Search results for: deep oxidation

Authors: Sirapop Nuannimnoi, Hojjat Baghban, Ching-Yao Huang

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Over the past decade, with the fast development of blockchain technology since the birth of Bitcoin, there has been a massive increase in the usage of Cryptocurrencies. Cryptocurrencies are not seen as an investment opportunity due to the market’s erratic behavior and high price volatility. With the recent success of deep reinforcement learning (DRL), portfolio management can be modeled and automated. In this paper, we propose a novel DRL-based multi-agent system to automatically make proper trading decisions on multiple cryptocurrencies and gain profits in the highly volatile cryptocurrency market. We also extend this multi-agent system with horizontal federated transfer learning for better adapting to the inclusion of new cryptocurrencies in our portfolio; therefore, we can, through the concept of diversification, maximize our profits and minimize the trading risks. Experimental results through multiple simulation scenarios reveal that this proposed algorithmic trading system can offer three promising key advantages over other systems, including maximized profits, minimized risks, and adaptability.

Keywords: cryptocurrency portfolio management, algorithmic trading, federated learning, multi-agent reinforcement learning

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Authors: Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini

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In vapor cycle systems, the mass flow sensor plays a key role for different monitoring and control purposes. However, physical sensors can be inaccurate, heavy, cumbersome, expensive, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor, based on other standard sensors, is a good alternative. This paper has two main objectives. Firstly, a data-driven model using a convolutional neural network is proposed to estimate the mass flow of the compressor. We show that it significantly outperforms the standard polynomial regression model (thermodynamic maps) in terms of the standard MSE metric and engineer performance metrics. Secondly, a semi-automatic segmentation method is proposed to compute the engineer performance metrics for real datasets, as the standard MSE metric may pose risks in analyzing the dynamic behavior of vapor cycle systems.

Keywords: deep learning, convolutional neural network, vapor cycle system, virtual sensor

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Authors: S. J. Addinell, A. F. Grabsch, P. D. Fawell, B. Evans

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The Deep Exploration Technologies Cooperative Research Centre (DET CRC) is researching and developing a new coiled tubing based greenfields mineral exploration drilling system utilising down-hole water-powered percussive drill tooling. This new drilling system is aimed at significantly reducing the costs associated with identifying mineral resource deposits beneath deep, barren cover. This system has shown superior rates of penetration in water-rich, hard rock formations at depths exceeding 500 metres. With fluid flow rates of up to 120 litres per minute at 200 bar operating pressure to energise the bottom hole tooling, excessive quantities of high quality drilling fluid (water) would be required for a prolonged drilling campaign. As a result, drilling fluid recovery and recycling has been identified as a necessary option to minimise costs and logistical effort. While the majority of the cuttings report as coarse particles, a significant fines fraction will typically also be present. To maximise tool life longevity, the percussive bottom hole assembly requires high quality fluid with minimal solids loading and any recycled fluid needs to have a solids cut point below 40 microns and a concentration less than 400 ppm before it can be used to reenergise the system. This paper presents experimental results obtained from the research program during laboratory and field testing of the prototype drilling system. A study of the morphological aspects of the cuttings generated during the percussive drilling process shows a strong power law relationship for particle size distributions. This data is critical in optimising solids control strategies and cuttings dewatering techniques. Optimisation of deployable solids control equipment is discussed and how the required centrate clarity was achieved in the presence of pyrite-rich metasediment cuttings. Key results were the successful pre-aggregation of fines through the selection and use of high molecular weight anionic polyacrylamide flocculants and the techniques developed for optimal dosing prior to scroll decanter centrifugation, thus keeping sub 40 micron solids loading within prescribed limits. Experiments on maximising fines capture in the presence of thixotropic drilling fluid additives (e.g. Xanthan gum and other biopolymers) are also discussed. As no core is produced during the drilling process, it is intended that the particle laden returned drilling fluid is used for top-of-hole geochemical and mineralogical assessment. A discussion is therefore presented on the biasing and latency of cuttings representivity by dewatering techniques, as well as the resulting detrimental effects on depth fidelity and accuracy. Data pertaining to the sample biasing with respect to geochemical signatures due to particle size distributions is presented and shows that, depending on the solids control and dewatering techniques used, it can have unwanted influence on top-of-hole analysis. Strategies are proposed to overcome these effects, improving sample quality. Successful solids control and cuttings dewatering for water-powered percussive drilling is presented, contributing towards the successful advancement of coiled tubing based greenfields mineral exploration.

Keywords: cuttings, dewatering, flocculation, percussive drilling, solids control

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Vishakha Kaim, Mookan Natarajan, Sandeep Kaur-Ghumaan

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Hydrogen is one of the most abundant elements present on earth’s crust and considered to be the simplest element in existence. It is not found naturally as a gas on earth and thus has to be manufactured. Hydrogen can be produced from a variety of sources, i.e., water, fossil fuels, or biomass and it is a byproduct of many chemical processes. It is also considered as a secondary source of energy commonly referred to as an energy carrier. Though hydrogen is not widely used as a fuel, it still has the potential for greater use in the future as a clean and renewable source of energy. Electrocatalysis is one of the important source for the production of hydrogen which could contribute to this prominent challenge. Metals such as platinum and palladium are considered efficient for hydrogen production but with limited applications. As a result, a wide variety of metal complexes with earth abundant elements and varied ligand environments have been explored for the electrochemical production of hydrogen. In nature, [FeFe] hydrogenase enzyme present in DesulfoVibrio desulfuricans and Clostridium pasteurianum catalyses the reversible interconversion of protons and electrons into dihydrogen. Since the first structure for the enzyme was reported in 1990s, a range of iron complexes has been synthesized as structural and functional mimics of the enzyme active site. Mn is one of the most desirable element for sustainable catalytic transformations, immediately behind Fe and Ti. Only limited number manganese complexes have been reported in the last two decades as catalysts for proton reduction. Furthermore, redox reactions could be carried out in a facile manner, due to the capability of manganese complexes to be stable at different oxidation states. Herein are reported, four µ2-thiolate bridged manganese complexes [Mn₂(CO)₆(μ-S₂N₄C₁₄H₁₀)] 1, [Mn₂(CO)7(μ- S₂N₄C₁₄H₁₀)] 2, Mn₂(CO)₆(μ-S₄N₂C₁₄H₁₀)] 3 and [Mn₂(CO)(μ- S₄N₂C₁₄H₁₀)] 4 have been synthesized and characterized. The cyclic voltammograms of the complexes displayed irreversible reduction peaks in the range - 0.9 to -1.3 V (vs. Fc⁺/Fc in acetonitrile at 0.1 Vs⁻¹). The complexes were catalytically active towards proton reduction in the presence of trifluoroacetic acid as seen from electrochemical investigations.

Keywords: earth abundant, electrocatalytic, hydrogen, manganese

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Authors: Khaled Mawardi

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Bay laurel (Laurus nobilis L.) is an evergreen of the Laurus genus of the Lauraceae Family. It is a plant native to the southern Mediterranean and widespread in Syria. It is a plant with enormous industrial applications. For instance, they are used as platform chemicals in food, pharmaceutical and cosmetic applications. Herein, we report an efficient extraction of Bay laurel oil from Bay laurel fruits via a comparative investigation of boiled water conventional extraction technique and microwave-assisted extraction (MAE) by microwave heating at atmospheric pressure. In order to optimize the extraction efficiency, we investigated several extraction parameters, such as extraction time and microwave power. In addition, to demonstrate the feasibility of the method, oil obtained under optimal conditions by method (MAE) was compared quantitatively and qualitatively with that obtained by the conventional method. After 1h of microwave-assisted extraction (power of 600W), an oil yield of 9.8% with identified lauric acid content of 22.7%. In comparison, an extended extraction of up to 4h was required to obtain a 9.7% yield of oil extraction with 21.2% of lauric acid content. The change in microwave power impacts the fatty acids profile and also the quality parameters of Laurel Oil. It was found that the profile of fatty acids changed with the power, where the lauric acid content increased from 22.7% at 600W to 30.5% at 1200W owing to a decrease of oleic acid content from 32.8% at 600W to 28.3% at 1200W and linoleic acid content from 22.3% at 600W to 20.6% at 1200W. In addition, we observed a decrease in oil yield from 9.8% at 600W to 5.1% at 1200W. Summarily, the overall results indicated that the extraction of laurel fruit oils could be successfully performed using (MAE) at a short extraction time and lower energy compared with the fixed oil obtained by conventional processes of extraction. Microwave heating exerted more aggressive effects on the oil. Indeed, microwave heating inflicted changes in the fatty acids profile of oil; the most affected fraction was the unsaturated fatty acids, with higher susceptibility to oxidation.

Keywords: microwaves, extraction, Laurel oil, solvent-free

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Authors: Thomas S. Abia II, Citlali Garcia-Saucedo

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A continuous copper precipitation treatment (CCPT) system was conceived at Intel Chandler Site to serve as a first-of-kind (FOK) facility-scale waste copper (Cu), nickel (Ni), and manganese (Mn) co-precipitation facility. The process was designed to treat highly variable wastewater discharged from a substrate packaging research factory. The paper discusses metals co-precipitation induced by internal changes for manufacturing facilities that lack the capacity for hardware expansion due to real estate restrictions, aggressive schedules, or budgetary constraints. Herein, operating parameters such as pH and oxidation reduction potential (ORP) were examined to analyze the ability of the CCPT System to immobilize various waste metals. Additionally, influential factors such as influent concentrations and retention times were investigated to quantify the environmental variability against system performance. A total of 2,027 samples were analyzed and statistically evaluated to measure the performance of CCPT that was internally retrofitted for Mn abatement to meet environmental regulations. In order to enhance the consistency of the influent, a separate holding tank was cannibalized from another system to collect and slow-feed the segregated Mn wastewater from the factory into CCPT. As a result, the baseline influent Mn decreased from 17.2+18.7 mg¹L^-1 at pre-pilot to 5.15+8.11 mg¹L^-1 post-pilot (70.1% reduction). Likewise, the pre-trial and post-trial average influent Cu values to CCPT were 52.0+54.6 mg¹L^-1 and 33.9+12.7 mg¹L^-1, respectively (34.8% reduction). However, the raw Ni content of 0.97+0.39 mg¹L^-1 at pre-pilot increased to 1.06+0.17 mg¹L^-1 at post-pilot. The average Mn output declined from 10.9+11.7 mg¹L^-1 at pre-pilot to 0.44+1.33 mg¹L^-1 at post-pilot (96.0% reduction) as a result of the pH and ORP operating setpoint changes. In similar fashion, the output Cu quality improved from 1.60+5.38 mg¹L^-1 to 0.55+1.02 mg¹L^-1 (65.6% reduction) while the Ni output sustained a 50% enhancement during the pilot study (0.22+0.19 mg¹L^-1 reduced to 0.11+0.06 mg¹L^-1). pH and ORP were shown to be significantly instrumental to the precipitative versatility of the CCPT System.

Keywords: copper, co-precipitation, industrial wastewater treatment, manganese, optimization, pilot study

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Authors: Ahmed Elbeltagy, Francesca Bertolini, Damarius Fleming, Angelica Van Goor, Chris Ashwell, Carl Schmidt, Donald Kugonza, Susan Lamont, Max Rothschild

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The major factor in shaping genomic variation of the African indigenous rural chicken is likely natural selection drives the development genetic footprints in the chicken genomes. To investigate such a hypothesis of a selection footprint, a total of 292 birds were randomly sampled from three indigenous ecotypes from East Africa (Uganda, Rwanda) and North Africa (Egypt) and two registered Egyptian breeds (Fayoumi and Dandarawi), and from the synthetic Kuroiler breed. Samples were genotyped using the Affymetrix 600K Axiom® Array. A total of 526,652 SNPs were utilized in the downstream analysis after quality control measures. The intra-population runs of homozygosity (ROH) that were consensuses in > 50% of individuals of an ecotype or > 75% of a breed were studied. To identify inter-population differentiation due to genetic structure, FST was calculated for North- vs. East- African populations in addition to population-pairwise combinations for overlapping windows (500Kb with an overlap of 250Kb). A total of 28,563 ROH were determined and were classified into three length categories. ROH and Fst detected sweeps were identified on several autosomes. Several genes in these regions are likely to be related to adaptation to local environmental stresses that include high altitude, diseases resistance, poor nutrition, oxidative and heat stresses and were linked to gene ontology terms (GO) related to immune response, oxygen consumption and heme binding, carbohydrate metabolism, oxidation-reduction, and behavior. Results indicated a possible effect of natural selection forces on shaping genomic structure for adaptation to local environmental stresses.

Keywords: African Chicken, runs of homozygosity, FST, selection footprints

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Authors: ELdessouky Sheta, Sayed Elzomor, Haithem Farghali, Kawkab A. Ahmed, Naglaa A. Abd Elkader

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The present investigation has been dealt with Spirocerca (S.) lupi infested mongrel dogs. This parasitic disease is highly infective to human beings and carnivores. The diagnosis march has been comprised the lateral contrast thoracic radiographs, fecal examination, blood profile, endoscopic examination and histopathological sections of deep seated pinch biopsies. These infested dogs have been put under an adopted treatment with Ivermectin injection combined with oral prednisolone. The obtained results reveal an absence of the pessimistic recognitions particularly after 3 weeks from the onset of treatment. Endoscopically the presented esophageal nodules are marked out in the distal third of infested dogs' esophagus as masses assigned into the esophageal lumen and fundus of stomach. The endoscopic outlook of Spirocerca lupi lesions has been considered an integral procedure of the diagnostic march and for evaluation of treatment follow up. The diagnostic procedures and the recommended treatment are the vet's guidance to care for Spirocerca lupi in dogs, hoping in future to prevent this disease from being spread among human beings and other carnivores.

Keywords: endoscopy, esophagus, stomach spirocercosis, dogs

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Authors: Siyao Zhu, Yifang Xu

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After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. The hands-free requirement from the first responders excludes the use of tedious manual control and operation. In unknown, unstructured, and obstructed environments, natural-language-based supervision is not amenable for first responders to formulate, and is difficult for robots to understand. Brain-computer interface is a promising option to overcome the limitations. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: consensus assessment, electroencephalogram, emergency response, human-robot collaboration, intention recognition, search and rescue

Procedia PDF Downloads 86

Authors: Ebasa Girma, Nathnael Minyelshowa, Lebsework Negash

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The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and models them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition, and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests show that the architecture offers a number of advantages that include fault-tolerance, robustness, developer, and user-friendliness.

Keywords: deep learning, multi-UAVs, precision agriculture, UAVs architecture

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Authors: Prashant Dhiman, Viranshu Kumar, Pradeep Joshi

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Lot of research is going on to study the effect of cryogenic treatment on materials. Cryogenic treatment is a heat treatment process which is used widely to enhance the mechanical and metallurgical properties of various materials whether the material is ferrous or non ferrous. In almost all ferrous metals, it is found that retained austenite is converted into martensite. Generally deep cryogenic treatment is done using liquid nitrogen having temperature of -195 ℃. The austenite is unstable at this stage and converts into martensite. In non ferrous materials there presents a microcavity and under the action of stress it becomes crack. When this crack propagates, fracture takes place. As the metal contract under low temperature, by doing cryogenic treatment these microcavities will be filled hence increases the soundness of the material. Properties which are enhanced by cryogenic treatment of both ferrous and non ferrous materials are hardness, tensile strength, wear rate, electrical and thermal conductivity, and others. Also there is decrease in residual stress. A large number of manufacturing process (EDM, CNC etc.) are using cryogenic treatment on different tools or workpiece to reduce their wear. In this Review paper the use of cryogenic heat treatment in different manufacturing has been shown along with their advantages.

Keywords: cyrogenic treatment, EDM (Electrical Discharge Machining), CNC (Computer Numeric Control), Mechanical and Metallurgical Properties

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Authors: Hye Jin Choi, Mirim Jin, Jeong June Choi

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Green tea, which is one of the most popular of tea, contains various ingredients that help health. Epigallocatechin gallate (EGCG) is one of the main active polyphenolic compound possessing diverse biologically beneficial effects such as anti-oxidation, anti-cancer founding in green tea. This study was performed to investigate the anti-diabetic effect of high-purity EGCG ( > 98%) in a spontaneous diabetic mellitus animal model, db/db mouse. Four-week-old male db/db mice, which was induced to diabetic mellitus by the high-fat diet, were orally administered with high-purity EGCG (10, 50 and 100 mg/kg) for 4 weeks. Daily weight and diet efficiency were examined, and blood glucose level was assessed once a week. After 4 weeks of EGCG administration, fasting blood glucose level was measured. Then, the mice were sacrificed and total abdominal fat was sampled to examine the change in fat weight. Plasma was separated from the blood and the levels of aspartate amino-transferase (ALT) and alanine amino-transferase (AST) were investigated. As results, blood glucose and body weight were significantly decreased by EGCG treatment compared to the control group. Also, the amount of abdominal fat was down-regulated by EGCG. However, ALT and AST levels, which are indicators of liver function, were similar to those of control group. Taken together, our study suggests that high purity EGCG is capable of treating diabetes mellitus based in db / db mice with safety and has a potent to develop a therapeutics for metabolic disorders. This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry (IPET) through High Value-added Food Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (317034-03-2-HD030)

Keywords: anti-diabetic effect, db/db mouse, diabetes mellitus, green tea, epigallocatechin gallate

Procedia PDF Downloads 180

Authors: Emi Yuda, Yutaka Yoshida, Junichiro Hayano

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Distinct circadian rhythm of activity, i.e., high activity during the day and deep rest at night are a typical feature of a healthy lifestyle. Exposure to the skylight is thought to be an important factor to increase arousal level and maintain normal circadian rhythm. To examine whether sunshine hours influence the day-night contract of activity, we analyzed the relationship between 24-hour heart rate (HR) and weather data of the recording day. We analyzed data in 36,500 males and 49,854 females of Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR) database in Japan. Median (IQR) sunshine duration was 5.3 (2.8-7.9) hr. While sunshine hours had only modest effects of increasing 24-hour average HR in either gender (P=0.0282 and 0.0248 for male and female) and no significant effects on nighttime HR in either gender, it increased daytime HR (P = 0.0007 and 0.0015) and day-night HF difference in both genders (P < 0.0001 for both) even after adjusting for the effects of average temperature, atmospheric pressure, and humidity. Our observations support for the hypothesis that longer sunshine hours enhance circadian rhythm of activity.

Keywords: big data, circadian rhythm, heart rate, sunshine

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Authors: Wafa Jahouach-Rabai, Khouloud Ouerghi, Zohra Azzouz-Berriche, Faouzi Hosni

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Widely used organic products in the pharmaceutical industry have been detected in environmental systems, essentially carboxylic acids. In this purpose, the degradation efficiency of these contaminants was evaluated using an advanced oxidation process (AOP), namely ionization process as an alternative to conventional water treatment technologies. This process permitted the generation of radical reactions to directly degrade organic pollutants in wastewater. In fact, gamma irradiation of aqueous solutions produces several reactive radicals, essentially hydroxyl radical (OH), to destroy recalcitrant pollutants. Different concentrations of aqueous solutions of Fumaric acid (FA) were considered in this study (0.1-1 mmol/L), which were treated by irradiation doses from 1 to 15 kGy with 6.1 kGy/h rate by ionizing system in pilot scale (⁶⁰Co irradiator). Variations of main parameters influencing degradation efficiency versus absorbed doses were released in the aim to optimize total mineralization of considered pollutants. Preliminary degradation pathway until complete mineralization into CO₂ has been suggested based on detection of residual degradation derivatives using different techniques, namely high performance liquid chromatography (HPLC) and electron paramagnetic resonance spectroscopy (EPR). Results revealed total destruction of treated compound, which improve the efficiency of this process in water remediation. We investigated the reactivity of hydroxyl radicals generated by irradiation on dicarboxylic acid (FA) in aqueous solutions, leading to its degradation into other smaller molecules. In fact, gamma irradiation of FA leads to the formation of hydroxylated intermediates such as hydroxycarbonyl radical which were identified by EPR spectroscopy. Finally, pilot plant irradiation facilities improved the applicability of radiation technology on large scale.

Keywords: AOP, radiolysis, fumaric acid, gamma irradiation, hydroxyl radical, EPR, HPLC

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Authors: Randy Price, Harbir Antil, Rainald Löhner, Fumiya Togashi

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Chemically reacting flows are common in engineering applications such as hypersonic flow, combustion, explosions, manufacturing process, and environmental assessments. The number of reactions in combustion simulations can exceed 100, making a large number of flow and combustion problems beyond the capabilities of current supercomputers. Motivated by this, deep neural networks (DNNs) will be introduced with the goal of eventually replacing the existing chemistry software packages with DNNs. The DNNs used in this paper are motivated by the Residual Neural Network (ResNet) architecture. In the continuum limit, ResNets become an optimization problem constrained by an ODE. Such a feature allows the use of ODE control techniques to enhance the DNNs. In this work, DNNs are constructed, which update the species un at the nᵗʰ timestep to uⁿ⁺¹ at the n+1ᵗʰ timestep. Parallel DNNs are trained for each species, taking in uⁿ as input and outputting one component of uⁿ⁺¹. These DNNs are applied to multiple species and reactions common in chemically reacting flows such as H₂-O₂ reactions. Experimental results show that the DNNs are able to accurately replicate the dynamics in various situations and in the presence of errors.

Keywords: chemical reacting flows, computational fluid dynamics, ODEs, residual neural networks, ResNets

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Authors: Farhad Ghorbandordinejad, Shamsoddin Aref

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Wikis and blogs, defined as educational tools in line with the objectives of collaborative writing, are regarded as innovative ways of writing addressing the problems of conventional types of writing. Although writing in wikis and blogs step in different contexts, they are both aiming at betterment of collaborative writing procedures. It is believed that due to certain reasons bringing in wikis and blogs to learners' life can lead to better performance of writing. This study aimed at dipping into pedagogical aspects of wikis and blogs in the hope of eliminating prior traditional mistakes and bringing students together in a more constructive L2 context. To this end, three groups of intermediate students were experimented in three settings of wiki-group, blog-group and conventional (control) group. Despite conventional group learners, participants in both experimental groups experienced L2 writing in a new telecollaborative context. An achievement test was administered after the treatment to check learners’ degree of improvement in EFL writing. The results of this study provide a deep insight towards the effectiveness of writing in the contexts of wikis and blogs compared with conventional writing procedures. The overall conclusion drawn from the distinction of conventional writing, on one hand, and wikis and blogs, on the other hand, indicates that the latter channels of writing are more constructive for learners’ writing improvements.

Keywords: collaborative writing, wikis, blogs, writing achievement

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Authors: Radka Gorejova, Ivana Sisolakova, Jana Shepa, Frederika Chovancova, Renata Orinakova

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Diabetes mellitus (DM) is a serious metabolic disease characterized by chronic hyperglycemia. Often, the symptoms are not sufficiently observable at early stages, and so hyperglycemia causes pathological and functional changes before the diagnosis of the DM. Therefore, the development of an electrochemical sensor that will be fast, accurate, and instrumentally undemanding is currently needful. Screen-printed carbon electrodes (SPCEs) can be considered as the most suitable matrix material for insulin sensors because of the small size of the working electrode. It leads to the analyst's volume reduction to only 50 µl for each measurement. The surface of bare SPCE was modified by a combination of chitosan, multi-walled carbon nanotubes (MWCNTs), and zinc nanoparticles (ZnNPs) to obtain better electrocatalytic activity towards insulin oxidation. ZnNPs were electrochemically deposited on the chitosan-MWCNTs/SPCE surface using the pulse deposition method. Thereafter, insulin was determined on the prepared electrode using chronoamperometry and electrochemical impedance spectroscopy (EIS). The chronoamperometric measurement was performed by adding a constant amount of insulin in 0.1 M NaOH and PBS (2 μl) with the concentration of 2 μM, and the current response of the system was monitored after a gradual increase in concentration. Subsequently, the limit of detection (LOD) of the prepared electrode was determined via the Randles-Ševčík equation. The LOD was 0.47 µM. Prepared electrodes were studied also as the impedimetric sensors for insulin determination. Therefore, various insulin concentrations were determined via EIS. Based on the performed measurements, the ZnNPs/chitosan-MWCNTs/SPCE can be considered as a potential candidate for novel electrochemical sensor for insulin determination. Acknowledgments: This work has been supported by the projects Visegradfund project number 22020140, VEGA 1/0095/21 of the Slovak Scientific Grant Agency, and APVV-PP-COVID-20-0036 of the Slovak Research and Development Agency.

Keywords: zinc nanoparticles, insulin, chronoamperometry, electrochemical impedance spectroscopy

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Authors: Mahshid Davoodpoor, Zahra Shamohammadi Ghahsareh, Saeid Razfar, Alaleh Dabbaghi

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Nowadays, air pollution has become a topic of great concern all over the world. One of the main sources of air pollution is automobile exhaust gas, which introduces a large number of toxic gases, including CO, unburned hydrocarbons (HCs), NOx, and non-methane hydrocarbons (NMHCs), into the air. The application of three-way catalysts (TWCs) is still the most effective strategy to mitigate the emission of these pollutants. Due to the stringent environmental regulations which continuously become stricter, studies on the TWCs are ongoing despite several years of research and development. This arises from the washcoat complexity and the several numbers of parameters involved in the redox reactions. The main objectives of these studies are the optimization of washcoat formulation and the investigation of different coating modes. Perovskite (ABO₃), as a promising class of materials, has unique features that make it versatile to use as an alternative to commonly mixed oxides in washcoats. High catalytic activity for oxidation reactions and its relatively high oxygen storage capacity are important properties of perovskites in catalytic applications. Herein, La₀.₈Ba₀.₂FeO₃ perovskite material was synthesized using the co-precipitation method and characterized by XRD, ICP, and BET analysis. The effect of synthesis conditions, including B site metal (Fe and Co), metal precursor concentration, and dopant (Ba), were examined on the phase purity of the products. The selected perovskite sample was used as one of the components in the TWC formulation to evaluate its catalytic performance through Light-off, oxygen storage capacity, and emission analysis. Results showed a remarkable increment in oxygen storage capacity and also revealed that T50 and emission of CO, HC, and NOx reduced in the presence of perovskite structure which approves the enhancement of catalytic performance for the new washcoat formulation. This study shows the brilliant future of advanced oxide structures in the TWCs.

Keywords: Perovskite, three-way catalyst, PGMs, PGMs reduction

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Authors: Radovan Čobanović, Milica Rankov Šicar

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The meat is in the group of perishable food which can be spoiled very rapidly if stored at room temperature. Salting in combination with smoke is intended to extend shelf life, and also to form the specific taste, odor and color. The smoke do not affect only on taste and flavor of the product, it has a bactericidal and oxidative effect and that is the reason because smoked products are less susceptible to oxidation and decay processes. According to mentioned the goal of this study was to evaluate shelf life of smoked ham, which is stored in conditions of high temperature (+15 °C). For the purposes of this study analyzes were conducted on eight samples of smoked ham every 7th day from the day of reception until 21st day. During this period, smoked ham is subjected to sensory analysis (appearance, odor, taste, color, aroma) and bacteriological analyzes (Listeria monocytogenes, Salmonella spp. and yeasts and molds) according to Serbian state regulation. All analyses were tested according to ISO methodology: sensory analysis ISO 6658, Listeria monocytogenes ISO 11 290-1, Salmonella spp ISO 6579 and yeasts and molds ISO 21527-2. Results of sensory analysis of smoked ham indicating that the samples after the first seven days of storage showed visual changes at the surface in the form of allocations of salt, most likely due to the process of drying out the internal parts of the product. The sample, after fifteen days of storage had intensive exterior changes, but the taste was still acceptable. Between the fifteenth and twenty-first day of storage, there is an unacceptable change on the surface and inside of the product and the occurrence of molds and yeasts but neither one analyzed pathogen was found. Based on the obtained results it can be concluded that this type of product cannot be stored for more than seven days at an elevated temperature of +15°C because there are a visual changes that would certainly have influence on decision of customers when purchase of this product is concerned.

Keywords: sustainability, smoked meat products, food engineering, agricultural process engineering

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Zi Zhu

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The last decade has witnessed increasing urban environmental uprisings in China, as thousands of citizens swarmed into streets to express their deep concerns about the environmental threat and public health through various collective actions. The prevalent western approaches to collective actions, which usually treat urban riots and social movements as distinct phenomenon, have plagued an adequate analysis of the urban environmental uprisings in China. The increasing urban environmental contention can neither be categorized into riots nor social movements, as they carry the features of both: at first sight, they are spontaneous, disorganized and disruptive with an absence of observable mobilization process; however, unlike riots in the west, these collective actions conveyed explicit demand in a mostly non-destructive way rather than a pure expression of frustration. This article proposes a different approach to urban environmental uprisings in China which concerns the diminishing boundaries between riots and social movements and points to the underlying structural causes to the unique forms of urban environmental contention. Taking the urban anti-PX protests as examples, this article analyzes the societal and political structural environment faced by the Chinese environmental protesters and its influence on the origin and development of their contention.

Keywords: urban environmental uprisings, China, anti-PX protests, opportunity structure

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Authors: Osamede Asowata

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The current resurgence of interest in the use of renewable energy is driven by the need to reduce the high environmental impact of fossil-based energy. The aim of this paper is to evaluate the effect of a stationary PV panel on the charging rate of deep-cycle valve regulated lead-acid (DCVRLA) batteries. Stationary PV panels are set to a fixed tilt and orientation angle, which plays a major role in dictating the output power of a PV panel and subsequently on the charging time of a DCVRLA battery. In a basic PV system, an energy storage device that stores the power from the PV panel is necessary due to the fluctuating nature of the PV voltage caused by climatic conditions. The charging and discharging times of a DCVRLA battery were determined for a twelve month period from January through December 2012. Preliminary results, which include regression analysis (R2), conversion-time per week and work-time per day, indicate that a 36 degrees tilt angle produces a good charging rate for a latitude of 26 degrees south throughout the year.

Keywords: tilt and orientation angles, solar chargers, PV panels, storage devices, direct solar radiation.

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Authors: Aleksandras Velykis, Antanas Satkus

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Clay loam and clay soils are typical for northern Lithuania. These soils are susceptible to physical degradation in the case of intensive use of heavy machinery for field operations. However, clayey soils having poor physical properties by origin require more intensive tillage to maintain proper physical condition for grown crops. Therefore not only choice of suitable tillage system is very important for these soils in the region, but also additional search of other measures is essential for good soil physical state maintenance. Research objective: To evaluate the long-term effects of different intensity tillage as well as its combinations with supplementary agronomic practices on improvement of soil physical conditions and environmental sustainability. The experiment examined the influence of deep and shallow ploughing, ploughless tillage, combinations of ploughless tillage with incorporation of lime sludge and cover crop for green manure and application of the same cover crop for mulch without autumn tillage under spring and winter crop growing conditions on clay loam (27% clay, 50% silt, 23% sand) Endocalcaric Endogleyic Cambisol. Methods: The indicators characterizing the impact of investigated measures were determined using the following methods and devices: Soil dry bulk density – by Eijkelkamp cylinder (100 cm3), soil water content – by weighing, soil structure – by Retsch sieve shaker, aggregate stability – by Eijkelkamp wet sieving apparatus, soil mineral nitrogen – in 1 N KCL extract using colorimetric method. Results: Clay loam soil physical state (dry bulk density, structure, aggregate stability, water content) depends on tillage system and its combination with additional practices used. Application of cover crop winter mulch without tillage in autumn, ploughless tillage and shallow ploughing causes the compaction of bottom (15-25 cm) topsoil layer. However, due to ploughless tillage the soil dry bulk density in subsoil (25-35 cm) layer is less compared to deep ploughing. Soil structure in the upper (0-15 cm) topsoil layer and in the seedbed (0-5 cm), prepared for spring crops is usually worse when applying the ploughless tillage or cover crop mulch without autumn tillage. Application of lime sludge under ploughless tillage conditions helped to avoid the compaction and structure worsening in upper topsoil layer, as well as increase aggregate stability. Application of reduced tillage increased soil water content at upper topsoil layer directly after spring crop sowing. However, due to reduced tillage the water content in all topsoil markedly decreased when droughty periods lasted for a long time. Combination of reduced tillage with cover crop for green manure and winter mulch is significant for preserving the environment. Such application of cover crops reduces the leaching of mineral nitrogen into the deeper soil layers and environmental pollution. This work was supported by the National Science Program ‘The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems’ [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: clay loam, endocalcaric endogleyic cambisol, mineral nitrogen, physical state

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Authors: Reihaneh Mahdavishahri

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This paper provides a brief introduction to emotionally focused therapy (EFT) and its culturally sensitive and informed application when working with immigrant couples. EFT's grounding in humanistic psychology prioritizes a non-pathologizing and empathic understanding of individuals' experiences, creating a safe space for couples to explore and create new experiences without imposing judgment or prescribing the couple "the right way of interacting" with one another. EFT's emphasis on attachment, bonding, emotions, and corrective emotional experiences makes it a fitting approach to work with multicultural couples, allowing for the corrective emotional experience to be shaped and informed by the couples' unique cultural background. This paper highlights the challenges faced by immigrant couples and explores how immigration adds a complex layer to each partner’s sense of self, their attachment bond, and their sense of safety and security within their relationships. Navigating a new culture, creating a shared sense of purpose, and re-establishing emotional bonds can be daunting for immigrant couples, often leading to a deep sense of disconnection and vulnerability. Reestablishing and fostering secure attachment between the partners in the safety of the therapeutic space can be a protective factor for these couples.

Keywords: attachment, culturally informed care, emotionally focused therapy, immigration

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Navajyoti Panda, R. S. Pawar

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In this study, process parameters like punch angle, die opening, grain direction, and pre-bend condition of the strip for deep draw of high strength low alloy steel HSLA 420 are investigated. The finite element method (FEM) in association with the Taguchi and the analysis of variance (ANOVA) techniques are carried out to investigate the degree of importance of process parameters in V-bending process for HSLA 420&ST12 grade material. From results, it is observed that punch angle had a major influence on the spring-back. Die opening also showed very significant role on spring back. On the other hand, it is revealed that grain direction had the least impact on spring back; however, if strip from flat sheet is taken, then it is less prone to spring back as compared to the strip from sheet metal coil. HyperForm software is used for FEM simulation and experiments are designed using Taguchi method. Percentage contribution of the parameters is obtained through the ANOVA techniques.

Keywords: bending, spring-back, v-bending, FEM, Taguchi, HSLA 420 and St12 materials, HyperForm, profile projector

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Authors: Karthick Prasad Gunasekaran, B. Chase Babrich, Saurabh Shirodkar, Hee Hwang

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Construction preparation is crucial for the success of a construction project. By involving project participants early in the construction phase, project managers can plan ahead and resolve issues early, resulting in project success and satisfaction. This study uses quantitative data from construction management projects to determine the relationship between the pre-construction phase, construction schedule, and customer satisfaction. This study examined a total of 65 construction projects and 93 clients per job to (a) identify the relationship between the pre-construction phase and program reduction and (b) the pre-construction phase and customer retention. Based on a quantitative analysis, this study found a negative correlation between pre-construction status and project schedule in 65 construction projects. This finding means that the more preparatory work done on a particular project, the shorter the total construction time. The Net Promoter Score of 93 clients from 65 projects was then used to determine the relationship between construction preparation and client satisfaction. The pre-construction status and the projects were further analyzed, and a positive correlation between them was found. This shows that customers are happier with projects with a higher ready-to-build ratio than projects with less ready-to-build.

Keywords: NLP, BERT, LLM, deep learning, classification

Procedia PDF Downloads 96