Local orbital-angular-momentum dependent surface states with topological protection

Chiral surface states along the zigzag edge of a valley photonic crystal in the honeycomb lattice are demonstrated. By decomposing the local fields into orbital angular momentum (OAM) modes, we find that the chiral surface states present OAM-dependent unidirectional propagation characteristics. Particularly, the propagation directivities of the surface states are quantified by the local OAM decomposition and are found to depend on the chiralities of both the source and surface states. These findings allow for the engineering control of the unidirectional propagation of electromagnetic energy without requiring an ancillary cladding layer. Furthermore, we examine the propagation of the chiral surface states against sharp bends. It turns out that although only certain states successfully pass through the bend, the unidirectional propagation is well maintained due to the topology of the structure.Comment: 9 pages, 6 figure

A new impedance matching method for an ultra-wide band and dual circularly polarised feed

In traditional antenna design, metal components are not placed in the central part of the antenna as they change the characteristics of near field radiation. However, we show that placing a metal ring in the centre of the strip lines, which connect the ends of folded high-frequency dipoles, does not damage the performance of the feed. Instead it significantly improves the voltage standing wave ratio of the feed whilst other performance indicators are not compromised. Thus, our findings show an excellent way of improving the wide band feed. Based on this foundation, a new circularly polarised feed for operation between 0.4 to 2 GHz is introduced for the Chinese Spectral Radioheliograph in this paper. The issue of a feed impedance matching network is investigated. By optimising the impedance matching, the performance of the feed is enhanced with respect to the previous realisations of the Eleven feed. The simulation and experimental results show that the gain of the feed is about 10 dBi, and the VSWR is less than 2:1. In addition, the feed has a low axial ratio, fixed phase centre location, and constant beam width in the range of 0.4 to 2 GHz

The application of daily interruption in the treatment of analgesia and sedation in general ICU

目的  探讨在综合ICU镇痛镇静患者护理方面实施每日唤醒疗法护理的方法和效果。方法  选择在ICU进行持续镇痛镇静治疗>24h的患者共90例，实施每日唤醒和相应护理计划并观察记录。结果  实施每日唤醒后，镇痛镇静药物用量减少，机械通气时间减少，与镇静相关的躁动、谵妄事件减少，住ICU时间缩短。结论  ICU镇痛镇静患者实施每日唤醒可有利于其疾病恢复，减少镇静相关不良事件，值得临床关注，但必须加强病情监测和安全护理。Objective: To explore method and effect of daily interruption therapy in the treatment of analgesia and sedation in general ICU. Methods: 90 patients in ICU, who had been treated with continuous analgesia therapy within 24 hours, were selected and would receive daily interruption therapy and corresponding nursing. Results: Analgesic dosage of sedative drugs was reduced, together with mechanical ventilation time, and delirium events associated with calm restless, ICU stay was shortened after the implement of daily interruption therapy and nursing. Conclusion: The application of daily interruption in general ICU is beneficial to their disease recovery, reduce adverse events related with calmness, worthy of clinical attention, but it is imperative to improve the security of condition monitoring and nursing

How complex is the microarray dataset? A novel data complexity metric for biological high-dimensional microarray data

Data complexity analysis quantifies the hardness of constructing a predictive model on a given dataset. However, the effectiveness of existing data complexity measures can be challenged by the existence of irrelevant features and feature interactions in biological micro-array data. We propose a novel data complexity measure, depth, that leverages an evolutionary inspired feature selection algorithm to quantify the complexity of micro-array data. By examining feature subsets of varying sizes, the approach offers a novel perspective on data complexity analysis. Unlike traditional metrics, depth is robust to irrelevant features and effectively captures complexity stemming from feature interactions. On synthetic micro-array data, depth outperforms existing methods in robustness to irrelevant features and identifying complexity from feature interactions. Applied to case-control genotype and gene-expression micro-array datasets, the results reveal that a single feature of gene-expression data can account for over 90% of the performance of multi-feature model, confirming the adequacy of the commonly used differentially expressed gene (DEG) feature selection method for the gene expression data. Our study also demonstrates that constructing predictive models for genotype data is harder than gene expression data. The results in this paper provide evidence for the use of interpretable machine learning algorithms on microarray data