Epidemiological studies evaluating the association of tea consumption and the risk of thyroid cancer risk have produced inconsistent results. Thus, we conducted a meta-analysis to assess the relationship between tea consumption and thyroid cancer risk.Pertinent studies were identified by a search in PubMed and Web of Knowledge. The random effect model was used based to combine the results. Publication bias was estimated using Egger's regression asymmetry test.Finally, 11 articles with 14 studies (2 cohort studies and 12 case-control studies) involving 2,955 thyroid cancer cases and 106,447 participants were included in this meta-analysis. The relative risk (95% confidence interval) of thyroid cancer for the highest versus the lowest category of tea consumption was 0.774 (95% CI = 0.619-0.967), and the associations were also significant in Europe and America, but not in the Asia. No publication bias was found.Our analysis indicated that higher tea consumption may have a protective effect on thyroid cancer, especially in Europe and America.
Abstract A magic-wavelength optical dipole trap (ODT) can eliminate the differential light shift of transition between atomic states, so that the transition frequency between the two states is the same as that in free space, which is of great significance for exploring atomic structure. Here we calculate the dynamic polarizabilities of the 6S 1/2 ground state and the 6P 1/2 excited state of the cesium atom which are connected by the D1 line, by using the relativistic coupled cluster method, and obtain magic wavelengths of ODT laser for trapping the two states simultaneously. By using these magic wavelengths, we calculate oscillator strengths of the 6P 1/2 → 7D 3/2 transition for linearly and circularly polarized lights. Furthermore, we analyze the influence of magic wavelengths on oscillator strengths.
We report on the frequency doubling of a tapered amplifier-boosted distributed-Bragg-reflector continuous-wave laser system at 795 nm, using a PPKTP crystal placed in an external ring cavity. A tunable 397.5 nm violet laser power of 130 mW with a mode-matched input 795 nm laser power of 416 mW is obtained (conversion efficiency η=31%), limited by thermally induced bistability. However, when the violet laser is at the maximum output, a stable operation more than 30 min is hard to reach due to thermal effects. With a scanning cavity, the peak violet laser power rises up to 180 mW, corresponding to an overall efficiency of η=43%. The generated 397.5 nm laser with good beam quality and satisfying power has huge potential use in quantum optics and cold-atom physics.
In the field of in situ measurement of high-temperature pressure, fiber-optic Fabry–Perot pressure sensors have been extensively studied and applied in recent years thanks to their compact size and excellent anti-interference and anti-shock capabilities. However, such sensors have high technological difficulty, limited pressure measurement range, and low sensitivity. This paper proposes a fiber-optic Fabry–Perot pressure sensor based on a membrane-hole-base structure. The sensitive core was fabricated by laser cutting technology and direct bonding technology of three-layer sapphire and develops a supporting large-cavity-length demodulation algorithm for the sensor’s Fabry–Perot cavity. The sensor exhibits enhanced sensitivity, a simplified structure, convenient preparation procedures, as well as improved pressure resistance and anti-harsh environment capabilities, and has large-range pressure sensing capability of 0–10 MPa in the temperature range of 20–370 °C. The sensor sensitivity is 918.9 nm/MPa, the temperature coefficient is 0.0695 nm/(MPa∙°C), and the error over the full temperature range is better than 2.312%.
The sensors with a wide gas pressure detection range are urgently demanded in many industrial applications. Here, we propose a gas pressure sensor based on an all-solid open Fabry-Pérot interferometer, which is prepared by using optical contact bonding to ensure high structural strength and high-quality factor of 8.8 × 105. The applied pressure induces a change in the refractive index of the air, leading to the shift of the resonant spectrum. The pressure is detected by calibrating this shift. The sensor exhibits a pressure sensitivity of 4.20 ± 0.01 nm/MPa in a pressure range of 0 to 10 MPa and has a minimum pressure resolution of 0.005 MPa. Additionally, it shows a lower temperature cross-sensitivity of -0.25 kPa/°C. These findings affirm that the sensor achieves high-sensitivity pressure sensing across a wide detection range. Moreover, owing to its exceptional mechanical strength, it holds great promise for applications in harsh environments, such as high temperature and high pressure.
Fiber optic sensors, prized for their light weight, compact size, high temperature resilience and resistance to electromagnetic interference, find extensive utility in various measurement applications. The performance of these sensors is primarily contingent on their sensitive units, with distinct structures of these units yielding varied performance outcomes. Traditional design methods primarily rely on finite element simulation and optimization, which are subjective and inefficient. Thus, the efficient on-demand design of sensitive structures is essential for different application scenarios. Here, we present a novel approach for both forward performance prediction and inverse structure design employing deep learning techniques based on symmetric bidirectional neural networks, with fiber optic vibration sensors serving as a design example. The proposed method can address the non-unique solution in traditional deep learning techniques for inverse design of three-dimensional (3D) complex structures. By learning the underlying relationships between complex non-intuitive sensitive structures and their performances, the approach can eliminate the need for numerous costly calculations that heavily depend on human experience or intuition. Furthermore, compared to the response surface optimization method, this approach saves 21.1 times the computation time and has an accuracy improvement of 34.6% when dealing with six samples. The results show that the efficiency of fiber optic sensor design can be significantly improved by employing this novel deep learning technique, which offers new insights for the rapid advancement of the fiber optic sensing field.
The ideal development direction of the fiber-optic acoustic sensor (FOAS) is toward broadband, a high sensitivity and a large dynamic range. In order to further promote the acoustic detection potential of the Fabry-Pérot etalon (FPE)-based FOAS, it is of great significance to study the acoustic performance of the FOAS with the quality (Q) factor of FPE as the research objective. This is because the Q factor represents the storage capability and loss characteristic of the FPE. The three FOASs with different Q factors all achieve a broadband response from 20 Hz to 70 kHz with a flatness of ±2 dB, which is consistent with the theory that the frequency response of the FOAS is not affected by the Q factor. Moreover, the sensitivity of the FOAS is proportional to the Q factor. When the Q factor is 1.04×106, the sensitivity of the FOAS is as high as 526.8 mV/Pa. Meanwhile, the minimum detectable sound pressure of 347.33 μPa/Hz1/2 is achieved. Furthermore, with a Q factor of 0.27×106, the maximum detectable sound pressure and dynamic range are 152.32 dB and 107.2 dB, respectively, which is greatly improved compared with two other FOASs. Separately, the FOASs with different Q factors exhibit an excellent acoustic performance in weak sound detection and high sound pressure detection. Therefore, different acoustic detection requirements can be met by selecting the appropriate Q factor, which further broadens the application range and detection potential of FOASs.
As a vibration signal acquisition device, the vibration sensor has important application prospects in aerospace, industrial manufacturing, and other fields. The traditional electrical vibration sensor is limited by its material and sensitive mechanism and has some bottleneck problems, such as poor anti-electromagnetic interference ability, poor temperature resistance, and inability to self-calibrate. Here, we report a high-temperature self-calibration fiber-optic vibration sensor based on an atomic frequency standard system for the first time. The absolute stability of the transition frequency between atomic states ensures the accuracy and effectiveness of the acceleration measurement under extreme conditions. The sensor is based on the Fabry–Pérot interference principle utilizing a sapphire material for the sensitive structure and enclosed in a high-reliability and rigid stainless-steel package for protection. The experimental results show that it operates at temperatures up to 600 °C with a sensitivity of 38.66 nm/g and a characteristic frequency of 2446 Hz. This work provides a new approach to improve the accuracy of fiber-optic sensing under harsh, on-site testing conditions.
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