Search results for: oil palm kernel shell

Authors: Bill Wason

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143 Crude Palm Oil Coop mills on Sumatra Island are participating in a program to transfer land from defaulted estates to small farmers while improving the sustainability of palm production to allow for biofuel & food production. GCarbon will be working with farmers to transfer technology, fertilizer, and trees to double the yield from the current baseline of 3.5 tons to at least 7 tons of oil per ha (25 tons of fruit bunches). This will be measured via evaluation of yield comparisons between participant and non-participant farms. We will also capture methane from Palm Oil Mill Effluent (POME)throughbelt press filtering. Residues will be weighed and a formula used to estimate methane emission reductions based on methodologies developed by other researchers. GCarbon will also cover mill ponds with a non-permeable membrane and collect methane for energy or steam production. A system for accelerating methane production involving ozone and electro-flocculation will be tested to intensifymethane generation and reduce the time for wastewater treatment. A meta-analysis of research on sweet potatoes and sorghum as rotation crops will look at work in the Rio Grande do Sul, Brazil where5 ha. oftest plots of industrial sweet potato have achieved yields of 60 tons and 40 tons per ha. from 2 harvests in one year (100 MT/ha./year). Field trials will be duplicated in Bom Jesus Das Selvas, Maranhaothat will test varieties of sweet potatoes to measure yields and evaluate disease risks in a very different soil and climate of NE Brazil. Hog methane will also be captured. GCarbon Brazil, Coop Sisal, and an Australian research partner will plant several varieties of agave and use agronomic procedures to get yields of 880 MT per ha. over 5 years. They will also plant new varieties expected to get 3500 MT of biomass after 5 years (176-700 MT per ha. per year). The goal is to show that the agave can adapt to Brazil’s climate without disease problems. The study will include a field visit to growing sites in Australia where agave is being grown commercially for biofuels production. Researchers will measure the biomass per hectare at various stages in the growing cycle, sugar content at harvest, and other metrics to confirm the yield of sugar per ha. is up to 10 times greater than sugar cane. The study will look at sequestration rates from measuring soil carbon and root accumulation in various plots in Australia to confirm carbon sequestered from 5 years of production. The agave developer estimates that 60-80 MT of sequestration per ha. per year occurs from agave. The three study efforts in 3 different countries will define a feedstock pathway for jet fuel that involves very high yield crops that can produce 2 to 10 times more biomass than current assumptions. This cost-effective and less land intensive strategy will meet global jet fuel demand and produce huge quantities of food for net zero aviation and feeding 9-10 billion people by 2050

Keywords: zero emission SAF, methane capture, food-fuel integrated refining, new crops for SAF

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Authors: Payel Das, Gnaneshwar Nelakanti

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In this paper we analyse the iterated discrete Legendre Galerkin method for Fredholm-Hammerstein integral equations with smooth kernel. Using sufficiently accurate numerical quadrature rule, we obtain superconvergence rates for the iterated discrete Legendre Galerkin solutions in both infinity and $L^2$-norm. Numerical examples are given to illustrate the theoretical results.

Keywords: hammerstein integral equations, spectral method, discrete galerkin, numerical quadrature, superconvergence

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Authors: Jibril Mohammed, Usman Dadum Hamza, Muhammad Idris Misau, Baba Yahya Danjuma, Yusuf Bode Raji, Abdulsalam Surajudeen

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Volatile organic compounds (VOCs) have been reported to be responsible for many acute and chronic health effects and environmental degradations such as global warming. In this study, a renewable and low-cost coconut shell activated carbon (PHAC) was synthesized and treated with ammonia (PHAC-AM) to improve its hydrophobicity and affinity towards VOCs. Removal efficiencies and adsorption capacities of the ammonia treated activated carbon (PHAC-AM) for benzene and toluene were carried out through batch and fixed-bed studies respectively. Langmuir, Freundlich and Tempkin adsorption isotherms were tested for the adsorption process and the experimental data were best fitted by Langmuir model and least fitted by Tempkin model; the favourability and suitability of fitness were validated by equilibrium parameter (RL) and the root square mean deviation (RSMD). Judging by the deviation of the predicted values from the experimental values, pseudo-second-order kinetic model best described the adsorption kinetics than the pseudo-first-order kinetic model for the two VOCs on PHAC and PHAC-AM. In the fixed-bed study, the effect of initial VOC concentration, bed height and flow rate on benzene and toluene adsorption were studied. The highest bed capacities of 77.30 and 69.40 mg/g were recorded for benzene and toluene respectively; at 250 mg/l initial VOC concentration, 2.5 cm bed height and 4.5 ml/min flow rate. The results of this study revealed that ammonia treated activate carbon (PHAC-AM) is a sustainable adsorbent for treatment of VOCs in polluted waters.

Keywords: volatile organic compounds, equilibrium and kinetics studies, batch and fixed bed study, bio-based activated carbon

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Authors: Andrey V. Timofeev

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The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. Results of practical usage are presented.

Keywords: Lipschitz Classifier, classifiers ensembles, LPBoost, C-OTDR systems

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Authors: Hamadi Djamal, Labiodh Bachir, Ounis Abdelhafid, Chaalane Mourad

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The objective of this work is setting numerically operational finite element CAXI_L for the axisymmetric and nonaxisymmetric shells. This element is based on the Reissner-Mindlin theory and mixed model formulation. The MATLAB language is used for the programming. In order to test the elaborated program, some applications are carried out.

Keywords: axisymmetric shells, nonaxisymmetric behaviour, finite element, MATLAB programming

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Authors: Elmeliani M’Hammed

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The work is carried out for the synthesis of antifungal effective against the fungus Fusarium oxysporum, Albedinis (Foa), the causative agent of bayoud, dates palm disease, through the use of raw clay as a green catalyst. The Aza-Michael reaction of amine addition to α, β-unsaturated alkene was carried out using the crude clay as a green catalyst to synthesize the antifungal agent bayoud. The reaction was carried out under favorable conditions, ambient temperature, without solvent, and a green catalyst "loves the environment" that the product that was synthesized gave us a high yield and excellent chemo selectivity.

Keywords: raw clay, amines, alkenes, environment, antifungal, bayoud, date palms

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Authors: Juliet Okoli

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The removal of BTEX (Benzene, toluene, Ethylbenzene and p-Xylene) from water by orange peel and eggshell compared to GAC were investigated. The influence of various factors such as contact time, dosage and pH on BTEX removal by virgin orange peel and egg shell were accessed using the batch adsorption set-up. These were also compared to that of GAC which serves as a benchmark for this study. Further modification (preparation of Activated carbon) of these virgin low-cost adsorbents was also carried out. The batch adsorption result showed that the optimum contact time, dosage and pH for BTEX removal by virgin LCAs were 180 minutes, 0.5g and 7 and that of GAC was 30mintues, 0.2g and 7. The maximum adsorption capacity for total BTEX showed by orange peel and egg shell were 42mg/g and 59mg/g respectively while that of GAC was 864mg/g. The adsorbent preference for adsorbate were in order of X>E>T>B. A comparison of batch and column set-up showed that the batch set-up was more efficient than the column set-up. The isotherm data for the virgin LCA and GAC prove to fit the Freundlich isotherm better than the Langmuir model, which produced n values >1 in case of GAC and n< 1 in case of virgin LCAs; indicating a more appropriate adsorption of BTEX onto the GAC. The adsorption kinetics for the three studied adsorbents were described well by the pseudo-second order, suggesting chemisorption as the rate limiting step. This was further confirmed by desorption study, as low levels of BTEX (<10%) were recovered from the spent adsorbents especially for GAC (<3%). Further activation of the LCAs which was compared to the virgin LCAs, revealed that the virgin LCAs had minor higher adsorption capacity than the activated LCAs. Economic analysis revealed that the total cost required to clean-up 9,600m3 of BTEX contaminated water using LCA was just 2.8% lesser than GAC, a difference which could be considered negligible. However, this area still requires a more detailed cost-benefit analysis, and if similar conclusions are reached; a low-cost adsorbent, easy to obtain are still promising adsorbents for BTEX removal from aqueous solution; however, the GAC are still more superior to these materials.

Keywords: activated carbon, BTEX removal, low cost adsorbents, water treatment

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Authors: Mustafa Taskin, Ozgur Demir, M. Mert Serveren

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The precise study of the impact of underwater explosions on structures is of great importance in the design and engineering calculations of floating structures, especially those used for military purposes, as well as power generation facilities such as offshore platforms that can become a target in case of war. Considering that ship and submarine structures are mostly curved surfaces, it is extremely important and interesting to examine the destructive effects of underwater explosions on curvilinear surfaces. In this study, geometric nonlinear dynamic analysis of cylindrical composite sandwich shells subjected to instantaneous pressure load is performed. The instantaneous pressure load is defined as an underwater explosion and the effects of the liquid medium are taken into account. There are equations in the literature for pressure due to underwater explosions, but these equations have been obtained for flat plates. For this reason, the instantaneous pressure load equations are arranged to be suitable for curvilinear structures before proceeding with the analyses. Fluid-solid interaction is defined by using Taylor's Plate Theory. The lower and upper layers of the cylindrical composite sandwich shell are modeled as composite laminate and the middle layer consists of soft core. The geometric nonlinear dynamic equations of the shell are obtained by Hamilton's principle, taken into account the von Kàrmàn theory of large displacements. Then, time dependent geometric nonlinear equations of motion are solved with the help of generalized differential quadrature method (GDQM) and dynamic behavior of cylindrical composite sandwich shells exposed to underwater explosion is investigated. An algorithm that can work parametrically for the solution has been developed within the scope of the study.

Keywords: cylindrical composite sandwich shells, generalized differential quadrature method, geometric nonlinear dynamic analysis, underwater explosion

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Authors: A. V. Samokhin, A. A. Fadeev, M. A. Sinaiskii, N. V. Alekseev, A. V. Kolesnikov

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Composite materials where metal matrix is reinforced by ceramic or metal particles are of great interest for use in the manufacturing of electrical contacts. Significant improvement of the composite physical and mechanical properties as well as increase of the performance parameters of composite-based products can be achieved if the nanoscale structure in the composite materials is obtained by using nanosized powders as starting components. The results of nanosized composite powders synthesis (Ag-SnO2, W-Cu and Cu-C) in the DC thermal plasma flows are presented in this paper. The investigations included the following processes: - Recondensation of micron powder mixture Ag + SnO2 in a nitrogen plasma; - The reduction of the oxide powders mixture (WO3 + CuO) in a hydrogen-nitrogen plasma; - Decomposition of the copper formate and copper acetate powders in nitrogen plasma. The calculations of equilibrium compositions of multicomponent systems Ag-Sn-O-N, W-Cu-O-H-N and Cu-O-C-H-N in the temperature range of 400-5000 K were carried to estimate basic process characteristics. Experimental studies of the processes were performed using a plasma reactor with a confined jet flow. The plasma jet net power was in the range of 2 - 13 kW, and the feedstock flow rate was up to 0.35 kg/h. The obtained powders were characterized by TEM, HR-TEM, SEM, EDS, ED-XRF, XRD, BET and QEA methods. Nanocomposite Ag-SnO2 (12 wt. %). Processing of the initial powder mixture (Ag-SnO2) in nitrogen thermal plasma stream allowed to produce nanopowders with a specific surface area up to 24 m2/g, consisting predominantly of particles with size less than 100 nm. According to XRD results, tin was present in the obtained products as SnO2 phase, and also as intermetallic phases AgxSn. Nanocomposite W-Cu (20 wt .%). Reduction of (WO3+CuO) mixture in the hydrogen-nitrogen plasma provides W-Cu nanopowder with particle sizes in the range of 10-150 nm. The particles have mainly spherical shape and structure tungsten core - copper shell. The thickness of the shell is about several nanometers, the shell is composed of copper and its oxides (Cu2O, CuO). The nanopowders had 1.5 wt. % oxygen impurity. Heat treatment in a hydrogen atmosphere allows to reduce the oxygen content to less than 0.1 wt. %. Nanocomposite Cu-C. Copper nanopowders were found as products of the starting copper compounds decomposition. The nanopowders primarily had a spherical shape with a particle size of less than 100 nm. The main phase was copper, with small amount of Cu2O and CuO oxides. Copper formate decomposition products had a specific surface area 2.5-7 m2/g and contained 0.15 - 4 wt. % carbon; and copper acetate decomposition products had the specific surface area 5-35 m2/g, and carbon content of 0.3 - 5 wt. %. Compacting of nanocomposites (sintering in hydrogen for Ag-SnO2 and electric spark sintering (SPS) for W-Cu) showed that the samples having a relative density of 97-98 % can be obtained with a submicron structure. The studies indicate the possibility of using high-intensity plasma processes to create new technologies to produce nanocomposite materials for electric contacts.

Keywords: electrical contact, material, nanocomposite, plasma, synthesis

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Authors: Hanizam Awang, Muhammad Hafiz Ahmad

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An experimental study was conducted on foamed concrete with synthetic and natural fibres consisting of AR-glass, polypropylene, steel, kenaf and oil palm fibre. The foamed concrete mixtures produced had a target density of 1000 kg/m3 and a mix ratio of (1:1.5:0.45). The fibres were used as additives. The inclusion of fibre was maintained at a volumetric fraction of 0.25 and 0.4 %. The water absorption, thermal and shrinkage were determined to study the effect of the fibre on the durability properties of foamed concrete. The results showed that AR-glass fibre has the lowest percentage value of drying shrinkage compared to others.

Keywords: foamed concrete, fibres, durability, construction, geological engineering

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Authors: Tarali Kalita, Karabi Dutta

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The North Eastern Region of India, with diverse climatic conditions and a wide range of ecological habitats, makes an ideal natural abode for a good number of silk-producing insects. Cocoon is the economically important life stage from where silk of economic importance is obtained. In recent years, silk-based biomaterials have gained considerable attention, which is dependent on the structure and properties of the silkworm cocoons as well as silk yarn. The present investigation deals with the morphological study of cocoons, including cocoon color, cocoon size, shell weight and shell ratio of eleven different species of silk insects collected from different regions of North East India. The Scanning Electron Microscopic study and X-ray photoelectron spectroscopy were performed to know the arrangement of silk threads in cocoons and the atomic elemental analysis, respectively. Further, collected cocoons were degummed and reeled/spun on a reeling machine or spinning wheel to know the filament length, linear density and tensile strength by using Universal Testing Machine. The study showed significant variation in terms of cocoon color, cocoon shape, cocoon weight and filament packaging. XPS analysis revealed the presence of elements (Mass %) C, N, O, Si and Ca in varying amounts. The wild cocoons showed the presence of Calcium oxalate crystals which makes the cocoons hard and needs further treatment to reel. In the present investigation, the highest percentage of strain (%) and toughness (g/den) were observed in Antheraea assamensis, which implies that the muga silk is a more compact packing of molecules. It is expected that this study will be the basis for further biomimetic studies to design and manufacture artificial fiber composites with novel morphologies and associated material properties.

Keywords: cocoon characterization, north-east India, prospects, silk characterization

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Ketaki D. Belsare, Nicholas F. Polizzi, Lior Shtayer, William F. DeGrado

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Nature utilizes metalloproteins to perform chemical transformations with activities and selectivities that have long been the inspiration for design principles in synthetic and biological systems. The chemical reactivities of metalloproteins are directly linked to local environment effects produced by the protein matrix around the metal cofactor. A complete understanding of how the protein matrix provides these interactions would allow for the design of functional metalloproteins. The de novo computational design of proteins have been successfully used in design of active sites that bind metals like di-iron, zinc, copper containing cofactors; however, precisely designing active sites that can bind small molecule ligands (e.g., substrates) along with metal cofactors is still a challenge in the field. The de novo computational design of a functional metalloprotein that contains a purposefully designed substrate binding site would allow for precise control of chemical function and reactivity. Our research strategy seeks to elucidate the design features necessary to bind the cofactor protoporphyrin IX (hemin) in close proximity to a substrate binding pocket in a four helix bundle. First- and second-shell interactions are computationally designed to control orientation, electronic structure, and reaction pathway of the cofactor and substrate. The design began with a parameterized helical backbone that positioned a single histidine residue (as an axial ligand) to receive a second-shell H-bond from a Threonine on the neighboring helix. The metallo-cofactor, hemin was then manually placed in the binding site. A structural feature, pi-bulge was introduced to give substrate access to the protoporphyrin IX. These de novo metalloproteins are currently being tested for their activity towards hydroxylation and epoxidation. The de novo designed protein shows hydroxylation of aniline to 4-aminophenol. This study will help provide structural information of utmost importance in understanding de novo computational design variables impacting the functional activities of a protein.

Keywords: metalloproteins, protein design, de novo protein, biocatalysis

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Authors: Alpana Tiwari, N. K. Gaur

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We have incorporated the translational rotational (TR) coupling effects in the framework of three body force shell model (TSM) to develop an extended TSM (ETSM). The dynamical matrix of ETSM has been applied to compute the phonon frequencies of orientationally disordered mixed crystal (ND4Br)x(KBr)1-x in (q00), (qq0) and (qqq) symmetry directions for compositions 0.10≤x≤0.50 at T=300K.These frequencies are plotted as a function of wave vector k. An unusual acoustic mode softening is found along symmetry directions (q00) and (qq0) as a result of translation-rotation coupling.

Keywords: orientational glass, phonons, TR-coupling, lattice dynamics

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Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: Raphaël Menard, Etienne Fayette, Paul Azzopardi

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This paper presents the method to generate the geometry of an actual glass canopy project in Rennes, France, by architect Bruno Gaudin, with aim to achieve the best structural efficiency possible using only quadrangle meshing. The paper includes equation of the translational surface generated, the level of accuracy in approximating the funicular shape and the method of constructive implementation.

Keywords: funicular shape, glass canopy, glass panels, lowered arches, mathematics, penalization, shell structure

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Authors: Marija Milašinović-Šeremešić, Valentina Semenčenko, Milica Radosavljević, Dušanka Terzić, Ljiljana Mojović, Ljubica Dokić

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Maize (Zea mays L.) is one of the most important cereal crops, and as such, one of the most significant naturally renewable carbohydrate raw materials for the production of energy and multitude of different products. The main goal of the present study was to investigate a suitability of selected maize hybrids of different genetic background produced in Maize Research Institute ‘Zemun Polje’, Belgrade, Serbia, for starch, bioethanol and animal feed production. All the hybrids are commercial and their detailed characterization is important for the expansion of their different uses. The starches were isolated by using a 100-g laboratory maize wet-milling procedure. Hydrolysis experiments were done in two steps (liquefaction with Termamyl SC, and saccharification with SAN Extra L). Starch hydrolysates obtained by the two-step hydrolysis of the corn flour starch were subjected to fermentation by S. cerevisiae var. ellipsoideus under semi-anaerobic conditions. The digestibility based on enzymatic solubility was performed by the Aufréré method. All investigated ZP maize hybrids had very different physical characteristics and chemical composition which could allow various possibilities of their use. The amount of hard (vitreous) and soft (floury) endosperm in kernel is considered one of the most important parameters that can influence the starch and bioethanol yields. Hybrids with a lower test weight and density and a greater proportion of soft endosperm fraction had a higher yield, recovery and purity of starch. Among the chemical composition parameters only starch content significantly affected the starch yield. Starch yields of studied maize hybrids ranged from 58.8% in ZP 633 to 69.0% in ZP 808. The lowest bioethanol yield of 7.25% w/w was obtained for hybrid ZP 611k and the highest by hybrid ZP 434 (8.96% w/w). A very significant correlation was determined between kernel starch content and the bioethanol yield, as well as volumetric productivity (48h) (r=0.66). Obtained results showed that the NDF, ADF and ADL contents in the whole maize plant of the observed ZP maize hybrids varied from 40.0% to 60.1%, 18.6% to 32.1%, and 1.4% to 3.1%, respectively. The difference in the digestibility of the dry matter of the whole plant among hybrids (ZP 735 and ZP 560) amounted to 18.1%. Moreover, the differences in the contents of the lignocelluloses fraction affected the differences in dry matter digestibility. From the results it can be concluded that genetic background of the selected maize hybrids plays an important part in estimation of the technological value of maize hybrids for various purposes. Obtained results are of an exceptional importance for the breeding programs and selection of potentially most suitable maize hybrids for starch, bioethanol and animal feed production.

Keywords: bioethanol, biomass quality, maize, starch

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Authors: Alexander P. Smirnov, Sergey A. Matveev, Dmitry A. Zheltkov, Eugene E. Tyrtyshnikov

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We propose a new computational technique for multidimensional (multicomponent) Smoluchowski coagulation equation. Using low-rank approximations in Tensor Train format of both the solution and the coagulation kernel, we accelerate the classical finite-difference Runge-Kutta scheme keeping its level of accuracy. The complexity of the taken finite-difference scheme is reduced from O(N^2d) to O(d^2 N log N ), where N is the number of grid nodes and d is a dimensionality of the problem. The efficiency and the accuracy of the new method are demonstrated on concrete problem with known analytical solution.

Keywords: tensor train decomposition, multicomponent Smoluchowski equation, runge-kutta scheme, convolution

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Authors: Edvard P. G. Bruun

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One of the best ways of improving our understanding of reinforced concrete is through large-scale experimental testing. The gathered information is critical in making inferences about structural mechanics and deriving the mathematical models that are the basis for finite element analysis programs and design codes. An effective way of measuring the strains across a region of a specimen is by using a system of surface mounted Linear Variable Differential Transformers (LVDTs). While a single LVDT can only measure the linear strain in one direction, by combining several measurements at known angles a Mohr’s circle of strain can be derived for the whole region under investigation. This paper presents a method that can be used by researchers, which improves the accuracy and removes experimental bias in the calculation of the Mohr’s circle, using four rather than three independent strain measurements. Obtaining high quality strain data is essential, since knowing the angular deviation (shear strain) and the angle of principal strain in the region are important properties in characterizing the governing structural mechanics. For example, the Modified Compression Field Theory (MCFT) developed at the University of Toronto, is a rotating crack model that requires knowing the direction of the principal stress and strain, and then calculates the average secant stiffness in this direction. But since LVDTs can only measure average strains across a plane (i.e., between discrete points), localized cracking and spalling that typically occur in reinforced concrete, can lead to unrealistic results. To build in redundancy and improve the quality of the data gathered, the typical experimental setup for a large-scale shell specimen has four independent directions (X, Y, H, and V) that are instrumented. The question now becomes, which three should be used? The most common approach is to simply discard one of the measurements. The problem is that this can produce drastically different answers, depending on the three strain values that are chosen. To overcome this experimental bias, and to avoid simply discarding valuable data, a more rigorous approach would be to somehow make use of all four measurements. This paper presents the derivation of a method to draw what is effectively a Mohr’s circle of 'best-fit', which optimizes the circle by using all four independent strain values. The four-strain optimized Mohr’s circle approach has been utilized to process data from recent large-scale shell tests at the University of Toronto (Ruggiero, Proestos, and Bruun), where analysis of the test data has shown that the traditional three-strain method can lead to widely different results. This paper presents the derivation of the method and shows its application in the context of two reinforced concrete shells tested in pure torsion. In general, the constitutive models and relationships that characterize reinforced concrete are only as good as the experimental data that is gathered – ensuring that a rigorous and unbiased approach exists for calculating the Mohr’s circle of strain during an experiment, is of utmost importance to the structural research community.

Keywords: reinforced concrete, shell tests, Mohr’s circle, experimental research

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Authors: Hassan Eshkiki, Benjamin Mora

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The Neighbor Migrating Generator is a simple and efficient approach to finding the closest potential neighbor(s) with a different label for a given instance and so without the need to calibrate any kernel settings at all. This allows determining and explaining the most important features that will influence an AI model. It can be used to either migrate a specific sample to the class decision boundary of the original model within a close neighborhood of that sample or identify global features that can help localising neighbor classes. The proposed technique works by minimizing a loss function that is divided into two components which are independently weighted according to three parameters α, β, and ω, α being self-adjusting. Results show that this approach is superior to past techniques when detecting the smallest changes in the feature space and may also point out issues in models like over-fitting.

Keywords: explainable AI, EX AI, feature importance, counterfactual explanations

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Authors: G. Alston Rush, Gus A. Rush III, M. F. Horstemeyer

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This study compares the shell-facemask responses of four commonly used American football helmets, under the National Operating Committee on Standards for Athletic Equipment (NOCSAE) drop impact test method, to show that the test standard would more accurately simulate in-use conditions by modification to include the facemask. In our study, the need for a more vigorous systematic approach to football helmet testing procedures is emphasized by comparing the Head Injury Criterion (HIC), the Gadd Severity Index (SI), and peak acceleration values for different helmets at different locations on the helmet under modified NOCSAE standard drop tower tests. Drop tests were performed on the Rawlings Quantum Plus, Riddell 360, Schutt Ion 4D, and Xenith X2 helmets at eight impact locations, impact velocities of 5.46 and 4.88 meters per second, and helmet configurations with and without facemasks. Analysis of NOCSAE drop test results reveal significant differences (p < 0.05) for when the facemasks were attached to helmets, as compared to the NOCSAE Standard, without facemask configuration. The boundary conditions of the facemask attachment can have up to a 50% decrease (p < 0.001) in helmet performance with respect to peak acceleration. While generally, all helmets with the facemasks gave greater HIC, SI, and acceleration values than helmets without the facemasks, significant helmet dependent variations were observed across impact locations and impact velocities. The variations between helmet responses could be attributed to the unique design features of each helmet tested, which include different liners, chin strap attachments, and faceguard attachment systems. In summary, these comparative drop test results revealed that the current NOCSAE standard test methods need improvement by attaching the facemasks to helmets during testing. The modified NOCSAE football helmet standard test gives a more accurate representation of a helmet’s performance and its ability to mitigate the on-field impact.

Keywords: football helmet testing, gadd severity index, head injury criterion, mild traumatic brain injury

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Authors: Cristina Opris, Bogdan Cojocaru, Madalina Tudorache, Simona M. Coman, Vasile I. Parvulescu, Camelia Bala, Bahir Duraki, Jeroen A. Van Bokhoven

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The tremendous achievements in the development of the society concretized by more sophisticated materials and systems are merely based on non-renewable resources. Consequently, after more than two centuries of intensive development, among others, we are faced with the decrease of the fossil fuel reserves, an increased impact of the greenhouse gases on the environment, and economic effects caused by the fluctuations in oil and mineral resource prices. The use of biomass may solve part of these problems, and recent analyses demonstrated that from the perspective of the reduction of the emissions of carbon dioxide, its valorization may bring important advantages conditioned by the usage of genetic modified fast growing trees or wastes, as primary sources. In this context, the abundance and complex structure of lignin may offer various possibilities of exploitation. However, its transformation in fuels or chemicals supposes a complex chemistry involving the cleavage of C-O and C-C bonds and altering of the functional groups. Chemistry offered various solutions in this sense. However, despite the intense work, there are still many drawbacks limiting the industrial application. Thus, the proposed technologies considered mainly homogeneous catalysts meaning expensive noble metals based systems that are hard to be recovered at the end of the reaction. Also, the reactions were carried out in organic solvents that are not acceptable today from the environmental point of view. To avoid these problems, the concept of this work was to investigate the synthesis of superparamagnetic core shell catalysts for the fragmentation of lignin directly in the aqueous phase. The magnetic nanoparticles were covered with a nanoshell of an oxide (niobia) with a double role: to protect the magnetic nanoparticles and to generate a proper (acidic) catalytic function and, on this composite, cobalt nanoparticles were deposed in order to catalyze the C-C bond splitting. With this purpose, we developed a protocol to prepare multifunctional and magnetic separable nano-composite Co@Nb2O5@Fe3O4 catalysts. We have also established an analytic protocol for the identification and quantification of the fragments resulted from lignin depolymerization in both liquid and solid phase. The fragmentation of various lignins occurred on the prepared materials in high yields and with very good selectivity in the desired fragments. The optimization of the catalyst composition indicated a cobalt loading of 4wt% as optimal. Working at 180 oC and 10 atm H2 this catalyst allowed a conversion of lignin up to 60% leading to a mixture containing over 96% in C20-C28 and C29-C37 fragments that were then completely fragmented to C12-C16 in a second stage. The investigated catalysts were completely recyclable, and no leaching of the elements included in the composition was determined by inductively coupled plasma optical emission spectrometry (ICP-OES).

Keywords: superparamagnetic core-shell catalysts, environmental production of fuels, renewable lignin, recyclable catalysts

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Authors: Li-Jun Ren, Wei Pan, Li-Li Xu, Shu-Qing An

Abstract:

This study evaluated whether different matrix composition volume ratio can improve water quality in the experiment. The mechanism and adsorption capability of wetland matrixes (oyster shell, coarse slag, and volcanic rock) and their different volume ratio in group configuration during pollutants removal processes were tested. When conditions unchanged, the residence time affects the reaction effect. The average removal efficiencies of four kinds of matrix volume ratio on the TN were 62.76%, 61.54%, 64.13%, and 55.89%, respectively.

Keywords: hydraulic residence time, matrix composition, removal efficiency, volume ratio

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Authors: Hanan. Al Chaghouri, Mohammad Azad Malik, P. John Thomas, Paul O’Brien

Abstract:

The process of assembling metal nanoparticles at the interface of two liquids has received a great interest over the past few years due to a wide range of important applications and their unusual properties compared to bulk materials. We present a low cost, simple and cheap synthesis of metal nanoparticles, core/shell structures and semiconductors followed by assembly of these particles between immiscible liquids. The aim of this talk is divided to three parts: firstly, to describe the achievement of a closed loop recycling for producing cadmium sulphide as powders and/or nanostructured thin films for solar cells or other optoelectronic devices applications by using a different chain length of commercially available secondary amines of dithiocarbamato complexes. The approach can be extended to other metal sulphides such as those of Zn, Pb, Cu, or Fe and many transition metals and oxides. Secondly, to synthesis significantly cheaper magnetic particles suited for the mass market. Ni/NiO nanoparticles with ferromagnetic properties at room temperature were among the smallest and strongest magnets (5 nm) were made in solution. The applications of this work can be applied to produce viable storage devices and the other possibility is to disperse these nanocrystals in solution and use it to make ferro-fluids which have a number of mature applications. The third part is about preparing and assembling of submicron silver, cobalt and nickel particles by using polyol methods and liquid/liquid interface, respectively. Noble metal like gold, copper and silver are suitable for plasmonic thin film solar cells because of their low resistivity and strong interactions with visible light waves. Silver is the best choice for solar cell application since it has low absorption losses and high radiative efficiency compared to gold and copper. Assembled cobalt and nickel as films are promising for spintronic, magnetic and magneto-electronic and biomedics.

Keywords: assembling nanoparticles, liquid/liquid interface, thin film, core/shell, solar cells, recording media

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Authors: Amani Tahat, Serdal Kirmizialtin

Abstract:

Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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Authors: Mohammad H. Fattahi

Abstract:

Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. The noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.

Keywords: chaotic behavior, wavelet, noise reduction, river flow

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Authors: Sue S. Feldman, Bradley Tipper, Benjamin Schooley

Abstract:

Nearly every community in the US has been impacted by opioid related addictions/deaths; it is a national problem that is threatening our social and economic welfare. Most believe that tackling this problem from a prevention perspective advances can be made toward breaking the chain of addiction. One mechanism, community based participatory research, involves the community in the prevention approach. This project combines that approach with a design science approach to develop an integrated solution. Findings suggested accountable care communities, transpersonal psychology, and social exchange theory as product kernel theories. Evaluation was conducted on a prototype.

Keywords: substance use and abuse recovery, community resource centers, accountable care communities, community based participatory research

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Authors: M. Z. Shazana, R. Rosazley, M. A. Izzati, A. W. Fareezal, I. Rushdan, A. B. Suriani, S. Zakaria

Abstract:

There is an increasing interest in the development of flexible energy storage for application of Carbon Nanotubes and nanofibrillated cellulose (NFC). In this study, nanocomposite is consisting of Carbon Nanotube (CNT) mixed with suspension of nanofibrillated cellulose (NFC) from Oil Palm Empty Fruit Bunch (OPEFB). The use of Carbon Nanotube (CNT) as additive nanocomposite was improved the conductivity and mechanical properties of nanocomposite from nanofibrillated cellulose (NFC). The nanocomposite were characterized for electrical conductivity and mechanical properties in uniaxial tension, which were tensile to measure the bond of fibers in nanocomposite. The processing route is environmental friendly which leads to well-mixed structures and good results as well.

Keywords: carbon nanotube (CNT), nanofibrillated cellulose (NFC), mechanical properties, electrical conductivity

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Authors: Deepak Kakde, Steve Howdle, Derek Irvine, Cameron Alexander

Abstract:

The poor aqueous solubility is one of the major obstacles in the formulation development of many drugs. Around 70% of drugs are poorly soluble in aqueous media. In the last few decades, micelles have emerged as one of the major tools for solubilization of hydrophobic drugs. Micelles are nanosized structures (10-100nm) obtained by self-assembly of amphiphilic molecules into the water. The hydrophobic part of the micelle forms core which is surrounded by a hydrophilic outer shell called corona. These core-shell structures have been used as a drug delivery vehicle for many years. Although, the utility of micelles have been reduced due to the lack of sustainable materials. In the present study, a novel methoxy poly(ethylene glycol)-b-poly(ε-decalactone) (mPEG-b-PεDL) copolymer was synthesized by ring opening polymerization (ROP) of renewable ε-decalactone (ε-DL) monomers on methoxy poly(ethylene glycol) (mPEG) initiator using 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) as a organocatalyst. All the reactions were conducted in bulk to avoid the use of toxic organic solvents. The copolymer was characterized by nuclear magnetic resonance spectroscopy (NMR), gel permeation chromatography (GPC) and differential scanning calorimetry (DSC).The mPEG-b-PεDL block copolymeric micelles containing indomethacin (IND) were prepared by nanoprecipitation method and evaluated as drug delivery vehicle. The size of the micelles was less than 40nm with narrow polydispersity pattern. TEM image showed uniform distribution of spherical micelles defined by clear surface boundary. The indomethacin loading was 7.4% for copolymer with molecular weight of 13000 and drug/polymer weight ratio of 4/50. The higher drug/polymer ratio decreased the drug loading. The drug release study in PBS (pH7.4) showed a sustained release of drug over a period of 24hr. In conclusion, we have developed a new sustainable polymeric material for IND delivery by combining the green synthetic approach with the use of renewable monomer for sustainable development of polymeric nanomedicine.

Keywords: dopolymer, ε-decalactone, indomethacin, micelles

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Authors: Chen Chuanzhi, Guo Yunyun

Abstract:

The large linear array feeding parabolic cylindrical antenna of the satellite has the ability of large-area line focusing, multi-directional beam clusters simultaneously in a certain azimuth plane and elevation plane, corresponding quickly to different orientations and different directions in a wide frequency range, dual aiming of frequency and direction, and combining space power. Therefore, the large-diameter parabolic cylindrical antenna has become one of the new development directions of spaceborne antennas. Limited by the size of the rocked fairing, the large-diameter spaceborne antenna is required to be small mass and have a deployment function. After being orbited, the antenna can be deployed by expanding and be stabilized. However, few types of structures can be used to construct large cylindrical shell structures in existing structures, which greatly limits the development and application of such antennas. Aiming at high structural efficiency, the geometrical characteristics of parabolic cylinders and mechanism topological mapping law to the expandable truss are studied, and the basic configuration of deployable truss with cylindrical shell is structured. Then a modular truss parabolic cylindrical antenna is designed in this paper. The antenna has the characteristics of stable structure, high precision of reflecting surface formation, controllable motion process, high storage rate, and lightweight, etc. On the basis of the overall configuration comprehensive theory and optimization method, the structural stiffness of the modular truss parabolic cylindrical antenna is improved. And the bearing density and impact resistance of support structure are improved based on the internal tension optimal distribution method of reflector forming. Finally, a truss-type cylindrical deployable support structure with high constriction-deployment ratio, high stiffness, controllable deployment, and low mass is successfully developed, laying the foundation for the application of large-diameter parabolic cylindrical antennas in satellite antennas.

Keywords: linear array feed antenna, truss type, parabolic cylindrical antenna, spaceborne antenna

Procedia PDF Downloads 131