Structure analysis of single- and multi-frequency subspace migrations in inverse scattering problems

In this literature, we carefully investigate the structure of single- and multi-frequency imaging functions, that are usually employed in inverse scattering problems. Based on patterns of the singular vectors of the Multi-Static Response (MSR) matrix, we establish a relationship between imaging functions and the Bessel function. This relationship indicates certain properties of imaging functions and the reason behind enhancement in the imaging performance by multiple frequencies. Several numerical simulations with a large amount of noisy data are performed in order to support our investigation.Comment: 11 pages, 10 figure

Expression of pepper cytoplasmic male sterility-associated open reading frame, orf507 in transgenic tobacco plants

Cytoplasmic male sterility (CMS) is a mitochondrial inherited trait that prevents a flower from producing normal pollen grains. CMS has widely been used to produce F1 hybrid seed in pepper. Though the orf507 gene is known to be associated with CMS in peppers, definitive and direct evidence that ORF507 induces male sterility is still lacking. In this study, a set of chimeric constructs were developed to confirm the hypothesis that ORF507 protein directly causes male sterility in tobacco. Tapetum-specific promoter TA29, mitochondrial transit sequence yeast coxIV pre-sequence, orf507 and green fluorescent protein (GFP) gene were cloned sequentially and designated as TCOGN (TA29-coxIV pre-sequnce-orf507-GFP-nos terminator). For developing control vectors, orf507 was cloned under TA29 promoter without coxIV pre-sequence (TOGN) or orf507 was cloned with or without coxIV pre-sequence under the constitutive CaMV35S promoter (SCOGN and SOGN). The four constructs (TCOGN, TOGN, SCOGN, and SOGN) were used to transform tobacco using leaf disk transformation mediated by agrobacterium. At flowering stage, transgenic plants will be scored for pollen production and viability. This study is expected to provide evidence that the expression of orf507 gene in the tapetum might be responsible for male sterility in pepper.OAIID:RECH_ACHV_DSTSH_NO:A201625326RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A076900CITE_RATE:FILENAME:하예성_2016육종학회.pdfDEPT_NM:식물생산과학부EMAIL:[email protected]_YN:FILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/6dd98ec4-fdab-4ecd-a747-0c4e1d86ec6d/linkCONFIRM:

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This study evaluated the characteristics of PM2.5 pollution and long-range atmospheric transport (LRAT) at the Baengnyeong and Jeju Air Quality Research Centers in South Korea during 2018~2020. The mean concentration of PM2.5 was constant in Baengnyeong but decreased in Jeju owing to COVID-19. The significant seasonal variations of OC, EC, and NO3 - in Baengnyeong and Jeju with the highest concentrations in winter may be due to the influence of high PM2.5 episodes. Meanwhile, the concentrations of SO4 2- and NH4 + were constant throughout the year in Baengnyeong, resulting from regional inflow from surrounding areas. The influence of anthropogenic sources and secondary formation of PM2.5 increased in summer and decreased in autumn at both sites, which was also observed at other background sites. The dominance of NO3 -, K+, and Cl- in Baengnyeong was due to the influence of combustion sources and LRAT. The source of SO4 2-, NH4 +, V, and Ni in Jeju was identified as industrial activities with the highest contribution in summer. The secondary formation of PM2.5 with external inflow effects was dominant in Baengnyeong and Jeju. The main emission source area of PM2.5 for both Baengnyeong and Jeju was East China (Hebei, Shandong, Jangsu, and Anhui), but the chemical composition and sources of PM2.5 were different between Baengnyeong and Jeju. The result of this study can be a basis for future monitoring and modeling studies on the influence of LRAT in background areas

Applicability of Machine-Learned Regression Models to Estimate Internal Air Temperature and CO₂ Concentration of a Pig House

Carbon dioxide (CO2) emissions from the livestock industry are expected to increase. A response strategy for CO2 emission regulations is required for pig production as this industry comprises a large proportion of the livestock industry and it is projected that per capita pork consumption will rise. A CO2 emission response strategy can be established by accurately measuring the CO2 concentrations in pig facilities. Here, we compared and evaluated the performance of three different machine learning (ML) models (ElasticNet, random forest regression (RFR), and support vector regression (SVR)) designed to predict CO2 concentration and internal air temperature (Ti) values in the pig house used to regulate a heating, ventilation, and air conditioning (HVAC) control system. For each ML model, the hyperparameter was optimised and the predictive accuracy was evaluated. The order of predictive accuracy for the ML models was ElasticNet i prediction by RFR, R2 ≥ 0.848 and the root mean square error (RMSE) and mean absolute error (MAE) were 0.235 °C and 0.160 °C, respectively, whilst for CO2 concentration prediction by RFR, R2 ≥ 0.885 and the RMSE and MAE were 64.39 ppm and ≤ 46.17 ppm, respectively

Applicability of Machine-Learned Regression Models to Estimate Internal Air Temperature and CO2 Concentration of a Pig House

Carbon dioxide (CO2) emissions from the livestock industry are expected to increase. A response strategy for CO2 emission regulations is required for pig production as this industry comprises a large proportion of the livestock industry and it is projected that per capita pork consumption will rise. A CO2 emission response strategy can be established by accurately measuring the CO2 concentrations in pig facilities. Here, we compared and evaluated the performance of three different machine learning (ML) models (ElasticNet, random forest regression (RFR), and support vector regression (SVR)) designed to predict CO2 concentration and internal air temperature (Ti) values in the pig house used to regulate a heating, ventilation, and air conditioning (HVAC) control system. For each ML model, the hyperparameter was optimised and the predictive accuracy was evaluated. The order of predictive accuracy for the ML models was ElasticNet < SVR < RFR. Hence, random forest regression provided superior prediction performance. Based on the test dataset, for Ti prediction by RFR, R2 ≥ 0.848 and the root mean square error (RMSE) and mean absolute error (MAE) were 0.235 °C and 0.160 °C, respectively, whilst for CO2 concentration prediction by RFR, R2 ≥ 0.885 and the RMSE and MAE were 64.39 ppm and ≤ 46.17 ppm, respectively

Effect of serum testosterone and percent tumor volume on extra-prostatic extension and biochemical recurrence after laparoscopic radical prostatectomy

Several studies have revealed that the preoperative serum testosterone and percent tumor volume (PTV) predict extra-prostatic extension (EPE) and biochemical recurrence (BCR) after radical prostatectomy. This study investigated the prognostic significance of serum testosterone and PTV in relation to EPE and BCR after laparoscopic radical prostatectomy (LRP). We reviewed 520 patients who underwent LRP between 2004 and 2012. PTV was determined as the sum of all visually estimated tumor foci in every section. BCR was defined as two consecutive increases in the postoperative prostate-specific antigen (PSA) >0.2 ng ml−1 . The threshold for serum total testosterone was 3.0 ng ml−1 . Multivariate logistic regression was used to define the effect of variables on the risk of EPE and BCR. A low serum testosterone (<3.0 ng ml−1 ) was associated with a high serum PSA, Gleason score, positive core percentage of the prostate biopsy, PTV, and all pathological variables. On multivariate analysis, similar to previous studies, the serum PSA, biopsy positive core percentage, Gleason score, and pathological variables predicted EPE and BCR. In addition, low serum testosterone (<3.0 ng ml−1 , adjusted OR, 8.52; 95% CI, 5.04-14.4, P= 0.001) predicted EPE and PTV (adjusted OR, 1.02; 95% CI, 1.01-1.05, P= 0.046) predicted BCR. In addition to previous predictors of EPE and BCR, low serum testosterone and PTV are valuable predictors of EPE and BCR after LRP