DIMENSION REDUCTION FOR POWER SYSTEM MODELING USING PCA METHODS CONSIDERING INCOMPLETE DATA READINGS

Principal Component Analysis (PCA) is a popular method for dimension reduction that can be used in many fields including data compression, image processing, exploratory data analysis, etc. However, traditional PCA method has several drawbacks, since the traditional PCA method is not efficient for dealing with high dimensional data and cannot be effectively applied to compute accurate enough principal components when handling relatively large portion of missing data. In this report, we propose to use EM-PCA method for dimension reduction of power system measurement with missing data, and provide a comparative study of traditional PCA and EM-PCA methods. Our extensive experimental results show that EM-PCA method is more effective and more accurate for dimension reduction of power system measurement data than traditional PCA method when dealing with large portion of missing data set.

Monitoring cerebral oxygenation during balloon occlusion with multichannel NIRS

We report on oxygenation changes noninvasively recorded by multichannel continuous-wave near infrared spectroscopy (CW-NIRS) during endovascular neuroradiologic interventions requiring temporary balloon occlusion of arteries supplying the cerebral circulation. Digital subtraction angiography (DSA) provides reference data on the site, timing, and effectiveness of the flow stagnation as well as on the amount and direction of collateral circulation. This setting allows us to relate CW-NIRS findings to brain specific perfusion changes. We focused our analysis on the transition from normal perfusion to vessel occlusion, i.e., before hypoxia becomes clinically apparent. The localization of the maximal response correlated either with the core (occlusion of the middle cerebral artery) or with the watershed areas (occlusion of the internal carotid artery) of the respective vascular territories. In one patient with clinically and angiographically confirmed insufficient collateral flow during carotid artery occlusion, the total hemoglobin concentration became significantly asymmetric, with decreased values in the ipsilateral watershed area and contralaterally increased values. Multichannel CW-NIRS monitoring might serve as an objective and early predictive marker of critical perfusion changes during interventions-to prevent hypoxic damage of the brain. It also might provide valuable human reference data on oxygenation changes as they typically occur during acute stroke.

The effect of inner speech on arterial CO2 and cerebral hemodynamics and oxygenation: a functional NIRS study

The aim of the present study was (i) to investigate the effect of inner speech on cerebral hemodynamics and oxygenation, and (ii) to analyze if these changes could be the result of alternations of the arterial carbon dioxide pressure (PaCO2). To this end, in seven adult volunteers, we measured changes of cerebral absolute [O2Hb], [HHb], [tHb] concentrations and tissue oxygen saturation (StO2) (over the left and right anterior prefrontal cortex (PFC)), as well as changes in end-tidal CO2 (PETCO2), a reliable and accurate estimate of PaCO2. Each subject performed three different tasks (inner recitation of hexameter (IRH) or prose (IRP) verses) and a control task (mental arithmetic (MA)) on different days according to a randomized crossover design. Statistical analysis was applied to the differences between pre-baseline, two tasks, and four post-baseline periods. The two brain hemispheres and three tasks were tested separately. During the tasks, we found (i) PETCO2 decreased significantly (p < 0.05) during the IRH ( ~ 3 mmHg) and MA ( ~ 0.5 mmHg) task. (ii) [O2Hb] and StO2 decreased significantly during IRH ( ~ 1.5 μM; ~ 2 %), IRP ( ~ 1 μM; ~ 1.5 %), and MA ( ~ 1 μM; ~ 1.5 %) tasks. During the post-baseline period, [O2Hb] and [tHb] of the left PFC decreased significantly after the IRP and MA task ( ~ 1 μM and ~ 2 μM, respectively). In conclusion, the study showed that inner speech affects PaCO2, probably due to changes in respiration. Although a decrease in PaCO2 is causing cerebral vasoconstriction and could potentially explain the decreases of [O2Hb] and StO2 during inner speech, the changes in PaCO2 were significantly different between the three tasks (no change in PaCO2 for MA) but led to very similar changes in [O2Hb] and StO2. Thus, the cerebral changes cannot solely be explained by PaCO2.

Independent measurement of biogenic silica in sediments by FTIR spectroscopy and PLS regression

We present an independent calibration model for the determination of biogenic silica (BSi) in sediments, developed from analysis of synthetic sediment mixtures and application of Fourier transform infrared spectroscopy (FTIRS) and partial least squares regression (PLSR) modeling. In contrast to current FTIRS applications for quantifying BSi, this new calibration is independent from conventional wet-chemical techniques and their associated measurement uncertainties. This approach also removes the need for developing internal calibrations between the two methods for individual sediments records. For the independent calibration, we produced six series of different synthetic sediment mixtures using two purified diatom extracts, with one extract mixed with quartz sand, calcite, 60/40 quartz/calcite and two different natural sediments, and a second extract mixed with one of the natural sediments. A total of 306 samples—51 samples per series—yielded BSi contents ranging from 0 to 100 %. The resulting PLSR calibration model between the FTIR spectral information and the defined BSi concentration of the synthetic sediment mixtures exhibits a strong cross-validated correlation ( R2cv = 0.97) and a low root-mean square error of cross-validation (RMSECV = 4.7 %). Application of the independent calibration to natural lacustrine and marine sediments yields robust BSi reconstructions. At present, the synthetic mixtures do not include the variation in organic matter that occurs in natural samples, which may explain the somewhat lower prediction accuracy of the calibration model for organic-rich samples.

Predicting prostate cancer-specific outcome after radical prostatectomy among men with very high-risk cT3b/4 PCa: a multi-institutional outcome study of 266 patients

BACKGROUND The value of radical prostatectomy (RP) as an approach for very high-risk prostate cancer (PCa) patients is controversial. To examine the risk of 10-year cancer-specific mortality (CSM) and other-cause mortality (OCM) according to clinical and pathological characteristics of very high-risk cT3b/4 PCa patients treated with RP as the primary treatment option. METHODS In a multi-institutional cohort, 266 patients with very high-risk cT3b/4 PCa treated with RP were identified. All patients underwent RP and pelvic lymph-node dissection. Competing-risk analyses assessed 10-year CSM and OCM before and after stratification for age and Charlson comorbidity index (CCI). RESULTS Overall, 34 (13%) patients died from PCa and 73 (28%) from OCM. Ten-year CSM and OCM rates ranged from 5.6% to 12.9% and from 10% to 38%, respectively. OCM was the leading cause of death in all subgroups. Age and comorbidities were the main determinants of OCM. In healthy men, CSM rate did not differ among age groups (10-year CSM rate for ⩽64, 65-69 and ⩾70 years: 16.2%, 11.5% and 17.1%, respectively). Men with a CCI ⩾1 showed a very low risk of CSM irrespective of age (10-year CSM: 5.6-6.1%), whereas the 10-year OCM rates increased with age up to 38% in men ⩾70 years. CONCLUSION Very high-risk cT3b/4 PCa represents a heterogeneous group. We revealed overall low CSM rates despite the highly unfavorable clinical disease. For healthy men, CSM was independent of age, supporting RP even for older men. Conversely, less healthy patients had the highest risk of dying from OCM while sharing very low risk of CSM, indicating that this group might not benefit from an aggressive surgical treatment. Outcome after RP as the primary treatment option in cT3b/4 PCa patients is related to age and comorbidity status.

Visible near infra-red (VisNIR) spectroscopy for predicting soil organic carbon in Ethiopia

Over the past few decades, the advantages of the visible-near infra-red (VisNIR) diffuse reflectance spectrometer (DRS) method have enabled prediction of soil organic carbon (SOC). In this study, SOC was predicted using regression models for samples taken from three sites (Gununo, Maybar and Anjeni) in Ethiopia. SOC was characterized in laboratory using conventional wet chemistry and VisNIR-DRS methods. Principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS) models were developed using Unscrambler X 10.2. PCA results show that the first two components accounted for a minimum of 96% variation which increased for individual sites and with data treatments. Correlation (r), coefficient of determination (R2) and residual prediction deviation (RPD) were used to rate four models built. PLS model (r, R2, RPD) values for Anjeni were 0.9, 0.9 and 3.6; for Gununo values 0.6, 0.3 and 1.2; for Maybar values 0.6, 0.3 and 0.9, and for the three sites values 0.7, 0.6 and 1.5, respectively. PCR model values (r, R2, RPD) for Anjeni were 0.9, 0.8 and 2.7; for Gununo values 0.5, 0.3 and 1; for Maybar values 0.5, 0.1 and 0.7, and for the three sites values 0.7, 0.5 and 1.2, respectively. Comparison and testing of models shows superior performance of PLS to PCR. Models were rated as very poor (Maybar), poor (Gununo and three sites) and excellent (Anjeni). A robust model, Anjeni, is recommended for prediction of SOC in Ethiopia.