Search Results (131)

Lidar serves as a vital active remote sensing instrument for exploring the atmosphere. However, the detection range of lidar is significantly constrained by the dynamic range of photo-detectors. To mitigate this limitation, atmospheric lidars are commonly equipped with multiple channels to capture signals from different altitude ranges, making the high-quality gluing of multi-channel echo signals crucial for accurate data retrieval. In this paper, an efficient dual-channel signal gluing method based on the improved whale optimization algorithm (IMWOA) and the entropy weight method (EWM), named IMWOA-EWM, was proposed. Here, the IMWOA method was used to optimize the fitness function, achieving higher computational efficiency. The weights of the correlation coefficient R, regression stability coefficient S and mean fit deviation D were determined using EWM, which together constitute the fitness function. Through signal gluing experiments conducted with ground-based aerosol lidar data, IMWOA-EWM can accurately identify the optimal gluing region, due to IMWOA’s excellent global search capability and the higher weight assigned to the objective function S by EWM. Meanwhile, regarding computational efficiency, its runtime is only half that of IGWO-RSD. Additionally, the applicable conditions of the weights in IMWOA-EWM were explored, which indicate that IMWOA-EWM has good robustness for atmospheric lidar signal gluing. Full article

In this paper, a high-precision 3D object detector—Voxel–RCNN—is adopted as the baseline detector, and an improved detector named RCAVoxel-RCNN is proposed. To address various issues present in current mainstream 3D point cloud voxelisation methods, such as the suboptimal performance of Region Proposal Networks (RPNs) in generating candidate regions and the inadequate detection of small-scale objects caused by overly deep convolutional layers in both 3D and 2D backbone networks, this paper proposes a Cascade Attention Network (CAN). The CAN is designed to progressively refine and enhance the proposed regions, thereby producing more accurate detection results. Furthermore, a 3D Residual Network is introduced, which improves the representation of small objects by reducing the number of convolutional layers while incorporating residual connections. In the Bird’s-Eye View (BEV) feature extraction network, a Residual Attention Network (RAN) is developed. This follows a similar approach to the aforementioned 3D backbone network, leveraging the spatial awareness capabilities of the BEV. Additionally, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to assign dynamic weights to features, allowing the network to focus more effectively on informative features. Experimental results on the KITTI validation dataset demonstrate the effectiveness of the proposed method, with detection accuracy for cars, pedestrians, and bicycles improving by 3.34%, 10.75%, and 4.61%, respectively, under the KITTI hard level. The primary evaluation metric adopted is the 3D Average Precision (AP), computed over 40 recall positions (R40). The Intersection over IoU thresholds used are 0.7 for cars and 0.5 for both pedestrians and bicycles. Full article

Communication via optical fiber is increasingly being used in harsh applications where environmental vibration is present. This study involves a Weibull reliability analysis focused on the performance of fiber optic connectors when they are subjected to mechanical random vibration stress to simulate real-world operating conditions, and the insertion loss (IL) degradation is measurable. By analyzing the testing times and stress levels, the Weibull shape ($\beta$) and scale ($\eta$) parameters are estimated directly from the maximal and minimal principal IL stresses (${\sigma }_1$, ${\sigma }_2$), enabling the prediction of the connector’s reliability with efficiency. The sample size n is derived from the desired reliability (R(t)), and the GR-326 mechanical vibration test (2.306 Grms for six hours) is performed on optical SC angled physical contact (PC) polish fiber endface connectors that are monitored during testing to evaluate the IL transient change in the optical transmission. The method is verified by an experiment performed with ${\sigma }_1=0.3960$ and ${\sigma }_2=0.1910$ where the IL measurements are captured with an Agilent N7745A source-detector optical equipment, and the Weibull statistical results provide a connector’s reliability R(t) = 0.8474, with a characteristic value of $\eta$ = 0.2750 dB and $\beta$ = 3. Finally, the connector’s reliability is as worthy of attention as the telecommunication sign conditions. Full article

Accurate fetal R-peak detection from low-SNR fetal electrocardiogram (FECG) signals remains a critical challenge as current NI-FECG methods struggle to extract high SNR FECG signals and conventional algorithms fail when signal quality deteriorates. We proposed a U-Net-based method that enables robust R-peak detection directly from low-SNR FECG signals (0–12 dB), bypassing the need for high-SNR inputs that are clinically difficult to acquire. The method was evaluated on both real (A&D FECG) and synthetic (FECGSYN) databases, comparing against ten state-of-the-art detectors. The proposed method significantly reduces false predictions compared to commonly used detection algorithms, achieving a PPV of 99.81%, an SEN of 100.00%, and an F1-score of 99.91% on the A&D FECG database and a PPV of 99.96%, an SEN of 99.93%, and an F1-score of 99.94% on the FECGSYN database. Further investigation of robustness in low-SNR conditions (0 dB, 5 dB, and 10 dB) achieved 87.38% F1-score at 0 dB SNR on real signals, surpassing the best-performing algorithm implemented in Neurokit by 13.58%. In addition, the algorithm showed ≤2.65% performance variation across tolerance windows (50 reduced to 20 ms), further underscoring its detection accuracy. Overall, this work reduces the reliance on high-SNR FECG signals by reliably extracting R-peaks from suboptimal signals, providing implications for the reliability of fetal heart rate variability analysis in real-world noisy environments. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Prognostication of neurodevelopmental outcomes for neonates with hypoxic–ischemic encephalopathy (HIE) is primarily reliant on structural assessment using conventional brain magnetic resonance imaging in the clinical setting. Diffuse optical tomography (DOT) can provide complementary information on brain function at the bedside, further enhancing prognostic accuracy. The predictive accuracy and generalizability of DOT-based neuroimaging markers are unknown. This study aims to test the feasibility of prospectively recruiting and retaining neonates for 12 months in a larger study that investigates the prognostic utility of DOT-based biomarkers of HIE. The study will recruit 25 neonates with HIE over one year and follow them beyond NICU discharge at 6 and 12 months of age. Study subjects will undergo resting-state DOT measurement within 7 days of life for a 30–45-min period without sedation. A customized neonatal cap with 10 sources and eight detectors per side will be used to quantify cortical functional connectivity and to generate brain networks using MATLAB-based software (version 24.2). The Ages and Stages Questionnaires—3rd edition will be used for standardized developmental assessments at follow-up. This feasibility study will help refine the design and sample-size calculation for an adequately powered larger study that determines the clinical utility of DOT-based neuroimaging in perinatal brain injury. Full article

Background: The Quantitative Analysis of Multi-components by Single-marker (QAMS) method has been developed as an alternative to the External Standards Method (ESM) for the quality control of medicinal herbs. Objectives: In this study, QAMS was developed to determine saponins in the raw materials of Panax vietnamensis using HPLC-PDA/ELSD. Methods: The method was developed and validated. The relative conversion factors F_x were calculated based on the linear regression for HPLC-PDA and the logarithm equation for HPLC-ELSD. The Standard Method Difference (SMD) was determined to indicate the difference in the results of QAMS and EMS. Results: Relative conversion factors (F_x) were determined for each detector to quantify five saponins (ginsenoside Rb₁, Rd, Rg₁, majnoside R2, and vina-ginsenoside R2) in VG root. The F_x values were calculated based on the ratio of the slopes of the regression equations of a single standard and an external standard. For HPLC-PDA, G-Rb1 was used as a single standard with the F_x values of 1.00 (G-Rb₁), 1.08 (G-Rd), 1.32 (G-Rg₁), and 0.04 (M-R2). For HPLC-ELSD, G-Rb₁ was used for determining the content of G-Rg₁ and G-Rb₁ with the F_x values of 1.00 (G-Rb₁) and 0.95 (G-Rg₁), while M-R2 was used for quantitating M-R2 and V-R2 with F_x of 1.00 (M-R2) and 1.05 (V-R2). An SMD value less than 5.00% confirms the close alignment of the QAMS method with ESM. Conclusions: The QAMS method proved to be a feasible and promising method for the quality control of P. vietnamensis. Full article

The stability of collagen, the most abundant protein in humans and many animals, is related to the hydroxylation of L-proline, a post-translational modification occurring at carbon 3 and 4 on its pyrrolidine ring. Collagens of different origins have shown different proline hydroxylation levels, making hydroxyprolines useful biomarkers in structure characterizations. The presence of two chiral carbon atoms, 3-hydroxyproline and 4-hydroxyproline, results in eight stereoisomers (four pairs of enantiomers) whose quantitation in collagen hydrolysates requires enantioselective analytical methods. Capillary electrophoresis was applied for the separation and quantitation of the eight stereoisomers of 3- and 4-hydroxyproline and D,L-proline in collagen hydrolysates. The developed method is based on the derivatization with the chiral reagent (R)-(-)-4-(3-Isothiocyanatopyrrolidin-yl)-7-nitro-2,1,3-benzoxadiazole, enabling the use of a light-emitting diode-induced fluorescence detector for high sensitivity. The separation of the considered compounds was accomplished in less than 10 min, using a 500 mM acetate buffer pH 3.5 supplemented with 5 mM of heptakis(2,6-di-O-methyl)-β-cyclodextrin as the chiral selector. The method was fully validated and showed the adequate sensitivity for the application to samples of collagen hydrolysates. The analysis of samples extracted from chicken Pectoralis major muscles affected by growth-related myopathies showed different stereoisomer patterns compared to those from the unaffected control samples. Full article

Using the Bridgman technique, GaSe single crystals were obtained which were mechanically split into plane-parallel plates with a wide range of thicknesses. By heat treatment in air at 820 °C and 900 °C, for 30 min and 6 h, micro- and nanocomposite layers of Ga₂Se₃–Ga₂O₃ and β–Ga₂O₃ (native oxide) with surfaces made of nanowires/nanoribbons were obtained. The obtained composite Ga₂Se₃–Ga₂O₃ and nanostructured β–Ga₂O₃ are semiconductor materials with band gaps of 2.21 eV and 4.60 eV (gallium oxide) and photosensitivity bands in the green–red and ultraviolet-C regions that peaked at 590 nm and 262 nm. For an applied voltage of 50 V, the dark current in the photodetector based on the nanostructured β–Ga₂O₃ layer was of 8.0 × 10⁻¹³ A and increased to 9.5 × 10⁻⁸ A upon 200 s excitation with 254 nm-wavelength radiation with a power density of 15 mW/cm². The increase and decrease in the photocurrent are described by an exponential function with time constants of τ_1r = 0.92 s, τ_2r = 14.0 s, τ_1d = 2.18 s, τ_2d = 24 s, τ_1r = 0.88 s, τ_2r = 12.2 s, τ_1d = 1.69 s, and τ_2d = 16.3 s, respectively, for the photodetector based on the Ga₂Se₃–Ga₂S₃–GaSe composite. Photoresistors based on the obtained Ga₂Se₃–Ga₂O₃ composite and nanostructured β–Ga₂O₃ layers show photosensitivity bands in the spectral range of electronic absorption bands of ozone in the same green–red and ultraviolet-C regions, and can serve as ozone sensors (detectors). Full article

Optical absorption spectra of 9 MeV electron-irradiated GaSe crystals measured at temperatures in the range from 9.5 to 300 K were analyzed. The absorption spectra with features caused by Ga vacancies in two charge states and direct interband transitions were fitted by a model equation. Temperature dependencies of the defect concentrations and optical transition energies, as well as of the GaSe band gap, were determined. Current- and capacitance-voltage characteristics and DLTS spectra were measured for as-grown and electron-irradiated GaSe slabs with Sc (barrier) and Pt (ohmic) contacts. An experimental Sc/GaSe Schottky barrier height of 1.12 eV was determined in close agreement with a theoretical estimate. The activation energy and the hole capture cross-section deduced from the DLTS data are 0.23 (0.66) eV and 1.5 × 10⁻¹⁹ (2.3 × 10⁻¹⁵) cm⁻² for the supposed $V_{\mathrm{Ga}}^{-1}$ ($V_{\mathrm{Ga}}^{-2}$) defect. For the electron-irradiated GaSe crystals, the found activation energies are close to the values inferred from the optical measurements. Full article

The Cryo-PoF project is an R&D project funded by the Italian Insitute for Nuclear Research (INFN) in Milano-Bicocca (Italy). The technology at the basis of the project is Power over Fiber (PoF). By sending laser light through an optical fiber, this technology delivers electrical power to a photovoltaic power converter, in order to power sensors or electrical devices. Among the several advantages this solution can provide, we can underline the spark-free operation when electric fields are present, the removal of noise induced by power lines, the absence of interference with electromagnetic fields, and robustness in hostile environments. R&D for the application of PoF in cryogenic environments started at Fermilab in 2020; for the DUNE Vertical Drift detector, it was needed to operate the Photon Detector System on a high-voltage cathode surface. Cryo-PoF, starting from this project, developed a single-laser input line system to power, at cryogenic temperatures, both an electronic amplifier and Photon Detection devices, tuning their bias by means of the input laser power, without adding ancillary fibers. The results obtained in Milano-Bicocca will be discussed, presenting the tests performed using power photosensors at liquid nitrogen temperature. Full article

The essential oil (EO) obtained from the roots of Valeriana rigida Ruiz & Pav. (Caprifoliaceae), collected in the moorland region of Chimborazo Province, Ecuador, was analyzed for the first time. The chemical profile was qualitatively and quantitatively analyzed using GC-MS and GC-FID, respectively. With both detectors, two stationary phases of different polarities were used. A total of 56 compounds were identified, and the most abundant components (>3% on at least one column) were a mixture of cyclosativene and α-ylangene (4.5–4.4%), α-copaene (9.0–8.8%), decanoic acid (16.0–15.6%), β-chamigrene (3.2–3.1%), δ-cadinene (9.7–9.5%), dodecanoic acid (13.4–12.3%), and 7-epi-α-eudesmol (5.0–4.9%), on a non-polar and polar stationary phase, respectively. Additionally, the enantioselective analysis showed (1S,5S)-(+)-α-pinene, (1R,4S)-(–)-camphene, (1S,5S)-(−)-β-pinene, and (1R,2S,6S,7S,8S)-(–)-α-copaene as enantiomerically pure compounds, whereas germacrene D exhibited both enantiomeric forms. The anti-inflammatory activity of V. rigida EO was comparable to that of aspirin, as indicated by the IC50 values, with no significant differences observed. Full article

Adversarial attacks threaten the reliability of machine learning models in critical applications like autonomous vehicles and defense systems. As object detectors become more robust with models like YOLOv8, developing effective adversarial methodologies is increasingly challenging. We present Truck Adversarial Camouflage Optimization (TACO), a novel framework that generates adversarial camouflage patterns on 3D vehicle models to deceive state-of-the-art object detectors. Adopting Unreal Engine 5, TACO integrates differentiable rendering with a Photorealistic Rendering Network to optimize adversarial textures targeted at YOLOv8. To ensure the generated textures are both effective in deceiving detectors and visually plausible, we introduce the Convolutional Smooth Loss function, a generalized smooth loss function. Experimental evaluations demonstrate that TACO significantly degrades YOLOv8’s detection performance, achieving an AP@0.5 of 0.0099 on unseen test data. Furthermore, these adversarial patterns exhibit strong transferability to other object detection models such as Faster R-CNN and earlier YOLO versions. Full article

The third-generation instrument era is approaching, and the Einstein Telescope (ET) giant interferometer is becoming a reality, with the potential to be installed at an underground site where seismic noise is about 100 times lower than at the surface. Moreover, new available technologies and the experience acquired from operating advanced detectors are key to further extending the detection bandwidth down to 2–3 Hz, with the possibility of suspending a cryogenic payload. The New Generation of Super-Attenuator (NGSA) is an R&D project aimed at the improvement of vibration isolation performance for thirrd-generation detectors of gravitational waves, assuming that the present mechanical system adopted for the advanced VIRGO interferometer (second generation) is compliant with a third-generation detector. In this paper, we report the preliminary results obtained from a simulation activity devoted to the characterization of a mechanical system based on a multi-stage pendulum and a double-inverted pendulum in a nested configuration (NIP). The final outcomes provide guidelines for the construction of a reduced-scale prototype to be assembled and tested in the “PLANET” laboratory at INFN Naples, where the multi-stage pendulum—equipped with a new magnetic anti-spring (nMAS)—will be hung from the NIP structure. Full article

To more intuitively demonstrate the locational distribution of spatial agglomeration of HQD (high-quality development) in China’s coal cities, this study uses the entropy value method, standard deviation ellipse, and geographic detector to investigate the law of dynamic evolution and driving factors of HQD in China’s coal cities from 2011 to 2020. The findings are as follows: (1) The HQD level of China’s coal cities is experiencing a positive trajectory, with the highest level of development in the east, followed by the regions located in the center and west of the country, and relatively low in the northeast. Throughout the “Twelfth Five-Year Plan” period, Suzhou made the greatest progress, while Fuxin had the greatest decline. Throughout the “13th Five-Year Plan” period, Xingtai and Handan made the greatest progress, while Qitaihe had the greatest decline. (2) The HQD level of China’s coal cities as a whole shows a northeast–southwest direction, the center of gravity shifts southward, indicating a concentration pattern. The eastern and central areas are oriented in a northwest–southeast direction; the center of gravity in the east shifts to the northwest, and the center of gravity in the middle shifts to the southeast; and both regions have a higher level of HQD in the east–west direction. The western and northeastern regions are in a northeast–southwest direction, with the center of gravity moving to the northeast: the western region shows a tendency toward diffusion, and the northeastern region shows an agglomeration trend. (3) Patent authorization per 10,000 people, foreign trade dependence, R&D investment intensity, and GDP per capita were important drivers for the HQD of China’s coal cities; The degree of government intervention is the best interaction factor, and the degree of opening to the outside world and the forest coverage rate are the best interaction objects. Full article