Juarezmalone1435

This dataset presents morphological features, elemental composition and functional groups of different pre- and post-gamma (γ)-irradiated chitosan (10kGy & 20kGy) prepared from shrimp waste. The γ-irradiated chitosan was characterized using Fourier transfer infrared (FTIR) spectroscopy and X-ray diffraction (XRD) analyses. Thermogravimetry/differential thermal analysis (TG/DTA) were performed using Perkin Elmer Pyris Diamond DSC with a heating rate of 10 °C/minute and dynamic synthetic atmospheric air set at flow rate of 100 ml/minute. We observed γ-irradiated chitosan to have shorter polymer size, small pores and compacted structure with active alkyl and hydroxyl groups when compared to non-irradiated chitosan. Our data provides baseline understanding for structure of shrimp chitosan after 60Co exposure which means, the biopolymer becomes more stable and is considered suitable for vast food industry applications.Every practicing orthodontist today is aware of the importance of considering arch form in the attainment of a functional orthodontic correction [1]. Arch perimeter or circumference prediction is an essential component when Tooth Size Arch Length Discrepancy (TSALD) is estimated. Arch perimeter is the distance from mesial contact of the permanent molar on one side to the mesial contact of the permanent molar on the other side, with the line connecting the buccal/incisor tip points in the intervening teeth. This is most evident when seeking to resolve dental crowding or arch-length discrepancy (ALD) [2]. The shape of the arch form of maxillary and mandible resembles that of the various geometric forms such as including ellipse, parabola, hyperbola, and catenary curve [3], [4], [5], [6]. Ellipse is the best form that fits the shape of the Maxillary arch [1,2]. The mathematical equation formulated by Srinivasan Ramanujan in 1914 for widely considered to be the most accurate for calculation of the circumference of an ellipse is [7]. The computation of the circumference of the ellipse by this equation requires two values- 'a' and 'b,' the semi-major and semi-minor axis [half of the major axis and minor axis of the ellipse] respectively [8]. EGFR inhibitor The perimeter (P) of an ellipse is given by the formulae; = π(a+b)1+(3h/(10-√(4-3h)); where h=(a-b)2/(a+b)2 and calculated Maxillary arch perimeter (CP) =1/2 P. This necessitates a complex series of steps, and to overcome this, a statistical formula is developed by algorithm steps for mathematical equation where perimeter can be directly obtained by just two inputs 'a' and 'b' in excel sheet. We correlated this calculated arch perimeter (CP) with directly measured perimeter (MP) and marginal difference estimated in three different classes of malocclusion.Pseudomonas aeruginosa generally forms strong biofilm during chronic condition of wound. The whole mechanism of biofilm formation works in tandem with quorum sensing circuit of the organism in order to produce virulence. Here we report the draft genome sequence of two diabetic foot ulcer Pseudomonas aeruginosa isolates (VIT PC 7 and VIT PC 9) displaying homoserine lactone, rhamnolipid producing, biofilm phenotype and antibiotic resistance genes related to carbapenem, aminoglycoside, beta- lactamase and tetracycline resistance. The whole genome sequencing library was prepared according to the Oxford Nanopore's SQK-LSK108 kit protocol on Oxford Nanopore's Minion platform. The 7.1 Mb and 6.3-Mb draft genome sequence with GC content of 65.8% and 66.4% respectively provides insight into their resistance mechanism and virulence factors.Coronavirus Disease 2019 (COVID-19) has been identified as a global pandemic by the World Health Organization (WHO). The breakout of COVID-19 in various countries and regions brings a great threat to people's life and adds an unprecedented high pressure on healthcare systems. Due to the high infectivity of COVID-19, high standard negative pressure isolation units are required to accommodate the patients with COVID-19 and protect health workers. A novel prefabricated negative pressure isolation medical unit was designed and constructed in Shenzhen, China to help to accommodate the patients with COVID-19. This article provides detailed construction cost, time and testing data for this isolation medical unit. Considering the construction happened during the lockdown in Shenzhen (and in China), the construction cost and time can provide precious and rare information as well as guidelines to construct or expand appropriate medical facilities to accommodate the patients with COVID-19.This data article describes datasets from a home improvement retail store located in Santiago, Chile. The datasets have been developed to simultaneously solve a staffing and tour scheduling problem that incorporates flexible contracts and multiskilled staff. This Data in Brief article is related to the published article "Hybrid flexibility strategy on personnel scheduling Retail case study" [1]. The datasets contain real, processed, and simulated data. Regarding the real and processed datasets, they are presented for three different store sizes (4, 5 or 6 departments). Real datasets include information about the employment-contract characteristics, cost parameters, and a forecast of the number of employees required in each department for each day of the week and each time period into which the operating day is divided. As regards the data processed for the case study, they include the set of skill sets considering that the employees can be trained in a maximum of two store departments. Regarding the simulated datasets, they include information about the random parameter of staff demand in each store department. The simulated data are presented in 90 text files classified by (i) Store size (4, 5 or 6 departments). (ii) Coefficient of variation (10, 20, 30%). (iii) Instance identification number (10 instances per scenario that resulted from combining the store sizes and coefficients of variation). Researchers can use the datasets for benchmarking the performance of different approaches with the one presented by Porto et al. [1], and in consequence, they can find solutions to the same (or similar) type of personnel scheduling problem. The dataset includes an Excel workbook that can be used to randomly generate staff demand instances according to a chosen coefficient of variation.