Search results for: deep layer

Authors: Wei-Song Hung

Abstract:

We utilized pressure, vacuum, and evaporation-assisted self-assembly techniques through which graphene oxide (GO) was deposited on modified polyacrylonitrile (mPAN). The fabricated composite GO/mPAN membranes were applied to dehydrate 1-butanol mixtures by pervaporation. Varying driving forces in the self-assembly techniques induced different GO assembly layer microstructures. XRD results indicated that the GO layer d-spacing varied from 8.3 Å to 11.5 Å. The self-assembly technique with evaporation resulted in a heterogeneous GO layer with loop structures; this layer was shown to be hydrophobic, in contrast to the hydrophilic layer formed from the other two techniques. From the pressure-assisted technique, the composite membrane exhibited exceptional pervaporation performance at 30 C: concentration of water at the permeate side = 99.6 wt% and permeation flux = 2.54 kg m-2 h-1. Moreover, the membrane sustained its operating stability at a high temperature of 70 C: a high water concentration of 99.5 wt% was maintained, and a permeation flux as high as 4.34 kg m-2 h-1 was attained. This excellent separation performance stemmed from the dense, highly ordered laminate structure of GO.

Keywords: graphene oxide, self-assembly, alcohol dehydration, polyacrylonitrile (mPAN)

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Authors: Meriem Boukais, B. Dennai, A. Ould- Abbas

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The efficiency of SiGe solar cells might be improved by a wide-band-gap window layer. In this work we were simulated using the one dimensional simulation program called analysis of microelectronic and photonic structures (AMPS-1D). In the modeling, the thickness of silicon window was varied from 80 to 150 nm. The rest of layer’s thicknesses were kept constant, by varying thickness of window layer the simulated device performance was demonstrate in the form of current-voltage (I-V) characteristics and quantum efficiency (QE).

Keywords: modeling, SiGe, AMPS-1D, quantum efficiency, conversion, efficiency

Procedia PDF Downloads 721

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

Procedia PDF Downloads 259

Authors: Boris Kolev, Matt Kokan, Mohammad Deriszadeh, Farshid Bateni

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Due to the increasing demand for real estate and the need for efficient land utilization in Greater Vancouver, developers have been increasingly considering the construction of high-rise structures with multiple below-grade parking. The temporary excavations required to allow for the construction of underground levels have recently reached up to 40 meters in depth. One of the challenges with deep excavations is the prediction of wall displacements and ground settlements due to their effect on the integrity of City utilities, infrastructure, and adjacent buildings. A large database of survey monitoring data has been collected for deep excavations in various soil conditions and shoring systems. The majority of the data collected is for tie-back anchors and shotcrete lagging systems. The data were categorized, analyzed and the results were evaluated to find a relationship between the most dominant parameters controlling the displacement, such as depth of excavation, soil properties, and the tie-back anchor loading and arrangement. For a select number of deep excavations, finite element modeling was considered for analyses. The lateral displacements from the simulation results were compared to the recorded survey monitoring data. The study concludes with a discussion and comparison of the available empirical and numerical modeling methodologies for evaluating lateral displacements in deep excavations.

Keywords: deep excavations, lateral displacements, numerical modeling, shoring walls, tieback anchors

Procedia PDF Downloads 181

Authors: Jenny Radeva, Anke-Gundula Roth, Christian Goebbert, Robert Niestroj-Pahl, Lars Daehne, Axel Wolfram, Juergen WIese

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The presented investigation studies the correlation between the process parameters of Layer-by-Layer (LbL) coatings and properties of the produced hybrid membranes for water treatment. The coating of alumina ceramic support membrane with polyelectrolyte multilayers on top results in hybrid membranes with increased fouling resistant behavior, high retention (up to 90%) of salt ions and various pharmaceuticals, selectivity to various organic molecules as known from LbL coated polyether sulfone membranes and the possibility of pH response control. Chosen polyelectrolytes were added to the support using the LbL-coating process. Parameters like the type of polyelectrolyte, ionic strength, and pH were varied in order to find the most suitable process conditions and to study how they influence the properties of the final product. The applied LbL-films was investigated in respect to its homogeneity and penetration depth. The analysis of the layer buildup was performed using fluorescence labeled polyelectrolyte molecules and Confocal Laser Scanning Microscopy as well as Scanning and Transmission Electron Microscopy. Furthermore, the influence of the coating parameters on the porosity, surface potential, retention, and permeability of the developed hybrid membranes were estimated. In conclusion, a comparison was drawn between the filtration performance of the uncoated alumina ceramic membrane and modified hybrid membranes.

Keywords: water treatment, membranes, ceramic membranes, hybrid membranes, layer-by-layer modification

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Authors: Dimassi Wissem

Abstract:

In this work, we investigate the effect of combination between the porous silicon (PS) layer passivized with Lithium Bromide (LiBr) and grooving of grain boundaries (GB) in multi crystalline silicon. The grain boundaries were grooved in order to reduce the area of these highly recombining regions. Using optimized conditions, grooved GB's enable deep phosphorus diffusion and deep metallic contacts. We have evaluated the effects of LiBr on the surface properties of porous silicon on the performance of silicon solar cells. The results show a significant improvement of the internal quantum efficiency, which is strongly related to the photo-generated current. We have also shown a reduction of the surface recombination velocity and an improvement of the diffusion length after the LiBr process. As a result, the I–V characteristics under the dark and AM1.5 illumination were improved. It was also observed a reduction of the GB recombination velocity, which was deduced from light-beam-induced-current (LBIC) measurements. Such grooving in multi crystalline silicon enables passivization of GB-related defects. These results are discussed and compared to solar cells based on untreated multi crystalline silicon wafers.

Keywords: Multicrystalline silicon, LiBr, porous silicon, passivation

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Authors: Bharat Kumar, Deepak Kumar, Vijay Chaudhry

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Zirconium alloys are used as core components of nuclear reactors due to their high wear resistance, good corrosion properties, and good mechanical stability at high temperatures. The present work simulates the contact between the calandria tube and the liquid injection shutdown system (LISS) nozzle. The Calandria tube is the outer covering of the pressure tube. Water flows inside the pressure tube through fuel claddings which produces vibration in the pressure tube along with vibration in the calandria tube. Fretting wear takes place at the point of contact between the calandria tube and the LISS nozzle. Fretting tests were performed under different conditions, such as; varying fretting duration (i.e., 1 to 4 hours), varying frequency (i.e., 5 to 6.5 Hz), and varying amplitude (100 to 400 µm). The formation of the oxide layer was observed during the fretting wear test; as a result, the worn product. The worn surfaces were analyzed with scanning electron microscopy (SEM) to analyze the wear mechanism involved in the fretting test, and Energy dispersive x-ray spectroscopy (EDS) and Raman spectroscopy were used to confirm the presence of an oxide layer on the worn surface. The oxide layer becomes more uniform with fretting duration in case of water submerged condition as compared to dry contact condition. The oxide layer is deeply removed at high amplitude due to the change of wear mechanism from adhesion to abrasion, as confirmed by the presence of micro ploughing and micro cutting. Low amplitude fretting favors the formation of the tribo-oxide layer.

Keywords: tribo-oxide layer, wear, mechanically mixed layer, zircaloy

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Authors: Boukais Meriem, B. Dennai, A. Ould-Abbas

Abstract:

The efficiency of SiGe solar cells might be improved by a wide-band-gap window layer. In this work we were simulated using the one dimensional simulation program called analysis of microelectronic and photonic structures (AMPS-1D). In the simulation, the thickness of silicon window was varied from 80 to 150 nm. The rest of layer’s thicknesses were kept constant, by varying thickness of window layer the simulated device performance was demonstrate in the form of current-voltage (I-V) characteristics and quantum efficiency (QE).

Keywords: SiGe, AMPS-1D, simulation, conversion, efficiency, quantum efficiency

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Authors: Torchane Lazhar

Abstract:

In this work, our task consists in optimizing the nitriding treatment at low-temperature of the steel 32CrMoV13 by the way of the mixtures of ammonia gas, nitrogen and hydrogen to improve the mechanical properties of the surface (good wear resistance, friction and corrosion), and of the diffusion layer of the nitrogen (good resistance to fatigue and good tenacity with heart). By limiting our work to the pure iron and to the alloys iron-chromium and iron-chrome-carbon, we have studied the various parameters which manage the nitriding: flow rate and composition of the gaseous phase, the interaction chromium-nitrogen and chromium-carbon by the help of experiments of nitriding realized in the laboratory by thermogravimetry. The acquired knowledge have been applied by the mastery of the growth of the combination layer on the diffusion layer in the case of the industrial steel 32CrMoV13.

Keywords: diffusion of nitrogen, gaseous nitriding, layer growth kinetic, steel

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Authors: R. N. Nataria, A. D. Paryuni, R. Wasito

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Layer farms in Indonesia have still obstacles to increasing their productivity, especially due to poultry diseases. The red onion (Allium ascalonicum L.) is a plant that contains flavonoid and saponin. Flavonoid is useful as anti-inflammatory and antioxidant while saponin is useful as antivirus, anti-inflammatory, antifungal, and immunomodulator. This study aimed to know and determine the effect of onion extracts to pulmonary histopathological lesions in layer chickens which raised in the battery cage. This study used eighteen layer chickens at seventeen days old. The eighteen layer chickens were divided into three groups of six each, namely without administration of red onion extract (Group I), with administration red onion extract through drinking water (Group II) and with administration red onion extract peroral (Group III). Every ten days, six chickens were necropsied and then the lungs were processed for histopathological preparations and stained with routine hematoxylin and eosin. The results showed that the lungs of the Group I had severe congestion and diffuse hemorrhages. In Group II, lungs had moderate congestion and hemorrhages. In group III, lungs had mild congestion and hemorrhages. It is concluded, that red onion extract apparently has reduced the lungs lesions in layer chickens.

Keywords: histopathological lesions, layers, lungs, poultry diseases, red onion extract

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Authors: Ngozi Nwogu, Mohammed Kajama, Emmanuel Anyanwu, Edward Gobina

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With the objective to create technologically advanced materials to be scientifically applicable, multi-layer silica alumina membranes were molecularly fabricated by continuous surface coating silica layers containing hybrid material onto a ceramic porous substrate for flue gas separation applications. The multi-layer silica alumina membrane was prepared by dip coating technique before further drying in an oven at elevated temperature. The effects of substrate physical appearance, coating quantity, the cross-linking agent, a number of coatings and testing conditions on the gas separation performance of the membrane have been investigated. Scanning electron microscope was used to investigate the development of coating thickness. The membrane shows impressive perm selectivity especially for CO2 and N2 binary mixture representing a stimulated flue gas stream

Keywords: gas separation, silica membrane, separation factor, membrane layer thickness

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Selvavinayagan Babiharan

Abstract:

This research aims to develop a deep learning platform to enhance mathematics education for secondary students in Sri Lanka. The platform will be designed to incorporate interactive and user-friendly features to engage students in active learning and promote their mathematical skills. The proposed platform will be developed using TensorFlow and Keras, two widely used deep learning frameworks. The system will be trained on a large dataset of math problems, which will be collected from Sri Lankan school curricula. The results of this research will contribute to the improvement of mathematics education in Sri Lanka and provide a valuable tool for teachers to enhance the learning experience of their students.

Keywords: information technology, education, machine learning, mathematics

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Authors: Rashi Jain

Abstract:

The Wireless Body Area Networks (WBANs) is an emerging interdisciplinary area where small sensors are placed on/within the human body. These sensors monitor the physiological activities and vital statistics of the body. The data from these sensors is aggregated and communicated to a remote doctor for immediate attention or to a database for records. On 6 Feb 2012, the IEEE 802.15.6 task group approved the standard for Body Area Network (BAN) technologies. The standard proposes the physical and MAC layer for the WBANs. The work provides an introduction to WBANs and overview of the physical and MAC layers of the standard. The physical layer specifications have been covered. A comparison of different protocols used at MAC layer is drawn. An introduction to the network layer and security aspects of the WBANs is made. The WBANs suffer certain limitations such as regulation of frequency bands, minimizing the effect of transmission and reception of electromagnetic signals on the human body, maintaining the energy efficiency among others. This has slowed down their implementation.

Keywords: vehicular networks, sensors, MicroController 8085, LTE

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Authors: Khiaira S. Hassan, Abbas S. Alwan, Muna K. Abbass

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This research aims to study the effect of the liquid nitriding and shot peening on the hardness, surface roughness, residual stress, microstructure and corrosion behavior of austenite stainless steel 316 L. Chemical surface heat treatment by liquid nitriding process was carried out at 500 °C for 1 h and followed by shot peening with using ball steel diameter of 1.25 mm in different exposure time of 10 and 20 min. Electrochemical corrosion test was applied in sea water (3.5% NaCl solution) by using potentostat instrument. The results showed that the nitride layer consists of a compound layer (white layer) and diffusion zone immediately below the alloy layer. It has been found that the mechanical treatment (shot peening) has led to the formation of compressive residual stresses in layer surface that increased the hardness of stainless steel surface. All surface treatment (nitriding and shot peening) processes have led to the formation of carbide of CrN in hard surface layer. It was shown that both processes caused an increase in surface hardness and roughness which increases with shot peening time. Also, the corrosion results showed that the liquid nitriding and shot peening processes increase the corrosion rate to values more than that of not treated stainless steel.

Keywords: stainless steel 316L, shot peening, nitriding, corrosion, hardness

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Bin Liu

Abstract:

Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: Degale Desta, Cheng Jian

Abstract:

The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: Donghee Noh, Seonhyeong Kim, Junhwan Choi, Heegon Kim, Sooho Jung, Keunho Park

Abstract:

In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest.

Keywords: aerial image, image process, machine vision, open field smart farm, segmentation

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Authors: Vaclav Heral

Abstract:

Ideas about the mechanism of heterogeneous catalytic hydrogenation are diverse. The Horiuti-Polanyi mechanism is most often referred to, based on the idea of a semi-hydrogenated state. In our opinion, it does not represent a satisfactory explanation of the hydrogenation mechanism, because, for example: (1) It neglects the fact that the bond of atomic hydrogen to the metal surface is strongly polarized, (2) It does not explain why a surface deprived of atomic hydrogen (by thermal desorption or by alkyne) loses isomerization capabilities, but hydrogenation capabilities remain preserved, (3) It was observed that during the hydrogenation of 1-alkenes, the reaction can be of the 0th order to hydrogen and to the alkene at the same time, which is excluded during the competitive adsorption of both reactants on the catalyst surface. We offer an alternative mechanism that satisfactorily explains many of the ambiguities: It is the idea of an independent course of olefin isomerization, catalyzed by acidic atomic hydrogen bonded on the surface of the catalyst, in addition to the hydrogenation itself, in which a two-layer complex appears on the surface of the catalyst: olefin bound to the surface and molecular hydrogen bound to it in the second layer. The rate-determining step of hydrogenation is the conversion of this complex into the final product. We believe that the Horiuti-Polanyi mechanism is flawed and we naturally think that our two-layer theory better describes the experimental findings.

Keywords: acidity of hydrogenation catalyst, Horiuti-Polanyi, hydrogenation, two-layer hydrogenation

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Authors: W. J. Stępniowski, W. Florkiewicz, M. Norek, M. Michalska-Domańska, E. Kościuczyk, T. Czujko

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In this paper, two options of anodic alumina barrier layer thinning have been demonstrated. The approaches varied with the duration of the voltage step. It was found that too long step of the barrier layer thinning process leads to chemical etching of the nanopores on their top. At the bottoms pores are not fully opened what is disadvantageous for further applications in nanofabrication. On the other hand, while the duration of the voltage step is controlled by the current density (value of the current density cannot exceed 75% of the value recorded during previous voltage step) the pores are fully opened. However, pores at the bottom obtained with this procedure have smaller diameter, nevertheless this procedure provides electric contact between the bare aluminum (substrate) and electrolyte, what is suitable for template assisted electrodeposition, one of the most cost-efficient synthesis method in nanotechnology.

Keywords: anodic aluminum oxide, anodization, barrier layer thinning, nanopores

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Authors: Edgardo V. Gubatanga Jr., Mark Joshua Salvacion

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Most aerial LiDAR systems have accompanying aerial cameras in order to capture not only the terrain of the surveyed area but also its true-color appearance. However, the presence of atmospheric clouds, poor lighting conditions, and aerial camera problems during an aerial survey may cause absence of aerial photographs. These leave areas having terrain information but lacking aerial photographs. Intensity images can be derived from LiDAR data but they are only grayscale images. A deep learning model is developed to create a complex function in a form of a deep neural network relating the pixel values of LiDAR-derived intensity images and true-color images. This complex function can then be used to predict the true-color images of a certain area using intensity images from LiDAR data. The predicted true-color images do not necessarily need to be accurate compared to the real world. They are only intended to look realistic so that they can be used as base maps.

Keywords: aerial LiDAR, colorization, deep learning, intensity images

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Authors: Morteza Karami, Soheila Dayani

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Deep Soil Mixing (DSM) is a soil improvement technique that involves mechanically mixing the soil with a binder material to improve its strength, stiffness, and durability. This technique is typically used in geotechnical engineering applications where weak or unstable soil conditions exist, such as in building foundations, embankment support, or ground improvement projects. In this study, the settlement of the foundation on the improved soil using the wet DSM technique has been analyzed for a case study. Before DSM production, the initial soil mixture has been determined based on the laboratory tests and then, the proper mix designs have been optimized based on the pilot scale tests. The results show that the spacing and depth of the DSM columns depend on the soil properties, the intended loading conditions, and other factors such as the available space and equipment limitations. Moreover, monitoring instruments installed in the pilot area verify that the settlement of the foundation has been placed in an acceptable range to ensure that the soil mixture is providing the required strength and stiffness to support the structure or load. As an important result, if the DSM columns touch or penetrate into the stiff soil layer, the settlement of the foundation can be significantly decreased. Furthermore, the DSM columns should be allowed to cure sufficiently before placing any significant loads on the structure to prevent excessive deformation or settlement.

Keywords: deep soil mixing, soil mixture, settlement, instrumentation, curing age

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Authors: Nabil Shawkat, Jamil Saquer

Abstract:

Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

Procedia PDF Downloads 136

Authors: Shafique Qadir Memon, Muhammad Safar Mirjat, Abdul Quadir Mughal, Nadeem Amjad

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The aims of this study were to determine direct input energy and indirect energy in maize production, to evaluate the inputs energy consumption and outputs energy gained for maize production in Islamabad, Pakistan for spring 2013. Results showed that grain yield was maximum under deep tillage as compared to conventional and zero tillage. Total energy input/output were maximum in deep tillage as compared to conventional tillage while lowest in zero tillage, net energy gain were found maximum under deep tillage.

Keywords: tillage, energy, grain yield, net energy gain

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Authors: Zelalem Abebe Bekele, Kun Lei, Xiukai Lan, Xiangyu Liu, Hui Wen, Kaiyou Wang

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Spin-polarized currents offer an efficient means of manipulating the magnetization of a ferromagnetic layer for big data and neuromorphic computing. Research has shown that the orbital Hall effect (OHE) can produce orbital currents, potentially surpassing the counter spin currents induced by the spin Hall effect. However, it’s essential to note that orbital currents alone cannot exert torque directly on a ferromagnetic layer, necessitating a conversion process from orbital to spin currents. Here, we present an efficient method for achieving perpendicularly magnetized spin-orbit torque (SOT) switching by harnessing the localized orbital Hall current generated from a Mo layer within a Mo/CoGd device. Our investigation reveals a remarkable enhancement in the interface-induced planar Hall effect (PHE) within the Mo/CoGd bilayer, resulting in the generation of a z-polarized planar current for manipulating the magnetization of CoGd layer without the need for an in-plane magnetic field. Furthermore, the Mo layer induces out-of-plane orbital current, boosting the in-plane and out-of-plane spin polarization by converting the orbital current into spin current within the dual-property CoGd layer. At the optimal Mo layer thickness, a low critical magnetization switching current density of 2.51×10⁶ A cm⁻² is achieved. This breakthrough opens avenues for all-electrical control energy-efficient magnetization switching through orbital current, advancing the field of spin-orbitronics.

Keywords: spin-orbit torque, orbital hall effect, spin hall current, orbital hall current, interface-generated planar hall current, anisotropic magnetoresistance

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Authors: M. Ashok Williams, T. V. Lakshmi Kumar

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The life cycle of Black carbon aerosols depends on their physical removal processes from the atmosphere during the precipitation events. Black Carbon (BC) mass concentration has been analysed during rainy and non-rainy days of Northeast (NE) Monsoon months of the years 2015 and 2017 over a semi-urban environment near Chennai (12.81 N, 80.03 E), located on the east coast of India. BC, measured using an Aethalometer (AE-31) has been related to the atmospheric boundary layer height (BLH) obtained from the ERA Interim Reanalysis data during rainy and non-rainy days on monthly mean basis to understand the wet removal of BC over the study location. The study reveals that boundary layer height has a profound effect on the BC concentration on rainy days and non rainy days. It is found that the BC concentration in the night time is lower on rainy days compared to non rainy days owing to wash out on rainy days and the boundary layer height remaining nearly the same on rainy and non rainy days. On the other hand, in the daytime, it is found that the BC concentration remains nearly the same on rainy and non rainy days whereas the boundary layer height is lower on rainy days compared to non rainy days. This reveals that in daytime, lower boundary layer heights compensate for the wet removal effect on BC concentration on rainy days. A quantitative relation is found between the product of BC and BLH during rainy and non-rainy days which indicates the extent of redistribution of BC during non-rainy days when compared to the rainy days. Further work on the wet removal processes of the BC is in progress considering the individual rain events and other related parameters like wind speed.

Keywords: black carbon aerosols, atmospheric boundary layer, scavenging processes, tropical coastal location

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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: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Yohannes Bisa Biramo

Abstract:

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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