Search Results (420)

The widespread presence of antibiotic residues, particularly norfloxacin (NFX), in food products and the environment has raised concern, underscoring the need for sensitive and selective detection methods. In this study, a novel broccoli-derived nitrogen-doped carbon quantum dots (N-CQDs) was synthesized via a green hydrothermal approach, 4-dimethylaminopyridine (DMAP) as both a nitrogen dopant and a functionalizing agent. The synthesized N-CQDs exhibit an average diameter of approximately ~4.2 nm and emit bright blue fluorescence, with a maximum emission at 445 nm upon excitation at 360 nm. A “Turn-ON” response toward NFX was achieved with a detection limit of 0.30 nM, attributed to hydrogen bonding and π–π stacking interactions that suppressed non-radiative decay. Moreover, the sensor demonstrates high selectivity for NFX, effectively distinguishing it from common interfering substances, including other antibiotics, organic acids, and biomolecules. The N-CQDs also exhibit excellent stability under diverse conditions, such as varying pH levels, high ionic strength, and prolonged irradiation. Finally, the practical applicability of the developed sensor was validated by detecting NFX in spiked broccoli extract and milk samples, with recovery rates ranging from 98.2% to 100.1% and relative standard deviations of less than 2.0%. This work presents a sustainable and efficient N-CQD-based fluorescent sensing platform, offering significant potential for rapid and reliable detection of NFX in food safety and environmental monitoring. Full article

Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. Full article

Many years later, as it faced the firing squad, the photopic luminous efficiency function was to remember that distant afternoon when its fathers developed it to quantify light. Drawing parallels from Gabriel García Márquez’s complex and multi-layered novel, this article details the cyclical nature of a recurring topic and the repetition of events across generations. About 100 years ago, the CIE 2° standard observer (V(λ)) was developed to enable a reliable, reproducible photometric system. V(λ)-derived photometric units (e.g., illuminance) were envisioned to predict a generic “visibility” response, but today they are widely used to quantify one of the most fundamental responses to light: brightness. Despite its limitations and several proposed alternatives, the lighting industry still uses V(λ) to calculate photometric measures. This study examines the predictive capacity of V(λ) and its three alternatives (CIE V₁₀(λ), V_F(λ), V_F,10(λ)) across three CCTs (2700 K, 4000 K, 6000 K) and three illuminance (50 lx, 100 lx, 300 lx) levels in predicting spatial brightness. Alternatives outperformed V(λ) under 2700 K and 50 lx, but overall photopic luminous efficiency functions (including V(λ)) could not predict spatial brightness consistently. Future studies should investigate the performance of specialized spatial brightness metrics. Full article

Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and C-band), SMAP (Soil Moisture Active Passive) soil moisture (SM) products, and in situ observation data. It mainly conducted a sensitivity analysis of τ to depth, climate type, vegetation coverage, and soil texture, and compared the sensitivity differences between signals of different frequencies. The statistical results of τ showed a pattern varying with sensing depth: C-band TB (0~3 cm) < L-band TB (0~5 cm) < in situ observation (4~8 cm), i.e., the shallower the depth, the faster the drying. τ was sensitive to Normalized Difference Vegetation Index (NDVI) when NDVI < 0.7 and climate types, but relatively insensitive to soil texture. The global median τ retrieved from TB aligned with the spatial pattern of climate classifications; drier climates and sparser vegetation coverage led to faster drying, and L-band TB was more sensitive to these factors than C-band TB. The attenuation magnitude of L-band TB was smaller than that of C-band TB, but the degree of change in its attenuation effect was greater than that of C-band TB, particularly regarding variations in NDVI and climate types. Furthermore, given the similar sensing depths of SMAP SM and L-band TB, their τ statistical characteristics were compared and found to differ, indicating that depth is not the sole reason SMAP SM dries faster than in situ observations. Full article

Interferometric scattering microscopy (iSCAT) is a powerful tool for single-particle detection. However, the detection sensitivity is significantly limited by high-frequency noise. In this paper, we have proposed a novel method leveraging frequency component analysis in the Fourier domain to enhance interference patterns, thus efficiently improving the detection accuracy. The bright–dark rings momentum feather has been effectively restored by a combined filter for high-frequency noise and aperture attenuation. The value of the structural similarity index measure has been improved from 0.73 to 0.98. We validate this method on gold nanoparticle samples. The results demonstrate its great potential to advance single-particle tracking by enhancing background suppression in iSCAT applications. Full article

Due to inaccurate reconnaissance parameters of the jammer and the decline in equipment performance over long-term usage, the false targets generated by the jammer usually have problems such as defocusing, abnormal brightness, and distortion, which fail to reach the ideal state. Therefore, experts or specialized assessment methods are needed to evaluate the interference effect. However, the human eye is not sensitive to minor changes. In such cases, the results obtained by some evaluation methods may be inconsistent with expert judgments. Therefore, this paper proposes an evaluation method that integrates three features of image edge information, brightness variation, and texture structure. This method mainly achieves two purposes: (1) Maintain a high evaluation effect when the image error does not change significantly, and be consistent with the expert’s judgment. (2) When the image error changes significantly, it can conduct effective effect evaluation like other methods, and has stronger universality and robustness. Furthermore, its robustness is reflected in the ability to reliably evaluate even in the presence of severe image distortion, without relying on geometric correction. Simulation experiments are conducted under three error conditions, and result comparisons verify the advantages and robustness of the proposed method. Finally, the effectiveness of proposed method is verified using real jamming data. Full article

Background/Objectives: Non-functional pituitary macroadenomas (NFPMA) are uncommon pituitary lesions that do not cause hormonal hypersecretion and are most often discovered at the macroadenoma stage. Consequently, they are more challenging to diagnose, often mimicking other non-secreting sellar masses, among which hypophysitis should be carefully considered. This study aimed to differentiate between non-functioning pituitary macroadenomas (NFPMA) and hypophysitis, two distinct sellar pathologies with overlapping MRI features, by developing a diagnostic score based on clinical, biological, and radiological criteria. Methods: We conducted a prospective study, including 56 patients with NFPMA and 16 patients with hypophysitis primarily of the lymphocytic subtype. A total of 31 clinical, biological, and radiological variables were analyzed using univariate and multivariate statistical methods to identify significant predictors and to establish a diagnostic score. Results: Nine significant criteria were identified: female sex, headaches, visual disturbances, corticotropic insufficiency, pituitary volume ≤ 7 cm³, loss of the posterior pituitary bright spot, cavernous sinus invasion, optic pathway compression, and pituitary stalk thickening. The established score demonstrated significant performance in predicting the diagnosis of hypophysitis (p < 0.001; Area Under the Curve = 0.967; 95% CI = 0.926–1). The sensitivity and specificity of this score were 93.8% and 87.5%, respectively, using a threshold ≥0.5. The median score was −2 (interquartile range = [−3.5; 0.5]), with extremes ranging from −6.5 to 9. Among these, pituitary stalk thickening emerged as a key diagnostic indicator. Conclusions: This simple and effective multi-parametric score enables rapid and accurate differentiation of hypophysitis from NFPMA, helping to avoid unnecessary surgical interventions and to improve the management of pituitary insufficiencies and may be especially valuable in settings when biopsy is unavailable or risky. Full article

In this study, the complete discrimination system for the polynomial method (CDSPM) is employed to analyze the integrable Gardner Equation (IGE). Through a traveling wave transformation, the model is reduced to a nonlinear ordinary differential equation, enabling the derivation of a wide class of exact solutions, including trigonometric, hyperbolic, rational, and Jacobi elliptic functions. For example, a bright soliton solution is obtained for parameters $A=1.3$, $\beta =-0.1$, and $\gamma =0.8$. Qualitative analysis reveals diverse phase portraits, indicating the presence of saddle points, centers, and cuspidal points depending on parameter values. Chaos and quasi-periodic dynamics are investigated via Poincaré maps and time-series analysis, where chaotic patterns emerge for values like ${\nu }_1=1.45$, ${\nu }_2=2.18$, ${\Xi }_0=4$, and $\lambda =2\pi$. Sensitivity analysis confirms the model’s sensitivity to initial conditions $\chi =2.2,2.4,2.6$, reflecting real-world unpredictability. Additionally, the energy balance method (EBM) is applied to approximate periodic solutions by conserving kinetic and potential energies. These results highlight the IGE’s ability to capture complex nonlinear behaviors relevant to fluid dynamics, plasma waves, and nonlinear optics. Full article

Augmented reality features, such as overlaying information in real time, modifying the projected scene, or dynamically adjusting parameters like contrast, zoom, and brightness, show promise in addressing the specific challenges faced by people with low vision. These tailored solutions enhance their visual experiences. When combined with mobile technology, these features significantly improve the personalization of visual aids and the monitoring of patients with low vision. Retiplus emerges as a personalized visual aid and rehabilitation system, utilizing smart glasses and augmented reality technology for visual aid functions, along with a mobile app for visual assessment, aid customization, and usage monitoring. This wearable system quickly assesses visual conditions, providing deep insights into the visual perception of patients with low vision. Designed to enhance autonomy and quality of life, Retiplus seamlessly integrates into indoor and outdoor environments, enabling the programming of rehabilitation exercises for both static and ambulatory activities at home. In collaboration with specialists, the system meticulously records patient interaction data for subsequent evaluation and feedback. A clinical study involving 30 patients with low vision assessed the effect of Retiplus, analyzing its impact on visual acuity, contrast sensitivity, visual field, and ambulation. The most notable finding was an average increase of 61% in visual field without compromising ambulation safety. Retiplus introduces a new user-centered approach that emphasizes collaboration among a multidisciplinary team for the customization of visual aids, thereby minimizing the gap between the perceptions of low vision specialists and technologists regarding user needs and the actual requirements of users. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

We report the RAD@home citizen science discovery of two rare spiral-host radio galaxies (NGC 3898 and WISEA J221656.57-132042434.1 or RAD-“Thumbs up” galaxy), both exhibiting asymmetric radio lobes extending over 30 to 220 kiloparsec scales. We present a multi-wavelength image analysis of these two sources using radio, optical, and ultraviolet data. Both host galaxies are young, star-forming systems with asymmetric or distorted stellar disks. These disks show similarities to those in galaxies undergoing ram pressure stripping, and the radio morphologies resemble those of asymmetric or bent FR-II and wide-angle-tailed radio galaxies. We suggest that non-uniform gas density in the environment surrounding the ram pressure-stripped disks may contribute to the observed asymmetry in the size, shape, and brightness of bipolar radio lobes. Such environmental effects, when properly accounted for, could help explain many of the non-standard radio morphologies observed in Seyfert galaxies and in recently identified populations of galaxies with galaxy-scale radio jets, which are now being revealed through deep and sensitive radio surveys with uGMRT, MeerKAT, LOFAR, and, in the future, SKAO. These findings also underscore the potential of citizen science to complement professional research and data-driven approaches involving machine learning and artificial intelligence in the analysis of complex radio sources. Full article

River-ice, a significant element of the cryosphere, plays a crucial role in hydrological processes. However, the effectiveness of current river-ice monitoring techniques on the Qinghai–Tibet Plateau is limited due to the complex interplay of environmental and topographical factors in this extensively ice-covered region. To overcome the inadequacies of traditional monitoring approaches in plateau settings, this research introduces a 4D-SfM photogrammetry method for river-ice monitoring. Experimental measurements of river-ice thickness were conducted on the upper reaches of the Heihe River in the Qilian Mountains during the freezing period of 2023–2024. The study evaluated accuracy variations across three different shooting distances: close-range (0.5 m–1.5 m), mid-range (3 m–10 m), and long-range (25 m–60 m). In this study, 4D-SfM photogrammetry not only accurately represents the nonlinear processes of river-ice formation and melting but also sensitively detects abrupt changes in thickness. Between 6 February and 4 April 2024, river-ice underwent a cumulative melt of 77.8 cm, followed by a cumulative growth of 72.2 cm between 26 November and 26 December 2024. Notably, between 24 and 25 December 2024, 4D-SfM photogrammetry successfully captured an extreme event in which river-ice thickness surged by approximately 30 cm. Measurement accuracy decreased with increasing shooting distance, as indicated by an increase in RMSE from 0.43 cm to 3.97 cm. Additionally, factors such as image brightness and ice surface irregularities significantly impact measurement precision. Moreover, the measurement area expanded from 11.38 m² to 2642 m² with increased shooting distances. Therefore, achieving a balance between shooting distance and measurement accuracy is essential when employing 4D-SfM photogrammetry for river-ice monitoring. This study provides a valuable resource for utilizing 4D-SfM photogrammetry to monitor river-ice thickness on the Qinghai–Tibet Plateau. Full article

The intensification of the urban thermal environment has brought attention to urban land surface temperature (ULST). Complex building geometry and manmade material lead to significant thermal radiation directionality (TRD) of the urban canopy, and the TRD effect directly influences the accuracy of ULST retrieval algorithms. Therefore, it is essential to understand and eliminate the TRD effect to achieve high-accuracy ULST. In this context, the hemispherical brightness temperature maximum–minimum discrepancy (BTD) was quantitatively analyzed via different spectral bands, component temperature thresholds, urban geometries, and component temperature differences. Meanwhile, the DART simulations database was used to systematically evaluate 1 single-kernel- and 30 dual-kernel-driven models (KDMs), which were combined from 5 base-shape kernels (RossThick, Vinnikov, uea, RossThin, and LSF) and 6 hotspot kernels (RL, Roujean, Vinnikov, LiSparseR, LiDense, and Chen). Results show that the BTD discrepancy (ΔBTD) can reach up to 0.91 K with different band emissivities, whereas the ΔBTD is over 10 K with different component temperature differences. The building density and ratio between building heights and road widths (H/W) also exhibit their importance over urban regions. In addition, the RossThick–/Vinnikov–Roujean dual-kernel KDMs demonstrate better performance with an overall RMSE of 1.12 K. The RL-series KDMs can describe the hotspot distribution well, but the uea-series KDMs outperform at the solar principal plane (SPP) and cross-solar principal plane (CSPP). Specifically, the performance of all KDMs is sensitive to the H/W and component temperature thresholds, and urban geometry can affect the TRD RMSE with increasing H/W and a depletion of high building density. The quantitative TRD analysis and comparison provide a comprehensive reference for understanding the distribution of thermal radiation, which is also a reliable basis for developing the new TRD model over urban regions. Full article

In this study, we designed a high-speed 16–50 mm F/1.8–2.8 zoom lens for advanced photo system type-C image sensors. Notably, this is the first attempt to implement a bright aperture of F/1.8 at the wide-angle end of a zoom lens. Compared to existing designs with fixed apertures of F/2.0–2.8 or F/2.8, our lens achieves a brighter open aperture while effectively suppressing aberrations and maintaining high resolution. Spherical aberration, chromatic aberration, and distortion are effectively controlled using three aspherical lenses and one extra-low dispersion lens. As a result, a modulation transfer function performance of over 80% at the center is achieved, even at F/1.8 in the wide-angle range. To enhance focusing performance, we adopted a second lens group with low aberration sensitivity as the autofocus driving group, contributing to reduced mechanical weight and improved stability. This design successfully realizes a high-speed zoom lens that delivers resolution comparable to or better than existing optical systems with brighter apertures, while also maintaining a well-balanced trade-off between optical performance and product size. Full article

Event-based vision sensors (EVSs), often referred to as neuromorphic cameras, operate by responding to changes in brightness on a pixel-by-pixel basis. In contrast, traditional framing cameras employ some fixed sampling interval where integrated intensity is read off the entire focal plane at once. Similar to traditional cameras, EVSs can suffer loss of sensitivity through scenes with high intensity and dynamic clutter, reducing the ability to see points of interest through traditional event processing means. This paper describes a method to reduce the negative impacts of these types of EVS clutter and enable more robust target detection through the use of individual pixel frequency analysis, background suppression, and statistical filtering. Additionally, issues found in normal frequency analysis such as phase differences between sources, aliasing, and spectral leakage are less relevant in this method. The statistical filtering simply determines what pixels have significant frequency content after the background suppression instead of focusing on the actual frequencies in the scene. Initial testing on simulated data demonstrates a proof of concept for this method, which reduces artificial scene noise and enables improved target detection. Full article