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BackgroundThe unfolding-synthesis technique is commonly used in digital pulse processing systems for nuclear radiation measurements.PurposeThis study aims to propose a novel flat-topped widened peak-shaping algorithm based on the pulse unfolding-synthesis technique is proposed.MethodsFirstly, the repetition and polynomials were utilizes to shape the digital pulses, and the nuclear pulse signal was unfolded into unit pulses series. Then, an impulse response system was employed to synthesize these unit pulses series to achieve the desired peak shape. Finally, the improved flat-topped peak-shaping algorithm was compared and analyzed against traditional filter shaping algorithms in terms of accuracy in amplitude extraction, interference resistance, and pile-up recognition.ResultsExperimental results demonstrate that, under the same shaping time, the energy resolution of the flat-topped peak-shaping algorithm for the γ characteristic peak of 137Cs is 7.2%, outperforming the trapezoidal, triangular, and Gaussian shaping algorithms. Additionally, it exhibits high counting rate performance.ConclusionsThe flat-topped peak-shaping algorithm of this study can effectively replace traditional pulse-shaping methods and be utilized for high-precision, high-count-rate γ spectroscopy measurements.

The efficient and accurate identification of traffic signs is crucial to the safety and reliability of active driving assistance and driverless vehicles. However, the accurate detection of traffic signs under extreme cases remains challenging. Aiming at the problems of missing detection and false detection in traffic sign recognition in fog traffic scenes, this paper proposes a recognition algorithm for traffic signs based on pix2pixHD+YOLOv5-T. Firstly, the defogging model is generated by training the pix2pixHD network to meet the advanced visual task. Secondly, in order to better match the defogging algorithm with the target detection algorithm, the algorithm YOLOv5-Transformer is proposed by introducing a transformer module into the backbone of YOLOv5. Finally, the defogging algorithm pix2pixHD is combined with the improved YOLOv5 detection algorithm to complete the recognition of traffic signs in foggy environments. Comparative experiments proved that the traffic sign recognition algorithm proposed in this paper can effectively reduce the impact of a foggy environment on traffic sign recognition. Compared with the YOLOv5-T and YOLOv5 algorithms in moderate fog environments, the overall improvement of this algorithm is achieved. The precision of traffic sign recognition of the algorithm in the fog traffic scene reached 78.5%, the recall rate was 72.2%, and mAP@0.5 was 82.8%. [ABSTRACT FROM AUTHOR]

Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks. [ABSTRACT FROM AUTHOR]

On the basis of processing principles of torus-involute gear,the longitudinal modification method based on continuous shifting is used for the pinion in the face gear drive,a modified face gear pair is obtained. At first,based on the differential geometry theory and the space mesh theory,the tooth surface equations of the modified face gear pair are derived. Then,the tooth contact analysis（ TCA） and curvature interference inspection are conducted. The numerical example shows that the proposed novel modified face gear pair has following features of can avoid the occurrence of gear deviation load and can decrease the sensitivity of the contact path to the installation errors,there is no curvature interference at meshing points. At last,based on the finite element method,the loaded tooth contact analysis（ LTCA） is conducted by ABAQUS.

In this study, we have investigated the chemopreventive role of 6‐shogaol (6‐SGL) on benzopyrene (BaP) exposed lung carcinogenesis by modulating PRDX1‐associated oxidative stress, inflammation, and proliferation in Swiss albino mouse models. Mice were exposed to BaP (50 mg/kg b.wt) orally twice a week for four consecutive weeks and maintained for 16 weeks, respectively. 6‐SGL (30 mg/kg b.wt) were orally administered to mouse 1 h before BaP exposure for 16 weeks. After the experiment's termination, 6‐SGL (30 mg/kg b.wt) prevented the loss in body weight, increased lung weight, and the total number of tumors in the mice. Moreover, we observed that 6‐SGL treatment reverted the activity of BaP‐induced lipid peroxidation and antioxidants in mice. Also, 6‐SGL impeded the phosphorylation of MAPK family proteins such as Erk1, p38, and Jnk1 in BaP‐exposed mice. PRDX1 is an essential antioxidant protein that scavenges toxic radicals and enhances several antioxidant proteins. Overexpression of PRDX1 substantially inhibits MAPKs, proliferation, and inflammation signaling axis. Hence, PRDX1 is thought to be a novel targeting protein for preventing BaP‐induced lung cancer. In this study, we have obtained the 6‐SGL treatment in a mouse model that reverted BaP‐induced depletion of PRDX1 expression. Moreover, pretreatment of 6‐SGL (30 mg/kg b.wt) significantly inhibited enhanced proinflammatory cytokines (TNF‐α, IL‐6, IL‐β1, IL‐10) and proliferative markers (Cyclin‐D1, Cyclin‐D2, and PCNA) in BaP‐exposed mice. The histopathological studies also confirmed that 6‐SGL effectively protected the cells with less damage. Thus, the study demonstrated that 6‐SGL could be a potential phytochemical and act as a chemopreventive agent in BaP‐induced lung cancer by enhancing PRDX1 expression. [ABSTRACT FROM AUTHOR]