Recent development on recovering bioactive peptides and phenolic compounds from under-utilised by-products during production of certain edible oil plants: current situation and future perspectives

During the edible oil production process, massive amounts of by-products are generated each year that are not efficiently utilised, posing economic and environmental challenges. Under-utilised products derived from valuable oil-plants such as oil-extracted wheat germ, oil-extracted moringa seed, oil-extracted walnut and flaxseed meal will be increased, and new integration concepts will be required as soon as possible. These underutilised products can be rich sources of phenolic compounds and bioactive peptides with appropriate health-promoting properties such as anti-diabetic, antioxidant, anti-hypertensive, anti-inflammatory, anticancer and antibacterial properties, according to this review. The recovery of bioactive compounds from these by-products can go a long way toward ensuring a sustainable food supply. Current challenges and requirements for recovering bioactive peptides and phenolic compounds from underutilised products derived from valuable oil-plants as well as the relationship between their biological activity and structure are discussed in this study.     

Incorporation of Iron Nanoparticles into Silicon Carbide Nanoparticles as Novel Antimicrobial Bimetallic Nanoparticles

Silicon - Zero valent iron nanoparticles have an attracting and ever growing interest in various research fields due to the fascinating potential. In the present work, antimicrobial activity of...     

Prediction of boundary shear stress in circular and trapezoidal channels with entropy concept

Relying on the power law to develop new relationships, the present study developed the Shannon entropy concept to predict shear stress distribution. The presented method was evaluated and compared with valid laboratory results and the shear stress distribution surveyed through this concept in circular, circular with flat bed and trapezoidal channels. By increasing the flow depth in circular channels, the model presented herein gives an improved prediction of shear stress distribution, while in circular channels with flat bed it is better in predicting shear stress both at lower flow depth and subcritical flow conditions. In comparison, in trapezoidal channels the model is much better in predicting the shear stress distribution at lower flow depths. Accordingly, with a mean error percentage of 1.99% in circular channels, 3.69% in circular channels with flat bed and 4.1% in trapezoidal channels, this novel model shows good ability in predicting shear stress distribution.     

A Hybrid GDM-SERVQUAL-QFD Approach for Service Quality Assessment in Hospitals

Quality is one of the most critical factors in health systems. To improve service quality in hospitals, we propose a hybrid approach incorporating Group decision-making (GDM), Service Quality measurement (SERVQUAL), and Quality function deployment (QFD). QFD is used to translate patient needs into quality characteristics (derived from SERVQUAL), while taking into account interrelationships between design requirements and quality characteristics. One of the most important steps in the QFD process is to prioritize quality characteristics from the patients’ viewpoint, which is inherently affected by two types of uncertainties: human mental perception and patient heterogeneity. In this research, a novel two-stage GDM approach is employed to cope with these uncertainties in an integrated manner. Furthermore, the proposed methodology is applied in a public hospital to effectively enhance quality characteristics, which will be considered in the QFD. Accordingly, the proposed methodology provides a framework for engineering professionals and managers in hospitals to assess and manage quality characteristics.     

Investigation on the effect of process parameter on physical properties of RF-sputtered Mo–Ni thin films as a back contact thin-film solar cell

Journal of Materials Science: Materials in Electronics - In this work, the influence of RF power and deposition time on the structural, chemical, morphological, optical, and electrical properties...     

European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation

Regarding the ability of data mining algorithms for post-processing the output of climate models, and on the other hand, the successful application of multi-model ensemble approaches in climate forecasts, in this paper, some important data mining algorithms are evaluated for the monthly forecast of precipitation over Iran. For this purpose, four European climate models, from DWD, ECMWF, CMCC and Meteo-France, with six lead times, are used to be post-processed by applying four different algorithms including artificial neural networks, support vector regression, decision tree and random forests. Based on the proposed approach, 72 different models are provided for 12 months, each month with six lead times. The approach is applied for the monthly forecast of precipitation over Iran. According to the results, the neural network and random forest methods performed better than the decision tree and the support vector machine. This advantage preserved for all months of the year. Also, the proposed multi-model approach outperformed any of the individual European models.

     

Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

Water Resources Management - An important issue in water engineering is predicting suspended sediment load (SSL). For the Telar River and its tributaries, this study employs an inclusive multiple...     

Mechanical and biological performance of rainbow trout collagen-boron nitride nanocomposite scaffolds for soft tissue engineering

We aim to investigate the potential of collagen extracted from rainbow trout for tissue engineering applications. In this regard, nanocomposite scaffolds based on the extracted collagen reinforced with various concentrations of boron nitride (BN) nanoparticles (0, 3, 6, 9, and 12 wt%) were developed. In addition, the role of various concentrations of BN nanoparticles and two-step cross-linking process on the physical and chemical properties of nanocomposite scaffolds were investigated. Our results demonstrated the isolation of Type I collagen with excellent thermal stability but with some structural and chemical differences compared to other sources. The synergic role of BN nanoparticles and two-step cross-linking process resulted in a noticeable improvement in the mechanical properties of collagen-BN scaffolds. Noticeably, incorporation of 6 wt% BN along with a two-step cross-linking process significantly increased the compressive strength (9.5 times) and elastic modulus (four times) of the collagen scaffold. Besides, nanocomposite scaffolds significantly improved proliferation and spreading of MG-63 cell line, confirming their biocompatibility. The results suggested that the incorporation of BN nanoparticles along with a two-step cross-linking process not only could promote the mechanical and thermal performances of collagen scaffolds, but also enhanced high cell viability, and proliferation supporting their potential in tissue engineering applications.     

Empirical Study of Wavelet Domain Image Joint Statistics and Proposition of an Efficient Correlation Map

This paper presents an empirical study of joint wavelet statistics for textures and other imagery to find an efficient correlation neighborhood. Since there is an established realization that modeling wavelet and other x-let coefficient relationships is crucial to any successful transform domain algorithm (such as Hidden Markov Trees), new works have been devoted to examine these dependencies from different aspects and propose an appropriate model. Because the time and computation complexity involved both in analyzing non-linear dependencies and in solving dependent models may restrict us to consider only a very small subset of contributing neighbors we focus our attention on linear dependencies (correlations) while having a squint on non-linear relations too. In this process, we study a collection of 5000 real images to corroborate our statistical analysis of the joint coefficient behavior and try to find an efficient and at the same time frugal relation map through different statistical means. The statistical observations are then certified by a coefficient significance measure and the competitiveness of the map is substantiated by plugging it into two dependent denoising frameworks.     

New photosensitive poly(amid‐imide)s containing chalcone moiety and hydantoin derivatives in the main chain: Synthesis and characterization

Six new poly(amid‐imide)s containing chalchone and hydantoin moieties in the main chain were synthesized through the polycondensation reaction of 1,3‐bis[4,4′‐bis(trimellityimido)phenyl]‐2‐propenone 6 with six hydantoin derivatives 7a‐f in a medium consisting of triphenyl phosphite, calcium chloride, pyridine, and N‐methyl‐2‐pyrrolidone. The polycondensation reaction produced a series of novel poly(amid‐imide)s 8a‐f in high yields with inherent viscosities between 0.26 and 0.42 dL/g. The resulting polymers were characterized by elemental analysis, viscosity measurements, solubility test, thermo gravimetric analysis (TGA and DTG), FTIR, and UV‐Vis spectroscopy. 1,3‐bis[4,4′‐bis(trimellityimido)phenyl]‐2‐propenone 6 was prepared from a three‐step reaction by using 4‐nitro benzaldehyde 1 and 4‐nitro acetophenone 2 as precursors. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009