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随着网络传输数据流量的爆炸性增长,传统电交换由于其有限的带宽和高功耗将很难满足网络带宽发展需求。光交换能直接在光域上完成光信道间信息的交换,具有高速、宽带、透明、低功耗以及潜在的低成本等诸多优点,能满足下一代全光交换网络、数据中心和高性能计算机光互连网络建设的迫切需求。文章介绍了集成光开关的发展现状及核心技术,包括采用氧化硅、III-V族半导体材料和硅材料来制作光开关的进展以及各种技术的特点。其中硅基集成光开关具有结构紧凑、功耗小、成本低以及与互补型金属-氧化物-半导体（CMOS）工艺兼容的优势,适合大规模光开关制作和量产,具有潜在的巨大市场商用价值。文章重点介绍了实现硅基光开关的核心单元器件以及几种代表性光开关阵列,并对光开关状态监控和调节以及光电封装做了阐述。

Copyright of Guihaia is the property of Guihaia Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To construct a prognostic model for breast cancer based on cuproptosis-related long non-coding RNA (lncRNA) and to validate its efficacy. Methods The transcriptional data and clinical information of female patients with breast cancer were obtained from the Cancer Genome Atlas (TCGA) database, and cuproptosis-related lncRNAs in breast cancer were identified by Pearson correlation analysis. Breast cancer patients with complete cuproptosis related lncRNA and clinical data were taken as an overall group and were randomly divided into the training group and validation group at a ratio of 1: 1. The cuproptosis-related lncRNAs which were closely related to the prognosis of breast cancer patients were screened out by univariate Cox regression analysis and LASSO regression analysis in the training group. The multivariate Cox regression analysis was used to construct a prognostic model in breast cancer based on the above-selected lncRNAs, and then the optimal prognostic model (with the smallest AIC value) was selected based on the AIC value. Risk scores were calculated for each patient based on the optimal breast cancer prognostic model. The median risk score of the patients in the training group was used as a cut-off value to categorize all patients into high- and low-risk groups. The differentiation ability of the prognostic model was validated by survival curves and subgroup survival analysis. The accuracy of the prognostic model was validated by receiver operator characteristic (ROC) and consistency index curves. The independence of prognostic models was verified by univariate and multivariate Cox regression. The clinical utility of the prognostic model was verified by Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes in the high- and low-risk groups, and immune infiltration analysis of patients in the high- and low-risk groups. Results The breast cancer prognostic model was constructed by 10 cuproptosis-related lncRNAs (AKT3. IT1, AL137847. 1, AL807757. 2, AC079766. 1, AL451123. 1, LINC02043, AL683813. 1, AC073127. 1, MFF. DT and AC091588. 1). The model formula was risk score = (-1. 129 216 501 573 150×expression of AKT3. IT1) + (-1. 166 095 685 256 72×expression of AL137847. 1) + (0. 729 804 497 137 164×expression of LINC02043)+ (0. 745 696 645 441 295×expression of AL683813. 1)+ (-0. 903 562 388 041 113×expression of AL807757. 2) + (1. 040 608 675 397 110×expression of AC073127. 1) + (2. 160 133 554 898 460×expression of MFF. DT) + (1. 417 144 256 517 410×expression of AC091588. 1) + (-0. 764 700 719 748 750×expression of AC079766. 1) + (-3. 608 177 447 126 010×expression of AL451123. 1). The survival curves demonstrated that patients in the high-risk group had a lower survival rate (P<0. 001). Subgroup survival analysis showed that there was significant difference in prognosis of breast cancer patients in different clinical stages, except for the M1 stage between the high- and low-risk groups (P<0. 05). ROC curves showed that the area under the curve of the model for 1-, 3-, and 5-year survival was 0. 807, 0. 739, and 0. 709, respectively. The multivariate ROC curves and the concordance index curves showed that the predictive efficacy of the risk score was superior to other clinical features. The univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic feature for breast cancer. The differentially expressed genes between the high- and low-risk groups were mainly enriched in immune- and drug resistance-related pathways. Patients in the high-risk group had lower levels of immune cell and stromal cell scores and a higher abundance of M2 macrophage infiltration in comparison with patients in the low-risk group (all P<0. 05). Conclusion The breast cancer prognostic model is constructed based on cuproptosis-related lncRNAs which has good differentiation ability, accuracy, independence, and clinical utility. [ABSTRACT FROM AUTHOR]

Copyright of Publishing Journal is the property of Wuhan University, School of Information Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shandong Medical Journal is the property of Shandong Medical Health Newspapers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of China Medical University is the property of Journal of China Medical University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

目的 探讨胃肠术后重症肺炎老年患者预后的影响因素，分析其在预后评价中的应用效能。方法 收集 29例老年（年龄≥65岁）胃肠术后重症肺炎患者的临床资料，分析患者预后的影响因素及其对患者预后评价的价值。 结果 患者平均年龄 73岁，院内病死率 58. 6%（17/29）。慢性肺病、急诊手术、累及多个肺叶、肺炎严重程度（PSI） 评分、序贯器官衰竭估计评分（SOFA）、乳酸（Lac）、降钙素原（PCT）是老年胃肠术后重症肺炎患者发生院内死亡的 独立危险因素（P 均<0. 05）。PSI 评分截断值取 164 分时，预测老年胃肠术后重症肺炎患者院内死亡的敏感度为 58. 8%，特异度为 83. 3%；SOFA 评分截断值取 6 分时，预测老年胃肠术后重症肺炎患者院内死亡的敏感度为 76. 5%，特异度为 58. 3%；PCT 截断值取 6. 81 ng/mL 时，预测老年胃肠术后重症肺炎患者院内死亡的敏感度为 82. 4%，特异度为 83. 3%；Lac 截断值取 2. 35 mmol/L 时，预测老年胃肠术后重症肺炎患者院内死亡敏感度为 82. 4%，特异度为 75. 0%。结论 胃肠术后重症肺炎的老年患者发生院内死亡独立危险因素为慢性肺病、急诊手 术、累及多个肺叶、PSI评分、SOFA 评分、血清 Lac、 PCT。SOFA 评分联合血清 Lac可作为预测胃肠术后重症肺炎老 年患者的预后指标。 [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to accurately predict future coal demand in China, Firstly, seven main influencing factors of coal demand in China were determined, and the Ensemble Learning Algorithm with Support Vector Regression algorithm which was adapted to regression analysis of small sample data were applied to coal demand prediction in China, and also the multi-verse optimizer were introduced to optimize the key parameters of Support Vector Regression. Finally, a prediction model of China's coal demand based on MVO-SVR-AdaBoost was constructed. Selecting historical data from 1980 to 2017 for experiment, the experimental results show that the adaptability of the forecasting model of coal demand was as high as 0.9791, and the average absolute error was only 4.2928%. Determining the fluctuation boundary value of the annual average growth rate of the influencing factors based on historical data, a random combination was introduced into the model to obtain the possible numerical range of China's coal demand in 2018-2020, which based on the case of low, medium and high GDP growth. [ABSTRACT FROM AUTHOR]

Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An adaptive Total Variation (TV)denoisingmethod based on noise detectionis proposed to restore the natural images corrupted by both additive Gaussian noise and random-valued impulse noise. The improved image restoration method uses a two-stage iterative framework: identification of pixels corrupted by impulse noise and TV based image restoration. Firstly, the local statistical value, i. e. Rank Ordered Absolute Difference(ROAD) is used to adaptively detect the pixels corrupted by impulse noise that contains no valid information. Secondly, the L2-TV method is used to restore noise images. The two steps are processed iteratively to obtain the denoisedimages. The introduction of impulse noise level parameters in the noise estimation process has the advantage of being able to detect the impulse noise more accurately. The L2-TV de-noising method can remove the Gaussian noise well. The combination of the two effectively solves the problem that the TV algorithm misjudges image impulse noise as edge and results in false edge. Compared withthe state-of-the-art methods, the proposed imagedenoising method, named TV-ROAD algorithm，can achieve superior restoration results and preserve image detail features. [ABSTRACT FROM AUTHOR]

In order to improve the denitration efficiency under low temperature，ethanol/NaCl/NaOH were selected as liquid additives and the experiment was carried out on both a small scale denitration experimental system and a medium scale denitration experimental system. In the small scale denitration experimental system，ethanol can improve the denitration efficiency from 5% to 45%，trace amounts of (20×10-6），NaCl improves the denitration efficiency from 800 ℃. In the medium scale denitration experimental system，different additives enhance the denitration process differently. Because of the best mixing condition，at different temperatures ethanol has different effects under different amounts.As the bed material containing Na，NaCl/NaOH does not have obvious effect on enhancing denitration process. The mixing condition between additives and flue gas，the composition of bed material，etc.make big differences in using additives in CFB boiler and the experimental result. [ABSTRACT FROM AUTHOR]