This paper proposes a novel salient object detection method for operational hole localization in metallurgical furnaces, addressing challenging industrial conditions including extreme illumination variations and strong electromagnetic interference to enable two-level measurement in aluminum electrolysis cells and impact position recognition of the front-of-furnace operation robot. It employs a multi-feature fusion framework combining foreground and background saliency maps with center prior maps. Foreground saliency maps are generated through spatial compactness and local contrast computations, enhancing discriminative features while suppressing shared foreground–background characteristics. Background saliency maps are constructed via sparse reconstruction to exploit redundant features. Then method integrates edge extraction and density clustering to generate center prior maps that emphasize foreground target centroids and mitigate background noise. Comprehensive evaluations on both a specialized operational hole dataset and six public datasets demonstrate superior performance compared to other methods. On the specialized dataset, it achieves a precision of 0.8954, a maximum F-measure of 0.8994, and an S-measure of 0.8662. While maintaining operational robustness, the method offers a practical solution for furnace monitoring and robotic operation guidance in metallurgical processes.

Ground-based remote sensing of seismic and geophysical displacements remains a major challenge due to environmental hazards, signal attenuation, and practical deployment limitations of traditional seismometers. In this study, we present a detailed design, implementation, and performance evaluation of a Moiré-based apparatus for remote ground displacement measurement. The system operates by detecting fringe shifts formed between a fixed and a displaced grating, with displacement magnified through controlled angular superposition. We systematically assess each component of the system, including telescope optics, imaging sensors, and grating configurations, to optimize spatial resolution, contrast, and robustness under varying environmental conditions. A digital approach for fringe generation was employed, allowing controlled magnification and improved sensitivity without the need for physical alignment of dual gratings. Indoor experiments under low-turbulence conditions validated the system’s capability to detect displacements as small as 50μm. Subsequent outdoor trials at different distances demonstrated successful measurement of both square-wave and seismic-like displacements despite increased atmospheric turbulence and wind. The results confirm the system’s ability to perform real-time, long-range, non-contact displacement monitoring with high accuracy and resilience to environmental variability. This study establishes a foundation for the application of Moiré-based sensing in challenging field conditions, including volcanic and seismic zones.

The migratory paths that are stereotypical of sexile are the core of some of the most recent manifestations of the so-called neorural literature or literatures of rurality. Under metronormative expectations, these narratives reproduce the spatial and temporal contrast between rural and urban queer lives, turning migration into a crucial element in queer studies. Drawing from the analysis of El Power Ranger rosa (Christo Casas, 2020) and La mancha (Enrique Aparicio, 2024), this paper analyses how time and space are intimately intertwined in the narrative trope of sexile. Both novels use the return of young queers to their villages as a pretext to thread temporal lines and perspectives in which we may identify the role of memory in identity, as well as the incoherence and fallibility of temporal linearity.

Air-coupled ultrasonic detection of microholes in flexible envelope materials.

The small-angle scattering form factors of two classes of composite particles with contrasting internal architectures have been studied: one consisting of inclusions of smaller spheres embedded within a larger sphere, and the other comprising a solid sphere with randomly distributed spherical voids. These systems serve as material- and void-based analogues, providing a model framework for examining how internal material distribution in porous particles influences scattering signatures. Monte Carlo simulations were used to generate scattering curves across a range of volume fractions and polydispersities, which were then employed to benchmark analytically derived form factor expressions. Steric repulsion between, respectively, spheres and voids was taken as hard-sphere interactions. The results reveal that internal structural asymmetries, especially in spatial correlations and contrast topology, significantly affect scattering patterns, despite the particles having similar overall structures and volume fractions. In particular, spheres-of-spheres structures exhibit features in the scattering signal from internal modulations, while void-based particles display smoother shell-like scattering features. The analytical models show excellent agreement with the simulated data, capturing both the global shape and fine structural characteristics. These findings demonstrate that relatively simple analytical approaches, validated against numerical simulations, can reliably describe complex heterogeneous particles. This methodology provides a robust basis for interpreting scattering data from porous and composite materials across a wide range of applications.

Primate vision has exceptionally high spatial acuity and contrast sensitivity. This performance originates in specialized photoreceptors of the fovea. These cones transduce light into electrical signals in the outer segment, and convey these signals to the presynaptic terminal for transmission. Backpropagating signals are also possible, as the terminal receives gap-junctional input from neighboring cones. Such signals could influence phototransduction itself. To test this idea, we recorded electrophysiologically from both ends of single cones dissociated from the macaque fovea. We found that backpropagation was effective despite the extreme slenderness and length of these cells. Backpropagation was also effective in a passive compartmental model, indicating that amplification by voltage-gated channels is not required. We then modeled mosaics of foveal cones coupled by terminal gap junctions. Despite faithful backpropagation of these inputs, they appear unlikely to influence phototransduction. Thus, even though foveal cones exhibit effective backpropagation, their encoding of visual information remains compartmentalized.

Abstract This study assesses the ability of eight High-Resolution Model Intercomparison Project (HighResMIP) simulations within the Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) to simulate West African rainfall from 1983 to 2014. Using multiple observational datasets, we assess the annual cycle, interannual variability, summer monsoon precipitation (June-July-August-September, JJAS), and extreme rainfall behaviour across the Sahel, the Guinea Coast, and the entire West African region. While the models capture the broad spatial contrast between the wetter Guinea Coast and the drier Sahel, substantial differences persist in their representation of seasonal timing, rainfall magnitude, and extremes. Several models reproduce the observed single August rainfall peak, but others generate bimodal structures or exhibit systematic wet or dry biases. HiRAM most accurately reproduces interannual variability, whereas INM-CM5 and BCC-CSM2 show large structural biases. Model performance reduces when evaluated over the full West African domain, and outliers in JJAS rainfall contribute to pronounced wet biases and uncertainty in extreme precipitation indices. The ensemble mean generally reduces individual model errors but inherits structural deficiencies from poorly performing models, particularly over the Guinea Coast. The results highlight persistent challenges in representing monsoon dynamics, convection, and regional climate drivers, demonstrating that higher spatial resolution alone does not eliminate major biases. Overall, the study highlights the importance of evaluating against multiple observational datasets and emphasises the need for further investigation into the physical processes and regional downscaling approaches necessary to enhance rainfall simulations over West Africa.

Event-based vision sensors offer microsecond temporal resolution and low power consumption, making them attractive for edge robotics and simultaneous localization and mapping (SLAM). Contrast maximization (CMAX) is a widely used direct geometric framework for rotational ego-motion estimation that aligns events by warping them and maximizing the spatial contrast of the resulting image of warped events (IWE). However, conventional CMAX is computationally inefficient because it repeatedly processes the full event set and a full-resolution IWE at every optimization iteration, including late-stage refinement, incurring both event-domain and image-domain costs. We propose coarse-to-fine contrast maximization (CCMAX), a computation-aware CMAX variant that aligns computational fidelity with the optimizer's coarse-to-fine convergence behavior. CCMAX progressively increases IWE resolution across stages and applies coarse-grid event subsampling to remove spatially redundant events in early stages, while retaining a final full-resolution refinement. On standard event-camera benchmarks with IMU ground truth, CCMAX achieves accuracy comparable to a full-resolution baseline while reducing floating-point operations (FLOPs) by up to 42%. Energy measurements on a custom RISC-V-based edge SoC further show up to 87% lower energy consumption for the iterative CMAX pipeline. These results demonstrate an energy-efficient motion-estimation front-end suitable for real-time edge SLAM on resource- and power-constrained platforms.

We investigate spatial localization and interference control in a three-level quantum dot (QD) system driven by two-dimensional standing-wave fields. Although QDs are embedded in solid-state environments and are heavier than natural atoms, their discrete energy levels and tunable tunneling couplings allow them to replicate atomic-like behaviors, offering greater flexibility than traditional atomic systems. Using the density matrix formalism, we calculate the optical susceptibility under the influence of a weak probe and a strong control field. This susceptibility explicitly incorporates inter-dot tunneling and phase-coherent interactions, ensuring that the resulting localization is not merely a classical field imprint but reflects quantum interference effects. Our results show that while the standing-wave fields define the spatial modulation template, the accuracy, contrast, and stability of localization are primarily determined by quantum coherence and tunneling-induced interference. Symmetric wavevector configurations lead to sharply confined, isotropic localization profiles, whereas asymmetries in wavevector alignments cause the distribution to broaden and degrade, reducing localization precision. These findings highlight the critical role of tunneling coherence and wavevector alignment in controlling localization behavior. The observations suggest that QDs, with their tunable energy levels and tailored tunneling couplings, provide versatile solid-state analogs for exploring localization phenomena. These systems hold significant potential for applications in spatially resolved quantum information processing, nanoscale sensing, and the development of coherent nanophotonic devices.

Compared to wide-field (WF) microscopy in the second near-infrared (NIR-II) window, NIR-II multifocal structured illumination microscopy (NIR-II MSIM) provides a twofold improvement in transverse spatial resolution, along with enhanced penetration depth and superior image contrast. These advantages position NIR-II MSIM as a promising platform for studying physiological processes in turbid specimens. However, significant background noise caused by cumulative photon scattering continues to severely degrade image resolution and fidelity. Moreover, the data processing workflow in NIR-II MSIM is highly sensitive to motion artifacts, which further limits its broader applications in live-tissue imaging. To address these challenges, we introduce NIR-II MSIM-eSRRF, a technique that combines NIR-II MSIM with enhanced super-resolution radial fluctuation (eSRRF). This hybrid approach facilitates advanced deep imaging with improved spatial resolution and contrast across turbid specimens, including Intralipid phantoms, ex-vivo pork tissue, and live mice. Notably, NIR-II MSIM-eSRRF could effectively suppress motion artifacts, yielding nearly artifact-free visualization of fine cerebral vessels in vivo. As such, the NIR-II MSIM-eSRRF platform will emerge as a powerful tool for investigating physiological processes in highly scattering environments, thereby accelerating advancements in intravital fluorescence microscopy.

BackgroundPostmortem MRI offers superior spatial resolution and contrast compared to antemortem MRI, enabling analysis of pathological processes underlying white matter hyperintensities (WMH). However, its correlation with antemortem WMH remains unclear due to postmortem changes, fixation effects, and tissue dehydration. This study compares WMH burden on antemortem and postmortem MRI in the same individuals to assess the suitability of postmortem MRI for structure‐pathology association studies.Method7T T2‐weighted postmortem and 3T FLAIR antemortem MRIs were acquired from 41 individuals (Table 1). WMH segmentations were generated using Purple‐MRI for postmortem MRI and WMH‐SynthSeg for antemortem MRI (Figure 1). One hemisphere was scanned postmortem, and the corresponding antemortem hemisphere was used for WMH segmentation. Linear regression models examined (I) Antemortem WMH volume as a predictor of postmortem WMH, (II) age at scan as a predictor of antemortem WMH volume, and (III) age at death as a predictor of postmortem WMH volume. Nuisance covariates included antemortem/postmortem intervals (AMI/PMI) and fixation time, as appropriate.ResultTable 1 provides a breakdown of demographics and neuropathological diagnoses. A moderate, statistically significant correlation was found between antemortem and postmortem WMH volume (r = 0.47, p = 0.002;Figure 2), remaining significant after adjusting for age and nuisance covariates (Table 1). Both antemortem and postmortem WMH volumes were significantly associated with age (Table 4 [antemortem MRI] and 3 [postmortem MRI]), with a greater antemortem association in absolute terms (t=2.8 vs. t=2.58, n.s.). Other covariates were not significant. Postmortem WMH volume was, on average, 102±121% higher than antemortem, with only a weak association between their relative difference and AMI (r=0.27, p = 0.04), suggesting that WMH increase is due to greater lesion visibility in postmortem MRI rather than WMH growth. This aligns with histological findings showing WMH‐linked pathology in surrounding normal‐appearing white matter.ConclusionThis study highlights both a significant association between antemortem and postmortem WMH volume, and a significant bias towards larger WMH in postmortem MRI, above and beyond factors like AMI and fixation time. In subsequent work, we will use matched MRI and histology to study the contribution of vascular and neurodegenerative pathologies to the extent and appearance of WMH on antemortem and postmortem MRI.

In hominins, the reduction of prognathism during craniofacial evolution was a significant derived trait differentiating Homo from earlier hominins and other apes and might have contributed significantly to calvarial expansion and encephalization. Gnathic remains of Australopithecus africanus, A. robustus, Homo habilis, and H. erectus from the Plio-Pleistocene boundary 5.3-2.6 MYA were studied for evidence of alveolar bone (AB) loss indicative of periodontal disease(s). AB loss may provide critical insights into craniofacial evolution and the divergence of Homo from the Australopithecines. AB loss in Plio-Pleistocene gnathic remains provides the fossilized hard evidence of the antiquity of periodontal diseases, the first recognized diseases in hominins' evolution. Seventy-one gnathic remains of Australopithecines and Homo species from Ditsong Museum of Natural History, Pretoria, and the School of Anatomy of the University of the Witwatersrand, Johannesburg, were examined using scanning electron microscopy (SEM) and macrophotography. Specimens were scanned at the microfocus X-ray tomography laboratory (MIXRAD) at Necsa, Pretoria, optimizing on highest spatial resolution and image contrast. Linear distances from the enamel-dentine junction (EDJ) to the remaining AB crest were also measured. Morphometric analyses showed that there is progressive AB loss as hominins speciated from Australopithecines to Homo. Homo remains showed statistically significant variation when analysing the linear distance between the EDJ and the remaining AB when compared with both Australopithecine taxa. AB loss was confirmed by microfocus X-ray tomography and, in Homo species only, showed a vertical pattern of bone loss with crateriform lesions and furcation defects. SEM and microfocus X-ray tomography, macrophotography, and linear measurements from the EDJ to the remaining alveolar bone showed that Homo had greater alveolar bone loss with intrabony defects and craters when compared with Australopithecines' taxa. There were no significant differences between the 2 Australopithecine species examined. The presented data show that Homo species developed significant AB loss. The data propose that random mutations of genes controlling odontometric values selected for a reduction of the size of the crowns during hominins' evolution. Smaller crowns ultimately resulted in weaker masticatory forces yet allowing masticatory function and thus survival in the presence of AB loss. Together with the speciation of smaller crowns, with reduction of masticatory muscle mass and thus masticatory forces, there was a reduction of prognathism leading to calvarial expansion with subsequent encephalization, speciating the Homo clade and later, the emergence of Homo sapiens.

Metacarpophalangeal Joints of the Long Fingers: Anatomy, Biomechanics, and Imaging Techniques.

Spatial and social cognition jointly determine multimodal demonstrative reference: Experimental evidence from Turkish and Spanish.

Ultrasound-guided nerve block is a safe and effective regional anesthesia technique; however, accurate identification of the brachial plexus remains challenging due to its small size and low contrast in ultrasound images. Recent advances in deep learning offer promising solutions to enhance brachial plexus segmentation and improve perioperative regional anesthesia precision and safety. This review systematically summarizes current deep learning approaches applied to ultrasound-based brachial plexus segmentation. We highlight key models, including Convolutional Neural Networks, the U-shaped Convolutional Neural Networks and their variants, Mask RegionBased Convolutional Neural Networks, and Generative Adversarial Network-based architectures, and compare their reported performances, with Dice Similarity Coefficients ranging from 0.5865 to 0.882 and Intersection over Union values up to 0.6957. Among them, U-Net remains the most frequently employed due to its balance of accuracy and computational efficiency. Moreover, novel models such as multi-objective brachial plexus segmentation network and BPMSegNet have demonstrated superior segmentation performance by incorporating attention mechanisms and spatial contrast features. Notwithstanding these advancements, challenges persist, particularly limited dataset availability and insufficient model generalization. This review provides a comprehensive overview of recent progress, evaluates comparative performance metrics, and outlines future directions to improve model robustness and clinical applicability and clinical applicability in the perioperative setting.

ABSTRACT Solution‐processed metal halide perovskites have rapidly emerged as promising candidates for direct X‐ray imaging, yet fundamental comparisons between hybrid and all‐inorganic systems in flat panel architectures remain missing. A systematic study of perovskite X‐ray imagers (PeroXIs) based on methylammonium lead iodide and cesium lead bromide films integrated onto identical active pixel arrays is presented. Fully inorganic imagers demonstrate excellent performance characteristics, achieving high spatial resolution, contrast, sensitivity, and operational stability. While hybrid perovskites also show promising results, the all‐inorganic systems exhibit characteristics that may be particularly advantageous for repeatable performance and therefore commercial deployment. Crucially, the all‐inorganic PeroXI overcomes the long‐standing trade‐off between spatial resolution and detection efficiency that has limited X‐ray detection technologies for decades, achieving high performance on both fronts. The results establish solution‐processed inorganic perovskites as a scalable, high‐performance platform capable of surpassing commercial standards, opening a new pathway toward low‐cost, high‐resolution, and low‐dose X‐ray imaging across medical, industrial, and scientific domains.

IntroductionWidespread visual deficits accompany normal aging, with most attributed to functional degradation of the visual cortex. Although perceptual learning can improve many visual functions in older adults, it remains unclear whether it can enhance spatial contrast sensitivity, a fundamental visual function known to decline significantly from around age forty.MethodsTo address this, we trained 29 older adults and 18 young controls using contrast perceptual learning. Training was conducted at seven spatial frequencies (from low to high) and/or at the individual cut-off spatial frequency. Spatial contrast sensitivity function (SCSF) and visual acuity (VA) were measured before and after training.ResultsTraining induced substantial improvements in both SCSF and VA in older adults, which were retained for at least several months. Analysis of transfer effects revealed that, compared to young controls, older adults exhibited a characteristic low-frequency shift in peak improvement and a slightly broader bandwidth.DiscussionThese results may be associated with age-related alterations in neuronal response properties within the primary visual cortex. Our findings demonstrate substantial neural plasticity in the aging visual system and support the potential of perceptual learning as a clinically viable intervention for mitigating age-related visual decline.

Electrical Impedance Tomography (EIT) is a vital non-invasive imaging technique for dynamic monitoring, such as lung ventilation. The primary challenge in EIT lies in the inverse problem, which is non-linear, ill-posed, and computationally slow, especially when high accuracy and real-time speed are simultaneously required. Conventional EIT reconstruction algorithms often yield blurred images and are highly susceptible to measurement noise and geometrical uncertainties, such as variations in electrode placement and unknown boundary shapes. This research proposes the Deep Physics-Informed Neural Network (D-PINN), an extended deep learning framework, to achieve accurate and real-time dynamic EIT reconstruction. Unlike purely data-driven methods, our D-PINN integrates the governing Laplace’s Equation directly into the network’s loss function, providing a strong physical constraint to significantly enhance image quality. The innovative focus of this study is addressing the critical gap in model uncertainty robustness. We develop a stochastic D-PINN training scheme that not only solves the conventional inverse problem (predicting conductivity) but also simultaneously accounts for small variations in boundary geometry or electrode positions. Initial simulation results are expected to show that D-PINN consistently:1. Reduces the reconstruction inference time to the millisecond scale, enabling true real-time monitoring. 2. Significantly improves the spatial resolution and image contrast (measured by the Structural Similarity Index / SSIM) compared to standard iterative methods. 3. Maintains high accuracy even when the input measurement data is noisy and the assumed forward geometrical model is intentionally perturbed, which is crucial for real-world instrumentation applications. This work is expected to advance EIT into a more reliable and robust real-time imaging tool.

BackgroundThe aging population in Europe has led to an expected increase in neurodegenerative diseases such as Alzheimer's. Neurologists are demanding customized tools to better address this growing challenge. Currently, there is no comprehensive toolbox for evaluating visual capacities linked to early detection of these disorders. BegiBrainTool aims to fill this gap by providing a computerized modular battery of visual tests designed to assess spatial and dynamic vision, as well as autonomic visual responses, making it a valuable tool for clinical and research applications.MethodBegiBrainTool consists of three interactive modules, all implemented in PsychoPy (Peirce et al., 2019). The first module, focused on spatial vision, evaluates elementary and semantic visual processing by employing stimuli that vary in spatial frequency, contrast or luminosity, and color thresholds. The second module examines dynamic vision and eye‐tracking performance through tasks that include moving stimuli, visual search paradigms, and flicker fusion threshold tests. This module is enhanced with integrated eye‐tracking, which enables precise measurement of gaze behavior and pupilometry. Finally, the autonomic response module measures unconscious reactions to elemental and emotional visual stimuli, utilizing metrics such as pupillary response, heart rate variability, and galvanic skin response.The modular structure of BegiBrainTool ensures that the tests are user‐friendly and customizable, facilitating adaptability to diverse clinical and research settings.ResultThis project is currently ongoing. Preliminary efforts focus on validating the toolbox components and establishing robust protocols for data collection. Anticipated outcomes include a validated open‐source tool ready for clinical deployment.ConclusionBegiBrainTool introduces a novel and interdisciplinary approach to visual assessment for neurodegenerative disorders. Using modular, computerized tools and advanced biometrics, it offers a powerful platform for patient monitoring and longitudinal analysis. BegiBrainTool has the potential to support neurologists in managing the increasing wave of neurodegenerative cases, while simultaneously contributing to research on the visual and autonomic markers of Alzheimer's disease.

Objective. Smaller scintillator crystals are commonly used in positron emission tomography (PET) to achieve higher resolution for preclinical research and clinical diagnosis, particularly in brain-dedicated PET scanners with depth-of-interaction (DOI) capability. Although DOI information provides parallax error correction, mispositioning of the line-of-response (LOR) induced by inter-crystal scattering (ICS) can lead to performance degradation, affecting both spatial resolution and image contrast. Previous studies primarily focused on ICS elimination or recovery on PET scanners that are equipped with one-to-one coupled scintillation detectors, with few studies proposing applicable methods for handling ICS events in brain-dedicated PET scanner equipped with light-sharing window detectors and DOI capability. This study specifically evaluates the impact of ICS on a system that have already undergone DOI correction.Approach. By positioning ICS events to their first interaction crystal in Monte Carlo simulations and removing ICS events through a contour-based mask in experimental studies, evaluations of the impact of ICS events on image quality in the context of DOI-corrected systems were conducted. Metrics including spatial resolution and peak-to-valley ratio (PVR) were used for quantitative analysis, while reconstructed images and line profiles provided qualitative comparisons.Main results. Three stages of images were compared in both simulation and experimental studies, revealing that although image quality could be improved by DOI correction, the presence of ICS still affects spatial resolution and image contrast. Applying a contour-based ICS removal method prior to image reconstruction showed a higher PVR, resulting in improved spatial resolution and better lesion detectability for the scanner, though at the cost of sensitivity loss.Significance. Through simulation and experimental evaluation, we have demonstrated that the presence of ICS can severely degrade the performance of high-resolution brain-dedicated PET scanners with DOI decoding capabilities, highlighting the necessity for accurate ICS event classification.