Search results for: harmonic filtering

Authors: Fisaha Asmelash

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The processing of textiles to achieve a desired handle is a crucial aspect of finishing technology. Softeners can enhance the properties of textiles, such as softness, smoothness, elasticity, hydrophilicity, antistatic properties, and soil release properties, depending on the chemical nature used. However, human skin is sensitive to rough textiles, making softeners increasingly important. Although synthetic softeners are available, they are often expensive and can cause allergic reactions on human skin. This paper aims to extract a natural softener from Ricinus communis and produce an eco-friendly and user-friendly alternative due to its 100% herbal and organic nature. Crushed Ricinus communis seeds were soaked in a mechanical oil extractor for one hour with a 100g cotton fabric sample. The defatted cake or residue obtained after the extraction of oil from the seeds, also known as Ricinus communis meal, was obtained by filtering the raffinate and then dried at 1030c for four hours before being stored under laboratory conditions for the softening process. The softener was applied directly to 100% cotton fabric using the padding process, and the fabric was tested for stiffness, crease recovery, and drape ability. The effect of different concentrations of finishing agents on fabric stiffness, crease recovery, and drape ability was also analyzed. The results showed that the change in fabric softness depends on the concentration of the finish used. As the concentration of the finish was increased, there was a decrease in bending length and drape coefficient. Fabrics with a high concentration of softener showed a maximum decrease in drape coefficient and stiffness, comparable to commercial softeners such as silicon. The highest decrease in drape coefficient was found to be comparable with commercial softeners, silicon. Maximum increases in crease recovery were seen in fabrics treated with Ricinus communis softener at a concentration of 30gpl. From the results, the extracted softener proved to be effective in the treatment of 100% cotton fabric

Keywords: ricinus communis, crease recovery, drapability, softeners, stiffness

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Authors: S. Behnia, S. Fathizadeh, A. Akhshani

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In the recent decades, DNA has increasingly interested in the potential technological applications that not directly related to the coding for functional proteins that is the expressed in form of genetic information. One of the most interesting applications of DNA is related to the construction of nanostructures of high complexity, design of functional nanostructures in nanoelectronical devices, nanosensors and nanocercuits. In this field, DNA is of fundamental interest to the development of DNA-based molecular technologies, as it possesses ideal structural and molecular recognition properties for use in self-assembling nanodevices with a definite molecular architecture. Also, the robust, one-dimensional flexible structure of DNA can be used to design electronic devices, serving as a wire, transistor switch, or rectifier depending on its electronic properties. In order to understand the mechanism of the charge transport along DNA sequences, numerous studies have been carried out. In this regard, conductivity properties of DNA molecule could be investigated in a simple, but chemically specific approach that is intimately related to the Su-Schrieffer-Heeger (SSH) model. In SSH model, the non-diagonal matrix element dependence on intersite displacements is considered. In this approach, the coupling between the charge and lattice deformation is along the helix. This model is a tight-binding linear nanoscale chain established to describe conductivity phenomena in doped polyethylene. It is based on the assumption of a classical harmonic interaction between sites, which is linearly coupled to a tight-binding Hamiltonian. In this work, the Hamiltonian and corresponding motion equations are nonlinear and have high sensitivity to initial conditions. Then, we have tried to move toward the nonlinear dynamics and phase space analysis. Nonlinear dynamics and chaos theory, regardless of any approximation, could open new horizons to understand the conductivity mechanism in DNA. For a detailed study, we have tried to study the current flowing in DNA and investigated the characteristic I-V diagram. As a result, It is shown that there are the (quasi-) ohmic areas in I-V diagram. On the other hand, the regions with a negative differential resistance (NDR) are detectable in diagram.

Keywords: DNA conductivity, Landauer resistance, negative dierential resistance, Chaos theory, mean Lyapunov exponent

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Authors: Rezvan Ravanfar Haghighi, V. C. Vani, Suresh Perumal, Sabyasachi Chatterjee, Pratik Kumar

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X-ray attenuation coefficient [µ(E)] of any substance, for energy (E), is a sum of the contributions from the Compton scattering [ μCom(E)] and photoelectric effect [µPh(E)]. In terms of the, electron density (ρe) and the effective atomic number (Zeff) we have µCom(E) is proportional to [(ρe)fKN(E)] while µPh(E) is proportional to [(ρeZeffx)/Ey] with fKN(E) being the Klein-Nishina formula, with x and y being the exponents for photoelectric effect. By taking the sample's HU at two different excitation voltages (V=V1, V2) of the CT machine, we can solve for X=ρe, Y=ρeZeffx from these two independent equations, as is attempted in DECT inversion. Since µCom(E) and µPh(E) are both energy dependent, the coefficients of inversion are also dependent on (a) the source spectrum S(E,V) and (b) the detector efficiency D(E) of the CT machine. In the present paper we tabulate these coefficients of inversion for different practical manifestations of S(E,V) and D(E). The HU(V) values from the CT follow: <µ(V)>=<µw(V)>[1+HU(V)/1000] where the subscript 'w' refers to water and the averaging process <….> accounts for the source spectrum S(E,V) and the detector efficiency D(E). Linearity of μ(E) with respect to X and Y implies that (a) <µ(V)> is a linear combination of X and Y and (b) for inversion, X and Y can be written as linear combinations of two independent observations <µ(V1)>, <µ(V2)> with V1≠V2. These coefficients of inversion would naturally depend upon S(E, V) and D(E). We numerically investigate this dependence for some practical cases, by taking V = 100 , 140 kVp, as are used for cardiological investigations. The S(E,V) are generated by using the Boone-Seibert source spectrum, being superposed on aluminium filters of different thickness lAl with 7mm≤lAl≤12mm and the D(E) is considered to be that of a typical Si[Li] solid state and GdOS scintilator detector. In the values of X and Y, found by using the calculated inversion coefficients, errors are below 2% for data with solutions of glycerol, sucrose and glucose. For low Zeff materials like propionic acid, Zeffx is overestimated by 20% with X being within1%. For high Zeffx materials like KOH the value of Zeffx is underestimated by 22% while the error in X is + 15%. These imply that the source may have additional filtering than the aluminium filter specified by the manufacturer. Also it is found that the difference in the values of the inversion coefficients for the two types of detectors is negligible. The type of the detector does not affect on the DECT inversion algorithm to find the unknown chemical characteristic of the scanned materials. The effect of the source should be considered as an important factor to calculate the coefficients of inversion.

Keywords: attenuation coefficient, computed tomography, photoelectric effect, source spectrum

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Authors: Wadha Alqahtani

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In recent times, polymers have seen a great surge in interest in the field of medicine, particularly chemotherapeutics. One recent innovation is the conversion of polymeric materials into “polymeric nanoparticles”. These nanoparticles can be designed and modified to encapsulate and transport drugs selectively to cancer cells, minimizing collateral damage to surrounding healthy tissues, and improve patient quality of life. In this study, we have synthesized pseudo-branched polyester polymers from bio-based small molecules, including sorbitol, glutaric acid and a propargylic acid derivative to further modify the polymer to make it “click-able" with an azide-modified target ligand. Melt polymerization technique was used for this polymerization reaction, using lipase enzyme catalyst NOVO 435. This reaction was conducted between 90- 95 °C for 72 hours. The polymer samples were collected in 24-hour increments for characterization and to monitor reaction progress. The resulting polymer was purified with the help of methanol dissolving and filtering with filter paper then characterized via NMR, GPC, FTIR, DSC, TGA and MALDI-TOF. Following characterization, these polymers were converted to a polymeric nanoparticle drug delivery system using solvent diffusion method, wherein DiI optical dye and chemotherapeutic drug Taxol can be encapsulated simultaneously. The efficacy of the nanoparticle’s apoptotic effects were analyzed in-vitro by incubation with prostate cancer (LNCaP) and healthy (CHO) cells. MTT assays and fluorescence microscopy were used to assess the cellular uptake and viability of the cells after 24 hours at 37 °C and 5% CO2 atmosphere. Results of the assays and fluorescence imaging confirmed that the nanoparticles were successful in both selectively targeting and inducing apoptosis in 80% of the LNCaP cells within 24 hours without affecting the viability of the CHO cells. These results show the potential of using biodegradable polymers as a vehicle for receptor-specific drug delivery and a potential alternative for traditional systemic chemotherapy. Detailed experimental results will be discussed in the e-poster.

Keywords: chemotherapeutic drug, click chemistry, nanoparticle, prostat cancer

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Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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Authors: Mohammad R. Jalali, Mohammad M. Jalali

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The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.

Keywords: Green–Naghdi equations, nonlinearity, numerical prediction, sloshing waves, solitary waves

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: K. Raghu, Sree Harsha

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The interrelationship between the technology and architecture of tall buildings is investigated from the emergence of tall buildings in late 19th century to the present. In the late 19th century early designs of tall buildings recognized the effectiveness of diagonal bracing members in resisting lateral forces. Most of the structural systems deployed for early tall buildings were steel frames with diagonal bracings of various configurations such as X, K, and eccentric. Though the historical research a filtering concept is developed original and remedial technology- through which one can clearly understand inter-relationship between the technical evolution and architectural esthetic and further stylistic transition buildings. Diagonalized grid structures – “diagrids” - have emerged as one of the most innovative and adaptable approaches to structuring buildings in this millennium. Variations of the diagrid system have evolved to the point of making its use non-exclusive to the tall building. Diagrid construction is also to be found in a range of innovative mid-rise steel projects. Contemporary design practice of tall buildings is reviewed and design guidelines are provided for new design trends. Investigated in depths are the behavioral characteristics and design methodology for diagrids structures, which emerge as a new direction in the design of tall buildings with their powerful structural rationale and symbolic architectural expression. Moreover, new technologies for tall building structures and facades are developed for performance enhancement through design integration, and their architectural potentials are explored. By considering the above data the analysis and design of 40-100 storey diagrids steel buildings is carried out using E-TABS software with diagrids of various angle to be found for entire building which will be helpful to reduce the steel requirement for the structure. The present project will have to undertake wind analysis, seismic analysis for lateral loads acting on the structure due to wind loads, earthquake loads, gravity loads. All structural members are designed as per IS 800-2007 considering all load combination. Comparison of results in terms of time period, top storey displacement and inter-storey drift to be carried out. The secondary effect like temperature variations are not considered in the design assuming small variation.

Keywords: diagrid, bracings, structural, building

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Authors: Md. Kausar Alam, Ramin Motamed

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The damaging effects of liquefaction-induced ground movements have been frequently observed in past earthquakes, such as the 2010-2011 Canterbury Earthquake Sequence (CES) in New Zealand and the 2011 Tohoku earthquake in Japan. To reduce the consequences of soil liquefaction at shallow depths, various ground improvement techniques have been utilized in engineering practice, among which this research is focused on experimentally evaluating the press-in sheet piling technique. The press-in sheet pile technique eliminates the vibration, hammering, and noise pollution associated with dynamic sheet pile installation methods. Unfortunately, there are limited experimental studies on the press-in sheet piling technique for liquefaction mitigation using 1g shake table tests in which all the controlling mechanisms of liquefaction-induced foundation settlement, including sand ejecta, can be realistically reproduced. In this study, a series of moderate scale 1g shake table experiments were conducted at the University of Nevada, Reno, to evaluate the performance of this technique in liquefiable soil layers. First, a 1/5 size model was developed based on a recent UC San Diego shaking table experiment. The scaled model has a density of 50% for the top crust, 40% for the intermediate liquefiable layer, and 85% for the bottom dense layer. Second, a shallow foundation is seated atop an unsaturated sandy soil crust. Third, in a series of tests, a sheet pile with variable embedment depth is inserted into the liquefiable soil using the press-in technique surrounding the shallow foundations. The scaled models are subjected to harmonic input motions with amplitude and dominant frequency properly scaled based on the large-scale shake table test. This study assesses the performance of the press-in sheet piling technique in terms of reductions in the foundation movements (settlement and tilt) and generated excess pore water pressures. In addition, this paper discusses the cost-effectiveness and carbon footprint features of the studied mitigation measures.

Keywords: excess pore water pressure, foundation settlement, press-in sheet pile, soil liquefaction

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Authors: Mohd Maniruzzaman, Md. Monjurul Alam, Md. Hafezur Rahaman, Anika Amir Mohona

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The consumption of water by various industries is increasing day by day, and the wastewaters from them are increasing as well. These wastewaters consist of various kinds of color, dissolved solids, toxic heavy metals, residual chlorine, and other non-degradable organic materials. If these wastewaters are exposed directly to the environment, it will be hazardous for the environment and personal health. So, it is very necessary to treat these wastewaters before exposing into the environment. In this research, we have demonstrated the successful processing and utilization of fully bio-based cellulose micro crystal (CMC) composite for the removal of heavy metals, dyes, and salinity from industrial wastewaters. Banana rachis micro-cellulose were prepared by acid hydrolysis (H₂SO₄) of banana (Musa acuminata L.) rachis fiber, and Bijoypur raw clay were treated by organic solvent tri-ethyl amine. Composites were prepared with varying different composition of banana rachis nano-cellulose and modified Bijoypur (north-east part in Bangladesh) clay. After the successful characterization of cellulose micro crystal (CMC) and modified clay, our targeted filter was fabricated with different composition of cellulose micro crystal and clay in the locally fabricated packing column with 7.5 cm as thickness of composites fraction. Waste-water was collected from local small textile industries containing basic yellow 2 as dye, lead (II) nitrate [Pb(NO₃)₂] and chromium (III) nitrate [Cr(NO₃)₃] as heavy metals and saline water was collected from Khulna to test the efficiency of banana rachis cellulose micro crystal-clay composite for removing the above impurities. The filtering efficiency of wastewater purification was characterized by Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction (X-RD), thermo gravimetric analysis (TGA), atomic absorption spectrometry (AAS), scanning electron microscopy (SEM) analyses. Finally, our all characterizations data are shown with very high expected results for in industrial application of our fabricated filter.

Keywords: banana rachis, bio-based filter, cellulose micro crystal-clay composite, wastewaters, synthetic dyes, heavy metal, water salinity

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Authors: Andrew Freiburger, Sergi Molins

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Freshwater is a vital resource; yet, the supply of clean freshwater is diminishing as the consequence of melting snow and ice from global warming, pollution from industry, and an increasing demand from human population growth. The unsustainable trajectory of diminishing water resources is projected to jeopardize water security for billions of people in the 21st century. Membrane desalination technologies may resolve the growing discrepancy between supply and demand by filtering arbitrary feed water into a fraction of renewable, clean water and a fraction of highly concentrated brine. The leading hindrance of membrane desalination is fouling, whereby the highly concentrated brine solution encourages micro-organismal colonization and/or the precipitation of occlusive minerals (i.e. scale) upon the membrane surface. Thus, an understanding of brine formation is necessary to mitigate membrane fouling and to develop efficacious desalination technologies that can bolster the supply of available freshwater. This study presents a reactive transport simulation of brine formation and scale deposition during reverse osmosis (RO) desalination. The simulation conceptually represents the RO module as a one-dimensional domain, where feed water directionally enters the domain with a prescribed fluid velocity and is iteratively concentrated in the immobile layer of a dual porosity model. Geochemical PHREEQC code numerically evaluated the conceptual model with parameters for the BW30-400 RO module and for real water feed sources – e.g. the Red and Mediterranean seas, and produced waters from American oil-wells, based upon peer-review data. The presented simulation is computationally simpler, and hence less resource intensive, than the existent and more rigorous simulations of desalination phenomena, like TOUGHREACT. The end-user may readily prepare input files and execute simulations on a personal computer with open source software. The graphical results of fouling-potential and brine characteristics may therefore be particularly useful as the initial tool for screening candidate feed water sources and/or informing the selection of an RO module.

Keywords: desalination, PHREEQC, reactive transport, scaling

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Authors: Emre Bayamlıoğlu

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This paper aims to provide an account of the legal and the regulative aspects of the algorithmic reputation systems with a special emphasis on the sharing economy (i.e., Uber, Airbnb, Lyft) business model. The first section starts with an analysis of the legal and commercial nature of the tripartite relationship among the parties, namely, the host platform, individual sharers/service providers and the consumers/users. The section further examines to what extent an algorithmic system of reputational information could serve as an alternative to legal regulation. Shortcomings are explained and analyzed with specific examples from Airbnb Platform which is a pioneering success in the sharing economy. The following section focuses on the issue of governance and control of the reputational information. The section first analyzes the legal consequences of algorithmic filtering systems to detect undesired comments and how a delicate balance could be struck between the competing interests such as freedom of speech, privacy and the integrity of the commercial reputation. The third section deals with the problem of manipulation by users. Indeed many sharing economy businesses employ certain techniques of data mining and natural language processing to verify consistency of the feedback. Software agents referred as "bots" are employed by the users to "produce" fake reputation values. Such automated techniques are deceptive with significant negative effects for undermining the trust upon which the reputational system is built. The third section is devoted to explore the concerns with regard to data mobility, data ownership, and the privacy. Reputational information provided by the consumers in the form of textual comment may be regarded as a writing which is eligible to copyright protection. Algorithmic reputational systems also contain personal data pertaining both the individual entrepreneurs and the consumers. The final section starts with an overview of the notion of reputation as a communitarian and collective form of referential trust and further provides an evaluation of the above legal arguments from the perspective of public interest in the integrity of reputational information. The paper concludes with certain guidelines and design principles for algorithmic reputation systems, to address the above raised legal implications.

Keywords: sharing economy, design principles of algorithmic regulation, reputational systems, personal data protection, privacy

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Authors: Xiuguo Zhao, Xudong Ren, Chen Su, Xinxi Xu, Fu Niu, Lingshuai Meng

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The respiratory diseases such as asthma, emphysema and bronchitis are connected with the air pollution and the number of these diseases tends to increase, which may attribute to the toxic aerosol deposition in human upper respiratory tract or in the bifurcation of human lung. The therapy of these diseases mostly uses pharmaceuticals in the form of aerosol delivered into the human upper respiratory tract or the lung. Understanding of airflow structures in human upper respiratory tract plays a very important role in the analysis of the “filtering” effect in the pharynx/larynx and for obtaining correct air-particle inlet conditions to the lung. However, numerical simulation based CFD (Computational Fluid Dynamics) technology has its own advantage on studying airflow structure in human upper respiratory tract. In this paper, a representative human upper respiratory tract is built and the CFD technology was used to investigate the air movement characteristic in the human upper respiratory tract. The airflow movement characteristic, the effect of the airflow movement on the shear stress distribution and the probability of the wall injury caused by the shear stress are discussed. Experimentally validated computational fluid-aerosol dynamics results showed the following: the phenomenon of airflow separation appears near the outer wall of the pharynx and the trachea. The high velocity zone is created near the inner wall of the trachea. The airflow splits at the divider and a new boundary layer is generated at the inner wall of the downstream from the bifurcation with the high velocity near the inner wall of the trachea. The maximum velocity appears at the exterior of the boundary layer. The secondary swirls and axial velocity distribution result in the high shear stress acting on the inner wall of the trachea and bifurcation, finally lead to the inner wall injury. The enhancement of breathing intensity enhances the intensity of the shear stress acting on the inner wall of the trachea and the bifurcation. If human keep the high breathing intensity for long time, not only the ability for the transportation and regulation of the gas through the trachea and the bifurcation fall, but also result in the increase of the probability of the wall strain and tissue injury.

Keywords: airflow structure, computational fluid dynamics, human upper respiratory tract, wall shear stress, numerical simulation

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Authors: Joshua Buli, David Pietrowski, Samuel Britton

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Processing SAR data usually requires constraints in extent in the Fourier domain as well as approximations and interpolations onto a planar surface to form an exploitable image. This results in a potential loss of data requires several interpolative techniques, and restricts visualization to two-dimensional plane imagery. The data can be interpolated into a ground plane projection, with or without terrain as a component, all to better view SAR data in an image domain comparable to what a human would view, to ease interpretation. An alternate but computationally heavy method to make use of more of the data is the basis of this research. Pre-processing of the SAR data is completed first (matched-filtering, motion compensation, etc.), the data is then range compressed, and lastly, the contribution from each pulse is determined for each specific point in space by searching the time history data for the reflectivity values for each pulse summed over the entire collection. This results in a per-3D-point reflectivity using the entire collection domain. New advances in GPU processing have finally allowed this rapid projection of acquired SAR data onto any desired reference surface (called backprojection). Mathematically, the computations are fast and easy to implement, despite limitations in SAR phase history data size and 3D-point cloud size. Backprojection processing algorithms are embarrassingly parallel since each 3D point in the scene has the same reflectivity calculation applied for all pulses, independent of all other 3D points and pulse data under consideration. Therefore, given the simplicity of the single backprojection calculation, the work can be spread across thousands of GPU threads allowing for accurate reflectivity representation of a scene. Furthermore, because reflectivity values are associated with individual three-dimensional points, a plane is no longer the sole permissible mapping base; a digital elevation model or even a cloud of points (collected from any sensor capable of measuring ground topography) can be used as a basis for the backprojection technique. This technique minimizes any interpolations and modifications of the raw data, maintaining maximum data integrity. This innovative processing will allow for SAR data to be rapidly brought into a common reference frame for immediate exploitation and data fusion with other three-dimensional data and representations.

Keywords: backprojection, data fusion, exploitation, three-dimensional, visualization

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Authors: Yelin Zhao, Xinxiu Li, Martin Smelik, Oleg Sysoev, Firoj Mahmud, Dina Mansour Aly, Mikael Benson

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Background: Early diagnosis of pancreatic cancer is clinically challenging due to vague, or no symptoms, and lack of biomarkers. Polygenic risk score (PRS) scores may provide a valuable tool to assess increased or decreased risk of PC. This study aimed to develop such PRS by filtering genetic variants identified by GWAS using transcriptional programs identified by single-cell RNA sequencing (scRNA-seq). Methods: ScRNA-seq data from 24 pancreatic ductal adenocarcinoma (PDAC) tumor samples and 11 normal pancreases were analyzed to identify differentially expressed genes (DEGs) in in tumor and microenvironment cell types compared to healthy tissues. Pathway analysis showed that the DEGs were enriched for hundreds of significant pathways. These were clustered into 40 “programs” based on gene similarity, using the Jaccard index. Published genetic variants associated with PDAC were mapped to each program to generate program PRSs (pPRSs). These pPRSs, along with five previously published PRSs (PGS000083, PGS000725, PGS000663, PGS000159, and PGS002264), were evaluated in a European-origin population from the UK Biobank, consisting of 1,310 PDAC participants and 407,473 non-pancreatic cancer participants. Stepwise Cox regression analysis was performed to determine associations between pPRSs with the development of PC, with adjustments of sex and principal components of genetic ancestry. Results: The PDAC genetic variants were mapped to 23 programs and were used to generate pPRSs for these programs. Four distinct pPRSs (P1, P6, P11, and P16) and two published PRSs (PGS000663 and PGS002264) were significantly associated with an increased risk of developing PC. Among these, P6 exhibited the greatest hazard ratio (adjusted HR[95% CI] = 1.67[1.14-2.45], p = 0.008). In contrast, P10 and P4 were associated with lower risk of developing PC (adjusted HR[95% CI] = 0.58[0.42-0.81], p = 0.001, and adjusted HR[95% CI] = 0.75[0.59-0.96], p = 0.019). By comparison, two of the five published PRS exhibited an association with PDAC onset with HR (PGS000663: adjusted HR[95% CI] = 1.24[1.14-1.35], p < 0.001 and PGS002264: adjusted HR[95% CI] = 1.14[1.07-1.22], p < 0.001). Conclusion: Compared to published PRSs, scRNA-seq-based pPRSs may be used not only to assess increased but also decreased risk of PDAC.

Keywords: cox regression, pancreatic cancer, polygenic risk score, scRNA-seq, UK biobank

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Authors: Atefe Malekkhani, Lee Beattie, Mohsen Mohammadzadeh

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Rapid urbanization and the growth of megacities have resulted in numerous risks, concerns, and problems across various aspects of urban life, including environmental, social, and economic domains. Cities and regions are inevitably moving toward urban digitalization owing to the fast-paced development of information and communication technologies (ICTs). The concept of 'smart city' represents an emerging solution to urban challenges arising from increased urbanization using ICTs. However, smart cities are often perceived primarily as technological initiatives and are implemented without considering the social and cultural contexts of cities and the needs of their residents. The implementation of smart city projects and initiatives has the potential to (un)intentionally exacerbate pre-existing social, spatial, and cultural segregation. Investigating the impact of smart city on social capital of people who are users of smart city systems and with governance as policymakers is worth exploring. 'Social capital' defined as the network of relationships and resources available to individuals through social interactions, is a glue that binds the community together. It is also a resource that benefits society by representing the collective amount of civic engagement, communal values, and interpersonal trust within a given geographic region or political entity. Despite the existing theoretical and practical knowledge, there is a significant research gap concerning the role and impact of smart city infrastructure in building social capital. Currently, there is no conclusive evidence indicating whether the impact of smart city infrastructure on social capital is positive or negative or to what extent smart city infrastructure can foster social capital in communities and cities. To shed light on this issue, this study aims to explore the domain of existing research in the field of smart city through the lens of social capital. This research will use the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) method to review relevant literature, focusing on the key concepts of 'Smart City' and 'Social Capital'. The studies will be selected Web of Science Core Collection, using a selection process that involves identifying literature sources, screening and filtering studies based on titles, abstracts, and full-text reading.

Keywords: Smart city, urban digitalisation, ICT, social capital

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Authors: Zilaila Zakaria, Muhd Azri Abdul Razak, Muhammad Murtadha Othman, Mohd Ainor Yahya, Ismail Musirin, Mat Nasir Kari, Mohd Fazli Osman, Mohd Zaini Hassan, Baihaki Azraee

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Energy efficiency can be realized by minimizing the power loss with a sufficient amount of energy used in an electrical distribution system. In this report, a detailed analysis of the energy efficiency of an electric distribution system was carried out with an implementation of the optimal capacitor placement and sizing (OCPS). The particle swarm optimization (PSO) will be used to determine optimal location and sizing for the capacitors whereas energy consumption and power losses minimization will improve the energy efficiency. In addition, a certain number of busbars or locations are identified in advance before the PSO is performed to solve OCPS. In this case study, three techniques are performed for the pre-selection of busbar or locations which are the power-loss-index (PLI). The particle swarm optimization (PSO) is designed to provide a new population with improved sizing and location of capacitors. The total cost of power losses, energy consumption and capacitor installation are the components considered in the objective and fitness functions of the proposed optimization technique. Voltage magnitude limit, total harmonic distortion (THD) limit, power factor limit and capacitor size limit are the parameters considered as the constraints for the proposed of optimization technique. In this research, the proposed methodologies implemented in the MATLAB® software will transfer the information, execute the three-phase unbalanced load flow solution and retrieve then collect the results or data from the three-phase unbalanced electrical distribution systems modeled in the SIMULINK® software. Effectiveness of the proposed methods used to improve the energy efficiency has been verified through several case studies and the results are obtained from the test systems of IEEE 13-bus unbalanced electrical distribution system and also the practical electrical distribution system model of Sultan Salahuddin Abdul Aziz Shah (SSAAS) government building in Shah Alam, Selangor.

Keywords: particle swarm optimization, pre-determine of capacitor locations, optimal capacitors placement and sizing, unbalanced electrical distribution system

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Authors: Nawras Kurzom, Avi Mendelsohn

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The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information.

Keywords: musical tension, declarative memory, learning and memory, musical perception

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

Procedia PDF Downloads 121

Authors: Alicia Yanadira Nava Fuentes, Ilse Cervantes Camacho, Amadeo José Argüelles Cruz, Ana María Balboa Verduzco

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Augmented reality promises being present in future driving, with its immersive technology let to show directions and maps to identify important places indicating with graphic elements when the car driver requires the information. On the other side, driving is considered a multitasking activity and, for some people, a complex activity where different situations commonly occur that require the immediate attention of the car driver to make decisions that contribute to avoid accidents; therefore, the main aim of the project is the instrumentation of a platform with biometric sensors that allows evaluating the performance in driving vehicles with the influence of augmented reality devices to detect the level of attention in drivers, since it is important to know the effect that it produces. In this study, the physiological sensors EPOC X (EEG), ECG06 PRO and EMG Myoware are joined in the driving test platform with a Logitech G29 steering wheel and the simulation software City Car Driving in which the level of traffic can be controlled, as well as the number of pedestrians that exist within the simulation obtaining a driver interaction in real mode and through a MSP430 microcontroller achieves the acquisition of data for storage. The sensors bring a continuous analog signal in time that needs signal conditioning, at this point, a signal amplifier is incorporated due to the acquired signals having a sensitive range of 1.25 mm/mV, also filtering that consists in eliminating the frequency bands of the signal in order to be interpretative and without noise to convert it from an analog signal into a digital signal to analyze the physiological signals of the drivers, these values are stored in a database. Based on this compilation, we work on the extraction of signal features and implement K-NN (k-nearest neighbor) classification methods and decision trees (unsupervised learning) that enable the study of data for the identification of patterns and determine by classification methods different effects of augmented reality on drivers. The expected results of this project include are a test platform instrumented with biometric sensors for data acquisition during driving and a database with the required variables to determine the effect caused by augmented reality on people in simulated driving.

Keywords: augmented reality, driving, physiological signals, test platform

Procedia PDF Downloads 134

Authors: A. G. Cunha, M. Inácio, M. C. Freitas, C. Antunes, T. Silva, C. Andrade, V. Lopes

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Saltmarshes are essential ecosystems both from an ecological and biological point of view. Furthermore, they constitute an important social niche, providing valuable economic and protection functions. Thus, understanding their rates and patterns of sedimentation is critical for functional management and rehabilitation, especially in an SLR scenario. The Sado estuary is located 40 km south of Lisbon. It is a bar built estuary, separated from the sea by a large sand spit: the Tróia barrier. Caldeira de Tróia is located on the free edge of this barrier, and encompasses a salt marsh with ca. 21,000 m². Sediment cores were collected in the high and low marshes and in the mudflat area of the North bank of Caldeira de Tróia. From the low marsh core, fifteen samples were chosen for ²¹⁰Pb and ¹³⁷Cs determination at University of Geneva. The cores from the high marsh and the mudflat are still being analyzed. A sedimentation rate of 2.96 mm/year was derived from ²¹⁰Pb using the Constant Flux Constant Sedimentation model. The ¹³⁷Cs profile shows a peak in activity (1963) between 15.50 and 18.50 cm, giving a 3.1 mm/year sedimentation rate for the past 53 years. The adopted sea level rise scenario was based on a model built with the initial rate of SLR of 2.1 mm/year in 2000 and an acceleration of 0.08 mm/year². Based on the harmonic analysis of Setubal-Tróia tide gauge of 2005 data, the tide model was estimated and used to build the tidal tables to the period 2000-2016. With these tables, the average mean water levels were determined for the same time span. A digital terrain model was created from LIDAR scanning with 2m horizontal resolution (APA-DGT, 2011) and validated with altimetric data obtained with a DGPS-RTK. The response model calculates a new elevation for each pixel of the DTM for 2050 and 2100 based on the sedimentation rates specific of each environment. At this stage, theoretical values were chosen for the high marsh and the mudflat (respectively, equal and double the low marsh rate – 2.92 mm/year). These values will be rectified once sedimentation rates are determined for the other environments. For both projections, the total surface of the marsh decreases: 2% in 2050 and 61% in 2100. Additionally, the high marsh coverage diminishes significantly, indicating a regression in terms of maturity.

Keywords: ¹³⁷Cs, ²¹⁰Pb, saltmarsh, sea level rise, response model

Procedia PDF Downloads 246

Authors: Folorunsho J. O., Umar M. A.

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Access to potable water supply in both the rural and urban regions of the world has been neglected, and this has severely affected man and the aesthetics of the natural environment of man. This has further worsened the issue of diseases prevalence. This study considered the effects of inadequate domestic water supply on human health in selected neighbourhoods of Lokoja. The study used descriptive statistics such as relative frequencies, percentages and inferential statistics to analyse the data obtained through the use of structured questionnaire. The results revealed that the females and male constituted 56% and 44% of the respondents respectively; 62% of the respondents married and 32% are unmarried; respondents between ages 31 and 40 years constitute majority of the study population, while respondents with tertiary education constituted 35%, and those with secondary education were 32% of the total respondents. Furthermore, civil servants constituted 40% and unemployed 16% of the total respondents. In terms of monthly income, 40% of the respondents was found to earn between ₦31,000 - 40,000 monthly. On the perception of households on the availability and adequacy of domestic water supply, the study revealed that 64.7% of the respondents have pipe-borne water as their main source of water supply, with only 28.5% out of the 64.7% have pipe-borne water supply daily. On the relationship between water supply characteristics and health status among households, the result shows that 76% of the respondents perceived a strong relationship between water supply and health status. Cumulatively, 67% of the respondents confirm that both the quality and quantity of water supplied play a critical role in determining health status of residents of the study area. The respondents also reported skin diseases (96%), diarrhoea (96%), malaria (91%), cholera (67%), dysentery (67%), and respiratory diseases (67%) as the most perceived and experienced in the area, the disease rate in the prevalence order of malaria (81%), diarrhoea (61%), skin diseases (58%), cholera (34%), dysentery (31%) and respiratory disease (14%) respectively. Finally, the results further showed how households cope with inadequate water supply with 52% of the respondents confirm that they regularly treat their water before it was deployed for domestic uses, while 35%, 26%, 25%, 10% and 4% of the 52% respectively, adopted boiling, addition of alums, filtering with fabrics, chlorination and bleaching as the preferred treatment methods. The study thus recommended policy options that will aggressively launch adequate potable water supply infrastructure in the study area.Keywords: Potable Water, Supply, Human Health, Perception, Chlorination

Keywords: potable water, human health, perception, chlorination

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Authors: Mohammad R. Jalali

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Movement of liquid with a free surface in a container is known as slosh. For instance, slosh occurs when water in a closed tank is set in motion by a free surface displacement, or when liquid natural gas in a container is vibrated by an external driving force, such as an earthquake or movement induced by transport. Slosh is also derived from resonant switching of a natural basin. During sloshing, different types of motion are produced by energy exchange between the liquid and its container. In present study, a numerical model is developed to simulate the nonlinear even harmonic oscillations of free surface sloshing of an initial disturbance to the free surface of a liquid in a closed square basin. The response of the liquid free surface is affected by amplitude and motion frequencies of its container; therefore, sloshing involves complex fluid-structure interactions. In the present study, nonlinear interaction of free surface sloshing of an initial Gaussian hump with its uneven container is predicted numerically. For this purpose, Green-Naghdi (GN) equations are applied as governing equation of fluid field to produce nonlinear second-order and higher-order wave interactions. These equations reduce the dimensions from three to two, yielding equations that can be solved efficiently. The GN approach assumes a particular flow kinematic structure in the vertical direction for shallow and deep-water problems. The fluid velocity profile is finite sum of coefficients depending on space and time multiplied by a weighting function. It should be noted that in GN theory, the flow is rotational. In this study, GN numerical simulations of initial Gaussian hump are compared with Fourier series semi-analytical solutions of the linearized shallow water equations. The comparison reveals that satisfactory agreement exists between the numerical simulation and the analytical solution of the overall free surface sloshing patterns. The resonant free surface motions driven by an initial Gaussian disturbance are obtained by Fast Fourier Transform (FFT) of the free surface elevation time history components. Numerically predicted velocity vectors and magnitude contours for the free surface patterns indicate that interaction of Gaussian hump with its container has localized effect. The result of this sloshing is applicable to the design of stable liquefied oil containers in tankers and offshore platforms.

Keywords: fluid-structure interactions, free surface sloshing, Gaussian hump, Green-Naghdi equations, numerical predictions

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Authors: Huseyin Guler, Cigdem Kosar

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The aim of this study is to propose Bridge estimator for seasonal unit root tests. Seasonality is an important factor for many economic time series. Some variables may contain seasonal patterns and forecasts that ignore important seasonal patterns have a high variance. Therefore, it is very important to eliminate seasonality for seasonal macroeconomic data. There are some methods to eliminate the impacts of seasonality in time series. One of them is filtering the data. However, this method leads to undesired consequences in unit root tests, especially if the data is generated by a stochastic seasonal process. Another method to eliminate seasonality is using seasonal dummy variables. Some seasonal patterns may result from stationary seasonal processes, which are modelled using seasonal dummies but if there is a varying and changing seasonal pattern over time, so the seasonal process is non-stationary, deterministic seasonal dummies are inadequate to capture the seasonal process. It is not suitable to use seasonal dummies for modeling such seasonally nonstationary series. Instead of that, it is necessary to take seasonal difference if there are seasonal unit roots in the series. Different alternative methods are proposed in the literature to test seasonal unit roots, such as Dickey, Hazsa, Fuller (DHF) and Hylleberg, Engle, Granger, Yoo (HEGY) tests. HEGY test can be also used to test the seasonal unit root in different frequencies (monthly, quarterly, and semiannual). Another issue in unit root tests is the lag selection. Lagged dependent variables are added to the model in seasonal unit root tests as in the unit root tests to overcome the autocorrelation problem. In this case, it is necessary to choose the lag length and determine any deterministic components (i.e., a constant and trend) first, and then use the proper model to test for seasonal unit roots. However, this two-step procedure might lead size distortions and lack of power in seasonal unit root tests. Recent studies show that Bridge estimators are good in selecting optimal lag length while differentiating nonstationary versus stationary models for nonseasonal data. The advantage of this estimator is the elimination of the two-step nature of conventional unit root tests and this leads a gain in size and power. In this paper, the Bridge estimator is proposed to test seasonal unit roots in a HEGY model. A Monte-Carlo experiment is done to determine the efficiency of this approach and compare the size and power of this method with HEGY test. Since Bridge estimator performs well in model selection, our approach may lead to some gain in terms of size and power over HEGY test.

Keywords: bridge estimators, HEGY test, model selection, seasonal unit root

Procedia PDF Downloads 333

Authors: Sanjay K. Behera, Debasish Saha, Paramesh Gadige, Ranjini Bandyopadhyay

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The zero shear viscosity (η₀) of a suspension of hard sphere colloids characterized by a significant polydispersity (≈10%) increases with increase in volume fraction (ϕ) and shows a dramatic increase at ϕ=ϕg with the system entering a colloidal glassy state. Fragility which is the measure of the rapidity of approach of these suspensions towards the glassy state is sensitive to its size polydispersity and stiffness of the particles. Soft poly(N-isopropylacrylamide) (PNIPAM) particles deform in the presence of neighboring particles at volume fraction above the random close packing volume fraction of undeformed monodisperse spheres. Softness, therefore, enhances the packing efficiency of these particles. In this study PNIPAM particles of a nearly constant swelling ratio and with polydispersities varying over a wide range (7.4%-48.9%) are synthesized to study the effects of polydispersity on the dynamics of suspensions of soft PNIPAM colloidal particles. The size and polydispersity of these particles are characterized using dynamic light scattering (DLS) and scanning electron microscopy (SEM). As these particles are deformable, their packing in aqueous suspensions is quantified in terms of effective volume fraction (ϕeff). The zero shear viscosity (η₀) data of these colloidal suspensions, estimated from rheometric experiments as a function of the effective volume fraction ϕeff of the suspensions, increases with increase in ϕeff and shows a dramatic increase at ϕeff = ϕ₀. The data for η₀ as a function of ϕeff fits well to the Vogel-Fulcher-Tammann equation. It is observed that increasing polydispersity results in increasingly fragile supercooled liquid-like behavior, with the parameter ϕ₀, extracted from the fits to the VFT equation shifting towards higher ϕeff. The observed increase in fragility is attributed to the prevalence of dynamical heterogeneities (DHs) in these polydisperse suspensions, while the simultaneous shift in ϕ₀ is ascribed to the decoupling of the dynamics of the smallest and largest particles. Finally, it is observed that the intrinsic nonlinearity of these suspensions, estimated at the third harmonic near ϕ₀ in Fourier transform oscillatory rheological experiments, increases with increase in polydispersity. These results are in agreement with theoretical predictions and simulation results for polydisperse hard sphere colloidal glasses and clearly demonstrate that jammed suspensions of polydisperse colloidal particles can be effectively fluidized with increasing polydispersity. Suspensions of these particles are therefore excellent candidates for detailed experimental studies of the effects of polydispersity on the dynamics of glass formation.

Keywords: dynamical heterogeneity, effective volume fraction, fragility, intrinsic nonlinearity

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Authors: Aime P. Lampad-Dela Pena, Rhodora V. Azanza, Cesar L. Villanoy, Ephrime B. Metillo, Aletta T. Yniguez

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Phytoplankton community changes in relation to environmental parameters were compared between and within, the three interconnected basins. Phytoplankton samples were collected from thirteen stations of Baler Bay and Casiguran Sound, Aurora last May 2013 by filtering 10 L buckets of surface water and 5 L Niskin samples at 20 meters and at 30 to 40 meters depths through a 20um sieve. Duplicate samples per station were preserved, counted, and identified up to genus level, in order to determine the horizontal and vertical spatial variation of different phytoplankton functional groups during the summer ebb and flood flow. Baler Bay, Outer and Inner Casiguran Sound had a total of 89 genera from four phytoplankton groups: Diatom (62), Dinoflagellate (25), Silicoflagellate (1) and Cyanobacteria (1). Non-toxic diatom Chaetoceros spp. bloom (averaged 2.0 x 105 to 2.73 x 106 cells L⁻¹) co-existed with Bacteriastrum spp. at surface waters in Inner and Outer Casiguran. Pseudonitzschia spp. (1.73 x 106 cells L⁻¹) bloomed at bottom waters of the innermost embayment near Casiguran mangrove estuary. Cyanobacteria Trichodesmium spp. significantly increased during ebb tide at the mid-water layers (20 meters depth) in the three basins (ranged from 6, 900 to 15, 125 filaments L⁻¹), forming another bloom. Gonyaulax spp. - dominated dinoflagellate did not significantly change with depth across the three basins. Overall, diatoms and dinoflagellates community assemblages significantly changed between sites (p < 0.001) while diatoms and cyanobacteria varied within Casiguran outer and inner sites (p < 0.001) only. Tidal fluctuations significantly affected dinoflagellates and diatom groups (p < 0.001) in inner and baler sites. Chlorophyll significantly varied between (KW, p < 0.001) and within each basins (KW, p < 0.05), no tidal influence, with the highest value at inner Casiguran and at deeper waters indicating deep chlorophyll maxima. Aurora’s distinct shelf morphology favoring counterclockwise circulation pattern, advective transport, and continuous stratification of the water column could basically affect the phytoplankton assemblages and water quality of Baler Bay and Casiguran inner and outer basins. Observed spatial phytoplankton community changes with multi-species diatom and cyanobacteria bloom at different water layers of the three inter-connected embayments would be vital for any environmental management initiatives in Aurora.

Keywords: aurora fronting Philippines Rise, intermonsoon, multi-species diatom bloom, spatial variability

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Authors: Shakila N. Senarath, Dinesh Asanka, Janaka Wijayanayake

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The necessity of cosmetic products is arising to fulfill consumer needs of personality appearance and hygiene. A cosmetic product consists of various chemical ingredients which may help to keep the skin healthy or may lead to damages. Every chemical ingredient in a cosmetic product does not perform on every human. The most appropriate way to select a healthy cosmetic product is to identify the texture of the body first and select the most suitable product with safe ingredients. Therefore, the selection process of cosmetic products is complicated. Consumer surveys have shown most of the time, the selection process of cosmetic products is done in an improper way by consumers. From this study, a content-based system is suggested that recommends cosmetic products for the human factors. To such an extent, the skin type, gender and price range will be considered as human factors. The proposed system will be implemented by using Machine Learning. Consumer skin type, gender and price range will be taken as inputs to the system. The skin type of consumer will be derived by using the Baumann Skin Type Questionnaire, which is a value-based approach that includes several numbers of questions to derive the user’s skin type to one of the 16 skin types according to the Bauman Skin Type indicator (BSTI). Two datasets are collected for further research proceedings. The user data set was collected using a questionnaire given to the public. Those are the user dataset and the cosmetic dataset. Product details are included in the cosmetic dataset, which belongs to 5 different kinds of product categories (Moisturizer, Cleanser, Sun protector, Face Mask, Eye Cream). An alternate approach of TF-IDF (Term Frequency – Inverse Document Frequency) is applied to vectorize cosmetic ingredients in the generic cosmetic products dataset and user-preferred dataset. Using the IF-IPF vectors, each user-preferred products dataset and generic cosmetic products dataset can be represented as sparse vectors. The similarity between each user-preferred product and generic cosmetic product will be calculated using the cosine similarity method. For the recommendation process, a similarity matrix can be used. Higher the similarity, higher the match for consumer. Sorting a user column from similarity matrix in a descending order, the recommended products can be retrieved in ascending order. Even though results return a list of similar products, and since the user information has been gathered, such as gender and the price ranges for product purchasing, further optimization can be done by considering and giving weights for those parameters once after a set of recommended products for a user has been retrieved.

Keywords: content-based filtering, cosmetics, machine learning, recommendation system

Procedia PDF Downloads 131

Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Alberto Mínguez-Martínez, Jesús de Vicente y Oliva

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Modern manufacturing processes have led to the miniaturization of systems and, as a result, parts at the micro-and nanoscale are produced. This trend seems to become increasingly important in the near future. Besides, as a requirement of Industry 4.0, the digitalization of the models of production and processes makes it very important to ensure that the dimensions of newly manufactured parts meet the specifications of the models. Therefore, it is possible to reduce the scrap and the cost of non-conformities, ensuring the stability of the production at the same time. To ensure the quality of manufactured parts, it becomes necessary to carry out traceable measurements at scales lower than one millimeter. Providing adequate traceability to the SI unit of length (the meter) to 2D and 3D measurements at this scale is a problem that does not have a unique solution in industrial environments. Researchers in the field of dimensional metrology all around the world are working on this issue. A solution for industrial environments, even if it is not complete, will enable working with some traceability. At this point, we believe that the study of the surfaces could provide us with a first approximation to a solution. Among the different options proposed in the literature, the areal topography methods may be the most relevant because they could be compared to those measurements performed using Coordinate Measuring Machines (CMM’s). These measuring methods give (x, y, z) coordinates for each point, expressing it in two different ways, either expressing the z coordinate as a function of x, denoting it as z(x), for each Y-axis coordinate, or as a function of the x and y coordinates, denoting it as z (x, y). Between others, optical measuring instruments, mainly microscopes, are extensively used to carry out measurements at scales lower than one millimeter because it is a non-destructive measuring method. In this paper, the authors propose a calibration procedure for the scales of optical measuring instruments, particularizing for a confocal microscope, using material standards easy to find and calibrate in metrology and quality laboratories in industrial environments. Confocal microscopes are measuring instruments capable of filtering the out-of-focus reflected light so that when it reaches the detector, it is possible to take pictures of the part of the surface that is focused. Varying and taking pictures at different Z levels of the focus, a specialized software interpolates between the different planes, and it could reconstruct the surface geometry into a 3D model. As it is easy to deduce, it is necessary to give traceability to each axis. As a complementary result, the roughness Ra parameter will be traced to the reference. Although the solution is designed for a confocal microscope, it may be used for the calibration of other optical measuring instruments by applying minor changes.

Keywords: industrial environment, confocal microscope, optical measuring instrument, traceability

Procedia PDF Downloads 145