Magnetic Properties of Zinc Ferrite Nanoparticles Synthesized by Coprecipitation Method

The influence of the precipitant and ferric concentration on the magnetic properties of coprecipitated zinc ferrite nanoparticles has been investigated. The nanoparticles were characterized using X-ray diffraction, scanning and transmission electron microscope, and vibrating sample magnetometer techniques. The results showed that the single-phase zinc ferrite with partially inverse spinel structures can be formed at high concentrations. The inversion coefficient calculated by the Rietveld method decreases with increasing of the concentrations, may be due to the crystal growth. The magnetic measurements exhibited that the coprecipitated zinc ferrite nanoparticles were superparamagnet and magnetization decreases with increasing of the concentrations through decreasing of inversion coefficient.     

Kinetics Inside,Outside and Through Cucurbiturils

This review describes the kinetics at play in Cucurbituril (CB[n])/guest recognition within a very wide time frame, from picoseconds to days. We cover the kinetics of (1) radiative and non-radiative decays of fluorophores inside and outside CB[n]s, (2) guest motions in the CB[n] cavity, and (3) ingression, egression and threading mechanisms into, out of and through the macrocycles. We then show that the kinetics of guest release or capture can be controlled using CB[n]-capped mesoporous systems and micelles, or by coupling those processes to the kinetics of ancillary reactions. The last section is devoted to the latest examples of reactions catalyzed and inhibited by CB[n]s.     

A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions

Industrialization and population growth have been accompanied by many problems such as waste management worldwide. Waste management and reduction have a vital role in national management. The presents study represents a multi-objective location-routing problem for hazardous wastes. The model was solved using Non dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Multi-Objective Invasive Weed Optimization, Pareto Envelope-based Selection Algorithm, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer algorithms. The findings revealed that the Multi-Objective Invasive Weed Optimization algorithm was the best and the most efficient among the algorithms used in this study. Obtaining income from the incineration of the wastes and reducing the risk of COVID-19 infection are the first innovation of the present study, which considered in the presented model. The second innovation is that uncertainty was considered for some of the crucial parameters of the model while the robust fuzzy optimization model was applied. Besides, the model was solved using several meta-heuristic algorithms such as Multi-Objective Invasive Weed Optimization, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer, which were rarely used in literature. Eventually, the most efficient algorithm was identified by comparing the considered algorithms.

     

Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

This study examines and compares the performance of four new attractive artificial intelligence techniques including artificial neural network (ANN), hybrid wavelet-artificial neural network (WANN), Genetic expression programming (GEP), and hybrid wavelet-genetic expression programming (WGEP) for daily mean streamflow prediction of perennial and non-perennial rivers located in semi-arid region of Zagros mountains in Iran. For this purpose, data of daily mean streamflow of the Behesht-Abad (perennial) and Joneghan (non-perennial) rivers as well as precipitation information of 17 meteorological stations for the period 1999–2008 were used. Coefficient of determination (R²) and root mean square error (RMSE) were used for evaluating the applicability of developed models. This study showed that although the GEP model was the most accurate in predicting peak flows, but in overall among the four mentioned models in both perennial and non-perennial rivers, WANN had the best performance. Among input patterns, flow based and coupled precipitation-flow based patterns with negligible difference to each other were determined to be the best patterns. Also this study confirmed that combining wavelet method with ANN and GEP and developing WANN and WGEP methods results in improving the performance of ANN and GEP models.     

Comparison of blood lipid-lowering effects of olive oil and other plant oils: A systematic review and meta‐analysis of 27 randomized placebo‐controlled clinical trials

Objective: We aim to report a systematic review and meta-analysis of randomized controlled trials (RCTs) on effects of olive oil consumption compared with other plant oils on blood lipids.

Methods: PubMed, web of science, Scopus, ProQuest, and Embase were systematically searched until September 2017, with no age, language and design restrictions. Weighed mean difference (WMD) and 95% confidence interval (CI) were expressed as effect size. Sensitivity analyses and pre specified subgroup was conducted to evaluate potential heterogeneity. Meta-regression analyses were performed to investigate association between blood lipid-lowering effects of olive oil and duration of treatment.

Results: Twenty-seven trials, comprising 1089 participants met the eligibility criteria. Results of this study showed that compared to other plant oils, high-density lipoprotein level increased significantly more for OO (1.37 mg/dl: 95% CI: 0.4, 2.36). Also OO consumption reduced total cholesterol (TC) (6.27 mg/dl, 95% CI: 2.8, 10.6), Low-density lipoprotein (LDL-c) (4.2 mg/dl, 95% CI: 1.4, 7.01), and triglyceride (TG) (4.31 mg/dl, 95% CI: 0.5, 8.12) significantly less than other plant oils. There were no significant effects on Apo lipoprotein A1 and Apo lipoprotein B.

Conclusion: This meta-analysis suggested that OO consumption decreased serum TC, LDL-c, and TG less but increased HDL-c more than other plant oils.     

A novel shape-stabilized PEG/novolac resin by sol–gel polymerization

In this study, a novel polyethylene glycol (PEG)/novolac shape-stabilized, composite, phase-change material was prepared with various mass fractions of PEG via sol–gel polymerization. The influences of PEG content on morphology, crystallinity, phase-change enthalpy efficiency and shift of melt temperature of the phase-change materials (super cooling effect) were investigated. Fourier transform infrared spectroscopy indicated no chemical reaction during the composite preparation and showed O–H bonding intensity increased with increase of novolac content. Field emission scanning electron microscopy images of the shape-stabilized phase-change material showed trapping of PEG chains into colloidal structure of novolac even at mass fraction of novolac as low as 10 wt%. Differential scanning calorimetry and X-ray diffraction techniques revealed well thermal properties of samples regarding phase-change material enthalpy as compared to their bulk forms. Among prepared samples in this work, samples with 15 wt% novolac showed improved phase-change enthalpy and crystallinity as compared to neat PEG. This work not only supplied efficient shape-stabilized phase-change materials without PCM leakage, but also improved the melt enthalpy and crystallinity of this new shape-stabilized PCM systems. This system give the opportunity to have improved phase change enthalpy and crystallization behavior of sample with 85 wt% of PEG in comparison to neat PEG.