Search Results (8,213)

High-resolution long-wave infrared imaging is critical for lunar mineralogy. However, it must balance a large FOV, a small F-number, chromatic aberration correction, optical efficiency, and system compactness. We introduce a push-broom multispectral imager employing a collaborative integrated filter array and an off-axis two-mirror Gregorian telescope. The system, utilizing an uncooled Vanadium Oxide detector, has an F-number of 1.0, an IFOV of 0.04943 mrad, and a 2.90° × 2.83° FOV that covers eight bands ranging between 7.38 and 14.3 μm. Optical simulation confirms that the modulation transfer function exceeds 0.25 at the Nyquist frequency of 42 lp/mm, with a maximum RMS spot radius of less than 12 μm. The system has remarkable versatility within an operating temperature range of 0 °C to 40 °C. Thermal background radiation analysis, stray light analysis, and detection sensitivity were conducted, which indicated that the system has good compliance with indicators and engineering feasibility. This high-throughput optical design meets the rigorous criteria for lunar remote sensing and provides a reliable device for site evaluation in future manned lunar missions. Full article

Extended reality (XR) has increasingly been applied to nursing practicum education; however, most systems rely on controller-based interfaces that limit precise capture of continuous fine motor performance and objective assessment. This study developed and validated a sensor-integrated, controller-free XR nursing practicum system (Smart Nursing v1.0) grounded in continuous precision sensing. Based on internationally standardized intravenous injection protocols, the system integrated optical hand tracking and speech recognition to quantify hand kinematics, spatial accuracy, procedural sequencing, and verbal compliance. A three-phase validation framework was implemented. Internal technical verification confirmed stable real-time performance (≥60 FPS) and consistent action recognition. In a user-based study involving 63 undergraduate nursing students, XR-based automated scores demonstrated high agreement with expert instructor ratings (ICC = 0.932, 95% CI = 0.91–0.96, p < 0.001). XR baseline scores significantly predicted post-training performance (β = 0.632, p < 0.001) and showed significant incremental validity beyond instructor pre-training scores (ΔR² = 0.186, p < 0.001). Independent verification confirmed high recognition accuracy (100%) and system stability. These findings indicate that precision sensing enables XR environments to function as reliable performance measurement systems, supporting standardized non-face-to-face nursing practicum education. Full article

Floods are among the most devastating natural hazards, intensified by climate change and rapid urbanization. This study introduces a novel deep learning framework, Transfer Learning-Enhanced FEDformer (TLE-FEDformer), designed for accurate and temporally consistent flood inundation mapping. The framework integrates pre-trained Xception backbones for robust multi-sensor feature extraction from Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery, a cross-modal fusion module to align heterogeneous modalities, and the Frequency Enhanced Decomposed Transformer (FEDformer) for efficient frequency-domain temporal modeling. This architecture effectively captures long-range dependencies and flood dynamics including onset, peak, duration, and recession, while addressing challenges such as cloud contamination, speckle noise, and limited labeled data. Comprehensive experiments demonstrate superior performance, achieving an overall accuracy of 98.12%, an F1-score of 98.55%, and an Intersection over Union (IoU) of 97.38%, outperforming baselines including Convolutional Neural Networks, Capsule Networks, and transfer learning alone. Ablation studies validate the contributions of each component, while sensitivity analyses confirm robustness across hyperparameters. Uncertainty quantification via Monte Carlo dropout highlights high confidence in core flooded regions. Preliminary generalization tests on independent events yield IoU > 94%, indicating strong transferability. TLE-FEDformer advances operational flood monitoring by providing reliable, scalable, and temporally consistent mapping from multi-sensor remote sensing data. This approach offers significant potential for real-time disaster response, early warning systems, and damage assessment in flood-prone regions worldwide. Full article

Accurate estimation of river width is of great importance for hydrological analysis and water-related applications. However, existing remote sensing-based river-width extraction methods are often complex and prone to error accumulation due to their multi-step processing pipelines. To address these limitations, this study proposes an end-to-end stage-guided multi-task network for river-width estimation (E2E-SGRWNet), which directly regresses continuous river-width values from optical remote sensing imagery. The model adopts a stage-wise guidance strategy that progressively incorporates river spatial semantic information and geometric structural information to effectively guide the learning of river width. At the task-guidance level, a cascaded multi-task framework is designed according to the dependency relationships among tasks, in which river-mask segmentation and centerline extraction serve as auxiliary tasks to guide river-width regression. At the feature-guidance level, a cross-branch feature fusion mechanism is introduced to fully exploit multi-scale spatial semantic features and geometric structural features, thereby jointly guiding fine-grained river-width regression. The experimental results on the self-constructed RiverWidth-HR Dataset show that E2E-SGRWNet reduces the mean absolute error (MAE) and Root-Mean-Square error (RMSE) by $1.1\%$ and $3.8\%$, respectively, compared with DeepRivWidth, the strongest existing multi-stage river-width estimation baseline. Overall, E2E-SGRWNet provides a concise and robust solution for high-precision, automated river-width estimation and offers new insights into end-to-end geometric parameter regression from remote sensing imagery. Full article

Under the global trend of population aging, providing a safe and reliable living environment for the elderly who live at home has become a major social issue. This study reports a monitoring technology for elderly-friendly residential buildings based on distributed acoustic sensing (DAS) and distributed temperature sensing (DTS), which is used to monitor and identify the physical behaviors of residents and temperature changes at different locations in the space. The results show that the distributed acoustic sensing (DAS) system can initially identify typical behavioral states such as walking, squatting, and falling. The fiber DTS technology can not only monitor the temperature distribution at different locations indoors, but also be used for the monitoring and early warning of local fires in different areas of the room. The sensing probes of the monitoring system proposed in this paper are linear optical cables, which have the advantages of easy installation, strong anti-interference ability, intrinsic explosion-proof, less likely to leak residents’ privacy, all-weather operation, precise event location, and low cost for large-scale distributed measurement systems. By integrating the sensing optical cables, fiber signal processing systems, and application software introduced in this paper, an intelligent management and early warning platform for elderly-friendly residential buildings can be established, providing a new solution for remote supervision of the living safety of the elderly. Full article

Alzheimer’s disease (AD) is the most common cause of dementia, affecting 55 million people worldwide. Its characteristics include the accumulation of senile plaques and neurofibrillary tangles. This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains. Due to the fact that neuropathological processes begin decades before the onset of cognitive symptoms, accurate detection of AD biomarkers is crucial for its early diagnosis. The combination of analytical techniques and machine learning methods plays a crucial role in medical innovation. Recently, efforts have been made to develop machine learning-assisted biosensors for AD diagnosis. This article provides an overview of the progress in machine learning-assisted sensing of AD biomarkers in bodily fluids. It mainly includes three parts: machine learning algorithms, machine learning-assisted electrochemical and optical biosensors, and challenges and future perspectives. We believe that this work will contribute to the development of innovative analytical devices based on artificial intelligence for monitoring and managing neurodegenerative diseases. Full article

Interest in non-centrosymmetric crystalline materials exhibiting second harmonic generation (SHG) has increased due to their potential applications in optical sensing and biosensing. Saccharide-based metal complexes are particularly attractive systems, as chiral sugars can promote non-centrosymmetric crystal packing. In this work, a new lanthanum–β-d-fructose compound, [La(C₆H₁₂O₆)(H₂O)₅]Cl₃ (LaFRUCl), was synthesized using a simple and low-cost method and characterized by single-crystal X-ray diffraction. The compound crystallizes in the orthorhombic space group P2₁2₁2₁ and consists of infinite (La³⁺–fructose)_n chains extending along the [001] direction, forming a one-dimensional Metal–Organic Framework. The nonlinear optical response was evaluated using the Kurtz–Perry powder technique with a Nd:YAG laser (1064 nm) and compared to a sucrose reference. The measured SHG efficiency is comparable to that of previously reported alkaline earth metal–sugar analogs. While the compound’s SHG emission is significant, evaluation of its structural stability under aqueous or physiological conditions is be required before considering biological applications. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

The low-frequency distributed acoustic sensing (LF-DAS) data acquired through fiber-optic cables cemented behind the fracturing well casing can dynamically capture the hydraulic fracturing process. After removing the thermal effect, the LF-DAS data can reveal the strain evolution induced by the initiation of hydraulic fractures. This paper presented an improved strain–temperature decoupling method for LF-DAS measurements based on joint LF-DAS/distributed temperature sensing (DTS) monitoring. The decoupling method was based on strain change and temperature change pre-processed from the raw DAS and DTS data to avoid the enhancement of DTS data noise. The moving window function method and the image processing parameter cosine similarity was introduced to cope with the differences in temporal and spatial resolution between LF-DAS and DTS data. The region significantly affected by temperature change could be identified automatically and the mechanical strain change could be extracted. The tensile strain response generally reached a local peak at perforation clusters and increased significantly at those with dominant fracture fluid inflow. By analyzing the evolution of strain profile during fracturing, the effectiveness of multi-cluster fracture initiation and fracture temporary plugging could be evaluated. Full article

Background/Objectives: Biopsy examination remains the gold standard for cancer diagnosis, relying on histopathological assessment of tissue samples to identify malignant changes. However, manual interpretation of histopathological slides is time-consuming, subjective, and susceptible to inter-observer variability. The digitization of histopathological images enables automated analysis and offers opportunities to support clinicians with more consistent and objective diagnostic tools. This study aims to enhance cancer diagnosis by proposing a hybrid framework that integrates deep-learning-based histopathological image analysis with Whispering Gallery Mode (WGM) optical sensing for complementary tissue characterization. Methods: The proposed framework combines automated tumor classification from histopathological images with biochemical signal analysis obtained from WGM optical sensors. Deep learning models, including EfficientNet-B0, InceptionV3, and Vision Transformer (ViT), were employed for binary and multi-class tumor classification using the BreakHis dataset. To address class imbalance, a Deep Convolutional Generative Adversarial Network (DCGAN) was utilized to generate synthetic histopathological images alongside conventional data augmentation techniques. In parallel, WGM optical sensors were incorporated to capture subtle tissue-specific signatures, with machine learning algorithms enabling automated feature extraction and classification of the acquired signals. Results: In multi-class classification, InceptionV3 combined with DCGAN-based augmentation achieved an accuracy of 94.45%, while binary classification reached 96.49%. Fine-tuned Vision Transformer models achieved a higher classification accuracy of 98% on the BreakHis dataset. The integration of WGM optical sensing provided additional biochemical information, offering complementary insights to image-based analysis and supporting more robust diagnostic decision-making. Conclusions: The proposed hybrid framework demonstrates the potential of combining deep-learning-based histopathological image analysis with WGM optical sensing to improve the accuracy and reliability of cancer classification. By integrating morphological and biochemical information, the framework offers a promising approach for enhanced, objective, and supportive cancer diagnostic systems. Full article

Optical remote sensing ship detection faces significant challenges in realistic maritime scenes due to strong background clutter (e.g., docks, shorelines, wake streaks), extreme scale variation, and the elongated geometry of ships with diverse orientations. These factors frequently lead to geometric misalignment, unstable localization, and false alarms, particularly in congested ports and complex sea states. To enhance robustness under clutter while retaining the set prediction efficiency of DETR, we propose the Directional Efficient Network (DENet), a structure-aware enhancement built upon RT-DETR. DENet introduces two complementary components. First, Directional Strip Convolution (DSConv) replaces the standard $3\times 3$ convolution for spatial mixing. By predicting offsets conditioned on input features, DSConv performs strip aggregation that aligns with slender hull structures, thereby suppressing interference from line-shaped background patterns. Second, Efficient Scale Fusion (ESF) augments the Hybrid Encoder as an additive residual correction. It combines multiple receptive field branches with lightweight differential compensation to balance low-frequency context and high-frequency structural transitions, ensuring stable multi-scale fusion in cluttered scenes. Extensive experiments demonstrate the effectiveness of DENet. On ShipRSImageNet, ${\mathrm{AP}}^{\mathrm{val}}$ improves from 58.8% to 63.2% and ${\mathrm{AP}}_{50}^{\mathrm{val}}$ increases from 68.5% to 73.6%. Consistent gains are also observed on NWPU VHR-10, where ${\mathrm{AP}}^{\mathrm{val}}$ reaches 63.0% and ${\mathrm{AP}}_{50}^{\mathrm{val}}$ reaches 94.6%, alongside improvements on the Infrared Ship Database and VisDrone2019-DET, validating the method’s generalization capabilities. Full article

Reliable ship detection in complex maritime optical imagery is a fundamental requirement for intelligent maritime monitoring and maritime automation systems. However, severe image degradation, large-scale variations, and background clutter often lead to feature ambiguity and unstable detection performance in real-world maritime environments. To address these challenges, this paper proposes a lightweight one-stage ship detection framework designed for robust real-time perception under degraded maritime sensing conditions. The proposed method incorporates an Adaptive Expert Selection Attention (AESA) mechanism to perform adaptive feature selection and background suppression under visually degraded conditions, together with a Geometry-Aware MultiScale Fusion (GAMF) module that enables orientation-aware aggregation of contextual information for elongated ship targets near complex sea–sky boundaries. In addition, a geometry-aware bounding box regression refinement is introduced to improve localization consistency in image space. Extensive experiments conducted on a unified real-world maritime benchmark demonstrate that the proposed framework consistently outperforms the baseline YOLO11n model by approximately 2–5 percentage points in terms of mAP@0.5 and mAP@0.5:0.95, while maintaining moderate computational complexity and real-time inference capability. These results indicate that the proposed method provides a practical and deployment-oriented perception solution for maritime automation applications, including onboard electro-optical sensing and coastal surveillance. Full article

To address the technical bottlenecks of strong background radiation interference and weak target signals in mid-wave infrared (MWIR) hyperspectral mineral detection over complex terrain, this paper proposes a “full-link background radiation suppression” methodological framework. A coupled illumination-terrain-atmosphere-sensor radiative transfer model is constructed to systematically quantify how multidimensional parameters—such as observation geometry, surface temperature, elevation, aerosol optical depth, and water vapor content—influence the target background radiation contrast. The findings reveal that daytime observation, lower surface temperature, higher altitude, dry atmosphere, and moderate solar and observation zenith angles are key factors for maximizing the signal-to-noise ratio. Comprehensive optimization analysis demonstrates that observations during midday in autumn and winter achieve optimal performance, with the target background relative contrast potentially enhanced by up to 6.29 times compared to unfavorable conditions such as summer nights. This work elucidates the physical mechanisms governing MWIR hyperspectral detection efficacy in complex scenarios, provides direct parameter-optimization strategies for intelligent mission planning of spaceborne imaging systems, and holds significant value for advancing mineral remote sensing from “passive acquisition” to “cognitive detection”. Full article

Organ-on-a-chip (OoC) technologies, also termed microphysiological systems (MPSs), integrate microfluidics, engineered biomaterials, human-derived cells, and on-chip biosensing to model human physiology in microscale devices that deliver quantitative, time-resolved readouts. This review surveys the 2010–2025 literature, emphasizing how sensing, standardized sampling, and analytics enable clinical concordance and fit-for-purpose regulatory use. We synthesize advances in (i) materials, fabrication, and microfluidic design; (ii) organ- and disease-focused case studies; and (iii) translational benchmarks that align chip outputs with clinical pharmacokinetics, toxicology, and biomarker datasets. Across organ systems, platforms increasingly incorporate vascularization, immune components, and organoid hybrids, paired with real-time measurements of barrier integrity, metabolism, electrophysiology, and secreted biomarkers using impedance (TEER), electrochemical, and optical modalities. Representative benchmarking studies report cardiac OoCs achieving AUROC ≥ 0.85 for torsadogenic risk classification, and renal chips improving prediction of transporter-mediated clearance relative to conventional in vitro assays. We summarize validation approaches and regulatory developments relevant to new approach methodologies, including the FDA Modernization Act 2.0, and discuss how AI and multi-omics can automate signal and image analysis, harmonize cross-platform datasets, and support digital-twin workflows that couple OoC measurements to in silico models. Overall, biosensor-enabled OoCs are progressing toward quantitatively benchmarked platforms for safety pharmacology, ADME/PK–PD, and precision medicine. Full article

Antimony oxide nanoparticles (Sb₂O₃ NPs) were synthesized via a chemical vapor deposition (CVD) method and systematically characterized to evaluate their multifunctional performance. Powder X-ray diffraction (PXRD) confirmed the formation of an orthorhombic Sb₂O₃ phase with an average crystallite size of 53.50 nm, while SEM analysis revealed elongated nanostructures with diameters in the range of 20–100 nm. The stoichiometric composition of Sb₂O₃ (Sb:O ≈ 2:3) was verified by EDAX, and optical studies indicated a direct band gap of 3.10 eV. The electrochemical sensing capability of Sb₂O₃ NPs was investigated using a modified nickel mesh electrode for the detection of Metronidazole (MTZ) in 0.1 N KOH. The presence of Sb₂O₃ NPs resulted in an additional irreversible reduction peak at −0.14 V, confirming enhanced electrocatalytic activity toward MTZ, along with excellent cycling stability (94.36% retention after 10 cycles). In addition, the photocatalytic performance of Sb₂O₃ NPs was evaluated through the degradation of Acid Orange (AO) dye under UV-Vis irradiation, achieving a degradation efficiency of 73.31%. These results demonstrate that Sb₂O₃ nanoparticles are promising multifunctional materials for environmental remediation and electrochemical sensing applications, highlighting their potential for industrial implementation. Full article