Changes in Volatile Compounds of Black Cumin Oil and Hazelnut Oil by Microwave Heating Process

Black cumin and hazelnut oils were subjected to a heating process in a microwave oven for a duration of 2, 4, 6 and 8 min at a constant frequency of 2450 MHz and a power of 0.45 kW. The ultraviolet absorption and volatile products of the oils were investigated in detail during the processes. The experimental evidences obtained show that K₂₃₂ and K₂₇₀ parameters reach values of 4.69 and 1.30 for black cumin oil, 3.22 and 1.75 for hazelnut oil, respectively with the increment of heating time. The headspace SPME method was used to analyze volatile compounds extracted from black cumin and hazelnut oils being exposed to the microwave heating process. The SPME–GC/MS method allowed the detection of 17 identified volatile compounds (hexanal, α‐thujene, α‐pinene, sabinene, β‐pinene, 2‐heptenal, α‐terpinene, limonene, p‐cymene, γ‐terpinene, E‐2‐octenal, nonanal, 4‐terpineol, thymoquinone, E,E‐2,4‐decadienal, α‐longipinene and isolongifolene) in black cumin oils. Of the products, hexanal, 2‐heptenal, E‐2‐octenal, nonanal and E,E‐2,4‐decadienal were determined to be the predominant volatile oxidation products. In fact, the hexanal was found as a major volatile oxidation compound and reached a local maximum point of 7.41 × 10⁶ AU at the end of heating. On the other hand, only 8 volatile oxidation products (hexanal, heptanal, 2‐heptenal, nonanal, E‐2‐decenal, E,Z‐2,4‐decadienal, E,E‐2,4‐decadienal and E‐2‐tridecenal) were identified in hazelnut oils as a consequence of the heating process. Based on the experimental evidence observed, it is reasonable to conclude that the nonanal content dramatically increased at the end of heating and reached a value of 9.22 × 10⁶ AU.     

Comparison of Different Mass Spectrometry Workflows for the Proteomic Analysis of Tear Fluid

The tear film is a multi-layer fluid that covers the corneal and conjunctival epithelia of the eye and provides lubrication, nutrients, and protection from the outside environment. Tear fluid contains a high concentration of proteins and has thus been recognized as a potential source of biomarkers for ocular disorders due to its proximity to disease sites on the ocular surface and the non-invasive nature of its collection. This is particularly true in the case of dry eye disease, which directly impacts the tear film and its components. Proteomic analysis of tear fluid is challenging mainly due to the wide dynamic range of proteins and the small sample volumes. However, recent advancements in mass spectrometry have revolutionized the field of proteomics enabling unprecedented depth, speed, and accuracy, even with small sample volumes. In this study using the Orbitrap Fusion Tribrid mass spectrometer, we compared four different mass spectrometry workflows for the proteomic analysis of tear fluid collected via Schirmer strips. We were able to establish a method of in-strip protein digestion that identified >3000 proteins in human tear samples from 11 healthy subjects. Our method offers a significant improvement in the number of proteins identified compared to previously reported methods without pooling samples.     

Dual gas jet-assisted fibre laser blind cutting of dry pine wood by statistical modelling

This paper presents a statistical analysis of the performance of an Ytterbium fibre laser utilising single and dual gas jets in a single pass to blind cut dry pine wood. Cutting wood with lasers is a multi-factor process and a proper combination of the parameters involved is needed to achieve high quality and optimum process efficiency. This study employs design of experiments and statistical modelling approach to investigate the significant process parameters along with their interactions. A high brightness, 1 kW IPG single mode Ytterbium-doped fibre laser was employed to produce blind cuts on samples of dry pine wood. The experiments were performed parallel to the direction of the wood fibre. The parameters investigated include laser power, focal plane position, traverse speed, gas pressure and single and dual gas jets. Results were compared using a number of process responses which define the efficiency of the cut in terms of kerf depth, mass removal and energy consumption as well as quality of the cut section considering the heat-affected zone, kerf width and roughness of the edge of the surface. It has been found that application of an additional gas jet produced a surface finish superior to the single jet.     

Poly(ε‐caprolactone) composites containing gentamicin‐loaded β‐tricalcium phosphate/gelatin microspheres as bone tissue supports

In this work, novel antibacterial composites were prepared by using poly(ε‐caprolactone) (PCL) as the main matrix material, and gentamicin‐loaded microspheres composed of β‐tricalcium phosphate (β‐TCP) and gelatin. The purpose is to use this biodegradable material as a support for bone tissue. This composite system is expected to enhance bone regeneration by the presence of β‐TCP and prevent a possible infection that might occur around the defected bone region by the release of gentamicin. The effects of the ratio of the β‐TCP/gelatin microspheres on the morphological, mechanical, and degradation properties of composite films as well as in vitro antibiotic release and antibacterial activities against Escherichia coli and Staphylococcus aureus were investigated. The results showed that the composites of PCL and β‐TCP/gelatin microspheres had antibacterial activities for both bacteria. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

An Analytical Model for Compressive Creep Behavior of HDPE Used in Plastic Pipe Manufacturing

The creep behavior of high-density polyethylene has been studied by low compressive creep tests, and empirical results were analyzed and compared with analytically predicted results. The tests are performed upon ASTM standard specimens at various stress levels in the linear region. After these analyses, a mathematical model is derived and used to predict creep strain for long periods of time from the strain-time data obtained at different stresses over a time interval of 24 h.     

Very fast H2 production from the methanolysis of NaBH4 by metal‐free poly(ethylene imine) microgel catalysts

The polyethyleneimine (PEI) microgels prepared via microemulsion polymerization are protonated by hydrochloric acid treatment (p‐PEI) and quaternized (q‐PEI) via modification reaction with methyl iodide and with bromo alkanes of different alkyl chain lengths such as 1‐bromoethane, 1‐bromobutane, 1‐bromohexane, and 1‐bromooctane. The bare p‐PEI and q‐PEI microgels are used as catalysts directly without any metal nanoparticles for the methanolysis reaction of sodium borohydride (NaBH₄). Various parameters such as the protonation/quaternization reaction on PEI microgels, the amount of catalyst, the amount of NaBH₄, and temperature are investigated for their effects on the hydrogen (H₂) production rate. The reaction of self‐methanolysis of NaBH₄ finishes in about 32.5 min, whereas the bare PEI microgel as catalyst finishes the methanolysis of NaBH₄ in 22 min. Surprisingly, it is found that when the protonated PEI microgels are used as catalyst, the same methanolysis of NaBH₄ is finished in 1.5 min. The highest H₂ generation rate value is observed for protonated PEI microgels (10 mg) with 8013 mL of H₂/(g of catalyst.min) for the methanolysis of NaBH₄. Moreover, activation parameters are also calculated with activation energy value of 23.7 kJ/mol, enthalpy 20.9 kJ/mol, and entropy ?158 J/K.mol. Copyright © 2016 John Wiley & Sons, Ltd.     

Application of two non-linear prediction tools to the estimation of tunnel boring machine performance

Predicting tunnel boring machine (TBM) performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling machine. Many models and equations have previously been introduced to estimate TBM performance based on properties of both rock and machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring machine performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM performance as a function of rock properties. For this purpose, rock properties and machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations.     

Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

In this paper, we present a new system for the classification of electrocardiogram (ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.