杭州湾南岸滩涂沉积物中HCHs与DDTs污染特征分析

摘 要：目的 了解六六六（hexachlorocyclohexanes,HCHs）与滴滴涕(dichloro diphenyl trichloroethanes,DDTs)在杭州湾南岸滩涂沉积物中的污染现状。方法 于2018年4月，采集杭州湾南岸地区4个采样点不同深度（0~80 cm）滩涂沉积物，利用气相色谱-串联质谱法（gas chromatography tandem mass spectrometry, GC-MS/MS）测定了该区域中HCHs与DDTs的含量，对其分布规律进行分析，使用共识沉积物质量基准法对其生态学风险进行了评估。结果 所有样品中均检出，HCHs与DDTs含量分别为4.20~6.24 μg/kg与3.37~4.73 μg/kg，主要以β-HCH、δ-HCH以及DDE为主，其污染主要来源于历史性工业品，整体污染处于较低水平；20.3%的沉积物样品中DDEs含量以及28.1%的样品DDTs总量介于阈值效应浓度(threshold concentration, TECs) 和可能效应浓度(probable effect concentration, PECs)之间。结论 杭州湾南岸沉积物中HCHs与DDTs的污染处于较低水平，具有较小的生态学风险。     

Nonuniform linear array DOA estimation using EM criterion

In this letter, we address the problem of Direction of Arrival (DOA) estimation with nonuniform linear array in the context of sparse Bayesian learning (SBL) framework. The nonuniform array output is deemed as an incomplete-data observation, and a hypothetical uniform linear array output is treated as an unavailable complete-data observation. Then the Expectation-Maximization (EM) criterion is directly utilized to iteratively maximize the expected value of the complete-data log likelihood under the posterior distribution of the latent variable. The novelties of the proposed method lie in its capability of interpolating the actual received data to a virtual uniform linear array, therefore extending the achievable array aperture. Simulation results manifests the superiority of the proposed method over off-the-shelf algorithms, specially on circumstances such as low SNR, insufficient snapshots, and spatially adjacent sources.     

多运营商高铁隧道移动网络覆盖方案

高铁移动网络覆盖是国内三大通信运营商的一个重点,而高铁隧道内移动网络覆盖更是运营商的一大难点痛点。文章根据我国中部省份某高铁线路覆盖规划实例,采用“设备+POI+泄漏电缆”模式,即三家运营商信号源设备通过同一POI(point of interface,多系统接入平台)接入,信号输出到泄漏电缆进行隧道覆盖,隧道口场坪站安装宽频切换天线对隧道外进行延伸覆盖,通过链路预算合理布置各运营商主设备信号源,从而实现隧道到室外的无缝覆盖。最后,根据已有成熟网络覆盖解决方案,对未来5G高铁隧道移动网络覆盖方案进行了探讨。     

渣油催化临氢热转化钼铁双金属催化剂的研究

Replacement of precious single metal catalysts with cost-effective, highly-dispersed composite catalysts for catalytic hydrothermal conversion of residue holds tremendous promise for the residue upgrading technologies. Organic metals were added to the feed as the oil-soluble precursors, and transformed into the catalytic active phases in this work. Physical properties and structures of the composite catalysts had been investigated by X-ray fluorescence spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, scanning electron microscope and transmission electron microscopy. The composite catalysts were found to be highly efficient in the catalytic hydrothermal conversion of both model compound and residue. Increased metal dispersion and synergistic effects of two metals played indispensable roles in such catalytic system. Results showed that under the test conditions in the article, the catalyst had the best catalytic performance when the mass ratio of molybdenum to iron was 1.5.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

Forging furnace with thermochemical waste-heat recuperation by natural gas reforming: Fuel saving and heat balance

This paper considers thermochemical recuperation (TCR) of waste-heat using natural gas reforming by steam and combustion products. Combustion products contain steam (H₂O), carbon dioxide (CO₂), and ballast nitrogen (N₂). Because endothermic chemical reactions take place, methane steam-dry reforming creates new synthetic fuel that contains valuable combustion components: hydrogen (H₂), carbon monoxide (CO), and unreformed methane (CH₄). There are several advantages to performing TCR in the industrial furnaces: high energy efficiency, high regeneration rate (rate of waste-heat recovery), and low emission of greenhouse gases (CO₂, NO_x). As will be shown, the use of TCR is significantly increasing the efficiency of industrial furnaces – it has been observed that TCR is capable of reducing fuel consumption by nearly 25%. Additionally, increased energy efficiency has a beneficial effect on the environment as it leads to a reduction in greenhouse gas emissions.     

计及Spark和属性权重的售电套餐推荐方法

针对电力市场用户群庞大，交易过程中售电套餐选择困难的问题，在Spark环境下设计了一种售电套餐推荐方法，同时也解决了售电套餐推荐过程中在大数据环境下的可扩展性及实时性问题。首先，计算出每个套餐属性的权重值，从而计算得到售电套餐综合相似度。然后，计及用户和套餐两方面提出一种售电套餐推荐方法，实现售电套餐的精准推荐。实验表明，提出的推荐方法能够明显提高推荐的准确度，并且在分布式环境下具有良好的推荐效率和可扩展性。     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

应用膨胀算法提取目标的直接定位算法

为了解决被动雷达系统中的多发射源定位问题，提出了一种基于多重信号分类(MUSIC)算法和图像膨胀(IE)算法的直接定位方法。该方法结合了谱分析中的MUSIC思想，通过对接收量测协方差矩阵进行特征分析求解目标的位置。首先，在目标个数未知的前提下，利用Akaike信息准则(AIC)来确定模型阶数；然后，推导了基于MUSIC的定位代价函数；之后，利用图像膨胀算法处理得到的代价函数平面；最后，膨胀处理后的输出为目标个数及目标位置的估计值。提出的算法有效地解决了目标检测及提取的问题，能够确定多个目标的位置坐标，为后续的定位性能分析提供可能性，也保证了算法的完整性。进一步地分析了多个临近目标情况下影响目标提取性能的主要因素。