Chiral Metasurface for Near-Field Imaging and Far-Field Holography Based on Deep Learning.

Chiral metasurfaces have great influence on the development of holography. Nonetheless, it is still challenging to design chiral metasurface structures on demand. As a machine learning method, deep learning has been applied to design metasurface in recent years. This work uses a deep neural network with a mean absolute error (MAE) of 0.03 to inverse design chiral metasurface. With the help of this approach, a chiral metasurface with circular dichroism (CD) values higher than 0.4 is designed. The static chirality of the metasurface and the hologram with an image distance of 3000 µm are characterized. The imaging results are clearly visible and demonstrate the feasibility of our inverse design approach.

Efficacy and safety of three species of Rhodiola L. in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis.

Background: Chronic obstructive pulmonary disease (COPD) is characterized by chronic hypoxia, inflammation, oxidative stress, and irreversible airflow limitations. Rhodiola L. is a genus of botanical drugs used in traditional medicine that may influence COPD. Objective: A systematic review of the safety and efficacy of Rhodiola L. in patients with COPD. Material and methods: We searched the PubMed, Embase, Cochrane Library, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), Chongqing VIP, Wanfang, and SinoMed databases. The search strategy used terms including "COPD" and "Rhodiola." Two independent reviewers conducted the literature screening, data extraction, and risk of bias assessment, with a third reviewer involved to resolve disagreements. Statistical analysis was conducted in Review Manager (version 5.4.1), following the Cochrane Handbook. Results: This review included nine studies, of which two focused on Rhodiola crenulata (Hook.f. and Thomson) H. Ohba (R. crenulata) and two on Rhodiola kirilowii (Regel) Maxim (R. kirilowii); the remaining five focused on Rhodiola wallichiana (Hook.) S.H.Fu (R. wallichiana). Compared with the placebo, patients who received Rhodiola L. presented no more adverse events (p = 0.65) but showed significant improvement in the percentage of forced expiratory volume in 1 s at prediction (FEV1%pred), forced expiratory volume in 1 s (FEV1), the ratio of forced expiratory volume in 1 s on forced vital capacity (FEV1/FVC), saturation of oxygen in arterial blood, partial pressure of oxygen in arterial blood (PaO2), partial pressure of carbon dioxide in arterial blood (PaCO2), systolic pulmonary arterial pressure, diastolic pulmonary arterial pressure, COPD assessment test, efficient rate, C-reactive protein, and N-terminal pro-B-type natriuretic peptide (all p < 0.01). Compared with ambroxol, R. kirilowii provided additional benefits to patients with COPD in FEV1%pred, FEV1, FEV1/FVC, PaO2, PaCO2, 8-iso-prostaglandin F2α, superoxide dismutase, glutathione, malondialdehyde, and total antioxidant capacity (all p < 0.01). Conclusion: Among the Rhodiola L. genus, this review included R. wallichiana, R. crenulata, and R. kirilowii, which might be safe and effective in COPD. Although this study has several limitations, further RCTs are needed. Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/ display_record.php?RecordID=302881], identifier [CRD42022361890].

Ultra-Broadband Tunable Terahertz Metamaterial Absorber Based on Double-Layer Vanadium Dioxide Square Ring Arrays.

Based on the phase transition of vanadium dioxide(VO2), an ultra-broadband tunable terahertz metamaterial absorber is proposed. The absorber consists of bilayer VO2 square ring arrays with different sizes, which are completely wrapped in Topas and placed on gold substrate. The simulation results show that the absorption greater than 90% has frequencies ranging from 1.63 THz to 12.39 THz, which provides an absorption frequency bandwidth of 10.76 THz, and a relative bandwidth of 153.5%. By changing the electrical conductivity of VO2, the absorption intensity can be dynamically adjusted between 4.4% and 99.9%. The physical mechanism of complete absorption is elucidated by the impedance matching theory and field distribution. The proposed absorber has demonstrated its properties of polarization insensitivity and wide-angle absorption, and therefore has a variety of application prospects in the terahertz range, such as stealth, modulation, and sensing.

Metamaterial Reverse Multiple Prediction Method Based on Deep Learning.

Metamaterials and their related research have had a profound impact on many fields, including optics, but designing metamaterial structures on demand is still a challenging task. In recent years, deep learning has been widely used to guide the design of metamaterials, and has achieved outstanding performance. In this work, a metamaterial structure reverse multiple prediction method based on semisupervised learning was proposed, named the partially Conditional Generative Adversarial Network (pCGAN). It could reversely predict multiple sets of metamaterial structures that can meet the needs by inputting the required target spectrum. This model could reach a mean average error (MAE) of 0.03 and showed good generality. Compared with the previous metamaterial design methods, this method could realize reverse design and multiple design at the same time, which opens up a new method for the design of new metamaterials.

Additive Manufacturing of Nerve Guidance Conduits for Regeneration of Injured Peripheral Nerves.

As a common and frequent clinical disease, peripheral nerve defect has caused a serious social burden, which is characterized by poor curative effect, long course of treatment and high cost. Nerve autografting is first-line treatment of peripheral nerve injuries (PNIs) but can result in loss of function of the donor site, neuroma formation, and prolonged operative time. Nerve guidance conduit (NGC) serves as the most promising alternative to autologous transplantation, but its production process is complicated and it is difficult to effectively combine growth factors and bioactive substances. In recent years, additive manufacturing of NGCs has effectively solved the above problems due to its simple and efficient manufacturing method, and it can be used as the carrier of bioactive substances. This review examines recent advances in additive manufacture of NGCs for PNIs as well as insight into how these approaches could be improved in future studies.

Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning.

The introduction of "metamaterials" has had a profound impact on several fields, including electromagnetics. Designing a metamaterial's structure on demand, however, is still an extremely time-consuming process. As an efficient machine learning method, deep learning has been widely used for data classification and regression in recent years and in fact shown good generalization performance. We have built a deep neural network for on-demand design. With the required reflectance as input, the parameters of the structure are automatically calculated and then output to achieve the purpose of designing on demand. Our network has achieved low mean square errors (MSE), with MSE of 0.005 on both the training and test sets. The results indicate that using deep learning to train the data, the trained model can more accurately guide the design of the structure, thereby speeding up the design process. Compared with the traditional design process, using deep learning to guide the design of metamaterials can achieve faster, more accurate, and more convenient purposes.