Study of Cherenkov Light Lateral Distribution Function around the Knee Region in Extensive Air Showers

The Cherenkov light lateral distribution function (LDF) was simulated with the CORSIKA code, in the energy range (10^13-10^16) eV. This simulation was performed for conditions and configurations of the Tunka EAS Cherenkov array for two primary particles (p and Fe). Basing on the simulated results, many approximated functions are structured for two primary particles and different zenith angles. This allowed us to reconstruct the EAS events, which is, to determine the type and energy of the primary particles that produced showers from signal amplitudes of Cherenkov radiation which measured with Tunka Cherenkov array experiment. Comparison of the calculated LDF of Cherenkov radiation with that measured at the Tunka EAS array shows the ability for identifying of the primary particle that initiated the EAS cascades determining of its primary energy around the knee region of the cosmic ray spectrum.Comment: 13 Pages, 8 figures, Submitted and accepted at the Serbian Astronomical Journa

Fluorescence spectroscopy for analysing deterioration of palm olein in batch deep-fat frying

Palm olein has been commercially used as frying medium in batch deep-fat frying. During frying, the oil usually deteriorates due to the exposure to high temperature. In this study, a fluorescence spectroscopy technique was applied to monitor the deterioration of refined, bleached, and deodorized palm olein (RBDPO) in batch deep-fat frying. 22.5 kg of French fries were used as the frying material. In 30 batches, the french fries were intermittently fried at 185 ± 5°C for eight hours a day over five consecutive days capturing 40 hours. The fluorescence intensity of the RBDPO was recorded with excitation at 390 nm and resulting emission of 465 nm. The fluorescence intensity of the RBDPO over five days of frying decreased considering the wavelength range of emission 430-640 nm and excitation 360-430 nm. The decreased in intensity of fluorescence emission and excitation spectra were inversely correlated with the FFA content of the oil samples. This study demonstrates the potential of fluorescence spectroscopy in monitoring the deterioration of RBDPO during batch deep-fat frying

Adjusting for verification bias in diagnostic accuracy measures when comparing multiple screening 2 tests - an application to the IP1-PROSTAGRAM study

Introduction Novel screening tests used to detect a target condition are compared against either a reference standard or other existing screening methods. However, as it is not always possible to apply the reference standard on the whole population under study, verification bias is introduced. Statistical methods exist to adjust estimates to account for this bias. We extend common methods to adjust for verification bias when multiple tests are compared to a reference standard using data from a prospective double blind screening study for prostate cancer. Methods Begg and Greenes method and multiple imputation are extended to include the results of multiple screening tests which determine condition verification status. These two methods are compared to the complete case analysis using the IP1-PROSTAGRAM study data. IP1-PROSTAGRAM used a paired84 cohort double-blind design to evaluate the use of imaging as alternative tests to screen for prostate 85 cancer, compared to a blood test called prostate specific antigen (PSA). Participants with positive imaging (index) and/or PSA (control) underwent a prostate biopsy (reference). Results When comparing complete case results to Begg and Greenes and methods of multiple imputation there is a statistically significant increase in the specificity estimates for all screening tests. Sensitivity estimates remained similar across the methods, with completely overlapping 95% confidence intervals. Negative predictive value (NPV) estimates were higher when adjusting for verification bias, compared to complete case analysis, even though the 95% confidence intervals overlap. Positive predictive value (PPV) estimates were similar across all methods. Conclusion Statistical methods are required to adjust for verification bias in accuracy estimates of screening tests. Expanding Begg and Greenes method to include multiple screening tests can be computationally intensive, hence multiple imputation is recommended, especially as it can be modified for low prevalence of the target condition

Isolation, Characterization, and Identification of Biological Control Agent for Potato Soft Rot in Bangladesh

A total of 91 isolates of probable antagonistic bacteria of potato soft rot bacterium Erwinia carotovora subsp. carotovora (Ecc) were extracted from rhizospheres and endophytes of various crop plants, different soil varieties, and atmospheres in the potato farming areas of Bangladesh. Antibacterial activity of the isolated probable antagonistic bacteria was tested in vitro against the previously identified most common and most virulent soft rot causing bacterial strain Ecc P-138. Only two isolates E-45 and E-65 significantly inhibited the in vitro growth of Ecc P-138. Physiological, biochemical, and carbon source utilization tests identified isolate E-65 as a member of the genus Bacillus and the isolate E-45 as Lactobacillus sp. The stronger antagonistic activity against Ecc P-138 was found in E-65 in vitro screening and storage potatoes. E-65 reduced the soft rot infection to 22-week storage potatoes of different varieties by 32.5–62.5% in model experiment, demonstrating its strong potential to be used as an effective biological control agent for the major pectolytic bacteria Ecc. The highest (62.5%) antagonistic effect of E-65 was observed in the Granola and the lowest (32.7%) of that was found in the Cardinal varieties of the Bangladeshi potatoes. The findings suggest that isolate E-65 could be exploited as a biocontrol agent for potato tubers

Effect of Growth Temperature and Mn Incorporation on GaN:Mn Thin Films Grown by Plasma-Assisted MOCVD

In this paper, the growth of GaN:Mn thin films by plasma-assisted metalorganic chemical vapor deposition (PAMOCVD) method is reported. The method used in this study, utilizes a microwave cavity as a cracking cell to produce nitrogen radicals, which in turn reduce the growth temperature. Trimethylgallium (TMGa), nitrogen (N2) and cyclopentadienyl manganese tricarbonyl (CpMnT) were used as a source of Ga, N and Mn, respectively, while hydrogen gas was used as a carrier gas for both TMGa and CpMnT. The effect of growth temperature and Mn incorporation on structural properties and surface morphology of GaN:Mn films are presented. The growth of GaN:Mn thin films were conducted at varied growth temperature in range of 625 oC to 700 oC and the Mn/Ga molar fraction in the range of 0.2 to 0.5. Energy dispersive of X-ray (EDX) and X-ray diffraction (XRD) methods were used to analyze atomic composition and crystal structure of the grown films, respectively. The surface morphology was then characterized using both atomic force microscopy (AFM) and scanning electron microscopy (SEM) images. A systematic XRD analysis reveal that maximum Mn incorporation that still produces single phase GaN:Mn (0002) is 6.4 % and 3.2 % for the film grown at 650 oC and 700 oC, respectively. The lattice constant and full width at half maximum (FWHM) of the single phase films depend on the Mn concentration. The decrease in lattice constant accompanied by the increase in FWHM is due to incorporation of substitutional Mn on the Ga sub-lattice. The maximum values of doped Mn atoms incorporated in the wurtzite structure of GaN:Mn as substitutional atoms on Ga sub-lattice are 2.0 % and 2.5 % at 650 oC and 700 oC, respectively. AFM and SEM images show that the film grown at lower growth temperature and Mn concentration has a better surface than that of film grown at higher growth temperature and Mn concentration

Value of systematic sampling in an mp-MRI targeted prostate biopsy strategy

The clinical utility of systematic prostate biopsy in addition to multi-parametric magnetic resonance imagining (mp-MRI) targeted biopsy pathways remains unclear. Despite radiological advancements in mp-MRI and utilisation of international standardised reporting systems (i.e., PI-RADS, LIKERT), undetected clinically significant prostate cancer (csPCa) on imaging persists. This has prevented the widespread adoption of an exclusively targeted biopsy approach. The current evidence on csPCa cancer detection rates in mp-MRI targeted alone and combined with a non-targeted systematic sampling is presented. Arguments for and against routine limited systematic sampling as an adjunct to an mp-MRI targeted biopsy are discussed. Our review will report the clinical utility of a combined sampling strategy on csPCa detection rate. The available evidence suggests that we are yet to reach a stage where non-targeted systematic prostate biopsy can be routinely omitted in mp-MRI targeted prostate biopsy pathways. Research should focus on improving the accuracy of mp-MRI, prostate biopsy techniques, and in identifying those men that will most benefit from a combined prostate biopsy. Such strategies may help future urologists reduce the burden of non-targeted cores in modern mp-MRI prostate biopsy pathways

Single class classifier using FMCD based non-metric distance for timber defect detection

In this work, we propose a robust Mahalanobis one class classifier with Fast Minimum Covariance Determinant estimator (MC-FMCD) for species independent timber defect detection. Having known in timber inspection research that there is a lack of defect samples compared to defect-free samples (imbalanced data), this unsupervised approach applies outlier detection concept with no training samples required. We employ a non-segmenting approach where a timber image will be divided into non-overlapping local regions and the statistical texture features will then be extracted from each of the region. The defect detection works by calculating the Mahalanobis distance (MD) between the features and the distribution average estimate. The distance distribution is approximated using chi-square distribution to determine outlier (defects). The approach is further improved by proposing a robust distribution estimator derived from FMCD algorithm which enhances the defect detection performance. The MC-FMCD is found to perform well in detecting various types of defects across various defect ratios and over multiple timber species. However, blue stain evidently shows poor performance consistently across all timber species. Moreover, the MC-FMCD performs significantly better than the classical MD which confirms that using the robust estimator clearly improved the timber defect detection over using the conventional mean as the average estimator