Correlation Study of Operational Data and System Performance of District Cooling System with Ice Storage

The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a monitoring and control platform for DCS with ice storage to analyze historical parameter values related to system operation and executed operations. We assess the distribution of cooling loads among various devices within the DCS, identify operational characteristics of the system through correlation analysis and principal component analysis (PCA), and subsequently determine key parameters affecting changes in cooling loads. Accurate forecasting of cooling loads is crucial for determining optimal control strategies. The research process can be summarized briefly as follows: data preprocessing, parameter analysis, parameter selection, and validation of load forecasting performance. The study reveals that while individual devices in the system perform well, there is considerable room for improving overall system efficiency. Six principal components have been identified as input parameters for the cold load forecasting model, with each of these components having eigenvalues greater than 1 and contributing to an accumulated variance of 87.26%, and during the dimensionality reduction process, we obtained a confidence ellipse with a 95% confidence interval. Regarding cooling load forecasting, the Relative Absolute Error (RAE) value of the light gradient boosting machine (lightGBM) algorithm is 3.62%, Relative Root Mean Square Error (RRMSE) is 42.75%, and R-squared value (R2) is 92.96%, indicating superior forecasting performance compared to other commonly used cooling load forecasting algorithms. This research provides valuable insights and auxiliary guidance for data analysis and optimizing operations in practical engineering applications. .

水中多氯代芳香化合物(PCAs)在自然沉积物及活性污泥中的吸附特征

对珠江三角洲水体中来源广、毒性大的多氯代芳香化合物（ＰＣＡｓ）在两种自然沉积物和活性污泥中的吸附特征进行了初步研究。研究结果表明，在珠江广州河段鸦岗底泥和顺德鱼塘底泥中，１，２，４－三氯苯、１，２，４，５－四氯苯、五氯苯主要呈现为Ｆｒｕｅｎｄｌｉｃｈ非线性吸附，六氯苯、２，４，６－三氯酚、七氯和Ａｒｏｃｌｏｒ１２６０则表现为线性吸附；而在活性污泥颗粒物上几种ＰＣＡｓ均表现为线性吸附。ＰＣＡｓ的吸附行为与Ｋｏｗ有一定关系，但主要与固体颗粒物中有机碳和粘土成分含量有关。有机碳和粘土成分的高低不仅影响ＰＣＡｓ在颗粒物上吸附容量的大小，其比值还决定了ＰＣＡｓ的吸附方式。

EPICS PCAS及其在BEPCII上的应用

非EPICS应用程序接入EPICS是加速器控制中的常见问题,EPICS提供了一组名为PCAS类接口,用于把非EPICS应用程序接入到EPICS中。为方便使用PCAS开发应用程序,本文开发了基于PCAS的动态链接库pcasdll,并对其CA访问响应性能进行了测试。另外,介绍了基于此动态链接库开发的BEPCII电子枪定时插件温度数据采集系统。

基于PCAS的Tektronix示波器与EPICS通信研究

现代大型实验物理和加速器装置中,高端示波器被越来越广泛地应用于信号采集和测量方面。EPICS是应用于大型实验物理和加速器控制系统的组态软件工具集,PCAS是EPICS用来端接EPICS异构系统的一种手段,已在多个加速器和EPICS工具上得到应用。论文介绍了利用PCAS把Tektronix示波器接入EPICS的方法。基于该方法,实现了通过EPICS对BEPCⅡ电子枪触发信号抖动值的实时监测。

大承气汤对PCAS兔心肺组织蛋白酶激活受体-1表达的影响

目的探讨大承气汤对心脏骤停后综合征（PCAS）兔心肺组织蛋白酶激活受体-1（PAR-1）表达的影响。方法将20只兔用来制备PCAS模型，另外10只作为正常对照组。采用窒息性心脏骤停制备兔PCAS，窒息性心脏骤停采用气管夹闭诱导。18只复苏成功兔随机分为PCAS组和大承气汤组。大承气汤组在自主循环恢复（ROSC）后15min给予大承气汤16g／kg灌胃；其他两组同时间点灌胃等量生理盐水。各组48h后处死动物，取心肺组织，采用相对定量实时逆转录-聚合酶链反应（RT—PCR）测定PAR-1mRNA表达。结果与正常对照组比较，PCAS组兔心肺组织中PAR-1mRNA表达较正常对照组显著升高（均P〈0．01）。大承气汤干预后心肺组织中PAR-1mRNA表达均较PCAS模型组显著下降（P〈0．01或P〈0．001）。结论大承气汤对PCAS兔心肺组织PAR-1表达有抑制作用，这可能是大承气汤治疗多器官功能障碍综合征（MODS）的机制之一。

基于PCAS及微信平台的PBL教学在消化科规范化培训医师带教中的经验

目的探讨基于PCAS及微信平台PBL教学在消化科规范化培训住院医师带教中的实践及体会。方法以123名规培医师为研究对象,分为常规教学组及PBL教学组。采用客观测评以及主观问卷调查对教学效果进行初步评价,比较常规教学和PBL教学在规培医师教学中的效果。结果PBL教学组学员认同人数更多,除"临床诊疗思维锻炼"外,组间比较差异均有统计学意义。PBL教学组出科考核成绩与常规教学组相比,在病史询问、体格检查、病历书写、理论考试上有所提高(P<0.05),而技能操作得分组间差异无统计学意义。教师座谈调查结果表示,教师也很支持PBL教学模式。结论在规培医师中开展基于PCAS及微信平台的PBL教学对其能力培养具有较大的帮助,在实际临床教学中切实可行,可操作性强,有利于提高学员培训质量。

Construction of project quality health monitoring system based on life-cycle theory

In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.

水中多氯代芳香化合物(PCAs)在自然沉积物及活性污泥中的吸附特征

本文对珠江三角洲水体中来源广、毒性大的多氯代芳香化合物(PCAs)在两种自然沉积物和活性污泥中的吸附特征进行了初步研究。研究结果表明,在珠江广州河段鸦岗底泥和顺德鱼塘底泥中,1,2,4—三氯苯、1,2,4,5—四氯苯、五氯苯主要呈现为Fruendlich非线性吸附,六氯苯、2,4,6—三氯酚、五氯酚、七氯和Aroclor1260则表现为线性吸附,而在活性污泥颗粒物上几种PCAs均表现为线性吸附。PCAs的吸附行为与Kow有一定关系,但主要与固体颗粒物中有饥碳和粘土成分含量有关。有机碳和粘土成分的高低不仅影响PCAs在颗粒物上吸附容量的大小,其比值还决定了PCAs的吸附方式。

大型机械摩擦故障的PCAS诊断方法

本文采用主分量自回归谱(Principal Components and Autoregressive Spectrum),简称PCAS方法,对大型离心压缩机设备中的摩擦故障作出了诊断.通过模糊隶属函数给出了摩擦故障的诊断判据.实践表明PCAS方法是有效的.

基于PCAS对砂土孔隙特征的识别与应用

为探究砂土粒径、颗粒级配与孔隙特征的关系,采用60目、80目、100目3种规格石英砂人工配置5种类型砂土,利用激光粒度仪和电镜扫描得到5种砂土的粒径累积百分数和135张砂土断面SEM(Scanning Electron Microscope)图像,使用PCAS软件和体积分形数学公式,分别对SEM图像和5种砂土的颗粒粒径累积百分数进行分析。分析显示,砂土颗粒在0. 8~1 000、50~500、0. 8~50μm粒级的体积分形维数数值范围分别为2. 093 77~2. 294 96、1. 298 7~2. 194 25、2. 014 9~2. 280 15;砂土孔隙分形维数数值和孔隙度分别为1. 117 9~1. 133 9、0. 519 4~0. 597 2。体积分形维数数值越大,砂土的颗粒级配越优;除砂土的粒径粗细程度影响孔隙分形维数外,颗粒级配也影响砂土的孔隙分形维数,颗粒级配越优,孔隙分形维数越大;相较于砂土的颗粒粒径大小,颗粒级配是影响砂土孔隙度的主要因素,颗粒级配越劣,砂土孔隙度越大。

Portable System for CWA Detection Using Micro Array Gas Sensor

Biochemistry weapon is raising new tools of war and terror.This research is an experiment about portable system for CWAs (chemical warfare agents) detection using micro array gas system.CWAs were divided into four types such as blood, nerve,vesicant,and choking agent.To detect various CWAs,semiconductor thick film sensor array based on tin oxide was fabricated and their gas responses were examined.Operating temperature was range of 250℃to 350℃and gas concentration was range of 0.1μg/g to 10μg/g.Sensor array was measured as operating temperatures,concentrations for four simulant agents of CWAs.Extracted parameters from results were carried out classification among CWAs through the principal component analysis (PCA).Also,classification of gases is studied using adaptive resonance theory (ART) that is one of neural network algorithm.Results displayed using PDA system.

Research on Indicator System of Regional Eco-efficiency:A Case Study of Chengyang District

Eco-efficiency is a valuable tool for managing and solving issues involving resource consumption and pollution emission in current production processes.Despite the popularity of the term"eco-efficiency"in business,limited attention has been paid to measuring and reporting regional eco-efficiency for local government policy makers.Based on the concept and method of eco-efficiency,an indicator system of regional eco-efficiency is established in this study.The indicator system comprises 22 indicators,which are divided in to three categories including socio-economic development,resources consumption, environmental pressure.As a promising statistical technique, principle component analysis is used to set the weight of indicators which attempts to calculate the eco-efficiency indices of Qingdao's Chengyang District.The results show that the eco-efficiency of Chengyang District has clearly improved 35.1% with small fluctuation from 1995 to 2003.Socio-economic development index and resources consumption index also represent obvious increasing trends.The correlation coefficient between soci-economic development index and resources consumption index is 0.979,which means the social progress and economic growth of Chengyang District depend on an extravagant consumption of resources.The environmental pressure index increased slowly before 1997 and declined gradually after 1997,due to more attention being paid to environmental protection by local government in recent years.Chengyang District still keeps the traditional economic development mode with a high consumption and high production,so the emphases of future development should put on improving the improving the efficient use of natural resources and promoting environmental management sustainability.The results show that the indicators system of regional eco-efficiency is a promising method to quantitatively evaluate resources and environmental efficiency and provide an effective decision-making support for local governments.

Degradation of malathion by Pseudomonas during activated sludge treatment system using principal component analysis (PCA)

Popular descriptive multivariate statistical method currently employed is the principal component analyses （PCA） method. PCA is used to develop linear combinations that successively maximize the total variance of a sample where there is no known group structure. This study aimed at demonstrating the performance evaluation of pilot activated sludge treatment system by inoculating a strain of Pseudomonas capable of degrading malathion which was isolated by enrichment technique. An intensive analytical program was followed for evaluating the efficiency of biosimulator by maintaining the dissolved oxygen （DO） concentration at 4.0 mg/L. Analyses by high performance liquid chromatographic technique revealed that 90% of malathion removal was achieved within 29 h of treatment whereas COD got reduced considerably during the treatment process and mean removal efficiency was found to be 78%. The mean pH values increased gradually during the treatment process ranging from 7.36-8.54. Similarly the mean ammonia-nitrogen （NH3-N） values were found to be fluctuating between 19.425-28.488 mg/L, mean nitrite-nitrogen （NO3-N） ranging between 1.301- 2.940 mg/L and mean nitrate-nitrogen （NO3-N） ranging between 0.0071-0.0711 mg/L. The study revealed that inoculation of bacterial culture under laboratory conditions could be used in bioremediation of environmental pollution caused by xenobiotics. The PCA analyses showed that pH, COD, organic load and total malathion concentration were highly correlated and emerged as the variables controlling the first component, whereas dissolved oxygen, NO3-N and NH3-N governed the second component. The third component repeated the trend exhibited by the first two components.

The PCA3 test for guiding repeat biopsy of prostate cancer and its cut-off score： a systematic review and meta-analysis

The specificity of prostate-specific antigen （PSA） for early intervention in repeat biopsy is unsatisfactory. Prostate cancer antigen 3 （PCA3） may be more accurate in outcome prediction than other methods for the early detection of prostate cancer （PCa）. However, the results were inconsistent in repeated biopsies. Therefore, we performed a systematic review and meta-analysis to evaluate the role of PCA3 in outcome prediction. A systematic bibliographic search was conducted for articles published before April 2013, using PubMed, Medline, Web of Science, Embase and other databases from health technology assessment agencies. The quality of the studies was assessed on the basis of QUADAS criteria. Eleven studies of diagnostic tests with moderate to high quality were selected. A meta-analysis was carried out to synthesize the results. The results of the meta-analyses were heterogeneous among studies. We performed a subgroup analysis （with or without inclusion of high-grade prostatic intraepithelial neoplasia （HGPIN） and atypical small acinar proliferation （ASAP））. Using a PCA3 cutoff of 20 or 35, in the two sub-groups, the global sensitivity values were 0.93 or 0.80 and 0.79 or 0.75, specificities were 0.65 or 0.44 and 0.78 or 0.70, positive likelihood ratios were 1.86 or 1.58 and 2.49 or 1.78, negative likelihood ratios were 0.81 or 0.43 and 0.91 or 0.82 and diagnostic odd ratios （ORs） were 5.73 or 3.45 and 7.13 or 4.11, respectively. The areas under the curve （AUCs） of the summary receiver operating characteristic curve were 0.85 or 0.72 and 0.81 or 0.69, respectively. PCA3 can be used for repeat biopsy of the prostate to improve accuracy of PCa detection. Unnecessary biopsies can be avoided by using a PCa cutoff score of 20.

Construction of composite indicator system based on simulation data mining

The indicator system is the foundation and emphasis in the effectiveness evaluation of system of systems(SoS). In the past, indicator systems were founded based on qualitative methods, and every indicator was mainly determined by the expert with experience. This paper proposed a brand-new method to construct indicator systems based on the repeated simulation of the scenario space, and calculated by quantitative data. Firstly, the selection of key indicators using the Gini indicator importance measure(IIM)is calculated by random forests(RFs). Then, principal component analysis(PCA) is applied when we use the selected indicators to construct the composite indicator system of SoS. Furthermore,a set of rulesare is developed to verify the practicability of the indicator system such as correlation, robustness, accuracy and convergence. Experiment shows that the algorithm achieves good results for the construction of composite indicators of So S.

Gold grade variation and stream sediment geochemistry of the Vaimba-Lidi drainage system, northern Cameroon (West Africa)

Stream sediment geochemistry remains a versatile tool in exploration especially in regions where knowledge of the primary mineralization is lacking and the exploration activities are still at reconnaissance stage. In this study, we investigate the concentrations of gold and associated elements in stream sediment samples from the Vaimba-Lidi drainage system in northern Cameroon;a relatively remote area where alluvial gold is worked locally, and exploration activities are at early stage. The main river and its principal first and second order tributaries were sampled, panned for gold grain recovery and the 100 μm size fraction analyzed for Au by fire assay with Ni finish. A suite of other elements were analyzed for by inductively coupled plasma mass spectrometry (ICP-MS). Gold grades estimated by the panning and weighing technique rarely exceed1 g/t while the Au concentrations from chemical analysis range from 3.0 to 354.0 ppm. The spatial distribution of gold and all the other elements are presented as point symbol maps and the data analyzed using multivariate statistics. From the principal component analysis (PCA), the As-Mo-W-Ag as well as the Au-Zn factors point to the presence of primary hydrothermal gold-sulphide mineralization in the area and this can be further investigated. These results highlight the importance of multielement analysis and multivariate statistical interpretation of sediment geochemical data in inferring the nature of the underlying primary mineralization in any region.

Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.

New pattern recognition system in the e-nose for Chinese spirit identification

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.

Simulation Tools for a Fiber-Optic Based Structural Health Monitoring System

Probability of detection(POD)graphics allow for a change from qualitative to quantitative assessment for every damage detection system,and as such it is a main request for conventional non-destructive testing(NDT)techniques.Its availability can greatly help towards the industrialization of the corresponding Structural health monitoring(SHM)system.But having in mind that for SHM systems the sensors are at fixed positions,and the location of a potential damage would change its detectability.Consequently robust simulation tools are required to obtain the model assisted probability of detection(MAPOD)which is needed to validate the SHM system.This tool may also help for the optimization of the sensor distribution,and finally will allow a probabilistic risk management.INDEUS,simulation of ultrasonic waves SHM system,was a main milestone in this direction.This article deals with the simulation tools for a strain based SHM system,using fiber optic sensors(FOS).FOS are essentially strain/temperature sensors,either with multi-point or with distributed sensing.The simulation tool includes the finite element model(FEM)for the original and damaged structure,and algorithms to compare the strain data at the pre-established sensors locations,and from this comparison to extract information about damage occurrence and location.The study has been applied to the structure of an all-composite unmanned aircraft vehicle(UAV)now under construction,designed at Universidad Politecnica de Madrid for the inspection of electrical utilities networks.Distributed sensing optical fibers were internally bonded at the fuselage and wing.Routine inspection is planned to be done with the aircraft at the test bench by imposing known loads.From the acquired strain data,damage occurrence may be calculated as slight deviations from the baselines.This is a fast inspection procedure without requiring trained specialists,and it would allow for detection of hidden damages.Simulation indicates that stringer partial debondings are detected before they become critical,while small delaminations as those produced by barely visible impact damages would require a prohibited number of sensing lines.These simulation tools may easily be applied to any other complex structure,just by changing the FEM models.From these results it is shown how a fiber optic based SHM system may be used as a reliable damage detection procedure.

Vision-Based Vehicle Detection for a Forward Collision Warning System

A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition;2) the local features are mutually processed to improve the reliability of vehicle detection;3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions;4) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.