Statistical approach to determination of overhaul and maintenance cost of loading equipment in surface mining

The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (UER) and multivariate linear regression (MLR) were used in this study. Loading equipment parameters such as bucket capacity, machine weight, engine power, boom length, digging depth, and dumping height were considered as variables. The results obtained by models and mean absolute error rate indicate that these models can be applied as the useful tool in determination of overhaul and maintenance cost of loading equipment. The results of this study can be used by the decision-makers for the specific surface mining operations.

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.

RAMAN SPECTROSCOPIC STUDY ON PREDICTION OF TREATMENT RESPONSE IN CERVICAL CANCERS

Concurrent chemoradiotherapy(CCRT)is the choice of treatment for locally adv anced cervical cancers;however,tumors exhibit diverse response to treatment.Early prediction of tumor response leads to individualizing treatment regimen.Response evaluation criteria in solid tumors(RECIST),the current modality of tumor response assessment,is often subject ive and carried out at the first visit after treatment,which is about four months.Hence,there is a need for better predictive tool for radioresponse.Optical spectroscopic techniques,sensitive to molecular alteration,are being pursued as potential diagnostic tools.Present pilot study ains to explore the fiber-optic-based Raman spectroscopy approach in prediction of tumor response to CCRT,before taking up extensive in vivo studies.Ex vivo Raman spectra were acquired from biopsies collected from 11 normal(148 spectra),16 tumor(201 spectra)and 13 complete response(151 CR spectra),one partial response(8 PR spectra)and one nonresponder(8 NR spectra)subjects.Data was analyzed using principal component linear discriminant analysis(PC-LDA)followed by leave-one-out cross-validation(LOO-CV).Findings suggest that normal tissues can be efficiently classified from both pre-and post-treated tumor biopsies,while there is an overlap between pre-and post-CCRT tumor tissues.Spectra of CR,PR and NR tissues were subjected to principal component analysis(PCA)and a tendency of classification was observed,corroborating previous studies.Thus,this study further supports the feasibility of Raman spectroscopy in prediction of tumor radioresponse and prospective noninvasive in vivo applications.

Incorporation of source contributions to improve the accuracy of soil heavy metal mapping using small sample sizes at a county scale

Estimating heavy metal(HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to detect the concentrations of eight HMs(As, Hg, Cu, Cr, Ni, Zn, Pb, and Cd) in the herb growing area of Luanping County, northeastern Hebei Province, China. An absolute principal component score-multiple linear regression(APCS-MLR) model was used to quantify pollution source contributions to soil HMs. Furthermore, the source contribution rates and environmental data of each sampling point were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HM spatial distributions. Results showed that 88% of Cu, 72% of Cr, and 72% of Ni came from natural sources;50% of Zn, 49% of Pb, and 59% of Cd were mainly caused by agricultural activities;and 44% of As and 56% of Hg originated from industrial activities. When three-type(natural, agricultural, and industrial) source contribution rates and environmental data were simultaneously incorporated into the stepwise linear regression model, the fitting accuracy was significantly improved and the model could explain 31%–86% of the total variance in soil HM concentrations. This study introduced three-type source contributions of each sampling point based on APCS-MLR analysis as new covariates to improve soil HM estimation precision, thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.

A high-linearity and high-resolution delay line structure with a calibration algorithm in delay-based LINC transmitters

In order to overcome the bottleneck of low linearity and low resolution, an improved delay line structure is proposed with a calibration algorithm to conquer PVT （process, voltage and temperature） variations for an all- digital design. The chip is implemented in 0.13 μm CMOS technology. Measurement results show that the proposed structure with the calibration algorithm can evidently improve the linearity and resolution of the delay line. The delay resolution is 2 ps and the root mean square jitter of the delay is 4.71 ps, leading to an error vector magnitude enhancement of 1.32 dB.

An all-digital synthesizable baseband for a delay-based LINC transmitter with reconfigurable resolution

The linear amplification with nonlinear component transmitter is a promising solution to high efficiency and high linearity amplification for non-constant envelope signals. An all-digital synthesizable baseband for a delay-based LINC transmitter is implemented. This paper proposes a standard-cell based synthesizable methodology which can be applied in the ASIC process efficiently without performance degradation compared to the manual layout. A scheme to overcome the limited resolution of conventional phase detectors is proposed. It employs alter- native phase detector structures to provide reconfigurability for higher resolution after fabricating, resulting in an 11 ps resolution improvement. Due to the PVT variation, an adaptive calibration scheme focusing on the inherent imbalance between two delay lines is depicted, which reveals an effective EVM enhancement of 5.37 dB. This baseband chip is implemented in 0.13 μm CMOS technology, and the transmitter with the baseband has an EVM of-28.96 dB and an ACPR of-29.51 dB, meeting the design requirement.

Occurrence of polycyclic aromatic hydrocarbons in the estuarine sediments of the Taihu Lake and their associated toxic effects on aquatic organisms

Polycyclic aromatic hydrocarbons(PAHs)are ubiquitous toxic organic pollutants in the natural environment that are strongly associated with socioeconomic activities.Exploring the distribution,sources,and ecological toxicity of PAHs is essential to abate their pollution and biological risks.The 16 priority PAHs in different lakeside city estuarine sediments in the northern Taihu Lake in China were determined using gas chromatography-mass spectrometry.Results showed that total concentrations of PAHs(ΣPAHs)ranged from 672.07 to 5858.34 ng g^(^(-1)),with a mean value of 2121.37 ng g^(^(-1)).High-molecular-weight PAHs(4-6 rings)were dominant,accounting for 85%of theΣPAHs detected.Due to the barrier of gate/dam in the estuarine area,the concentrations of PAHs in the sediments were significantly different between the river mouth and lake side.Changes in total organic carbon(TOC)content and the spatial distribution of PAHs in the sediments were consistent.Sediment pollution assessment explored using the fuzzy evaluation model indicated 75%of slight PAH pollution.Some estuarine sediments(22%)concentrated in the east of the Wuli Lake in the Meiliang bay of the Taihu Lake were moderately or heavily polluted.The PAHs may lead to occasional detrimental biological consequences in the area.Diagnostic ratios and principal component analysis-multiple linear regression suggested vehicle emission and natural gas combustion as the primary PAH contributors(81%).