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.     

Long-term exposure to air pollution and cerebrovascular disease: findings from Beijing Health Management Cohort study

● This study explored the long-term association by double robust additive models. ● Individual exposure concentrations were assessed by integrating GAM, LUR and BPNN. ● PM_2.5, SO₂ and NO₂ are positively associated with cerebrovascular disease. ● CO could reduce the risk of cerebrovascular disease with the highest robustness. ● The elderly, women and people with normal BMI are at higher risk for air pollution. The relationship between air pollution and cerebrovascular disease has become a popular topic, yet research findings are highly heterogeneous. This study aims to investigate this association based on detailed individual health data and a precise evaluation of their exposure levels. The integrated models of generalized additive model, land use regression model and back propagation neural network were used to evaluate the exposure concentrations. And doubly robust additive model was conducted to explore the association between cerebrovascular disease and air pollution after adjusted for demographic characteristics, physical examination, disease information, geographic and socioeconomic status. A total of 25097 subjects were included in the Beijing Health Management Cohort from 2013 to 2018. With a 1 μg/m³ increase in the concentrations of PM_2.5, SO₂ and NO₂, the incidence risk of cerebrovascular disease increased by 1.02 (95% CI: 1.008–1.034), 1.06 (95% CI: 1.034–1.095) and 1.02 (95% CI: 1.010–1.029) respectively. Whereas CO exposure could decrease the risk, with an odds ratio of 0.38 (95% CI: 0.212–0.626). In the subgroup analysis, individuals under the age of 50 with normal BMI were at higher risk caused by PM_2.5, and SO₂ was considered more hazardous to women. Meanwhile, the protective effect of CO on women and those with normal BMI was stronger. Successful reduction of long-term exposure to PM_2.5, SO₂ and NO₂ would lead to substantial benefits for decrease the risk of cerebrovascular disease especially for the health of the susceptible individuals.     

悬浮态水泥生料固硫效率试验研究

在水泥工业领域,水泥生料作为悬浮预热预分解系统脱硫过程中的固硫剂,为了研究固气比、温度、气体流速、SO2浓度以及水泥生料比表面积对悬浮态水泥固硫效率影响规律,采用水泥工厂的生料,利用悬浮电阻炉对水泥生料固硫效率进行试验研究,研究发现固气比和温度对水泥生料固硫效率影响较大。     

北京市大气PM10源解析研究

于2004年在北京市定陵、车公庄、古城、亦庄、房山和奥体中心6个采样点采集大气PM10环境样品,针对北京市颗粒物主要排放源采集土壤尘、建筑水泥尘、燃煤等污染源PM10样品,分别对其中的无机元素、离子、有机碳(OC)和元素碳(EC)进行测定。采用代表北京市颗粒物主要排放源PM10组分特征的成分谱,利用CMB受体模型对PM10来源进行解析。结果表明,PM10的最大来源为土壤尘,其它贡献源类依次为燃煤排放、机动车/燃油排放、二次粒子(SO42-、NO3-和NH4 )、建筑水泥尘。污染源贡献具有明显的季节变化,并存在一定的地域变化。     

铝厂工人尿中1-羟基芘的变化

分别对炼铝厂工人、铝厂附近小学生和对照组人群尿中的1-羟基芘进行了测定.铝厂工人尿中的1-羟基芘为小学生的7.6倍;为对照组人群的43倍.结合多环芳烃的污染情况讨论了用尿中1-羟基芘作为评价铝厂多环芳烃污染的人体接触指标的可行性。     

根据现代企业理论探讨煤炭企业安全管理问题

煤炭行业属于高危险性行业,降低其事故发生率,提高安全生产水平是煤炭工作者孜孜以求的目标。笔者应用委托——代理理论对煤炭安全管理问题进行探讨,分析了安全投入与安全水平、安全投入与安全报酬、事故赔偿与安全水平等之间的关系,提出了煤炭企业改善安全现状的管理对策及建议,并认为事故赔偿金过低,不足以对企业形成足够的压力,不利于企业主动提高安全水平,应加大惩罚力度,提高惩罚成本和惩处率。     

地下工程中空气离子环境和氡害

近年来国内外大量研究资料表明:环境中空气离子和氡是两个与人舒适感及人体健康密切相关的环境因素。地下工程中空气离子和氡问题比地面室内环境更为突出。随着地下空间广为开发利用,如何改善地下工程中空气离子环境及防氡害问题正在引起人们的极大关注。我们对我国五个地区的37个     

室内小煤炉采暖对尿中1-羟基芘的影响

用尿中1-羟基芘作为指标,观察了室内用小煤炉采暖、暖气采暖和非采暖季节时相同人群尿中1-羟基芘的变化,自身对照和人群对比的测定结果,经数理统计检验后均证明:用小煤炉采暖时人尿中的1-羟基芘显著高于甩暖气采暖或不采暖时的结果,表明用小煤炉采暖造成了室内空气中多环芳烃的严重污染,用尿中1-羟基芘可做为人体接触空气中多环芳烃污染的一个特异、灵敏的指标。     

北京市不同功能区人体接触多环芳烃的生化指标——人尿中1-羟基芘的水平

本文报告北京市不同功能区人尿中1-羟基芘的测定结果,自身对照和群体测定均显示出采暖期与非采暖期的显著差别以及不同功能区样品的差异.尿中1-羟基芘的浓度以焦化厂为最高,为其它各区的4—25倍,依次为工业区、商业区、居民区和对照区,证明用尿中1-羟基芘作为人体接触环境中多环芳烃的一个生物化学指标是可行的.     

宁夏电力通信网支撑设备巡检系统的设计与研究

宁夏电力通信网支撑设备众多,机房日常巡检过程中,填写巡视记录工作消耗的时间占比较大,巡检效率低,容易出现运维工作的遗漏。通过开发通信支撑设备巡检系统软件,提升了设备巡检的效率,避免了设备维护遗漏情况的发生,实现了巡检历史数据导出及归档功能。