Constrained Balancing of Two Industrial Rotor Systems: Least Squares and Min-Max Approaches

Rotor vibrations caused by rotor mass unbalance distributions are a major source of maintenance problems in high-speed rotating machinery. Minimizing this vibration by balancing under practical constraints is quite important to industry. This paper considers balancing of two large industrial rotor systems by constrained least squares and min-max balancing methods. In current industrial practice, the weighted least squares method has been utilized to minimize rotor vibrations for many years. One of its disadvantages is that it cannot guarantee that the maximum value of vibration is below a specified value. To achieve better balancing performance, the min-max balancing method utilizing the Second Order Cone Programming (SOCP) with the maximum correction weight constraint, the maximum residual response constraint as well as the weight splitting constraint has been utilized for effective balancing. The min-max balancing method can guarantee a maximum residual vibration value below an optimum value and is shown by simulation to significantly outperform the weighted least squares method

High-throughput metabolic profiling of diverse green Coffea arabica beans identified tryptophan as a universal discrimination factor for immature beans.

The maturity of green coffee beans is the most influential determinant of the quality and flavor of the resultant coffee beverage. However, the chemical compounds that can be used to discriminate the maturity of the beans remain uncharacterized. We herein analyzed four distinct stages of maturity (immature, semi-mature, mature and overripe) of nine different varieties of green Coffea arabica beans hand-harvested from a single experimental field in Hawaii. After developing a high-throughput experimental system for sample preparation and liquid chromatography-mass spectrometry (LC-MS) measurement, we applied metabolic profiling, integrated with chemometric techniques, to explore the relationship between the metabolome and maturity of the sample in a non-biased way. For the multivariate statistical analyses, a partial least square (PLS) regression model was successfully created, which allowed us to accurately predict the maturity of the beans based on the metabolomic information. As a result, tryptophan was identified to be the best contributor to the regression model; the relative MS intensity of tryptophan was higher in immature beans than in those after the semi-mature stages in all arabica varieties investigated, demonstrating a universal discrimination factor for diverse arabica beans. Therefore, typtophan, either alone or together with other metabolites, may be utilized for traders as an assessment standard when purchasing qualified trading green arabica bean products. Furthermore, our results suggest that the tryptophan metabolism may be tightly linked to the development of coffee cherries and/or beans

Multivariate statistical analyses.

<p>(A) The principal component analysis (PCA) score plot showed that the first two components (PC1/PC2) represented 14.2% of the variation among all samples. Each sample is colored according to the maturity in green (immature), pink (semi-mature), red (mature) or umber (overripe). The ellipse represents Hotelling’s T2, with 95% confidence in the score plots. (B) The partial least squares (PLS) regression model. The relationship between the four maturities as the rank-ripeness [immature (1), semi-mature (2), mature (3) and overripe (4)] and the 117 samples was modeled by the PLS method. One PLS component described 8.4% (R²X) of the variation among all samples. The goodness of fit value, R²Y, and the goodness of prediction value, Q²Y (cross-validated R²Y), were 0.822 and 0.776, respectively. The root-mean-square of the error of estimation (RMSEE) was 0.4714. (C) The validation plot (after 200 permutations) of the one-dimensional PLS model. The Y-axis represents R²Y (triangle in green) and Q²Y (squares in blue) for every model, and the X-axis designates the Pearson correlation coefficient between the original and permutated rank-ripeness. (D) The VIP plot of the PLS model. The top 24 important variables are shown according to their VIP values.</p

Development of a high-throughput analytical method for sample preparation and LC-MS measurement.

<p>First, green coffee beans were subjected to pulverization using a MultiBeads Shocker (Yasui Kikai, Japan), allowing us to process 18 samples in 20 seconds at once, while a conventional method using a mortar takes a few minutes for a single sample. Second, the extracted metabolites were subjected to LC-MS using a KINETEX C18 column, which allows a rapid (10 minute) separation for a single sample, which requires one-third less time than the conventional separation method.</p

A list of the samples included in the LC-MS analysis.

<p><i>Coffea arabica</i> cherries of nine different varieties [Catimor 5175-1, Red Catuai, F1 hybrid of Catimor and Tall Mokka (5175-1 xMA2-7), Maragogipe, Tall Mokka MA2-7, SL28, Typica, Yellow Bourbon and Yellow Catuai] and four distinct maturities (immature, semi-mature, mature and overripe) were harvested at the HARC in Hawaii.</p

Tryptophan is a specific marker of diverse immature <i>Coffea arabica</i> green beans.

<p>(A) The total ion chromatograms derived from the LC-MS analyses of either the catimore 5175-1 immature or mature bean extract. The red arrowhead indicates a characteristic peak that specifically appeared in the green bean sample. (B) The extracted ion chromatogram (XIC) of 205.0941 (+) retained at 5.00–5.40 minutes and the mass spectrum are shown. (C) Box-and-whisker plots derived from the tryptophan in diverse varieties. The mass intensities (Y-axis) were normalized to the sum total ion counts obtained from each sample. The developmental stages (X-axis) were abbreviated as I (immature), SM (semi-mature), M (mature) and OR (overripe).</p

Identification of 3‑Methylbutanoyl Glycosides in Green <i>Coffea arabica</i> Beans as Causative Determinants for the Quality of Coffee Flavors

The quality of coffee green beans is generally evaluated by the sensory cupping test, rather than by chemical compound-based criteria. In this study, we examined the relationship between metabolites and cupping scores for 36 varieties of beans, using a nontargeted LC–MS-based metabolic profiling technique. The cupping score was precisely predicted with the metabolic information measured using LC–MS. Two markers that strongly correlated with high cupping scores were determined to be isomers of 3-methylbutanoyl disaccharides (3MDs; 0.01–0.035 g/kg of beans) by spectroscopic analyses after purification, and one of them was a novel structure. Further, both the 3MDs were determined to be precursors of 3-methylbutanoic acid that enhance the quality of coffee. The applicability of 3MDs as universal quality indicators was validated with another sample set. It was concluded that 3MDs are the causative metabolites determining beverage quality and can be utilized for green bean selection and as key compounds for improving the beverage quality

Real-Time Fluorescence Imaging Using Indocyanine Green to Assess Therapeutic Effects of Near-Infrared Photoimmunotherapy in Tumor Model Mice

Background: Near-infrared photoimmunotherapy (NIR-PIT) is a cancer therapy that causes an increase in tumor perfusion, a phenomenon termed the super-enhanced permeability and retention effect. Currently, in vivo treatment efficacy of NIR-PIT is observable days after treatment, but monitoring would be improved by more acute detection of intratumor change. Fluorescence imaging may detect increased tumor perfusion immediately after treatment. Methods: In the first experiment, athymic nude mouse models bearing unilateral subcutaneous flank tumors were treated with either NIR-PIT or laser therapy only. In the second experiment, mice bearing bilateral flank tumors were treated with NIR-PIT only on the left-sided tumor. In both groups, immediately after treatment, indocyanine green was injected at different doses intravenously, and mice were monitored with the Shimadzu LIGHTVISION fluorescence imaging system for 1 hour. Results: Tumor-to-background ratio of fluorescence intensity increased over the 60 minutes of monitoring in treated mice but did not vary significantly in control mice. Tumor-to-background ratio was highest in the 1 mg kg −1 and 0.3 mg kg −1 doses. In mice with bilateral tumors, tumor-to-untreated tumor ratio increased similarly. Conclusions: Acute changes in tumor perfusion after NIR-PIT can be detected by real-time fluorescence imaging