套箍加固RC轴压中长柱的承载力理论研究∗

为研究套箍加固RC轴压中长柱的承载力理论计算分析方法,采用切线模量理论,根据材料本构关系和内外力平衡条件,推导了套箍加固RC轴压中长柱的弹塑性稳定承载力计算方法。将理论计算结果与试验结果进行了对比分析,结果表明:理论计算值均低于实测值,且二者的偏差在15%以内。根据所推导的计算分析方法,研究了核心柱初始荷载水平β、柱子长细比l0/b、套箍层混凝土强度、套箍层配筋量四种参数对加固柱承载力Nu的影响规律,结果表明:Nu总体上随β增大而减小,当β<0.7时,减小趋势不够明显,β>0.7后,减小趋势变得明显;l0/b对Nu有明显影响,Nu随l0/b的增大而减小,但二者不是线性关系;Nu随套箍层混凝土强度提高而增大,随套箍层配筋量增大而增大。     

核环境地下水评价源项模型及浓度计算

结合美国地下核试验场公开资料,分析了地下核爆炸的源项核素特征,在源项释放和核素迁移过程概化的基础上,建立以水力释放和玻璃体浸出为主要机制的源项释放浓度计算模式,对3H、90Sr和239Pu释放到地下水的浓度进行了理论计算。结果表明:3H、90Sr和239Pu向地下水的释放是一个连续、缓慢的过程,就核素释放浓度曲线递减程度而言,3H>90Sr>239Pu,但浓度并不是短期内显著降低。在地下水污染安全评价中,应重视源强及其持续时间的分析,以使环境模型能更为合理地反映核素迁移行为。     

基于GF-LSTM和GAN网络的小样本集人工水体溶解氧浓度预测

采用推流方式改善人工水体溶解氧分布不均衡以防止富营养化时,需要对其分布进行预测来提高推流效率,为此构建了基于生成式对抗网络(GAN,Generative Adversarial Networks)和长短期记忆神经网络(LSTM,Long-Short Term Memory Network)的溶解氧浓度预测模型。以广西大学镜湖35 m2的一片水体区域为研究对象,采用不同电压直流水泵推流,用无人船搭载在线检测仪在一段时间内定时定点采集水体溶解氧浓度数据作为原始数据样本,并采用GAN扩充数据样本。利用遗传算法和改进的一阶滤波算法进行溶解氧的噪声数据处理,结合LSTM网络构建溶解氧浓度预测模型GF-LSTM(Genetic And Filtering Algorithm-Long Short Term Memory Network)。结果表明:相比常用的BP网络,GF-LSTM网络预测的平均误差降低了62%,均方误差降低了75%;相比传统的LSTM网络,GF-LSTM网络预测的平均误差降低了22%,均方误差降低了50%。     

Fragmentation effects on an endangered species across a gradient from the interior to edge of its range

Understanding how habitat fragmentation affects individual species is complicated by challenges associated with quantifying species-specific habitat and spatial variability in fragmentation effects within a species’ range. We aggregated a 29-year breeding survey data set for the endangered marbled murrelet (Brachyramphus marmoratus) from >42,000 forest sites throughout the Pacific Northwest (Oregon, Washington, and northern California) of the United States. We built a species distribution model (SDM) in which occupied sites were linked with Landsat imagery to quantify murrelet-specific habitat and then used occupancy models to test the hypotheses that fragmentation negatively affects murrelet breeding distribution and that these effects are amplified with distance from the marine foraging habitat toward the edge of the species’ nesting range. Murrelet habitat declined in the Pacific Northwest by 20% since 1988, whereas the proportion of habitat comprising edges increased by 17%, indicating increased fragmentation. Furthermore, fragmentation of murrelet habitat at landscape scales (within 2 km of survey stations) negatively affected occupancy of potential breeding sites, and these effects were amplified near the range edge. On the coast, the odds of occupancy decreased by 37% (95% confidence interval [CI] –54 to 12) for each 10% increase in edge habitat (i.e., fragmentation), but at the range edge (88 km inland) these odds decreased by 99% (95% CI 98 to 99). Conversely, odds of murrelet occupancy increased by 31% (95% CI 14 to 52) for each 10% increase in local edge habitat (within 100 m of survey stations). Avoidance of fragmentation at broad scales but use of locally fragmented habitat with reduced quality may help explain the lack of murrelet population recovery. Further, our results emphasize that fragmentation effects can be nuanced, scale dependent, and geographically variable. Awareness of these nuances is critical for developing landscape-level conservation strategies for species experiencing broad-scale habitat loss and fragmentation.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

多级机站通风系统在武山铜矿的应用

介绍了武山铜矿北矿带西部通风系统实施多级机站改造的技术和方法,及改造后的效果。     

剧场建筑的空调设计探讨

针对剧场建筑的特殊性,对其观众厅及舞台的空调系统的设计方式进行探讨,并得出如下结论:（1）观众厅采用下送风方式;（2）舞台采用侧送、顶送及下回风方式。     

醋酸乙烯酯卸料系统的改进

介绍了卸料系统改造前的状况,对卸料系统流程和操作步骤的改造。改造后保证了腈纶装置安全运行,减少了环境污染,减轻了工人的劳动强度。     

化学实验室火灾、爆炸原因分析及防火防爆措施

从化学实验室内使用的化学物品的易燃易爆性能,存在的各种火源入手,分析了化学实验室发生火灾爆炸事故的原因和爆炸事故的特点,在此基础上提出了防火防爆的具体措施。     

高空吊架坠落伤亡事故故障树分析

对高空吊架坠落伤亡事故产生的原因进行了分析,应用安全系统工程中的故障树分析方法,编制了故障树,进行定性分析。找出了导致吊架坠落伤亡事故发生的途径,并提出了一些预防措施。