Hull Form Factor Prediction of Mini Submarine Model Using Prohaska Method

Speaking of prisoners on mini-submarines is certainly different Fnom the type of surface vessels in general. This is related to differences in the shape of the sub's hull when compared to surface ship. In addition to differences in the shape of the hull, the difference in the operational area of ​​the ship is also different, where the submarine's hull operates at full water depth, while the surface ship the ship hull partly operates at sea level. If the submarine model is tested then the value of the coefficient of resistance will be very different. Where the component of the coefficient of resistance (CT) consists of the coefficient of Fniction (CF), form factor (1+K), and Correlate Allowance (CA). Because the hull shape is different Fnom the surface ship, then the hull form factor coefficient is the focus of this study. The prediction of the hull form factor can be searched using the PROHASKA method. Where this method is done using a mini-submarine model test. By the known value of the hull form factor, then it can be used to find the value of the coefficient of resistance and can know the resistance of the ship

Assessment of influence of block wheelset dynamics on wear of freight car bearing

The paper presents a method for comparative assessment of wear of support surfaces of freight car bogie bearings with block and standard wheelsets. With the use of wear factor coefficient the authors have established that the calculated values of wear energy of bogie bearing equipped with block wheelsets are significantly lower in comparison with the bogie bearing with standard wheelsets.

Spectrophotometric indicators of the stability of anthocyanin-containing extracts depending on the color of plant materials

This paper aims at spectrophotometric determination of changes in stability of extractable anthocyanins during drying of plant materials depending on their color. Raw and dried colored parts of 50 plant species from 25 families were used for the study. The extracts were prepared over 95% ethanol acidified with hydrochloric acid (pH ~ 1). The absorption spectra were registered within the range of 210 to 680 nm. The extinction variability factor, coefficient of intensity absorption relative and generalized stability factor were used to determine the anthocyanin degradation. The highest values of the stability factor were obtained for the extracts from fruit shells of burgundy or violet color within the range of 0.934±0.024 to 0.973±0.024, while the extracts from flower petals of the same care featured the stability factor that was 1.19 to 1.44 times less. The values of the stability factor of the extracts from black, red and blue materials are 1.15 to 1.19 times, 1.74 to 2.48 times and 4.65 to 4.84 times less respectively than those of the extracts from violet-burgundy materials. It is appropriate to apply the spectrophotometric factors of anthocyanins stability used in this study to selection of promising plants for industrial cultivation as material of anthocyanin-containing herbal preparations. The most stable anthocyanins are those of burgundy-purple and black fruits.

Spatial analysis of Samara region land degradation and differentiation of antierosion territory organization

In the research, a spatial analysis of the degradation of Samara region agricultural land with the assessment of economic losses due to water erosion is carried out. A map chart of the distribution of districts with different degrees of erosion has also been developed. The values of the degradation factor coefficient and economic losses due to the influence of erosion processes are calculated. The key points of antierosion territory organization and land regulation depending on landscape nature and kind of damage are represented. The plan of action for the antierosion territory organization of a farm in Kinelsky district is proposed and its effectiveness to stop and prevent erosion processes, as well as for rational use of land and increase soil fertility is shown.

Prognostic Score for Adults with Acute Myeloid Leukemia in First Complete Remission

Background Current prognostic scoring systems in acute myeloid leukemia (AML) help predict outcomes in newly diagnosed patients at the time of diagnosis. In AML, achievement of complete remission (CR) is essential for the success of treatment. Therefore, development of a prognostic score for patients who enter first CR would be an important tool for physicians to guide their decisions for further treatment at the time of CR. Patients and Methods We developed a prognostic score based on data from 938 newly diagnosed adult AML (non-APL) patients (median age 59 years, range [18 - 88]) diagnosed and treated at the University of Texas MD Anderson Cancer Center, USA between [1999-2012], who achieved 1st CR/CRp and were not transplanted in 1st CR. Median follow-up was 22.3 months [range 1.4-165.1 months]. Information on MRC 2010 classification was available for 896 patients (156 (17.4%) patients in favorable, 550 (61.4%) in intermediate, and 190 (21.2%) in adverse group); ELN classification could be determined for 478 patients (160 (33.5%) in favorable, 88 (18.4%) in intermediate-1, 103 (21.5%) in intermediate-2, and 127 (26.6%) patients in adverse risk group. For survival analysis Kaplan-Meier method was used. Survival distributions were compared using log-rank tests. Univariable and multivariable Cox regression analyses were performed to evaluate an impact of clinically significant prognostic variables (P<0.05). Each significant variable in the multivariate analysis was then assigned points based on the weight of the coefficient. A sum of the points led to a score that segregated patients into different prognostic subgroups (Table 1 and 2). Results Based on multivariable analysis, age and MRC/ELN subgroup were statistically significant prognostic factors (p<0.001) and were subsequently included in the prognostic score model. Since age (<60 and ≥60 years old) is an important prognostic factor not directly accounted for in the original MRC2010 and ELN classification systems, this was added to each of the new prognostic scores: "MRC+age" and "ELN+age". In "MRC +age" prognostic score four groups were identified: favorable, intermediate-1, intermediate-2 and poor (Table 1B). In "ELN+age" score five groups were identified: favorable, intermediate-1, intermediate-2, poor and very poor (Table 2B). According to "MRC+age" prognostic score, the 3- and 5-year overall survivals (OS), respectively were: 80% and 77% in the favorable group; 53% and 45% in the intermediate-1 group; 31% and 19% in the intermediate-2 group; and 8% and 6 % in the poor group. According to "ELN+age" prognostic score, the 3- and 5-year OS, respectively, were 78% and 77% in the favorable group; 68% and 50% in the intermediate-1 group; 46% and 37% in the intermediate-2 group; 27% and 22% in poor group; and 3% and 2% in the very poor group (Figure 1; A and B). Conclusion Despite the new discoveries in AML (DNA sequencing, genomic mutations, etc.) many clinics worldwide still don't have access to those tools and they stratify patients according to MRC or ELN classifications. The prognostic score presented above could be a powerful tool for physicians to risk-stratify patients with AML in first CR for post-remission therapy based on age and basic cytogenetic and/or molecular testing. Table 1. Prognostic score for AML in 1st complete remission according to Age and MRC classification Prognostic factor Coefficient Points Age <60 0 0 ≥60 0.513 1 MRC subgroup Favorable 0 0 Intermediate 1.070 2 Adverse 1.957 4 Table 2. Prognostic score for AML in 1st complete remission according to Age and ELN classification MRC + age Total score Favorable 0 Intermediate-1 1-2 Intermediate-2 3 Poor 4-5 Table 3. Prognostic factor Coefficient Points Age <60 years 0 0 ≥60 years 0.224 0.5 ELN subgroup Favorable 0 0 Intermediate - 1 1.570 3 Intermediate - 2 1.128 2.5 Adverse 2.013 4 Table 4. ELN + age Total score Favorable 0 Intermediate-1 0.5 Intermediate-2 2.5 Poor 3 Very poor 3.5 - 4.5 Figure 1. Cumulative rates of overall survival in patients with newly diagnosed AML in 1st complete remission according to A) MRC + Age prognostic score, and B) ELN + Age prognostic score Figure 1. Cumulative rates of overall survival in patients with newly diagnosed AML in 1st complete remission according to A) MRC + Age prognostic score, and B) ELN + Age prognostic score Disclosures Tamamyan: Conquer Cancer Foundation of the American Society of Clinical Oncology: Other: Long-term International Fellowship (LIFe). Cortes:Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Teva: Research Funding; BMS: Consultancy, Research Funding; BerGenBio AS: Research Funding; Ariad: Consultancy, Research Funding; Astellas: Consultancy, Research Funding; Ambit: Consultancy, Research Funding; Arog: Research Funding; Celator: Research Funding; Jenssen: Consultancy. Pemmaraju:Stemline: Research Funding; Incyte: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; LFB: Consultancy, Honoraria. Konopleva:Novartis: Research Funding; AbbVie: Research Funding; Stemline: Research Funding; Calithera: Research Funding; Threshold: Research Funding.

Estimating the Strength of a General Factor: Coefficient Omega Hierarchical

Relying on work described by Jackson (2003), Ree, Carretta, and Teachout (2015) recommended researchers use the first unrotated principal component associated with a principal components analysis (PCA) to estimate the strength of a general factor. Arguably, such a recommendation is based on rather old work. Furthermore, it is not a method that can be relied on to yield an accurate solution. For example, it is well known that the first component extracted from a correlation matrix of the Wechsler intelligence subtests is biased toward the verbal comprehension subtests (Ashton, Lee, & Vernon, 2001).