Search results for: deep oxidation

Authors: Khaled S. Al Salhen

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Aldehyde oxidase (AO) and xanthine oxidoreductase (XOR) catalyze the oxidation of many different N-heterocyclic compounds as well as aliphatic and aromatic aldehydes to their corresponding lactam and carboxylic acids respectively. The present study examines the oxidation of dimethylamino-cinnamaldehyde (DMAC), vanillin and phenanthridine by AO and xanthine by XOR from Drosophila cytosol. Therefore, the results obtained in the present study showed the DMAC, vanillin and phenanthridine substrates used were found to be good substrates of Drosophila AO and xanthine is the preferred substrate for Drosophila XOR. Km values of AO substrates were observed with DMAC (50±5.4 µM), phenanthridine (80±9.1 µM) and vanillin (303±11.7 µM) respectively for Drosophila cytosol. The Km values for DMAC and phenanthridine were ~6 and ~4 fold lower than that for vanillin as a substrate. The Km for XOR with xanthine using NAD+ as an electron acceptor was 27±4.1 µM. Relatively low Vmax values were obtained with phenanthridine (1.78±0.38 nmol/min/mg protein) and DMAC (1.80±0.35 nmol/min/mg protein). The highest Vmax was obtained from Drosophila cytosol with vanillin (7.58±2.11 nmol/min/mg protein). It is concluded these results that AO and XOR in Drosophila were able to catalyse the biotransformation of numerous substrates of the well-characterised mammalian AO and XOR. The kinetic parameters have indicated that the activity of AO of Drosophila may be a significant factor the oxidation of aromatic aldehyde compounds.

Keywords: aldehyde oxidase, xanthine oxidoreductase, dimethylamino-cinnamaldehyde, vanillin, phenanthridine, Drosophila melanogaster

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Authors: Ilkham Gafurovich Atabaev, Khimmatali Nomozovich Juraev

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The activation energy of conductivity in p-i-n SiC structures fabricated by doping with Aluminum using the new low-temperature diffusion method is investigated. In this method, diffusion is stimulated by the flux of carbon and silicon vacancies created by surface oxidation. The activation energy of conductivity in the p - layer is 0.25 eV and it is close to the ionization energy of Aluminum in 4H-SiC from 0.21 to 0.27 eV for the hexagonal and cubic positions of aluminum in the silicon sublattice for weakly doped crystals. The conductivity of the i-layer (measured in the reverse biased diode) shows 2 activation energies: 0.02 eV and 0.62 eV. Apparently, the 0.62 eV level is a deep trap level and it is a complex of Aluminum with a vacancy. According to the published data, an analogous level system (with activation energies of 0.05, 0.07, 0.09 and 0.67 eV) was observed in the ion Aluminum doped 4H-SiC samples.

Keywords: activation energy, aluminum, low temperature diffusion, SiC

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Authors: G. Blugan, B. Jiang, J. Thornberry, P. Sturzenegger, U. Gonzenbach, M. Misson, D. Cartlidge, R. Stenerud, J. Kuebler

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Low-cost ceramic spheres were developed and manufactured from the engineering ceramic aluminium oxide. Hollow spheres of 50 mm diameter with a wall thickness of 0.5-1.0 mm were produced via an adapted slip casting technique. It was possible to produce the spheres with good repeatability and with no defects or failures in the spheres due to the manufacturing process. The spheres were developed specifically for use in buoyancy devices for deep-sea exploration conditions at depths of 3000 m below sea level. The spheres with a 1.0 mm wall thickness exhibit a buoyancy of over 54% while the spheres with a 0.5 mm wall thickness exhibit a buoyancy of over 73%. The mechanical performance of the spheres was confirmed by performing a hydraulic burst pressure test on individual spheres. With a safety factor of 3, all spheres with 1.0 mm wall thickness survived a hydraulic pressure of greater than 150 MPa which is equivalent to a depth of more than 5000 m below sea level. The spheres were then incorporated into a buoyancy module. These hollow aluminium oxide ceramic spheres offer an excellent possibility of deep-sea exploration to depths greater than the currently used technology.

Keywords: buoyancy, ceramic spheres, deep-sea, oil exploration

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Authors: Krittiya Pornmai, Sumaeth Chavadej

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The increasing in global energy demand has affected the climate change caused by the generation of greenhouse gases. Therefore, the objective of this work was to investigate a direct production of synthesis gas from a CO₂-containing natural gas by using gliding arc discharge plasma technology. In this research, the effects of steam reforming, combined steam reforming and partial oxidation, and using multistage gliding arc discharge system on the process performance have been discussed. The simulated natural gas used in this study contains 70% methane, 5% ethane, 5% propane, and 20% carbon dioxide. In comparison with different plasma reforming processes (under their optimum conditions), the steam reforming provides the highest H₂ selectivity resulting from the cracking reaction of steam. In addition, the combined steam reforming and partial oxidation process gives a very high CO production implying that the addition of both oxygen and steam can offer the acceptably highest synthesis gas production. The stage number of plasma reactor plays an important role in the improvement of CO₂ conversion. Moreover, 3 stage number of plasma reactor is considered as an optimum stage number for the reforming of CO₂-containing natural gas with steam and partial oxidation in term of providing low energy consumption as compared with other plasma reforming processes.

Keywords: natural gas, reforming process, gliding arc discharge, plasma technology

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Authors: Yohannes Bisa Biramo

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This study examined the meditational role of surface acting and deep acting on the relationship between calling orientation and work engagement of teachers in secondary schools of Wolaita Zone, Wolaita, Ethiopia. A predictive non-experimental correlational design was performed among 300 secondary school teachers. Stratified random sampling followed by a systematic random sampling technique was used as the basis for selecting samples from the target population. To analyze the data, Structural Equation Modeling (SEM) was used to test the association between the independent variables and the dependent variables. Furthermore, the goodness of fit of the study variables was tested using SEM to see and explain the path influence of the independent variable on the dependent variable. Confirmatory factor analysis (CFA) was conducted to test the validity of the scales in the study and to assess the measurement model fit indices. The analysis result revealed that calling was significantly and positively correlated with surface acting, deep acting and work engagement. Similarly, surface acting was significantly and positively correlated with deep acting and work engagement. And also, deep acting was significantly and positively correlated with work engagement. With respect to mediation analysis, the result revealed that surface acting mediated the relationship between calling and work engagement and also deep acting mediated the relationship between calling and work engagement. Besides, by using the model of the present study, the school leaders and practitioners can identify a core area to be considered in recruiting and letting teachers teach, in giving induction training for newly employed teachers and in performance appraisal.

Keywords: calling, surface acting, deep acting, work engagement, mediation, teachers

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Ajay Kumar Choubey, Geeta Agnihotri, C. Sasikumar, Rashmi Dwivedi

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Deep-drawing operations are performed widely in industrial applications. It is very important for efficiency to achieve parts with no or minimum defects. Deep drawn parts are used in high performance, high strength and high reliability applications where tension, stress, load and human safety are critical considerations. Wrinkling is a kind of defect caused by stresses in the flange part of the blank during metal forming operations. To avoid wrinkling appropriate blank-holder pressure/force or drawbead can be applied. Now-a-day computer simulation plays a vital role in the field of manufacturing process. So computer simulation of manufacturing has much advantage over previous conventional process i.e. mass production, good quality of product, fast working etc. In this study, a two dimensional elasto-plastic Finite Element (F.E.) model for Mild Steel material blank has been developed to study the behavior of the flange wrinkling and deep drawing parameters under different Blank-Holder Pressure (B.H.P.). For this, commercially available Finite Element software ANSYS 14 has been used in this study. Simulation results are critically studied and salient conclusions have been drawn.

Keywords: ANSYS, deep drawing, BHP, finite element simulation, wrinkling

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Hesheng Wang, Haoyu Wang, Chungang Zhuang

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Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.

Keywords: pose estimation, deep learning, point cloud, bin-picking, 3D computer vision

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Authors: Nabi Ullah

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The single-step solvothermal method is used to prepare Cu/CuSe as an electrocatalyst for methanol electro-oxidation reaction (MOR). 1,3-butane-diol is selected as a reaction medium, whose viscosity and complex formation with Cu(II) ions dictate the catalyst morphology. The catalyst has a macroporous structure, which is composed of nanoballs with a high purity, crystallinity, and uniform morphology. The electrocatalyst is excellent for MOR, as it delivers a current density of 37.28 mA/mg at a potential of 0.6 V (vs Ag/AgCl) in the electrolyte of 1 M KOH and 0.75 M methanol at a 50 mV/s scan rate under conditions of cyclic voltammetry. The catalyst also shows good stability for 3600 s with negligible charge transfer resistance and a high electrochemical active surface area (ECSA) value of 0.100 mF/cm².

Keywords: MOR, copper selenide, electocatalyst, energy application

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Authors: A.Velavan, C. G. Saravanan, M. Vikneswaran, E. James Gunasekaran

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In present investigation, experiments were carried out to compare the effect of the ceramic coated piston crown and uncoated piston on combustion, performance and emission characteristics of a port injected Spark Ignited engine. The piston crown was coated with aluminium alloy in the form ceramic oxide layer of thickness 500 µm using micro-arc oxidation technique. This ceramic coating will act as a thermal barrier which reduces in-cylinder heat rejection and increases the durability of the piston by withstanding high temperature and pressure produced during combustion. Flame visualization inside the combustion chamber was carried out using AVL Visioscope combustion analyzer to predict the type of combustion occurs at different load condition. Based on the experimental results, it was found that the coated piston shows an improved thermal efficiency when compared to uncoated piston. This is because more heat presents in the combustion chamber which helps efficient combustion of the fuel. The CO and HC emissions were found to be reduced due to better combustion of the fuel whereas NOx emission was increased due to increase in combustion temperature for ceramic coated piston.

Keywords: coated piston, micro-arc oxidation, thermal barrier, thermal efficiency, visioscope

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Askar Sabet, Abdolrasoul Fakhraee, Motahahre Ramezanpour, Noorallah Alipour

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An efficient and green method for oxidation of thiols to the corresponding disulfides is reported using ionic liquid [HSO3N(C2H4OSO3H)3] in the presence of free nano-Fe2O3 at 60°C. Ionic liquid is selective oxidant for S-S Coupling variety aliphatic and aromatic of thiols to corresponding disulfide in the presence of free nano-Fe2O3 as recoverable catalyst. Reaction has been performed in methanol as an inexpensive solvent. This reaction is clean and easy work-up with no side reaction.

Keywords: thiol, disulfide, ionic liquid, free nano-Fe2O3, oxidation, coupling

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Authors: A. V. Shelke, P. S. More

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This research aimed to study the kinetic reaction of reused cooking oil and to find the optimum condition of its process. The feedstock was collected from the street sellers and also prepared at laboratory. From this research, it is found that the kinetic reaction of reused sunflower oil (auto-oxidation) is obtained in terms of variation of the absorption coefficient of unexposed sunflower oil as 0.05 which is very close to that of exposed sunflower oil 0.075. At room temperature, the optimum intensity obtained from optical absorption spectroscopy study is 0.267 for unexposed sunflower oil and 0.194 for exposed sunflower oil. However, results indicated that FTIR spectroscopy is accurate and precise enough for such determination. Free Fatty Acid (FFA% = 026), acid ~53% and safonication ~%192 get reduce in exposed oil was investigated.

Keywords: friction, oxidation, sunflower oil, vegetable oils

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: O. Tovide, N. Jahed, N. Mohammed, C. E. Sunday, H. R. Makelane, R. F. Ajayi, K. M. Molapo, A. Tsegaye, M. Masikini, S. Mailu, A. Baleg, T. Waryo, P. G. Baker, E. I. Iwuoha

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A graphenated–polyaniline (GR-PANI) nanocomposite sensor was constructed and used for the determination of anthracene. The direct electro-oxidation behavior of anthracene on the GR-PANI modified glassy carbon electrode (GCE) was used as the sensing principle. The results indicate thatthe response profile of the oxidation of anthracene on GR-PANI-modified GCE provides for the construction of sensor systems based onamperometric and potentiometric signal transductions. A dynamic linear range of 0.12- 100 µM anthracene and a detection limit of 0.044 µM anthracene were established for the sensor system.

Keywords: electrochemical sensors, environmental pollutants, graphenated-polymers, polyaromatic hydrocarbon

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Arwa Abdelhay, Inshad Jum’h, Abeer Albsoul, Khalideh Alrawashdeh, Dina Al Tarazi

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Treated wastewater reuse has been considered recently as one of the successful management strategies to overcome water shortage in countries suffering from water scarcity. The non-readily biodegradable and recalcitrant pollutants in wastewater cannot be destructed by conventional treatment methods. This paper deals with the electrochemical treatment of dairy wastewater using a promising non-conventional Boron-Doped Diamond (BDD) anode. During the electrochemical process, different operating parameters were investigated, such as electrolysis time, current density, supporting electrolyte, chemical oxygen demand (COD), turbidity as well as absorbance/color. The experimental work revealed that electrochemical oxidation carried out with no added electrolyte has significantly reduced the COD, turbidity, and color (absorbance) by 72%, 76%, and 78% respectively. Results also showed that raising the current density from 5.1 mA/cm² to 7.7 mA/cm² has boosted COD, and color removal to 82.5%, and 83% respectively. However, the current density did not show any significant effect on the turbidity. Interestingly, it was observed that adding Na₂SO₄ and FeCl₃ as supporting electrolytes brought the COD removal to 91% and 97% respectively. Likewise, turbidity and color removal has been enhanced by the addition of the same supporting electrolytes.

Keywords: boron doped-diamond anode, dairy wastewater, electrochemical oxidation, supporting electrolytes

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Authors: P. Acosta-Santamaría, A. Ibatá-Soto, A. López-Vásquez

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Organic compounds in wastewaters coming from textile and pharmaceutical industry generated multiple harmful effects on the environment and the human health. One of them is the methyl orange (MeO), an azoic dye considered to be a recalcitrant compound. The heterogeneous photocatalysis emerges as an alternative for treating this type of hazardous compounds, through the generation of OH radicals using radiation and a semiconductor oxide. According to the author’s knowledge, catalysts such as TiO₂ doped with metals show high efficiency in degrading MeO; however, this presents economic limitations on industrial scale. Black sand can be considered as a naturally doped catalyst because in its structure is common to find compounds such as titanium, iron and aluminum oxides, also elements such as zircon, cadmium, manganese, etc. This study reports the photocatalytic activity of the mineral black sand used as semiconductor in the discoloration of MeO by oxidation and reduction photocatalytic techniques. For this, magnetic composites from the mineral were prepared (RM, M1, M2 and NM) and their activity were tested through MeO discoloration while TiO₂ was used as reference. For the fractions, chemical, morphological and structural characterizations were performed using Scanning Electron Microscopy with Energy Dispersive X-Ray (SEM-EDX), X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF) analysis. M2 fraction showed higher MeO discoloration (93%) in oxidation conditions at pH 2 and it could be due to the presence of ferric oxides. However, the best result to reduction process was using M1 fraction (20%) at pH 2, which contains a higher titanium percentage. In the first process, hydrogen peroxide (H₂O₂) was used as electron donor agent. According to the results, black sand mineral can be used as natural semiconductor in photocatalytic process. It could be considered as a photocatalyst precursor in such processes, due to its low cost and easy access.

Keywords: black sand mineral, methyl orange, oxidation, photocatalysis, reduction

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Authors: Malika Foudia, Larbi Zerroual

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The objective of this work is to improve the electrical proprieties of lead-acid battery with the addition of additives in electrolyte and in the cured plates before oxidation. The results showed that the addition of surfactant in sulfuric acid and 3% mineral additive in the cured plates change the morphology and the crystallite size of PAM after oxidation. The discharge capacity increases with the decrease of the crystallite size and the resistance of the active mass. This shows that the addition of mineral additive and the surfactant additive to the PAM, the electrical performance and the cycle life of lead- acid battery are significantly increases.

Keywords: lead-acid battery, additives, positive plate, impedance (EIS).

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Authors: Amal Khalifa, Nicolas Vana Santos

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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Yong Wee Foo, Lai Meng Tang

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Artificial Intelligence (AI), particularly the usage of deep neural networks for hierarchical representations from data, has found numerous complex applications across various domains, including computer vision, robotics, autonomous vehicles, and other scientific fields. However, their inherent “black box” nature can sometimes make it challenging for early researchers or school students of various levels to comprehend and trust the results they produce. Consequently, there has been a growing demand for reliable visualization tools in engineering and science education to help learners understand, trust, and explain a deep learning network. This has led to a notable emphasis on the visualization of AI in the research community in recent years. AI visualization tools are increasingly being adopted to significantly improve the comprehension of complex topics in deep learning. This paper proposes a novel approach to empower students to actively explore the inner workings of deep neural networks by combining the student-centered learning approach of flipped classroom models with the investigative power of AI visualization tools, namely, the TensorFlow Playground, the Local Interpretable Model-agnostic Explanations (LIME), and the SHapley Additive exPlanations (SHAP), for delivering an AI education curriculum. Combining the two factors is vital in fostering ownership, responsibility, and critical thinking skills in the age of AI.

Keywords: deep learning, explainable AI, AI visualization, representation learning

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Authors: Azubuike Ekwere

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The shallow and deep aquiferous horizons of the fractured and weathered crystalline basement Oban Massif of south-eastern Nigeria were studied during the dry and wet seasons. The criteria were ascertaining hydrochemistry relative to seasonal and spatial variations across the study area. Results indicate that concentrations of major cations and anions exhibit the order of abundance; Ca>Na>Mg>K and HCO3>SO4>Cl respectively, with minor variations across sampling seasons. Major elements Ca, Mg, Na and K were higher for the shallow aquifers than the deep aquifers across seasons. The major anions Cl, SO4, HCO3, and NO3 were higher for the deep aquifers compared to the shallow ones. Two water types were identified for both aquifer types: Ca-Mg-HCO3 and Ca-Na-Cl-SO4. Most of the parameters considered were within the international limits for drinking, domestic and irrigation purposes. Assessment by use of sodium absorption ratio (SAR), percent sodium (%Na) and the wilcox diagram reveals that the waters are suitable for irrigation purposes.

Keywords: shallow aquifer, deep aquifer, seasonal variation, hydrochemistry, Oban massif, Nigeria

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Authors: Alessandro Marano

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The author describes the use of deep plane face lifting and platysmaplasty to treat sagging neck with minimal scars. Series of case study. The author uses a selective deep plane face lift with a minimal access scar that not extend behind the ear lobe, neck liposuction and platysmaplasty to restore the sagging neck; the scars are minimal and no require drainage post-op. The deep plane face lifting can achieve a good result restoring vertical vectors in aging and sagging face, neck district can be treated without cutting the skin behind the ear lobe combining the SMAS vertical suspension and platysmaplasty; surgery can be performed in local anesthesia with sedation in day surgery and fast recovery. Restoring neck sagging without extend scars behind ear lobe is possible in selected patients, procedure is fast, safe, no drainage required, patients are satisfied and healing time is fast and comfortable.

Keywords: face lifting, aesthetic, face, neck, platysmaplasty, deep plane

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Authors: Anastasios Rodis

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Background: Case studies of walkers, climbers, and campers exposed to cold and wet conditions without limb water/windproof protection revealed experiences of muscle weakness and fatigue. It is reasonable to assume that a part of the fatigue could occur due to an alteration in substrate utilization, since reduction of performance in extreme cold conditions, may partially be explained by higher anaerobic glycolysis, reflecting higher carbohydrate oxidation and an increase accumulation rate of blood lactate. The aim of this study was to assess the effects of pre-exercise lower limb cooling on substrate utilization rate during sub-maximal exercise. Method: Six male university students (mean (SD): age, 21.3 (1.0) yr; maximal oxygen uptake (V0₂ max), 49.6 (3.6) ml.min⁻¹; and percentage of body fat, 13.6 (2.5) % were examined in random order after either 30min cold water (12°C) immersion utilized as the cooling strategy up to the gluteal fold, or under control conditions (no precooling), with tests separated by minimum of 7 days. Exercise consisted of 60min cycling at 50% V0₂ max, in a thermoneutral environment of 20°C. Subjects were also required to record a diet diary over the 24hrs prior to the each trial. Means (SD) for the three macronutrients during the 1 day prior to each trial (expressed as a percentage of total energy) 52 (3) % carbohydrate, 31 (4) % fat, and 17 (± 2) % protein. Results: The following responses to lower limb cooling relative to control trial during exercise were: 1) Carbohydrate (CHO) oxidation, and blood lactate (Bₗₐc) concentration were significantly higher (P < 0.05); 2) rectal temperature (Tᵣₑc) was significantly higher (P < 0.05), but skin temperature was significantly lower (P < 0.05); no significant differences were found in blood glucose (Bg), heart rate (HR) and oxygen consumption (V0₂). Discussion: These data suggested that lower limb cooling prior to submaximal exercise will shift metabolic processes from Fat oxidation to CHO oxidation. This shift from Fat to CHO oxidation will probably have important implications in the surviving scenario, since people facing accidental localized cooling of their limbs either through wading/falling in cold water or snow even if they do not perform high intensity activity, they have to rely on CHO availability.

Keywords: exercise in wet conditions, leg cooling, outdoors exercise, substrate utilization

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Authors: F. Javier Benitez, Juan L. Acero, Francisco J. Real, Elena Rodriguez

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Five selected pollutants which are frequently present in waters and wastewaters have been degraded by the advanced oxidation process constituted by UV radiation activated with the additional presence of persulfate (UV/PS). These pollutants were 1H-benzotriazole (BZ), N,N-diethyl-m-toluamide or DEET (DT), chlorophene (CP), 3-methylindole (ML), and nortriptyline hydrochloride (NH).While UV radiation alone almost not degraded these substances, the addition of PS generated the very reactive and oxidizing sulfate radical SO₄⁻. The kinetic study provided the second order rate constants for the reaction between this radical and each pollutant. An increasing dose of PS led to an increase in the degradation rate, being the highest results obtained at near neutral pH. Several water matrices were tested, and the presence of bicarbonate showed different effects: a decrease in the elimination of DT, BZ, and NH; and an increase in the oxidation of CP and ML. The additional presence of humic acids (AH) decreased this degradation, because of several effects: light screening and radical scavenging. The presence of several natural substances in waters (both types, inorganic and organic matter) usually diminishes the oxidation rates of organic pollutants, but this combination UV/PS process seems to be an efficient solution for the removal of the selected contaminants when are present in contaminated waters.

Keywords: water purification, UV activated persulfate, kinetic study, sulfate radicals

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Authors: Omar Masaud Almrhag, Frage Lhadi Abookleesh

Abstract:

Different types of food like banana, potato and chicken affect the quality of oil during deep fat frying. The changes in the quality of oil were evaluated and compared. Four different types of edible oils, namely, corn oil, soybean, canola, and palm oil were used for deep fat frying at 180°C ± 5°C for 5 h/d for six consecutive days. A potato was sliced into 7-8 cm length wedges and chicken was cut into uniform pieces of 100 g each. The parameters used to assess the quality of oil were total polar compound (TPC), iodine value (IV), specific extinction E1% at 233 nm and 269 nm, fatty acid composition (FAC), free fatty acids (FFA), viscosity (cp) and changes in the thermal properties. Results showed that, TPC, IV, FAC, Viscosity (cp) and FFA composition changed significantly with time (P< 0.05) and type of food. Significant differences (P< 0.05) were noted for the used parameters during frying of the above mentioned three products.

Keywords: frying potato, chicken, frying deterioration, quality of oil

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Authors: Firas Gerges, Frank Y. Shih

Abstract:

Malignant melanoma, known simply as melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient's death. When detected early, melanoma is curable. In this paper, we propose a deep learning model (convolutional neural networks) in order to automatically classify skin lesion images as malignant or benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: deep learning, skin cancer, image processing, melanoma

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

Abstract:

The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

Procedia PDF Downloads 52