Search results for: deep composite kernel

Authors: Manel Hannachi, Mustapha Nechiche, Said Azem

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This study focuses on biodegradable materials made from Poly-lactic acid (PLA) and vegetal reinforcements. Three materials are developed from PLA, as a matrix, and : (i) olive kernels (OK); (ii) alfa (α) short fibers and (iii) OK+ α mixture, as reinforcements. After processing of PLA pellets and olive kernels in powder and alfa stems in short fibers, three mixtures, namely PLA-OK, PLA-α, and PLA-OK-α are prepared and homogenized in Turbula®. These mixtures are then compacted at 180°C under 10 MPa during 15 mn. Scanning Electron Microscopy (SEM) examinations show that PLA matrix adheres at surface of all reinforcements and the dispersion of these ones in matrix is good. X-ray diffraction (XRD) analyses highlight an increase of PLA inter-reticular distances, especially for the PLA-OK case. These results are explained by the dissociation of some molecules derived from reinforcements followed by diffusion of the released atoms in the structure of PLA. This is consistent with Fourier Transform Infrared Spectroscopy (FTIR) and Differential Scanning Calorimetry (DSC) analysis results.

Keywords: alfa short fibers, biodegradable composite, olive kernels, poly-lactic acid

Procedia PDF Downloads 142

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

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

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

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Authors: Masato Nishi, Tetsushi Kaburagi, Masashi Kurose, Tei Hirashima, Tetsusei Kurasiki

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The process of thermoforming a carbon fiber reinforced thermoplastic (CFRTP) has increased its presence in the automotive industry for its wide applicability to the mass production car. A non-isothermal forming for CFRTP can shorten its cycle time to less than 1 minute. In this paper, the textile reinforcement FE model which the authors proposed in a previous work is extended to the CFRTP model for non-isothermal forming simulation. The effect of thermoplastic is given by adding shell elements which consider thermal effect to the textile reinforcement model. By applying Reuss model to the stress calculation of thermoplastic, the proposed model can accurately predict in-plane shear behavior, which is the key deformation mode during forming, in the range of the process temperature. Using the proposed model, thermoforming simulation was conducted and the results are in good agreement with the experimental results.

Keywords: carbon fiber reinforced thermoplastic, finite element analysis, pre-impregnated textile composite, non-isothermal forming

Procedia PDF Downloads 423

Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

Procedia PDF Downloads 121

Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Chia-Chia Chang, Jhen-Ting Huang, Hu-Cheng Weng, An-Ya Lo

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This study developed a simple hierarchical porous carbon (HPC) synthesis process and used for supercapacitor application. In which, mesopore provides huge specific surface area, meanwhile, macropore provides excellent mass transfer. Thus the hierarchical porous electrode improves the charge-discharge performance. On the other hand, cerium oxide (CeO2) have also got a lot research attention owing to its rich in content, low in price, environmentally friendly, good catalytic properties, and easy preparation. Besides, a rapid redox reaction occurs between trivalent cerium and tetravalent cerium releases oxygen atom and increase the conductivity. In order to prevent CeO2 from disintegration under long-term charge-discharge operation, the CeO2 carbon porous materials were was integrated as composite material in this study. For in the ex-situ analysis, scanning electron microscope (SEM), X-ray diffraction (XRD), transmission electron microscope (TEM) analysis were adopted to identify the surface morphology, crystal structure, and microstructure of the composite. 77K Nitrogen adsorption-desorption analysis was used to analyze the porosity of each specimen. For the in-situ test, cyclic voltammetry (CV) and chronopotentiometry (CP) were conducted by potentiostat to understand the charge and discharge properties. Ragone plot was drawn to further analyze the resistance properties. Based on above analyses, the effect of macropores/mespores and the CeO2/HPC ratios on charge-discharge performance were investigated. As a result, the capacitance can be greatly enhanced by 2.6 times higher than pristine mesoporous carbon electrode.

Keywords: hierarchical porous carbon, cerium oxide, supercapacitor

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Authors: Thanaa Abdel Moghny, Mohamed Keshawy, Mahmoud Fathy, Abdul-Raheim M. Abdul-Raheim, Khalid I. Kabel, Ahmed F. El-Kafrawy, Mahmoud Ahmed Mousa, Ahmed E. Awadallah

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Ecological pollution is of great concern for human health and the environment. Numerous organic and inorganic pollutants usually discharged into the water caused carcinogenic or toxic effect for human and different life form. In this respect, this work aims to treat water contaminated by organic and inorganic waste using sorbent based on polystyrene. Therefore, two different series of adsorbent material were prepared; the first one included the preparation of polymeric sorbent from the reaction of styrene acrylate ester and alkyl acrylate. The second series involved syntheses of composite ion exchange resins of waste polystyrene and   amorphous carbon thin film (WPS/ACTF) by solvent evaporation using micro emulsion polymerization. The produced ACTF/WPS nanocomposite was sulfonated to produce cation exchange resins ACTF/WPSS nanocomposite. The sorbents of the first series were characterized using FTIR, ¹H NMR, and gel permeation chromatography. The thermal properties of the cross-linked sorbents were investigated using thermogravimetric analysis, and the morphology was characterized by scanning electron microscope (SEM). The removal of organic pollutant was determined through absorption tests in a various organic solvent. The chemical and crystalline structure of nanocomposite of second series has been proven by studies of FTIR spectrum, X-rays, thermal analysis, SEM and TEM analysis to study morphology of resins and ACTF that assembled with polystyrene chain. It is found that the composite resins ACTF/WPSS are thermally stable and show higher chemical stability than ion exchange WPSS resins. The composite resin was evaluated for calcium hardness removal. The result is evident that the ACTF/WPSS composite has more prominent inorganic pollutant removal than WPSS resin. So, we recommend the using of nanocomposite resin as new potential applications for water treatment process.

Keywords: nanocomposite, sorbent materials, waste water, waste polystyrene

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Kumlachew Zelalem Walle, Chun-Chen Yang

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Solid-state Lithium metal batteries (SSLMBs) that contain polymer and ceramic solid electrolytes have received considerable attention as an alternative to substitute liquid electrolytes in lithium metal batteries (LMBs) for highly safe, excellent energy storage performance and stability under elevated temperature situations. Here, a novel fast Li-ion conducting material, LiTa₂PO₈ (LTPO), was synthesized and electrochemical performance of as-prepared powder and LTPO-incorporated composite solid polymer electrolyte (LTPO-CPE) membrane were investigated. The as-prepared LTPO powder was homogeneously dispersed in polymer matrices, and a hybrid solid electrolyte membrane was synthesized via a simple solution-casting method. The room temperature total ionic conductivity (σt) of the LTPO pellet and LTPO-CPE membrane were 0.14 and 0.57 mS cm-1, respectively. A coin battery with NCM811 cathode is cycled under 1C between 2.8 to 4.5 V at room temperature, achieving a Coulombic efficiency of 99.3% with capacity retention of 74.1% after 300 cycles. Similarly, the LFP cathode also delivered an excellent performance at 0.5C with an average Coulombic efficiency of 100% without virtually capacity loss (the maximum specific capacity is at 27th: 138 mAh g−1 and 500th: 131.3 mAh g−1). These results demonstrates the feasibility of a high Li-ion conductor LTPO as a filler, and the developed polymer/ceramic hybrid electrolyte has potential to be a high-performance electrolyte for high-voltage cathodes, which may provide a fresh platform for developing more advanced solid-state electrolytes.

Keywords: li-ion conductor, lithium-metal batteries, composite solid electrolytes, liTa2PO8, high-voltage cathode

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Authors: Behrang Tavousi Tehrani, Mohammad-Zaman Kabir

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Shape memory alloys (SMA) are often implemented in smart structures as the active components. Their ability to recover large displacements has been used in many applications, including structural stability/response enhancement and active structural acoustic control. SMA wires or fibers can be embedded with composite cylinders to increase their critical buckling load, improve their load-deflection behavior, and reduce the radial deflections under various thermo-mechanical loadings. This paper presents a semi-analytical investigation on the non-linear load-deflection response of SMA-reinforced composite circular cylindrical shells. The cylinder shells are under uniform external pressure load. Based on first-order shear deformation shell theory (FSDT), the equilibrium equations of the structure are derived. One-dimensional simplified Brinson’s model is used for determining the SMA recovery force due to its simplicity and accuracy. Airy stress function and Galerkin technique are used to obtain non-linear load-deflection curves. The results are verified by comparing them with those in the literature. Several parametric studies are conducted in order to investigate the effect of SMA volume fraction, SMA pre-strain value, and SMA activation temperature on the response of the structure. It is shown that suitable usage of SMA wires results in a considerable enhancement in the load-deflection response of the shell due to the generation of the SMA tensile recovery force.

Keywords: airy stress function, cylindrical shell, Galerkin technique, load-deflection curve, recovery stress, shape memory alloy

Procedia PDF Downloads 185

Authors: Jun-Lun Jiang, Bing-Sheng Yu

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Processing of flat-panel displays generates huge amount of wasted glass polishing powder, with high concentration of cerium and other elements such as lanthanum. According to the current statistics, consumption of polishing powder was approximately ten thousand tons per year in the world. Nevertheless, wasted polishing powder was usually buried or burned. If the lanthanum and cerium compounds in the wasted polishing powder could be recycled, that will greatly reduce enterprise cost and implement waste circulation. Cathodes of SOFCs are the principal consisting of rare earth elements such as lanthanum and cerium. In this study, we recycled the lanthanum and cerium from wasted glass polishing powder by acid-solution method, and synthesized La0.8Sr0.05Ca0.15Fe0.8Co0.8O3-δ and Gd0.1Ce0.9O2 (LSCCF-GDC) composite cathode material for SOFCs by glycinenitrate combustion (GNP) method. The results show that the recovery rates of lanthanum and cerium could accomplish up to 80% and 100% under 10N nitric acid solution within one hour. Comparing with the XRD data of the commercial LSCCF-GDC powder and the LSCCF-GDC product synthesized with chemicals, we find that the LSCCF-GDC was successfully synthesized with the recycled La & Ce solution by GNP method. The effect of adding ammonia to the product was also discussed, the grain size is finer and recovery rate of the product is higher without the addition of ammonia to the solution.

Keywords: glass polishing powder, acid solution, recycling, composite cathodes of solid oxide fuel, cell (SOFC), perovskite, glycine-nitrate combustion(GNP) method

Procedia PDF Downloads 267

Authors: Rajnarayan Damor, Gayatri Swarnakar

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Fasciola gigantica are present in the biliary ducts of liver and gall bladder of domestic buffaloes. They are very harmful and causes significant lose to live stock owners, on account of poor growth and lower productivity of domestic buffaloes. Synthetic veterinary drugs have been used to eliminate parasites from cattle but these drugs are unaffordable and inaccessible for poor cattle farmers. The in vitro anthelmintic effect of Citrullus colocynthis fruit extract against Fasciola gigantica parasites were observed by light and scanning electron microscopy. Fruit extracts of C. colocynthis exhibit highest mortality 100% at 50 mg/ml in 15th hour of exposure. The oral and ventral sucker appeared to be slightly more swollen than control and synthetic drug albendazole. The tegument showed submerged spines by the swollen tegument around them. The tegument of the middle region showed deep furrows, folding and submerged spines which either lied very flat against the surface or had become submerged in the tegument by the swollen tegument around them leaving deep furrows. Posterior region showed with deep folding in the tegument, completely disappearance of spines and swelling of the tegument led to completely submerged spines leaving spine socket. The present study revealed that fruit extracts of Citrullus colocynthis found to be potential sources for novel anthelmintic and justify their ethno-veterinary use.

Keywords: anthelmintic, buffalo, Citrullus colocynthis, Fasciola gigantica, mortality, tegument

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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World-wide spread of coronavirus grows the concern about planning for the excess demand of hospital services in response to COVID-19 pandemic. The surge in the hospital services demand beyond the current capacity leads to shortage of ICU beds and ventilators in some parts of US. In this study, we forecast the required number of hospital beds and possible shortage of beds in US during COVID-19 pandemic to be used in the planning and hospitalization of new cases. In this paper, we used a data on COVID-19 deaths and patients’ hospitalization besides the data on hospital capacities and utilization in US from publicly available sources and national government websites. we used a novel ensemble modelling of deep learning networks, based on stacking different linear and non-linear layers to predict the shortage in hospital beds. The results showed that our proposed approach can predict the excess hospital beds demand very well and this can be helpful in developing strategies and plans to mitigate this gap.

Keywords: COVID-19, deep learning, ensembled models, hospital capacity planning

Procedia PDF Downloads 148

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

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Authors: Yildiray Korkmaz, Mehmet Aksoy

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In today's operations, countries want to reach their aims in the shortest way due to economical, political and humanitarian aspects. The most effective way of achieving this goal is to be able to penetrate strategic targets. Strategic targets are generally located deep inside of the countries and are defended by modern and efficient surface to air missiles (SAM) platforms which are operated as integrated with Intelligence, Surveillance and Reconnaissance (ISR) systems. On the other hand, these high valued targets are buried deep underground and hardened with strong materials against attacks. Therefore, to penetrate these targets requires very detailed intelligence. This intelligence process should include a wide range that is from weaponry to threat assessment. Accordingly, the framework of the attack package will be determined. This mission package has to execute missions in a high threat environment. The way to minimize the risk which depends on loss of life is to use packages which are formed by UAVs. However, some limitations arising from the characteristics of UAVs restricts the performance of the mission package consisted of UAVs. So, the mission package should be formed with UAVs under the leadership of a fifth generation manned aircraft. Thus, we can minimize the limitations, easily penetrate in the deep inside of the enemy territory with minimum risk, make a decision according to ever-changing conditions and finally destroy the strategic targets. In this article, the strengthens and weakness aspects of UAVs are examined by SWOT analysis. And also, it revealed features of a mission package and presented as an example what kind of a mission package we should form in order to get marginal benefit and penetrate into strategic targets with the development of autonomous mission execution capability in the near future.

Keywords: UAV, autonomy, mission package, strategic attack, mission planning

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Authors: Maham Malik, Amjad Ali, Muhammad Asif

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Current study aims to examine the relationship of emotional labor strategies - deep acting and surface acting - with employees' job satisfaction, organizational commitment and intentions to quit. The study also examines the mediating role of job satisfaction and organizational commitment for relationship of emotional labor strategies with intentions to quit. Data were conveniently collected from 307 nurses by using self-administered questionnaire. Linear regression test was applied to find the relationship between the variables. Mediation was checked through Baron and Kenny Model and Sobel test. Results prove the existence of partial mediation of job satisfaction between the emotional labor strategies and quitting intentions. The study recommends that deep acting should be promoted because it is positively associated with quality of work life, work engagement and organizational citizenship behavior of employees.

Keywords: emotional labor strategies, intentions to quit, job satisfaction, organizational commitment, nursing

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Authors: T. D. Ibragimov, A. R. Imamaliyev, G. M. Bayramov

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Influence of barium titanate particles on electro-optic properties of liquid crystal 4-cyano-4′-pentylbiphenyl (5CB) with positive dielectric anisotropy and the liquid crystalline (LC) mixture Н-37 consisting of 4-methoxybezylidene-4'–butylaniline and 4-ethoxybezylidene-4'–butylaniline with negative dielectric anisotropy was investigated. It was shown that a presence of particles inside 5СВ and H-37 decreased the clearing temperature from 35.2 °С to 32.5°С and from 61.2 oC to 60.1oC, correspondingly. The threshold voltage of the Fredericksz effect became 0.3 V for the BaTiO3-5CB colloid while the beginning of this effect of the pure 5СВ was observed at 2.1 V. Threshold voltage of the Fredericksz effect increased from 2.8 V to up 3.1 V at additive of particles into H-37. A rise time of the BaTiO3-5CB colloid improved while a decay time worsened in comparison with the pure 5CB at all applied voltages. The inverse trends were observed for the H-37 matrix, namely, a rise time worsened and a decay time improved. Among other things, the effect of fast light modulation was studied at application of the rectangular impulse with direct bias to an electro-optical cell with the BaTiO3 particles+5CB and the pure 5CB. At this case, a rise time of the composite worsened, a decay time improved in comparison with the pure 5CB. The pecularities of electrohydrodynamic instability (EHDI) formation was also investigated into the composite with the H-37 matrix. It was found that the voltage of the EHDI formation decreased, a rise time increased and a decay time decreased in comparison with the pure H-37. First of all, experimental results are explained by appearance of local electric fields near the polarized ferroelectric particles at application of external electric field and an existence of the additional obstacles (particles) for movement of ions.

Keywords: liquid crystal, ferroelectric particles, composite, electro-optics

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Authors: Harpreet Singh

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Finding relevant information on the World Wide Web is becoming highly challenging day by day. Web usage mining is used for the extraction of relevant and useful knowledge, such as user behaviour patterns, from web access log records. Web access log records all the requests for individual files that the users have requested from the website. Web usage mining is important for Customer Relationship Management (CRM), as it can ensure customer satisfaction as far as the interaction between the customer and the organization is concerned. Web usage mining is helpful in improving website structure or design as per the user’s requirement by analyzing the access log file of a website through a log analyzer tool. The focus of this paper is to enhance the accessibility and usability of a guitar selling web site by analyzing their access log through Deep Log Analyzer tool. The results show that the maximum number of users is from the United States and that they use Opera 9.8 web browser and the Windows XP operating system.

Keywords: web usage mining, web mining, log file, data mining, deep log analyzer

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Authors: Seyedmahdi Mousavihashemi

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Natural Akermanite (NAM) has been successfully prepared by a modified sol-gel method. Optimization in calcination temperature and mechanical ball milling resulted in a pure and nano-sized powder which characterized by means of scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM) and Fourier transform infrared Spectroscopy (FT–IR). We hypothesized that nano-sized Akermanite (AM) would mimic more efficiently the nanocrystal structure and function of natural bone apatite, owing to the higher surface area, compare to conventional micron-size Akermanite (AM). Accordingly, we used the unique advantage of nanotechnology to improve novel nano akermanite particles as a potential candidate for bone tissue regeneration whether as a per implant filling powder or in combination with other biomaterials as a composite scaffold. Pure Akermanite (PAM) powders were successfully obtained via a simple sol-gel method followed by calcination at 1250 °C. Mechanical grinding in a ceramic ball mill for 7 hours resulted in akermanite (AM) nanoparticles in the range of about 30- 45 nm.

Keywords: biomedical engineering, nano composite, SEM, TEM

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Authors: Adil Basuki Ahza, Michael Liong, Subarna Suryatman

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Healthy chips now dominating the snack market shelves. More than 80% processed snack foods in the market are chips. This research takes the advantages of twin extrusion technology to produce two types of product, i.e. directly expanded and intermediate ready-to-fry or microwavable chips. To improve the functional quality, the cereal-tuber based mix was enriched with antioxidant rich mix of temurui, celery, carrot and isolated soy protein (ISP) powder. Objectives of this research were to find best composite cassava-sorghum ratio, i.e. 60:40, 70:30 and 80:20, to optimize processing conditions of extrusion and study the microstructural, physical and sensorial characteristics of the final products. Optimization was firstly done by applying metering section of extruder barrel temperatures of 120, 130 and 140 °C with screw speeds of 150, 160 and 170 rpm to produce direct expanded product. The intermediate product was extruded in 100 °C and 100 rpm screw speed with feed moisture content of 35, 40 and 45%. The directly expanded products were analyzed for color, hardness, density, microstructure, and organoleptic properties. The results showed that interaction of ratio of cassava-sorghum and cooking methods affected the product's color, hardness, and bulk density (p<0.05). Extrusion processing conditions also significantly affected product's microstructure (p<0.05). The direct expanded snacks of 80:20 cassava-sorghum ratio and fried expanded one 70:30 and 80:20 ratio shown the best organoleptic score (slightly liked) while baking the intermediate product with microwave were resulted sensorial not acceptable quality chips.

Keywords: cassava-sorghum composite, extrusion, microstructure, physical characteristics

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Authors: Neha Batra Bali, Monika Tomar, Vinay Gupta

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A reagentless biosensor for detection of urea based on ZnO-CuO composite thin film is presented in following work. Biosensors have immense potential for varied applications ranging from environmental to clinical testing, health care, and cell analysis. Immense growth in the field of biosensors is due to the huge requirement in today’s world to develop techniques which are both cost effective and accurate for prevention of disease manifestation. The human body comprises of numerous biomolecules which in their optimum levels are essential for functioning. However mismanaged levels of these biomolecules result in major health issues. Urea is one of the key biomolecules of interest. Its estimation is of paramount significance not only for healthcare sector but also from environmental perspectives. If level of urea in human blood/serum is abnormal, i.e., above or below physiological range (15-40mg/dl)), it may lead to diseases like renal failure, hepatic failure, nephritic syndrome, cachexia, urinary tract obstruction, dehydration, shock, burns and gastrointestinal, etc. Various metal nanoparticles, conducting polymer, metal oxide thin films, etc. have been exploited to act as matrix to immobilize urease to fabricate urea biosensor. Amongst them, Zinc Oxide (ZnO), a semiconductor metal oxide with a wide band gap is of immense interest as an efficient matrix in biosensors by virtue of its natural abundance, biocompatibility, good electron communication feature and high isoelectric point (9.5). In spite of being such an attractive candidate, ZnO does not possess a redox couple of its own which necessitates the use of electroactive mediators for electron transfer between the enzyme and the electrode, thereby causing hindrance in realization of integrated and implantable biosensor. In the present work, an effort has been made to fabricate a matrix based on ZnO-CuO composite prepared by pulsed laser deposition (PLD) technique in order to incorporate redox properties in ZnO matrix and to utilize the same for reagentless biosensing applications. The prepared bioelectrode Urs/(ZnO-CuO)/ITO/glass exhibits high sensitivity (70µAmM⁻¹cm⁻²) for detection of urea (5-200 mg/dl) with high stability (shelf life ˃ 10 weeks) and good selectivity (interference ˂ 4%). The enhanced sensing response obtained for composite matrix is attributed to the efficient electron exchange between ZnO-CuO matrix and immobilized enzymes, and subsequently fast transfer of generated electrons to the electrode via matrix. The response is encouraging for fabricating reagentless urea biosensor based on ZnO-CuO matrix.

Keywords: biosensor, reagentless, urea, ZnO-CuO composite

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Authors: Asimina Paparrigopoulou, John Pavlopoulos, Maria Konstantinidou

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Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. By experimenting with data obtained from the Collaborative Database of Dateable Greek Bookhands and the PapPal online collections of objectively dated Greek papyri, this study shows that deep learning dating models, pre-trained on generic images, can achieve accurate chronological estimates for a test subset (67,97% accuracy for book hands and 55,25% for documents). To compare the estimates of these models with those of humans, experts were asked to complete a questionnaire with samples of literary and documentary hands that had to be sorted chronologically by century. The same samples were dated by the models in question. The results are presented and analysed.

Keywords: image classification, papyri images, dating

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Ubon Asuquo Essien, Okwudili Bismark Ibeagwa, Daberechi Peace Ubabuko

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The Broiler Poultry subsector is dominated by small scale production with low aggregate output. The high cost of inputs currently experienced in Nigeria tends to aggravate the situation; hence many broiler farmers struggle to break-even. This study was designed to examine income and input factors in small scale deep liter broiler production in Imo state, Nigeria. Specifically, the study examined; socio-economic characteristics of small scale deep liter broiler producing Poultry farmers; estimate cost and returns of broiler production in the area; analyze input factors in broiler production in the area and examined marketability, age and profitability of the enterprise. A multi-stage sampling technique was adopted in selecting 60 small scale broiler farmers who use deep liter system from 6 communities through the use of structured questionnaire. The socioeconomic characteristics of the broiler farmers and the profitability/ marketability age of the birds were described using descriptive statistical tools such as frequencies, means and percentages. Gross margin analysis was used to analyze the cost and returns to broiler production, while Cobb Douglas production function was employed to analyze input factors in broiler production. The result of the study revealed that the cost of feed (P<0.1), deep liter material (P<0.05) and medication (P<0.05) had a significant positive relationship with the gross return of broiler farmers in the study area, while cost of labour, fuel and day old chicks were not significant. Furthermore, Gross profit margin of the farmers who market their broiler at the 8th week of rearing was 80.7%; and 78.7% and 60.8% for farmers who market at the 10th week and 12th week of rearing, respectively. The business is, therefore, profitable but at varying degree. Government and Development partners should make deliberate efforts to curb the current rise in the prices of poultry feeds, drugs and timber materials used as bedding so as to widen the profit margin and encourage more farmers to go into the business. The farmers equally need more technical assistance from extension agents with regards to timely and profitable marketing.

Keywords: broilers, factor analysis, income, small scale

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Authors: Sarita Agarwal, Deepika Delsa Dean

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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.

Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation

Procedia PDF Downloads 119

Authors: Danute Karcauskiene, Dalia Ambrazaitiene, Regina Skuodiene, Monika Vilkiene, Regina Repsiene, Ieva Jokubauskaite

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The main ambition for nowadays agriculture is to get the economically effective yield and to secure the soil ecological sustainability. According to the effect on the main soil quality indexes, tillage systems may be separated into two types, conventional and conservation tillage. The goal of this study was to determine the impact of conservation and conventional primary soil tillage methods and soil fertility improvement measures on soil properties and crop productivity. Methods: The soil of the experimental site is Dystric Glossic Retisol (WRB 2014) with texture of sandy loam. The trial was established in 2003 in the experimental field of crop rotation of Vėžaičiai Branch of Lithuanian Research Centre for Agriculture and Forestry. Trial factors and treatments: factor A- primary soil tillage in (autumn): deep ploughing (20-25cm), shallow ploughing (10-12cm), shallow ploughless tillage (8-10cm); factor B – soil fertility improvement measures: plant residues, plant residues + straw, green manure 1st cut + straw, farmyard manure 40tha-1 + straw. The four - course crop rotation consisted of red clover, winter wheat, spring rape and spring barley with undersown. Results: The tillage had no statistically significant effect on topsoil (0-10 cm) pHKCl level, it was 5.5 - 5.7. During all experiment period, the highest soil pHKCl level (5.65) was in the shallow ploughless tillage. The organic fertilizers particularly the biomass of grass and farmyard manure had tendency to increase the soil pHKCl. The content of plant - available phosphorus and potassium significantly increase in the shallow ploughing compared with others tillage systems. The farmyard manure increases those elements in whole arable layer. The dissolved organic carbon concentration was significantly higher in the 0 - 10 cm soil layer in the shallow ploughless tillage compared with deep ploughing. After the incorporation of clover biomass and farmyard manure the concentration of dissolved organic carbon increased in the top soil layer. During all experiment period the largest amount of water stable aggregates was determined in the soil where the shallow ploughless tillage was applied. It was by 12% higher compared with deep ploughing. During all experiment time, the soil moisture was higher in the shallow ploughing and shallow ploughless tillage (9-27%) compared to deep ploughing. The lowest emission of CO2 was determined in the deep ploughing soil. The highest rate of CO2 emission was in shallow ploughless tillage. The addition of organic fertilisers had a tendency to increase the CO2 emission, but there was no statistically significant effect between the different types of organic fertilisers. The crop yield was larger in the deep ploughing soil compared to the shallow and shallow ploughless tillage.

Keywords: reduced tillage, soil structure, soil pH, biological activity, crop productivity

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Authors: Isil Kerti, Onur Okur, Sibel Daglilar, Recep Calin

Abstract:

Particulate reinforced aluminium matrix composites have gained more importance in automotive, aeronautical and defense industries due to their specific properties like as low density, high strength and stiffness, good fatigue strength, dimensional stability at high temperature and acceptable tribological properties. In this study, 2014 Aluminium alloy used as a matrix material and B₄C and SiC were selected as reinforcements components. For production of composites materials, vacuum infiltration method was used. In the experimental studies, the reinforcement volume ratios were defined by mixing as totally 10% B₄C and SiC. Aging treatment (T6) was applied to the specimens. The effect of T6 treatment on hardness was determined by using Brinell hardness test method. The effects of the aging treatment on microstructure and chemical structure were analysed by making XRD, SEM and EDS analysis on the specimens.

Keywords: metal matrix composite, vacumm infiltration method, aluminum metal matrix, mechanical feature

Procedia PDF Downloads 306

Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

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Authors: K. Tomolya, D. Janovszky, A. Sycheva, M. Sveda, A. Roosz

Abstract:

CuZrAl amorphous alloys have attracted high interest due to unique physical and mechanical properties, which can be enhanced by adding of Ni and Ti elements. It is known that this properties can be enhanced by crystallization of amorphous alloys creating nanocrystallines in the matrix. The present work intends to produce nanosized crystalline parti-cle reinforced amorphous matrix composite powders by crystallization of amorphous powders. As the first step the amorphous powders were synthe-tized by ball-milling of crystalline powders. (Cu49Zr45Al6) 80Ni10Ti10 and (Cu49Zr44Al7) 80Ni10Ti10 (at%) alloys were ball-milled for 12 hours in order to reach the fully amorphous structure. The impact en-ergy of the balls during milling causes the change of the structure in the powders. Scanning electron microscopical (SEM) images shows that the phases mixed first and then changed into a fully amorphous matrix. Furthermore, nanosized particles in the amorphous matrix were crystallized by heat treatment of the amorphous powders that was confirmed by TEM measurement. It was of importance to define the tem-perature when the amorphous phase starts to crystal-lize. Amorphous alloys have a special heating curve and characteristic temperatures, which can be meas-ured by differential scanning calorimetry (DSC). A typical DSC curve of an amorphous alloy exhibits an endothermic event characteristic of the equilibrium glass transition (Tg) and a distinct undercooled liquid region, followed by one or two exothermic events corresponding to crystallization processes (Tp). After measuring the DSC traces of the amorphous powders, the annealing temperatures should be determined between Tx and Tp. In our experiments several temperatures from the annealing temperature range were selected and de-pendency of crystallized nanoparticles fraction on their hardness was investigated.

Keywords: amorphous structure, composite, mechanical milling, powder, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), transmission electronmocroscopy (TEM)

Procedia PDF Downloads 441