A new method of producing high strength oil palm shell lightweight concrete

This paper presents a new method to produce high strength lightweight aggregate concrete (HSLWAC) using an agricultural solid waste, namely oil palm shell (OPS). This method is based on crushing large old OPS. Crushed OPS are hard and have a strong physical bond with hydrated cement paste. The 28 and 56 days compressive strength achieved in this study were about 53 and 56 MPa, respectively. Furthermore, it was observed that it was possible to produce grade 30 OPS concrete without the addition of any cementitious materials. Compared to previous studies, significantly lower cement content was used to produce this grade of concrete. Unlike OPS concrete incorporating uncrushed OPS aggregate, this study found that there is a strong correlation between the short term and 28-day compressive strength.     

Ink‐Free Reversible Optical Writing in Monolayers by Polymerization of a Trifunctional Monomer: Toward Rewritable “Molecular Paper”

A Langmuir–Blodgett film consisting of a dense array of trifunctional monomers bearing three 1,8‐diazaanthracene units is polymerized at an air/water interface or after transfer on solid substrates. The transfer does not affect the excimer fluorescence of the film, indicating that the monomers' packing with their diazaanthracene units stacked face‐to‐face is retained—a prerequisite for successful polymerization. The monomer film can be polymerized in confined areas on solid substrates by UV irradiation with a confocal microscope laser. The underlying chemistry of the polymerization, a [4+4]‐cycloaddition of the diazaanthracene units, leads to disappearance of the fluorescence in the irradiated regions which enables writing into the monolayer on a µm scale—thus the term “molecular paper.” The reaction can be reversed by heating which leads to a recovery of the fluorescence and to erasing of the writing. Alternative pathways for this phenomenon are discussed and control experiments are conducted to rule them out.     

Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision

Orange is a citrus fruit which is rich in vitamins and minerals and other nutrients. Getting the relationship between physical properties of orange and its mass can create tremendous change in the packaging industry. The Iranian orange fruits used in this study consisted of Bam cultivars that they got from Kermanshah–Iran (longitude: 7.03°E; latitude: 4.22°N). One hundred samples were randomly selected. All the measurements were carried out at the laboratory with temperature of 24 °C during 2 days. Fourteen parameters were got by image processing for each orange. ANFIS and SPSS models were employed to predict the mass based on perimeter and (width/length) value as inputs. The coefficient of determination (R²) for ANFIS and SPSS were 0.936999 and 0.919 respectively. To evaluate the ANFIS model, samples were divided into two sets, 70% of data was used for training the model and 30% of data was used to test the model. So, The ANFIS model with less error percentage can be used to design and develop sizing systems.     

Syntheses of immobilized G protein-coupled receptor chromatographic stationary phases: characterization of immobilized mu and kappa opioid receptors

Opioid receptors are members of the superfamily of G protein-coupled receptors (GPCRs). Liquid chromatographic stationary phases containing either the human mu or kappa opioid receptor subtypes have been developed to study the binding between the opioid receptors and their ligands. The receptors were obtained from Chinese hamster ovary cells stably transfected with human mu or kappa receptor subtypes. The receptors were isolated through partial solubilization with sodium cholate detergent, and the partially purified receptor complex was immobilized in the phospholipid analogue monolayer of an immobilized artificial membrane liquid chromatographic stationary phase. The resulting phase was packed into a glass column (1.8 x 0.5 (i.d.) cm) and used in the on-line chromatographic determination of drug/ligand binding affinities to the immobilized opioid receptors. Preliminary on-line binding studies employing frontal chromatographic techniques were conducted with the known mu antagonist (naloxone) and a kappa agonist (U69593). The calculated dissociation constants (Kd) were 110 nM for naloxone and 84 nM for U69593. The results indicate that the immobilized receptors retained their ability to specifically bind ligands and were active for 1200 column volumes applied over two weeks. Zonal chromatographic experiments were also conducted, and competitive displacements of the marker ligands were observed. The results suggest that the immobilized opioid receptor stationary phases can be used to qualitatively assess the binding affinities of compounds to the immobilized receptors and represent the first example of the use of immobilized GPCRs in a chromatographic system.     

Crystallization kinetics study of dynamically vulcanized PA6/NBR/HNTs nanocomposites by nonisothermal differential scanning calorimetry

Investigation of crystallization behavior and kinetics of thermoplastic elastomer nanocomposites was the subject of limited works because of complexities associated with semiexperimental modeling of such phenomenon in a system containing components having completely different behavior in the molten state. Nonisothermal crystallization kinetics of dynamically vulcanized PA6/NBR/HNTs thermoplastic elastomer nanocomposites was mathematically modeled applying well‐known Avrami, Ozawa, and Mo theoretical models to the differential scanning calorimetry data gathered at various cooling rates. It was found that HNTs contribute as nucleating agents to the crystallization kinetics and cause acceleration of crystallization. Activation energy of the crystallization was calculated by correlating the crystallization peak temperature with the cooling rate using Kissinger model. It was found that Mo equation could properly describe nonisothermal crystallization kinetics of the PA6/NBR/HNTs thermoplastic elastomer nanocomposites. This was recognized from the obtained parameters of Mo equation in terms of HNTs loading level, which suggested a higher rate for dynamic crystallization. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46488.     

The Growth‐Inhibiting Effects of Beef Fatty Acids on MCF‐7 Cells Are Influenced Mostly by the Depot Location and Inconsistently by the Biohydrogenation Intermediate Content

Biohydrogenation intermediates (BHI) including conjugated linoleic acid (CLA) isomers are formed during ruminal biohydrogenation of polyunsaturated fatty acids (PUFA) in ruminants. Although many studies have examined the anticarcinogenic effects of CLA, few studies have reported the anticarcinogenic properties of BHI in their natural form found in dairy and beef fats. The present study compared the growth‐inhibitory effects of fatty acids from beef perirenal fat (PRF) or subcutaneous fat (SCF) with low or high levels of BHI in MCF‐7 human breast cancer cells. Cells were exposed for 72 h to media containing increasing doses (50 to 400 μM) of different beef fat treatments. Fatty‐acid analysis showed that BHI were readily incorporated into cell phospholipids (PL) in a treatment‐dependent manner, but higher BHI in PL did not consistently inhibit growth. Culturing with low‐BHI PRF or high‐BHI PRF did not lead to growth inhibition, but low‐BHI SCF inhibited growth, and inhibition was further increased by high‐BHI SCF. Other classes of fatty acids may, therefore, be interacting with BHI resulting in differential effects on growth inhibition in human breast cancer cells.     

Using Hidden Markov Models for paper currency recognition

Accurate characterization is an important issue in paper currency recognition system. This paper proposes a robust paper currency recognition method based on Hidden Markov Model (HMM). By employing HMM, the texture characteristics of paper currencies are modeled as a random process. The proposed algorithm can be used for distinguishing paper currency from different countries. A similarity measure has been used for the classification in the proposed algorithm. To evaluate the performance of the proposed algorithm, experiments have been conducted on more than 100 denominations from different countries. The results indicate 98% accuracy for recognition of paper currency.     

Optimum model for bearing capacity of concrete-steel columns with AI technology via incorporating the algorithms of IWO and ABC

In a composite column, the performance of both the concrete and steel has a considerable effect on the structural behaviour under different loading conditions. This study applies several artificial intelligence (AI) techniques to optimise the bearing capacity of concrete-filled steel tube (CFST) columns. First, the bearing capacity values of the CFST columns are estimated by an artificial neural network (ANN) technique. Using 303 datasets, the outer diameter, concrete compressive strength, tensile yield stress of the steel column, thickness of the steel cover, and length of the applied samples are considered as the model inputs. Following a series of analyses, several ANN models are developed. The ANN model with 8 neurons and 250 iterations is determined as the best model to predict the bearing capacity of the CFST columns. Subsequently, the invasive weed optimisation (IWO) technique, which is considered the most current optimisation algorithm, is developed to maximise the results of the bearing capacity by considering the selected ANN model. To highlight the ability of IWO, the artificial bee colony (ABC) algorithm is also applied. Consequently, it is found that both optimisation algorithms can design input parameters such that the maximum value of the bearing capacity can be obtained. The bearing capacity of the CFST columns from the ABC and IWO techniques indicates that IWO has a better capability of maximising the bearing. Thus, IWO can optimise similar problems with a high rate of performance.

     

Implicit Finite-Volume Scheme to Solve Coupled Saint-Venant and Darcy–Forchheimer Equations for Modeling Flow Through Porous Structures

In hydrodynamic modeling of flow through porous structures, the solution domain might encounter discontinuities. These include, for example, porous structure-open channel interface, porous dam-break, and heterogeneous porous structures. The treatment of discontinuity is challenging within a numerical scheme as it can be a source of instabilities. This study proposes a finite-volume method to solve coupled Saint-Venant and Darcy–Forchheimer equations for simulating free-surface flow through porous structures. For capturing shocks arising at discontinuous regions, an upwind scheme is utilized to maintain the solution monotone. Fully implicit methods can allow the choice of longer time steps. Since the current problem involves two nonlinear systems, namely the open-channel and seepage flow equations, the Picard method is adopted to linearize the system of equations. Unlike typical implicit schemes of seepage flows, herein, both flow depth and velocity matrices appear within the iterative process, threatening the convergence criterion. To converge iteration, the continuity equation's flux term is treated using the dynamic wave equation under the relaxation method. The present model is applicable to simulate gradually and rapidly unsteady flow through homogeneous and heterogeneous porous media under laminar, transitional, and fully developed turbulent flow regimes within various closed and/or open boundary conditions.

     

Business model innovation driven by the internet of things technology,in internet service providers’ business context

Business model innovation (BMI) describes the efforts made by the business in finding new business logic or new ways of value creation. Technological change is deemed to be the main driver of BMI. This study focused on the emergence of the internet of things (IoT) as a technological driver of BMI in internet service providers’ (ISPs) business context, in the scope of wired access (WA) and fixed wireless access (FWA) providers, and addressed new ways of value creation for ISPs driven by IoT. To this end, a four-stage BMI process, including; initiation, ideation, integration, and evaluation, was used. In the implementation of the BMI process, we used the data extracted from the literature of IoT, BMI, and ISP business, as well as those obtained through interviews with experts. As a result of the process implementation, we identified possible ideas for the value creation of ISPs in the IoT domain, based on connectivity service providing, cloud service providing, technical solution providing, and business solution providing. Then, we proposed ISPs’ business models in the IoT domain, in accordance with the identified ideas, based on Hedman and Kalling’s ontology. To boost the validity of the proposed business models, the stress testing approach was recruited at the final stage of the BMI process. Implementing BMI, driven by IoT, in the ISPs’ context, reduces constraints imposed by the paucity of knowledge in both BMI and IoT, helps ISPs’ managers to anticipate and identify the IoT-based opportunities, and provides a starting point for further studies on new ways of value creation in other businesses in the telecom industry.