear interpolation, effectively eliminating artifacting while achieving superior memory efficiency through customized buffering architectures. A comprehensive evaluation demonstrates highly competitive performance, achieving a PSNR of 19.91/16.62 and an SSIM of 0.591/0.475 on LSRW-Huawei/Nikon datasets, alongside NIQE scores of 2.065/3.025 on DCIM and TM-DIED datasets. The hardware implementation attains 42.5 GOPS/W power efficiency, representing 35.4× and 7.3× improvements over conventional CPU and GPU platforms, establishing a comprehensive edge deployment solution for next-generation intelligent image processing systems. Full article

The Pushbroom Imager for Cloud and Aerosol Research and Development (PICARD) visible through shortwave infrared imaging spectrometer was developed to carry a calibration laboratory environment to high altitudes, while also providing high-dynamic-range bright cloud-top radiance measurements across a field of view just under 50 degrees. The in-flight performance of this new spectroradiometer was validated in comparison to multiple reference data sources and targets using imagery collected aboard NASA’s ER-2 high-altitude aircraft during the Western Diversity Time Series (WDTS) airborne science campaign in April 2023 and the September 2024 Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) Postlaunch Airborne eXperiment (PACE-PAX), both operating out of southern California. PICARD measurements from flights over Railroad Valley Playa, Nevada, USA, were compared to high-resolution radiance spectra of the dry lakebed provided by the Radiometric Calibration Network (RadCalNet) Working Group. Direct comparison to satellite cloud radiometry was enabled by the ER-2 flying in coordination with simultaneous overpasses of the Terra, Aqua, and NOAA-20 Earth-observing satellites during WDTS and with the PACE observatory during PACE-PAX. To account for large spectral differences between incandescent laboratory sources and solar illumination, PICARD calibration relies on measurements using the Goddard Laser for Absolute Measurements of Radiance (GLAMR) to characterize and minimize spectral stray light from the instrument’s twin Offner grating spectrometers. Good agreement in comparison to reference measurements demonstrates PICARD’s ability to provide imagery for environmental science or for testing new sensor designs and retrieval algorithms for cloud and aerosol research with verified laboratory calibrations at high altitudes. Full article

Satellite DNA (satDNA) is a family of tandemly repeated non-coding sequences in eukaryotic genomes involved in shaping genome architecture and regulation of various biological functions. Within a species, all satDNA families collectively form the satellitome. Satellitomes of Allium species has been explored only superficially, largely due to enormous genome sizes, high transposable element content, and a general lack of reference genomic resources. The emergence of reference genome assemblies now makes it possible to conduct a more in-depth study. Here, we applied a comprehensive bioinformatics approach to study the satellitomes of Allium cepa and Allium fistulosum. Using two complementary bioinformatics pipelines along with available reference genome assemblies, we have created the most complete collection of consensus satDNA sequences of A. cepa and A. fistulosum so far, consisting of 83 and 97 consensus sequences, respectively. The in silico analysis of the genomic distribution allowed the identification of 11 novel candidates for cytogenetic marker panels, including chromosome-specific satDNA families. Validation of satDNA using PCR and FISH confirmed the reliability of the created satellitomes. Furthermore, comparative analysis of satDNA genomic organization and abundance provided insights into the evolution of these species satellitomes. These findings provide a foundational resource that will help illuminate the evolutionary dynamics of Allium satellitomes and pave the way for future cytogenetic studies of Allium species. Full article

Existing buildings with large glazing ratios within subtropical Mediterranean climates face substantial challenges for thermal and visual control of their indoor environment. Previous research by the same authors has already identified the potential of incorporating both solar–PDLC (polymer-dispersed liquid crystal) and SPD (suspended particle device) switchable films within facades exposed to high solar insolation to provide a wide dynamic range of visual transparencies. This paper identifies a novel application for switchable laminates within a dynamic external shading device that permits the casting of a shadow on demand onto existing fenestration. This study compares the degree of glare within an enclosed space attained by a conventional opaque overhang over a window to that achieved with glass shading overhangs incorporating two types of switchable films. Using a scale model in a field test setting, indoor illumination and glare measurements are investigated under different states of switchable films and compared to those provided by conventional static glazing, with and without ordinary external overhangs under identical field test conditions. Results show that switchable overhangs in their transparent/bleached state can allow the ingress of daylight without creating excessive glare, whereas in their translucent/tinted state, switchable shades can deliver a level of glare protection similar to that provided by an opaque shading overhang. Full article