Search Results (80,272)

To validate the effectiveness of the grooved diaphragm with back peninsulas and trenches (GDPT) and the radial basis function neural network (RBFNN)-based dimension generator in the development of a 1 kPa piezoresistive micropressure sensor, this paper presents a comprehensive investigation into the design, fabrication, and characterization of the anticipative sensor prototype. The GDPT achieves favorable sensing stress and reduced deflection, enabling a favorable trade-off between sensitivity and nonlinearity; the RBFNN-based generator efficiently determines practicable dimensions for the complex structure, offering a significant improvement over the conventional trial-and-error process. Characterization results demonstrate that the fabricated sensor achieves sensitivity of 13.01 mV/(V·kPa) and nonlinearity of 0.26% FS within the pressure range of 0–1 kPa, in good agreement with the preset design target. This work verifies the validity of the proposed approach and offers a holistic methodology for developing high-performance piezoresistive sensors. Full article

The bacterial Quorum Sensing system refers to the recognition of signaling molecules called autoinducers produced by bacteria when a certain cell density is reached in the environment. Those cell-density-dependent autoinducers regulate and coordinate diverse functional processes, such as bioluminescence, biofilm production, sporulation, and even the expression of some virulence factors, among others. There is a wide variety of autoinducers, and for Gram-negative bacteria, the canonical autoinducers are the N-acyl-homoserine lactones (AI-1). Presently, the production of autoinducers in Leptospira has not been described; therefore, the objective of this study was to detect and identify autoinducers in this bacterial genus. We report here the expression of AI-1 in cultures ≥2.4 × 10⁸ of Leptospira meyeri. Ethyl acetate extracts of Leptospira culture supernatants were capable of activating the β-galactosidase system in the biosensor Agrobacterium tumefaciens strain NTL4. Partial identification of the leptospiral supernatant extracts was done by thin-layer chromatography (TLC), showing a similar retention factor to the synthetic standard N-Octanoyl-DL-homoserine lactone (C8-AHL) in the Leptospira supernatant extracts. In addition, infrared spectroscopy (IR) analysis showed peaks corresponding to the lactone and amide groups in both the C8-AHL standard and the Leptospira meyeri culture extracts. Moreover, High-Performance Liquid Chromatography–Mass Spectrometry (HPLC-MS/MS) confirmed the same retention time (10.7 ± 0.1 min) in both the Leptospira meyeri supernatant extracts and the C8-AHL standard. These results show that Leptospira meyeri synthesizes N-acyl homoserine lactone family autoinducers, particularly the N-Octanoyl-DL-homoserine lactone, and lay the groundwork for future research on Quorum Sensing systems in Leptospira. Full article

Recent statistics reported by the World Health Organization and the International Agency for Research on Cancer indicate that the global incidence of bladder cancer has continued to increase in recent years, particularly in industrialized countries. Therefore, the timely diagnosis of early-stage bladder cancer is of great clinical importance for improving patient prognosis and treatment outcomes. In this context, computational optical sensing frameworks that integrate Spectrum-Aided Visual Enhancer (SAVE) technology with cystoscopy have attracted significant attention to overcome the limitations of conventional visual data interpretation. In this study, an AI-driven optical biosensing framework was evaluated using 1372 white-light cystoscopy (WLC) images of bladder cancer (RGB-WLC) collected in collaboration with Chung Shan Medical University Hospital. Hyperspectral conversion technology was applied to extract precise spectral information from the white-light images. Subsequently, dimensionality reduction was performed based on the characteristic wavelengths of narrow-band imaging cystoscopy at 415 nm and 540 nm to generate hyperspectral reconstructed narrow-band images. The images were categorized into Ta stage (Ta), above T1 stage (Above T1), and four additional classes. The dataset was divided into training and testing sets to establish both a standard white-light cystoscopy model (RGB-WLC) and an advanced hyperspectral biosensing model utilizing the YOLOv8 architecture for enhanced pattern recognition. Model performance was evaluated using sensitivity, F1-score, and overall accuracy. The standard RGB-WLC model achieved an accuracy of 0.852, whereas the SAVE-based biosensing model achieved an accuracy of 0.948, representing an improvement of approximately 11.27%. The results demonstrate that combining algorithmic hyperspectral reconstruction with deep learning architectures effectively addresses the challenges of clinical data interpretation and significantly enhances the detection and staging performance of bladder cancer imaging. Full article

Accurate channel estimation in millimeter-wave MIMO-OFDM systems is hindered by limited pilot resources and the high sensitivity of physical priors to propagation conditions. This paper proposes PERL, a physics-enhanced residual learning framework that integrates ray-tracing (RT) priors with deep neural networks to refine coarse RT-based estimates. Unlike direct channel reconstruction, PERL learns a residual correction atop the RT baseline, with the correction magnitude adaptively gated according to noise level and RT reliability, thereby relying more on physical priors under low SNR and exploiting pilot observations for refinement at high SNR. The framework leverages a Transformer-based multimodal attention mechanism to deeply fuse sparse pilot observations, path-level propagation features (delay, angle, power, and phase), and scene-level statistical descriptors, enabling physical constraints and data-driven refinement to interact effectively. Experiments on a 28 GHz urban macro-cellular MIMO-OFDM scenario demonstrate that PERL achieves an overall NMSE of −28.82 dB, outperforming the RT baseline by 5.30 dB and the MMSE estimator by over 20 dB, with notably larger gains under non-line-of-sight conditions where the RT prior is less accurate. Link-level evaluations further confirm improved error vector magnitude and maintained bit/block error rates relative to the RT baseline, validating that the proposed physical-data collaborative paradigm not only enhances estimation accuracy but also preserves communication reliability, while offering a promising foundation for future detection-aware and integrated-sensing-and-communication optimizations. Full article

Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors from device-level transduction to system-level integration. At the device level, sensing strategies have evolved from direct sensing toward indirect sensing architectures, improving stability and reusability. At the system level, conventional off-chip implementations have progressively shifted toward fully integrated on-chip implementations. Furthermore, this review highlights the emerging paradigm of in-sensor computing, in which sensing, memory, and computation are co-located within a single physical platform. This approach enables reduced data movement and supports energy-efficient operation for point-of-care applications. Finally, key challenges—including CMOS compatibility, device variability, and reliable multi-threshold sensing operation—are discussed as critical factors for the practical realization of memristive-device-based electrochemical biosensing systems. Full article

Glacial lakes are sensitive indicators of climate-driven cryospheric change, and their accurate mapping provides fundamental spatial information for water-resource assessment and glacial lake outburst flood (GLOF) hazard assessment. In southeastern Tibet, automatic extraction remains difficult because glacial lakes are small and easily confused with snow, mountain shadows, dark bedrock, riverine wetlands, and non-glacial water bodies. In this study, we integrate optical bands and water indices from Sentinel-2, topographic information derived from a digital elevation model, and radar backscatter from Sentinel-1 into a nine-channel multimodal dataset, and develop an improved YOLO11-seg model that combines spatial-to-depth downsampling, multi-scale attention, long-range context modeling, and content-aware upsampling to enhance small-lake detection, background suppression, and boundary delineation. Compared with U-Net, DeepLabV3+, YOLOv8-seg, YOLO11-seg, YOLO12-seg, and YOLO26-seg, the proposed model achieved the highest F1-Score of 0.9205 and an mAP50(M) of 0.9331, while its F1-Score and IoU on the independent test set reached 0.9209 and 0.8533, respectively. Using remote sensing imagery acquired in 2024, the model extracted 4766 glacial lakes in southeastern Tibet, covering 428.13 km²; 80.84% of these lakes were smaller than 0.10 km². The results demonstrate an effective and reproducible framework for automatic glacial lake mapping in complex alpine environments. Full article

Smart chemical sensors have emerged as promising tools for real-time monitoring of food spoilage in both fermented and non-fermented products. By detecting key spoilage indicators—including biogenic amines, ammonia, hydrogen sulfide, methane, pH variations, and microbial volatile organic compounds (MVOCs)—these systems enable rapid, on-site assessment of food quality, offering a viable alternative to conventional, time-consuming laboratory analyses. Recent advances encompass diverse sensing mechanisms, including chemiresistive platforms based on conducting polymers and MEMS (Microelectromechanical Systems); optical/colorimetric systems using dyes, metal–organic frameworks, and porphyrins; and electrochemical and biosensing approaches employing enzymes, antibodies, aptamers, and whole-cell recognition elements. These sensors demonstrate high sensitivity (ppb–ppm range), enabling early detection of spoilage before sensory perception or microbiological threshold exceedance. Their applicability has been validated across a wide range of food matrices, including meat, fish, dairy products, vegetables, beverages, and fermented foods. Despite significant progress, key challenges persist, including signal drift, limited specificity, susceptibility to environmental factors such as humidity and temperature, and interference from complex food matrices. Furthermore, integration into intelligent packaging requires the development of flexible, food-safe, and regulatory-compliant materials. Emerging approaches that combine sensor arrays with machine learning and MVOC pattern recognition are enhancing predictive accuracy and enabling food classification across commodity types. Overall, smart chemical sensing technologies are rapidly transitioning from laboratory prototypes to practical applications in intelligent packaging and wireless monitoring systems, with ongoing research focused on improving robustness, standardization, and scalability for commercial deployment. This article provides an overview of the topic, drawing on the available bibliography from the last five years and the most-cited scientific databases. Full article

Localized surface plasmon resonance biosensors are promising devices for label-free detection of live cells and biomolecules. However, typical plasmonic sensors have limited surface area, planar electromagnetic fields, and poor compatibility with three-dimensional (3D) interactions with cells or biomolecules. In this study, a 3D plasmonic sensor around microposts was developed using reversal nanoimprint lithography for highly sensitive cell and DNA detection. Au nanopillars were conformally integrated onto the bottom, sidewall, and top of microposts, forming additional sensing surface area along the sidewall of microposts for plasmonic sensing. The 3D plasmonic sensors exhibited tunable resonance peaks and refractive index (RI) sensitivities by varying the micropost height. The highest sensitivity of 1306 nm per RI unit was obtained from the sensor with 10 μm-tall microposts at a resonance wavelength of 1315 nm, which was significantly higher than that of typical planar plasmonic sensors. The platform was applied to live MC3T3-E1 cell detection, showing a resonance peak shift of 71 ± 11.6 nm at a cell concentration of 10⁶ cells/mL with a cell concentration ranging from 10² to 10⁶ cells/mL. In addition, DNA hybridization detection was demonstrated over a concentration range of 10⁻¹⁵–10⁻⁷ M complementary target DNA, with a resonance shift of 68 ± 2.5 nm observed at 10⁻⁷ M target DNA concentration. The 3D plasmonic sensor provides a scalable device for additional plasmonic biointerfaces with enhanced analyte accessibility and light–matter interactions. This platform offers high-sensitivity biosensing involving live cells, nucleic acids, and other biological targets. Full article

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has emerged as a promising tool for monitoring crop physiological status and supporting precision disease management. However, the capacity of multispectral vegetation indices to detect foliar diseases under commercial field conditions remains insufficiently understood, particularly in perennial crops grown in humid tropical environments. This study evaluated the potential of UAV-derived multispectral vegetation indices to assess the physiological response of avocado (Persea americana Mill.) canopies affected by anthracnose caused by Colletotrichum fructicola in Amazonas, Peru. Disease incidence and severity were assessed through field evaluations, while multispectral imagery was acquired using a UAV equipped with a MicaSense RedEdge-MX Dual sensor. Vegetation indices including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge Index (NDRE), Red Edge Chlorophyll Index (CIred-edge), Plant Senescence Reflectance Index (PSRI), and Visible Atmospherically Resistant Index (VARI) were calculated, and their relationships with anthracnose incidence were analyzed using Spearman’s rank correlation. Field observations confirmed a high incidence of foliar anthracnose with a heterogeneous spatial distribution across the orchard. Multispectral imagery successfully characterized spatial variability in the canopy physiological condition, revealing differences in vegetation vigor, chlorophyll-related reflectance, and senescence among experimental blocks. Nevertheless, none of the evaluated vegetation indices showed statistically significant correlations with anthracnose incidence (p > 0.05), indicating that spectral variability primarily reflected the general canopy physiological status rather than a disease-specific spectral response. These findings demonstrate that UAV-derived multispectral vegetation indices are valuable for monitoring spatial variability in the avocado canopy condition but have limited capability for independently detecting anthracnose under humid tropical field conditions. Future studies integrating multi-temporal UAV acquisitions, hyperspectral and thermal imagery, LiDAR, environmental variables, and machine-learning approaches are expected to improve the early detection and spatial prediction of anthracnose in avocado production systems. Full article

Purpose: Marking the one hundredth anniversary of quantum mechanics in 2025, quantum science has become foundational for the development of contemporary technologies, enabling advances in sensing, imaging, computing, and materials engineering. Cancer radiotherapy, although traditionally developed within the scope of classical dosimetric models and phenomenological biological frameworks, is fundamentally initiated by quantum-mechanical radiation-matter interactions. Radiation-induced DNA damage, which ultimately determines therapeutic effectiveness, originates from primary quantum-mechanical processes involving particle transport, electronic excitation and ionisation, followed by successive physicochemical and chemical stages including water radiolysis and radical formation. As scientific disciplines undergo a rapid “quantum transition,” radiation cancer treatment is increasingly positioned to benefit from deeper integration of quantum principles and emerging quantum technologies. Methods: This review examines how quantum mechanics governs the primary radiation-matter interactions that initiate the physical, physicochemical, chemical, and ultimately biological stages of radiation action at the (sub)cellular level, with particular emphasis on track structure, water radiolysis, DNA damage induction, and multiscale biological response. Contemporary approaches to DNA damage modeling are discussed, including track-structure Monte Carlo methods, nanodosimetric frameworks, and multi-scale simulation approaches that connect microscopic interaction events with biological outcomes. Key quantum concepts relevant to radiation therapy are outlined, together with emerging quantum technologies such as nanoscale quantum sensing, quantum lasers, quantum dots, and quantum computing, which are evaluated for their potential roles in dosimetry, imaging, treatment planning, and radiation transport simulations. In this context, artificial intelligence (AI) is considered a complementary tool to accelerate computation and integrate quantum-informed data across multiple scales. Results: The review highlights that quantum-informed modeling enables a more consistent description of radiation-induced processes across spatial and temporal scales, linking microscopic interaction mechanisms to DNA damage formation and macroscopic biological outcomes. Recent advances in track-structure and radiobiological modeling provide new opportunities for improving predictions of radiation effects and treatment response. Emerging quantum technologies show potential to enhance measurement sensitivity, improve simulation efficiency, and enable more precise control of radiation delivery. Furthermore, AI-assisted approaches facilitate the extraction of predictive patterns from complex datasets, supporting faster and more accurate estimation of biological endpoints such as DNA damage and cell survival. Conclusions: The quantum aspects of advanced treatment modalities, including proton and heavy-ion therapy, ultrafast radiation delivery, and the FLASH effect, as well as future concepts such as laser-plasma-driven and coherence-informed radiotherapy systems, indicate a promising direction for next-generation cancer treatment. By critically assessing both opportunities and limitations, this work provides a coherent framework for integrating DNA damage modeling, quantum principles, quantum-inspired techniques, emerging quantum technologies, and advanced computational tools to guide future developments in radiation oncology. Full article

Benefiting from information complementarity, the fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for land cover classification has attracted significant attention in the remote sensing community. However, due to modality imbalance and information redundancy, effectively extracting and integrating complementary knowledge from HSI and LiDAR data remains a major challenge. To address the aforementioned issues, a class semantic prototype guided fusion network (CSPGFNet) is proposed to realize efficient and accurate classification by task-relevant feature mining, fusion, and interaction. Specifically, feature extraction sub-networks with multi-scale and multi-type convolutional structures are designed for each modality to mitigate semantic imbalance caused by inherent dimensional discrepancies. Moreover, a feature fusion-interaction module based on cross-attention mechanism is designed to fuse and interact task-relevant spatial–spectral and elevation information from modalities with class semantic prototype (CSP) as the bridge. Furthermore, a composite loss that incorporates multi-factor constraints is optimized to ensure information complementarity, semantic consistency and task relevance of the whole network. Experimental evaluations on three benchmark datasets demonstrate the effectiveness of the proposed method. Full article

Although the bounding boxes produced by an object detector provide real-time target localization cues for anti-UAV visual perception, they remain susceptible to geometric deviations under long-range small-target conditions, complex backgrounds, motion blur, and cross-domain environmental variations. Consequently, these detector outputs cannot always serve directly as stable observations for state estimation, trajectory prediction, and interception control. To address this issue, this paper proposes CDBR-Net, a causally conditioned domain-robust observation correction network for post-detection UAV bounding-box refinement. CDBR-Net employs a shared encoder, disentangled multi-branch representations, and a quality-aware gating mechanism to jointly produce a corrected observation box, a robust representation, and an observation uncertainty estimate. To preserve this conditional environment-transformation symmetry without suppressing geometry-induced symmetry breaking, CDBR-Net constructs geometry-conditioned cross-domain sample pairs and imposes cross-domain consistency and geometry-sensitivity preservation constraints. After training on 8749 post-detection observations, CDBR-Net is evaluated on 997 aligned validation observations from three simulated scene domains. It reduces the YOLO bounding-box mean absolute error (MAE) by 6.8%, from 0.002924 to 0.002725, and increases the intersection over union (IoU) by 0.007979, from 0.852167 to 0.860146. It further reduces MAE by 1.7% and increases IoU by 0.002031 relative to the YOLO + MLP Residual baseline. Ablation studies demonstrate the complementary roles of geometry-conditioned cross-domain consistency and geometry-sensitivity preservation. These results indicate that CDBR-Net provides a more stable and geometrically consistent post-detection observation interface for anti-UAV visual perception. Full article

Monitoring complex river networks in Myanmar is challenging due to the high spatial heterogeneity and fragmentation of surface water bodies. Accurate identification of surface water resources is therefore essential for water resource management and for improving preparedness against climate change–induced hydrological hazards. To address this problem, this study proposes an adaptive interwoven deep learning–based segmentation framework that jointly utilizes multispectral reflectance information and topographic elevation data to enhance the extraction of fragmented water bodies. The framework is designed to coordinate feature interaction across spectral, spatial, and topographic dimensions by integrating channel-wise feature recalibration and attention-guided feature modulation within the encoding–decoding architecture. Experimental results demonstrate that the proposed method outperforms several traditional water index–based approaches, conventional machine learning algorithms and deep learning models. Across five independent training runs, the proposed framework achieves an average precision of 91.0%, recall of 93.5%, and F1 score of 92.3% (95% confidence interval: 91.8–93.0), demonstrating stable performance for fragmented water-body extraction. Cross-site experiments across three within-country study areas further demonstrate the spatial transferability and robustness of the proposed framework across diverse hydrological conditions within Myanmar. Overall, the proposed approach provides a reliable solution for fragmented water body extraction under heterogeneous hydrological conditions within Myanmar. Full article

Metal–Organic Frameworks (MOFs) have undergone remarkable development in recent decades, transforming them into one of the most dynamic classes of emerging composite materials. These crystalline, porous coordination networks, built from metal ions or metal clusters interconnected by organic linkers, form architectures with tunable porosity, large specific surface area, and chemical functionality. Due to their remarkable stability and customizable functionalities, MOFs have attracted significant attention in recent years as promising materials for different applications. In 2025, Susumu Kitagawa, Omar Yaghi, and Richard Robson were awarded the Nobel Prize in Chemistry for pioneering the development of MOF crystalline materials with spacious internal cavities that can store, filter or catalyze molecules. This review systematically consolidates the recent literature (since 2020) on MOF-based systems, covering state-of-the-art performance, synthesis advantages and limitations, and the influence of reaction parameters on morphology, structure, and luminescent properties. The rapid yearly increase in MOF-related publications, continuing strongly into 2026, reflects the growing global interest and highlights the rising importance of their design and applications. This trend motivates the central focus of this paper, which, in a single work, emphasizes the integrated use of MOFs in luminescent sensing, biosensing, the removal of heavy metals, microplastics, and organic dyes in water treatment, and hydrogen storage. Finally, the challenges, conclusions and future perspectives of MOF-based materials will be highlighted with the aim of providing guidelines for their further development and additional applications. Full article

Acrylamide (AA) is a common contaminant in foods processed at high temperatures and has attracted significant attention due to its potential neurotoxicity and carcinogenicity. Therefore, the development of a highly sensitive, highly selective sensing technology suitable for on-site detection is of great importance for ensuring food safety. In this study, an aptamer (Apt)-based capacitive AA sensor was developed based on the alternating current electrokinetics (ACEK) effect. The sensor utilizes an aptamer as the biomimetic recognition element, which can specifically recognize AA, thereby enabling quantitative detection. Additionally, a detachable detection fixture and data acquisition system were designed to enhance the detection stability and convenience of sensor. Within the linear range of 1 nmol/L to 10 µmol/L, the sensor response (dC/dt) exhibited a good linear relationship with AA concentration, with a detection limit as low as 0.4235 nmol/L. The sensor exhibits good selectivity toward structural analogs of AA, with a recovery relative standard deviations (RSDs) of less than 5.42% in spiked coffee samples. This portable detection system provides a sensitive and user-friendly tool for the analysis of acrylamide in food and holds great potential for application in food safety. Full article