Search results for: engineering applications

Authors: Sam Bartlett, Thomas Bacquart, Arul Murugan, Abigail Morris

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Fuel cell electric vehicles provide the potential to decarbonise road transport, create new economic opportunities, diversify national energy supply, and significantly reduce the environmental impacts of road transport. A potential issue, however, is that the catalyst used at the fuel cell cathode is susceptible to degradation by impurities, especially sulphur-containing compounds. A recent European Directive (2014/94/EU) stipulates that, from November 2017, all hydrogen provided to fuel cell vehicles in Europe must comply with the hydrogen purity specifications listed in ISO 14687-2; this includes reactive and toxic chemicals such as ammonia and total sulphur-containing compounds. This requirement poses great analytical challenges due to the instability of some of these compounds in calibration gas standards at relatively low amount fractions and the difficulty associated with undertaking measurements of groups of compounds rather than individual compounds. Without the available reference materials and analytical infrastructure, hydrogen refuelling stations will not be able to demonstrate compliance to the ISO 14687 specifications. The hydrogen purity laboratory at NPL provides world leading, accredited purity measurements to allow hydrogen refuelling stations to evidence compliance to ISO 14687. Utilising state-of-the-art methods that have been developed by NPL’s hydrogen purity laboratory, including a novel method for measuring total sulphur compounds at 4 nmol/mol and a hydrogen impurity enrichment device, we provide the capabilities necessary to achieve these goals. An overview of these capabilities will be given in this paper. As part of the EMPIR Hydrogen co-normative project ‘Metrology for sustainable hydrogen energy applications’, NPL are developing a validated analytical methodology for the measurement of speciated sulphur-containing compounds in hydrogen at low amount fractions pmol/mol to nmol/mol) to allow identification and measurement of individual sulphur-containing impurities in real samples of hydrogen (opposed to a ‘total sulphur’ measurement). This is achieved by producing a suite of stable gravimetrically-prepared primary reference gas standards containing low amount fractions of sulphur-containing compounds (hydrogen sulphide, carbonyl sulphide, carbon disulphide, 2-methyl-2-propanethiol and tetrahydrothiophene have been selected for use in this study) to be used in conjunction with novel dynamic dilution facilities to enable generation of pmol/mol to nmol/mol level gas mixtures (a dynamic method is required as compounds at these levels would be unstable in gas cylinder mixtures). Method development and optimisation are performed using gas chromatographic techniques assisted by cryo-trapping technologies and coupled with sulphur chemiluminescence detection to allow improved qualitative and quantitative analyses of sulphur-containing impurities in hydrogen. The paper will review the state-of-the art gas standard preparation techniques, including the use and testing of dynamic dilution technologies for reactive chemical components in hydrogen. Method development will also be presented highlighting the advances in the measurement of speciated sulphur compounds in hydrogen at low amount fractions.

Keywords: gas chromatography, hydrogen purity, ISO 14687, sulphur chemiluminescence detector

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Authors: S. A. Faizi, S. Yoshitomi

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The optimal design procedure of buckling restrained braces (BRBs) in reinforced concrete (RC) building structures can provide the distribution of horizontal stiffness of BRBs at each story, which minimizes story drift response of the structure under the constraint of specified total stiffness of BRBs. In this paper, a simple rule is proposed to convert continuous horizontal stiffness of BRBs into sectional sizes of BRB which are available from standardized section list assuming realistic structural design stage.

Keywords: buckling restrained brace, building engineering, optimal damper placement, structural engineering

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Authors: Hanna Abbo, Siyasanga Noganta, Salam Titinchi

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The global lack of clean water for human sanitation and other purposes has become an emerging dilemma for human beings. The presence of organic pollutants in wastewater produced by textile industries, leather manufacturing and chemical industries is an alarming matter for a safe environment and human health. For the last decades, conventional methods have been applied for the purification of water but due to industrialization these methods fall short. Advanced oxidation processes and their reliable application in degradation of many contaminants have been reported as a potential method to reduce and/or alleviate this problem. Lately it has been assumed that incorporation of some metal nanoparticles such as magnetite nanoparticles as photocatalyst for Fenton reaction which could improve the degradation efficiency of contaminants. Core/shell nanoparticles, are extensively studied because of their wide applications in the biomedical, drug delivery, electronics fields and water treatment. The current study is centred on the synthesis of silver-doped Fe3O4/SiO2/TiO2 photocatalyst. Magnetically separable Fe3O4@SiO2@TiO2 composite with core–shell structure were synthesized by the deposition of uniform anatase TiO2 NPs on Fe3O4@SiO2 by using titanium butoxide (TBOT) as titanium source. Then, the silver is doped on SiO2 layer by hydrothermal method. Integration of magnetic nanoparticles was suggested to avoid the post separation difficulties associated with the powder form of the TiO2 catalyst, increase of the surface area and adsorption properties. The morphology, structure, composition, and magnetism of the resulting composites were characterized and their photocatalytic activities were also evaluated. The results demonstrate that TiO2 NPs were uniformly deposited on the Fe3O4@SiO2 surface. The silver nanoparticles were also uniformly distributed on the surface of TiO2 nanoparticles. The aim of this work is to study the suitability of photocatalysis for the treatment of aqueous streams containing organic pollutants such as methylene blue which is selected as a model compound to represent one of the pollutants existing in wastewaters. Various factors such as initial pollutant concentration, photocatalyst dose and wastewater matrix were studied for their effect on the photocatalytic degradation of the organic model pollutants using the as synthesized catalysts and compared with the commercial titanium dioxide (Aeroxide P25). Photocatalysis was found to be a potential purification method for the studied pollutant also in an industrial wastewater matrix with the removal percentages of over 81 % within 15 minutes. Methylene blue was removed most efficiently and its removal consumed the least of energy in terms of the specific applied energy. The magnetic Ag/SiO2/TiO2 composites show high photocatalytic performance and can be recycled three times by magnetic separation without major loss of activity, which meant that they can be used as efficient and conveniently renewable photocatalyst.

Keywords: Magnetite nanoparticles, Titanium, Photocatalyst, Organic pollutant, Water treatment

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Authors: Betty Kibirige

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Students entering the University of Zululand (UNIZULU) Science Faculty mostly come with skills that allow them to prepare for exams and pass them in order to satisfy requirements for entry into a tertiary Institution. Some students hail from deep rural schools with limited facilities, while others come from well-resourced schools. Personal experience has shown that it may take a student the whole time at a tertiary institution following the same skills as those acquired in high school as a sure means of entering the next level in their development, namely a postgraduate program. While it is apparent that at this point in human history, it is totally impossible to teach all the possible content in any one subject, many academics approach teaching and learning from the traditional point of view. It therefore became apparent to explore ways of developing a graduate that will be able to approach life with skills that allows them to navigate knowledge by applying conceptual and critical thinking skills. Recently, the Science Faculty at the University of Zululand introduced two Engineering programs. In an endeavour to approach the development of the Engineering graduate in this institution to be able to tackle problem-solving in the present-day excessive information availability, it became necessary to study and review approaches used by various academics in order to settle for a possible best approach to the challenge at hand. This paper focuses on the development of a deep rural child in a graduate with conceptual and critical thinking skills as major attributes possessed upon graduation. For this purpose, various approaches were studied. A combination of these approaches was repackaged to form an approach that may appear novel to UNIZULU and the rural child, especially for the Engineering discipline. The approach was checked by offering quiz questions to students participating in an engineering module, observing test scores in the targeted module and make comparative studies. Test results are discussed in the article. It was concluded that students’ graduate attributes could be tailored subconsciously to indeed include conceptual and critical thinking skills, but through more than one approach depending mainly on the student's high school background.

Keywords: graduate attributes, conceptual skills, critical thinking skills, traditional approach

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Authors: Claudio Lamilla, Misael Riquelme, Victoria Saez, Fernanda Sepulveda, Monica Pavez, Leticia Barrientos

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Biosurfactants are compounds synthesized by microorganisms that show various chemical structures, including glycolipids, lipopeptides, polysaccharide-protein complex, phospholipids, and fatty acids. These molecules have attracted attention in recent years due to the amphipathic nature of these compounds, which allows their application in various activities related to emulsification, foaming, detergency, wetting, dispersion and solubilization of hydrophobic compounds. Microorganisms that produce biosurfactants are ubiquitous, not only present in water, soil, and sediments but in extreme conditions of pH, salinity or temperature such as those present in Antarctic ecosystems. Due to this, it is of interest to study biosurfactants producing bacterial strains isolated from Antarctic environments, with the potential to be used in various biotechnological processes. The objective of this research was to characterize biosurfactants produced by bacterial strains isolated from Antarctic environments, with potential use in biotechnological processes for the cleaning of sites contaminated with hydrocarbons. The samples were collected from soils and sediments in the South Shetland Islands and the Antarctic Peninsula, during the Antarctic Research Expedition INACH 2016, from both pristine and human occupied areas (influenced). The bacteria isolation was performed from solid R2A, M1 and LB media. The selection of strains producing biosurfactants was done by hemolysis test on blood agar plates (5%) and blue agar (CTAB). From 280 isolates, it was determined that 10 bacterial strains produced biosurfactants after stimulation with different carbon sources. 16S rDNA taxonomic markers, using the universal primers 27F-1492R, were used to identify these bacterias. Biosurfactants production was carried out in 250 ml flasks using Bushnell Hass liquid culture medium enriched with different carbon sources (olive oil, glucose, glycerol, and hexadecane) during seven days under constant stirring at 20°C. Each cell-free supernatant was characterized by physicochemical parameters including drop collapse, emulsification and oil displacement, as well as stability at different temperatures, salinity, and pH. In addition, the surface tension of each supernatant was quantified using a tensiometer. The strains with the highest activity were selected, and the production of biosurfactants was stimulated in six liters of culture medium. Biosurfactants were extracted from the supernatants with chloroform methanol (2:1). These biosurfactants were tested against crude oil and motor oil, to evaluate their displacement activity (detergency). The characterization by physicochemical properties of 10 supernatants showed that 80% of them produced the drop collapse, 60% had stability at different temperatures, and 90% had detergency activity in motor and olive oil. The biosurfactants obtained from two bacterial strains showed a high activity of dispersion of crude oil and motor oil with halos superior to 10 cm. We can conclude that bacteria isolated from Antarctic soils and sediments provide biological material of high quality for the production of biosurfactants, with potential applications in the biotechnological industry, especially in hydrocarbons -contaminated areas such as petroleum.

Keywords: antarctic, bacteria, biosurfactants, hydrocarbons

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Authors: Javed Iqbal

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The growing need for energy by the human society and depletion of conventional energy sources demands a renewable, safe, infinite, low-cost and omnipresent energy source. One of the most suitable ways to solve the foreseeable world’s energy crisis is to use the power of the sun. Photovoltaic devices are especially of wide interest as they can convert solar energy to electricity. Recently the best performing solar cells are silicon-based cells. However, silicon cells are expensive, rigid in structure and have a large timeline for the payback of cost and electricity. Organic photovoltaic cells are cheap, flexible and can be manufactured in a continuous process. Therefore, organic photovoltaic cells are an extremely favorable replacement. Organic photovoltaic cells utilize sunlight as energy and convert it into electricity through the use of conductive polymers/ small molecules to separate electrons and electron holes. A major challenge for these new organic photovoltaic cells is the efficiency, which is low compared with the traditional silicon solar cells. To overcome this challenge, usually two straightforward strategies have been considered: (1) reducing the band-gap of molecular donors to broaden the absorption range, which results in higher short circuit current density (JSC) of devices, and (2) lowering the highest occupied molecular orbital (HOMO) energy of molecular donors so as to increase the open-circuit voltage (VOC) of applications devices.8 Keeping in mind the cost of chemicals it is hard to try many materials on test basis. The best way is to find the suitable material in the bulk. For this purpose, we use computational approach to design molecules based on our organic chemistry knowledge and determine their physical and electronic properties. In this study, we did DFT calculations with different options to get high open circuit voltage and after getting suitable data from calculation we finally did synthesis of a novel D–π–A–π–D type low band-gap small molecular donor material (ZOPTAN-TPA). The Aarylene vinylene based bis(arylhalide) unit containing a cyanostilbene unit acts as a low-band- gap electron-accepting block, and is coupled with triphenylamine as electron-donating blocks groups. The motivation for choosing triphenylamine (TPA) as capped donor was attributed to its important role in stabilizing the separated hole from an exciton and thus improving the hole-transporting properties of the hole carrier.3 A π-bridge (thiophene) is inserted between the donor and acceptor unit to reduce the steric hindrance between the donor and acceptor units and to improve the planarity of the molecule. The ZOPTAN-TPA molecule features a low HOMO level of 5.2 eV and an optical energy gap of 2.1 eV. Champion OSCs based on a solution-processed and non-annealed active-material blend of [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) and ZOPTAN-TPA in a mass ratio of 2:1 exhibits a power conversion efficiency of 1.9 % and a high open-circuit voltage of over 1.0 V.

Keywords: high open circuit voltage, donor, triphenylamine, organic solar cells

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Authors: David X. Dong, Qingming Zhang, Meng Lu

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Nitrites enter waterways as runoff from croplands and are discharged from many industrial sites. Excessive nitrite inputs to water bodies lead to eutrophication. On-site rapid detection of nitrite is of increasing interest for managing fertilizer application and monitoring water source quality. Existing methods for detecting nitrites use spectrophotometry, ion chromatography, electrochemical sensors, ion-selective electrodes, chemiluminescence, and colorimetric methods. However, these methods either suffer from high cost or provide low measurement accuracy due to their poor selectivity to nitrites. Therefore, it is desired to develop an accurate and economical method to monitor nitrites in environments. We report a low-cost optical sensor, in conjunction with a machine learning (ML) approach to enable high-accuracy detection of nitrites in water sources. The sensor works under the principle of measuring molecular absorptions of nitrites at three narrowband wavelengths (295 nm, 310 nm, and 357 nm) in the ultraviolet (UV) region. These wavelengths are chosen because they have relatively high sensitivity to nitrites; low-cost light-emitting devices (LEDs) and photodetectors are also available at these wavelengths. A regression model is built, trained, and utilized to minimize cross-sensitivities of these wavelengths to the same analyte, thus achieving precise and reliable measurements with various interference ions. The measured absorbance data is input to the trained model that can provide nitrite concentration prediction for the sample. The sensor is built with i) a miniature quartz cuvette as the test cell that contains a liquid sample under test, ii) three low-cost UV LEDs placed on one side of the cell as light sources, with each LED providing a narrowband light, and iii) a photodetector with a built-in amplifier and an analog-to-digital converter placed on the other side of the test cell to measure the power of transmitted light. This simple optical design allows measuring the absorbance data of the sample at the three wavelengths. To train the regression model, absorbances of nitrite ions and their combination with various interference ions are first obtained at the three UV wavelengths using a conventional spectrophotometer. Then, the spectrophotometric data are inputs to different regression algorithm models for training and evaluating high-accuracy nitrite concentration prediction. Our experimental results show that the proposed approach enables instantaneous nitrite detection within several seconds. The sensor hardware costs about one hundred dollars, which is much cheaper than a commercial spectrophotometer. The ML algorithm helps to reduce the average relative errors to below 3.5% over a concentration range from 0.1 ppm to 100 ppm of nitrites. The sensor has been validated to measure nitrites at three sites in Ames, Iowa, USA. This work demonstrates an economical and effective approach to the rapid, reagent-free determination of nitrites with high accuracy. The integration of the low-cost optical sensor and ML data processing can find a wide range of applications in environmental monitoring and management.

Keywords: optical sensor, regression model, nitrites, water quality

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Authors: Lilah Inzelberg, David Rand, Stanislav Steinberg, Moshe David Pur, Yael Hanein

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Human facial expressions carry important psychological and neurological information. Facial expressions involve the co-activation of diverse muscles. They depend strongly on personal affective interpretation and on social context and vary between spontaneous and voluntary activations. Smiling, as a special case, is among the most complex facial emotional expressions, involving no fewer than 7 different unilateral muscles. Despite their ubiquitous nature, smiles remain an elusive and debated topic. Smiles are associated with happiness and greeting on one hand and anger or disgust-masking on the other. Accordingly, while high-resolution recording of muscle activation patterns, in a non-interfering setting, offers exciting opportunities, it remains an unmet challenge, as contemporary surface facial electromyography (EMG) methodologies are cumbersome, restricted to the laboratory settings, and are limited in time and resolution. Here we present a wearable and non-invasive method for objective mapping of facial muscle activation and demonstrate its application in a natural setting. The technology is based on a recently developed dry and soft electrode array, specially designed for surface facial EMG technique. Eighteen healthy volunteers (31.58 ± 3.41 years, 13 females), participated in the study. Surface EMG arrays were adhered to participant left and right cheeks. Participants were instructed to imitate three facial expressions: closing the eyes, wrinkling the nose and smiling voluntary and to watch a funny video while their EMG signal is recorded. We focused on muscles associated with 'enjoyment', 'social' and 'masked' smiles; three categories with distinct social meanings. We developed a customized independent component analysis algorithm to construct the desired facial musculature mapping. First, identification of the Orbicularis oculi and the Levator labii superioris muscles was demonstrated from voluntary expressions. Second, recordings of voluntary and spontaneous smiles were used to locate the Zygomaticus major muscle activated in Duchenne and non-Duchenne smiles. Finally, recording with a wireless device in an unmodified natural work setting revealed expressions of neutral, positive and negative emotions in face-to-face interaction. The algorithm outlined here identifies the activation sources in a subject-specific manner, insensitive to electrode placement and anatomical diversity. Our high-resolution and cross-talk free mapping performances, along with excellent user convenience, open new opportunities for affective processing and objective evaluation of facial expressivity, objective psychological and neurological assessment as well as gaming, virtual reality, bio-feedback and brain-machine interface applications.

Keywords: affective expressions, affective processing, facial EMG, high-resolution electromyography, independent component analysis, wireless electrodes

Procedia PDF Downloads 246

Authors: Muhammad Tariq, Muhammad Waseem, Muhammad Hidayat Rasool

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Isolation and characterization of chromium tolerant Staphylococcus aureus from industrial wastewater and their potential use to bioremediate environmental chromium. Objectives: Chromium with its great economic importance in industrial use is major metal pollutant of the environment. Chromium are used in different industries for various applications such as textile, dyeing and pigmentation, wood preservation, manufacturing pulp and paper, chrome plating, steel and tanning. The release of untreated chromium in industrial effluents causes serious threat to environment and human health, therefore, the current study designed to isolate chromium tolerant Staphylococcus aureus for removal of chromium prior to their final discharge into the environment due to its cost effective and beneficial advantage over physical and chemical methods. Methods: Wastewater samples were collected from discharge point of different industries. Heavy metal analysis by atomic absorption spectrophotometer and microbiological analysis such as total viable count, total coliform, fecal coliform and Escherichia coli were conducted. Staphylococcus aureus was identified through gram’s staining, biomeriux vitek 2 microbial identification system and 16S rRNA gene amplification by polymerase chain reaction. Optimum growth conditions with respect to temperature, pH, salt concentrations and effect of chromium on the growth of bacteria, resistance to other heavy metal ions, minimum inhibitory concentration and chromium uptake ability of Staphylococcus aureus strain K1 was determined by spectrophotometer. Antibiotic sensitivity pattern was also determined by disc diffusion method. Furthermore, chromium uptake ability was confirmed by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope equipped with Oxford Energy Dipersive X-ray (EDX) micro analysis system. Results: The results presented that optimum temperature was 35ᵒC, pH was 8.0 and salt concentration was 0.5% for growth of Staphylococcus aureus K1. The maximum uptake ability of chromium by bacteria was 20mM than other heavy metal ions. The antibiotic sensitivity pattern revealed that Staphylococcus aureus was vancomycin and methicillin sensitive. Non hemolytic activity on blood agar and negative coagulase reaction showed that it was non-pathogenic. Furthermore, the growth of bacteria decreases in the presence of chromium and maximum chromium uptake by bacteria observed at optimum growth conditions. Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and Energy dispersive X-ray (EDX) analysis confirmed the presence of chromium uptake by Staphylococcus aureus K1. Conclusion: The study revealed that Staphylococcus aureus K1 have the potential to bio-remediate chromium toxicity from wastewater. Gradually, this biological treatment becomes more important due to its advantage over physical and chemical methods to protect environment and human health.

Keywords: wastewater, staphylococcus, chromium, bioremediation

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Authors: Till Fehlauer, Blanche Collin, Bernard Angeletti, Andrea Somogyi, Claire Lallemand, Perrine Chaurand, Cédric Dentant, Clement Levard, Jerome Rose

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Over the last decades, yttrium (Y) has gained importance in high-tech applications. It is an essential part of alloys and compounds used for lasers, displays, or cell phones, for example. Due to its chemical similarities with the lanthanides, Y is often considered a rare earth element (REE). Despite their increased usage, the environmental behavior of REEs remains poorly understood. Especially regarding their interactions with plants, many uncertainties exist. On the one hand, Y is known to have a negative effect on root development and germination, but on the other hand, it appears to promote plant growth at low concentrations. In order to understand these phenomena, a precise knowledge is necessary about how Y is absorbed by the plant and how it is handled once inside the organism. Contradictory studies exist, stating that due to a similar ionic radius, Y and the other REEs might be absorbed through Ca²⁺-channels, while others suspect that Y has a shared pathway with Al³⁺. In this study, laser ablation coupled ICP-MS, and synchrotron-based micro-X-ray fluorescence (µXRF, beamline Nanoscopium, SOLEIL, France) have been used in order to localize Y within the plant tissue and identify associated elements. The plant used in this study is Saxifraga paniculata, a rugged alpine plant that has shown an affinity for Y in previous studies (in prep.). Furthermore, Saxifraga paniculata performs guttation, which means that it possesses phloem sap secreting openings on the leaf surface that serve to regulate root pressure. These so-called hydathodes could provide special insights in elemental transport in plants. The plants have been grown on Y doped soil (500mg/kg DW) for four months. The results showed that Y was mainly concentrated in the roots of Saxifraga paniculata (260 ± 85mg/kg), and only a small amount was translocated to the leaves (10 ± 7.8mg/kg). µXRF analysis indicated that within the root transects, the majority of Y remained in the epidermis and hardly penetrated the stele. Laser ablation coupled ICP-MS confirmed this finding and showed a positive correlation in the roots between Y, Fe, Al, and to a lesser extent Ca. In the stem transect, Y was mainly detected in a hotspot of approximately 40µm in diameter situated in the endodermis area. Within the stem and especially in the hotspot, Y was highly colocalized with Al and Fe. Similar-sized Y hotspots have been detected in/on the leaves. All of them were strongly colocalized with Al and Fe, except for those situated within the hydathodes, which showed no colocalization with any of the measured elements. Accordingly, a relation between Y and Ca during root uptake remains possible, whereas a correlation to Fe and Al appears to be dominant in the aerial parts, suggesting common storage compartments, the formation of complexes, or a shared pathway during translocation.

Keywords: laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), Phytoaccumulation, Rare earth elements, Saxifraga paniculata, Synchrotron-based micro-X-ray fluorescence, Yttrium

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Authors: Ehsan Javanmardemamgheisi, Javad Mehr, Jason Ali-Lavroff, Damien Holloway, Michael Davis

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During the last decades, a growing interest in faster and more efficient waterborne transportation has led to the development of high-speed vessels for both commercial and military applications. To satisfy this global demand, a wide variety of arrangements of high-speed crafts have been proposed by designers. Among them, high-speed catamarans have proven themselves to be a suitable Roll-on/Roll-off configuration for carrying passengers and cargo due to widely spaced demi hulls, a wide deck zone, and a high ratio of deadweight to displacement. To improve passenger comfort and crew workability and enhance the operability and performance of high-speed catamarans, mitigating the severity of motions and structural loads using Ride Control Systems (RCS) is essential.In this paper, a set of towing tank tests was conducted on a 2.5 m scaled model of a 112 m Incat Tasmania high-speed catamaran in irregular head seas to investigate the effect of different ride control algorithms including linear and nonlinear versions of the heave control, pitch control, and local control on motion responses and passenger comfort of the full-scale ship. The RCS included a centre bow-fitted T-Foil and two transom-mounted stern tabs. All the experiments were conducted at the Australian Maritime College (AMC) towing tank at a model speed of 2.89 m/s (37 knots full scale), a modal period of 1.5 sec (10 sec full scale) and two significant wave heights of 60 mm and 90 mm, representing full-scale wave heights of 2.7 m and 4 m, respectively. Spectral analyses were performed using Welch’s power spectral density method on the vertical motion time records of the catamaran model to calculate heave and pitch Response Amplitude Operators (RAOs). Then, noting that passenger discomfort arises from vertical accelerations and that the vertical accelerations vary at different longitudinal locations within the passenger cabin due to the variations in amplitude and relative phase of the pitch and heave motions, the vertical accelerations were calculated at three longitudinal locations (LCG, T-Foil, and stern tabs). Finally, frequency-weighted Root Mean Square (RMS) vertical accelerations were calculated to estimate Motion Sickness Dose Value (MSDV) of the ship based on ISO 2631-recommendations. It was demonstrated that in small seas, implementing a nonlinear pitch control algorithm reduces the peak pitch motions by 41%, the vertical accelerations at the forward location by 46%, and motion sickness at the forward position by around 20% which provides great potential for further improvement in passenger comfort, crew workability, and operability of high-speed catamarans.

Keywords: high-speed catamarans, ride control system, response amplitude operators, vertical accelerations, motion sickness, irregular waves, towing tank tests.

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Authors: Leo Nnamdi Ozurumba-Dwight

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Messenger ribooxynucleic acid (mRNA) molecules are compositional, protein-based. These proteins, encoding mRNA molecules (which collectively connote the transcriptome), when analyzed by RNA sequencing (RNAseq), unveils the nature of gene expression in the RNA. The obtained gene expression provides clues of cellular traits and their dynamics in presentations. These can be studied in relation to function and responses. RNAseq is a practical concept in Genomics as it enables detection and quantitative analysis of mRNA molecules. Single cell and spatial transcriptomics both present varying avenues for expositions in genomic characteristics of single cells and pooled cells in disease conditions such as cancer, auto-immune diseases, hematopoietic based diseases, among others, from investigated biological tissue samples. Single cell transcriptomics helps conduct a direct assessment of each building unit of tissues (the cell) during diagnosis and molecular gene expressional studies. A typical technique to achieve this is through the use of a single-cell RNA sequencer (scRNAseq), which helps in conducting high throughput genomic expressional studies. However, this technique generates expressional gene data for several cells which lack presentations on the cells’ positional coordinates within the tissue. As science is developmental, the use of complimentary pre-established tissue reference maps using molecular and bioinformatics techniques has innovatively sprung-forth and is now used to resolve this set back to produce both levels of data in one shot of scRNAseq analysis. This is an emerging conceptual approach in methodology for integrative and progressively dependable transcriptomics analysis. This can support in-situ fashioned analysis for better understanding of tissue functional organization, unveil new biomarkers for early-stage detection of diseases, biomarkers for therapeutic targets in drug development, and exposit nature of cell-to-cell interactions. Also, these are vital genomic signatures and characterizations of clinical applications. Over the past decades, RNAseq has generated a wide array of information that is igniting bespoke breakthroughs and innovations in Biomedicine. On the other side, spatial transcriptomics is tissue level based and utilized to study biological specimens having heterogeneous features. It exposits the gross identity of investigated mammalian tissues, which can then be used to study cell differentiation, track cell line trajectory patterns and behavior, and regulatory homeostasis in disease states. Also, it requires referenced positional analysis to make up of genomic signatures that will be sassed from the single cells in the tissue sample. Given these two presented approaches to RNA transcriptomics study in varying quantities of cell lines, with avenues for appropriate resolutions, both approaches have made the study of gene expression from mRNA molecules interesting, progressive, developmental, and helping to tackle health challenges head-on.

Keywords: transcriptomics, RNA sequencing, single cell, spatial, gene expression.

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Authors: Cristina Diaz, Lucia Perez, Simone Visigalli, Giuseppe Di Florio, Gonzalo Fuentes, Roberto Canziani, Paolo Gronchi

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During the last 50 years, the selection of materials is one of the main decisions in machine design for different industrial applications. It is due to numerous physical, chemical, mechanical and technological factors to consider in it. Corrosion effects are related with all of these factors and impact in the life cycle, machine incidences and the costs for the life of the machine. Corrosion affects the deterioration or destruction of metals due to the reaction with the environment, generally wet. In food industry, dewatering industry, concrete industry, paper industry, etc. corrosion is an unsolved problem and it might introduce some alterations of some characteristics in the final product. Nowadays, depending on the selected metal, its surface and its environment of work, corrosion prevention might be a change of metal, use a coating, cathodic protection, use of corrosion inhibitors, etc. In the vast majority of the situations, use of a corrosion resistant material or in its defect, a corrosion protection coating is the solution. Stainless steels are widely used in machine design, because of their strength, easily cleaned capacity, corrosion resistance and appearance. Typical used are AISI 304 and AISI 316. However, their benefits don’t fit every application, and some coatings are required against corrosion such as some paintings, galvanizing, chrome plating, SiO₂, TiO₂ or ZrO₂ coatings, etc. In this work, some coatings based in a bilayer made of Titanium-Tantalum, Titanium-Niobium, Titanium-Hafnium or Titanium-Zirconium, have been developed used magnetron sputtering configuration by PVD (Physical Vapor Deposition) technology, for trying to reduce corrosion effects on AISI 304, AISI 316 and comparing it with Titanium alloy substrates. Ti alloy display exceptional corrosion resistance to chlorides, sour and oxidising acidic media and seawater. In this study, Ti alloy (99%) has been included for comparison with coated AISI 304 and AISI 316 stainless steel. Corrosion tests were conducted by a Gamry Instrument under ASTM G5-94 standard, using different electrolytes such as tomato salsa, wine, olive oil, wet compost, a mix of sand and concrete with water and NaCl for testing corrosion in different industrial environments. In general, in all tested environments, the results showed an improvement of corrosion resistance of all coated AISI 304 and AISI 316 stainless steel substrates when they were compared to uncoated stainless steel substrates. After that, comparing these results with corrosion studies on uncoated Ti alloy substrate, it was observed that in some cases, coated stainless steel substrates, reached similar current density that uncoated Ti alloy. Moreover, Titanium-Zirconium and Titanium-Tantalum coatings showed for all substrates in study including coated Ti alloy substrates, a reduction in current density more than two order in magnitude. As conclusion, Ti-Ta, Ti-Zr, Ti-Nb and Ti-Hf coatings have been developed for improving corrosion resistance of AISI 304 and AISI 316 materials. After corrosion tests in several industry environments, substrates have shown improvements on corrosion resistance. Similar processes have been carried out in Ti alloy (99%) substrates. Coated AISI 304 and AISI 316 stainless steel, might reach similar corrosion protection on the surface than uncoated Ti alloy (99%). Moreover, coated Ti Alloy (99%) might increase its corrosion resistance using these coatings.

Keywords: coatings, corrosion, PVD, stainless steel

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Authors: M. Saard, A. Kolk, K. Sepp, L. Pertens, L. Reinart, C. Kööp

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Introduction: Social competence is often impaired in children with acquired brain injury (ABI), but evidence-based rehabilitation for social skills has remained undeveloped. Modern technology-based methods create effective and safe learning environments for pediatric social skills remediation. The aim of the study was to implement our structured model of neuro rehab for socio-cognitive deficit using multitouch-multiuser tabletop (MMT) computer-based platforms and virtual reality (VR) technology. Methods: 40 children aged 8-13 years (yrs) have participated in the pilot study: 30 with ABI -epilepsy, traumatic brain injury and/or tic disorder- and 10 healthy age-matched controls. From the patients, 12 have completed the training (M = 11.10 yrs, SD = 1.543) and 20 are still in training or in the waiting-list group (M = 10.69 yrs, SD = 1.704). All children performed the first individual and paired assessments. For patients, second evaluations were performed after the intervention period. Two interactive applications were implemented into rehabilitation design: Snowflake software on MMT tabletop and NoProblem on DiamondTouch Table (DTT), which allowed paired training (2 children at once). Also, in individual training sessions, HTC Vive VR device was used with VR metaphors of difficult social situations to treat social anxiety and train social skills. Results: At baseline (B) evaluations, patients had higher deficits in executive functions on the BRIEF parents’ questionnaire (M = 117, SD = 23.594) compared to healthy controls (M = 22, SD = 18.385). The most impaired components of social competence were emotion recognition, Theory of Mind skills (ToM), cooperation, verbal/non-verbal communication, and pragmatics (Friendship Observation Scale scores only 25-50% out of 100% for patients). In Sentence Completion Task and Spence Anxiety Scale, the patients reported a lack of friends, behavioral problems, bullying in school, and social anxiety. Outcome evaluations: Snowflake on MMT improved executive and cooperation skills and DTT developed communication skills, metacognitive skills, and coping. VR, video modelling and role-plays improved social attention, emotional attitude, gestural behaviors, and decreased social anxiety. NEPSY-II showed improvement in Affect Recognition [B = 7, SD = 5.01 vs outcome (O) = 10, SD = 5.85], Verbal ToM (B = 8, SD = 3.06 vs O = 10, SD = 4.08), Contextual ToM (B = 8, SD = 3.15 vs O = 11, SD = 2.87). ToM Stories test showed an improved understanding of Intentional Lying (B = 7, SD = 2.20 vs O = 10, SD = 0.50), and Sarcasm (B=6, SD = 2.20 vs O = 7, SD = 2.50). Conclusion: Neurorehabilitation based on the Structured Model of Neurorehab for Socio-Cognitive Deficit in children with ABI were effective in social skills remediation. The model helps to understand theoretical connections between components of social competence and modern interactive computerized platforms. We encourage therapists to implement these next-generation devices into the rehabilitation process as MMT and VR interfaces are motivating for children, thus ensuring good compliance. Improving children’s social skills is important for their and their families’ quality of life and social capital.

Keywords: acquired brain injury, children, social skills deficit, technology-based neurorehabilitation

Procedia PDF Downloads 120

Authors: Mohkammad Nur Cahyadi, Imam Wahyu Farid, Ronny Mardianto, Agung Budi Cahyono, Eko Yuli Handoko, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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Unmanned aerial vehicle (UAV) technology has cost efficiency and data retrieval time advantages. Using technologies such as UAV, GNSS, and LiDAR will later be combined into one of the newest technologies to cover each other's deficiencies. This integration system aims to increase the accuracy of calculating the volume of the land stockpile of PT. Garam (Salt Company). The use of UAV applications to obtain geometric data and capture textures that characterize the structure of objects. This study uses the Taror 650 Iron Man drone with four propellers, which can fly for 15 minutes. LiDAR can classify based on the number of image acquisitions processed in the software, utilizing photogrammetry and structural science principles from Motion point cloud technology. LiDAR can perform data acquisition that enables the creation of point clouds, three-dimensional models, Digital Surface Models, Contours, and orthomosaics with high accuracy. LiDAR has a drawback in the form of coordinate data positions that have local references. Therefore, researchers use GNSS, LiDAR, and drone multi-sensor technology to map the stockpile of salt on open land and warehouses every year, carried out by PT. Garam twice, where the previous process used terrestrial methods and manual calculations with sacks. Research with LiDAR needs to be combined with UAV to overcome data acquisition limitations because it only passes through the right and left sides of the object, mainly when applied to a salt stockpile. The UAV is flown to assist data acquisition with a wide coverage with the help of integration of the 200-gram LiDAR system so that the flying angle taken can be optimal during the flight process. Using LiDAR for low-cost mapping surveys will make it easier for surveyors and academics to obtain pretty accurate data at a more economical price. As a survey tool, LiDAR is included in a tool with a low price, around 999 USD; this device can produce detailed data. Therefore, to minimize the operational costs of using LiDAR, surveyors can use Low-Cost LiDAR, GNSS, and UAV at a price of around 638 USD. The data generated by this sensor is in the form of a visualization of an object shape made in three dimensions. This study aims to combine Low-Cost GPS measurements with Low-Cost LiDAR, which are processed using free user software. GPS Low Cost generates data in the form of position-determining latitude and longitude coordinates. The data generates X, Y, and Z values to help georeferencing process the detected object. This research will also produce LiDAR, which can detect objects, including the height of the entire environment in that location. The results of the data obtained are calibrated with pitch, roll, and yaw to get the vertical height of the existing contours. This study conducted an experimental process on the roof of a building with a radius of approximately 30 meters.

Keywords: LiDAR, unmanned aerial vehicle, low-cost GNSS, contour

Procedia PDF Downloads 95

Authors: S. Dalvi, N. Sane, V. Patil, D. Bansode, A. Tharakan, V. Mathur

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Measurement of routine clinical electrolyte tests is common in labs worldwide for screening of illness or diseases. All the analyzers for the measurement of electrolyte parameters have sensors, reagents, sampler, pump tubing, valve, other tubing’s separate that are either expensive, require heavy maintenance and have a short shelf-life. Moreover, the costs required to maintain such Lab instrumentation is high and this limits the use of the device to only highly specialized personnel and sophisticated labs. In order to provide Healthcare Diagnostics to ALL at affordable costs, there is a need for an All-in-one Universal Modular Cartridge that contains sensors, reagents, sampler, valve, pump tubing, and other tubing’s in one single integrated module-in-module cartridge that is affordable, reliable, easy-to-use, requires very low sample volume and is truly modular and maintenance-free. DiaSys India has developed a World’s first, Patent Pending, Versatile All-in-one Universal Module-in-Module Cartridge based Electrolyte Analyzer (QDx InstaLyte) that can perform sodium, potassium, chloride, calcium, pH, lithium tests. QDx InstaLyte incorporates High Performance, Inexpensive All-in-one Universal Cartridge for rapid quantitative measurement of electrolytes in body fluids. Our proposed methodology utilizes Advanced & Improved long life ISE sensors to provide a sensitive and accurate result in 120 sec with just 100 µl of sample volume. The All-in-One Universal Cartridge has a very low reagent consumption capable of maximum of 1000 tests with a Use-life of 3-4 months and a long Shelf life of 12-18 months at 4-25°C making it very cost-effective. Methods: QDx InstaLyte analyzers with All-in-one Universal Modular Cartridges were independently evaluated with three R&D lots for Method Performance (Linearity, Precision, Method Comparison, Cartridge Stability) to measure Sodium, Potassium, Chloride. Method Comparison was done against Medica EasyLyte Plus Na/K/Cl Electrolyte Analyzer, a mid-size lab based clinical chemistry analyzer with N = 100 samples run over 10 days. Within-run precision study was done using modified CLSI guidelines with N = 20 samples and day-to-day precision study was done for 7 consecutive days using Trulab N & P Quality Control Samples. Accelerated stability testing was done at 45oC for 4 weeks with Production Lots. Results: Data analysis indicates that the CV for within-run precision for Na is ≤ 1%, for K is ≤2%, and for Cl is ≤2% and with R2 ≥ 0.95 for Method Comparison. Further, the All-in-One Universal Cartridge is stable up to 12-18 months at 4-25oC storage temperature based on preliminary extrapolated data. Conclusion: The Developed Technology Platform of All-in-One Universal Module-in-Module Cartridge based QDx InstaLyte is Reliable and meets all the performance specifications of the lab and is Truly Modular and Maintenance-Free. Hence, it can be easily adapted for low cost, sensitive and rapid measurement of electrolyte tests in low resource settings such as in urban, semi-urban and rural areas in the developing countries and can be used as a Point-of-care testing system for worldwide applications.

Keywords: all-in-one modular catridge, electrolytes, maintenance free, QDx instalyte

Procedia PDF Downloads 31

Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

Procedia PDF Downloads 125

Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

Procedia PDF Downloads 276

Authors: Shelley Y. Hawkins

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Introduction/Background: As technology has grown exponentially in healthcare, nurse educators must prepare Advanced Practice Registered Nurse (APRN) graduates with the knowledge and skills in information systems/technology to support and improve patient care and health care systems. APRN’s are expected to lead in caring for populations who lack accessibility and availability through the use of technology, specifically telehealth. The capacity to effectively and efficiently use technology in patient care delivery is clearly delineated in the American Association of Colleges of Nursing (AACN) Doctor of Nursing Practice (DNP) and Master of Science in Nursing (MSN) Essentials. However, APRN’s have minimal, or no, exposure to formalized telehealth education and lack necessary technical skills needed to incorporate telehealth into their patient care. APRN’s must successfully master the technology using telehealth/telemedicine, electronic health records, health information technology, and clinical decision support systems to advance health. Furthermore, APRN’s must be prepared to lead the coordination and collaboration with other healthcare providers in their use and application. Aim/Goal/Purpose: The purpose of this presentation is to establish and operationalize telehealth-focused educational partnerships between one University School of Nursing and two health care systems in order to enhance the preparation of APRN NP students for practice, teaching, and/or scholarly endeavors. Methods: The proposed project was initially presented by the project director to selected multidisciplinary stakeholders including leadership, home telehealth personnel, primary care providers, and decision support systems within two major health care systems to garner their support for acceptance and implementation. Concurrently, backing was obtained from key university-affiliated colleagues including the Director of Simulation and Innovative Learning Lab and Coordinator of the Health Care Informatics Program. Technology experts skilled in design and production in web applications and electronic modules were secured from two local based technology companies. Results: Two telehealth-focused APRN Program academic/practice partnerships have been established. Students have opportunities to engage in clinically based telehealth experiences focused on: (1) providing patient care while incorporating various technology with a specific emphasis on telehealth; (2) conducting research and/or evidence-based practice projects in order to further develop the scientific foundation regarding incorporation of telehealth with patient care; and (3) participating in the production of patient-level educational materials related to specific topical areas. Conclusions: Evidence-based APRN student telehealth clinical experiences will assist in preparing graduates who can effectively incorporate telehealth into their clinical practice. Greater access for diverse populations will be available as a result of the telehealth service model as well as better care and better outcomes at lower costs. Furthermore, APRN’s will provide the necessary leadership and coordination through interprofessional practice by transforming health care through new innovative care models using information systems and technology.

Keywords: academic/practice partnerships, advanced practice nursing, nursing education, telehealth

Procedia PDF Downloads 242

Authors: Andrey A. Tyuftin, David Clarke, Malco C. Cruz-Romero, Declan Bolton, Seamus Fanning, Shashi K. Pankaj, Carmen Bueno-Ferrer, Patrick J. Cullen, Joe P. Kerry

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Prolonging of shelf-life is essential in order to address issues such as; supplier demands across continents, economical profit, customer satisfaction, and reduction of food wastage. Smart packaging solutions presented in the form of naturally occurred antimicrobially-active packaging may be a solution to these and other issues. Gelatin film forming solution with adding of natural sourced antimicrobials is a promising tool for the active smart packaging. The objective of this study was to coat conventional plastic hydrophobic packaging material with hydrophilic antimicrobial active beef gelatin coating and conduct shelf life trials on beef sub-primal cuts. Minimal inhibition concentration (MIC) of Caprylic acid sodium salt (SO) and commercially available Auranta FV (AFV) (bitter oranges extract with mixture of nutritive organic acids) were found of 1 and 1.5 % respectively against bacterial strains Bacillus cereus, Pseudomonas fluorescens, Escherichia coli, Staphylococcus aureus and aerobic and anaerobic beef microflora. Therefore SO or AFV were incorporated in beef gelatin film forming solution in concentration of two times of MIC which was coated on a conventional plastic LDPE/PA film on the inner cold plasma treated polyethylene surface. Beef samples were vacuum packed in this material and stored under chilling conditions, sampled at weekly intervals during 42 days shelf life study. No significant differences (p < 0.05) in the cook loss was observed among the different treatments compared to control samples until the day 29. Only for AFV coated beef sample it was 3% higher (37.3%) than the control (34.4 %) on the day 36. It was found antimicrobial films did not protect beef against discoloration. SO containing packages significantly (p < 0.05) reduced Total viable bacterial counts (TVC) compared to the control and AFV samples until the day 35. No significant reduction in TVC was observed between SO and AFV films on the day 42 but a significant difference was observed compared to control samples with a 1.40 log of bacteria reduction on the day 42. AFV films significantly (p < 0.05) reduced TVC compared to control samples from the day 14 until the day 42. Control samples reached the set value of 7 log CFU/g on day 27 of testing, AFV films did not reach this set limit until day 35 and SO films until day 42 of testing. The antimicrobial AFV and SO coated films significantly prolonged the shelf-life of beef steaks by 33 or 55% (on 7 and 14 days respectively) compared to control film samples. It is concluded antimicrobial coated films were successfully developed by coating the inner polyethylene layer of conventional LDPE/PA laminated films after plasma surface treatment. The results indicated that the use of antimicrobial active packaging coated with SO or AFV increased significantly (p < 0.05) the shelf life of the beef sub-primal. Overall, AFV or SO containing gelatin coatings have the potential of being used as effective antimicrobials for active packaging applications for muscle-based food products.

Keywords: active packaging, antimicrobials, edible coatings, food packaging, gelatin films, meat science

Procedia PDF Downloads 303

Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

Procedia PDF Downloads 196

Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

Procedia PDF Downloads 41

Authors: John M. Ikome, Martha E. Ikome, Therese Van Wyk

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Productivity optimization of a company can significantly increase the company’s output and productivity which can be in the form of corrective actions of ineffective activities, process simplification, and reduction of variations, responsiveness, and reduction of set-up-time which are all under the classification of waste within the manufacturing environment. Deriving a means to eliminate a number of these issues has a key importance for manufacturing organization. This paper focused on a number of industrial engineering techniques which include a cause and effect diagram, to identify and optimize the method or systems being used. Based on our results, it shows that there are a number of variations within the production processes that can significantly disrupt the expected output.

Keywords: optimization, fishbone, diagram, productivity

Procedia PDF Downloads 312

Authors: Jing Zhang, Yvonne Reinwald, Nick Poulson, Alicia El Haj, Chung See, Mike Somekh, Melissa Mather

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Biomedical imaging of native tissue using light offers the potential to obtain excellent structural and functional information in a non-invasive manner with good temporal resolution. Image contrast can be derived from intrinsic absorption, fluorescence, or scatter, or through the use of extrinsic contrast. A major challenge in applying optical microscopy to in vivo tissue imaging is the effects of light attenuation which limits light penetration depth and achievable imaging resolution. Recently Selective Plane Illumination Microscopy (SPIM) has been used to map the 3D distribution of fluorophores dispersed in biological structures. In this approach, a focused sheet of light is used to illuminate the sample from the side to excite fluorophores within the sample of interest. Images are formed based on detection of fluorescence emission orthogonal to the illumination axis. By scanning the sample along the detection axis and acquiring a stack of images, 3D volumes can be obtained. The combination of rapid image acquisition speeds with the low photon dose to samples optical sectioning provides SPIM is an attractive approach for imaging biological samples in 3D. To date all implementations of SPIM rely on the use of fluorescence reporters be that endogenous or exogenous. This approach has the disadvantage that in the case of exogenous probes the specimens are altered from their native stage rendering them unsuitable for in vivo studies and in general fluorescence emission is weak and transient. Here we present for the first time to our knowledge a label-free implementation of SPIM that has downstream applications in the clinical setting. The experimental set up used in this work incorporates both label-free and fluorescent illumination arms in addition to a high specification camera that can be partitioned for simultaneous imaging of both fluorescent emission and scattered light from intrinsic sources of optical contrast in the sample being studied. This work first involved calibration of the imaging system and validation of the label-free method with well characterised fluorescent microbeads embedded in agarose gel. 3D constructs of mammalian cells cultured in agarose gel with varying cell concentrations were then imaged. A time course study to track cell proliferation in the 3D construct was also carried out and finally a native tissue sample was imaged. For each sample multiple images were obtained by scanning the sample along the axis of detection and 3D maps reconstructed. The results obtained validated label-free SPIM as a viable approach for imaging cells in a 3D gel construct and native tissue. This technique has the potential use in a near-patient environment that can provide results quickly and be implemented in an easy to use manner to provide more information with improved spatial resolution and depth penetration than current approaches.

Keywords: bioimaging, optics, selective plane illumination microscopy, tissue imaging

Procedia PDF Downloads 249

Authors: Luiza Melo De Lima, Tito Trindade, Jose M. Oliveira

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The development of thermoplastic-based graphene nanocomposites has been of great interest not only to the scientific community but also to different industrial sectors. Due to the possible improvement of performance and weight reduction, thermoplastic nanocomposites are a great promise as a new class of materials. These nanocomposites are of relevance for the automotive industry, namely because the emission limits of CO2 emissions imposed by the European Commission (EC) regulations can be fulfilled without compromising the car’s performance but by reducing its weight. Thermoplastic polymers have some advantages over thermosetting polymers such as higher productivity, lower density, and recyclability. In the automotive industry, for example, poly(propylene) (PP) is a common thermoplastic polymer, which represents more than half of the polymeric raw material used in automotive parts. Graphene-based materials (GBM) are potential nanofillers that can improve the properties of polymer matrices at very low loading. In comparison to other composites, such as fiber-based composites, weight reduction can positively affect their processing and future applications. However, the properties and performance of GBM/polymer nanocomposites depend on the type of GBM and polymer matrix, the degree of dispersion, and especially the type of interactions between the fillers and the polymer matrix. In order to take advantage of the superior mechanical strength of GBM, strong interfacial strength between GBM and the polymer matrix is required for efficient stress transfer from GBM to the polymer. Thus, chemical compatibilizers and physicochemical modifications have been reported as important tools during the processing of these nanocomposites. In this study, PP-based nanocomposites were obtained by a simple melt blending technique, using a Brabender type mixer machine. Graphene nanoplatelets (GnPs) were applied as structural reinforcement. Two compatibilizers were used to improve the interaction between PP matrix and GnPs: PP graft maleic anhydride (PPgMA) and PPgMA modified with tertiary amine alcohol (PPgDM). The samples for tensile and Charpy impact tests were obtained by injection molding. The results suggested the GnPs presence can increase the mechanical strength of the polymer. However, it was verified that the GnPs presence can promote a decrease of impact resistance, turning the nanocomposites more fragile than neat PP. The compatibilizers’ incorporation increases the impact resistance, suggesting that the compatibilizers can enhance the adhesion between PP and GnPs. Compared to neat PP, Young’s modulus of non-compatibilized nanocomposite increase demonstrated that GnPs incorporation can promote a stiffness improvement of the polymer. This trend can be related to the several physical crosslinking points between the PP matrix and the GnPs. Furthermore, the decrease of strain at a yield of PP/GnPs, together with the enhancement of Young’s modulus, confirms that the GnPs incorporation led to an increase in stiffness but to a decrease in toughness. Moreover, the results demonstrated that incorporation of compatibilizers did not affect Young’s modulus and strain at yield results compared to non-compatibilized nanocomposite. The incorporation of these compatibilizers showed an improvement of nanocomposites’ mechanical properties compared both to those the non-compatibilized nanocomposite and to a PP sample used as reference.

Keywords: graphene nanoplatelets, mechanical properties, melt blending processing, poly(propylene)-based nanocomposites

Procedia PDF Downloads 187

Authors: Geoffrey A. Wright

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In an attempt to address shortcomings revealed in international assessments and lamented in legislation, many schools are reducing or eliminating elective courses, applying the rationale that replacing "non-essential" subjects with core subjects, such as mathematics and language arts, will better position students in the global market. However, there is evidence that systematically pairing a core subject with another complementary subject may lead to greater overall learning in both subjects. In this paper, we outline the methods and preliminary findings from a study we conducted analyzing the influence learning programming has on student mathematical comprehension and ability. The purpose of this research is to demonstrate in what ways two subjects might complement each other, and to better understand the principles and conditions that encourage what we call lateral transfer, the synergistic effect that occurs when a learner studies two complementary subjects.

Keywords: programming, engineering, technology, complementary subjects

Procedia PDF Downloads 357

Authors: Piotr Kacejko, Mariusz Duk, Miroslaw Wendeker

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This paper presents an adaptive controller to track the maximum power point of a photovoltaic modules (PV) under fast irradiation change on the city-bus roof. Photovoltaic systems have been a prominent option as an additional energy source for vehicles. The Municipal Transport Company (MPK) in Lublin has installed photovoltaic panels on its buses roofs. The solar panels turn solar energy into electric energy and are used to load the buses electric equipment. This decreases the buses alternators load, leading to lower fuel consumption and bringing both economic and ecological profits. A DC–DC boost converter is selected as the power conditioning unit to coordinate the operating point of the system. In addition to the conversion efficiency of a photovoltaic panel, the maximum power point tracking (MPPT) method also plays a main role to harvest most energy out of the sun. The MPPT unit on a moving vehicle must keep tracking accuracy high in order to compensate rapid change of irradiation change due to dynamic motion of the vehicle. Maximum power point track controllers should be used to increase efficiency and power output of solar panels under changing environmental factors. There are several different control algorithms in the literature developed for maximum power point tracking. However, energy performances of MPPT algorithms are not clarified for vehicle applications that cause rapid changes of environmental factors. In this study, an adaptive MPPT algorithm is examined at real ambient conditions. PV modules are mounted on a moving city bus designed to test the solar systems on a moving vehicle. Some problems of a PV system associated with a moving vehicle are addressed. The proposed algorithm uses a scanning technique to determine the maximum power delivering capacity of the panel at a given operating condition and controls the PV panel. The aim of control algorithm was matching the impedance of the PV modules by controlling the duty cycle of the internal switch, regardless of changes of the parameters of the object of control and its outer environment. Presented algorithm was capable of reaching the aim of control. The structure of an adaptive controller was simplified on purpose. Since such a simple controller, armed only with an ability to learn, a more complex structure of an algorithm can only improve the result. The presented adaptive control system of the PV system is a general solution and can be used for other types of PV systems of both high and low power. Experimental results obtained from comparison of algorithms by a motion loop are presented and discussed. Experimental results are presented for fast change in irradiation and partial shading conditions. The results obtained clearly show that the proposed method is simple to implement with minimum tracking time and high tracking efficiency proving superior to the proposed method. This work has been financed by the Polish National Centre for Research and Development, PBS, under Grant Agreement No. PBS 2/A6/16/2013.

Keywords: adaptive control, photovoltaic energy, city bus electric load, DC-DC converter

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Authors: Mohammed Sabbah, Prospero Di Pierro, Raffaele Porta

Abstract:

Protein-based edible films and coatings have attracted an increasing interest in recent years since they might be used to protect pharmaceuticals or improve the shelf life of different food products. Among them, several plant proteins represent an abundant, inexpensive and renewable raw source. These natural biopolymers are used as film forming agents, being able to form intermolecular linkages by various interactions. However, without the addition of a plasticizing agent, many biomaterials are brittle and, consequently, very difficult to be manipulated. Plasticizers are generally small and non-volatile organic additives used to increase film extensibility and reduce its crystallinity, brittleness and water vapor permeability. Plasticizers normally act by decreasing the intermolecular forces along the polymer chains, thus reducing the relative number of polymer-polymer contacts, producing a decrease in cohesion and tensile strength and thereby increasing film flexibility allowing its deformation without rupture. The most commonly studied plasticizers are polyols, like glycerol (GLY) and some mono or oligosaccharides. In particular, GLY not only increases film extensibility but also migrates inside the film network often causing the loss of desirable mechanical properties of the material. Therefore, replacing GLY with a different plasticizer might help to improve film characteristics allowing potential industrial applications. To improve film properties, it seemed of interest to test as plasticizers some cationic small molecules like polyamines (PAs). Putrescine, spermidine (SPD), and spermine are PAs widely distributed in nature and of particular interest for their biological activities that may have some beneficial health effects. Since PAs contains amino instead of hydroxyl functional groups, they are able to trigger ionic interactions with negatively charged proteins. Bitter vetch (Vicia ervilia; BV) is an ancient grain legume crop, originated in the Mediterranean region, which can be found today in many countries around the world. This annual Vicia genus shows several favorable features, being their seeds a cheap and abundant protein source. The main objectives of this study were to investigate the effect of different concentrations of SPD on the mechanical and permeability properties of films prepared with native or heat denatured BV proteins in the presence of different concentrations of SPD and/or GLY. Therefore, a BV seed protein concentrate (BVPC), containing about 77% proteins, was used to prepare film forming solutions (FFSs), whereas GLY and SPD were added as film plasticizers, either singly or in combination, at various concentrations. Since a primary plasticizer is generally defined as a molecule that when added to a material makes it softer, more flexible and easier to be processed, our findings lead to consider SPD as a possible primary plasticizer of protein-based films. In fact, the addition of millimolar concentrations of SPD to BVPC FFS allowed obtaining handleable biomaterials with improved properties. Moreover, SPD can be also considered as a secondary plasticizer, namely an 'extender', because of its ability even to enhance the plasticizing performance of GLY. In conclusion, our studies indicate that innovative edible protein-based films and coatings can be obtained by using PAs as new plasticizers.

Keywords: edible films, glycerol, plasticizers, polyamines, spermidine

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Authors: Rajashekar Badam, Vedarajan Raman, Noriyoshi Matsumi

Abstract:

Efficiency of energy converting and storage systems like fuel cells and Li-Air battery principally depended on oxygen reduction reaction (ORR) which occurs at cathode. As the kinetics of the ORR is very slow, it becomes the rate determining step. Exploring carbon substrates for enhancing the dispersion and activity of the metal catalyst and commercially viable simple preparation method is a very crucial area of research in the field of energy materials. Hence, many researchers made large number of carbon-based ORR materials today. But, there are hardly few studies on the effect of interaction between Pt-carbon and carbon-electrolyte on activity. In this work, we have prepared functionalized carbon-based Pt catalyst (Pt-FAB) with enhanced interfacial properties that lead to efficient ORR catalysis. The present work deals with a single-pot method to exfoliate and functionalized acetylene black with enhanced interaction with Pt as well as electrolyte. Acetylene black was functionalized and exfoliated using a facile single pot acid treatment method. The resulted FAB was further decorated with Pt-nano particles (Pt-np). The TEM images of Pt-FAB with uniformly decorated Pt-np of ~3 nm. Further, XPS studies of Pt 4f peak revealed that Pt0 peak was shifted by 0.4 eV in Pt-FAB compared to binding energy of typical Pt⁰ found in Pt/C. The shift can be ascribed to the modulation of electronic state and strong electronic interaction of Pt with carbon. Modulated electronic structure of Pt and strong electronic interaction of Pt with FAB enhances the catalytic activity and durability respectively. To understand the electrode electrolyte interface, electrochemical impedance spectroscopy was carried out. These measurements revealed that the charge transfer resistance of electrode to electrolyte for Pt-FAB is 10 times smaller than that of conventional Pt/C. The interaction with electrolyte helps reduce the interface boundaries, which in turn affects the overall catalytic performance of the electrode. Cyclic voltammetric measurements in 0.1M HClO₄ aq. at a potential scan rate of 50 mVs-1 was employed to evaluate electrochemical surface area (ECSA) of Pt. ECSA of Pt-FAB was found to be as high as 67.2 m²g⁻¹. The three-electrode system showed very high ORR catalytic activity. Mass activity at 0.9 V vs. RHE showed 460 A/g which is much higher than the DOE target values for the year 2020. Further, it showed enhanced performance by showing 723 mW/cm² of highest power density and 1006 mA/cm² of current density at 0.6 V in fuel cell single cell type configuration and 1030 mAhg⁻¹ of rechargeable capacity in Li air battery application. The higher catalytic activity can be ascribed to the improved interaction of FAB with Pt and electrolyte. The aforementioned results evince that Pt-FAB will be a promising cathode material for efficient ORR with significant cyclability for its application in fuel cells and Li-Air batteries. In conclusion, a disordered material was prepared from AB and was systematically characterized. The extremely high ORR activity and ease of preparation make it competent for replacing commercially available ORR materials.

Keywords: functionalized acetylene black, oxygen reduction reaction, fuel cells, Functionalized battery

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

Abstract:

The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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