Search Results (166)

Rapid urbanization and associated greenhouse gas emissions pose severe challenges to global climate goals. Accurately estimating urban carbon emissions at fine administrative scales is a critical prerequisite for spatially differentiated mitigation policies and achieving carbon neutrality. However, while current research has validated the feasibility of using nighttime light (NTL) remote sensing for carbon estimation, most studies predominantly focus on macro scales, paying limited attention to intra-urban spatial heterogeneity and the value of high-resolution imagery. Using Nanjing, China, as a case study, this study examines the optimal scale, model, and data source for estimating urban total carbon emissions. NTL features from NPP/VIIRS and Luojia1-01 imagery were extracted at the district and township levels. Spatial lag and spatial error models were compared, and geographically weighted regression was further applied at the township level. The results show that urban carbon emissions in Nanjing exhibit clear scale effects and spatial non-stationarity. At the township level, the total indicator (TCE-TNLI) better reflects emission expansion in peripheral areas, while the intensity indicator (CI-ANLI) shows better predictive performance and robustness. With high-resolution Luojia1-01 imagery, the intensity model further reduces the effects of pixel saturation and administrative scale differences, achieving better model performance. These findings establish a robust methodological framework for fine-scale urban carbon accounting, demonstrating that integrating high-resolution imagery with intensity-based models is crucial for supporting spatially differentiated low-carbon planning in high-density megacities. Full article

Silicon photomultipliers (SiPMs) are vital for calorimetric applications in high-energy physics and medical imaging due to their high gain, compactness, and insensitivity to magnetic fields. However, their finite pixel count induces non-linear response behaviour at high photon fluxes, affecting energy resolution and systematic accuracy. This work presents a comprehensive methodology to characterise SiPM response functions and derive correction curves using a single-step laser-based measurement approach. Three SiPMs with varying pixel sizes (15, 25 and 50 µm) are studied under controlled temperature conditions, with response functions extracted across different overvoltages and integration windows. The correction method, independent of precise light source calibration, effectively linearises the response up to saturation levels exceeding 100% of the pixel count, achieving deviations of the order of 3% across a broad operational parameter space, and outperforming the traditional calibration model. The analysis demonstrates minimal dependence of the correction on temperature, overvoltage, and pixel size, indicating universal applicability. These findings enhance SiPM performance in high-energy calorimetry and offer a practical framework for improving detector linearity and dynamic range extensions in large-scale applications. Full article

Standard fringe-based structured-light processing estimates wrapped phase from phase-shifted sinusoidal images and commonly relies on phase unwrapping to obtain a globally consistent phase representation. In practical measurements, this approach may become unstable on reflective objects and under low or non-uniform illumination, where the recorded fringe signal is distorted and the recovered phase becomes unreliable. To address these limitations, we propose a correspondence extraction method based on subpixel peak localization performed directly on phase-domain images. The wrapped phase is transformed into absolute value phase profiles, $\mathsf{\Phi }=\left|{\varphi }_w\right|$, whose local structure follows the projected fringe pattern and is less affected by object-dependent intensity variations. The proposed method reformulates correspondence extraction as a local signal-based estimation problem in the phase-domain, thereby reducing reliance on global phase-consistency constraints at the correspondence stage. A practical advantage observed in the evaluated examples is that the method remained usable in some regions where the phase became locally flat because of low modulation, saturation, or reflective surface effects. In such regions, conventional processing relies on sufficiently reliable phase gradients and subsequent unwrapping, whereas the proposed method uses local peak geometry in the transformed phase representation. In the implementation used here, Gray-code information is employed only for pixel-wise phase extension and reference indexing, not as a spatial phase-unwrapping mechanism. The method does not require machine learning models or training data and can be integrated as a correspondence analysis stage in practical structured-light systems. Full article

Accurate anatomical landmark localization in clinical images requires millimeter-level spatial precision, yet whether increasing model scale improves such precision in structured medical imaging tasks remains unclear. Five YOLO26 pose-estimation variants (N, S, M, L, and X) were evaluated on 3679 RGB distal-arm images from 262 participants under a standardized overhead imaging protocol, with five anatomical landmarks annotated across the proximal forearm, mid-forearm, and hand. Localization error was quantified in millimeters using ArUco-marker-based pixel-to-millimeter calibration; all models were initialized from COCO-pretrained weights, fine-tuned under identical conditions, and assessed using COCO-style detection metrics and physically grounded localization error. Detection performance saturated across all scales (mAP@0.5 = 99.5%), while localization performance differed substantially; YOLO26N achieved the lowest mean error (2.76 ± 0.96 mm) and the highest proportion of predictions within 4 mm (88.0%), whereas YOLO26X produced the highest mean error (4.08 ± 2.59 mm) despite a 26.9× higher computational cost. Landmark-wise analysis revealed a consistent proximal-to-distal error gradient, with the largest degradation at anatomically ambiguous proximal landmarks in larger models. These findings suggest that increasing model capacity does not improve clinically meaningful localization precision in structured distal-arm imaging, and lightweight models may offer the most favorable accuracy-efficiency trade-off in resource-constrained clinical settings. Full article

Physically unclonable functions (PUFs) are critical security primitives used in authentication and cryptographic key generation. Among these, structural color-based PUFs offer distinct advantages, including fade resistance and the ability to conceal multi-dimensional information. However, current fabrication methods rely heavily on wet processes and laser ablation. Consequently, there is a significant need for flexible PUF labels capable of being produced through a facile and dry process. Here, we present stress-relief modulated photonic crystal PUF labels designed for multi-level dynamic encryption. We achieve random patterning of nanograting-based photonic crystals by leveraging curved pinning edge-induced interruptions and the uncontrolled bulking of the polymeric elastomer due to the uneven adhesion force from the tape. Using artificial intelligence-based deep learning algorithms, we authenticate the labels by extracting structural color, brightness, and saturation, which are determined by the grating periodicity, depth, and orderliness of each pixel. Furthermore, we integrated these photonic crystal patterns with dynamically modulated optical erasure to extend encryption capacity from the spatial to the temporal dimension. We anticipate this approach will enable advanced wearable anti-counterfeiting labels and multi-level digital encryption systems. Full article

Background/Objectives: Modern tomographic reconstruction methods—including physics-informed and AI-based approaches—can produce very high fidelity images. In this regime, widely used global image quality metrics often approach saturation, making it harder to distinguish residual differences between methods and identify remaining performance gaps. This study introduces a physically grounded and standardized evaluation framework designed to retain sensitivity beyond conventional global metrics and support both comparison and systematic improvement in tomographic reconstruction methods. Methods: The proposed framework defines standardized reference images—“Source”, “Detector”, “Ideal”, and “Realistic”—using Monte Carlo simulations, with the Ideal Image serving as a physically grounded benchmark. Reconstruction performance is evaluated using pixel-wise difference and ${\chi }^2$ maps, Region-of-Interest analysis, intensity (gray-value) histogram comparisons, and the Structure and Contrast Index (SCI), computed on difference maps. Demonstrations use simulated SPECT data reconstructed with ART, MLEM, and RISE-1. Results: Across case studies, SCI and ${\chi }^2$-based diagnostics reveal structured residuals and localized deficiencies not evident from global similarity metrics such as SSIM or NMSE. Comparative analyses show that methods with similar global scores can exhibit distinct residual structures and region-specific performance variations, while improved agreement in the sinogram domain does not necessarily translate into improved image fidelity. Histogram-based diagnostics provide complementary information on intensity redistribution not captured by pixel-domain summaries. Conclusions: The framework provides a reproducible, physically meaningful, and sensitive approach for evaluating tomographic reconstruction performance in the high-fidelity regime. By combining standardized reference images with multi-domain and multi-metric analysis, it enables robust benchmarking and supports physically consistent interpretation of reconstruction quality. Full article

Optical reflective markers are widely used in precision medicine, computer-assisted surgery, and robotic interventions. Nevertheless, intraoperative tracking still faces challenges such as sensor saturation, Point Spread Function (PSF) blooming, and flat-top artifacts, which affect localization precision and stability. Traditional deep learning detectors perform well in general object recognition but are limited in handling saturated infrared reflective markers due to their neglect of optical physics and inability to separate signal from blooming interference. This paper presents a physics-prior-guided network integrating a Brightness-Prior-Enhanced Spatial Attention (BPESA) mechanism for high-precision sub-pixel marker localization under saturation conditions. The method achieves a Root Mean Square (RMS) error of 0.52 pixels (approximately 0.11 mm) on a dataset of 8000 binocular images and reduces the localization error by approximately 54.4% compared with the baseline YOLOv8 model, while maintaining an inference speed of 134.6 FPS. The results demonstrate that optical blooming interference can be effectively mitigated by a learnable physics-prior branch, providing accurate marker coordinates that form a foundation for potential downstream tracking or navigation tasks. Full article

The accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is critical for assessing their carbon sequestration potential and supporting sustainable management. Satellite-based approaches are often constrained by signal saturation and mixed-pixel effects, whereas Unmanned Aerial Vehicle (UAV) imagery enables precise individual tree detection, overcoming these limitations. In this study, we propose a stand density (SD)-driven AGB estimation framework using high-resolution UAV RGB imagery. Individual bamboo positions were extracted using the Revised Local Maximum (RLM) algorithm, which achieved an optimal accuracy at a 2.5 m sampling interval (OA = 82.20%). Using 85 ground-truth plots, we developed six SD-AGB models and evaluated them via 10-fold cross-validation and independent UAV validation (10 plots). The Artificial Neural Network (ANN) model outperformed others, with strong calibration (R² = 0.94, RMSE = 3.78 Mg/ha), robust cross-validation (R² = 0.84 ± 0.06, RMSE = 5.24 ± 0.67 Mg/ha), and reliable independent validation (R² = 0.87, RMSE = 4.56 Mg/ha). Spatial mapping revealed a total of 14,190 bamboo plants with an average AGB of 32.80 Mg/ha. This UAV-based SD-AGB framework provides a robust, scalable, and cost-effective tool for precise biomass estimation, supporting sustainable bamboo forest management and carbon sequestration strategies and progress towards SDG 15. Full article

Objectives: To develop and validate a deep-learning model capable of detecting and quantifying intraoperative bleeding to objectively evaluate visual field impairment in unilateral biportal endoscopic spine surgery (UBE). Methods: Overall, 223,568 still images were extracted from 20 UBE videos and used to train a U-Net++ segmentation model based on the red masks generated using hue, saturation, and value (HSV) thresholding. The model was fine-tuned using 350 manually annotated images that differentiated clinically relevant bleeding (red masks) from non-bleeding red regions (zero masks). The model performance was evaluated against 180 ground-truth images annotated by three spine surgeons, which were extracted from videos that were separate from those used for training and fine-tuning. Dice and intersection-over-union (IoU) scores were calculated, and correlation analyses were performed based on inter-annotator agreement. Results: The HSV-based model reproduced the red regions with high fidelity; however, it showed limited agreement with the ground-truth bleeding regions (median Dice = 0.57, IoU = 0.40). The fine-tuned model improved substantially. For image-wise binary classification of bleeding presence, the model achieved an accuracy of 86%, with a sensitivity of 93% and a specificity of 60%. For pixel-level segmentation performance, the model achieved a median Dice score of 0.79 and a median IoU of 0.65 on ground-truth-positive images. Dice performance exceeded 0.80 in cases with strong inter-surgeon ground-truth concordance (≥0.80) and substantial bleeding area (>20%). Conclusions: This deep-learning model can accurately detect clinically meaningful intraoperative bleeding in UBE and quantify visual field impairments in still images and surgical videos. Future applications include the evaluation of hemostatic techniques, postoperative image-based assessment of surgical quality, and real-time intraoperative bleeding alerts to support surgical decision-making. Full article

Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration. Full article

For assessing forest resource quality and carbon sequestration, both optical and synthetic aperture radar (SAR) remote sensing data have been widely used to map forest aboveground carbon storage (AGC), demonstrating considerable potential across diverse forest types. However, the fusion approaches between SAR and optical data remain technically challenging, particularly when combining multi-source optical and multi-temporal SAR datasets. In this study, multiple optical datasets with varying spatial resolutions and spectral bands (Landsat-9, Sentinel-2, GF-6 PMS, and GF-6 WFV) and time-series Sentinel-1 data acquired within the same year were employed to develop an optical–SAR fusion framework for mapping forest AGC in mixed broadleaf–coniferous forests. Firstly, a multi-level collaborative fusion strategy (MLC) was developed using multi-source optical data by integrating the strengths of both pixel-level and feature-level fusion. Subsequently, a multi-temporal SAR combining approach was designed based on seasonal variation patterns using one-year time-series Sentinel-1 data. Finally, an optical–SAR modeling approach was established to map forest AGC using multiple machine learning models combined with the sequential forward feature selection method. The results demonstrate that the proposed MLC fused method for multi-source optical data offers significant advantages in enhancing estimation accuracy and improving model robustness. Furthermore, when multi-temporal Sentinel-1 data were integrated with the MLC-fused optical data, the optical–SAR collaborative approach further improved the coefficient of determination (R²), effectively mitigating the saturation effect commonly observed in optical data. The highest performance was achieved using spring-acquired multi-temporal Sentinel-1 data within the SVR model, yielding an R² of 0.69 and reducing rRMSE to 18.03%. It is indicated that an appropriate fusing strategy for integrating optical and SAR data can substantially enhance both accuracy and reliability in mapping forest AGC. Full article

Fringe projection profilometry has been widely adopted in various fields due to its non-contact nature, high accuracy, high speed, and full-field measurement capability. However, when measuring objects with highly reflective surfaces, saturation often occurs due to the limited dynamic range of the camera. To effectively address this issue, this paper proposes a novel adaptive fringe projection method. First, an intensity transfer model is established, which uses uniform grayscale images to compute surface reflectance coefficients and accurately determines the optimal projection intensity in the camera coordinate system. Subsequently, low-intensity orthogonal fringe patterns are employed to compute a smoothed absolute phase in saturated regions, establishing a coordinate mapping. The mapped pixel intensities are diffused into their neighborhoods, and the minimum value is taken in overlapping areas to generate an optimal projection intensity template in the projector coordinate system. Finally, adaptive fringe patterns are generated based on this template. Experimental results demonstrate that the proposed method achieves high-precision and high-completeness 3D measurement for objects with highly reflective surfaces. Full article

This essay contends that Jean-Luc Godard’s late digital cinema elaborates a geopolitical aesthetics in which Europe confronts the return of its repressed histories through the very instability of the digital image. While Europe has long functioned in Godard’s work as both theme and epistemic horizon—echoing the Hegelian cartographies—Film Socialisme (2010) and The Image Book (2018) transform this Eurocentrism into a site of crisis. In these films, what Fredric Jameson terms the “political unconscious” (1981) emerges through the spectral return of Palestine and the Arab world, compelling a reckoning with colonial legacies and the limits of representation. The digital turn proves decisive. Godard mobilizes pixelation, saturation, glitch, and decomposed sound to reveal what might be called the technological unconscious of the medium. I develop the concept of “Digital Orientalism” to designate how Orientalist chronotopes persist in the digital age yet are unsettled by Godard’s experimental manipulation of audiovisual fragments. Through close readings of Film Socialisme and The Image Book, which incorporates works by Arab filmmakers including Youssef Chahine, Nacer Khemir, Ossama Mohammed, and Wiam Simav Bedirxan, I show how Godard’s fractured montages produce symptomatic cartographies of the world-system where repression, memory, and accident collide. Full article

The implementation of FLASH Radiotherapy (FLASH-RT), characterized by ultra-high dose rates (UHDRs) frequently exceeding ${10}^6$ Gy/s in microsecond pulses, imposes stringent requirements on real-time dosimetry. Conventional ionization chambers suffer severe ion recombination and space-charge limitations under these conditions. This review summarizes the state of SSD technologies—including conventional standard silicon diodes, advanced SiC diodes, Low-Gain Avalanche Detectors (LGADs), and pixel detectors—and compares their performance, linearity, and dynamic range in UHDR environments. Particular attention is devoted to operational modes (integrating vs. counting), saturation mechanisms, and readout electronics, which frequently dominate detector behavior at FLASH conditions. We discuss the experimental results from recent UHDR beamlines and highlight emerging concepts that will shape future clinical translation. Full article

Future space detectors for Ultra High Energy neutrinos and cosmic rays will utilize Cherenkov telescopes to detect forward-beamed Cherenkov light produced by charged particles in Extensive Air Showers (EASs). A Cherenkov detector can be equipped with an array of Silicon Photo-Multiplier (SiPM) pixels, which offer several advantages over traditional Photo-Multiplier Tubes (PMTs). SiPMs are compact and lightweight and operate at lower voltages, making them well-suited for space-based experiments. The SiSMUV (SiPM-based Space Monitor for UV-light) is developing a SiPM-based Cherenkov camera for PBR (POEMMA Baloon with Radio) at INFN Napoli. To understand the response of such an instrument, a comprehensive simulation of the response of individual SiPM pixels to incident light is needed. For the accurate simulation of a threshold trigger, this simulation must reproduce the current produced by a SiPM pixel as a function of time. Since a SiPM pixel is made of many individual Avalanche Photo-Diodes (APDs), saturation and pileup in APDs must also be simulated. A Gaussian mixture fit to ADC count spectrum of a SiPM pixel exposed to low levels of laser light at INFN Napoli shows a significant amount of samples between the expected PE (Photo Electron) peaks. Thus, noise sources such as dark counts and afterpulses, which result in partially integrated APD pulses, must be accounted for. With static, reasonable values for noise rates, the simulation chain presented in this work uses the characteristics of individual APDs to produce the aggregate current produced by a SiPM pixel. When many such pulses are simulated and integrated, the ADC spectra generated by low levels of laser light at the INFN Napoli SiSMUV test setup can be accurately reproduced. Full article