Search results for: deep oxidation

Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

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Authors: Neşe Taşci, Soner Çubuk, Ece Kök Yetimoğlu, M. Vezir Kahraman

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Bisphenol-A (BPA), 2,2-bis(4-hydroxyphenly)propane, is one of the highest usage volume chemicals in the world. Studies showed that BPA maybe has negative effects on the central nervous system, immune and endocrine systems. Several of analytical methods for the analysis of BPA have been reported including electrochemical processes, chemical oxidation, ozonization, spectrophotometric, chromatographic techniques. Compared with other conventional analytical techniques, optic sensors are reliable, providing quick results, low cost, easy to use, stands out as a much more advantageous method because of the high precision and sensitivity. In this work, a new photocured polymeric fluorescence sensor was prepared and characterized for Bisphenol-A (BPA) analysis. Characterization of the membrane was carried out by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Scanning Electron Microscope (SEM) techniques. The response characteristics of the sensor including dynamic range, pH effect and response time were systematically investigated. Acknowledgment: This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 115Y469.

Keywords: bisphenol-a, fluorescence, photopolymerization, polymeric sensor

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Authors: Fatima Al-Aswadi, Chan Yong

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With the large volume of unstructured data that increases day by day on the web, the motivation of representing the knowledge in this data in the machine processable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The goal of Ontology learning from text is to elicit and represent domain knowledge in the machine readable form. This paper aims to give a follow-up review on the ontology learning systems from text and some of their defects. Furthermore, it discusses how far the ontology learning process will enhance in the future.

Keywords: concept discovery, deep learning, ontology learning, semantic relation, semantic web

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Authors: Bayan Alqassem, Israa A. Othman, Mohammed Abu Haija, Fawzi Banat

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The treatment of wastewater from highly toxic pollutants is one of the most challenging issues for humanity. In this study, the advanced oxidation process (AOP) was employed to study the catalytic degradation of phenol using different ferrite catalysts which are CoFe₂O₄, CrFe₂O₄, CuFe₂O₄, MgFe₂O₄, MnFe₂O₄, NiFe₂O₄ and ZnFe₂O₄. The ferrite catalysts were prepared via sol-gel and co-precipitation methods. Different ferrite composites were also prepared either by varying the metal ratios or incorporating chemically reduced graphene oxide in the ferrite cluster. The effect of phosphoric acid treatment on the copper ferrite activity. All of the prepared catalysts were characterized using infrared spectroscopy (IR), X-ray diffraction (XRD) and scanning electron microscopy (SEM). The ferrites catalytic activities were tested towards phenol degradation using high performance liquid chromatography (HPLC). The experimental results showed that ferrites prepared through sol-gel route were more active than those of the co-precipitation method towards phenol degradation. In both cases, CuFe₂O₄ exhibited the highest degradation of phenol compared to the other ferrites. The photocatalytic properties of the ferrites were also investigated.

Keywords: ferrite catalyst, ferrite composites, phenol degradation, photocatalysis

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Authors: Raúl García de la Cruz

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In the state of Hidalgo and to the vicinity to the state of Mexico, there is a network of people who also share a valley bordered by hills with agave landscape of cacti and shared a bond of building traditions inherited from pre-Hispanic times and according to their material resources, habits and needs have been adapted in time. Weather has played an important role in the way buildings and roofs are constructed. Throughout the centuries, the population has developed very sophisticated building techniques like the flat roof, made out of a layer of earth; that is usually identified as belonging to architecture of the desert, but it can also be found in other climates, such as semi-arid and even template climates. It is an example of a constructive logic applied efficiently to various cultures proving its thermal isolation. So far it has done a review and analysis of the use of the roof in different areas, from pre-Hispanic architecture to traditional Moroccan architecture , finding great similarities in the elements of the system to be incorporated into the contemporary architecture. The rescue of a lore that dissolves with the changing environment, depends in principle on the links created towards the use of environmental resources as the anchor of the people to retain and preserve a building tradition which has viability deep league with the possibility of obtaining the raw material from the immediate environment. The objective of the research is the documentation of existing earth flat roofs in the state of Hidalgo and Mexico, as evidence of the importance of constructive system and its historical value in the area, considering its environmental, social aspects, also understanding the process of transformation of public housing at the time replaced the traditional techniques for industrial materials on a path towards urbanization. So far it has done a review and analysis of the use of the roof in different areas, from pre-Hispanic architecture to traditional Moroccan architecture, finding great similarities in the elements of the system to be incorporated into the contemporary architecture. The rescue of a lore that dissolves with the changing environment, depends in principle on the links created towards the use of environmental resources as the anchor of the people to retain and preserve a building tradition which has viability deep league with the possibility of obtaining the raw material from the immediate environment.

Keywords: earth roof, low impact building system, sustainable architecture, vernacular architecture

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Authors: Soma Ghosh, Balaram Mohapatra, Pinaki Sar, Abhijeet Mukherjee

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Microbial role in arsenic (As) mobilization in the groundwater aquifers of Brahmaputra river basin (BRB) in India, severely threatened by high concentrations of As, remains largely unknown. The present study, therefore, is a molecular and ecophysiological characterization of an indigenous bacterium strain IIIJ3-1 isolated from As contaminated groundwater of BRB and application of this strain in several microcosm set ups differing in their organic carbon (OC) source and terminal electron acceptors (TEA), to understand its role in As dissolution under aerobic and anaerobic conditions. Strain IIIJ3-1 was found to be a new facultative anaerobic, gram-positive, endospore-forming strain capable of arsenite (As3+) oxidation and dissimilatory arsenate (As5+) reduction. The bacterium exhibited low genomic (G+C)% content (45 mol%). Although, its 16S rRNA gene sequence revealed a maximum similarity of 99% with Bacillus cereus ATCC 14579(T) but the DNA-DNA relatedness of their genomic DNAs was only 49.9%, which remains well below the value recommended to delimit different species. Abundance of fatty acids iC17:0, iC15:0 and menaquinone (MK) 7 though corroborates its taxonomic affiliation with B. cereus sensu-lato group, presence of hydroxy fatty acids (HFAs), C18:2, MK5 and MK6 marked its uniqueness. Besides being highly As resistant (MTC=10mM As3+, 350mM As5+), metabolically diverse, efficient aerobic As3+ oxidizer; it exhibited near complete dissimilatory reduction of As5+ (1 mM). Utilization of various carbon sources with As5+ as TEA revealed lactate to serve as the best electron donor. Aerobic biotransformation assay yielded a lower Km for As3+ oxidation than As5+ reduction. Arsenic homeostasis was found to be conferred by the presence of arr, arsB, aioB, and acr3(1) genes. Scanning electron microscopy (SEM) coupled with energy dispersive X-ray (EDX) analysis of this bacterium revealed reduction in cell size upon exposure to As and formation of As-rich electron opaque dots following growth with As3+. Incubation of this strain with sediment (sterilised) collected from BRB aquifers under varying OC, TEA and redox conditions revealed that the strain caused highest As mobilization from solid to aqueous phase under anaerobic condition with lactate and nitrate as electron donor and acceptor, respectively. Co-release of highest concentrations of oxalic acid, a well known bioweathering agent, considerable fold increase in viable cell counts and SEM-EDX and X-ray diffraction analysis of the sediment after incubation under this condition indicated that As release is consequent to microbial bioweathering of the minerals. Co-release of other elements statistically proves decoupled release of As with Fe and Zn. Principle component analysis also revealed prominent role of nitrate under aerobic and/or anaerobic condition in As release by strain IIIJ3-1. This study, therefore, is the first to isolate, characterize and reveal As mobilization property of a strain belonging to the Bacillus cereus sensu lato group isolated from highly As contaminated aquifers of Brahmaputra River Basin.

Keywords: anaerobic microcosm, arsenic rich electron opaque dots, Arsenic release, Bacillus strain IIIJ3-1

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Authors: Pantip Kayee

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17α-ethinylestradiol (EE2) is a recalcitrant micropollutant which is found in small amounts in municipal wastewater. But these small amounts still adversely affect for the reproductive function of aquatic organisms. Evidence in the past suggested that full-scale WWTPs equipped with nitrification process enhanced the removal of EE2 in the municipal wastewater. EE2 has been proven to be able to be transformed by ammonia oxidizing bacteria (AOB) via co-metabolism. This research aims to clarify the EE2 degradation pattern by different consortium of ammonia oxidizing microorganism (AOM) including AOA (ammonia oxidizing archaea) and investigate contribution between the existing ammonia monooxygenase (AMO) and new synthesized AOM. The result showed that AOA or AOB of N. oligotropha cluster in enriched nitrifying activated sludge (NAS) from 2mM and 5mM, commonly found in municipal WWTPs, could degrade EE2 in wastewater via co-metabolism. Moreover, the investigation of the contribution between the existing ammonia monooxygenase (AMO) and new synthesized AOM demonstrated that the new synthesized AMO enzyme may perform ammonia oxidation rather than the existing AMO enzyme or the existing AMO enzyme may has a small amount to oxidize ammonia.

Keywords: 17α-ethinylestradiol, nitrification, ammonia oxidizing bacteria, ammonia oxidizing archaea

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Authors: L. Chetibi, D. Hamana, T. O. Busko, M. P. Kulish, S. Achour

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TiO2 nanostructures have attracted much attention due to their optical, dielectric and photocatalytic properties as well as applications including optical coating, photocatalysis and photoelectrochemical solar cells. This work aims to prepare TiO2 nanofibers (NFs) on titanium substrate (Ti) by in situ oxidation of Ti foils in a mixture solution of concentrated H2O2 and NaOH followed by proton exchange and calcinations. Scanning Electron microscopy (SEM) revealed an obvious network of TiO2 nanofibers. The photoluminescence (PL) spectra of these nanostructures revealed a broad intense band in the visible light range with a reduced near edge band emission. The PL bands in the visible region, mainly, results from surface oxygen vacancies and others defects. After irradiation with Ti+ ions (the irradiation energy was E = 140 keV with doses of 1013 ions/cm2), the intensity of the PL spectrum decreased as a consequence of the radiation treatment. The irradiation with Ti+ leads to a reduction of defects and generation of non irradiative defects near to the level of the conduction band as evidenced by the PL results. On the other hand, reducing the surface defects on TiO2 nanostructures may improve photocatalytic and optoelectronic properties of this nanostructure.

Keywords: TiO2, nanofibers, photoluminescence, irradiation

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Authors: Kumaresh Selvakumar, Man Young Kim

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Catalytic combustor substantially reduces emission entailing fuel-air premixing at very low equivalence ratios. The catalytic combustion of natural gas has the potential to become sufficiently active at light off temperature by the convection of heat from the catalyst surface. Only one channel is selected to investigate both the gas and surface reactions in the catalyst bed because of the honeycomb structure of the catalytic combustor. The objective of the present study is to find the methane catalytic combustion behavior inside the catalytic combustor, where the gas phase kinetics is employed by homogeneous methane combustion and surface chemistry is described with the heterogeneous catalysis of the oxidation of methane on a platinum catalyst. The reaction of the premixed mixture in the catalytic regime improves flame stability with complete combustion for lower operating flame temperature. An overview of the flow behavior is presented inside the single channel catalytic combustor including the operation of catalytic combustion with various F/A ratios and premixed inlet temperature.

Keywords: catalytic combustor, equivalence ratios, flame temperature, heterogeneous catalysis, homogeneous combustion

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Authors: Eun Gyeong Lee, Ji Young Park, Hyun Ae Woo

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Reactive oxygen species (ROS) production is involved in ischemia/reperfusion (I/R) injury in kidney of mice. Oxidative stress develops from an imbalance between ROS production and reduced antioxidant defenses. Many enzymatic and nonenzymatic antioxidant systems including peroxiredoxins (Prxs) are present in kidney to maintain an appropriate level of ROS and prevent oxidative damage. Prxs are a family of peroxidases that reduce peroxides, with a conserved cysteine residue serving as the site of oxidation by peroxides. In this study, we examined the protective role of Prx V against I/R-induced acute kidney injury (AKI) using Prx V wild type (WT) and knockout (KO) mice. We compared the response of Prx V WT and KO mice in mice model of I/R injury. Renal structure, functions, oxidative stress markers, protein levels of oxidative damage marker were worse in Prx V KO mice. Ablation of Prx V enhanced susceptibility to I/R-induced oxidative stress. Prx V KO mice were seen to have more severe renal damage than Prx V WT mice in mice model of I/R injury. Our results demonstrate that Prx V is protective against I/R-induced AKI.

Keywords: peroxiredoxin, ischemia/reperfusion, kidney, oxidative stress

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Authors: Athena Johnson

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Veganism has seen a rapid increase in members over recent years. Understanding the mechanisms of social change associated with these dietary practices in relation to gender is significant as these groups may seem small, but they have a large impact as they influence many and change the food market. This research article's basic methodology is primarily a deep article research literature review with empirical research. The research findings show that the popularity of veganism is growing, in large part due to the extensive use of social media, which dispels longstanding gendered connotations with food, such as the correlations between meat and masculinity.

Keywords: diversity, gender roles, social media, veganism

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Authors: Dong Nyoung Heo, Il Keun Kwon

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Three-dimensional (3D) technology is a promising method for bone tissue engineering. In order to enhance bone tissue regeneration, it is important to have ideal 3D constructs with biomimetic mechanical strength, structure interconnectivity, roughened surface, and the presence of chemical functionality. In this respect, a 3D printing system combined with cold atmospheric plasma (CAP) was developed to fabricate a 3D construct that has a rough surface with polar functional chemical groups. The CAP-etching process leads to oxidation of chemical groups existing on the polycaprolactone (PCL) surface without conformational change. The surface morphology, chemical composition, mean roughness of the CAP-treated PCL surfaces were evaluated. 3D printed constructs composed of CAP-treated PCL showed an effective increment in the hydrophilicity and roughness of the PCL surface. Also, an in vitro study revealed that CAP-treated 3D PCL constructs had higher cellular behaviors such as cell adhesion, cell proliferation, and osteogenic differentiation. Therefore, a 3D printing system with CAP can be a highly useful fabrication method for bone tissue regeneration.

Keywords: bone tissue engineering, cold atmospheric plasma, PCL, 3D printing

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Authors: Juliana V. Pereira, Adriana S. M. Batista, Jefferson P. Nascimento, Clascídia A. Furtado, Luiz O. Faria

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The combination of the properties of graphene oxide (OG) and PVDF homopolymer makes their combined composite materials as multifunctional systems with great potential. Knowledge of the molecular structure is essential for better use. In this work, the degradation of PVDF polymer exposed to gamma irradiation in oxygen atmosphere in high dose rate has been studied and compared to degradation of PVDF/OG composites. The samples were irradiated with a Co-60 source at constant dose rate, with doses ranging from 100 kGy to 1,000 kGy. In FTIR data shown that the formation of oxidation products was at the both samples with formation of carbonyl and hydroxyl groups amongst the most prevalent products in the pure PVDF samples. In the other hand, the composites samples exhibit less presence of degradation products with predominant formation of carbonyl groups, these results also seen in the UV-Vis analysis. The results show that the samples of composites may have greater resistance to the irradiation process, since they have less degradation products than pure PVDF samples seen by spectroscopic techniques.

Keywords: gamma irradiation, PVDF, PVDF/OG composites, spectroscopic techniques

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Authors: Mohamed Deyab

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The global lubricant additives market size was USD 14.35 billion in 2015. The industry is characterized by increasing additive usage in base oil blending for longer service life and performance. These additives improve the viscosity of oil, act as detergents, defoamers, antioxidants, and antiwear agents. Since additives play a significant role in base oil blending and subsequent formulations as they are critical materials in improving specification and performance of oils. Herein, we report on the synthesis and characterization of three imidazolium derivatives and their application as antioxidants, detergents and antiwear agents. The molecular structure and characterizations of these ionic liquids were confirmed by elemental analysis, FTIR, X-Ray Diffraction (XRD) and 1HNMR spectroscopy. Thermo gravimetric analysis (TGA), is used to study the degradation and thermal stability of the studied base stock samples. It was found that all the prepared ionic liquids additives have excellent power of dispersion and detergency. The ionic liquids as additives to engine oil reduced the friction (38%) and wear volume (76%) of steel balls. The obtained results show that the ionic liquids have an oxidation inhibitor up to 95%.

Keywords: reused lubricating oil, waste, petroleum, ionic liquids

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Authors: Constanza Levicán, Andrés Aparicio, Rodrigo F. Cádiz

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The artistic usage of Brain-computer interfaces (BCI), initially intended for medical purposes, has increased in the past few years as they become more affordable and available for the general population. One interesting question that arises from this practice is whether it is possible to compose or perform music by using only the brain as a musical instrument. In order to approach this question, we propose a BCI for musical composition, based on the representation of some mental states as the musician thinks about sounds. We developed software, called Insight2OSC, that allows the usage of the Emotiv Insight device as a musical instrument, by sending the EEG data to audio processing software such as MaxMSP through the OSC protocol. We provide two compositional applications bundled with the software, which we call Mapping your Mental State and Thinking On. The signals produced by the brain have different frequencies (or rhythms) depending on the level of activity, and they are classified as one of the following waves: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (30-50 Hz). These rhythms have been found to be related to some recognizable mental states. For example, the delta rhythm is predominant in a deep sleep, while beta and gamma rhythms have higher amplitudes when the person is awake and very concentrated. Our first application (Mapping your Mental State) produces different sounds representing the mental state of the person: focused, active, relaxed or in a state similar to a deep sleep by the selection of the dominants rhythms provided by the EEG device. The second application relies on the physiology of the brain, which is divided into several lobes: frontal, temporal, parietal and occipital. The frontal lobe is related to abstract thinking and high-level functions, the parietal lobe conveys the stimulus of the body senses, the occipital lobe contains the primary visual cortex and processes visual stimulus, the temporal lobe processes auditory information and it is important for memory tasks. In consequence, our second application (Thinking On) processes the audio output depending on the users’ brain activity as it activates a specific area of the brain that can be measured using the Insight device.

Keywords: BCI, music composition, emotiv insight, OSC

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Authors: Doo Ki Lee, Kumaresh Selvakumar, Man Young Kim

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Catalytic combustion is an environmentally friendly technique to combust fuels in gas turbines. In this paper, the behavior of surface reaction rate on catalytic combustion is studied with respect to the heterogeneous oxidation of methane-air mixture in a catalytic reactor. Plug flow reactor (PFR), the simplified single catalytic channel assists in investigating the catalytic combustion phenomenon over the Pt catalyst by promoting the desired chemical reactions. The numerical simulation with multi-step elementary surface reactions is governed by the availability of free surface sites onto the catalytic surface and thereby, the catalytic combustion characteristics are demonstrated by examining the rate of the reaction for lean fuel mixture. Further, two different surface reaction mechanisms are adopted and compared for surface reaction rates to indicate the controlling heterogeneous reaction for better fuel conversion. The performance of platinum catalyst under heterogeneous reaction is analyzed under the same temperature condition, where the catalyst with the higher kinetic rate of reaction would have a maximum catalytic activity for enhanced methane catalytic combustion.

Keywords: catalytic combustion, heterogeneous reaction, plug flow reactor, surface reaction rate

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Authors: Yen-Ping Peng, Ku-Fan Chen, Ken-Lin Chang, Jian Sun

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In this study, palladium loaded titanium dioxide nanotube arrays (Pd/TNAs) was successfully synthesized by anodic oxidation etching method combined with microwave hydrothermal method, using tea or coffee as a green reductant. Pd/TNAs was employed as an electrode in a photoelectrochemcial (PEC) system to simultaneously remove azo-dye and to generate hydrogen in the anodic and cathodic chamber, respectively. The chemical and physical properties of as-synthesized Pd/TNAs were characterized by scanning electron microscopy (SEM), ultraviolet–visible spectroscopy (UV-vis), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). SEM image indicates the diameter and the length of Pd/TNAs were approximately 300 nm and 2.5 μm, respectively. XPS analyses indicate that 1.13% (atomic %) of Pd was loaded onto the surface of TNAs. UV-vis results show that the band gap of TNAs was reduced from 3.2 eV to 2.37 eV after Pd loading. In addition, the electrochemical performances of Pd/TNAs were investigated by photocurrent density test and electrochemical impedance spectroscopy (EIS). The photocurrent (4.0 mA/cm²) of Pd /TNAs was higher than that of the uncoated TNAs (1.4 mA/cm²) at a bias potential of 1 V (vs. Ag/AgCl), indicating that Pd/TNAs-C can effectively separate photogenerated electrons and holes. The mechanism of our PEC system was proposed and discussed in detail in this study.

Keywords: Pd/TNAs, photoelectrochemical, azo-dye degradation, hydrogen generation

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Authors: H. B. Rekha, Usha N. Murthy, Prashanth, Ashoka

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Dyes are manufactured to have high chemical resistance because they are normally species, very difficult to degrade (reactive dyes). It damages flora and fauna. Furthermore, coloured components are highly hazardous. So removal of dyes becomes a challenge for both textile industry and water treatment facility. Dyeing wastewater is usually treated by conventional methods such as biological oxidation and adsorption but nowadays them becoming in-adequate because of large variability of composition of waste water. In the present investigation, mild steel electrodes of varying surface area were used for treatment of synthetic textile dye. It appears that electro-chemical coagulation could be very effective in removing coloured from wastewater; it could also be used to remove other parameters like chlorides, COD, and solids to some extent. In the present study, coloured removal up to 99% was obtained for surface area of mild steel electrode of 80 cm2 and 96% of surface area of mild steel electrode of 50 cm2. The findings from this study could be used to improve the design of electro-chemical treatment systems and modify existing systems to improve efficiency.

Keywords: electrochemical coagulation, mild steel, colour, environmental engineering

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

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Authors: Alona Faktor

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In this work, we present a NN-based computational model that can perform attention shifts according to high-level instruction. The instruction specifies the type of attentional shift using explicit geometrical relation. The instruction also can be of cognitive nature, specifying more complex human-human interaction or human-object interaction, or object-object interaction. Applying this approach sequentially allows obtaining a structural description of an image. A novel data-set of interacting humans and objects is constructed using a computer graphics engine. Using this data, we perform systematic research of relational segmentation shifts.

Keywords: cognitive science, attentin, deep learning, generalization

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Authors: Atilla Evcin, Bahri Ersoy, Süleyman Akpınar, I. Sinan Atlı

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Ceramic, polymer and composite nanofibers are nowadays begun to be utilized in many fields of nanotechnology. By the means of dimensions, these fibers are as small as nano scale but because of having large surface area and microstructural characteristics, they provide unique mechanic, optical, magnetic, electronic and chemical properties. In terms of nanofiber production, electrospinning has been the most widely used technique in recent years. In this study, carbon nanofibers have been synthesized from solutions of Polyacrylonitrile (PAN)/ N,N-dimethylformamide (DMF) by electrospinning method. The carbon nanofibers have been stabilized by oxidation at 250 °C for 2 h in air and carbonized at 750 °C for 1 h in H2/N2. Images of carbon nanofibers have been taken with scanning electron microscopy (SEM). The images have been analyzed to study the fiber morphology and to determine the distribution of the fiber diameter using FibraQuant 1.3 software. Then polymer composites have been produced from mixture of carbon nanofibers and silicone polymer. The final polymer composites have been characterized by X-ray diffraction method and scanning electron microscopy (SEM) energy dispersive X-ray (EDX) measurements. These results have been reported and discussed. At result, homogeneous carbon nanofibers with 100-167 nm of diameter were obtained with optimized electrospinning conditions.

Keywords: electrospinning, characterization, composites, nanofiber

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Authors: Isahakyan Heghine

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Intertextuality, being in the centre of attention of both linguists and literary critics, is vividly expressed in the outstanding British novelist and philosopher J. Fowles' works. 'The Magus’ is a deep psychological and philosophical novel with vivid intertextual links with the Greek mythology and authors from different epochs. The aim of the paper is to show how intertextuality might serve as a dialogue between two authors (J. Fowles and J. Donne) disguised in the dialogue of two protagonists of the novel : Conchis and Nicholas. Contrastive viewpoints concerning man's isolation, loneliness are stated in the dialogue. Due to the conceptual analysis of the text it becomes possible both to decode the conceptual information of the text and find out its intertextual links.

Keywords: dialogue, conceptual analysis, isolation, intertextuality

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Authors: Rakesh Dogra, Arun Kumar, Arvind Kumar Sharma

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This paper reports the thermoluminescence as well as optical properties of rare earth doped lithium fluoride (LiF) nanophosphor, synthesized via chemical route. The rare earth impurities (Yb and Sm) have been observed to increase the deep trap center capacity, which, in turn, enhance the radiation resistance of the LiF. This suggests the viability of these materials to be used as high dose thermoluminescent detectors at high temperature. Further, optical absorption measurements revealed the formation of radiation induced stable color centers in LiF at room temperature, which are independent of the rare earth dopant.

Keywords: lithium flouride, thermoluminescence, UV-VIS spectroscopy, Gamma radiations

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Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

Abstract:

Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: Asmaa Zaki M., Ahmed Abd El Aziz, Heba A. Fayed, Moustafa H. Aly

Abstract:

ISLs are the most popular choice for deep space communications because these links are attractive alternatives to present day microwave links. This paper explored the allowable high data rate in this link over different orbits, which is affected by variation in modulation scheme and detector type. Moreover, the objective of this paper is to optimize and analyze the performance of ISL in terms of Q-factor and Minimum Bit Error Rate (Min-BER) based on different detectors comprising some parameters.

Keywords: free space optics (FSO), field of view (FOV), inter satellite link (ISL), optical wireless communication (OWC)

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Authors: Zhaojun Zheng, Yuanfa Liu, Jiaxin Li, Jinwei Li, Yong-jiang Xu, Chen Cao

Abstract:

Black bean is an excellent protein source for preparing hydrolysates, which attract much attention due to their biological activity. The objective of this study was to characterize the physicochemical and antioxidant properties of black bean protein, hydrolyzed by ficin, bromelain or alcalase until 300 min of hydrolysis. Results showed that bromelain and alcalase hydrolysates possessed a higher degree of hydrolysis (DH) than that of ficin, thereby presenting different ultraviolet absorption, fluorescence intensity, and circular dichroism. Moreover, all hydrolysates possessed the capacity to scavenge DPPH radical with the lowest IC₅₀ of 21.11 µg/mL, as well as to chelate ferrous ion (Fe²⁺) with the IC₅₀ values ranging from 6.82 to 30.68 µg/mL. Intriguingly, the oxidation of linoleic acid, sunflower oil, and sunflower oil-in-water emulsion was remarkedly retarded by the three selected protein hydrolysates, especially by bromelain-treated protein hydrolysate, which might attribute to their high hydrophobicity and emulsifying properties. These findings can provide strong support for black bean protein hydrolysates to be employed in food products acting as natural antioxidant alternatives.

Keywords: antioxidant activity, black bean protein hydrolysate, emulsion physicochemical properties, sunflower oil

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Authors: Fatima Ammari, Meriem Chenouf

Abstract:

Synthesis of gold nano particles has attracted much attention since the pioneering discovery of the high catalytic activity of supported gold nano particles in the reaction of CO oxidation at low temperature. In this research field, we used Na-montmorillonite for gold nanoparticles stabilization; different loading percentage 1, 2 and 5%. The gold nano particles were obtained using chemical reduction method using NaBH4 as reductant agent. The obtained gold nano particles Au-mont stabilized in Na-montmorillonite were used as catalysts for reduction of 4-nitrophenol to aminophenol with sodium borohydride at room temperature. The UV-Vis results confirm directly the gold nano particles formation. The XRD and N2 adsorption results showed the formation of gold nano particles in the pores of montmorillonite with an average size of 5 nm obtained on samples with 2%Au-mont. The gold particles size increased with the increase of gold loading percentage. The reduction reaction of 4-nitrophenol into 4-aminophenol with NaBH4 catalyzed by Au-Na-montmorillonite catalyst exhibits remarkably a high activity; the reaction was completed within 9 min for 1Au-mont and within 3 min for 2Au-mont.

Keywords: chemical reduction, gold, montmorillonite, nano particles, 4-nitrophenol

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Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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

Abstract:

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

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

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