Search Results (582)

Background/Objectives: Children with adolescent idiopathic scoliosis (AIS) require repeated imaging, primarily standing spine radiography, while CT may be required for surgical planning, resulting in higher radiation exposure. Spectral shaping using a tin filter can reduce radiation dose in non-contrast chest CT. This study evaluated the efficacy of spectral shaping using a tin filter for reducing radiation dose in CT imaging in AIS and its impact on image quality. Methods: We retrospectively analyzed 51 AIS patients who underwent spine CT between February 2017 and March 2022, and divided them into two groups: normal-dose CT (NDCT) and low-dose CT with spectral shaping with a tin filter (LDCT). Radiation doses and image quality were compared between the groups. Radiation dose was recorded as the volume CT dose index (CTDIvol) and the dose length product emitted from the device, and effective and equivalent doses obtained from simulations. Results: The use of spectral shaping with a tin filter resulted in a 75% reduction in radiation dose compared to conventional CT without any reduction in image quality. Conclusions: Spectral shaping CT with a tin filter can substantially reduce radiation dose while maintaining image quality. It may be considered a safer alternative to conventional CT when clinically indicated in AIS patients. Full article

The classification of very-low-frequency and low-frequency (VLF/LF) lightning-radiation electric-field waveforms is of paramount importance for lightning-disaster prevention and mitigation. However, traditional waveform classification methods suffer from the complex characteristics of lightning waveforms, such as non-stationarity, strong noise interference, and feature coupling, limiting classification accuracy and generalization. To address this problem, a novel framework is proposed for VLF/LF lightning-radiated electric-field waveform classification. Firstly, an improved Kalman filter (IKF) is meticulously designed to eliminate possible high-frequency interferences (such as atmospheric noise, electromagnetic radiation from power systems, and electronic noise from measurement equipment) embedded within the waveforms based on the maximum entropy criterion. Subsequently, an attention-based multi-fusion convolutional neural network (AMCNN) is developed for waveform classification. In the AMCNN architecture, waveform information is comprehensively extracted and enhanced through an optimized feature fusion structure, which allows for a more thorough consideration of feature diversity, thereby significantly improving the classification accuracy. An actual dataset from Anhui province in China is used to validate the proposed classification framework. Experimental results demonstrate that our framework achieves a classification accuracy of 98.9% within a processing time of no more than 5.3 ms, proving its superior classification performance for lightning-radiation electric-field waveforms. Full article

With the advancement of autonomous driving technology, the performance of LiDAR in adverse weather conditions has garnered increasing attention. Traditional denoising algorithms, including intensity-based methods like LIOR (a representative intensity-based filter that relies solely on signal intensity), have limited effectiveness in handling snow noise, especially in removing dynamic noise points and distinguishing them from environmental features. This paper proposes a Dynamic Vertical and Low-Intensity Outlier Removal (DVIOR) algorithm, specifically designed to optimize LiDAR point cloud data under snowy conditions. The DVIOR algorithm, as an extension of intensity-based filtering augmented with vertical height information, dynamically adjusts filter parameters by combining the height and intensity information of the point cloud, effectively filtering out snow noise while preserving environmental features. In our experiments, the DVIOR algorithm was evaluated on several publicly available adverse weather datasets, including the Winter Adverse Driving Scenarios (WADS), the Canadian Adverse Driving Conditions (CADC), and the Radar Dataset for Autonomous Driving in Adverse weather conditions (RADIATE) datasets. Compared with both the mainstream dynamic distance–intensity hybrid algorithm in recent years, Dynamic Distance–Intensity Outlier Removal (DDIOR), and the representative intensity-based filter LIOR, DVIOR achieved notable improvements: it gained a 10.2-point higher F1-score than DDIOR and an 11.8-point higher F1-score than LIOR (79.00) on the WADS dataset. Additionally, DVIOR performed excellently on the CADC and RADIATE datasets, achieving F1-scores of 87.35 and 86.68, respectively—representing an improvement of 19.82 and 36.9 points over DDIOR and 4.67 and 17.95 points over LIOR (82.68 and 68.73). These results demonstrate that the DVIOR algorithm outperforms existing methods, including both distance–intensity hybrid approaches and intensity-based filters like LIOR, in snow noise removal, particularly in complex snowy environments. Full article

Tropical cyclones (TCs) rank among the most destructive natural hazards globally, with core damaging potential originating from regions of intense wind shear and steep wind speed gradients within the eyewall and spiral rainbands. Accurately characterizing these fine-scale structural features is therefore critical for understanding TC intensity evolution, wind hazard distribution, and disaster mitigation. Recently, the deep learning-based downscaling methods have shown significant advantages in efficiently obtaining high-resolution wind field distributions. However, existing methods are mainly used to downscale general wind fields, and research on downscaling extreme wind field events remains limited. There are two main difficulties in downscaling TC wind fields. The first one is that high-quality datasets for TC wind fields are scarce; the other is that general deep learning frameworks lack the ability to capture the dynamic characteristics of TCs. Consequently, this study proposes a novel deep learning framework, CycloneWind, for downscaling TC surface wind fields: (1) a high-quality dataset is constructed by integrating Cyclobs satellite observations with ERA5 reanalysis data, incorporating auxiliary variables like low cloud cover, surface pressure, and top-of-atmosphere incident solar radiation; (2) we propose CycloneWind, a dynamically constrained Transformer-based architecture incorporating three wind field dynamical operators, along with a wind dynamics-constrained loss function formulated to enforce consistency in wind divergence and vorticity; (3) an Adaptive Dynamics-Guided Block (ADGB) is designed to explicitly encode TC rotational dynamics using wind shear detection and wind vortex diffusion operators; (4) Filtering Transformer Layers (FTLs) with high-frequency filtering operators are used for modeling wind field small-scale details. Experimental results demonstrate that CycloneWind successfully achieves an 8-fold spatial resolution reconstruction in TC regions. Compared to the best-performing baseline model, CycloneWind reduces the Root Mean Square Error (RMSE) for the U and V wind components by 9.6% and 4.9%, respectively. More significantly, it achieves substantial improvements of 23.0%, 22.6%, and 20.5% in key dynamical metrics such as divergence difference, vorticity difference, and direction cosine dissimilarity. Full article

We report a method of improving the precision and resolution of sensing systems based on position sensitive detectors (PSDs). In the method, we improved the precision and resolution by reducing the gain of the condition circuit and conducting spatial filtering on the measured spot position. To demonstrate the method, we experimentally built a PSD-based displacement sensing system. With the system, a precision of 0.3 μm and a resolution of 0.5 μm were obtained. The precision is two orders of magnitude better than that obtained with the use of a commercial condition circuit (SPC02, SiTek, Partille, Sweden) and without using any filter. Moreover, we tested the radiation-resistance performance of the system using a ⁶⁰Co radiation source. The system kept the precision and resolution after exposure to radiation with a dose set to 100 krad. Our study is very useful to realize high-precision PSD-based sensing in space. Full article

Background: [⁶⁸Ga]Ga-DOTATOC is widely used in PET imaging of neuroendocrine tumors (NETs) due to its high affinity for somatostatin receptors. Given the short physical half-life of gallium-68 (~68 min), rapid, reproducible, and GMP-compliant synthesis is essential for clinical application. Methods: An optimized cassette-based automated synthesis protocol was developed using a commercial cassette. Improvements included direct generator elution into the reactor without pre-purification, use of a SepPak^® C18 Plus Light cartridge for purification, replacement of HEPES with 0.3 M sodium acetate buffer (final pH ~3.8), and implementation of a non-vented sterile filter enabling automated pressure-hold integrity testing. Results: Across all batches, the synthesis yielded [⁶⁸Ga]Ga-DOTATOC with high radiochemical purity (> 97%) and reproducible decay-corrected radiochemical yields up to 88.3 ± 0.6%. Total synthesis time was approximately 13 min. The final product remained stable for at least 3 h post-synthesis. The use of acetate buffer eliminated the need for HEPES-specific testing, streamlining the workflow. Automated filter testing improved GMP-compliant documentation and reduced radiation exposure for personnel. Conclusions: This optimized, cassette-based synthesis protocol enables fast, high-yield, and GMP-compliant production of [⁶⁸Ga]Ga-DOTATOC. It supports clinical theranostic workflows by ensuring product quality, process standardization, and regulatory compliance. Full article

In order to address the challenge of achieving optimal prediction accuracy when a single prediction model faced with changes in meteorological conditions of different weather types, this paper proposes a photovoltaic (PV) power prediction method based on the combination of similar day clustering and convolutional neural network (CNN)-gated recurrent unit (GRU). The Pearson correlation coefficient and Spearman’s correlation coefficient are used to filter out the key features such as total solar radiation and module temperature to construct a new input dataset; the K-means algorithm is used to perform clustering analysis on the data, and the data are classified into sunny, cloudy, and rainy days; the spatial correlation features of the meteorological factors are extracted by using the convolutional neural network (CNN), and the CNN-GRU model is established by combining with the gated recurrent units (GRUs). The PV output power is predicted based on the PV power data and the corresponding meteorological data from a place in Ningxia, collected during June to August 2020, and the method proposed in the article is tested. Validation results show that, compared to other models, the model proposed in this paper reduces MAE and RMSE by 66.1% and 65.7% on average under three different weather type scenarios, and improves R² by 19.8% on average. This verifies that the model has high prediction accuracy and generalization ability, achieving better results in PV output power prediction. The CNN-GRU model demonstrates superior capability in modeling short- and long-term dependencies compared to other deep learning hybrid approaches, while also achieving higher computational efficiency and faster training convergence. Full article

A self-diplexing, full-mode, substrate-integrated waveguide (SIW) rectangular cavity-backed antenna based on an inverted Z-shaped radiating slot with filtering characteristics is investigated in this work. The proposed design allows for individual control through the loading of four different slots, namely, a combination of horizontal and diagonal slots, called inverted Z-shaped slots. The two diagonal slots make 45° angles between them, and this flexible rotation gives the design flexibility regarding control of the bands. By combining these slots into a modified inverted Z-shaped slot, a SIW rectangular cavity is configured and energized with two separate 50 Ω microstrip feed lines to resonate at two different frequencies—11.63 GHz and 13.27 GHz—and TE₂₁₀ and TE₂₂₀ modes are obtained for X- and Ku-band wireless purposes. In an experimental analysis, reflection coefficients of S₁₁ < −10 dB were noted for both operating frequencies of 7.4% (11.23–12.09 GHz) and 3.0% (13.15–13.55 GHz), respectively. The average gain of the proposed antenna design in the two different operating conditions is 6.14 and 6.16 dBi, respectively. In addition, the proposed self-diplexing antenna attained high isolation, greater than 28 dB between both operating channels, and showed overall measured efficiency of 87.32%. Moreover, it features a single-layer structure, operates in dual bands, provides broadside linear polarization, and exhibits filtering capabilities. Full article

The use of Acousto-Optical Tunable Filters (AOTFs) is well known in ground- and space-based applications. These devices are used in several optical instruments and payloads for monitoring and other purposes. To make use of the filter capability of the AOTF, a dedicated Radio Frequency (RF) chain, consisting of an RF generator and RF amplifier, is needed. An RF generator can be designed in several ways. However, the design of these steering devices for space applications comes with several difficulties and limitations. The mechanical stress due to shock and vibration, the temperature variation, as well as the vacuum environment and radiation levels in which these devices have to perform limits the selection of possible techniques. This paper aims at giving an in-depth overview of space-qualified RF generator techniques using Commercial-Off-The-Shelf available components that usable in the harsh environment of space and applicable in driving AOTFs. Several analog as well as digital generator principles are discussed, substantiated by test results. Full article

Short-wave-infrared (SWIR) imaging can extend drone operations into fog and rain, yet the optimum spectral strategy remains unclear. We evaluated a drone-borne quantum-dot SWIR camera inside a climate-controlled tunnel that generated calibrated advection fog, radiation fog, and rain. Images were captured with a broadband 400–1700 nm setting and three sub-band filters, each at four lens apertures (f/1.8–5.6). Entropy, structural-similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were computed for every weather–aperture–filter combination. Broadband SWIR consistently outperformed all filtered configurations. The gain stems from higher photon throughput, which outweighs the modest scattering reduction offered by narrowband selection. Under passive illumination, broadband SWIR therefore represents the most robust single-camera choice for unmanned aerial vehicles (UAVs), enhancing situational awareness and flight safety in fog and rain. Full article

In this paper, a compact wideband filtering monopole is presented for remote terrestrial omnidirectional communication systems. The presented antenna features a sleeve monopole structure integrating with two key components: the lumped parallel RLC circuits and an embedded high–low impedance structure within the sleeve section. The integrated high–low impedance structure enables the monopole to achieve excellent filtering characteristics while maintaining the monopole compactly. Meanwhile, the combination of the RLC loads and the sleeve monopole ensures wideband omnidirectional radiation performance. To validate the design, a prototype operating from 200 to 1500 MHz is fabricated and tested. The measurement results demonstrate that the monopole achieves a VSWR below 3 across the entire operating band and a measured gain exceeding 0 dB. Furthermore, the monopole exhibits satisfactory out-of-band rejection from 1700 to 4000 MHz, confirming its effective filtering capability. Full article

This paper proposes a compact, broadband, and low-profile filtering antenna designed for Sub-6 GHz communication. By applying characteristic mode analysis to the radiating elements, the operational mechanism of the antenna is clearly elucidated. The current cancellation among different radiating elements results in two radiation nulls in the primary radiation direction, effectively enhancing the filtering effect. The antenna achieves a wide operational bandwidth ($S_{11}\le -10$ dB) of 35.9% (4.3–6.4 GHz), making it highly suitable for Sub-6 GHz communication systems. Despite its compact size of 25 × 25 mm², the antenna consistently maintains stable broadside radiation patterns, with a peak gain of 6.14 dBi and a minimal gain fluctuation of less than 1 dBi at 4.6–6.45 GHz. This design ensures reliable and robust communication performance for V2V systems operating in the designated frequency band. Full article

The fraction of absorbed photosynthetically active radiation (FAPAR) stands as a pivotal parameter within the Earth system, quantifying the energy exchange between vegetation and solar radiation. Accordingly, there is an urgent need for comprehensive validation studies to accurately quantify uncertainties and improve the reliability of FAPAR-based applications. This study validated five global FAPAR products, MOD15A2H, MYD15A2H, VNP15A2H, GEOV2, and GEOV3, over four boreal forest sites in North America. Qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) of each product were analyzed. Time series high-resolution reference FAPAR maps were developed using the Harmonized Landsat and Sentinel-2 dataset. The reference FAPAR maps revealed a strong agreement with the in situ FAPAR from AmeriFlux (correlation coefficient (R) = 0.91; root mean square error (RMSE) = 0.06). The results revealed that global FAPAR products show similar uncertainties (RMSE: 0.16 ± 0.04) and moderate agreement with the reference FAPAR (R = 0.75 ± 0.10). On average, 34.47 ± 6.91% of the FAPAR data met the goal requirements of the Global Climate Observing System (GCOS), while 54.41 ± 6.89% met the threshold requirements of the GCOS. Deciduous forests perform better than evergreen forests, and the products tend to underestimate the reference data, especially for the beginning and end of growing seasons in evergreen forests. There are no obvious quality differences at different QQFs, and the relative QQI can be used to filter high-quality values. To enhance the regional applicability of global FAPAR products, further algorithm improvements and expanded validation efforts are essential. Full article

The lips, due to their unique anatomical characteristics of a thin stratum corneum, the absence of sebaceous glands, and limited melanin content are particularly vulnerable to ultraviolet (UV) radiation, necessitating specialized photoprotective care. While facial sunscreens are widely available, the development of lip-specific sun protection products remains underexplored. This study aims to analyze technological trends and innovations in lip photoprotection by reviewing patents published between 2014 and 2024. A comprehensive patent search using the IPC code A61Q19 and the keywords “lip” and “sunscreen” identified 17 relevant patents across China, the United States, and Japan. The patents were examined for active ingredients, formulation strategies, and use of botanical or sustainable excipients. The findings revealed that patented formulations predominantly rely on well-established UV filters such as zinc oxide, titanium dioxide, octyl methoxycinnamate, and avobenzone, often combined with antioxidants like ferulic acid and rutin for enhanced efficacy. Lipid-based excipients were widely used to improve texture, hydration, and product stability. Although many formulations exhibit a conservative ingredient profile, the strategic combination of UV filters with natural antioxidants and moisturizing lipids demonstrates a multifunctional approach aimed at enhancing both protection and user experience. Full article

To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540–1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable output, based on high irradiance and spectral uniformity. A compound beam homogenization structure—combining a multimode fiber and an apodizator—achieves 85.8% far-field uniformity over a 200 mm aperture. A power–spectrum co-optimization strategy is introduced for filter design, achieving a spectral matching degree of 78%. The system supports a tunable output from 2.5 to 130 mW with a 50× dynamic range and maintains power control accuracy within ±0.9%. To suppress internal background interference, a BRDF-based optical scattering model is established to trace primary and secondary stray light paths. Simulation results show that by maintaining the surface roughness of key mirrors below 2 nm and incorporating a U-shaped reflective light trap, stray light levels can be reduced to 5.13 × 10⁻¹² W, ensuring stable detection of a 10⁻¹⁰ W signal at a 10:1 signal-to-background ratio. Experimental validation confirms that the system can faithfully reproduce solar outage conditions within a ±3° field of view, achieving consistent performance in spectrum shaping, irradiance uniformity, and background suppression. The proposed platform provides a standardized and practical testbed for ground-based anti-interference assessment of optical communication terminals. Full article