Search results for: spectral domain optical coherence tomography

Authors: Chuyi Ye, Minsang Kim, Cheol-Hee Moon

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The organic light emitting diode(OLED), is a potential organic optical functional materials which is considered as the next generation display technology with the advantages such as all-solid state, ultra-thin thickness, active luminous and flexibility. Due to the development of polymer-inorganic substrate, it becomes possible to achieve the flexible OLED display. However the organic light-emitting material is very sensitive to the oxygen and water vapor, and the encapsulation requires water vapor transmission rate(WVTR) and oxygen transmission rate(OTR) as lower as 10-6 g/(m2.d) and 10-5 cm3/(m2.d) respectively. In current situation, the rigorous WVTR and OTR have restricted the application of the OLED display. Traditional epoxy/getter or glass frit approaches, which have been widely applied on glass-substrate-based devices, are not suitable for transparent flexible organic devices, and mechanically flexible thin-film approaches are required. To ensure the OLED’s lifetime, the encapsulation material of the OLED package is very important. In this paper, a low melting point alloy(LMPA)-epoxy mixture in the encapsulation process is introduced. There will be a phase separation when the mixture is heated to the melting of LMPA and the formation of the double line structure between two substrates: the alloy barrier has extremely low WVTR and OTR and the epoxy fills the potential tiny cracks. In our experiment, the PI film is chosen as a flexible transparent substrate, and Mo and Cu are deposited on the PI film successively. Then the two metal layers are photolithographied to the sealing pattern line. The Mo is a transition layer between the PI film and Cu, at the same time, the Cu has a good wettability with the LMPA(Sn-58Bi). At last, pattern is printed with LMPA layer and applied voltage, the gathering Joule heat melt the LMPA and form the double line structure and the OLED package is sealed in the same time. In this research, the double-line encapsulating structure of LMPA and epoxy on the PI film is manufactured for the flexible OLED encapsulation, and in this process it is investigated whether the encapsulation satisfies the requirement of WVTR and OTR for the flexible OLED.

Keywords: encapsulation, flexible, low melting point alloy, OLED

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Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have played an indispensable role in controlling illness and death associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global healthcare problem. Many antibiotics had lost their effectiveness since the beginning of the antibiotic era because many bacteria have adapted defenses against these antibiotics. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing require the isolation of the pathogen from a clinical specimen by culturing on the appropriate media (this culturing stage lasts 24 h-first culturing). Then, chosen colonies are grown on media containing antibiotic(s), using micro-diffusion discs (second culturing time is also 24 h) in order to determine its bacterial susceptibility. Other methods, genotyping methods, E-test and automated methods were also developed for testing antimicrobial susceptibility. Most of these methods are expensive and time-consuming. Fourier transform infrared (FTIR) microscopy is rapid, safe, effective and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria; nonetheless, its true potential in routine clinical diagnosis has not yet been established. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The UTI E.coli bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 700 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 90% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E.coli, FTIR, multivariate analysis, susceptibility, UTI

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Authors: Carlos Amnael Orozco-Diaz, Robert David Moorehead, Gwendolen Reilly, Fiona Gilchrist, Cheryl Ann Miller

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The current gold-standard for maxillofacial reconstruction surgery (MRS) utilizes auto-grafted cancellous bone as a filler. This study was aimed towards developing a polylactic-acid/hydroxyapatite (PLA-HA) composite suitable for fused-deposition 3D printing. Functionalization of the polymer through the addition of HA was directed to promoting bone-regeneration properties so that the material can rival the performance of cancellous bone grafts in terms of bone-lesion repair. This kind of composite enables the production of MRS implants based off 3D-reconstructions from image studies – namely computed tomography – for anatomically-correct fitting. The present study encompassed in-vitro degradation and in-vitro biocompatibility profiling for 3D-printed PLA and PLA-HA composites. PLA filament (Verbatim Co.) and Captal S hydroxyapatite micro-scale HA powder (Plasma Biotal Ltd) were used to produce PLA-HA composites at 5, 10, and 20%-by-weight HA concentration. These were extruded into 3D-printing filament, and processed in a BFB-3000 3D-Printer (3D Systems Co.) into tensile specimens, and were mechanically challenged as per ASTM D638-03. Furthermore, tensile specimens were subjected to accelerated degradation in phosphate-buffered saline solution at 70°C for 23 days, as per ISO-10993-13-2010. This included monitoring of mass loss (through dry-weighing), crystallinity (through thermogravimetric analysis/differential thermal analysis), molecular weight (through gel-permeation chromatography), and tensile strength. In-vitro biocompatibility analysis included cell-viability and extracellular matrix deposition, which were performed both on flat surfaces and on 3D-constructs – both produced through 3D-printing. Discs of 1 cm in diameter and cubic 3D-meshes of 1 cm3 were 3D printed in PLA and PLA-HA composites (n = 6). The samples were seeded with 5000 MG-63 osteosarcoma-like cells, with cell viability extrapolated throughout 21 days via resazurin reduction assays. As evidence of osteogenicity, collagen and calcium deposition were indirectly estimated through Sirius Red staining and Alizarin Red staining respectively. Results have shown that 3D printed PLA loses structural integrity as early as the first day of accelerated degradation, which was significantly faster than the literature suggests. This was reflected in the loss of tensile strength down to untestable brittleness. During degradation, mass loss, molecular weight, and crystallinity behaved similarly to results found in similar studies for PLA. All composite versions and pure PLA were found to perform equivalent to tissue-culture plastic (TCP) in supporting the seeded-cell population. Significant differences (p = 0.05) were found on collagen deposition for higher HA concentrations, with composite samples performing better than pure PLA and TCP. Additionally, per-cell-calcium deposition on the 3D-meshes was significantly lower when comparing 3D-meshes to discs of the same material (p = 0.05). These results support the idea that 3D-printable PLA-HA composites are a viable resorbable material for artificial grafts for bone-regeneration. Degradation data suggests that 3D-printing of these materials – as opposed to other manufacturing methods – might result in faster resorption than currently-used PLA implants.

Keywords: bone regeneration implants, 3D-printing, in vitro testing, biocompatibility, polymer degradation, polymer-ceramic composites

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Authors: İlkay Özer Erselcan, Abdi Kükner, Gökhan Ceylan

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The power generated by eight point absorber type wave energy converters each having a different buoy are calculated in order to investigate the performances of buoys in this study. The calculations are carried out by modeling three different sea states observed in two different locations in the Black Sea. The floats analyzed in this study have two basic geometries and four different draft/radius (d/r) ratios. The buoys possess the shapes of a semi-ellipsoid and a semi-elliptic paraboloid. Additionally, the draft/radius ratios range from 0.25 to 1 by an increment of 0.25. The radiation forces acting on the buoys due to the oscillatory motions of these bodies are evaluated by employing a 3D panel method along with a distribution of 3D pulsating sources in frequency domain. On the other hand, the wave forces acting on the buoys which are taken as the sum of Froude-Krylov forces and diffraction forces are calculated by using linear wave theory. Furthermore, the wave energy converters are assumed to be taut-moored to the seabed so that the secondary body which houses a power take-off system oscillates with much smaller amplitudes compared to the buoy. As a result, it is assumed that there is not any significant contribution to the power generation from the motions of the housing body and the only contribution to power generation comes from the buoy. The power take-off systems of the wave energy converters are high pressure oil hydraulic systems which are identical in terms of their characteristic parameters. The results show that the power generated by wave energy converters which have semi-ellipsoid floats is higher than that of those which have semi elliptic paraboloid floats in both locations and in all sea states. It is also determined that the power generated by the wave energy converters follow an unsteady pattern such that they do not decrease or increase with changing draft/radius ratios of the floats. Although the highest power level is obtained with a semi-ellipsoid float which has a draft/radius ratio equal to 1, other floats of which the draft/radius ratio is 0.25 delivered higher power that the floats with a draft/radius ratio equal to 1 in some cases.

Keywords: Black Sea, buoys, hydraulic power take-off system, wave energy converters

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Authors: Mona Alharbi

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Melioidosis has emerged as a lethal disease. Unfortunately, the molecular mechanisms of virulence and pathogenicity of Burkholderia pseudomallei remain unknown. However, proteomics research has selected putative targets in B. pseudomallei that might play roles in the B. pseudomallei virulence. BPSL 2418 putative protein has been predicted as a free methionine sulfoxide reductase and interestingly there is a link between the level of the methionine sulfoxide in pathogen tissues and its virulence. Therefore in this work, we describe the cloning expression, purification, and crystallization of BPSL 2418 and the solution of its 3D structure using X-ray crystallography. Also, we aimed to identify the substrate binding and reduced forms of the enzyme to understand the role of BPSL 2418. The gene encoding BPSL2418 from B. pseudomallei was amplified by PCR and reclone in pETBlue-1 vector and transformed into E. coli Tuner DE3 pLacI. BPSL2418 was overexpressed using E. coli Tuner DE3 pLacI and induced by 300μM IPTG for 4h at 37°C. Then BPS2418 purified to better than 95% purity. The pure BPSL2418 was crystallized with PEG 4000 and PEG 6000 as precipitants in several conditions. Diffraction data were collected to 1.2Å resolution. The crystals belonged to space group P2 21 21 with unit-cell parameters a = 42.24Å, b = 53.48Å, c = 60.54Å, α=γ=β= 90Å. The BPSL2418 binding MES was solved by molecular replacement with the known structure 3ksf using PHASER program. The structure is composed of six antiparallel β-strands and four α-helices and two loops. BPSL2418 shows high homology with the GAF domain fRMsrs enzymes which suggest that BPSL2418 might act as methionine sulfoxide reductase. The amino acids alignment between the fRmsrs including BPSL 2418 shows that the three cysteines that thought to catalyze the reduction are fully conserved. BPSL 2418 contains the three conserved cysteines (Cys⁷⁵, Cys⁸⁵ and Cys¹⁰⁹). The active site contains the six antiparallel β-strands and two loops where the disulfide bond formed between Cys⁷⁵ and Cys¹⁰⁹. X-ray structure of free methionine sulfoxide binding and native forms of BPSL2418 were solved to increase the understanding of the BPSL2418 catalytic mechanism.

Keywords: X-Ray Crystallography, BPSL2418, Burkholderia pseudomallei, Melioidosis

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Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Mathodzi Nditsheni, Olawole Emmanuel Aina, Joshua Oluwole Olowoyo

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The ever-increasing world population is putting extreme pressure on the already limited agricultural resources for food production. Different soil enhancers were introduced by famers to meet the need of the ever-increasing population demand for food. One of the soil enhancers is the municipal waste sludge. This research investigated the differences in the concentrations of trace metals and levels of phytochemicals in four different tomato varieties cultivated on soil treated with municipal waste sludge in Pretoria, South Africa. Fruits were harvested at maturity and analyzed for trace metals and phytochemicals contents using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and a High-Performance Liquid Chromatography (HPLC) respectively. A one-way analysis of variance (ANOVA) was used to determine the differences in the concentrations of trace metals and phytochemical from different tomato varieties were significant. From the study, Rodade tomato bioaccumulated the highest concentrations of Mn, Cr, Cu and Ni, Roma bioaccumulated the highest concentrations of, Cd, Fe and Pb while Heinz bioaccumulated the highest concentrations of As and Zn. Cherry tomato on the other hand, recorded the lowest concentrations for most metals, Cd, Cr, Cu, Mn, Ni, Pb and Zn. The results of the study further showed that phenolic and flavonoids content were higher in the Solanum lycopersicum fruit grown in soils treated with municipal waste sludge. The study also showed that there was an inverse relationship between the levels of trace metals and phytochemicals. The calculated contamination factor values of trace metals like Cr, Cu, Pb and Zn were above the safe value of 1 which indicated that the tomato fruits may be unsafe for human consumption. However, the contamination factor values for the remaining trace metals were well below the safe value of 1. From the results obtained either for the control group or the treatment, the tomato varieties used in the study, bioaccumulated the toxic trace metals in their fruits and some of the values obtained were higher than the acceptable limit, which may then imply that the varieties of tomato used in this study bio accumulated the toxic trace metals from the soil, hence care should be taken when these tomato varieties are either cultivated or harvested from polluted areas

Keywords: trace metals, flavonoids, phenolics, waste sludge, tomato, contamination factors

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Authors: Mohammed Al Sulaimani, Hamad Al Manhi

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With the development of Remote Sensing technology, the resolution of optical Remote Sensing images has greatly improved, and images have become largely available. Numerous detectors have been developed for detecting different types of objects. In the past few years, Remote Sensing has benefited a lot from deep learning, particularly Deep Convolution Neural Networks (CNNs). Deep learning holds great promise to fulfill the challenging needs of Remote Sensing and solving various problems within different fields and applications. The use of Unmanned Aerial Systems in acquiring Aerial Photos has become highly used and preferred by most organizations to support their activities because of their high resolution and accuracy, which make the identification and detection of very small features much easier than Satellite Images. And this has opened an extreme era of Deep Learning in different applications not only in feature extraction and prediction but also in analysis. This work addresses the capacity of Machine Learning and Deep Learning in detecting and extracting Oil Leaks from Flowlines (Onshore) using High-Resolution Aerial Photos which have been acquired by UAS fixed with RGB Sensor to support early detection of these leaks and prevent the company from the leak’s losses and the most important thing environmental damage. Here, there are two different approaches and different methods of DL have been demonstrated. The first approach focuses on detecting the Oil Leaks from the RAW Aerial Photos (not processed) using a Deep Learning called Single Shoot Detector (SSD). The model draws bounding boxes around the leaks, and the results were extremely good. The second approach focuses on detecting the Oil Leaks from the Ortho-mosaiced Images (Georeferenced Images) by developing three Deep Learning Models using (MaskRCNN, U-Net and PSP-Net Classifier). Then, post-processing is performed to combine the results of these three Deep Learning Models to achieve a better detection result and improved accuracy. Although there is a relatively small amount of datasets available for training purposes, the Trained DL Models have shown good results in extracting the extent of the Oil Leaks and obtaining excellent and accurate detection.

Keywords: GIS, remote sensing, oil leak detection, machine learning, aerial photos, unmanned aerial systems

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Authors: Payman Davoodi-Nasab, Ahmad Rahbar-Kelishami, Jaber Safdari, Hossein Abolghasemi

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The high purity rare earth elements (REEs) have been vastly used in the field of chemical engineering, metallurgy, nuclear energy, optical, magnetic, luminescence and laser materials, superconductors, ceramics, alloys, catalysts, and etc. Neodymium is one of the most abundant rare earths. By development of a neodymium–iron–boron (Nd–Fe–B) permanent magnet, the importance of neodymium has dramatically increased. Solvent extraction processes have many operational limitations such as large inventory of extractants, loss of solvent due to the organic solubility in aqueous solutions, volatilization of diluents, etc. One of the promising methods of liquid membrane processes is emulsion liquid membrane (ELM) which offers an alternative method to the solvent extraction processes. In this work, a study on Nd extraction through multi-walled carbon nanotubes (MWCNTs) assisted ELM using response surface methodology (RSM) has been performed. The ELM composed of diisooctylphosphinic acid (CYANEX 272) as carrier, MWCNTs as nanoparticles, Span-85 (sorbitan triooleate) as surfactant, kerosene as organic diluent and nitric acid as internal phase. The effects of important operating variables namely, surfactant concentration, MWCNTs concentration, and treatment ratio were investigated. Results were optimized using a central composite design (CCD) and a regression model for extraction percentage was developed. The 3D response surfaces of Nd(III) extraction efficiency were achieved and significance of three important variables and their interactions on the Nd extraction efficiency were found out. Results indicated that introducing the MWCNTs to the ELM process led to increasing the Nd extraction due to higher stability of membrane and mass transfer enhancement. MWCNTs concentration of 407 ppm, Span-85 concentration of 2.1 (%v/v) and treatment ratio of 10 were achieved as the optimum conditions. At the optimum condition, the extraction of Nd(III) reached the maximum of 99.03%.

Keywords: emulsion liquid membrane, extraction of neodymium, multi-walled carbon nanotubes, response surface method

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Authors: Mustapha A. Tijjani, Fanna I. Abdulrahman, Irfan Z. Khan, Umar K. Sandabe, Cong Li

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Air dried sample V. doniana after collection and identification was extracted with ethanol and further partition with chloroform, ethyl acetate and n-butanol. Ethanolic extract (11.9g) was fractionated on a silica gel accelerated column chromatography using solvents such as n-hexane, ethyl acetate and methanol. Each eluent fractions (150ml aliquots) were collected and monitored with thin layer chromatography. Fractions with similar Rf values from same solvents system were pooled together. Phytochemical test of all the fractions were performed using standard procedure. Complete elution yielded 48 fractions (150ml/fraction) which were pooled to 24 fractions base on the Rf values. It was further recombined and 12 fractions were obtained on the basis on Rf values and coded Vd1 to Vd12 fractions. Vd8 was further eluted with ethylacetate and methanol and gave fourteen sub fractions Vd8-a, -Vd8-m. Fraction Vd8-a (56mg) gave a white crystal compound coded V1. It was further checked on TLC and observed under ultraviolet lamp and was found to give a single spot. The Rf values were calculated to be 0.433. The melting point was determined using Gallenkamp capillary melting point apparatus and found to be 241-243°C uncorrected. Characterization of the isolated compound coded V1 was done using FT-infra-red spectroscopy, HNMR, 13CNMR(1and 2D) and HRESI-MS. The IR spectrum of compound V1 shows prominent peaks that corresponds to OHstr (3365cm-1) and C=0 (1652cm-1) etc. This spectrum suggests that among the functional moiety in compound V1 are the carbonyl and hydroxyl group. The 1H NMR (400 MHz) spectrum of compound V1 in DMSO-d6 displayed five singlet signals at δ 0.72 (3H, s, H-18), 0.79 (3H, s, H-19), 1.03 (3H, s, H-21), 1.04 (3H, s, H-26), 1.06 (3H, s, H-27) each integrating for three protons indicating the five methyl functional groups present in the compound. It further showed a broad singlet at δ 5.58 integrated for 1 H due to an olefinic H-atom adjacent to the carbonyl carbon atom. Three signals at δ 3.10 (d, J = 9.0 Hz, H-22), 3.59 (m, 1H, 2H-a) and 3.72 (m, 1H, 3H-e), each integrating for one proton is due to oxymethine protons indicating that three oxymethine H-atoms are present in the compound. These all signals are characteristic to the ecdysteroid skeletons. The 13C-NMR spectrum showed the presence of 27 carbon atoms, suggesting that may be steroid skeleton. The DEPT-135 experiment showed the presence of five CH3, eight CH2, and seven CH groups, and seven quaternary C-atoms. The molecular formula was established as C27H44O7 by high resolution electron spray ionization-mass spectroscopy (HRESI-MS) positive ion mode m/z 481.3179. The signals in mass spectrum are 463, 445, and 427 peaks corresponding to losses of one, two, three, or four water molecules characteristic for ecdysterone skeleton reported in the literature. Based on the spectral analysis (HNMR, 13CNMR, DEPT, HMQC, IR, HRESI-MS) the compound V1 is thus concluded to have ecdysteriod skeleton and conclusively conforms with 2β, 3β 14α, 20R, 22R, 25-hexahydroxy-5 β cholest-7-ene-6- one, or 2, 3, 14, 20, 22, 25 hexahydroxy cholest-7-ene-6-one commonly known as 20-hydroxyecdysone.

Keywords: vitex, phytochemical, purification, isolation, chromatography, spectroscopy

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Authors: Zahid Ullah, Atlas Khan

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In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society.

Keywords: mathematics, bridging theory and applications, data-driven world, mathematical models

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Authors: Afzal Hussain, Mohammad A. Altamimi, Syed Sarim Imam, Mudassar Shahid, Osamah Abdulrahman Alnemer

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Exhaustive application of sulfamethoxazole (SUX) became as a global threat for human health due to water contamination through diverse sources. The addressed combined application of Hansen solubility (HSPiP software) parameters and Quality by Design tool for developing various green nanoemulsions. HSPiP program assisted to screen suitable excipients based on Hansen solubility parameters and experimental solubility data. Various green nanoemulsions were prepared and characterized for globular size, size distribution, zeta potential, and removal efficiency. Design Expert (DoE) software further helped to identify critical factors responsible to have direct impact on percent removal efficiency, size, and viscosity. Morphological investigation was visualized under transmission electron microscopy (TEM). Finally, the treated was studied to negate the presence of the tested drug employing ICP-OES (inductively coupled plasma optical emission microscopy) technique and HPLC (high performance liquid chromatography). Results showed that HSPiP predicted biocompatible lipid, safe surfactant (lecithin), and propylene glycol (PG). Experimental solubility of the drug in the predicted excipients were quite convincing and vindicated. Various green nanoemulsions were fabricated, and these were evaluated for in vitro findings. Globular size (100-300 nm), PDI (0.1-0.5), zeta potential (~ 25 mV), and removal efficiency (%RE = 70-98%) were found to be in acceptable range for deciding input factors with level in DoE. Experimental design tool assisted to identify the most critical variables controlling %RE and optimized content of nanoemulsion under set constraints. Dispersion time was varied from 5-30 min. Finally, ICP-OES and HPLC techniques corroborated the absence of SUX in the treated water. Thus, the strategy is simple, economic, selective, and efficient.

Keywords: quality by design, sulfamethoxazole, green nanoemulsion, water treatment, icp-oes, hansen program (hspip software

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Authors: Omar Qaftan

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Earthquakes can cause tremendous loss of human life and can result in severe damage to a several of civil engineering structures especially the tall buildings. The response of a multistory structure subjected to earthquake loading is a complex task, and it requires to be studied by physical and numerical modelling. For many circumstances, the scale models on shaking table may be a more economical option than the similar full-scale tests. A shaking table apparatus is a powerful tool that offers a possibility of understanding the actual behaviour of structural systems under earthquake loading. It is required to use a set of scaling relations to predict the behaviour of the full-scale structure. Selecting the scale factors is the most important steps in the simulation of the prototype into the scaled model. In this paper, the principles of scaling modelling procedure are explained in details, and the simulation of scaled multi-storey concrete structure for dynamic studies is investigated. A procedure for a complete dynamic simulation analysis is investigated experimentally and numerically with a scale factor of 1/50. The frequency domain accounting and lateral displacement for both numerical and experimental scaled models are determined. The procedure allows accounting for the actual dynamic behave of actual size porotype structure and scaled model. The procedure is adapted to determine the effects of the tall multi-storey structure on a raft foundation. Four generated accelerograms were used as inputs for the time history motions which are in complying with EC8. The output results of experimental works expressed regarding displacements and accelerations are compared with those obtained from a conventional fixed-base numerical model. Four-time history was applied in both experimental and numerical models, and they concluded that the experimental has an acceptable output accuracy in compare with the numerical model output. Therefore this modelling methodology is valid and qualified for different shaking table experiments tests.

Keywords: structure, raft, soil, interaction

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Authors: Li Shihao, Dieter Trau

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Bilirubin is a yellow substance that is made when the body breaks down old red blood cells. High levels of bilirubin can cause jaundice, a condition that makes the newborn's skin and the white part of the eyes look yellow. Jaundice is a serial-killer in developing countries in Southeast Asia such as Myanmar and most parts of Africa where jaundice screening is largely unavailable. Worldwide, 60% of newborns experience infant jaundice. One in ten will require therapy to prevent serious complications and lifelong neurologic sequelae. Limitations of current solutions: - Blood test: Blood tests are painful may largely unavailable in poor areas of developing countries, and also can be costly and unsafe due to the insufficient investment and lack of access to health care systems. - Transcutaneous jaundice-meter: 1) can only provide reliable results to caucasian newborns, due to skin pigmentations since current technologies measure bilirubin by the color of the skin. Basically, the darker the skin is, the harder to measure, 2) current jaundice meters are not affordable for most underdeveloped areas in Africa like Kenya and Togo, 3) fat tissue under the skin also influences the accuracy, which will give overestimated results, 4) current jaundice meters are not reliable after treatment (phototherapy) because bilirubin levels underneath the skin will be reduced first, while overall levels may be quite high. Thus, there is an urgent need for a low-cost non-invasive device, which can be effective not only for caucasian babies but also Asian and African newborns, to save lives at the most vulnerable time and prevent any complications like brain damage. Instead of measuring bilirubin on skin, we proposed a new method to do the measurement on the sclera, which can avoid the difference of skin pigmentations and ethnicities, due to the necessity for the sclera to be white regardless of racial background. This is a novel approach for measuring bilirubin by an optical method of light reflection off the white part of the eye. Moreover, the device is connected to a smart device, which can provide a user-friendly interface and the ability to record the clinical data continuously A disposable eye cap will be provided avoiding contamination and fixing the distance to the eye.

Keywords: Jaundice, bilirubin, non-invasive, sclera

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Authors: Dina Ibrahim Abouelamaiem, Ana Jorge Sobrido, Magdalena Titirici, Paul R. Shearing, Daniel J. L. Brett

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Global warming and scarcity of fossil fuels have had a radical impact on the world economy and ecosystem. The urgent need for alternative energy sources has hence elicited an extensive research for exploiting efficient and sustainable means of energy conversion and storage. Among various electrochemical systems, supercapacitors attracted significant attention in the last decade due to their high power supply, long cycle life compared to batteries and simple mechanism. Recently, the performance of these devices has drastically improved, as tuning of nanomaterials provided efficient charge and storage mechanisms. Carbon materials, in various forms, are believed to pioneer the next generation of supercapacitors due to their attractive properties that include high electronic conductivities, high surface areas and easy processing and functionalization. Cellulose has eco-friendly attributes that are feasible to replace man-made fibers. The carbonization of cellulose yields carbons, including activated carbon and graphite fibers. Activated carbons successively are the most exploited candidates for supercapacitor electrode materials that can be complemented with pseudocapacitive materials to achieve high energy and power densities. In this work, the optimum functionalization conditions of cellulose have been investigated for supercapacitor electrode materials. The precursor was treated with potassium hydroxide (KOH) at different KOH/cellulose ratios prior to the carbonization process in an inert nitrogen atmosphere at 850 °C. The chalky products were washed, dried and characterized with different techniques including transmission electron microscopy (TEM), x-ray tomography and nitrogen adsorption-desorption isotherms. The morphological characteristics and their effect on the electrochemical performances were investigated in two and three-electrode systems. The KOH/cellulose ratios of 0.5:1 and 1:1 exhibited the highest performances with their unique hierarchal porous network structure, high surface areas and low cell resistances. Both samples acquired the best results in three-electrode systems and coin cells with specific gravimetric capacitances as high as 187 F g-1 and 20 F g-1 at a current density of 1 A g-1 and retention rates of 72% and 70%, respectively. This is attributed to the morphology of the samples that constituted of a well-balanced micro-, meso- and macro-porosity network structure. This study reveals that the electrochemical performance doesn’t solely depend on high surface areas but also an optimum pore size distribution, specifically at low current densities. The micro- and meso-pore contribution to the final pore structure was found to dominate at low KOH loadings, reaching ‘equilibrium’ with macropores at the optimum KOH loading, after which macropores dictate the porous network. The wide range of pore sizes is detrimental for the mobility and penetration of electrolyte ions in the porous structures. These findings highlight the influence of various morphological factors on the double-layer capacitances and high performance rates. In addition, they open a platform for the investigation of the optimized conditions for double-layer capacitance that can be coupled with pseudocapacitive materials to yield higher energy densities and capacities.

Keywords: carbon, electrochemical performance, electrodes, KOH/cellulose optimized ratio, morphology, supercapacitor

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Authors: M. Shemis, O. Maher, G. Casterou, F. Gauffre

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Hepatitis C virus (HCV) is a major cause of chronic liver disease with a global 170 million chronic carriers at risk of developing liver cirrhosis and/or liver cancer. Egypt reports the highest prevalence of HCV worldwide. Currently, two classes of assays are used in the diagnosis and management of HCV infection. Despite the high sensitivity and specificity of the available diagnostic assays, they are time-consuming, labor-intensive, expensive, and require specialized equipment and highly qualified personal. It is therefore important for clinical and economic terms to develop a low-tech assay for the direct detection of HCV RNA with acceptable sensitivity and specificity, short turnaround time, and cost-effectiveness. Such an assay would be critical to control HCV in developing countries with limited resources and high infection rates, such as Egypt. The unique optical and physical properties of gold nanoparticles (AuNPs) have allowed the use of these nanoparticles in developing simple and rapid colorimetric assays for clinical diagnosis offering higher sensitivity and specificity than current detection techniques. The current research aims to develop a detection assay for HCV RNA using gold nanoparticles (AuNPs). Methods: 200 anti-HCV positive samples and 50 anti-HCV negative plasma samples were collected from Egyptian patients. HCV viral load was quantified using m2000rt (Abbott Molecular Inc., Des Plaines, IL). HCV genotypes were determined using multiplex nested RT- PCR. The assay is based on the aggregation of AuNPs in presence of the target RNA. Aggregation of AuNPs causes a color shift from red to blue. AuNPs were synthesized using citrate reduction method. Different sets of probes within the 5’ UTR conserved region of the HCV genome were designed, grafted on AuNPs and optimized for the efficient detection of HCV RNA. Results: The nano-gold assay could colorimetrically detect HCV RNA down to 125 IU/ml with sensitivity and specificity of 91.1% and 93.8% respectively. The turnaround time of the assay is < 30 min. Conclusions: The assay allows sensitive and rapid detection of HCV RNA and represents an inexpensive and simple point-of-care assay for resource-limited settings.

Keywords: HCV, gold nanoparticles, point of care, viral load

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

Procedia PDF Downloads 190

Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

Procedia PDF Downloads 279

Authors: Mahtab Makaremi Masouleh, Günter Wozniak

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This work is part of a major research project concerning the design of a light temporary installable textile flood control structure. The motivation for this work is the great need of applying light structures for the protection of coastal areas from detrimental effects of rapid water runoff. The prime objective of the study is the numerical analysis of the interaction among free surface water flow and slender shaped pliable structures, playing a key role in safety performance of the intended system. First, the behavior of down scale membrane is examined under hydrostatic pressure by the Abaqus explicit solver, which is part of the finite element based commercially available SIMULIA software. Then the procedure to achieve a stable and convergent solution for strongly coupled media including fluids and structures is explained. A partitioned strategy is imposed to make both structures and fluids be discretized and solved with appropriate formulations and solvers. In this regard, finite element method is again selected to analyze the structural domain. Moreover, computational fluid dynamics algorithms are introduced for solutions in flow domains by means of a commercial package of Star CCM+. Likewise, SIMULIA co-simulation engine and an implicit coupling algorithm, which are available communication tools in commercial package of the Star CCM+, enable powerful transmission of data between two applied codes. This approach is discussed for two different cases and compared with available experimental records. In one case, the down scale membrane interacts with open channel flow, where the flow velocity increases with time. The second case illustrates, how the full scale flexible flood barrier behaves when a massive flotsam is accelerated towards it.

Keywords: finite element formulation, finite volume algorithm, fluid-structure interaction, light pliable structure, VOF multiphase model

Procedia PDF Downloads 192

Authors: Ruijia Hu, Susanna T.Y. Tong

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Light detection and ranging (LiDAR) and four-channel red, green, blue, and near-infrared (RGBI) remote sensed imageries allow an accurate quantification and contiguous measurement of vegetation characteristics and forest structures. This information facilitates the generation of habitat structure variables for forest species distribution modelling. However, applications of remote sensing data, especially the combination of structural and spectral information, to support evidence-based decisions in forest managements and conservation practices at local scale are not widely adopted. In this study, we examined the habitat requirements of pileated woodpecker (Dryocopus pileatus) (PW) in Hamilton County, Ohio, using ecologically relevant forest structural and vegetation characteristics derived from LiDAR and RGBI data. We hypothesized that the habitat of PW is shaped by vegetation characteristics that are directly associated with the availability of food, hiding and nesting resources, the spatial arrangement of habitat patches within home range, as well as proximity to water sources. We used 186 PW presence or absence locations to model their presence and absence in generalized additive model (GAM) at two scales, representing foraging and home range size, respectively. The results confirm PW’s preference for tall and large mature stands with structural complexity, typical of late-successional or old-growth forests. Besides, the crown size of dead trees shows a positive relationship with PW occurrence, therefore indicating the importance of declining living trees or early-stage dead trees within PW home range. These locations are preferred by PW for nest cavity excavation as it attempts to balance the ease of excavation and tree security. In addition, we found that PW can adjust its travel distance to the nearest water resource, suggesting that habitat fragmentation can have certain impacts on PW. Based on our findings, we recommend that forest managers should use different priorities to manage nesting, roosting, and feeding habitats. Particularly, when devising forest management and hazard tree removal plans, one needs to consider retaining enough cavity trees within high-quality PW habitat. By mapping PW habitat suitability for the study area, we highlight the importance of riparian corridor in facilitating PW to adjust to the fragmented urban landscape. Indeed, habitat improvement for PW in the study area could be achieved by conserving riparian corridors and promoting riparian forest succession along major rivers in Hamilton County.

Keywords: deadwood detection, generalized additive model, individual tree crown delineation, LiDAR, pileated woodpecker, RGBI aerial imagery, species distribution models

Procedia PDF Downloads 61

Authors: Elaf Ahmed, Shahid Rasul, Ohoud Alharbi, Peng Wang

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Ultra-Small Nanoparticles of metals (USNPs) have attracted the attention from the perspective of both basic and developmental science in a wide range of fields. These NPs exhibit electrical, optical, magnetic, and catalytic phenomena. In addition, they are considered effective catalysts because of their enormously large surface area. Many chemical methods of synthesising USNPs are reported. However, the drawback of these methods is the use of different capping agents and ligands in the process of the production such as Polyvinylpyrrolidone, Thiol and Ethylene Glycol. In this research ultra-small nanoparticles of gold, palladium and platinum metal have been successfully produced using electrochemically active biofilm (EAB) after optimising the pH of the media. The production of ultra-small nanoparticles has been conducted in a reactor using a simple two steps method. Initially biofilm was grown on the surface of a carbon paper for 7 days using Shewanella Loihica bacteria. Then, biofilm was employed to synthesise platinum, palladium and gold nanoparticles in water using sodium lactate as electron donor without using any toxic chemicals at mild operating conditions. Electrochemically active biofilm oxidise the electron donor and produces electrons in the solution. Since these electrons are a strong reducing agent, they can reduce metal precursors quite effectively and quickly. The As-synthesized ultra-small nanoparticles have a size range between (2-7nm) and showed excellent catalytic activity on the degradation of methyl orange. The growth of metal USNPs is strongly related to the condition of the EAB. Where using low pH for the synthesis was not successful due to the fact that it might affect and destroy the bacterial cells. However, increasing the pH to 7 and 9, led to the successful formation of USNPs. By changing the pH value, we noticed a change in the size range of the produced NPs. The EAB seems to act as a Nano factory for the synthesis of metal nanoparticles by offering a green, sustainable and toxic free synthetic route without the use of any capping agents or ligands and depending only on their respiration pathway.

Keywords: electrochemically active biofilm, electron donor, shewanella loihica, ultra-small nanoparticles

Procedia PDF Downloads 195

Authors: Pallavi Gajjar, Vinayak Malhotra

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Rockets have been known to have played a predominant role in spacecraft propulsion. The quintessential aspect of combustion-related requirements of a rocket engine is the minimization of the surrounding risks/hazards. Over time, it has become imperative to understand the combustion rate variation in presence of external energy source(s). Rocket propulsion represents a special domain of chemical propulsion assisted by high speed flows in presence of acoustics and thermal source(s). Jet noise leads to a significant loss of resources and every year a huge amount of financial aid is spent to prevent it. External heat source(s) induce high possibility of fire risk/hazards which can sufficiently endanger the operation of a space vehicle. Appreciable work had been done with justifiable simplification and emphasis on the linear variation of external energy source(s), which yields good physical insight but does not cater to accurate predictions. Present work experimentally attempts to understand the correlation between inter-energy conversions with the non-linear placement of external energy source(s). The work is motivated by the need to have better fire safety and enhanced combustion. The specific objectives of the work are a) To interpret the related energy transfer for combustion in presence of alternate external energy source(s) viz., thermal and acoustic, b) To fundamentally understand the role of key controlling parameters viz., separation distance, the number of the source(s), selected configurations and their non-linear variation to resemble real-life cases. An experimental setup was prepared using incense sticks as potential fuel and paraffin wax candles as the external energy source(s). The acoustics was generated using frequency generator, and source(s) were placed at selected locations. Non-equidistant parametric experimentation was carried out, and the effects were noted on regression rate changes. The results are expected to be very helpful in offering a new perspective into futuristic rocket designs and safety.

Keywords: combustion, acoustic energy, external energy sources, regression rate

Procedia PDF Downloads 144

Authors: Lan Luo, Jingjing Huang, Huafang Li

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Introduction: Distressing low sexual desire is common in China, while the lack of reliable and valid instruments to evaluate symptoms of hypoactive sexual desire disorder (HSDD) impedes related research and clinical services. Aim: This study aimed to validate the reliability and validity of the Female Sexual Function Index (FSFI) and the Female Sexual Distress Scale-Desire/Arousal/Orgasm (FSDS-DAO) in Chinese female HSDD patients. Methods: We administered FSFI and FSDS-DAO in a convenient sample of Chinese adult women. Participants were diagnosed by a psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR). Results: We had a valid analysis sample of 279 Chinese women, of which 107 were HSDD patients. The Cronbach's α of FSFI and FSDS-DAO were 0.947 and 0.956, respectively, and the intraclass correlation coefficients of which were 0.86 and 0.89, respectively (the interval was 13-15 days). The correlation coefficient between the Revised Adult Attachment Scale (RAAS) and FSFI (or FSDS-DAO) did not exceed 0.4; the area under the receiver operating characteristic (ROC) curve was 0. 83 when combined FSFI-d (the desire domain of FSFI) and FSDS-DAO to diagnose HSDD, which was significantly different from that of using these scales individually. FSFI-d of less than 2.7 (1.2-6) and FSDS-DAO of no less than 15 (0-60) (Sensitivity 65%, Specificity 83%), or FSFI-d of no more than 3.0 (1.2-6) and FSDS-DAO of no less than 14 (0-60) (Sensitivity 74%, Specificity 77%) can be used as cutoff scores in clinical research or outpatient screening. Clinical implications: FSFI (including FSFI-d) and FSDS-DAO are suitable for the screening and evaluation of Chinese female HSDD patients of childbearing age. Strengths and limitations: Strengths include a thorough validation of FSFI and FSDS-DAO and the exploration of the cutoff score combing FSFI-d and FSDS-DAO. Limitations include a small convenience sample and the requirement of being sexually active for HSDD patients. Conclusion: FSFI (including FSFI-d) and FSDS-DAO have good internal consistency, test-retest reliability, construct validity, and criterion validity in Chinese female HSDD patients of childbearing age.

Keywords: sexual desire, sexual distress, hypoactive sexual desire disorder, scale

Procedia PDF Downloads 80

Authors: Hassan Qandil

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Using a statistical algorithm incorporated in MATLAB, four types of non-imaging Fresnel lenses are designed; spot-flat, linear-flat, dome-shaped and semi-cylindrical-shaped. The optimization employs a statistical ray-tracing methodology of the incident light, mainly considering effects of chromatic aberration, varying focal lengths, solar inclination and azimuth angles, lens and receiver apertures, and the optimum number of prism grooves. While adopting an equal-groove-width assumption of the Poly-methyl-methacrylate (PMMA) prisms, the main target is to maximize the ray intensity on the receiver’s aperture and therefore achieving higher values of heat flux. The algorithm outputs prism angles and 2D sketches. 3D drawings are then generated via AutoCAD and linked to COMSOL Multiphysics software to simulate the lenses under solar ray conditions, which provides optical and thermal analysis at both the lens’ and the receiver’s apertures while setting conditions as per the Dallas-TX weather data. Once the lenses’ characterization is finalized, receivers are designed based on its optimized aperture size. Several cavity shapes; including triangular, arc-shaped and trapezoidal, are tested while coupled with a variety of receiver materials, working fluids, heat transfer mechanisms, and enclosure designs. A vacuum-reflective enclosure is also simulated for an enhanced thermal absorption efficiency. Each receiver type is simulated via COMSOL while coupled with the optimized lens. A lab-scale prototype for the optimum lens-receiver configuration is then fabricated for experimental evaluation. Application-based testing is also performed for the selected configuration, including that of a photovoltaic-thermal cogeneration system and solar furnace system. Finally, some future research work is pointed out, including the coupling of the collector-receiver system with an end-user power generator, and the use of a multi-layered genetic algorithm for comparative studies.

Keywords: COMSOL, concentrator, energy, fresnel, optics, renewable, solar

Procedia PDF Downloads 158

Authors: Sara Khamseh, Kambiz Javanruee, Hamid Khorsand

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Plenty of metallic materials are used for biomedical applications like hip joints and screws. Besides, it is reported that metal platforms such as stainless steel show significant deterioration because of wear and friction. The surface of metal substrates has been coated with a variety of multicomponent coatings to prevail these problems. The carbon-based multicomponent coatings such as metal-added amorphous carbon and diamond coatings are crucially important because of their remarkable tribological performance and chemical stability. In the current study, H-D contained Nb: (a-C) multicomponent coatings (H-D: hexagonal diamond, a-C: amorphous carbon) coated on A 304 steel substrates using an unbalanced magnetron (UBM) sputtering system. The effects of Nb and H-D content and ID/IG ratio on microstructure, mechanical and tribological characteristics of (Nb: H-D: a-C) composite coatings were investigated. The results of Raman spectroscopy represented that a-C phase with a Graphite-like structure (GLC with high value of sp2 carbon bonding) is formed, and its domain size increased with increasing Nb content of the coatings. Moreover, the Nb played a catalyst for the formation of the H-D phase. The nanoindentation hardness value of the coatings ranged between ~17 to ~35 GPa and (Nb: H-D: a-C) composite coatings with more H-D content represented higher hardness and plasticity index. It seems that the existence of extra-hard H-D particles straightly increased hardness. The tribological performance of the coatings was evaluated using the pin-on-disc method under the wet environment of SBF (Simulated Body Fluid). The COF value of the (Nb: H-D: a-C) coatings decreased with an increasing ID/IG ratio. The lower coefficient of friction is a result of the lamelliform array of graphitic domains. Also, the wear rate of the coatings decreased with increasing H-D content of the coatings. Based on the literature, a-C coatings with high hardness and H3/E2 ratio represent lower wear rates and better tribological performance. According to the nanoindentation analysis, hardness and H3/E2 ratio of (Nb: H-D: a-C) multicomponent coatings increased with increasing H-D content, which in turn decreased the wear rate of the coatings. The mechanical and tribological potency of (Nb: H-D: a-C) composite coatings on A 304 steel substrates paved the way for the development of innovative advanced coatings to ameliorate the performance of A 304 steel for biomedical applications.

Keywords: COF, mechanical properties, (Nb: H-D: a-C) coatings, wear rate

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Authors: Priyanka Tiwari, Uma Devi Ranjit, Ritesh Thapa

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Background and Significance: Cerebral Palsy (CP) influences not only the child's everyday functioning but also the functioning of whole family. Application of study findings can be used in clinical or community setting to screen the parents of children with cerebral palsy in order to identify the compromised domain of stress which in turn will help to improve the interaction between parent and child with disability and thus ultimately affect the progress that a child makes in his or her therapeutic or educational programs. Objective: The objective of the study was to assess the level of stress in mothers of children with CP by adopting mixed method design. Methodology: Cross-sectional descriptive design was adopted in the quantitative design where Parental Stress Scale (PSS) was utilized to collect data from a convenient sample of 40 mothers of children with CP who were under regular follow-up by home visitor of Self-help Group for Cerebral Palsy while embedded qualitative design was used to explore the stress of mothers of CP affected children. From the parent population of quantitative sample 4 mothers were chosen for in-depth exploration, regarding their stress by means of case study method. Descriptive statistics like frequency, percentage, mean, median, standard deviation, correlation and inferential statistics like Mann-Whitney U test and Kruskal-Wallis H test were used to describe and assess relationship between variables. Findings: The mean stress experienced by mothers of children with cerebral palsy was 53.62±9.53 with 15% percent of the mothers experiencing severe stress. There was significant association between age group of mother and total stress score and negative themes of stress. Similarly, signification association was found between educational status of the mother and positive themes of stress which was convergent with the qualitative finding as well, where literate mothers had more positive view of their child's disability which could be attributed to their educational level as education provides us with a broad perspective to look at a situation. Conclusions: Still one-sixth of the mothers experienced severe stress so if we want to ensure the well-being of the children affected by cerebral palsy, then parents caring for them need to be looked after as well.

Keywords: stress, cerebral palsy, mothers, mixed method

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

Procedia PDF Downloads 54

Authors: Mercedes Bertotto, Marcelo Bello, Hector Goicoechea, Veronica Fusca

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The National Service of Agri-Food Health and Quality (SENASA), controls honey to detect contamination by synthetic or natural chemical substances and establishes and controls the traceability of the product. The utility of near-infrared spectroscopy for the detection of adulteration of honey with high fructose corn syrup (HFCS) was investigated. First of all, a mixture of different authentic artisanal Argentinian honey was prepared to cover as much heterogeneity as possible. Then, mixtures were prepared by adding different concentrations of high fructose corn syrup (HFCS) to samples of the honey pool. 237 samples were used, 108 of them were authentic honey and 129 samples corresponded to honey adulterated with HFCS between 1 and 10%. They were stored unrefrigerated from time of production until scanning and were not filtered after receipt in the laboratory. Immediately prior to spectral collection, honey was incubated at 40°C overnight to dissolve any crystalline material, manually stirred to achieve homogeneity and adjusted to a standard solids content (70° Brix) with distilled water. Adulterant solutions were also adjusted to 70° Brix. Samples were measured by NIR spectroscopy in the range of 650 to 7000 cm⁻¹. The technique of specular reflectance was used, with a lens aperture range of 150 mm. Pretreatment of the spectra was performed by Standard Normal Variate (SNV). The ant colony optimization genetic algorithm sample selection (ACOGASS) graphical interface was used, using MATLAB version 5.3, to select the variables with the greatest discriminating power. The data set was divided into a validation set and a calibration set, using the Kennard-Stone (KS) algorithm. A combined method of Potential Functions (PF) was chosen together with Partial Least Square Linear Discriminant Analysis (PLS-DA). Different estimators of the predictive capacity of the model were compared, which were obtained using a decreasing number of groups, which implies more demanding validation conditions. The optimal number of latent variables was selected as the number associated with the minimum error and the smallest number of unassigned samples. Once the optimal number of latent variables was defined, we proceeded to apply the model to the training samples. With the calibrated model for the training samples, we proceeded to study the validation samples. The calibrated model that combines the potential function methods and PLSDA can be considered reliable and stable since its performance in future samples is expected to be comparable to that achieved for the training samples. By use of Potential Functions (PF) and Partial Least Square Linear Discriminant Analysis (PLS-DA) classification, authentic honey and honey adulterated with HFCS could be identified with a correct classification rate of 97.9%. The results showed that NIR in combination with the PT and PLS-DS methods can be a simple, fast and low-cost technique for the detection of HFCS in honey with high sensitivity and power of discrimination.

Keywords: adulteration, multivariate analysis, potential functions, regression

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Authors: Jérémie Ochin

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Soccer analytics are built on two data sources: the frame-by-frame position of each player on the terrain and the sequences of events, such as ball drive, pass, cross, shot, throw-in... With more than 2000 ball-events per soccer game, their precise and exhaustive annotation, based on a monocular video stream such as a TV broadcast, remains a tedious and costly manual task. State-of-the-art methods for spatio-temporal action detection from a monocular video stream, often based on 3D convolutional neural networks, are close to reach levels of performances in mean Average Precision (mAP) compatibles with the automation of such task. Nevertheless, to meet their expectation of exhaustiveness in the context of data analytics, such methods must be applied in a regime of high recall – low precision, using low confidence score thresholds. This setting unavoidably leads to the detection of false positives that are the product of the well documented overconfidence behaviour of neural networks and, in this case, their limited access to contextual information and understanding of the game: their predictions are highly unstructured. Based on the assumption that professional soccer players’ behaviour, pose, positions and velocity are highly interrelated and locally driven by the player performing a ball-action, it is hypothesized that the addition of information regarding surrounding player’s appearance, positions and velocity in the prediction methods can improve their metrics. Several methods are compared to build a proper representation of the game surrounding a player, from handcrafted features of the local graph, based on domain knowledge, to the use of Graph Neural Networks trained in an end-to-end fashion with existing state-of-the-art 3D convolutional neural networks. It is shown that the inclusion of information regarding surrounding players helps reaching higher metrics.

Keywords: fine-grained action recognition, human action recognition, convolutional neural networks, graph neural networks, spatio-temporal action recognition

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Authors: Juan C. Sanchez, Jose L. Endrino, Alejandro Toro, Hugo A. Estupinan, Glenn Leighton

Abstract:

For years, the reduction of friction and wear has been a matter of interest in the engineering field. Several solutions have been proposed to address this issue, including the use of lubricants and coatings to reduce the frictional forces and to increase the surface wear resistance. Alternatively, texturing processes have been used in a wide variety of materials, in many cases inspired in natural surfaces. Nature has shown how species adapt to the environment and the engineers try to understand natural surfaces for particular applications by analyzing outstanding species such as gecko for high adhesion, lotus leaves for hydrophobicity, sharks for reduced flow resistance and snakes for optimized frictional response. Texturized surfaces have shown a superior performance in terms of the frictional response in many situations, and the control of its behavior greatly depends on the manufacturing process. The focus of this work is to evaluate the tribological behavior of AISI 52100 steel samples texturized by Photochemical Machining (PCM). The surface texture was inspired by several features of the snakeskin such as aspect ratio of fibrils and mean fibril spacing. Two coatings were applied on the texturized surface, namely Diamond-like Carbon (DLC) and Molybdenum Disulphide (MoS₂), and their tribological behavior after pin-on-disk tests were compared with that of the non-texturized and uncovered surfaces. The samples were characterised through Stereoscopic Microscope (SM), Scanning Electron Microscope (SEM), Optical Microscope (OM), Profilometer, Raman Spectrometer (RS) and X-Ray Diffractometer (XRD). The Coefficient of Friction (COF) measured in pin-on-disk tests showed correlations with the sliding direction (relative to the texture features) and the aspect ratio of the texture features. Regarding the coated surfaces, the DLC and MoS₂ coating had a good performance in terms of wear rate and coefficient of friction compared with the uncoated and non-texturized surfaces. On the other hand, for the uncoated surfaces, the texture showed an influence in the tribological performance with respect to the non-texturized surface.

Keywords: coating, coefficient of friction, deterministic surface, photochemical machining

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