Search results for: dual band band-pass filter

Authors: Suparna Roy, Anatharaman Perumal

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The industrial effluent with high amount of heavy metals is known to have adverse effects on the environment. For the removal of heavy metals from aqueous environment, different conventional treatment technologies had been applied gradually which are not economically beneficial and also produce huge quantity of toxic chemical sludge. So, bio-sorption of heavy metals by marine plant is an eco-friendly innovative and alternative technology for removal of these pollutants from aqueous environment. The aim of this study is to evaluate the capacity of heavy metals accumulation and removal by some selected marine macroalgae (seaweeds) from marine environment. Methods: Seaweeds Acanthophora spicifera (Vahl.) Boergesen, Codium tomentosum Stackhouse, Halimeda gracilis Harvey ex. J. Agardh, Gracilaria opuntia Durairatnam.nom. inval. Valoniopsis pachynema (Martens) Boergesen, Caulerpa racemosa var. macrophysa (Sonder ex Kutzing) W. R. Taylor and Hydroclathrus clathratus (C. Agardh) Howe were collected from Olaikuda (09°17.526'N-079°19.662'E), Rameshwaram, south east coast of India during post monsoon period (April’2016). Seaweeds were washed with sterilized and filtered in-situ seawater repeatedly to remove all the epiphytes and debris and clean seaweeds were kept for shade drying for one week. The dried seaweeds were grinded to powder, and one gm powder seaweeds were taken in a 250ml conical flask, and 8 ml of 10 % HNO3 (70 % pure) was added to each sample and kept in room temperature (28 ̊C) for 24 hours and then samples were heated in hotplate at 120 ̊C, boiled to evaporate up to dryness and 20 ml of Nitric acid: Percholoric acid in 4:1 were added to it and again heated to hotplate at 90 ̊C up to evaporate to dryness, then samples were kept in room temperature for few minutes to cool and 10ml 10 % HNO3 were added to it and kept for 24 hours in cool and dark place and filtered with Whatman (589/2) filter paper and the filtrates were collected in 250ml clean conical flask and diluted accurately to 25 ml volume with double deionised water and triplicate of each sample were analysed with Inductively-Coupled plasma analysis (ICP-OES) to analyse total eleven heavy metals (Ag, Cd, B, Cu, Mn, Co, Ni, Cr, Pb, Zn, and Al content of the specified species and data were statistically evaluated for standard deviation. Results: Acanthophora spicifera contains highest amount of Ag (0.1± 0.2 mg/mg) followed by Cu (0.16±0.01 mg/mg), Mn (1.86±0.02 mg/mg), B (3.59±0.2 mg/mg), Halimeda gracilis showed highest accumulation of Al (384.75±0.12mg/mg), Valoniopsis pachynema accumulates maximum amount of Co (0.12±0.01 mg/mg), Zn (0.64±0.02 mg/mg), Caulerpa racemosa var. macrophysa contains Zn (0.63±0.01), Cr (0.26±0.01 mg/mg ), Ni (0.21±0.05), Pb (0.16±0.03 ) and Cd ( 0.02±00 ). Hydroclathrus clathratus, Codium tomentosum and Gracilaria opuntia also contain adequate amount of heavy metals. Conclusions: The mentioned species of seaweeds are contributing important role for decreasing the heavy metals pollution in marine environment by bioaccumulation. So, we can utilise this species to remove excess amount of heavy metals from polluted area.

Keywords: heavy metals pollution, seaweeds, bioaccumulation, eco-friendly, phyco-remediation

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Authors: Sujosh Nandi, Proshanta Guha

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Synthetic plastics are preferred for food packaging due to high strength, stretch-ability, good water vapor and gas barrier properties, transparency and low cost. However, environmental pollution generated by these synthetic plastics is a major concern of modern human civilization. Therefore, use of biodegradable polymers as a substitute for synthetic non-biodegradable polymers are encouraged to be used even after considering drawbacks related to mechanical and barrier properties of the films. Starch is considered one of the potential raw material for the biodegradable polymer, encounters poor water barrier property and mechanical properties due to its hydrophilic nature. That apart, recrystallization of starch molecules occurs during aging which decreases flexibility and increases elastic modulus of the film. The recrystallization process can be minimized by blending of other hydrocolloids having similar structural compatibility, into the starch matrix. Therefore, incorporation of guar gum having a similar structural backbone, into the starch matrix can introduce a potential film into the realm of biodegradable polymer. However, hydrophilic nature of both starch and guar gum, water barrier property of the film is low. One of the prospective solution to enhance this could be modification of the potato starch/guar gum (PSGG) composite film using cross-linker. Over the years, several cross-linking agents such as phosphorus oxychloride, sodium trimetaphosphate, etc. have been used to improve water vapor permeability (WVP) of the films. However, these chemical cross-linking agents are toxic, expensive and take longer time to degrade. Therefore, naturally available carboxylic acid (tartaric acid, malonic acid, succinic acid, etc.) had been used as a cross-linker and found that water barrier property enhanced substantially. As per our knowledge, no works have been reported with tartaric acid and succinic acid as a cross-linking agent blended with the PSGG films. Therefore, the objective of the present study was to examine the changes in water vapor barrier property and mechanical properties of the PSGG films after cross-linked with tartaric acid (TA) and succinic acid (SA). The cross-linkers were blended with PSGG film-forming solution at four different concentrations (4, 8, 12 & 16%) and cast on teflon plate at 37°C for 20 h. From the fourier-transform infrared spectroscopy (FTIR) study of the developed films, a band at 1720cm-1 was observed which is attributed to the formation of ester group in the developed films. On the other hand, it was observed that tensile strength (TS) of the cross-linked film decreased compared to non-cross linked films, whereas strain at break increased by several folds. Moreover, the results depicted that tensile strength diminished with increasing the concentration of TA or SA and lowest TS (1.62 MPa) was observed for 16% SA. That apart, maximum strain at break was also observed for TA at 16% and the reason behind this could be a lesser degree of crystallinity of the TA cross-linked films compared to SA. However, water vapor permeability of succinic acid cross-linked film was reduced significantly, but it was enhanced significantly by addition of tartaric acid.

Keywords: cross linking agent, guar gum, organic acids, potato starch

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Authors: Gianmarco Taveri, Ivo Dlouhy

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Geopolymers are well-suited materials to abate CO2 emission coming from the Portland cement production, and then replace them, in the near future, in building and other applications. The cost of production of geopolymers may be seen the only weakness, but the use of wastes as raw materials could provide a valid solution to this problem, as demonstrated by the successful incorporation of fly-ash, a by-product of thermal power plants, and waste glasses. Recycled glass in waste-derived geopolymers was lately employed as a further silica source. In this work we present, for the first time, the introduction of recycled borosilicate glass (BSG). BSG is actually a waste glass, since it derives from dismantled pharmaceutical vials and cannot be reused in the manufacturing of the original articles. Owing to the specific chemical composition (BSG is an ‘alumino-boro-silicate’), it was conceived to provide the key components of zeolitic networks, such as amorphous silica and alumina, as well as boria (B2O3), which may replace Al2O3 and contribute to the polycondensation process. The solid–state MAS NMR spectroscopy was used to assess the extent of boron oxide incorporation in the structure of geopolymers, and to define the degree of networking. FTIR spectroscopy was utilized to define the degree of polymerization and to detect boron bond vibration into the structure. Mechanical performance was tested by means of 3 point bending (flexural strength), chevron notch test (fracture toughness), compression test (compressive strength), micro-indentation test (Vicker’s hardness). Spectroscopy (SEM and Confocal spectroscopy) was performed on the specimens conducted to failure. FTIR showed a characteristic absorption band attributed to the stretching modes of tetrahedral boron ions, whose tetrahedral configuration is compatible to the reaction product of geopolymerization. 27Al NMR and 29Si NMR spectra were instrumental in understanding the extent of the reaction. 11B NMR spectroscopies evidenced a change of the trigonal boron (BO3) inside the BSG in favor of a quasi-total tetrahedral boron configuration (BO4). Thanks to these results, it was inferred that boron is part of the geopolymeric structure, replacing the Si in the network, similarly to the aluminum, and therefore improving the quality of the microstructure, in favor of a more cross-linked network. As expected, the material gained as much as 25% in compressive strength (45 MPa) compared to the literature, whereas no improvements were detected in flexural strength (~ 5 MPa) and superficial hardness (~ 78 HV). The material also exhibited a low fracture toughness (0.35 MPa*m1/2), with a tangible brittleness. SEM micrographies corroborated this behavior, showing a ragged surface, along with several cracks, due to the high presence of porosity and impurities, acting as preferential points for crack initiation. The 3D pattern of the surface fracture, following the confocal spectroscopy, evidenced an irregular crack propagation, whose proclivity was mainly, but not always, to follow the porosity. Hence, the crack initiation and propagation are largely unpredictable.

Keywords: borosilicate glass, characterization, fly-ash, geopolymerization

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Authors: Satya Narayan

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India, a rapidly growing economy with a burgeoning population, grapples with the dual challenge of meeting rising energy demands and reducing its carbon footprint. Geothermal energy, an often overlooked and underutilized renewable source, holds immense potential for addressing this challenge. Geothermal resources offer a valuable, consistent, and sustainable energy source, and may significantly contribute to India's energy. This paper discusses the importance of geothermal exploration in India, emphasizing its role in achieving sustainable energy production while mitigating environmental impacts. It also delves into the methodology employed to assess geothermal resource feasibility, including geophysical surveys and borehole drilling. The results and discussion sections highlight promising geothermal sites across India, illuminating the nation's vast geothermal potential. It detects potential geothermal reservoirs, characterizes subsurface structures, maps temperature gradients, monitors fluid flow, and estimates key reservoir parameters. Globally, geothermal energy falls into high and low enthalpy categories, with India mainly having low enthalpy resources, especially in hot springs. The northwestern Himalayan region boasts high-temperature geothermal resources due to geological factors. Promising sites, like Puga Valley, Chhumthang, and others, feature hot springs suitable for various applications. The Son-Narmada-Tapti lineament intersects regions rich in geological history, contributing to geothermal resources. Southern India, including the Godavari Valley, has thermal springs suitable for power generation. The Andaman-Nicobar region, linked to subduction and volcanic activity, holds high-temperature geothermal potential. Geophysical surveys, utilizing gravity, magnetic, seismic, magnetotelluric, and electrical resistivity techniques, offer vital information on subsurface conditions essential for detecting, evaluating, and exploiting geothermal resources. The gravity and magnetic methods map the depth of the mantle boundary (high-temperature) and later accurately determine the Curie depth. Electrical methods indicate the presence of subsurface fluids. Seismic surveys create detailed sub-surface images, revealing faults and fractures and establishing possible connections to aquifers. Borehole drilling is crucial for assessing geothermal parameters at different depths. Detailed geochemical analysis and geophysical surveys in Dholera, Gujarat, reveal untapped geothermal potential in India, aligning with renewable energy goals. In conclusion, geophysical surveys and borehole drilling play a pivotal role in economically viable geothermal site selection and feasibility assessments. With ongoing exploration and innovative technology, these surveys effectively minimize drilling risks, optimize borehole placement, aid in environmental impact evaluations, and facilitate remote resource exploration. Their cost-effectiveness informs decisions regarding geothermal resource location and extent, ultimately promoting sustainable energy and reducing India's reliance on conventional fossil fuels.

Keywords: geothermal resources, geophysical methods, exploration, exploitation

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Authors: Zanele D. Ngwenya, Mustapha Mohammed, Felix D. Dakora

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Legumes are a major source of biological nitrogen, and therefore play a crucial role in maintaining soil productivity in smallholder agriculture in southern Africa. Through their ability to fix atmospheric nitrogen in root nodules, legumes are a better option for sustainable nitrogen supply in cropping systems than chemical fertilisers. For decades, farmers have been highly receptive to the use of rhizobial inoculants as a source of nitrogen due mainly to the availability of elite rhizobial strains at a much lower compared to chemical fertilisers. To improve the efficiency of the legume-rhizobia symbiosis in African soils would require the use of highly effective rhizobia capable of nodulating a wide range of host plants. This study assessed the morphogenetic diversity, photosynthetic functioning and relative symbiotic effectiveness (RSE) of groundnut, jack bean and soybean microsymbionts in Eswatini soils as a first step to identifying superior isolates for inoculant production. According to the manufacturer's instructions, rhizobial isolates were cultured in yeast-mannitol (YM) broth until the late log phase and the bacterial genomic DNA was extracted using GenElute bacterial genomic DNA kit. The extracted DNA was subjected to enterobacterial repetitive intergenic consensus-PCR (ERIC-PCR) and a dendrogram constructed from the band patterns to assess rhizobial diversity. To assess the N2-fixing efficiency of the authenticated rhizobia, photosynthetic rates (A), stomatal conductance (gs), and transpiration rates (E) were measured at flowering for plants inoculated with the test isolates. The plants were then harvested for nodulation assessment and measurement of plant growth as shoot biomass. The results of ERIC-PCR fingerprinting revealed the presence of high genetic diversity among the microsymbionts nodulating each of the three test legumes, with many of them showing less than 70% ERIC-PCR relatedness. The dendrogram generated from ERIC-PCR profiles grouped the groundnut isolates into 5 major clusters, while the jack bean and soybean isolates were grouped into 6 and 7 major clusters, respectively. Furthermore, the isolates also elicited variable nodule number per plant, nodule dry matter, shoot biomass and photosynthetic rates in their respective host plants under glasshouse conditions. Of the groundnut isolates tested, 38% recorded high relative symbiotic effectiveness (RSE >80), while 55% of the jack bean isolates and 93% of the soybean isolates recorded high RSE (>80) compared to the commercial Bradyrhizobium strains. About 13%, 27% and 83% of the top N₂-fixing groundnut, jack bean and soybean isolates, respectively, elicited much higher relative symbiotic efficiency (RSE) than the commercial strain, suggesting their potential for use in inoculant production after field testing. There was a tendency for both low and high N₂-fixing isolates to group together in the dendrogram from ERIC-PCR profiles, which suggests that RSE can differ significantly among closely related microsymbionts.

Keywords: genetic diversity, relative symbiotic effectiveness, inoculant, N₂-fixing

Procedia PDF Downloads 220

Authors: D. Ficai, A. Ficai, D. Gudovan, I. A. Gudovan, I. Ardelean, R. Trusca, E. Andronescu, V. Mitran, A. Cimpean

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In the last years, scientists struggled hardly to mimic bone structures to develop implants and biostructures which present higher biocompatibility and reduced rejection rate. One way to obtain this goal is to use similar materials as that of bone, namely collagen/hydroxyapatite composite materials. However, it is very important to tailor both compositions but also the microstructure of the bone that would ensure both the optimal osteointegartion and the mechanical properties required by the application. In this study, new collagen/hydroxyapatites composite materials doped with Cu, Li, Mn, Zn were successfully prepared. The synthesis method is described below: weight the Ca(OH)₂ mass, i.e., 7,3067g, and ZnCl₂ (0.134g), CuSO₄ (0.159g), LiCO₃ (0.133g), MnCl₂.4H₂O (0.1971g), and suspend in 100ml distilled water under magnetic stirring. The solution thus obtained is added a solution of NaH₂PO₄*H2O (8.247g dissolved in 50ml distilled water) under slow dropping of 1 ml/min followed by adjusting the pH to 9.5 with HCl and finally filter and wash until neutral pH. The as-obtained slurry was dried in the oven at 80°C and then calcined at 600°C in order to ensure a proper purification of the final product of organic phases, also inducing a proper sterilization of the mixture before insertion into the collagen matrix. The collagen/hydroxyapatite composite materials are tailored from morphological point of view to optimize their biocompatibility and bio-integration against mechanical properties whereas the addition of the dopants is aimed to improve the biological activity of the samples. The addition of transitional metals can improve the biocompatibility and especially the osteoblasts adhesion (Mn²⁺) or to induce slightly better osteoblast differentiation of the osteoblast, Zn²⁺ being a cofactor for many enzymes including those responsible for cell differentiation. If the amount is too high, the final material can become toxic and lose all of its biocompatibility. In order to achieve a good biocompatibility and not reach the cytotoxic effect, the amount of transitional metals added has to be maintained at low levels (0.5% molar). The amount of transitional metals entering into the elemental cell of HA will be verified using inductively-coupled plasma mass spectrometric system. This highly sensitive technique is necessary, because, at such low levels of transitional metals, the difference between biocompatible and cytotoxic is a very thin line, thus requiring proper and thorough investigation using a precise technique. In order to determine the structure and morphology of the obtained composite materials, IR spectroscopy, X-Ray diffraction (XRD), scanning electron microscopy (SEM), and Energy Dispersive X-Ray Spectrometry (EDS) were used. Acknowledgment: The present work was possible due to the EU-funding grant POSCCE-A2O2.2.1-2013-1, Project No. 638/12.03.2014, code SMIS-CSNR 48652. The financial contribution received from the national project “Biomimetic porous structures obtained by 3D printing developed for bone tissue engineering (BIOGRAFTPRINT), No. 127PED/2017 is also highly acknowledged.

Keywords: collagen, composite materials, hydroxyapatite, bone tissue engineering

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Authors: Sutapa Mondal Roy, Suban K. Sahoo

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The primary aim of this work is to synthesis silver nanoparticles (AgNPs) through environmentally benign routes to avoid any chemical toxicity related undesired side effects. The nanoparticles were stabilized with drug ciprofloxacin (Cp) and were studied for their effectiveness as drug delivery agent. Targeted drug delivery improves the therapeutic potential of drugs at the diseased site as well as lowers the overall dose and undesired side effects. The small size of nanoparticles greatly facilitates the transport of active agents (drugs) across biological membranes and allows them to pass through the smallest capillaries in the body that are 5-6 μm in diameter, and can minimize possible undesired side effects. AgNPs are non-toxic, inert, stable, and has a high binding capacity and thus can be considered as biomaterials. AgNPs were synthesized from the nutrient broth supernatant after the culture of environment-friendly bacteria Bacillus subtilis. The AgNPs were found to show the surface plasmon resonance (SPR) band at 425 nm. The Cp capped Ag nanoparticles formation was complete within 30 minutes, which was confirmed from absorbance spectroscopy. Physico-chemical nature of the AgNPs-Cp system was confirmed by Dynamic Light Scattering (DLS), Transmission Electron Microscopy (TEM) etc. The AgNPs-Cp system size was found to be in the range of 30-40 nm. To monitor the kinetics of drug release from the surface of nanoparticles, the release of Cp was carried out by careful dialysis keeping AgNPs-Cp system inside the dialysis bag at pH 7.4 over time. The drug release was almost complete after 30 hrs. During the drug delivery process, to understand the AgNPs-Cp system in a better way, the sincere theoretical investigation is been performed employing Density Functional Theory. Electronic charge transfer, electron density, binding energy as well as thermodynamic properties like enthalpy, entropy, Gibbs free energy etc. has been predicted. The electronic and thermodynamic properties, governed by the AgNPs-Cp interactions, indicate that the formation of AgNPs-Cp system is exothermic i.e. thermodynamically favorable process. The binding energy and charge transfer analysis implies the optimum stability of the AgNPs-Cp system. Thus, the synthesized Cp-Ag nanoparticles can be effectively used for biological purposes due to its environmentally benign routes of synthesis procedures, which is clean, biocompatible, non-toxic, safe, cost-effective, sustainable and eco-friendly. The Cp-AgNPs as biomaterials can be successfully used for drug delivery procedures due to slow release of drug from nanoparticles over a considerable period of time. The kinetics of the drug release show that this drug-nanoparticle assembly can be effectively used as potential tools for therapeutic applications. The ease of synthetic procedure, lack of possible chemical toxicity and their biological activity along with excellent application as drug delivery agent will open up vista of using nanoparticles as effective and successful drug delivery agent to be used in modern days.

Keywords: silver nanoparticles, ciprofloxacin, density functional theory, drug delivery

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: S. Cichosz, A. Masek

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A new cellulose hybrid modification approach, which is undoubtedly a scientific novelty, is introduced. The study reports the properties of cellulose (Arbocel UFC100 – Ultra Fine Cellulose) and characterizes cellulose filled polymer composites based on an ethylene-norbornene copolymer (TOPAS Elastomer E-140). Moreover, the approach of physicochemical two-stage cellulose treatment is introduced: solvent exchange (to ethanol or hexane) and further chemical modification with maleic anhydride (MA). Furthermore, the impact of the drying process on cellulose properties was investigated. Suitable measurements were carried out to characterize cellulose fibers: spectroscopic investigation (Fourier Transform Infrared Spektrofotometer-FTIR, Near InfraRed spectroscopy-NIR), thermal analysis (Differential scanning calorimetry, Thermal gravimetric analysis ) and Karl Fischer titration. It should be emphasized that for all UFC100 treatments carried out, a decrease in moisture content was evidenced. FT-IR reveals a drop in absorption band intensity at 3334 cm-1, the peak is associated with both –OH moieties and water. Similar results were obtained with Karl Fischer titration. Based on the results obtained, it may be claimed that the employment of ethanol contributes greatly to the lowering of cellulose water absorption ability (decrease of moisture content to approximately 1.65%). Additionally, regarding polymer composite properties, crucial data has been obtained from the mechanical and thermal analysis. The highest material performance was noted in the case of the composite sample that contained cellulose modified with MA after a solvent exchange with ethanol. This specimen exhibited sufficient tensile strength, which is almost the same as that of the neat polymer matrix – in the region of 40 MPa. Moreover, both the Payne effect and filler efficiency factor, calculated based on dynamic mechanical analysis (DMA), reveal the possibility of the filler having a reinforcing nature. What is also interesting is that, according to the Payne effect results, fibers dried before the further chemical modification are assumed to allow more regular filler structure development in the polymer matrix (Payne effect maximum at 1.60 MPa), compared with those not dried (Payne effect in the range 0.84-1.26 MPa). Furthermore, taking into consideration the data gathered from DSC and TGA, higher thermal stability is obtained in case of the materials filled with fibers that were dried before the carried out treatments (degradation activation energy in the region of 195 kJ/mol) in comparison with the polymer composite samples filled with unmodified cellulose (degradation activation energy of approximately 180 kJ/mol). To author’s best knowledge this work results in the introduction of a novel, new filler hybrid treatment approach. Moreover, valuable data regarding the properties of composites filled with cellulose fibers of various moisture contents have been provided. It should be emphasized that plant fiber-based polymer bio-materials described in this research might contribute significantly to polymer waste minimization because they are more readily degraded.

Keywords: cellulose fibers, solvent exchange, moisture content, ethylene-norbornene copolymer

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Authors: Su Mon Saw, Joe Rothnagel

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Short open reading frames (sORFs) located in 5’UTR of mRNAs are known as uORFs. Characterization of uORF-encoded peptides (uPEPs) i.e., a subset of short open reading frame encoded peptides (sPEPs) and their translation regulation lead to understanding of causes of genetic disease, proteome complexity and development of treatments. Existence of uORFs within cellular proteome could be detected by LC-MS/MS. The ability of uORF to be translated into uPEP and achievement of uPEP identification will allow uPEP’s characterization, structures, functions, subcellular localization, evolutionary maintenance (conservation in human and other species) and abundance in cells. It is hypothesized that a subset of sORFs are translatable and that their encoded sPEPs are functional and are endogenously expressed contributing to the eukaryotic cellular proteome complexity. This project aimed to investigate whether sORFs encode functional peptides. Liquid chromatography-mass spectrometry (LC-MS) and bioinformatics were thus employed. Due to probable low abundance of sPEPs and small in sizes, the need for efficient peptide enrichment strategies for enriching small proteins and depleting the sub-proteome of large and abundant proteins is crucial for identifying sPEPs. Low molecular weight proteins were extracted using SDS-PAGE from Human Embryonic Kidney (HEK293) cells and Strong Cation Exchange Chromatography (SCX) from secreted HEK293 cells. Extracted proteins were digested by trypsin to peptides, which were detected by LC-MS/MS. The MS/MS data obtained was searched against Swiss-Prot using MASCOT version 2.4 to filter out known proteins, and all unmatched spectra were re-searched against human RefSeq database. ProteinPilot v5.0.1 was used to identify sPEPs by searching against human RefSeq, Vanderperre and Human Alternative Open Reading Frame (HaltORF) databases. Potential sPEPs were analyzed by bioinformatics. Since SDS PAGE electrophoresis could not separate proteins <20kDa, this could not identify sPEPs. All MASCOT-identified peptide fragments were parts of main open reading frame (mORF) by ORF Finder search and blastp search. No sPEP was detected and existence of sPEPs could not be identified in this study. 13 translated sORFs in HEK293 cells by mass spectrometry in previous studies were characterized by bioinformatics. Identified sPEPs from previous studies were <100 amino acids and <15 kDa. Bioinformatics results showed that sORFs are translated to sPEPs and contribute to proteome complexity. uPEP translated from uORF of SLC35A4 was strongly conserved in human and mouse while uPEP translated from uORF of MKKS was strongly conserved in human and Rhesus monkey. Cross-species conserved uORFs in association with protein translation strongly suggest evolutionary maintenance of coding sequence and indicate probable functional expression of peptides encoded within these uORFs. Translation of sORFs was confirmed by mass spectrometry and sPEPs were characterized with bioinformatics.

Keywords: bioinformatics, HEK293 cells, liquid chromatography-mass spectrometry, ProteinPilot, Strong Cation Exchange Chromatography, SDS-PAGE, sPEPs

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Authors: Jiahui You, Kyung Jae Lee

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Hydraulic fracturing fluid (HFF) is widely used in shale reservoir productions. HFF contains diverse chemical additives, which result in the dissolution and precipitation of minerals through multiple chemical reactions. In this study, a new pore-scale Darcy–Brinkman–Stokes (DBS) model coupled with Level Set Method (LSM) is developed to address the microscopic phenomena occurring during the iron–HFF interaction, by numerically describing mass transport, chemical reactions, and pore structure evolution. The new model is developed based on OpenFOAM, which is an open-source platform for computational fluid dynamics. Here, the DBS momentum equation is used to solve for velocity by accounting for the fluid-solid mass transfer; an advection-diffusion equation is used to compute the distribution of injected HFF and iron. The reaction–induced pore evolution is captured by applying the LSM, where the solid-liquid interface is updated by solving the level set distance function and reinitialized to a signed distance function. Then, a smoothened Heaviside function gives a smoothed solid-liquid interface over a narrow band with a fixed thickness. The stated equations are discretized by the finite volume method, while the re-initialized equation is discretized by the central difference method. Gauss linear upwind scheme is used to solve the level set distance function, and the Pressure–Implicit with Splitting of Operators (PISO) method is used to solve the momentum equation. The numerical result is compared with 1–D analytical solution of fluid-solid interface for reaction-diffusion problems. Sensitivity analysis is conducted with various Damkohler number (DaII) and Peclet number (Pe). We categorize the Fe (III) precipitation into three patterns as a function of DaII and Pe: symmetrical smoothed growth, unsymmetrical growth, and dendritic growth. Pe and DaII significantly affect the location of precipitation, which is critical in determining the injection parameters of hydraulic fracturing. When DaII<1, the precipitation uniformly occurs on the solid surface both in upstream and downstream directions. When DaII>1, the precipitation mainly occurs on the solid surface in an upstream direction. When Pe>1, Fe (II) transported deeply into and precipitated inside the pores. When Pe<1, the precipitation of Fe (III) occurs mainly on the solid surface in an upstream direction, and they are easily precipitated inside the small pore structures. The porosity–permeability relationship is subsequently presented. This pore-scale model allows high confidence in the description of Fe (II) dissolution, transport, and Fe (III) precipitation. The model shows fast convergence and requires a low computational load. The results can provide reliable guidance for injecting HFF in shale reservoirs to avoid clogging and wellbore pollution. Understanding Fe (III) precipitation, and Fe (II) release and transport behaviors give rise to a highly efficient hydraulic fracture project.

Keywords: reactive-transport , Shale, Kerogen, precipitation

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Authors: Ife Elegbeleye, Nnditshedzeni Eric, Regina Maphanga, Femi Elegbeleye, Femi Agunbiade

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The global potential of other renewable energy sources such as wind, hydroelectric, bio-mass, and geothermal is estimated to be approximately 13 %, with hydroelectricity constituting a larger percentage. Sunlight provides by far the largest of all carbon-neutral energy sources. More energy from the sunlight strikes the Earth in one hour (4.3 × 1020 J) than all the energy consumed on the planet in a year (4.1 × 1020 J), hence, solar energy remains the most abundant clean, renewable energy resources for mankind. Photovoltaic (PV) devices such as silicon solar cells, dye sensitized solar cells are utilized for harnessing solar energy. Polyene-diphenylaniline organic molecules are important sets of molecules that has stirred many research interest as photosensitizers in TiO₂ semiconductor-based dye sensitized solar cells (DSSCs). The advantages of organic dye molecule over metal-based complexes are higher extinction coefficient, moderate cost, good environmental compatibility, and electrochemical properties. The polyene-diphenylaniline organic dyes with basic configuration of donor-π-acceptor are affordable, easy to synthesize and possess chemical structures that can easily be modified to optimize their photocatalytic and spectral properties. The enormous interest in polyene-diphenylaniline dyes as photosensitizers is due to their fascinating spectral properties which include visible light to near infra-red-light absorption. In this work, density functional theory approach via GPAW software, Avogadro and ASE were employed to study the effect of methoxy functionalized group on the spectral properties of polyene-diphenylaniline dyes and their photons absorbing characteristics in the visible region to near infrared region of the solar spectrum. Our results showed that the two-phenyl based complexes D5 and D7 exhibits maximum absorption peaks at 750 nm and 850 nm, while D9 and D11 with methoxy group shows maximum absorption peak at 800 nm and 900 nm respectively. The highest absorption wavelength is notable for D9 and D11 containing additional polyene and methoxy groups. Also, D9 and D11 chromophores with the methoxy group shows lower energy gap of 0.98 and 0.85 respectively than the corresponding D5 and D7 dyes complexes with energy gap of 1.32 and 1.08. The analysis of their electron injection kinetics ∆Ginject into the band gap of TiO₂ shows that D9 and D11 with the methoxy group has higher electron injection kinetics of -2.070 and -2.030 than the corresponding polyene-diphenylaniline complexes without the addition of polyene group with ∆Ginject values of -2.820 and -2.130 respectively. Our findings suggest that the addition of functionalized group as an extension of the organic complexes results in higher light harvesting efficiencies and bathochromic shift of the absorption spectra to higher wavelength which suggest higher current densities and open circuit voltage in DSSCs. The study suggests that the photocatalytic properties of organic chromophores/complexes with donor-π-acceptor configuration can be enhanced by the addition of functionalized groups.

Keywords: renewable energy resource, solar energy, dye sensitized solar cells, polyene-diphenylaniline organic chromophores

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Authors: Carolina Busco, Daniela Osses

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The development of digital literacy has become a fundamental element that allows for citizen inclusion, access to quality jobs, and a labor market capable of responding to the digital economy. There are no methodological instruments available in Chile to measure the workforce’s digital literacy and improve national policies on this matter. Thus, the objective of this research is to develop a survey to measure digital literacy in a sample of 200 Chilean workers. Dimensions considered in the instrument are sociodemographics, access to infrastructure, digital education, digital skills, and the ability to use e-government services. To achieve the research objective of developing a digital literacy model of indicators and a research instrument for this purpose, along with an exploratory analysis of data using factor analysis, we used an empirical, quantitative-qualitative, exploratory, non-probabilistic, and cross-sectional research design. The research instrument is a survey created to measure variables that make up the conceptual map prepared from the bibliographic review. Before applying the survey, a pilot test was implemented, resulting in several adjustments to the phrasing of some items. A validation test was also applied using six experts, including their observations on the final instrument. The survey contained 49 items that were further divided into three sets of questions: sociodemographic data; a Likert scale of four values ranked according to the level of agreement; iii) multiple choice questions complementing the dimensions. Data collection occurred between January and March 2022. For the factor analysis, we used the answers to 12 items with the Likert scale. KMO showed a value of 0.626, indicating a medium level of correlation, whereas Bartlett’s test yielded a significance value of less than 0.05 and a Cronbach’s Alpha of 0.618. Taking all factor selection criteria into account, we decided to include and analyze four factors that together explain 53.48% of the accumulated variance. We identified the following factors: i) access to infrastructure and opportunities to develop digital skills at the workplace or educational establishment (15.57%), ii) ability to solve everyday problems using digital tools (14.89%), iii) online tools used to stay connected with others (11.94%), and iv) residential Internet access and speed (11%). Quantitative results were discussed within six focus groups using heterogenic selection criteria related to the most relevant variables identified in the statistical analysis: upper-class school students; middle-class university students; Ph.D. professors; low-income working women, elderly individuals, and a group of rural workers. The digital divide and its social and economic correlations are evident in the results of this research. In Chile, the items that explain the acquisition of digital tools focus on access to infrastructure, which ultimately puts the first filter on the development of digital skills. Therefore, as expressed in the literature review, the advance of these skills is radically different when sociodemographic variables are considered. This increases socioeconomic distances and exclusion criteria, putting those who do not have these skills at a disadvantage and forcing them to seek the assistance of others.

Keywords: digital literacy, digital society, workforce digitalization, digital skills

Procedia PDF Downloads 66

Authors: Nadeen Felemban, Hamsa Banjer, Rabaah Jaafari

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One of the most common hazards in the pharmaceutical industry is the chemical hazard, which can cause harm or develop occupational health diseases/illnesses due to chronic exposures to hazardous substances. Therefore, a chemical agent management system is required, including hazard identification, risk assessment, controls for specific hazards and inspections, to keep your workplace healthy and safe. However, routine management monitoring is also required to verify the effectiveness of the control measures. Moreover, Betamethasone Valerate and Clobetasol Propionate are some of the APIs (Active Pharmaceutical Ingredients) with highly hazardous classification-Occupational Hazard Category (OHC 4), which requires a full containment (ECA-D) during handling to avoid chemical exposure. According to Safety Data Sheet, those chemicals are reproductive toxicants (reprotoxicant H360D), which may affect female workers’ health and cause fatal damage to an unborn child, or impair fertility. In this study, qualitative (chemical Risk assessment-qCRA) was conducted to assess the chemical exposure during handling of Betamethasone Valerate and Clobetasol Propionate in pharmaceutical laboratories. The outcomes of qCRA identified that there is a risk of potential chemical exposure (risk rating 8 Amber risk). Therefore, immediate actions were taken to ensure interim controls (according to the Hierarchy of controls) are in place and in use to minimize the risk of chemical exposure. No open handlings should be done out of the Steroid Glove Box Isolator (SGB) with the required Personal Protective Equipment (PPEs). The PPEs include coverall, nitrile hand gloves, safety shoes and powered air-purifying respirators (PAPR). Furthermore, a quantitative assessment (personal air sampling) was conducted to verify the effectiveness of the engineering controls (SGB Isolator) and to confirm if there is chemical exposure, as indicated earlier by qCRA. Three personal air samples were collected using an air sampling pump and filter (IOM2 filters, 25mm glass fiber media). The collected samples were analyzed by HPLC in the BV lab, and the measured concentrations were reported in (ug/m3) with reference to Occupation Exposure Limits, 8hr OELs (8hr TWA) for each analytic. The analytical results are needed in 8hr TWA (8hr Time-weighted Average) to be analyzed using Bayesian statistics (IHDataAnalyst). The results of the Bayesian Likelihood Graph indicate (category 0), which means Exposures are de "minimus," trivial, or non-existent Employees have little to no exposure. Also, these results indicate that the 3 samplings are representative samplings with very low variations (SD=0.0014). In conclusion, the engineering controls were effective in protecting the operators from such exposure. However, routine chemical monitoring is required every 3 years unless there is a change in the processor type of chemicals. Also, frequent management monitoring (daily, weekly, and monthly) is required to ensure the control measures are in place and in use. Furthermore, a Similar Exposure Group (SEG) was identified in this activity and included in the annual health surveillance for health monitoring.

Keywords: occupational health and safety, risk assessment, chemical exposure, hierarchy of control, reproductive

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Authors: Nitika Sharma, Yogesh Jain

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Introduction: An efficient Information System helps in improving the service delivery as well provides the foundation for policy and regulation of other building blocks of Health System. Health care organizations require an integrated working of its various sub-systems. An efficient EMR software boosts the teamwork amongst the various sub-systems thereby resulting in improved service delivery. Although there has been a huge impetus to EMR under the Digital India initiative, it has still not been mandated in India. It is generally implemented in huge funded public or private healthcare organizations only. Objective: The study was conducted to understand the factors that lead to the successful adoption of an affordable EMR in the low level healthcare organization. It intended to understand the design of the EMR and address the solutions to the challenges faced in adoption of the EMR. Methodology: The study was conducted in a non-profit registered Healthcare organization that has been providing healthcare facilities to more than 2500 villages including certain areas that are difficult to access. The data was collected with help of field notes, in-depth interviews and participant observation. A total of 16 participants using the EMR from different departments were enrolled via purposive sampling technique. The participants included in the study were working in the organization before the implementation of the EMR system. The study was conducted in one month period from 25 June-20 July 2018. The Ethical approval was taken from the institute along with prior approval of the participants. Data analysis: A word document of more than 4000 words was obtained after transcribing and translating the answers of respondents. It was further analyzed by focused coding, a line by line review of the transcripts, underlining words, phrases or sentences that might suggest themes to do thematic narrative analysis. Results: Based on the answers the results were thematically grouped under four headings: 1. governance of organization, 2. architecture and design of the software, 3. features of the software, 4. challenges faced in adoption and the solutions to address them. It was inferred that the successful implementation was attributed to the easy and comprehensive design of the system which has facilitated not only easy data storage and retrieval but contributes in constructing a decision support system for the staff. Portability has lead to increased acceptance by physicians. The proper division of labor, increased efficiency of staff, incorporation of auto-correction features and facilitation of task shifting has lead to increased acceptance amongst the users of various departments. Geographical inhibitions, low computer literacy and high patient load were the major challenges faced during its implementation. Despite of dual efforts made both by the architects and administrators to combat these challenges, there are still certain ongoing challenges faced by organization. Conclusion: Whenever any new technology is adopted there are certain innovators, early adopters, late adopters and laggards. The same pattern was followed in adoption of this software. He challenges were overcome with joint efforts of organization administrators and users as well. Thereby this case study provides a framework of implementing similar systems in public sector of countries that are struggling for digitizing the healthcare in presence of crunch of human and financial resources.

Keywords: EMR, healthcare technology, e-health, EHR

Procedia PDF Downloads 105

Authors: Sudeshna Chandra, Christian Gäbler, Christian Schliebe, Heinrich Lang

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Highly branched dendrimers provide structural homogeneity, controlled composition, comparable size to biomolecules, internal porosity and multiple functional groups for conjugating reactions. Electro-active dendrimers containing multiple redox units have generated great interest in their use as electrode modifiers for development of biosensors. The electron transfer between the redox-active dendrimers and the biomolecules play a key role in developing a biosensor. Ferrocenes have multiple and electrochemically equivalent redox units that can act as electron “pool” in a system. The ferrocenyl-terminated polyamidoamine dendrimer is capable of transferring multiple numbers of electrons under the same applied potential. Therefore, they can be used for dual purposes: one in building a film over the electrode for immunosensors and the other for immobilizing biomolecules for sensing. Electrochemical immunosensor, thus developed, exhibit fast and sensitive analysis, inexpensive and involve no prior sample pre-treatment. Electrochemical amperometric immunosensors are even more promising because they can achieve a very low detection limit with high sensitivity. Detection of the cancer biomarkers at an early stage can provide crucial information for foundational research of life science, clinical diagnosis and prevention of disease. Elevated concentration of biomarkers in body fluid is an early indication of some type of cancerous disease and among all the biomarkers, IgG is the most common and extensively used clinical cancer biomarkers. We present an IgG (=immunoglobulin) electrochemical immunosensor using a newly synthesized redox-active ferrocenyl dendrimer of generation 2 (G2Fc) as glassy carbon electrode material for immobilizing the antibody. The electrochemical performance of the modified electrodes was assessed in both aqueous and non-aqueous media using varying scan rates to elucidate the reaction mechanism. The potential shift was found to be higher in an aqueous electrolyte due to presence of more H-bond which reduced the electrostatic attraction within the amido groups of the dendrimers. The cyclic voltammetric studies of the G2Fc-modified GCE in 0.1 M PBS solution of pH 7.2 showed a pair of well-defined redox peaks. The peak current decreased significantly with the immobilization of the anti-goat IgG. After the immunosensor is blocked with BSA, a further decrease in the peak current was observed due to the attachment of the protein BSA to the immunosensor. A significant decrease in the current signal of the BSA/anti-IgG/G2Fc/GCE was observed upon immobilizing IgG which may be due to the formation of immune-conjugates that blocks the tunneling of mass and electron transfer. The current signal was found to be directly related to the amount of IgG captured on the electrode surface. With increase in the concentration of IgG, there is a formation of an increasing amount of immune-conjugates that decreased the peak current. The incubation time and concentration of the antibody was optimized for better analytical performance of the immunosensor. The developed amperometric immunosensor is sensitive to IgG concentration as low as 2 ng/mL. Tailoring of redox-active dendrimers provides enhanced electroactivity to the system and enlarges the sensor surface for binding the antibodies. It may be assumed that both electron transfer and diffusion contribute to the signal transformation between the dendrimers and the antibody.

Keywords: ferrocenyl dendrimers, electrochemical immunosensors, immunoglobulin, amperometry

Procedia PDF Downloads 335

Authors: Nikola Toshikj, Michel Ramonda, Sylvain Catrouillet, Jean-Jacques Robin, Sebastien Blanquer

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Biodegradable and biocompatible block copolymers have risen as the golden materials in both medical and environmental applications. Moreover, if their architecture is of controlled manner, higher applications can be foreseen. In the meantime, organocatalytic ROP has been promoted as more rapid and immaculate route, compared to the traditional organometallic catalysis, towards efficient synthesis of block copolymer architectures. Therefore, herein we report novel organocatalytic pathway with guanidine molecules (TBD) for supported synthesis of trimethylene carbonate initiated by poly(caprolactone) as pre-polymer. Pristine PTMC-b-PCL-b-PTMC block copolymer structure, without any residual products and clear desired block proportions, was achieved under 1.5 hours at room temperature and verified by NMR spectroscopies and size-exclusion chromatography. Besides, when elaborating block copolymer films, further stability and amelioration of mechanical properties can be achieved via additional reticulation step of precedently methacrylated block copolymers. Subsequently, stimulated by the insufficient studies on the phase-separation/crystallinity relationship in these semi-crystalline block copolymer systems, their intrinsic thermal and morphology properties were investigated by differential scanning calorimetry and atomic force microscopy. Firstly, by DSC measurements, the block copolymers with χABN values superior to 20 presented two distinct glass transition temperatures, close to the ones of the respecting homopolymers, demonstrating an initial indication of a phase-separated system. In the interim, the existence of the crystalline phase was supported by the presence of melting temperature. As expected, the crystallinity driven phase-separated morphology predominated in the AFM analysis of the block copolymers. Neither crosslinking at melted state, hence creation of a dense polymer network, disturbed the crystallinity phenomena. However, the later revealed as sensible to rapid liquid nitrogen quenching directly from the melted state. Therefore, AFM analysis of liquid nitrogen quenched and crosslinked block copolymer films demonstrated a thermodynamically driven phase-separation clearly predominating over the originally crystalline one. These AFM films remained stable with their morphology unchanged even after 4 months at room temperature. However, as demonstrated by DSC analysis once rising the temperature above the melting temperature of the PCL block, neither the crosslinking nor the liquid nitrogen quenching shattered the semi-crystalline network, while the access to thermodynamical phase-separated structures was possible for temperatures under the poly (caprolactone) melting point. Precisely this coexistence of dual crosslinked/crystalline networks in the same copolymer structure allowed us to establish, for the first time, the shape-memory properties in such materials, as verified by thermomechanical analysis. Moreover, the response temperature to the material original shape depended on the block copolymer emplacement, hence PTMC or PCL as end-block. Therefore, it has been possible to reach a block copolymer with transition temperature around 40°C thus opening potential real-life medical applications. In conclusion, the initial study of phase-separation/crystallinity relationship in PTMC-b-PCL-b-PTMC block copolymers lead to the discovery of novel shape memory materials with superior properties, widely demanded in modern-life applications.

Keywords: biodegradable block copolymers, organocatalytic ROP, self-assembly, shape-memory

Procedia PDF Downloads 128

Authors: Mona Isazad Mashinchi, Davood Roshan Sangachin, Francis J. Sullivan, Dietrich Rebholz-Schuhmann

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Background: Decision-making processes are nowadays driven by data, data analytics and Business Intelligence (BI). BI as a software platform can provide a wide variety of capabilities such as organization memory, information integration, insight creation and presentation capabilities. Visualizing data through dashboards is one of the BI solutions (for a variety of areas) which helps managers in the decision making processes to expose the most informative information at a glance. In the healthcare domain to date, dashboard presentations are more frequently used to track performance related metrics and less frequently used to monitor those quality parameters which relate directly to patient outcomes. Providing effective and timely care for patients and improving the health outcome are highly dependent on presenting and visualizing data and information. Objective: In this research, the focus is on the presentation capabilities of BI to design a dashboard for prostate cancer (PC) data that allows better decision making for the patients, the hospital and the healthcare system related to a cancer dataset. The aim of this research is to customize a retrospective PC dataset in a dashboard interface to give a better understanding of data in the categories (risk factors, treatment approaches, disease control and side effects) which matter most to patients as well as other stakeholders. By presenting the outcome in the dashboard we address one of the major targets of a value-based health care (VBHC) delivery model which is measuring the value and presenting the outcome to different actors in HC industry (such as patients and doctors) for a better decision making. Method: For visualizing the stored data to users, three interactive dashboards based on the PC dataset have been developed (using the Tableau Software) to provide better views to the risk factors, treatment approaches, and side effects. Results: Many benefits derived from interactive graphs and tables in dashboards which helped to easily visualize and see the patients at risk, better understanding the relationship between patient's status after treatment and their initial status before treatment, or to choose better decision about treatments with fewer side effects regarding patient status and etc. Conclusions: Building a well-designed and informative dashboard is related to three important factors including; the users, goals and the data types. Dashboard's hierarchies, drilling, and graphical features can guide doctors to better navigate through information. The features of the interactive PC dashboard not only let doctors ask specific questions and filter the results based on the key performance indicators (KPI) such as: Gleason Grade, Patient's Age and Status, but may also help patients to better understand different treatment outcomes, such as side effects during the time, and have an active role in their treatment decisions. Currently, we are extending the results to the real-time interactive dashboard that users (either patients and doctors) can easily explore the data by choosing preferred attribute and data to make better near real-time decisions.

Keywords: business intelligence, dashboard, decision making, healthcare, prostate cancer, value-based healthcare

Procedia PDF Downloads 140

Authors: Kaustav Mukherjee

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In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

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Authors: Sunil Chandra, Himanshu Rawat, Vikas Gusain, Triparna Barman

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within reserved forests, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differenced normalized burnt ratio (dNBR) index approach that uses the burnt ratio values generated using the Short-Wave Infrared (SWIR) band and Near Infrared (NIR) bands of the Sentinel-2 image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel-2 bands. The training and testing data are generated from the Sentinel-2 data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated using spectral unmixing methods, which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: categorical data, log linear modeling, neural network, shifting cultivation

Procedia PDF Downloads 53

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 38

Authors: Kyoungrean Kim

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Importance of deep-sea mineral resources is dramatically increasing due to the depletion of land mineral resources corresponding to increasing human’s economic activities. Korea has acquired exclusive exploration licenses at four areas which are the Clarion-Clipperton Fracture Zone in the Pacific Ocean (2002), Tonga (2008), Fiji (2011) and Indian Ocean (2014). The preparation for commercial mining of Nautilus minerals (Canada) and Lockheed martin minerals (USA) is expected by 2020. The London Protocol 1996 (LP) under International Maritime Organization (IMO) and International Seabed Authority (ISA) will set environmental guidelines for deep-sea mining until 2020, to protect marine environment. In this research, the applicability of washing/extraction treatment for the remediation of deep-sea mining tailings was mainly evaluated in order to present preliminary data to develop practical remediation technology in near future. Polymetallic nodule samples were collected at the Clarion-Clipperton Fracture Zone in the Pacific Ocean, then stored at room temperature. Samples were pulverized by using jaw crusher and ball mill then, classified into 3 particle sizes (> 63 µm, 63-20 µm, < 20 µm) by using vibratory sieve shakers (Analysette 3 Pro, Fritsch, Germany) with 63 µm and 20 µm sieve. Only the particle size 63-20 µm was used as the samples for investigation considering the lower limit of ore dressing process which is tens to 100 µm. Rhamnolipid and sodium alginate as biosurfactant and aluminum sulfate which are mainly used as flocculant were used as environmentally friendly additives. Samples were adjusted to 2% liquid with deionized water then mixed with various concentrations of additives. The mixture was stirred with a magnetic bar during specific reaction times and then the liquid phase was separated by a centrifugal separator (Thermo Fisher Scientific, USA) under 4,000 rpm for 1 h. The separated liquid was filtered with a syringe and acrylic-based filter (0.45 µm). The extracted heavy metals in the filtered liquid were then determined using a UV-Vis spectrometer (DR-5000, Hach, USA) and a heat block (DBR 200, Hach, USA) followed by US EPA methods (8506, 8009, 10217 and 10220). Polymetallic nodule was mainly composed of manganese (27%), iron (8%), nickel (1.4%), cupper (1.3 %), cobalt (1.3%) and molybdenum (0.04%). Based on remediation standards of various countries, Nickel (Ni), Copper (Cu), Cadmium (Cd) and Zinc (Zn) were selected as primary target materials. Throughout this research, the use of rhamnolipid was shown to be an effective approach for removing heavy metals in samples originated from manganese nodules. Sodium alginate might also be one of the effective additives for the remediation of deep-sea mining tailings such as polymetallic nodules. Compare to the use of rhamnolipid and sodium alginate, aluminum sulfate was more effective additive at short reaction time within 4 h. Based on these results, sequencing particle separation, selective extraction/washing, advanced filtration of liquid phase, water treatment without dewatering and solidification/stabilization may be considered as candidate technologies for the remediation of deep-sea mining tailings.

Keywords: deep-sea mining tailings, heavy metals, remediation, extraction, additives

Procedia PDF Downloads 154

Authors: Manish Saha, Manish Kumar Niranjan, Saket Asthana

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The K₀.₅Na₀.₅NbO₃ (KNN) system has emerged as one of the most promising lead-free piezoelectric over the years. In this work, we perform a comprehensive investigation of electronic structure, lattice dynamics and dielectric/ferroelectric properties of the room temperature phase of KNN by combining ab-initio DFT-based theoretical analysis and experimental characterization. We assign the symmetry labels to KNN vibrational modes and obtain ab-initio polarized Raman spectra, Infrared (IR) reflectivity, Born-effective charge tensors, oscillator strengths etc. The computed Raman spectrum is found to agree well with the experimental spectrum. In particular, the results suggest that the mode in the range ~840-870 cm-¹ reported in the experimental studies is longitudinal optical (LO) with A_1 symmetry. The Raman mode intensities are calculated for different light polarization set-ups, which suggests the observation of different symmetry modes in different polarization set-ups. The electronic structure of KNN is investigated, and an optical absorption spectrum is obtained. Further, the performances of DFT semi-local, metal-GGA and hybrid exchange-correlations (XC) functionals, in the estimation of KNN band gaps are investigated. The KNN bandgap computed using GGA-1/2 and HSE06 hybrid functional schemes are found to be in excellant agreement with the experimental value. The COHP, electron localization function and Bader charge analysis is also performed to deduce the nature of chemical bonding in the KNN. The solid-state reaction and hydrothermal methods are used to prepare the KNN ceramics, and the effects of grain size on the physical characteristics these ceramics are examined. A comprehensive study on the impact of different synthesis techniques on the structural, electrical, and photocatalytic properties of ferroelectric ceramics KNN. The KNN-S prepared by solid-state method have significantly larger grain size as compared to that for KNN-H prepared by hydrothermal method. Furthermore, the KNN-S is found to exhibit higher dielectric, piezoelectric and ferroelectric properties as compared to KNN-H. On the other hand, the increased photocatalytic activity is observed in KNN-H as compared to KNN-S. As compared to the hydrothermal synthesis, the solid-state synthesis causes an increase in the relative dielectric permittivity (ε^') from 2394 to 3286, remnant polarization (P_r) from 15.38 to 20.41 μC/cm^², planer electromechanical coupling factor (k_p) from 0.19 to 0.28 and piezoelectric coefficient (d_33) from 88 to 125 pC/N. The KNN-S ceramics are also found to have a lower leakage current density, and higher grain resistance than KNN-H ceramic. The enhanced photocatalytic activity of KNN-H is attributed to relatively smaller particle sizes. The KNN-S and KNN-H samples are found to have degradation efficiencies of RhB solution of 20% and 65%, respectively. The experimental study highlights the importance of synthesis methods and how these can be exploited to tailor the dielectric, piezoelectric and photocatalytic properties of KNN. Overall, our study provides several bench-mark important results on KNN that have not been reported so far.

Keywords: lead-free piezoelectric, Raman intensity spectrum, electronic structure, first-principles calculations, solid state synthesis, photocatalysis, hydrothermal synthesis

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Authors: Walter W. Loo

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China declared the “zero discharge rules which leave no toxics into our living environment and deliver blue sky, green land and clean water to many generations to come”. The achievement of ZWD will provide conservation of water, soil and energy and provide drastic increase in Gross Domestic Products (GDP). Our society’s engine needs a major tune up; it is sputtering. ZWD is achieved in world’s space stations – no toxic air emission and the water is totally recycled and solid wastes all come back to earth. This is all done with solar power. These are all achieved under extreme temperature, pressure and zero gravity in space. ZWD can be achieved on earth under much less fluctuations in temperature, pressure and normal gravity environment. ZWD systems are not expensive and will have multiple beneficial returns on investment which are both financially and environmentally acceptable. The paper will include successful case histories since the mid-1970s. ZWD discharge can be applied to the following types of projects: nuclear and coal fire power plants with a closed loop system that will eliminate thermal water discharge; residential communities with wastewater treatment sump and recycle the water use as a secondary water supply; waste water treatment Plants with complete water recycling including water distillation to produce distilled water by very economical 24-hours solar power plant. Landfill remediation is based on neutralization of landfilled gas odor and preventing anaerobic leachate formation. It is an aerobic condition which will render landfill gas emission explosion proof. Desert development is the development of recovering soil moisture from soil and completing a closed loop water cycle by solar energy within and underneath an enclosed greenhouse. Salt-alkali land development can be achieved by solar distillation of salty shallow water into distilled water. The distilled water can be used for soil washing and irrigation and complete a closed loop water cycle with energy and water conservation. Heavy metals remediation can be achieved by precipitation of dissolved toxic metals below the plant or vegetation root zone by solar electricity without pumping and treating. Soil and groundwater remediation - abandoned refineries, chemical and pesticide factories can be remediated by in-situ electrobiochemical and bioventing treatment method without pumping or excavation. Toxic organic chemicals are oxidized into carbon dioxide and heavy metals precipitated below plant and vegetation root zone. New water sources: low temperature distilled water can be recycled for repeated use within a greenhouse environment by solar distillation; nano bubble water can be made from the distilled water with nano bubbles of oxygen, nitrogen and carbon dioxide from air (fertilizer water) and also eliminate the use of pesticides because the nano oxygen will break the insect growth chain in the larvae state. Three dimensional high yield greenhouses can be constructed by complete water recycling using the vadose zone soil as a filter with no farming wastewater discharge.

Keywords: greenhouses, no discharge, remediation of soil and water, wastewater

Procedia PDF Downloads 343

Authors: Kimberly Samaha

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In order to attract new types of investors into the emerging Bio-Economy, new methodologies to analyze investment risk are needed. The Bio-Hub Ecosystem model was developed to address a critical area of concern within the global energy market regarding the use of biomass as a feedstock for power plants. This study looked at repurposing existing biomass-energy plants into Circular Zero-Waste Bio-Hub Ecosystems. A Bio-Hub model that first targets a ‘whole-tree’ approach and then looks at the circular economics of co-hosting diverse industries (wood processing, aquaculture, agriculture) in the vicinity of the Biomass Power Plants facilities. This study modeled the economics and risk strategies of cradle-to-cradle linkages to incorporate the value-chain effects on capital/operational expenditures and investment risk reductions using a proprietary techno-economic model that incorporates investment risk scenarios utilizing the Monte Carlo methodology. The study calculated the sequential increases in profitability for each additional co-host on an operating forestry-based biomass energy plant in West Enfield, Maine. Phase I starts with the base-line of forestry biomass to electricity only and was built up in stages to include co-hosts of a greenhouse and a land-based shrimp farm. Phase I incorporates CO2 and heat waste streams from the operating power plant in an analysis of lowering and stabilizing the operating costs of the agriculture and aquaculture co-hosts. Phase II analysis incorporated a jet-fuel biorefinery and its secondary slip-stream of biochar which would be developed into two additional bio-products: 1) A soil amendment compost for agriculture and 2) A biochar effluent filter for the aquaculture. The second part of the study applied the Monte Carlo risk methodology to illustrate how co-location derisks investment in an integrated Bio-Hub versus individual investments in stand-alone projects of energy, agriculture or aquaculture. The analyzed scenarios compared reductions in both Capital and Operating Expenditures, which stabilizes profits and reduces the investment risk associated with projects in energy, agriculture, and aquaculture. The major findings of this techno-economic modeling using the Monte Carlo technique resulted in the masterplan for the first Bio-Hub to be built in West Enfield, Maine. In 2018, the site was designated as an economic opportunity zone as part of a Federal Program, which allows for Capital Gains tax benefits for investments on the site. Bioenergy facilities are currently at a critical juncture where they have an opportunity to be repurposed into efficient, profitable and socially responsible investments, or be idled and scrapped. The Bio-hub Ecosystems techno-economic analysis model is a critical model to expedite new standards for investments in circular zero-waste projects. Profitable projects will expedite adoption and advance the critical transition from the current ‘take-make-dispose’ paradigm inherent in the energy, forestry and food industries to a more sustainable Bio-Economy paradigm that supports local and rural communities.

Keywords: bio-economy, investment risk, circular design, economic modelling

Procedia PDF Downloads 100

Authors: Nalika S. Rajapaksha, James Airton, Amina Aboobakar, Nick Chappell, Andy Dyer

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Nutrient pollution and their impact on water quality is a key concern in England. Many water quality issues originate from multiple sources of pollution spread across the catchment. The river water quality in England has improved since 1990s and wastewater effluent discharges into rivers now contain less phosphorus than in the past. However, excess phosphorus is still recognised as the prevailing issue for rivers failing Water Framework Directive (WFD) good ecological status. To achieve WFD Phosphorus objectives, Wastewater Treatment Works (WwTW) permit limits are becoming increasingly stringent. Nevertheless, in some rural catchments, the apportionment of Phosphorus pollution can be greater from agricultural runoff and other sources such as septic tanks. Therefore, the challenge of meeting the requirements of watercourses to deliver WFD objectives often goes beyond water company activities, providing significant opportunities to co-deliver activities in wider catchments to reduce nutrient load at source. The aim of this study was to apply the United Utilities' Catchment Systems Thinking (CaST) strategy and pilot an innovative permitting approach - Catchment Nutrient Balancing (CNB) in a rural catchment in Cumbria (the River Petteril) in collaboration with the regulator and others to achieve WFD objectives and multiple benefits. The study area is mainly agricultural land, predominantly livestock farms. The local ecology is impacted by significant nutrient inputs which require intervention to meet WFD obligations. There are a range of Phosphorus inputs into the river, including discharges from wastewater assets but also significantly from agricultural contributions. Solely focusing on the WwTW discharges would not have resolved the problem hence in order to address this issue effectively, a CNB trial was initiated at a small WwTW, targeting the removal of a total of 150kg of Phosphorus load, of which 13kg were to be reduced through the use of catchment interventions. Various catchment interventions were implemented across selected farms in the upstream of the catchment and also an innovative polonite reactive filter media was implemented at the WwTW as an alternative to traditional Phosphorus treatment methods. During the 3 years of this trial, the impact of the interventions in the catchment and the treatment works were monitored. In 2020 and 2022, it respectively achieved a 69% and 63% reduction in the phosphorus level in the catchment against the initial reduction target of 9%. Phosphorus treatment at the WwTW had a significant impact on overall load reduction. The wider catchment impact, however, was seven times greater than the initial target when wider catchment interventions were also established. While it is unlikely that all the Phosphorus load reduction was delivered exclusively from the interventions implemented though this project, this trial evidenced the enhanced benefits that can be achieved with an integrated approach, that engages all sources of pollution within the catchment - rather than focusing on a one-size-fits-all solution. Primarily, the CNB approach and the act of collaboratively engaging others, particularly the agriculture sector is likely to yield improved farm and land management performance and better compliance, which can lead to improved river quality as well as wider benefits.

Keywords: agriculture, catchment nutrient balancing, phosphorus pollution, water quality, wastewater

Procedia PDF Downloads 64

Authors: Tomasz Balabanski, Anna Biedunkiewicz

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Research on basic bacteriological and physicochemical parameters conducted by state institutions (Provincial Sanitary and Epidemiological Station and District Sanitary and Epidemiological Station) are limited to bathing waters under constant sanitary and epidemiological supervision. Unfortunately, no routine or monitoring tests are carried out for the presence of microfungi. This also applies to beach sand used for recreational purposes. The purpose of the planned own research was to determine the diversity of the mycobiota present on supervised and unsupervised sandy beaches, on the shores of lakes, of municipal baths used for recreation. The research material consisted of microfungi isolated from April to October 2019 from sandy beaches of supervised and unsupervised lakes located within the administrative boundaries of the city of Olsztyn (North-Eastern Poland, Europe). Four lakes, out of the fifteen available (Tyrsko, Kortowskie, Skanda, and Ukiel), whose bathing waters are subjected to routine bacteriological tests, were selected for testing. To compare the diversity of the mycobiota composition on the surface and below the sand mixing layer, samples were taken from two depths (10 cm and 50 cm), using a soil auger. Micro-fungi from sand samples were obtained by surface inoculation on an RBC medium from the 1st dilution (1:10). After incubation at 25°C for 96-144 h, the average number of CFU/dm³ was counted. Morphologically differing yeast colonies were passaged into Sabouraud agar slants with gentamicin and incubated again. For detailed laboratory analyses, culture methods (macro- and micro-cultures) and identification methods recommended in diagnostic mycological laboratories were used. The conducted research allowed obtaining 140 yeast isolates. The total average population ranged from 1.37 × 10⁻² CFU/dm³ before the bathing season (April 2019), 1.64 × 10⁻³ CFU/dm³ in the season (May-September 2019), and 1.60 × 10⁻² CFU/dm³ after the end of the season (October 2019). More microfungi were obtained from the surface layer of sand (100 isolates) than from the deeper layer (40 isolates). Reported microfungi may circulate seasonally between individual elements of the lake ecosystem. From the sand/soil from the catchment area beaches, they can get into bathing waters, stopping periodically on the coastal phyllosphere. The sand of the beaches and the phyllosphere are a kind of filter for the water reservoir. The presence of microfungi with various pathogenicity potential in these places is of major epidemiological importance. Therefore, full monitoring of not only recreational waters but also sandy beaches should be treated as an element of constant control by appropriate supervisory institutions, allowing recreational areas for public use so that the use of these places does not involve the risk of infection. Acknowledgment: 'Development Program of the University of Warmia and Mazury in Olsztyn', POWR.03.05.00-00-Z310/17, co-financed by the European Union under the European Social Fund from the Operational Program Knowledge Education Development. Tomasz Bałabański is a recipient of a scholarship from the Programme Interdisciplinary Doctoral Studies in Biology and Biotechnology (POWR.03.05.00-00-Z310/17), which is funded by the 'European Social Fund'.

Keywords: beach, microfungi, sand, yeasts

Procedia PDF Downloads 102

Authors: Tibor Ajtai, Noémi Utry, Máté Pintér, Gábor Szabó, Zoltán Bozóki

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Beyond its climate and health related issues ambient light absorbing carbonaceous particulate matter (LAC) has also become a great scientific interest in terms of its regulations recently. It has been experimentally demonstrated in recent studies, that LAC is dominantly composed of traffic and wood burning aerosol particularly under wintertime urban conditions, when the photochemical and biological activities are negligible. Several methods have been introduced to quantitatively apportion aerosol fractions emitted by wood burning and traffic but most of them require costly and time consuming off-line chemical analysis. As opposed to chemical features, the microphysical properties of airborne particles such as optical absorption and size distribution can be easily measured on-line, with high accuracy and sensitivity, especially under highly polluted urban conditions. Recently a new method has been proposed for the apportionment of wood burning and traffic aerosols based on the spectral dependence of their absorption quantified by the Aerosol Angström Exponent (AAE). In this approach the absorption coefficient is deduced from transmission measurement on a filter accumulated aerosol sample and the conversion factor between the measured optical absorption and the corresponding mass concentration (the specific absorption cross section) are determined by on-site chemical analysis. The recently developed multi-wavelength photoacoustic instruments provide novel, in-situ approach towards the reliable and quantitative characterization of carbonaceous particulate matter. Therefore, it also opens up novel possibilities on the source apportionment through the measurement of light absorption. In this study, we demonstrate an in-situ spectral characterization method of the ambient carbon fraction based on light absorption and size distribution measurements using our state-of-the-art multi-wavelength photoacoustic instrument (4λ-PAS) and Single Mobility Particle Sizer (SMPS) The carbonaceous particulate selective source apportionment study was performed for ambient particulate matter in the city center of Szeged, Hungary where the dominance of traffic and wood burning aerosol has been experimentally demonstrated earlier. The proposed model is based on the parallel, in-situ measurement of optical absorption and size distribution. AAEff and AAEwb were deduced from the measured data using the defined correlation between the AOC(1064nm)/AOC(266nm) and N100/N20 ratios. σff(λ) and σwb(λ) were determined with the help of the independently measured temporal mass concentrations in the PM1 mode. Furthermore, the proposed optical source apportionment is based on the assumption that the light absorbing fraction of PM is exclusively related to traffic and wood burning. This assumption is indirectly confirmed here by the fact that the measured size distribution is composed of two unimodal size distributions identified to correspond to traffic and wood burning aerosols. The method offers the possibility of replacing laborious chemical analysis with simple in-situ measurement of aerosol size distribution data. The results by the proposed novel optical absorption based source apportionment method prove its applicability whenever measurements are performed at an urban site where traffic and wood burning are the dominant carbonaceous sources of emission.

Keywords: absorption, size distribution, source apportionment, wood burning, traffic aerosol

Procedia PDF Downloads 226

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: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

Procedia PDF Downloads 176