Several physical properties of almond nut and kernel were evaluated as functions of moisture content. The average length, width, thickness, the geometric mean diameter, unit mass and volume of nuts ...were 25.49, 17.03, 13.12, 18.13 mm, 2.64 g and 2.61 cm
3 respectively. Corresponding values for kernel were 21.19, 14.34, 6.38, 11.42 mm, 0.69 g and 0.71 cm
3 respectively. In the moisture range from 2.77 to 24.97 db, studies on re-wetted almond nut showed that the bulk density decreased from 655 to 525 kg/m
3, true density increased from 1015 to 1115 kg/m
3, porosity increased from 35.32% to 53.21%, projected area increased from 3.74 to 3.9 cm
2, and terminal velocity increased from 5.62 to 7.98 m/s. For the kernel, the corresponding values changed from 595 to 475 kg/m
3, 900 to 995 kg/m
3, 34.23% to 50.29%, 1.68 to 2.39 cm
2 and from 5.62 to 7.2 m/s respectively. The rupture strength of almond nut and kernel decreased with increasing moisture content. The highest rupture strength occurred when loaded along the
X-axis. In the same moisture range 2.77–24.97 db the static coefficient of friction varied from 0.28 to 0.83 for almond nut and from 0.53 to 0.78 for kernel over different material surfaces.
•Sn-doped ZnO films were prepared via facile sol–gel spin coating method.•The grain size of the films changes from 39.23 to 71.84nm with Sn doping.•The refractive index dispersion of the films obeys ...the single oscillator model.
Thin films of Sn-doped ZnO were prepared via facile sol–gel spin coating method. The structural and optical properties of the films were investigated by means of X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and UV-VIS-NIR spectrophotometer. The X-ray results confirmed that all the ZnO thin films are polycrystalline with a hexagonal wurtzite structure with a preferential orientation of (002) plane. The crystallite size and lattice parameter values of the films were obtained. Atomic force microscopy results indicate that the Sn-doped ZnO films have the nanostructure. The grain size values of the films were found to vary from 39.23 to 71.84nm with Sn doping. The nanostructure of the Sn-doped ZnO films was also confirmed by scanning electron microcopy. The optical bandgaps of the films were calculated for the various Sn contents. The refractive index dispersion curves obey the single oscillator model. The optical constants and dispersion parameters of the ZnO films were changed with Sn doping. The obtained results suggest that the structural and optical properties of ZnO films can be controlled by Sn doping.
A new method through Gauss–Helmert model of adjustment is presented for the solution of the similarity transformations, either 3D or 2D, in the frame of errors-in-variables (EIV) model. EIV model ...assumes that all the variables in the mathematical model are contaminated by random errors. Total least squares estimation technique may be used to solve the EIV model. Accounting for the heteroscedastic uncertainty both in the target and the source coordinates, that is the more common and general case in practice, leads to a more realistic estimation of the transformation parameters. The presented algorithm can handle the heteroscedastic transformation problems, i.e., positions of the both target and the source points may have full covariance matrices. Therefore, there is no limitation such as the isotropic or the homogenous accuracy for the reference point coordinates. The developed algorithm takes the advantage of the quaternion definition which uniquely represents a 3D rotation matrix. The transformation parameters: scale, translations, and the quaternion (so that the rotation matrix) along with their covariances, are iteratively estimated with rapid convergence. Moreover, prior least squares (LS) estimation of the unknown transformation parameters is not required to start the iterations. We also show that the developed method can also be used to estimate the 2D similarity transformation parameters by simply treating the problem as a 3D transformation problem with zero (0) values assigned for the z-components of both target and source points. The efficiency of the new algorithm is presented with the numerical examples and comparisons with the results of the previous studies which use the same data set. Simulation experiments for the evaluation and comparison of the proposed and the conventional weighted LS (WLS) method is also presented.
In this study, manganese sillenite nano-powders were produced by hydrothermal method by adding graphene oxide in different mass amounts. Structural, morphological, thermal, optical and electrical ...properties of the obtained undoped and graphene-oxide-doped samples were investigated. XRD results showed that the produced samples had nano-sizes and the crystal sizes changed with the addition of graphene oxide. SEM and TEM images showed that the microstructures of the samples were nano-sized and composed of randomly oriented particles without orientation relationship between them. From FT-IR spectra of the samples, chemical properties and bond structures were determined. With the help of thermal analysis measurements, reactions in the sample structures at increasing temperatures, reaction temperatures, reaction energies and total mass losses were determined. The forbidden energy ranges of the samples decreased at significant rates with the addition of graphene oxide. By checking the P–E hysteresis cycles, the samples are understood to show ferroelectric properties. Dielectric measurements and temperature-dependent electrical conductivity measurements of the produced nano-powders confirmed that the samples exhibited semiconductor behaviour. The obtained results showed that manganese sillenites can be synthesised by low-temperature hydrothermal method and physical, chemical and thermal properties can be controlled by adding different amounts of graphene oxide for use in electronic and energy applications.
Abstract
Background
Determining the modifiable risk factors for postoperative complications is particularly significant in patients undergoing colorectal surgery since those are associated with worse ...long-term outcomes.
Methods
Consecutive newly diagnosed 104 colorectal cancer patients were prospectively included in this single-center observational study. Preoperative serum 25-OH vitamin D levels were measured and analyzed for infectious and postoperative complications.
Results
Serum 25-OH vitamin D levels were found to be < 20 ng/ml in 74 patients (71.2%) and ≥ 20 ng/ml in 30 patients (28.8%); and the mean serum 25-OH vitamin D level was 15.95 (± 9.08) ng/ml. In patients with surgical site infection and infectious complications, 25-OH vitamin D levels were significantly lower than patients without complications (p = 0.036 and p = 0.026). However, no significant difference was demonstrated in 25-OH vitamin D levels according to overall postoperative complications.
Conclusions
Our results suggest that vitamin D levels might be a potential risk factor for infectious complications in patients undergoing colorectal cancer surgery.
In this article, the bound state solution of the modified radial Schrodinger equation is obtained for the sum of Cornell and inverse quadratic potential. Here in, the developed scheme is used to ...overcome the centrifugal part at the finite temperature and the energy eigenvalues and corresponding radial wave functions are defined for any angular momentum case via the Nikiforov-Uvarov methods. The present result are applied on the charmonium and bottomonuim masses at finite and zero temperature. Our result are in goog agreement with other theoretical and experimental results. The zero temperature limit of the energy spectrum and eigenfunctions is also founded. It is shown that the present approach can successfully be apply to the quarkonium systems at the finite temperature as well.
Abstract While total knee arthroplasty (TKA) has been shown to have excellent outcomes, a significant proportion of patients experience relatively poor post-operative function. In this study, we test ...the hypothesis that the level of osteoarthritic symptoms in the contralateral knee at the time of TKA is associated with poorer post-operative outcomes in the operated knee. Using longitudinal cohort data from the Osteoarthritis Initiative (OAI), we included 171 patients who received a unilateral TKA. We compared pre-operative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores in the contralateral knee to post-operative WOMAC scores in the index knee. Pre-operative contralateral knee WOMAC scores were associated with post-operative index knee WOMAC Total scores, indicating that the health of the pre-operative contralateral knee is a significant factor in TKA outcomes.
Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using ...such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.
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