A comparison of the effect of supplementation and sunlight exposure on serum vitamin D and parathyroid hormone: A systematic review and meta-analysis

Abstract

Background: Supplementation and getting sunlight exposure are two treatments for vitamin D deficiency. However, studies reported conteroversial findings regarding the efficacy of these two methods.

Objective: To compare the effect of oral vitamin D supplementation with sunlight exposure on serum vitamin D and parathyroid hormone (PTH).

Methods: A computer-based literature search through PubMed, Scopus and Google scholar search engines was conducted until April 2019 to find clinical trials which compared the effect of oral vitamin D supplementation with sunlight exposure on serum vitamin D and PTH. Means for serum 25-hydroxy vitamin D3 (25(OH) D3) and PTH concentration were extracted. A subgroup analysis was used to detect potential sources of inter-study heterogeneity. Mean differences (MD) were analyzed using a random-effects model (the DerSimonian-Laird approach).

Results: A total of seven papers were included in the meta-analysis. Pooled analysis showed that vitamin D supplementation significantly elevated levels of serum 25(OH) D3 in comparison with sunlight exposure (MD: 8.56nmol/l, 95%CI: 4.15, 12.97, T² = 40.32%, H² = 9.45%, P for heterogeneity p?<?0.001). Also, the difference between the effect of vitamin D supplementation and sun exposure was lower in studies which used UVB radiation compared with studies which applied direct sunlight (MD: 11.65?nmol/l, 95%CI: 7.02, 16.28; P for between subgroup heterogeneity = 0.001).

Conclusion: Vitamin D supplementation was more effective than sun exposure at increasing serum 25(OH) D3. The difference between efficacy of vitamin D supplementation and sun exposure was lower in studies which used long-term sun exposure or applied UVB treatment instead of direct sunlight.     

Peel Adhesion of Natural Rubber Bonded to Polyethylene Terephthalate: Effect of Crosslinking on Rate/Temperature Response

Laminates consisting of natural rubber (NR) sandwiched between cloth fabric and polyester film were pulled apart at various rates and temperatures in a T-peel geometry. Peel energies for joints containing uncrosslinked or lightly-crosslinked NR did not obey simple time-temperature superposition. This behavior is attributed to strain-induced crystallization during peeling. However, when the rubber was highly crosslinked, strain crystallization seems to be absent, as peel energies now can be WLF shifted to form a mastercurve.     

Tailoring the Mechanical Properties of Copper-Added Transformation-Induced Plasticity-Aided Multiphase Steel by Thermomechanical Processing

Herein, the microstructure and mechanical properties of a low-alloy Cu-bearing transformation-induced plasticity (TRIP) steel and their dependence on the processing history are investigated. The distributions of the retained austenite and bainite are found to be dependent on the initial cold-rolled microstructure, which can be tailored by the preceding hot-rolling route (unidirectional/cross rolling) and the subsequent cooling rate (furnace/air cooling). Mechanical properties and TRIP effect strongly depend on the initial microstructure, where the air-cooled sheet shows remarkable mechanical properties; while the cross-rolled sheet shows isotropic properties. The addition of copper results in an increase in the amount of the retained austenite, enhancement of strength-ductility balance, and improvement of the hardening behavior of TRIP-assisted high-performance steel.     

Analyzing electrical field effect on asphaltene deposition

Asphaltenes are the heaviest and most complicated fraction in a crude oil sample and consist of condensed polynuclear aromatics, small amounts of heteroatoms (e.g., S, N, and O), and some traces of metal elements (e.g., nickel and vanadium). The main mechanisms of asphaltene deposition are precipitation (formation of asphaltene solids out of liquid phase), aggregation (formation of larger asphaltene particles), and deposition (adsorption and adhesion onto the surface). Asphaltene deposition is a major unresolved flow assurance problem in the petroleum industry, which may occur anywhere in the production system consists of reservoir, wellbore, through flowing and the separator. Asphaltene moieties in crude oil are found to carry residual surface electric charge, so by exerting an electrical field in a specific length of pipe, asphaltenes will deposit and we will have no blockage problem. Determining asphaltene electric charge is an important issue that will be done by static experiment, and then effect of electrical field on asphaltene deposition in dynamic state should be investigated. This paper discusses electric field effect on asphaltene deposition and represents a way to deposit asphaltene moieties in specific location.     

The application of nanofluids for recovery of asphaltenic oil

In the recent years, requirement of suitable enhanced oil recovery (EOR) technique as a more proficient technology becomes significant because of increasing demand for energy. Nanofluids have great potential in order to improve oil recovery. In our study, the effect of SiO₂, Al₂O₃, and MgO nanoparticles on oil recovery was investigated by using core flooding apparatus. Zeta potential and particle size distribution measurements were carried out to investigate the stability of nano particles and results showed SiO₂ has more stability than other ones. Interfacial tension and contact angle measurements between nanofluids and crude oil used to demonstrate that how nanoparticles enhance oil recovery. Experimental data reveals that SiO₂ nanoparticles introduce as the greatest agent among these nanoparticles for enhanced oil recovery. Lowest damage for SiO₂ nanofluids was observed and also it was observed that the concentration and injection rate have straight effects on permeability reductions.     

Simple, fast and accurate two-diode model for photovoltaic modules

This paper proposes an improved modeling approach for the two-diode model of photovoltaic (PV) module. The main contribution of this work is the simplification of the current equation, in which only four parameters are required, compared to six or more in the previously developed two-diode models. Furthermore the values of the series and parallel resistances are computed using a simple and fast iterative method. To validate the accuracy of the proposed model, six PV modules of different types (multi-crystalline, mono-crystalline and thin-film) from various manufacturers are tested. The performance of the model is evaluated against the popular single diode models. It is found that the proposed model is superior when subjected to irradiance and temperature variations. In particular the model matches very accurately for all important points of the I-V curves, i.e. the peak power, short-circuit current and open circuit voltage. The modeling method is useful for PV power converter designers and circuit simulator developers who require simple, fast yet accurate model for the PV module.     

A critical evaluation of EA computational methods for Photovoltaic cell parameter extraction based on two diode model

Due to its ability to handle nonlinear functions regardless of the derivatives information, evolutionary algorithms (EA) are envisaged to be very effective for extracting parameter of photovoltaic (PV) cell. This paper presents critical evaluation of the parameters extraction of two diode PV model using three EA methods, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). For DE, two variations are proposed: (1) boundary based differential evolution (B-DE) and (2) penalty based differential evolution (P-DE). The performance of each method is evaluated based on several factors: accuracy and consistency of solution; speed of convergence; computational efficiency and the required number of control parameters. Comparisons are carried out using synthetic data and are validated by six PV modules of different types (multi-crystalline, mono-crystalline, and thin-film) from various manufacturers. Information derived from these critical evaluations can be useful to determine the best computational method to build an efficient and accurate PV system simulator.     

Using the mixture design approach to predict the rheological properties of low-calorie dairy desserts containing gum tragacanth exuded by three Iranian Astragalus species

Food Science and Biotechnology - In this study, the flow behavior and creep parameters of saffron desserts containing gum tragacanth combinations of three species were modeled by the mixture design...     

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.     

Thermo-acoustic random response of temperature-dependent functionally graded material panels

A nonlinear finite element model is provided for the nonlinear random response of functionally graded material panels subject to combined thermal and random acoustic loads. Material properties are assumed to be temperature-dependent, and graded in the thickness direction according to a simple power law distribution in terms of the volume fractions of the constituents. The governing equations are derived using the first-order shear-deformable plate theory with von Karman geometric nonlinearity and the principle of virtual work. The thermal load is assumed to be steady state constant temperature distribution, and the acoustic excitation is considered to be a stationary white-Gaussian random pressure with zero mean and uniform magnitude over the plate surface. The governing equations are transformed to modal coordinates to reduce the computational efforts. Newton–Raphson iteration method is employed to obtain the dynamic response at each time step of the Newmark implicit scheme for numerical integration. Finally, numerical results are provided to study the effects of volume fraction exponent, temperature rise, and the sound pressure level on the panel response.