Effects of nano-chitosan coatings incorporating with free/nano-encapsulated essential oil of Golpar (Heracleum persicum L.) on quality characteristics and safety of rainbow trout (Oncorhynchus mykiss).

This study investigated the effect of nano-chitosan coating containing free/nano forms of Heracleum persicum L. essential oil (HPEO) on microbial, chemical and sensory properties of rainbow trout (Oncorhynchus mykiss) at 4 °C during storage. In this study, nanoliposomes were prepared using different soy lecithin/cholesterol ratios (30:30, 40:20, 50:10 and 60:0) and sonication-hydration techniques with sizes of 78.70-123 nm. Liposome with a ratio of 50:10 had the greatest EE%. The zeta potantial was between -5.62 ± 0.28 to -3.71 ± 0.31 mV. The index of polydispersity and encapsulation efficiency (EE) were 0.452-0.481 and 51.93-68.13 %, respectively. Based on the results of microbiological and chemical analyses, samples treated with with nanochitosan/nano essential oil (NCS-NEO) had a better effect at the end of the study than other samples (NCS-EO, NCS and control). For NCS-NEO treatment at the end of storage time, analysis the value of total volatile basic nitrogen (TVB-N), pH, thiobarbituric acid reactive substances (TBARs) and peroxide value (PV) were, 27.95 (mg/100 g), 6.9, 0.37 (mEq/kg), 5.13 (mg MDA/kg), respectively, and the total pseudomonads count (TPC), total viable counts (TVC) and lactic acid bacteria (LAB) were, 8, 6.8, and 6.5 CFU/g, respectively. Further, the NCS-NEO treatment (at the end of study) achieved the highest sensory characteristics. HPEO encapsulation decreases diffusion rates, so improving activity of antioxidant and antimicrobial, and characteristics of sensory. This study suggested that coating nano forms of HPEO with NCS could be used to enhance shelf life of rainbow trout fish.

A Comparative and Review Study on Shape and Stress Sensing of Flat/Curved Shell Geometries Using C0-Continuous Family of iFEM Elements.

In this study, we methodologically compare and review the accuracy and performance of C0-continuous flat and curved inverse-shell elements (i.e., iMIN3, iQS4, and iCS8) for inverse finite element method (iFEM) in terms of shape, strain, and stress monitoring, and damage detection on various plane and curved geometries subjected to different loading and constraint conditions. For this purpose, four different benchmark problems are proposed, namely, a tapered plate, a quarter of a cylindrical shell, a stiffened curved plate, and a curved plate with a degraded material region in stiffness, representing a damage. The complexity of these test cases is increased systematically to reveal the advantages and shortcomings of the elements under different sensor density deployments. The reference displacement solutions and strain-sensor data used in the benchmark problems are established numerically, utilizing direct finite element analysis. After performing shape-, strain-, and stress-sensing analyses, the reference solutions are compared to the reconstructed solutions of iMIN3, iQS4, and iCS8 models. For plane geometries with sparse sensor configurations, these three elements provide rather close reconstructed-displacement fields with slightly more accurate stress sensing using iCS8 than when using iMIN3/iQS4. It is demonstrated on the curved geometry that the cross-diagonal meshing of a quadrilateral element pattern (e.g., leading to four iMIN3 elements) improves the accuracy of the displacement reconstruction as compared to a single-diagonal meshing strategy (e.g., two iMIN3 elements in a quad-shape element) utilizing iMIN3 element. Nevertheless, regardless of any geometry, sensor density, and meshing strategy, iQS4 has better shape and stress-sensing than iMIN3. As the complexity of the problem is elevated, the predictive capabilities of iCS8 element become obviously superior to that of flat inverse-shell elements (e.g., iMIN3 and iQS4) in terms of both shape sensing and damage detection. Comprehensively speaking, we envisage that the set of scrupulously selected test cases proposed herein can be reliable benchmarks for testing/validating/comparing for the features of newly developed inverse elements.

Oxidative stability of virgin olive oil: evaluation and prediction with an adaptive neuro-fuzzy inference system (ANFIS).

BACKGROUND: An adaptive neuro-fuzzy inference system (ANFIS) was employed to predict the oxidative stability of virgin olive oil (VOO) during storage as a function of time, storage temperature, total polyphenol, α-tocopherol, fatty acid profile, ultraviolet (UV) extinction coefficient (K268 ), and diacylglycerols (DAGs). RESULTS: The mean total quantities of polyphenols and DAGs were 1.1 and 1.9 times lower in VOOs stored at 25 °C than in the initial samples, and the mean total quantities of polyphenols and DAGs were 1.3 and 2.26 times lower in VOOs stored at 37 °C than in the initial samples, respectively. In a single sample, α-tocopherol was reduced by between 0.52 and 0.91 times during storage, regardless of the storage temperature. The mean specific UV extinction coefficients (K268 ) for VOO stored at 25 and 37 °C were reported as 0.15 (ranging between 0.06-0.39) and 0.13 (ranging between 0.06-0.35), respectively. The ANFIS model created a multi-dimensional correlation function, which used compositional variables and environmental conditions to assess the quality of VOO. The ANFIS model, with a generalized bell-shaped membership function and a hybrid learning algorithm (R2  = 0.98; MSE = 0.0001), provided more precise predictions than other algorithms. CONCLUSION: Minor constituents were found to be the most important factors influencing the preservation status and freshness of VOO during storage. Relative changes (increases and reductions) in DAGs were good indicators of oil oxidative stability. The observed effectiveness of ANFIS for modeling oxidative stability parameters confirmed its potential use as a supplemental tool in the predictive quality assessment of VOO. © 2019 Society of Chemical Industry.

Age at Natural Menopause and Related Factors in Isfahan, Iran.

OBJECTIVE: This study was aimed to evaluate the age at natural menopause and related factors among women in a population based study in 2015 in Isfahan, Islamic Republic of Iran. METHODS: In this cross-sectional study 960 menopausal women were selected by cluster sampling. Demographic, socioeconomic, lifestyle behavior and reproductive history aspects were collected using a structured questionnaire. Woman and her husband's educational level and occupation with family income were the variables to construct socioeconomic status using principal component analysis. RESULTS: Mean and median of natural menopause age were 48.66 and 48 years, respectively. Women body mass index (BMI) more than 30 kg/m(2) had significantly higher menopausal age than women with lower BMI (P value = 0.022). The mean of menopausal age was not statistically significant in regard to marital status, physical activity, smoking status, menarche age, age at first pregnancy and history of abortion. Menopause age with pregnancy numbers and age at last pregnancy had a significant positive association. Women with better socioeconomic status had significantly higher natural menopause age. Multiple linear regression shows significant relationship between lower age at menopause with higher age at marriage, higher number of pregnancy and lower socioeconomic status. CONCLUSION: Age at menopause in our studied sample is similar to previous estimates reported for other Iranian populations. Age at marriage, higher number of pregnancy and lower socioeconomic status were the significant factors in relations to age at menopause.