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In this paper, the stresses and buckling behaviors of a thick-walled micro sandwich panel with a flexible foam core and carbon nanotube reinforced composite (CNTRC) face sheets are considered based on the high-order shear deformation theory (HSDT) and the modified couple stress theory (MCST). The governing equations of equilibrium are obtained based on the total potential energy principle. The effects of various parameters such as the aspect ratio, elastic foundation, temperature changes, and volume fraction of the canbon nanotubes (CNTs) on the critical buckling loads, normal stress, shear stress, and deflection of the thick-walled micro cylindrical sandwich panel considering different distributions of CNTs are examined. The results are compared and validated with other studies, and showing an excellent compatibility. CNTs have become very useful and common candidates in sandwich structures, and they have been extensively used in many applications including nanotechnology, aerospace, and micro-structures. This paper also extends further applications of reinforced sandwich panels by providing the modified equations and formulae.

The aim of this study was to compare the fatigue effect on the viscoelastic behavior of the musculoskeletal system between the two sexes in a landing task. Stiffness and shock absorption capacity were studied from ground reaction force data using a mass-spring-damper model. A parametric simulation was also performed to demonstrate the effect of different values of model parameters on the vertical ground reaction force. Following fatigue, the shock absorption capacity reduced in men. According to the parametric simulation, this reduction can result in lower rate of force development which may make men less susceptible to impact injuries than women.

In recent years, thin silver nanowires (Ag NWs) with diameters smaller than 150 nm have been synthesized by implementation of NaCl or FeCl3 as reaction promoters and high molecular weight polyvinylpyrrolidone (PVP) as the capping agent. However, the yield of Ag NWs still remains low, mostly due to the insufficient aspect ratio (AR) of the synthesized nanostructures and the production of Ag nanoparticles, which is an undesirable by product. This study proposes a modified technique to alleviate the problem by using a mixture of FeCl3/CuCl2 as the reaction promoter and two different types of PVP with molecular weight of 360 k and 40 k as the capping agents. The appropriate mixtures of FeCl3/CuCl2 (molar ratio of 1.48:1.45) and PVP 360 k/40 k (molar ratio of 2:1) allow for the rapid synthesis of NWs with diameter of about 110 nm and length of up to 50 μm. It is revealed that the ARs of the synthesized NWs are much greater. The NWs are then used for the fabrication of transparent conductive films (TCFs). The obtained TCF prepared by a scalable, in-house, and cost-efficient spray apparatus possesses a transmittance of 91%, sheet resistance of 10 Ω/sq, and optical haze value of 6.6%. In fact, the AR of the NWs is increased as much as 1135%, and further the figure␣of merit of the TCFs is improved by as much as 2785%.

In this article, vibration behavior of a micro cylindrical sandwich panel with foam core and reinforced graphene platelet composite layers on the top and bottom resting on elastic foundation based on modified couple stress theory is investigated. Hamilton’s principle is used to determine the governing equations of motion. These equations are solved by Navier’s method to obtain the natural frequencies. The results are compared with the extracted results by the other literatures. The effects of different parameters such as temperature change, volume fraction of graphene platelet, length to radius ratio, and the elastic foundation on the natural frequencies have been carried out. Also, the effects of reinforced materials for layers is discussed and compared with unreinforced composites layers. Sandwich structures are wildly used in different applications such as spacecraft, aeronautical, pressurized gas tanks, boilers, aircraft fuselage, marines, and civil structures, and these cases need high strength and low weight. The present work is a theoretical background for more explorations and further experimental researchers in the field of cylindrical reinforced panels.

Purpose This paper aims to improve some properties of poly (methyl methacrylate) by copolymerization with butyl acrylate (BA) monomer along with the incorporation of the stable and economical synthesized silicone-containing monomer hexamethyldisilazanomethacryloxyphenyl ketone (HDMK) into the copolymer matrix. Design/methodology/approach For this target solution copolymerization of methyl methacrylate (MMA), BA and HDMK were carried out using a 250 mL four-necked round-bottom flask. Before solution polymerization start-up, the reaction vessel was first charged with 34.8 mL toluene and heated to 170 °C with stirring and reflux cooling. A monomer mixture of 25.86 g (260 mmol) MMA, 26.40 g (200 mmol) BA, 3.00 g (8.60 mmol) HDMK and 0.45 g (2.00 mmol) dibenzoyl peroxide was added continuously from the dropping funnel over a period of 4 h. Findings The HDMK was successfully synthesized and the water resistance of acrylic resins was improved because of the existence of HDMK. Research limitations/implications The materials that were used in this research paper had a reasonably low cost. Also, the procedures for synthesis of monomers and polymers were extremely easy because there was no need for high pressure or temperature and no dangerous solvents were used. Practical implications The acrylic resin that contained HDMK was used to synthesis a white architectural paint for exterior coating. Examining the paint characteristics has shown acceptable washing and abrasion resistance, good brushing, excellent storage stability and great surface coating. Originality/value HDMK was synthesized for the first time.

In the current study, a numerical method is used to investigate the thermal energy transfer and pressure drop augmentation in helically coiled tube heat exchanger with a coiled wire insert made from copper. The impact of geometrical parameters of the inserts like diameter and cross sectional form on the intensification of the Nusselt and the friction factor number is studied. The Transition SST model is used to simulate the impact of turbulence. The model validation is performed by comparing the results with the empirical equations of prior experimental works. Results indicate that, using a circular cross section coiled wire inserts will enhance the Nusselt number and friction factor by 340.9%, 536.1%, respectively. Furthermore, using inserts with concentric circular cross section with diameter of 0.008 m and two rectangular cross sections are recommended for the intensification of heat transfer at the inlet mass flow rate 0.05 kg/s while, all inserts are suggested at the inlet mass flow rate of 0.075 kg/s. As a part of the study, a correlation is proposed for estimating the Nusselt number of these heat exchangers.

Electrochemical exfoliation of graphite has recently attracted a big attention as a simple, fast and scalable method for the preparation of high quality graphene, but there are some drawbacks that hinder its application. Direct deposition is one of the most critical challenges that makes it difficult to deposit uniform, compact and large scale graphene thin films. This work develops a facile electrophoretic deposition route to fabricate exfoliated graphene (EG) film on Ag nanowires (NWs) networks with a controllable film thickness in nanometers scale. EG thin films are deposited with different applied potentials and times from an EG dispersion in N, N -dimethylformamide solvent. Since confirmed by light transmittance results, the increase in deposition time or applied potential lead to an increase in the number of deposited EG layers. The scanning electron microscopy results confirm the uniformity and compactness of EG films deposited on Ag NWs network. Furthermore, transparency and conductivity of Ag NWs films before and after the deposition of EG are investigated. It shows significant improvement in its performance, as a result, it leads to Ag NWs/EG hybrid films exhibiting a sheet resistance of R s = 30 Ω sq -1 , and 85% light transmittance, comparable to conventional transparent conductive oxide films.

Hybridization of natural fiber with synthetic fiber to reinforce polymer matrix composites is an effective way of increasing fatigue strength of composites with substantial amount of bio-based content. Flax is the strongest type of bast natural fiber, possessing excellent mechanical and damping properties. Fatigue properties of flax fiber hybridized with synthetic carbon fiber reinforced polymer matrix composites were studied. Fatigue properties of inter-ply hybrid flax-carbon fiber reinforced composite were compared to intra-ply hybrid flax-carbon fiber reinforced composites through tensile fatigue testing at 70% load of ultimate tensile strength and with a loading frequency of 3 Hz. For similar amount (by mass) of flax and carbon fiber, intra-ply flax-carbon fiber hybrid reinforced composite exhibited a very large increase (>2000%) in fatigue life compared to inter-ply flax-carbon fiber hybrid reinforced composites. Suitable hybridization can produce hybrid composites that are as strong as synthetic fiber composites while containing a high bio-based content of natural fibers.

The main goal of the present work is to explore the three dimensional (3-D) atomic force microscopy (AFM) images of human teeth and investigating their micromorphology. For this purpose, 10 fresh and permanent canine teeth were selected from a group of 40-year-old men who were candidate for the experimental processes. Afterward, they were all applied for studying the morphology of their hard tissues. The tapping mode of AFM was used to characterize the surface micromorphology on the square areas of 1 μm × 1 μm (512 × 512 pts). AFM results and surface stereometric analysis indicate the relationships between the micromorphology of the surface and the structural properties of these tissues across the length scales. As can be seen, the surface of cementum has the most irregular topography (D = 2.87 ± 0.01) while the most regular topography (D = 2.43 ± 0.01) is found in dentin. Furthermore, the more and less regularity of the surface have been found in inner enamel (Sq = 26.26 nm) and dentin (Sq = 41.28 nm), respectively. Stereometric and fractal analyses give valuable information about human canine teeth via 3-D micromorphology.

Because of excellent physical properties and wide range of applications in electrodes, electrical connections, microelectronic packaging, and heating mineral, Tungsten–copper (W–Cu) composite powders have been significantly remarkable. The substantial differences in the density and lack of fusion of copper and tungsten in both solid and liquid phases, led to occur some problems in W–Cu composite powders. Among the various methods of preparing tungsten–copper composites, the electroless plating process is one of the conventional methods as it is easy to use, efficient, and inexpensive. In this paper, the copper electrolyte solution containing copper sulfate as the main salt, formaldehyde as the reducing agent, sodium ethylene diamine tetra-acetate as the sophisticated agent, has been employed in the electroless copper plating process of tungsten powder. The effects of main parameters of electroless copper plating including NaOH concentration, time and operating temperature, and pre-activation of tungsten powder on the amount of deposited copper layer were investigated. Then, the phase behavior, morphology, mechanical, and corrosion behavior were evaluated using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and energy-dispersive X-ray spectroscopy(EDS), Vickers hardness tester, and electrochemical impedance spectroscopy (EIS), respectively. Results demonstrated an improvement in the morphology and amount of the copper, which was deposited on the surface of the activated tungsten powder. Indeed, the defects formed on the surface of the tungsten powder increased the sites for nucleation and growth of copper particles. In agreement with the Fick's 2nd law, the maximum deposition rate of 7.9 vol% was obtained at 14 g/l NaOH, 100 °C and processing time of 90 min, with a (111) orientation on the activated tungsten powder surface. The higher capacitance and the charge transfer resistance along with the highest ndl at the 14 g/l NaOH concentration demonstrated the homogeneity and integrity of W–Cu composite, the lowest hardness of 149.0 ± 2.8 Hv and a compressive strength of 187 ± 11 MPa.

The finite volume method is employed to analyze the fluid flow, heat transfer and entropy generation within a fluid channel. The fluid channel is filled with CuO-water nanofluid. The KKL model is used to estimate the dynamic viscosity and consider the Brownian motion. On the other hand, the influence of nanoparticles' shapes on the thermal conductivity is considered. The fluid channel is included with hot and cold fluid injection pipes which are modeled as active bodies in 2D model. Three different governing parameters are utilized such as Rayleigh number in range of 103

Multi-label learning (MLL) is a special supervised learning task, where any single instance possibly belongs to several classes simultaneously. Nowadays, MLL methods are increasingly required by modern applications, such as protein function classification, speech recognition and textual data classification. In this paper, a structural least square twin support vector machine (SLSTSVM) classifier for multi-label learning is presented. This proposed ML-SLSTSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. This method is extended to a nonlinear version by the kernel trick. Experimental results demonstrate that proposed method is superior in generalization performance to other classifiers.

Classification of data with imbalanced class distributions is a major problem in the data mining community. Imbalanced classification is a challenging task in the presence of outliers. In this pape...

In the present work, a numerical method is used for the simulation of the heat transfer and entropy generation for the non-premixed combustion of CH4-air. The standard K-e model is established for the simulation of turbulence. Furthermore, the PDF (Probability Density Function) approach is used for the simulation of the reaction between the species. Twenty species are considered in this simulation. The model is validated by comparing the values of the outlet species mass fraction between the present numerical work and the previous experimental works. The effect of the equivalence ratio, on the heat flux, heat transfer coefficient, wall temperature, entropy generation rate and the outlet species mass fraction is obtained. Based on the results, two correlations are proposed for the prediction of the maximum heat transfer coefficient. These correlations show that if the equivalence ratio is doubled the maximum heat transfer coefficient is doubled too for ∅ 1. Furthermore, it was found that in the first region of the tube the maximum values of the heat flux, wall temperature and heat transfer coefficient occur.

Scalable coding methods are widely considered as promising coding approaches for image/video transmission in heterogeneous environments. To copyright protection of scalable coded videos, scalable video watermarking was introduced which enables reconstructing various scales of watermark from watermarked videos. This paper proposes a novel human vision system-based, spread spectrum method to scalable video watermarking. The host video is decomposed using newly proposed spatio-temporal motion-compensated 3D wavelet decomposition, called k( $$\hbox {t}+2\hbox {D}$$ ) DWT decomposition, to frequency sub-bands. A scalable decomposition of the watermark is inserted into the entire frequency sub-bands of decomposed video. At each frequency sub-band, the watermark data are inserted into the selected coefficients of the sub-band frames in a way that the watermark embedding visual artifact occurs in the highly textured, highly contrasted, and very dark/bright areas of the video frames. The experimental results show that the watermarked test videos are highly transparent and robust against scalable video coding even at very low decoding bit-rates and some other video processing attacks. The proposed approach can guarantee copyright protection for scalable coded videos, especially over heterogeneous networks.

Application of additive manufacturing (AM) technology in production of ceramic parts is considered as a state-of-the-art technique which has been recently introduced to industry. In the current study the imperative microstructural characteristics of the alumina manufactured via laser stereolithography (SLA) has been investigated. The microstructural characteristics of the developed ceramic parts and components are still unknown and require detailed investigation. A combination of optical microscopy and scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), image analysis, X-ray diffraction (XRD) analysis, transmission electron microscopy (TEM) and micro-computed tomography (micro-CT) scans was used to evaluate the microstructural features of the alumina samples after each step of the manufacturing process (i.e. printing, debinding and sintering). In addition, the apparent density of each sample was measured using water displacement method. Results indicated that the porosity of printed alumina samples was significantly reduced after sintering process. EDS analysis confirmed elimination of binder material after debinding and sintering processes. XRD analysis detected existence of α-Al 2 O 3 in initial printed samples which was not changed during debinding and sintering processes. Due to detection of identical peaks for all samples, only one set of lattice parameters ( a and c ) was calculated from XRD patterns of all samples which was close to the ones reported in literature for alumina. TEM micrographs and corresponding diffraction patterns confirmed polycrystalline structure from different layers of the samples. High resolution transmission electron microscopy (HRTEM) and diffraction patterns from single layers were used to calculate lattice parameters for each sample. A slight increase was noticed in unit cell and grain size after sintering process. The obtained results help for better understanding of the properties through microstructural characteristics of the 3D printed ceramic parts.

Data distribution has an important role in classification. The problem of imbalanced data has occurred when the distribution of one class, which usually attends more interest, is negligible compared with other class. Furthermore, by the existence of outliers and noise, the classification of these data confronts more challenges. Despite these challenges, doing fast classification with good performance is desired. One of the successful classifier methods for dealing with imbalanced data and outliers is weighted relaxed support vector machines (WRSVMs). In this paper, the improved twin version of this classifier, which is called twin-bounded weighted relaxed support vector machines, is introduced to confront the mentioned challenges; besides, it performs in a significant fast manner and it is more accurate in most cases. This method benefits from the fast classification manner of twin-bounded support vector machines and outlier robustness capability of WRSVM in the imbalanced problems. The experimentally, the proposed method is compared with the WRSVM and other standard SVM-based methods on the public benchmark datasets. The results confirm the efficiency of the proposed method.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. Limited research on biomineralization in cement-based systems suggested that self-healing of surface cracks could be obtained by triggering biogenic calcium carbonate (CaCO3) precipitation within the cracks. While this is encouraging, there is not enough information regarding the influence of admixtures on crack remediation and durability of the biogenic CaCO3 against weathering conditions. In this study, the microorganisms were introduced to mortar with their growth medium, which included corn steep liquor (CSL) and urea. With this approach, the cracks on mortar surface were sealed with the CaCO3 and the water absorption capacity of the so-called self-healed mortar decreased compared to its counterpart cracked mortar samples. The biogenic CaCO3 precipitate was found to be durable against freeze-thaw; however the precipitate was unstable under rain water and light. While the addition of air entraining agents (AEA) did not influence the self-healing ability of cells, use of superplasticizers improved the self-healing ability in terms of crack sealing, water absorption, and durability of the precipitate. TÜBİTAK

This paper proposes a novel human vision system based, spread spectrum method to scalable image watermarking. A scalable decomposition of the watermark is spread into the entire frequency sub-bands of the wavelet decomposed image. At each wavelet sub-band, the watermark data are inserted into the selected coefficients of the sub-band in a manner that the watermark embedding visual artifact occurs in the highly textured, highly contrasted and very dark/bright areas of the image. In the lowest frequency sub-band of wavelet transform, the coefficients are selected by independent analysis of texture, contrast and luminance information. In high frequency sub-bands, the coefficient selection is done by analyzing coefficients amplitude and local entropy. The experimental results show that the watermarked test images are highly transparent and robust against scalable wavelet-based image coding even at very low bit-rate coding. The proposed approach can guarantee content authentication for scalable coded images, especially on heterogeneous networks which different users with different process capabilities and network access bandwidth use unique multimedia sources.

The heat transfer enhancement in a D-shaped heat exchanger using nanofluid is analyzed using a modern numerical method. The double-MRT lattice Boltzmann method is utilized to simulate the fluid flow and heat transfer. On the experimental phase, the thermal conductivity and viscosity of Al2O3-EG/W(60/40 vol%) are measured using experiments with modern devices. Some correlations based on experimental data are developed as a function of temperature for different nanoparticle concentrations. These temperature/nanoparticle concentration-based correlations are used in the numerical simulations to enhance the accuracy of results. In the present investigation, the impacts of Rayleigh number (103

The natural convection heat transfer and fluid flow is analyzed using lattice Boltzmann numerical method . The entropy generation analysis and heatline visualization are used to study the convective flow field comprehensively. The hollow L-shaped cavity is considered and filled with SiO2-TiO2/Water-EG (60:40) hybrid nanofluid . The thermal conductivity and dynamic viscosity of nanofluid are measured experimentally. To use the experimental data of thermal conductivity and dynamic viscosity, two sets of correlations based on temperature for six different solid volume fractions of 0.5, 1, 1.5, 2, 2.5 and 3 vol% are derived. The influences of different governing parameters such different aspect ratios, solid volume fractions of nanofluid and Rayleigh numbers on the fluid flow, temperature filed, average/local Nusselt number , total/local entropy generation and heatlines are presented.

Ground source heat pumps are known as a high-efficiency renewable energy heating and cooling system. The ground heat exchanger (GHE) plays an important role to achieve a higher coefficient of performance in GSHP system. GHE technology has known very well and a significant research has been done over the last decades. The aim of this paper is to overview the previous research on various GHE parametAli Amiriers to improve the efficiency and their effect on factors such as heat exchanger type, heat exchange rate, outlet temperature, pressure loss, thermal resistance, thermal interference, thermal conductivity, soil temperature, thermo-economic and etc. According to effective parameters, this paper is divided into three parts. The first part is dedicated to inside the pipe parameters (velocity, inlet temperatures and working fluid). The pipe parameters including pipe diameter, pitch, pipe center-to-center distance, pipe arrangement and pipe material are reviewed in the second part. Outside of pipe parameters including pipe length, borehole depth and diameter, and backfill material is reviewed in third part. It is concluded that, the inlet-temperature and the circulating fluid velocity, higher center-to-center distance of the vertical pipe and pitch for the spiral pipe and, thermal conductivity of the backfill material, have the greatest impact on increasing the system performance compare to other parameters.

Real-time transient stability assessment (TSA) of power systems is an important real world problem in electrical energy engineering and pattern recognition scope. The definition of most discriminative trajectory features and proper supervised trajectory-based classifier has remained a motivational challenge for scholars vis-à-vis real-time TSA. In addition, increase in the consumption of electrical energy along with constraints such as amortization of network equipment induces electric power system inadequacy risk. The retrieval of power system adequacy involves network expansion planning such as installing new power plants for the network. This policy affects the structure and electrical specification of the network significantly. Furthermore, due to sudden or the scheduled tripping of network equipment stemming from action of protection devices or maintenance procedures, the network must undergo shallow structural changes. The different level of changes in network specification is becoming a potential barrier for network analysis tools like real-time TSA platform. In fact, the lack of consideration of the incompatibility of TSA tool with expansion planning affects the performance of TSA learning model that is trained using the preexpansion network. However, this paradoxical problem can be solved by generalized learning for power system size & type independent (PSs&tInd) real-time TSA. For this purpose, first, we used a set of PSs&tInd trajectory features. Next, we presented a trajectory-based deep neuro classifier to eliminate kernel functions weaknesses plugged into the hyperplane-based classifier. Finally, experimental comparisons were conducted to assess the efficacy of the proposed framework. The results showed that the proposed technique offered high-generalization capacity on real-time TSA during network expansion.

Carbon fiber composite frames were first used in Tour de France in 1986. With recent growth in research and development of composite frames, carbon fiber composites have become more popular in bicy...

There has been tremendous research progress in estimating the depth of a scene from a monocular camera image. Existing methods for single-image depth prediction are exclusively based on deep neural networks, and their training can be unsupervised using stereo image pairs, supervised using LiDAR point clouds, or semi-supervised using both stereo and LiDAR. In general, semi-supervised training is preferred as it does not suffer from the weaknesses of either supervised training, resulting from the difference in the cameras and the LiDARs field of view, or unsupervised training, resulting from the poor depth accuracy that can be recovered from a stereo pair. In this paper, we present our research in single image depth prediction using semi-supervised training that outperforms the state-of-the-art. We achieve this through a loss function that explicitly exploits left-right consistency in a stereo reconstruction, which has not been adopted in previous semi-supervised training. In addition, we describe the correct use of ground truth depth derived from LiDAR that can significantly reduce prediction error. The performance of our depth prediction model is evaluated on popular datasets, and the importance of each aspect of our semi-supervised training approach is demonstrated through experimental results. Our deep neural network model has been made publicly available., Comment: Submitted to IROS2019

This protocol describes a method for rapid manufacturing of soft pneumatic actuators and robots with an ultrathin form factor using a heat press and a laser cutter machine. The method starts with the lamination of thermoplastic polyurethane (TPU) sheets using a heat press for 10 min at the temperature of ~93 °C. Next, the parameters of the laser cutter machine are optimized to produce a rectangular balloon with maximum burst pressure. Using the optimized parameters, the soft actuators are laser cut/welded three times sequentially. Next, a dispensing needle is attached to the actuator, allowing it to be inflated. The effect of geometrical parameters on the deflection of the actuator are studied systematically by varying the channel width and length. Finally, the performance of the actuator is characterized using an optical camera and a fluid dispenser. Conventional fabrication methods of soft pneumatic actuators based on silicone molding are time consuming (several hours). They also result in strong but bulky actuators, which limits the actuator's applications. Moreover, microfabrication of thin pneumatic actuators is both time-consuming and expensive. The proposed manufacturing method in the current work resolves these issues by introducing a fast, simple, and cost-effective fabrication method of ultrathin pneumatic actuators.

Funding Information: This research was funded by the Ministry of the Environment of Finland, CarbonSinkCity grant (310283). The views expressed by the authors do not necessarily reflect those of the funder. Publisher Copyright: © 2021 The Author(s) Copyright: Copyright 2021 Elsevier B.V., All rights reserved. The construction and use of buildings consume a significant proportion of global energy and natural resources. Leadership in Energy and Environmental Design (LEED) is arguably the most international green building certification system and attempts to take actions to limit energy use of buildings and construct them sustainably. While there has been a wide range of research mainly focused on energy use and emission production during the operation phase of LEED-certified buildings, research on embodied emissions is rare. The aim of this study is to evaluate the efficiency of LEED regarding initial (pre-use) embodied emissions using life cycle assessment (LCA). The study comprised several steps using a designed model. In the first step, three optional building material scenarios were defined (optimized concrete, hybrid concrete-wood, and wooden buildings) in addition to the base case concrete building located in Iceland. Second, an LCA was conducted for each scenario. Finally, the number of LEED points and the level of LEED certification was assessed for all studied scenarios. In addition, a comparison regarding embodied emissions consideration between LEED and Building Research Establishment Environmental Assessment Method (BREEAM) as mostly used green certificate was conducted in the discussion section. The LCA showed the lowest environmental impact for the wooden building followed by the hybrid concrete wood building. In the LEED framework, wooden and hybrid scenarios obtained 14 and 8 points that were related to material selection. Among these points, only 3 (out of a total of 110 available points) were directly accredited to embodied emissions. The study recommends that the green building certificates increase the weight of sustainable construction materials since the significance of embodied emissions is substantially growing along with the current carbon neutrality goals. As most of the materials for building construction are imported into Iceland, this study is useful for locations similar to Iceland, while overall it is beneficial for the whole world regarding climate change mitigation. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Density is a fundamental physical property of a reinforcement in composite materials. It appears widely in calculations, which are mostly used for engineering designs. The purpose of this research project is to establish a standard test method to measure the density of flax fiber using Archimedes (buoyancy) method. Different immersion fluids were considered in the density measurement of flax fiber with and without use of a vacuum oven. Results of gas pycnometry tests of the same flax sample were used as the reference for comparison. Variables such as, specimen size, vacuum pressure, vacuum time and type of immersion fluid were studied by the ruggedness test. Results of this study suggest that use of certified lab grade soybean oil as test fluid, and submerging flax specimens in test fluid under vacuum pressure of 90–100 kPa for 10 min yields the closest values of density to those acquired by gas pycnometry method.

Carbon fiber-reinforced polymers are being used in advanced structural applications such as aerospace, automotive, and naval industries. Therefore, there is a rising need for predicting their fatigue life and improving their fatigue behavior. In this study, the fatigue behavior and changes in flexural modulus of bidirectional carbon fiber-reinforced polymers due to cyclic fully reversed bending are investigated. A unique fixture is designed and manufactured to perform fully reversed four-point bending fatigue tests on (0 °/90 °)15 carbon/polyester specimens with a stress ratio of R = −1 and frequency of 5 Hz. The expected downward trend in fatigue life with increasing maximum applied stress was observed in the S–N curves of samples. Based on the decay in the flexural modulus of the specimens, a modified exponential model is proposed to predict the life of carbon fiber-reinforced polymers under fully reversed bending. The empirical constants in the model are calculated based on the results of experiments. The model is applied to predict the fatigue life of the samples that did not fail during the tests and cycle-to-failure of the specimens are found.

Composites having high bio-based content with properties and costs rivaling those consisting of synthetic constituents are a goal of much current research. The obvious material choices — vegetable oil based resins and natural fibers — present the challenges of poor resin properties and weak fiber/matrix bonding, respectively. Conventional methods of overcoming poor resin quality involve the incorporation of additives, which dilute the resulting composite's bio-content and increases cost. To overcome these limitations while maintaining high bio-content, in this study, a newly developed biobased resin, epoxidized sucrose soyate (ESS), is combined with surface-treated flax fiber to produce novel bio-composites. Properties of manufactured composites were compared against those using epoxy resin reinforced with flax fiber. In addition to mechanical property evaluation, accelerated weathering was performed on the manufactured composites to evaluate the mechanical properties after exposing to UV and moisture. The results of this study revealed the successful production of bio-composites having properties that meet or exceed those of conventional pultruded members while having 85% bio-content. Moreover, composites using treated flax fiber and ESS resin showed less degradation in properties and appearance after accelerated weathering exposure. Keywords: Biocomposites, Degrading, Natural fibers, Natural resins, Weathering

The present study suggest modified twin support vector regression (MTSVR) for data regression. In the MTSVR model, the regression function is determined using a pair of unparalleled up and down bound functions. In any optimization problem, a new term is added to obtain structural information of the input data based on the concept of structural granularity. Furthermore, Successive Over relaxation is used to accelerate the training process of optimization problems. Particle Swarm Optimization (PSO) algorithm is used to determine the parameters of the MTSVR model. According to the results of the artificial and real datasets, the prediction accuracy and generalization capability of the MTSVR model is significantly increased.

Developing soft actuators and sensors by means of 3D printing has become an exciting research area. Compared to conventional methods, 3D printing enables rapid prototyping, custom design, and single-step fabrication of actuators and sensors that have complex structure and high resolution. While 3D printed sensors have been widely reviewed in the literature, 3D printed actuators, on the other hand, have not been adequately reviewed thus far. This paper presents a comprehensive review of the existing 3D printed actuators. First, the common processes used in 3D printing of actuators are reviewed. Next, the existing mechanisms used for stimulating the printed actuators are described. In addition, the materials used to print the actuators are compared. Then, the applications of the printed actuators including soft-manipulation of tissues and organs in biomedicine and fragile agricultural products, regenerative design, smart valves, microfluidic systems, electromechanical switches, smart textiles, and minimally invasive surgical instruments are explained. After that, the reviewed 3D printed actuators are discussed in terms of their advantages and disadvantages considering power density, elasticity, strain, stress, operation voltage, weight, size, response time, controllability, and biocompatibility. Finally, the future directions of 3D printed actuators are discussed.

One major service provided by cloud computing is Software as a Service (SaaS). As competition in the SaaS market intensifies, it becomes imperative for a SaaS provider to design and configure its computing system properly. This paper studies the application placement problem encountered in computer clustering in SaaS networks. This problem involves deciding which software applications to install on each computer cluster of the provider and how to assign customers to the clusters in order to minimize total cost. Given the complexity of the problem, we propose two algorithms to solve it. The first one is a probabilistic greedy algorithm which includes randomization and perturbation features to avoid getting trapped in a local optimum. The second algorithm is based on a reformulation of the problem where each cluster is to be assigned an application configuration from a properly generated subset of configurations. We conducted an extensive computational study using large data sets with up to 300 customers and 50 applications. The results show that both algorithms outperform a standard branch-and-bound procedure for problem instances with large sizes. The probabilistic greedy algorithm is shown to be the most efficient in solving the problem.

Flax fiber composites have been found to exhibit suitable mechanical properties for general applications. Natural fibers exhibit lower mechanical properties than their synthetic counterparts as well as have a high degree of fiber to fiber mechanical variability based on growing conditions and plant varieties which lowers their mechanical performance predictability. However, Thermoset resins reinforced with flax fibers exhibit nonlinear behavior when subjected to loading which results in energy loss. This is beneficial in applications where vibration damping is anticipated. One potential method for increasing performance and strength of flax fiber is to hybridize it with synthetic fibers with the goal of improving composite mechanical properties as well as reducing composite to composite mechanical variability. In this study carbon fiber and flax fiber were used to manufacture different hybridized composites with varying flax fiber volume fractions. Resulting composites were characterized using tensile, flexural, impact and vibration tests. Also, results of experiments were compared against predictions of rule of mixture's model and Halpin-Tsai model. Findings of this study provides valuable information for designers for hybridizing flax and carbon fibers and results suggest that hybridizing synthetic fibers with natural fibers is an effective method of improving the mechanical properties and controlling vibration damping. Keywords: Bio-composites, Carbon, Epoxy, Flax, Hybridization, Sports equipment

This article describes an efficient control-oriented model of a soft robot made of electroactive polymers. The proposed soft robot is constructed from two flexible links and has a multiphysics dynamic model consisting of both an electrochemical and electromechanical model. The electrochemical model is based on a distributed RC line approach, and the electromechanical model, considering the continuum vibration of the robot, is derived based on Hamilton's principle. The governing equation of the soft robot is solved by means of the Rayleigh-Ritz-Meirovitch substructure synthesis method, and the Laplace operator is used to obtain the transfer function of the soft robot as a 2 by 2 multiple-input multiple-output system.

This paper studies the reliable application placement problem encountered in computer clustering in Software as a Service (SaaS) networks. The problem involves deciding which software applications to install on each computer cluster of the provider and how to assign customers to the clusters in order to provide primary and backup service to customers in case of a cluster failure, while minimizing total cost. Given the complexity of the reliable application placement problem, we propose two algorithms to solve it. The first one is a probabilistic greedy algorithm and the second one is based on a reformulation of the problem where each cluster is to be assigned an application configuration from among all possible configurations or from a properly generated subset of configurations. Results of an extensive computational study show that the two algorithms are more effective than a standard branch-and-bound procedure based on the linear programming relaxation of the problem in solving problem instances with large sizes. This paper studies a problem encountered in a SaaS network.The network provides reliable service to customers in case of a cluster failure.The problem is an interesting combinatorial problem related to several OR problems.The problem is complex and difficult to solve with a standard B&B algorithm.We proposed two effective algorithms to solve the problem.

There has been a vast growth in manufacturing of fiber reinforced plastics by means of liquid composite molding such as resin transfer molding and vacuum-assisted resin transfer molding processes. In these processes, compression of the porous media and pressure of the injected resin result in in-mold forces that need to be determined. Limited information exists regarding the processing parameters and extent of reinforcing potential natural fibers have in polymer matrices. Current study investigates the effect of different variables such as fiber volume fraction, shive content, fiber size, wax content, and resin viscosity on permeability of five different natural fiber mats. Flax fiber with low-, medium-, and high-shive content as well as hemp and kenaf fiber mats was selected for this study and an original experimental device was setup to measure the permeability of the mentioned fiber mats based on different variables. It was found that increasing fiber volume fraction will result in reduction of permeability of all mats. The presence of shive and larger fiber size increased the permeability. Higher wax content lowered the permeability. These competing factors could be used by manufacturers to produce a mat which had optimum permeability while still maintaining acceptable strength.

The success of SVM in solving pattern recognition problems has encouraged researcher to extend the development of different versions. They are well-known for their robustness and good generalization performance. In many real-world applications, the data to be trained are available on-line in a sequential fashion and because of space and time requirements, batch training methods are not suitable.This paper proposes a new fast on-line algorithm called OTWISVM. It defines two optimization problems and incremental learning is done based of them. Two hyperplanes are generated as decision functions thus each of them is closer to one of the two classes and is as far as possible from the other. The solution is constructed via two subsets of linearly independent samples seen so far, and is always bounded. Good accuracy and notable speed of the method was tested and affirmed both on ordinary and noisy data sets as opposed to similar algorithms. On-line twin independent support vector machine (OTWISVM) is proposed.OTWISWM uses way two nonparallel hyper planes as decision functions.The hyperplanes are described by SVs and are updated when new sample is received.The SVs are constructed as a set of linearly independent samples iteratively.Assessments confirm effectiveness of OTWISVM on conventional and noisy data.

We consider the multiple knapsack problem with setups which is a generalization of the classical knapsack problem. In this problem, there are multiple knapsacks and the items belong to families such that an item can be placed in a knapsack only if its family is selected and assigned to that knapsack. We present an algorithm based on a Lagrangean relaxation of the problem that takes advantage of the structure of the problem. The algorithm produces solutions whose quality can be assessed automatically without ever knowing the optimal solutions. We carry out a computational study to evaluate the performance of the proposed algorithm on a benchmark data set from the literature. The results show a great performance of the algorithm compared to published state-of-the-art approaches, as it generates better values for 9% of the instances and the same values for 90% of the instances. The algorithm generated new best-known values for 6% of the instances compared to both the published state-of-the-art approaches and the commercial solver CPLEX. We also report the results of an additional computational study that show that the algorithm scales up extremely well, solving near-optimally very large instances with up to 5 knapsacks, 200 families, and 300,000 items in a reasonable amount of time on a desktop PC.

We consider the knapsack problem with setups which is a generalization of the classical knapsack problem where the items belong to families and an item can be placed in the knapsack only if its family is selected. The problem has received increasing attention by researchers because of its theoretical significance and practical applications related to resource allocation. This paper presents an algorithm based on a Lagrangean relaxation of the problem that produces solutions whose quality can be assessed automatically with the algorithm itself without ever knowing the optimal solutions. We report results of an extensive computational study which show that the method can solve near optimally very large instances of the problem with up to 500 families and 2,000,000 items in reasonable amount of time. This study shows the merit of using the Lagrangean relaxation method to solve the current problem when the constraints to relax are chosen properly.

Pneumatically actuated soft robots address many challenges with interfacing with delicate objects, but these actuators/robots are still bulky and require many hours to fabricate, limiting their widespread use. This article reports a novel design and manufacturing method for ultrathin soft robots and actuators (∼70 μm) using a laser-cutting machine that cuts/welds sheets of thermoplastic polyurethane (TPU) from a 2D CAD drawing. Using this method, five different soft actuators (e.g., bending, rotating, contracting) are designed, fabricated, and characterized with both planar and nonplanar motions. Furthermore, we show how stacking multiple sheets of TPU enables rapid fabrication of multifunctional actuators. Finally, a portable four-arm swimming robot is designed and fabricated without any assembly steps. This rapid fabrication method enables soft robots to go from concept to operational within minutes, and creates a new subclass of soft robots suitable for applications requiring a robot to be ultrathin, lightweight, and/or fit within small volumes.

In this study, a robust neuro-adaptive controller for cable-driven parallel robots is proposed. The robust neuro-adaptive control system is comprised of a computation controller and a robust controller. The computation controller containing a neural-network-estimator with radial basis function activator is the principal controller and the robust controller is designed to achieve tracking performance. An on-line tuning method is derived to tune the parameters of the neural network for estimating the controlled system dynamic function. To investigate the effectiveness of the robust adaptive control, the design methodology is applied to control a cable-driven parallel robot. Simulation results demonstrate that the proposed robust adaptive control system can achieve favorable tracking performances for the robot.

Summary This paper presents a novel approach for intelligent islanding detection in a multi-machine distribution network using the Adaptive Boosting (AdaBoost) algorithm. The AdaBoost algorithm is an iterative procedure that improves the accuracy of classification models by combining multiple base classifiers. In this paper, two different case studies are specified so as to assess the performance of the AdaBoost in islanding detection. In order to train the AdaBoost algorithm, various scenarios are implemented in the case studies. A number of electrical parameters including frequency and voltage are utilized as a feature vector to describe the designed scenarios. In order to determine the accuracy of AdaBoost in detecting the islanding phenomenon, the proposed method is subsequently tested in further scenarios. Response time and non-detection zone of the suggested method are also evaluated in the case studies as the two other main criteria. The results indicate that this method can successfully detect islanding operation in a variety of operating states in the networks under study. All the simulations presented here are produced in the Simulink toolbox of MATLAB. Copyright © 2015 John Wiley & Sons, Ltd.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. Early age microcracks are generally the primary cause for a decrease in service life of cement-based structures. Recent studies suggested that it might be possible to develop a smart cement-based material that could self-heal microcracks. The use of microbial induced calcium carbonate precipitation (MICP) in cement-based materials is a novel approach to trigger self-healing and it has become an interesting field of research. MICP is a biochemical process where calcium carbonate (CaCO3) precipitation is obtained via metabolic pathways for microorganism and MICP via urea hydrolysis is the most common approach used in cement-based materials. Through the literature the most commonly used nutrient media for urea hydrolysis was composed of yeast extract and urea. However, use of yeast extract as a carbon source not only resulted with a severe retardation of initial setting and it increases the cost of the application. This study investigates the suitability of corn steep liquor (CSL) as an alternative replacement of yeast extract. CSL was found to be a suitable alternative for MICP applications without compromising bacterial growth, ability to promote CaCO3 precipitation. In addition, use of a nutrient medium including CSL and urea did not have such an adverse effect on initial set and compressive strength as compared to a urea and yeast extract medium. (C) 2018 Elsevier Ltd. All rights reserved.

Reliable feature correspondence between frames is a critical step in visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) algorithms. In comparison with existing VO and V-SLAM algorithms, semi-direct visual odometry (SVO) has two main advantages that lead to state-of-the-art frame rate camera motion estimation: direct pixel correspondence and efficient implementation of probabilistic mapping method. This paper improves the SVO mapping by initializing the mean and the variance of the depth at a feature location according to the depth prediction from a single-image depth prediction network. By significantly reducing the depth uncertainty of the initialized map point (i.e., small variance centred about the depth prediction), the benefits are twofold: reliable feature correspondence between views and fast convergence to the true depth in order to create new map points. We evaluate our method with two outdoor datasets: KITTI dataset and Oxford Robotcar dataset. The experimental results indicate that the improved SVO mapping results in increased robustness and camera tracking accuracy., Comment: 6 pages, 5 figures, submitted to ICRA 2019

Fiber-reinforced composites are widely used in advanced structural applications, including aerospace. The reinforcement in these composites is usually synthetic or mineral fibers, such as carbon or glass fibers. Due to the advantages of natural fibers over their synthetic or mineral counterparts, they have been the center of attention for the past couple of decades. However, there are challenges to overcome before natural composites become more widespread in the advanced structural industry. In this study, two newly developed bio-based resins, derived from soybean oil, are combined with surface-treated flax fiber to produce novel bio-composites. Mechanical properties such as tensile and flexural strength, tensile and flexural moduli, interlaminar shear strength, and color gloss of the composite specimens before and after exposure to accelerated weathering (UV and moisture) have been studied. The results of this study reveal that bio-based composites using vinyl ester resin or 100% vegetable oil-based resins have the potential to be used in structural applications such as aerospace applications. In this study, the mechanical properties of manufactured composites showed improvement when using alkaline-treated flax fiber as reinforcement. In addition, after being exposed to accelerated weathering, the mechanical properties of composites using treated flax fiber showed lower reductions, with the exception of flexural strength. In general, it has been shown that the bio-based composites manufactured in this study meet, and most of the time exceed, the target properties set, based on commercially available pultruded fiberglass composites.

There has been a substantial increase in the usage of natural fibers and biodegradable polymers in composite materials due to the recent focus on sustainability of materials. Flax fibers have exhibited higher mechanical properties compared to most other natural fibers available. However, one of the major challenges faced in the use of flax fiber is its hydrophilicity. In this study, the tensile behavior of flax fiber tows removed from commercially available woven fabrics were investigated at different moisture levels. The breaking tenacity of fiber tows was shown to increase with an increase in moisture content of up to 25%. After this point, additional absorption of moisture resulted in a decrease of fiber tenacity. In addition, the diffusion process through flax fiber mat with different areal densities was investigated and the diffusion coefficients were determined using the desorption curves. Diffusion rates were not found to significantly change with varying areal densities of 200 to 400 gsm, but were significantly different when exposed to temperatures of 55 &deg, C versus 80 &deg, C.

The curse of dimensionality resulted from insufficient training samples and redundancy is considered as an important problem in the supervised classification of hyperspectral data. This problem can be handled by Feature Subset Selection (FSS) methods and Support Vector Machine (SVM). The FSS methods can manage the redundancy by removing redundant spectral bands. Moreover, kernel based methods, especially SVM have a high ability to classify limited-sample data sets. This paper mainly aims to assess the capability of a FSS method and the SVM in curse of dimensional circumstances and to compare results with the Artificial Neural Network (ANN), when they are used to classify alteration zones of the Hyperion hyperspectral image acquired from the greatest Iranian porphyry copper complex. The results demonstrated that by decreasing training samples, the accuracy of SVM was just decreased 1.8% while the accuracy of ANN was highly reduced i.e. 14.01%. In addition, a hybrid FSS was applied to reduce the dimension of Hyperion. Accordingly, among the 165 useable spectral bands of Hyperion, 18 bands were only selected as the most important and informative bands. Although this dimensionality reduction could not intensively improve the performance of SVM, ANN revealed a significant improvement in the computational time and a slightly enhancement in the average accuracy. Therefore, SVM as a low-sensitive method respect to the size of training data set and feature space can be applied to classify the curse of dimensional problems. Also, the FSS methods can improve the performance of non-kernel based classifiers by eliminating redundant features. Keywords: Curse of dimensionality, Feature Subset Selection, Hydrothermal alteration, Hyperspectral, SVM

The applications of self-healing in cement-based materials via biomineralization processes are developing quickly. The main challenge is to find a microorganism that can tolerate the restricted environment of cement paste matrix (i.e. very high pH, lack of oxygen and nutrients, small pore size etc.). The focus of this work was to determine the possible use of an ammonium salt-based air-entraining admixture (AEA) as a protection method to improve the survival of incorporated Sporosarcina pasteurii cells in cement-based mortar. Bacterial cells were directly added to the mortar mix with and without nutrients. Nutrients should be provided to keep the microorganisms viable even at early ages (i.e. 7 days). Surface charge of the bacterial cells and in vitro biogenic calcium carbonate (CaCO3) precipitation were not affected by the incorporation of AEA. However, introducing AEA did not influence the viability in mortar samples, which might be attributed to the type and chemistry of AEA used.

Video stabilization and mosaicking is an important task when a video stream of a large area, such as the video stream from a UAV or a blimp, is received. In this paper we propose a novel approach for video stabilization and mosaicking from shaky video streams. We present a feature-based real-time video mosaicking pipeline performing image alignment by combining feature point detector, descriptor, a robust statistical selection methodology (RANSAC), and other filters. By using this method, we can eliminate the mechanical stabilizer. The stabilization is done using SURF feature detectors, BRSIK descriptor, and affine transition matrix. To reach real-time performance, we used C++ OpenCV libraries for implementation and compared most feature detectors and descriptors by their time consumption factor to find out which one is better for real-time approach. Finally, the approach has been tested on real video streams whic showed good performance.