用光电离技术探测钐原子的奇宇称束缚激发态的光谱

采用光电离探测方法,对钐原子奇宇称束缚激发态进行了系统研究.通过设计三条不同的激发路径, 采用共振激发方式,先将钐原子分三步从基态激发到不同的束缚激发态,然后采用光电离手段对其进行探测. 通过对第三步激发光的波长进行大范围的扫描,在同一能域内获得了三组不同的光谱. 通过比对三条路径所得到的三组光谱,不仅精确确定了大量奇宇称束缚激发态的能级位置, 而且还获得了相应跃迁的相对强度的信息.最后,通过运用三条不同的激发路径的选择定则, 还确定了上述能级的总角动量. 关键词： 钐原子 束缚激发态 奇宇称 光电离探测     

自由电子激光的医学应用

自由电子激光这种新型光源非常适合于医学应用。通过对医用自由电子激光器的简要介绍,探讨了自由电子激光与人体组织的相互作用及其在医学上的应用前景。     

Mechanical damage and thermal effect induced by ultrasonic treatment in olive leaf tissue. Impact on polyphenols recovery

The influence of ultrasound treatment (US) on cellular damage of olive leaf tissue was studied. Mechanical damage and thermal effect of US were characterized. The level of tissue damage was defined by the diffusivity disintegration index Z_D based on the diffusivity of solutes extracted from olive leaves differently treated. The Arrhenius form using the temperature dependences of the thermal treatment time within the temperature interval 20–90 °C was observed for the thermal process. The corresponding activation energy ΔUT was estimated as 57 kJ/mol. The temperature dependences of electrical conductivity were measured for extracts of intact and maximally treated olive leaves. Then the diffusivity disintegration index Z_D and total phenolic compounds recovery for three studied US powers were calculated (100, 200, and 400 W). The results evidenced that the mechanically stimulated damage in olive leaf tissue can occur even at a low US power of 100 W if treatment time is long enough (t = 3.5 h). The US treatment noticeably accelerated the diffusion process mechanically in addition to its thermal effect. Trials in aqueous solution revealed the dependence of polyphenols extraction on damage level with respect to the US power applied.     

Effects of reservoir rock pore geometries and ultrasonic parameters on the removal of asphaltene deposition under ultrasonic waves

Asphaltene deposition around the wellbore is a major cause of formation damage, especially in heavy oil reservoirs Ultrasonic stimulation, rather than chemical injection, is thought to be a more cost-effective and environmentally friendly means of removing asphaltene deposition. However, it seems to be unclear how crucial features like reservoir pore geometries and ultrasonic parameters affect this ultrasound treatment.In this work, five two-dimensional glass micromodels with different pore geometries were designed to assess the impact of pore geometries on the ultrasonic removal of asphaltene deposition. Experiments were undertaken in an ultrasound bath at a set frequency (20 kHz) and adjustable powers (100–1000 W). Direct image analysis before, during and after sonication was used to assess the impact of pore geometry and a change in ultrasonic parameter on the removal of asphaltene deposition. The effectiveness of ultrasound treatment at various sonication periods were found to be reliant on the pore geometries of the individual micromodels. For micromodels with throat sizes 300 µm and pore shapes as circle, square and triangle, an increase in ultrasonic power from 400 to 1000 W resulted in an increase in the percentage of removed asphaltene deposition after 2 h from 12.6 to 14.7, 11.5 to 14.63, and 5.8 to 7.1 percent, respectively.     

Meaningful Secret Image Sharing with Saliency Detection

Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme.     

Synthesizing Red Fluorescent Carbon Dots from Rigid Polycyclic Conjugated Molecules: Dual-Mode Sensing and Bioimaging in Biochemical Applications

Red fluorescent carbon dots (R-CDs) are special desirable for biochemical analysis due to good biological compatibility and deep penetration; however, they remain as bottlenecks due to difficulties in expanding the sp² domain, especially those are fused from rigid polycyclic conjugated molecules (RPCMs) with heteroatom substituents due to huge steric hindrance and heteroatom blockage toward graphic lattice. Here, an RPCM with heteroatom substituents, 1,5-diamino-4,8-dihydroxyanthraquinone (DDAQ), based self-doped R-CDs with PL emission at 635 nm is reported. Further investigations reveal that the expanding, hybrid sp² domain with indanthrone tannin structure from DDAQ is mainly responsible for the obtained red fluorescence of R-CDs. Taking advantage of optical properties, R-CDs are considered to construct a colorimetric/fluorescent dual mode sensing array for quantifying trace levels of Fe³⁺ and glyphosate based on the static quenching, and a biomarker for cell imaging. The CD-based sensors exhibit outstanding recovery, high selectivity, and sensitivity, also facilitated dual-mode detection with the naked-eye. The R-CDs have low cytotoxicity, good cell membrane penetration for rapid cell entry, and high resolution, demonstrating their potential for biolabeling and bioanalytic applications.     

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.     

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.     

Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization

When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.