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Background and Objectives: In the past, women traditionally have been supported by other women during labor. Although, in recent decades the issue of continuous support during labor in all hospitals in the world has been discussed, this issue has become an exeption rather than a routine. Therefore, this study aimed at assessing the effect of supportive behaviours in comparrison with usual care on labor pain process.Methods: This quasi-experimental study was conducted on 80 primigravidas who had referred to Zanjan Valieasr hospital because of labor pain or rapture of membrane. At first, samples were selected by purposeful sampling, so they were randomly allocated into two groups; case and control. In the case group, supportive behaviors were conducted by a midwife. The control group only received usual care. For data gathering, a questionnaire and pain evaluation scale (VAS) were used. The data were analyzed by various statistical tests such as T test, Chi-Square, Mann Whitney test (p=0.05) in SPSS10 software. Results: The findings of Independent T-test showed a significant difference between case and control groups in mean pain severity in three situations (dill: 5cm, dill: 7cm, full dilatation) (P

A systematic investigation of magnetic nanoparticles and the formation of a core-shell structure, consisting of multiple maghemite (γ-Fe2O3) nanoparticles as the core and silica as the shell, has been performed using various techniques. High-resolution transmission electron microscopy clearly shows isolated maghemite nanoparticles with an average diameter of 13 nm and the formation of a core-shell structure. Low temperature Mössbauer spectroscopy reveals the presence of pure maghemite nanoparticles with all vacancies at the B-sites. Isothermal magnetization and zero-field-cooled and field-cooled measurements are used for investigating the magnetic properties of the nanoparticles. The magnetization results are in good accordance with the contents of the magnetic core and the non-magnetic shell. The multiple-core γ-Fe2O3 nanoparticles show similar behavior to isolated particles of the same size.

This review provides a summary of the growth, progress, and characterization studies of benzimidazole single crystals and their derivatives. Various methods, including SEST, Vertical Bridgeman, and Sankaran Narayanan methods, were employed to grow organic nonlinear optical (NLO) single crystals of BMZ derivatives. The developed crystals underwent different characterization analyses such as UV-Vis-NIR spectroscopy, microhardness testing, and Second Harmonic Generation. The focus of this review is specifically on the advancement of the NLO characteristics of the BMZ single crystal. [ABSTRACT FROM AUTHOR]

In this study the structural and magnetization properties of a CoFe2O4-based ferrofluid was investigated using x-ray diffraction (XRD), transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy (EDS), Mössbauer spectroscopy, and magnetic Compton scattering (MCS) measurements. The XRD diagram indicates that the nanoparticles in the ferrofluid are inverse spinel and TEM graph shows that the ferrofluid consists of spherical nanoparticles with an average diameter of 18± 1 nm, in good agreement with the size, 19.4 nm, extracted from line broadening of the XRD peaks. According to EDS measurements the composition of the nanoparticles is CoFe2O4. Mössbauer spectroscopy shows that the cation distributions are (Co0.38Fe0.62)[Co0.62Fe1.38]O4. The MCS measurement, performed at 10 K, indicates that the magnetization of the nanoparticles is similar to magnetization of maghemite and magnetite. While the magnetization of the inverse spinels are in [111] direction, interestingly, the magnetization deduced from MCS is in [100] direction. The CoFe2O4-based ferrofluid is found to be stable at ambient conditions, which is important for applications.

The non‐hydrostatic Model for Prediction Across Scales‐Atmosphere (MPAS‐A) dynamical core has recently been adapted for the Specified Chemistry Whole Atmosphere Community Climate Model (SC‐WACCM). In this study, the mean zonal wind and temperature climatology from SC‐WACCM/MPAS‐A is compared with the results from SC‐WACCM using the finite volume and spectral element dynamical cores, as well as the zonal wind and temperature climatology of Upper Atmosphere Research Satellite mission and SABER. The simulations have been performed at horizontal resolutions of ∼100 km. Generally a good agreement is seen between the results from the three dynamical cores, which verifies that the new dynamical core is working with WACCM. Plain Language Summary: Development of whole atmosphere models are focusing increasingly more on geospace applications. This focus stems from our society becoming dependent on advanced technology which can be vulnerable to space weather. In these regions the accuracy of the hydrostatic approximation becomes problematic. In this work the Whole Atmosphere Community Climate Model at the National Center for Atmospheric Research is developed and tested with the non‐hydrostatic Model for Prediction Across Scales‐Atmosphere dynamical core. Key Points: Whole Atmosphere Community Climate Model (WACCM) with the non‐hydrostatic Model for Prediction Across Scales‐Atmosphere (MPAS‐A) dynamical core has been developedTemperature and wind climatology from WACCM/MPAS‐A is compared with results from the spectral element (SE) and finite volume (FV) dynamical cores, as well as available observationsPreliminary validation demonstrates agreement with the result from the SE and FV dynamical cores [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: To address the potential harm caused by the intensive use of pesticides in pest control in agriculture, there is a need for accurate and efficient methods to detect and monitor pesticide residues. Therefore, this study aimed to develop a biosensor that can detect organophosphate pesticides highly toxic to humans and the environment. METHODS: Biosensor organophosphate pesticides using a single film containing acetylcholinesterase/choline oxidase have been designed using silver-based plasmonic nanoparticles as a colorimetric indicator. In the presence of acetylcholinesterase, acetylcholine is hydrolyzed to choline and acetic acid, then choline oxidase catalyzes the oxidation of choline to hydrogen peroxide and betaine. Hydrogen peroxide reacts with the silver nanoparticles, and the discoloration of the brown solution occurs due to the oxidation of silver+. FINDINGS: As a biosensor indicator, silver nanoparticles were extremely accurate, sensitive, and stable over a long period of storage. Transmission Electron Microscope images confirmed the reduction in size of nanoparticles from 16.82 ± 4.36 to 9.63 ± 2.29 nanometers. The analyte profenofos, one of the organophosphate pesticides, inhibits the activity of acetylcholinesterase, thereby reducing the concentration decrease of silver nanoparticles by releasing less hydrogen peroxide. Optimum conditions for biosensors were achieved with a potential of Hydrogen of 7, buffer, and acetylcholinesterase concentrations of 7 and 70 millimolar, respectively, with an incubation time of 5 minutes. Biosensor response showed a linear range at profenofos concentrations of 0.05-2.00 milligrams per liter, with limits of detection and quantization of 0.04 and 0.13 milligrams/liter, respectively. Biosensor also has excellent sensitivity, reproducibility, and stability, with a Relative Standard Deviation of 2.5 percent and a stable response of up to 4 months. Subsequently, using a biosensor in the chilli as a sample resulted in a profonefos level of 0.04 milligrams per liter, making it safe for consumption. CONCLUSION: Biosensor measurement outcome aligned with the gas chromatographymass spectrometry result, which is the accepted standard method for detecting profenofos. Additionally, the proposed biosensor offers several advantages such as ease of use, fast, low-cost, and on-site analysis. Hence, this method is suitable for monitoring and controlling pesticide residues, particularly organophosphate, in agricultural products and the environment. [ABSTRACT FROM AUTHOR]

Flood causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal and environmental damage. The aim of this study was assessing the performance of different machine learning methods in predicting flood in Karkheh basin. To aim this, we used Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM), Feed Forward Back Propagation Neural Network (FFBPNN), and Radial Basis Function Neural Network (RBFNN) to simulate monthly streamflow in the study area. Furthermore, the performance of models was compared in predicting flood. All four models indicated good performance in simulating streamflow. However, LSSVM model had the highest accuracy compared with other models with R2 and RMSE of 85.89% and 30.02 m3/s during testing periods, respectively. Similarly, LSSVM model performed better in predicting annual maximum streamflow in comparison with other machine learning models. [ABSTRACT FROM AUTHOR]