Chemical composition, antioxidant and antibacterial properties of the essential oils of Etlingera elatior and Cinnamomum pubescens Kochummen

BACKGROUND: Plant essential oils are widely used as fragrances and flavours. Therefore, the essential oils from the leaves of Cinnamomum pubescens Kochummen (CP) and the whole plant of Etlingera elatior (EE) were investigated for their antioxidant, antibacterial and phytochemical properties. RESULTS: CP and EE were found to contain appreciable levels of total phenolic contents (50.6 and 33.41 g kg^?1 as gallic acid equivalent) and total flavonoid contents (205.6 and 244.8 g kg^?1 as rutin equivalent), respectively. DPPH free radical scavenging activity of CP is superior to EE (P < 0.05) showing IC₅₀ of 77.2 and 995.1 µg mL^?1, respectively. Methicillin‐resistant Staphylococcus aureus (MRSA), Bacillus subtilis, Pseudomonas aeruginosa and Salmonella choleraesuis were tested against CP and EE. Only MRSA was the most susceptible bacteria to CP. GC/MS studies resulted in the identification of 79 and 73 compounds in CP and EE, respectively. The most abundant components of EE included β‐pinene (24.92%) and 1‐dodecene (24.31%). While the major compound in CP were 1,6‐octadien‐3‐ol,3,7‐dimethyl (11.55%), cinnamaldehyde (56.15%) and 1‐phenyl‐propane‐2,2‐diol diethanoate (11.38%). CONCLUSION: This study suggests that the essential oils from Cinnamomum pubescens Kochummen and Etlingera elatior could be potentially used as a new source of natural antioxidant and antibacterial in the food and pharmaceutical industries. Copyright © 2010 Society of Chemical Industry     

Effect of polyethylenimine addition and washing on stability and electrophoretic deposition of Co3O4 nanoparticles

In this work, the parameters of cobalt oxide suspension such as conductivity, zeta potential, particle size, stability, and finally the electrophoretic behavior of particles in the absence and presence of polyethylenimine (PEI) in acetone medium were investigated. Also, the effects of washing on the stability and electrophoretic deposition of Co₃O₄ were studied. Characterization of the obtained layer by optical microscopy revealed that there was no deposition in the suspension without PEI, while a uniform layer was formed in the presence of PEI additive. Scanning electron microscopy (SEM) results confirmed the uniformity of layer obtained in acetone using PEI additive. Moreover, SEM results demonstrated that more porous microstructures were obtained at longer deposition durations. The difference in the porosity of the layers, as indicated by the SEM micrographs, is attributed to increase in the deposition time.     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

Graph attention-based collaborative filtering for user-specific recommender system using knowledge graph and deep neural networks

Knowledge and Information Systems - Collaborative filtering suffers from the issues of data sparsity and cold start. Due to which recommendation models that only rely on the user–item...     

Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines

A data assimilation (DA) methodology that uses two state-of-the-art techniques, relevance vector machines (RVMs) and support vector machines (SVMs), is applied to retrieve surface (0–6 cm) soil moisture content (SMC) and SMC at a depth of 30 cm. RVMs and SVMs are known for their robustness, efficiency and sparseness and provide a statistically sound approach to solve inverse problems and thus to build statistical models. Here, we build a statistical model that produces acceptable estimations of SMC by using inexpensive and readily available data. The study area for this research is the Walnut Creek watershed in Ames, south-central Iowa, USA. The data were obtained from Soil Moisture Experiments 2002 (SMEX02) conducted at Ames, Iowa. The DA methodology combines remotely sensed inputs with field measurements, crop physiological characteristics, soil temperature, soil water-holding capacity and meteorological data to build a two-step model to estimate SMC using both techniques, i.e. RVMs and SVMs. First, the RVM is used to build a model that retrieves surface (0–6 cm) SMC. This information serves as a boundary condition for the second step of this model, which estimates SMC at a depth of 30 cm. An exactly similar routine is followed with an SVM for estimation of surface (0–6 cm) SMC and SMC at a depth of 30 cm. The results from the RVM and SVM models are compared and statistics show that RVMs perform better (root mean square error (RMSE)?= 0.014 m³ m^?3) when compared with SVMs (RMSE?= 0.017 m³ m^?3) with a reduced computational complexity and more suitable real-time implementation. Cross-validation techniques are used to optimize the model. Bootstrapping is used to check over/under-fitting and uncertainty in model estimates. Computations show good agreement with the actual SMC measurements with coefficients of determination (R ²) for RVM equal to 0.92 and for SVM equal to 0.88. Statistics indicate a good model generalization capability with indexes of agreement (IoAs) for RVM equal to 0.97 and for SVM equal to 0.96.     

Numerical investigation of effective parameters in convective heat transfer of nanofluids flowing under a laminar flow regime

This article presents a numerical investigation on heat transfer performance and pressure drop of nanofluids flows through a straight circular pipe in a laminar flow regime and constant heat flux boundary condition. Al₂O₃, CuO, carbon nanotube (CNT) and titanate nanotube (TNT) nanoparticles dispersed in water and ethylene glycol/water with particle concentrations ranging between 0 and 6 vol.% were used as working fluids for simulating the heat transfer and flow behaviours of nanofluids. The proposed model has been validated with the available experimental data and correlations. The effects of particle concentrations, particle diameter, particles Brownian motions, Reynolds number, type of the nanoparticles and base fluid on the heat transfer coefficient and pressure drop of nanofluids were determined and discussed in details. The results indicated that the particle volume concentration, Brownian motion and aspect ratio of nanoparticles similar to flow Reynolds number increase the heat transfer coefficient, while the nanoparticle diameter has an opposite effect on the heat transfer coefficient. Finally, the present study provides some considerations for the appropriate choice of the nanofluids for practical applications.     

Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics

Ultra-high-performance concrete (UHPC) is a recent class of concrete with improved durability, rheological and mechanical and durability properties compared to traditional concrete. The production cost of UHPC is considerably high due to a large amount of cement used, and also the high price of other required constituents such as quartz powder, silica fume, fibres and superplasticisers. To achieve specific requirements such as desired production cost, strength and flowability, the proportions of UHPC’s constituents must be well adjusted. The traditional mixture design of concrete requires cumbersome, costly and extensive experimental program. Therefore, mathematical optimisation, design of experiments (DOE) and statistical mixture design (SMD) methods have been used in recent years, particularly for meeting multiple objectives. In traditional methods, simple regression models such as multiple linear regression models are used as objective functions according to the requirements. Once the model is constructed, mathematical programming and simplex algorithms are usually used to find optimal solutions. However, a more flexible procedure enabling the use of high accuracy nonlinear models and defining different scenarios for multi-objective mixture design is required, particularly when it comes to data which are not well structured to fit simple regression models such as multiple linear regression. This paper aims to demonstrate a procedure integrating machine learning (ML) algorithms such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) to develop high-accuracy models, and a metaheuristic optimisation algorithm called Particle Swarm Optimisation (PSO) algorithm for multi-objective mixture design and optimisation of UHPC reinforced with steel fibers. A reliable experimental dataset is used to develop the models and to justify the final results. The comparison of the obtained results with the experimental results validates the capability of the proposed procedure for multi-objective mixture design and optimisation of steel fiber reinforced UHPC. The proposed procedure not only reduces the efforts in the experimental design of UHPC but also leads to the optimal mixtures when the designer faces strength-flowability-cost paradoxes.

     

Dynamic mechanical behavior and nanostructure morphology of hyperbranched‐modified polypropylene blends

Polypropylene (PP) was modified utilizing two types of polyesteramide‐based hyperbranched polymers (amphiphilic PS and hydrophilic PH). A maleicanhydride‐modified PP (PM) was used as a reactive dispersing agent to enhance the modification by grafting the hyperbranched polymers onto the PP chains. Pure PP, two different non‐reactively modified samples, i.e. excluding PM, and two different reactively modified samples, i.e. including PM, were studied. Investigating the morphology of the samples was performed by scanning electron microscopy. To follow the effect of the modification on the dynamic mechanical properties, dynamic mechanical analysis experiments both in the melt (rheometric mechanical spectrometry) and in solid state (dynamic mechanical thermal analysis) were carried out. In the next step, the nanocrystalline structure of the samples was studied by small angle X‐ray scattering (SAXS) in two different modes, i.e. static and recrystallization. Hundreds of SAXS patterns were analyzed automatically using procedures written in PV‐WAVE image‐processing software. The chord distribution function (CDF) was calculated and the long period (l_p) of the crystal lamellae was extracted from the CDFs. The rheometric mechanical spectrometry results show that both hyperbranched polymers decrease complex viscosity η^* and enhance liquid‐like behavior. This happens more significantly when PM is included. The dynamic mechanical thermal analysis results reveal that T_g decreases when PS and PH are added. In the reactively modified samples this reduction is compensated most probably because of the crosslinked structure formed through the grafting reaction between the hyperbranched polymers and PM. Such structure is confirmed by SAXS data and calculated CDFs in the recrystallization mode. Static SAXS data also show enhancement in the crosshatched morphology of the crystalline lamellae of PP for reactively modified samples compared with non‐reactively modified samples. © 2013 Society of Chemical Industry     

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool

In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non‐linear controllers. The related gains of the designed controllers are optimised using genetic algorithm and simultaneously are adapted by employing the Fuzzy scheduling method. The cancer dynamic model is extracted with five differential equations, including normal cells, endothelial cells, cancer cells, and the amount of two chemotherapy and anti‐angiogenic drugs left in the body as the engaged state variables, while double drug injection is considered as the corresponding controlling signals of the mentioned state space. This treatment aims to reduce the tumour cells by providing a timely schedule for drug dosage. In chemotherapy, not only the cancer cells are killed but also other healthy cells will be destroyed, so the rate of drug injection is highly significant. It is shown that the simultaneous application of chemotherapy and anti‐angiogenic therapy is more efficient than single chemotherapy. Two different non‐linear controllers are employed and their performances are compared. Simulation results and comparison studies show that not only adding the anti‐angiogenic reduce the side effects of chemotherapy but also the proposed robust controller of sliding mode provides a faster and stronger treatment in the presence of patient parametric uncertainties in an optimal way. As a result of the proposed closed‐loop drug treatment, the tumour cells rapidly decrease to zero, while the normal cells remain healthy simultaneously. Also, the injection rate of the chemotherapy drug is very low after a short time and converges to zero.     

Effect of high temperature annealing on scratch behavior of acrylonitrile styrene acrylate copolymers

The scratch behavior of butyl-acrylate rubber-modified styrene-acrylonitrile (ASA) upon high temperature annealing is investigated following the ASTM D7027/ISO 19252 linearly increasing normal load test methodology. The critical normal loads at the onset of the major deformation transitions along the scratch path, such as groove formation, scratch visibility, microcrack formation, and plowing, are reported and quantitatively analyzed. It is found that the scratch resistance improves with high temperature annealing, i.e., 30 °C above T_g, as compared to annealing below or around T_g. Microscopic investigation suggests that the increase in scratch resistance is related to the changes in surface morphology of the polymer. It is concluded that performing high temperature annealing enhances the scratch performance without compromising ASA bulk properties. Implication of the present study for improving scratch resistance of polymers is discussed.