Search results for: deep layer

Authors: Khansa Hayat

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Organizational stress is one of the vital phenomena which is having its roots deep down in has deep roots in management, psychology, and organizational behavior research. In the meanwhile, keeping its focus on the positive strength of humans rather than the traditional negativity oriented research, positive psychology has emerged as a separate branch of organizational behavior. The current study investigates the interactive effects of Challenge and hindrance stressors and core Self Evaluations (CSE’s) of the individual on job performances including the in-role performance and extra role performances. The study also aims to investigate the supporting/buffering role of the human dispositions (i.e., self esteem, self efficacy, locus of control and emotional stability). The results show that Challenge stressors have a significant positive effect on in role performance and extra role performance of the individual. The findings of the study indicate that Core Self evaluations strengthen the relationship between challenge stressors and in role performance of the individual. In case of Hindrance Stressors the Core self Evaluations lessen the negative impact of Hindrance stressors and they let the individual perform at a better and normal position even when the Hindrance stressors are high. The relationship and implication of conservation of resource theory are also discussed. The limitations, future research directions and implications of the study are also discussed.

Keywords: challenge-hindrance stressors, core self evaluations, in-role performance, extra-role performance

Procedia PDF Downloads 277

Authors: Yu Chen, Xiaoguang Yang, Lin Lin

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Traffic engineering construction is an important infrastructure for economic and social development. In the process of construction and operation, the ability to make a correct evaluation of the project's environmental impact appears to be crucial to the rational operation of existing transportation projects, the correct development of transportation engineering construction and the adoption of corresponding measures to scientifically carry out environmental protection work. Most of the existing research work on ecological and environmental impact assessment is limited to individual aspects of the environment and less to the overall evaluation of the environmental system; in terms of research conclusions, there are more qualitative analyses from the technical and policy levels, and there is a lack of quantitative research results and quantitative and operable evaluation models. In this paper, a comprehensive analysis of the ecological and environmental impacts of transportation construction projects is conducted, and factors such as the accessibility of data and the reliability of calculation results are comprehensively considered to extract indicators that can reflect the essence and characteristics. The qualitative evaluation indicators were screened using the expert review method, the qualitative indicators were measured using the fuzzy statistics method, the quantitative indicators were screened using the principal component analysis method, and the quantitative indicators were measured by both literature search and calculation. An environmental impact evaluation index system with the general objective layer, sub-objective layer and indicator layer was established, dividing the environmental impact of the transportation construction project into two periods: the construction period and the operation period. On the basis of the evaluation index system, the index weights are determined using the hierarchical analysis method, and the individual indicators to be evaluated are dimensionless, eliminating the influence of the original background and meaning of the indicators. Finally, the thesis uses the above research results, combined with the actual engineering practice, to verify the correctness and operability of the evaluation method.

Keywords: transportation construction projects, ecological and environmental impact, analysis and evaluation, indicator evaluation system

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Authors: K. Jafar, R. Nazar, A. Ishak, I. Pop

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The present analysis considers the steady stagnation point flow and heat transfer towards a permeable sheet in an upper-convected Maxwell (UCM) electrically conducting fluid, with a constant magnetic field applied in the transverse direction to flow, and a local heat generation within the boundary layer with a heat generation rate proportional to (T-T_inf)^p. Using a similarity transformation, the governing system of partial differential equations is first transformed into a system of ordinary differential equations, which is then solved numerically using a finite-difference scheme known as the Keller-box method. Numerical results are obtained for the flow and thermal fields for various values of the shrinking/stretching parameter lambda, the magnetic parameter M, the elastic parameter K, the Prandtl number Pr, the suction parameter s, the heat generation parameter Q, and the exponent p. The results indicate the existence of dual solutions for the shrinking sheet up to a critical value lambda_c whose value depends on the value of M, K, and s. In the presence of internal heat absorbtion (Q<0), the surface heat transfer rate decreases with increasing p but increases with parameter Q and s, when the sheet is either stretched or shrunk.

Keywords: magnetohydrodynamic (MHD), boundary layer flow, UCM fluid, stagnation point, shrinking sheet

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Authors: Jisoo Kim, Jin-Young Choi, MinSu Lee, Sunmook Lee

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Effects of ceramic nanoparticles on the improvement of durability of bone fixation devices have been investigated by assessing the durability of nanocomposite materials consisting of bioabsorbable polymer and ceramic nanoparticles, which could be applied for bone fixation devices such as plates and screws. Various composite ratios were used for the synthesis of nanocomposite materials by blending polylactic acid (PLA) and polyglycolic acid (PGA) as bioabsorbable polymer, and hydroxyapatite (HA) and tri-calcium phosphate (TCP) as ceramic nanoparticles. It was found that the addition of ceramic nanoparticles significantly enhanced the mechanical properties of the bone fixation devices compared to those fabricated with pure biopolymers. Particularly, the layer-by-layer approach for the fabrication of nanocomposites also had an effect on the improvement of bending strength. Durability tests were performed by measuring the changes in the bending strength of nanocomposite samples under varied temperature conditions for the accelerated degradation tests. It was found that Weibull distribution was the most proper one for describing the life distribution of devices in the present study. The mean lifetime was predicted by adopting Arrhenius Eq. Model for Stress-Life relationship.

Keywords: bioabsorbable, bone fixation device, ceramic nanoparticles, durability assessment, nanocomposite

Procedia PDF Downloads 326

Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Fatima Zohra Ibn Majdoub Hassani, Ivan Lavandera, Joseph Kreit

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This study explored the effects of different organic solvents, temperature, and the amount of glycerol on the alcohol dehydrogenase (ADH)-catalysed stereoselective reduction of different ketones. These conversions were then analyzed by gas chromatography. It was found that when the amount of deep eutectic solvents (DES) increases, it can improve the stereoselectivity of the enzyme although reducing its ability to convert the substrate into the corresponding alcohol. Moreover, glycerol was found to have a strong stabilizing effect on the ADH from Ralstonia sp. (E. coli/ RasADH). In the case of organic solvents, it was observed that the best conversions into the alcohols were achieved with DMSO and hexane. It was also observed that temperature decreased the ability of the enzyme to convert the substrates into the products and also affected the selectivity. In addition to that, the recycling of DES up to three times gave good conversions and enantiomeric excess results and glycerol showed a positive effect in the stability of various ADHs. Using RasADH, a good conversion and enantiomeric excess into the S-alcohol were obtained. It was found that an enhancement of the temperature disabled the stabilizing effect of glycerol and decreased the stereoselectivity of the enzyme. However, for other ADHs a temperature increase had an opposite positive effect, especially with ADH-T from Thermoanaerobium sp. One of the objectives of this study was to see the effect of cofactors such as NAD(P) on the biocatlysis activities of ADHs.

Keywords: alcohol dehydrogenases, DES, gas chromatography, RasADH

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Cam Minh Tri Tien, Richard Fenrich, Tristan Shelley, Nam Mai-Duy, Allan Malano, Xuesen Zeng

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Since additive manufacturing is a layer-by-layer deposition approach, good bonding quality between adjacent layers is critically important to achieve optimal mechanical performance, including applications in pressure vessels. The need to enhance the strength of printed products, especially in the build direction where layup gaps and voids exist between the printed layers, has garnered significant attention. The proposed research will focus on improving the current Fused Deposition Modelling (FDM) process to produce polymers reinforced with chopped fibers, utilizing a controlled heat zone to enhance the adhesion between printed layers. Energy will be applied to both printed and printing layers to improve the bonding strength between adjacent layers. Through the enhanced FDM process, the mechanical performance of composite parts will experience a substantial improvement, particularly in the build direction, as compared to current FDM methods. A combination of experimental, numerical, and analytical methods will be employed to demonstrate the enhanced performance of heat-controlled 3D printed parts.

Keywords: 3D Printing, pressure vessels, interlayer bonding, controlled heat

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Authors: Mehrdad Shafiei Dizaji, Hoda Azari

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The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

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Authors: S. Talebnezhad, S. Pourmahdian, D. Soudbar, M. Khosravani, J. Merasi

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Adding toughened polypropylene and plastomer in FFS 3 layered blown film formulation resulted in superior dart impact and MD tear resistance along with acceptable tensile properties in TD and MD. The optimum loading of toughened polypropylene and plastomer in each layer depends on miscibility of polypropylene in polyethylene medium, mechanical properties, welding characteristics in bags top and bottoms and friction coefficient of film surfaces. Film property tests and efficiency of FFS machinery during processing in industrial scale showed that about 4% loading of plastomer and 16% of toughened polypropylene (reactor grade) in middle layer and loading of 0-1% plastomer and 5-19% of toughened polypropylene in other layers results optimum characteristics in the formulation based on 1-butene LLDPE grade with MFR of 0.9 and LDPE grade with MFI of 0.3. Both the plastomer and toughened polypropylene had the MFI of blow 1 and the TiO2 and processing aid masterbatches loading was 2%. The friction coefficient test results also represented the anti-block masterbatch could be omitted from formulation with adding toughened polypropylene due to partial miscibility of PP in PE which makes the surface of films somewhat bristly.

Keywords: FFS 3 layered blown film, toughened polypropylene, plastomer, dart impact, tear resistance

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: Kbrom Mearg Haile

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Introduction: Membrane fouling is the bottleneck for the anaerobic membrane bioreactor (AnMBR) robust continuous operation, primarily caused by the mixed liquor suspended solids (MLSS) characteristics formed by aggregated flocs and a scaffold of microbial self-produced extracellular polymeric substances (EPS), which dictates the flocs integrity. Accordingly, the adhesion of EPS to the membrane surface versus their role in forming firm, elastic, and mechanically stable flocs under the reactor’s hydraulic shear is critical for minimizing interactions between EPS and colloids originating from the MLSS flocs with the membrane. This study aims to gain insight and investigate the effect of MLSS flocs properties, EPS adhesion and viscoelasticity, viscoelastic properties of the sludge, and membrane fouling propensity. Experimental: As a working hypothesis, to alter the aforementioned flocs’ and EPS’s properties, the addition of either coagulant or surfactant was carried out during the AnMBR operation. In the AnMBR, two flat-sheet 300 kDa pore size polyether sulfone (PES) membranes with a total filtration area of 352 cm2 were immersed in the AnMBR system treating municipal wastewater of Midreshet Ben-Gurion village at the Negev highlands, Israel. The system temperature, pH, biogas recirculation, and hydraulic retention time were regulated. TMP fluctuations during a 30-day experiment were recorded under three operating conditions: Baseline (without the addition of coagulating or dispersing agent), coagulant addition (FeCl3), and surfactant addition (sodium dodecyl sulfate). At the end of each experiment, EPS were extracted from the MLSS and from the fouled membrane, characterized for their protein, polysaccharides, and DOC contents, and correlated with the fouling tendency of the submerged UF membrane. The EPS adherence and viscoelastic properties were revealed using QCM-D via the PES-coated gold sensor used as a membrane-mimicking surface providing a detailed real-time EPS adhesion. The associated shifts in the resonance frequency and dissipation at different overtones were further modeled using the Voigt-based viscoelastic model (using Dfind software, Q-Sense Biolin Scientific) in which the thickness, shear modulus, and shear viscosity values of the adsorbed EPS layers on the PES coated sensor were calculated. Results and discussion: The observations obtained from the QCM-D analysis indicate a greater decrease in the frequency shift for the elevated membrane fouling scenarios, likely due to an observed decrease in the calculated shear viscosity and shear modulus of the EPS adsorbed layer, coupled with an increase in EPS layer hydrated thickness and fluidity (ΔD/Δf slopes). Further analysis is being conducted for the three major operating conditions-analyzing their effects on sludge rheology, dewaterability (capillary suction time-CST) and settle ability (SVI). The biofouling layer is further characterized microscopically using a confocal laser scanning microscope (CLSM) and scanning electron microscope (SEM), for analyzing the consistency of the development of the biofouling layer with sludge characteristics, i.e., thicker biofouling layer on the membrane surface when operated with surfactant addition, due to flocs with reduced integrity and availability of EPS/colloids to the membrane. Conversely, a thinner layer when operated with coagulant compared to the baseline experiment, due to elevation in flocs integrity.

Keywords: viscoelasticity, biofouling, viscoelastic, AnMBR, EPS, elocintegrity

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Authors: Wadea Ameen, Abdulrahman Al-Ahmari, Osama Abdulhameed

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Electron beam melting (EBM) is one of the modern additive manufacturing (AM) technologies. In EBM, the electron beam melts metal powder into a fully solid part layer by layer. Since EBM is a new technology, most designers are unaware of the capabilities and the limitations of EBM technology. Also, many engineers are facing many challenges to utilize the technology because of a lack of design rules for the technology. The aim of this study is to identify the capabilities and the limitations of EBM technology in fabrication of small features and overhang structures and develop a design rules that need to be considered by designers and engineers. In order to achieve this objective, a series of experiments are conducted. Several features having varying sizes were designed, fabricated, and evaluated to determine their manufacturability limits. In general, the results showed the capabilities and limitations of the EBM technology in fabrication of the small size features and the overhang structures. In the end, the results of these investigation experiments are used to develop design rules. Also, the results showed the importance of developing design rules for AM technologies in increasing the utilization of these technologies.

Keywords: additive manufacturing, design for additive manufacturing, electron beam melting, self-supporting overhang

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Authors: M. Solairaju, Nithin J. Thomas, S. Ganesan

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Each and every rotating part of a machine element consists of bearings within its structure. In particular, the rolling element bearings such as cylindrical roller bearing and deep groove ball bearings are frequently used. Improper handling, excessive loading, improper lubrication and sealing cause bearing damage. Hence health monitoring of bearings is an important aspect for radiation pattern of bearing vibration is computed using the dipole model. Sound pressure level for defect-free and race defect the prolonged life of machinery and auto motives. This paper presents modeling and analysis of Acoustic response of deep groove ball bearing with localized race defects. Most of the ball bearings, especially in machine tool spindles and high-speed applications are pre-loaded along an axial direction. The present study is carried out with axial preload. Based on the vibration response, the orbit motion of the inner race is studied, and it was found that the oscillation takes place predominantly in the axial direction. Simplified acoustic is estimated. Acoustic response shows a better indication in identifying the defective bearing. The computed sound signal is visualized in diagrammatic representation using Symmetrised Dot Pattern (SDP). SDP gives better visual distinction between the defective and defect-free bearing

Keywords: bearing, dipole, noise, sound

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Authors: Harshavardhan Reddy, Balaji Subramanian

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A systematic parametric study to find the optimum bellmouth profile by relating geometric and performance parameters to satisfy a set of specifications is reported. A careful aerodynamic design of bellmouth intake is critical to properly direct the flow with minimal losses and maximal flow uniformity into the honeycomb located inside the settling chamber of an indraft wind tunnel, thus improving the efficiency of the entire unit. Design charts for elliptically profiled bellmouths with two different contraction ratios (9 and 18) and three different test section speeds (25 m/s, 50 m/s, and 75 m/s) were presented. A significant performance improvement - especially in the Coefficient of discharge and in the flow angularity and boundary layer thickness at the honeycomb inlet - was observed when an entry corner radius (r/D = 0.08) was added to the bellmouth profile. The nonuniformity at the honeycomb inlet drops by about three times (~1% to 0.3%) when moving from square to regular octagonal cross-section. An octagonal cross-sectioned bellmouth intake with L/d = 0.55, D/d = 1.625, and r/D = 0.08 met all the four target performance specifications and is proposed as the best choice for a low-speed wind tunnel.

Keywords: bellmouth intake, low-speed wind tunnel, coefficient of discharge, nonuniformity, flow angularity, boundary layer thickness, CFD, aerodynamics

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Authors: Andrew N. Saylor, James R. Peters

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Scoliosis is a complex 3D deformity of the thoracic and lumbar spines, clinically diagnosed by measurement of a Cobb angle of 10 degrees or more on a coronal X-ray. The Cobb angle is the angle made by the lines drawn along the proximal and distal endplates of the respective proximal and distal vertebrae comprising the curve. Traditionally, Cobb angles are measured manually using either a marker, straight edge, and protractor or image measurement software. The task of measuring the Cobb angle can also be represented by a function taking the spine geometry rendered using X-ray imaging as input and returning the approximate angle. Although the form of such a function may be unknown, it can be approximated using artificial neural networks (ANNs). The performance of ANNs is affected by many factors, including the choice of activation function and network architecture; however, the effects of these parameters on the accuracy of scoliotic deformity measurements are poorly understood. Therefore, the objective of this study was to systematically investigate the effect of ANN architecture and activation function on Cobb angle measurement from the coronal X-rays of scoliotic subjects. The data set for this study consisted of 609 coronal chest X-rays of scoliotic subjects divided into 481 training images and 128 test images. These data, which included labeled Cobb angle measurements, were obtained from the SpineWeb online database. In order to normalize the input data, each image was resized using bi-linear interpolation to a size of 500 × 187 pixels, and the pixel intensities were scaled to be between 0 and 1. A fully connected (dense) ANN with a fixed cost function (mean squared error), batch size (10), and learning rate (0.01) was developed using Python Version 3.7.3 and TensorFlow 1.13.1. The activation functions (sigmoid, hyperbolic tangent [tanh], or rectified linear units [ReLU]), number of hidden layers (1, 3, 5, or 10), and number of neurons per layer (10, 100, or 1000) were varied systematically to generate a total of 36 network conditions. Stochastic gradient descent with early stopping was used to train each network. Three trials were run per condition, and the final mean squared errors and mean absolute errors were averaged to quantify the network response for each condition. The network that performed the best used ReLU neurons had three hidden layers, and 100 neurons per layer. The average mean squared error of this network was 222.28 ± 30 degrees2, and the average mean absolute error was 11.96 ± 0.64 degrees. It is also notable that while most of the networks performed similarly, the networks using ReLU neurons, 10 hidden layers, and 1000 neurons per layer, and those using Tanh neurons, one hidden layer, and 10 neurons per layer performed markedly worse with average mean squared errors greater than 400 degrees2 and average mean absolute errors greater than 16 degrees. From the results of this study, it can be seen that the choice of ANN architecture and activation function has a clear impact on Cobb angle inference from coronal X-rays of scoliotic subjects.

Keywords: scoliosis, artificial neural networks, cobb angle, medical imaging

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Authors: Josef Novák, Alena Kohoutková, Vladimír Křístek, Jan Vodička

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Numerical analysis software belong to the main tools for simulating the real behavior of various concrete structures and elements. In comparison with experimental tests, they offer an affordable way to study the mechanical behavior of structures under various conditions. The contribution deals with a precast element of an innovative airfield pavement system which is being developed within an ongoing scientific project. The proposed system consists a two-layer surface course of precast concrete panels positioned on a two-layer base of fiber-reinforced concrete with recycled aggregate. As the panels are supposed to be installed directly on the hardened base course, imperfections at the interface between the base course and surface course are expected. Considering such circumstances, three various behavior patterns could be established and considered when designing the precast element. Enormous costs of full-scale experiments force to simulate the behavior of the element in a numerical analysis software using finite element method. The simulation was conducted on a nonlinear model in order to obtain such results which could fully compensate results from the experiments. First, several loading schemes were considered with the aim to observe the critical one which was used for the simulation later on. The main objective of the simulation was to optimize reinforcement of the element subject to quasi-static loading from airplanes. When running the simulation several parameters were considered. Namely, it concerns geometrical imperfections, manufacturing imperfections, stress state in reinforcement, stress state in concrete and crack width. The numerical simulation revealed that the precast element should be heavily reinforced to fulfill all the demands assumed. The main cause of using high amount of reinforcement is the size of the imperfections which could occur at real structure. Improving manufacturing quality, the installation of the precast panels on a fresh base course or using a bedding layer underneath the surface course belong to the main steps how to reduce the size of imperfections and consequently lower the consumption of reinforcement.

Keywords: nonlinear analysis, numerical simulation, precast concrete, pavement

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Authors: S. Yakut, D. Deger, K. Ulutas, D. Bozoglu

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Plasma poly(ethylene oxide) (pPEO) thin films were deposited between Aluminum (Al) electrodes on glass substrates by plasma assisted physical vapor deposition (PAPVD). The deposition was operated inside Argon plasma under 10⁻³ Torr and the thicknesses of samples were determined as 20, 100, 250, 500 nm. The plasma was produced at 5 W by magnetron connected to RF power supply. The capacitance C and dielectric loss factor tan δ were measured by Novovontrol Alpha-A high frequency empedance analyzer at freqquency and temperature intervals of 0,1 Hz and 1MHz, 193-353K, respectively. AC conductivity was derived from these values. AC conductivity results exhibited three different conductivity regions except for 20 nm. These regions can be classified as low, mid and high frequency regions. Low frequency region is observed at around 10 Hz and 300 K while mid frequency region is observed at around 1 kHz and 300 K. The last one, high frequency region, is observed at around 1 kHz and 200 K. There are some coinciding definitions for conduction regions, because these regions shift depending on temperature. Low frequency region behaves as DC-like conductivity while mid and high frequency regions show conductivities corresponding to mechanisms such as classical hopping, tunneling, etc. which are observed for amorphous materials. Unlike other thicknesses, for 20 nm sample low frequency region can not be detected in the investigated freuency range. It is thought that this is arised because of the presence of dead layer behavior.

Keywords: plasma polymers, dead layer, dielectric spectroscopy, AC conductivity

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Authors: Omar Hamdy, Schichen Zhao, Hussein Abd El-Atty, Ayman Ragab, Muhammad Salem

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One of the most risky areas in Aswan is Abouelreesh, which is suffering from flood disasters, as heavy deluge inundates urban areas causing considerable damage to buildings and infrastructure. Moreover, the main problem was the urban sprawl towards this risky area. This paper aims to identify the urban areas located in the risk areas prone to flash floods. Analyzing this phenomenon needs a lot of data to ensure satisfactory results; however, in this case the official data and field data were limited, and therefore, free sources of satellite data were used. This paper used ArcGIS tools to obtain the storm water drains network by analyzing DEM files. Additionally, historical imagery in Google Earth was studied to determine the age of each building. The last step was to overlay the urban area layer and the storm water drains layer to identify the vulnerable areas. The results of this study would be helpful to urban planners and government officials to make the disasters risk estimation and develop primary plans to recover the risky area, especially urban areas located in torrents.

Keywords: risk area, DEM, storm water drains, GIS

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Authors: Taras Agryzkov, Jose L. Oliver, Leandro Tortosa, Jose Vicent

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This paper aims to demonstrate how network models can be used to understand and to deal with some aspects of urban complexity. As it is well known, the Theory of Architecture and Urbanism has been using for decades’ intellectual tools based on the ‘sciences of complexity’ as a strategy to propose theoretical approaches about cities and about architecture. In this sense, it is possible to find a vast literature in which for instance network theory is used as an instrument to understand very diverse questions about cities: from their commercial activity to their heritage condition. The contribution of this research consists in adding one step of complexity to this process: instead of working with one single primal graph as it is usually done, we will show how new network models arise from the consideration of two different primal graphs interacting in two layers. When we model an urban network through a mathematical structure like a graph, the city is usually represented by a set of nodes and edges that reproduce its topology, with the data generated or extracted from the city embedded in it. All this information is normally displayed in a single layer. Here, we propose to separate the information in two layers so that we can evaluate the interaction between them. Besides, both layers may be composed of structures that do not have to coincide: from this bi-layer system, groups of interactions emerge, suggesting reflections and in consequence, possible actions.

Keywords: graphs, mathematics, networks, urban studies

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Authors: Kyoungok Jung, Chang Hong Bak, Gyeong Cheon Jo, Jin-Baek Kim

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Nanostructured arrays have drawn extensive attention because of their unique properties resulting from nanoscale features. However, it is difficult to achieve uniform and freestanding 1D nanostrcutures over a large area. Here, a simple and novel method was developed for fabrication of universal nanoporous templates for high-density nanostructure arrays, by combining self-assembly of a Si-containing block copolymer with a bilayer lithography system. We introduced a dually responsive photoresist bottom layer into which the nanopatterns of block copolymer are transferred by oxygen reactive ion etching. Because the dually responsive layer becomes cross-linked by heating, it can be used as a hard template during the etching process. It becomes soluble again by chain scission upon exposure to light. Therefore, it can be easily removed by the lift-off process. The template was applicable to the various conducting substrates due to the compatibility of the photoresist with a wide range of substrates and was used in electrodeposition for well-aligned and high-density inorganic and organic nanoarrays. We successfully obtained vertically aligned and highly ordered gold nanorods and polypyrrole dots on the substrate without aggregation, and these arrays did not collapse after removing the dually responsive templates by the simple lift-off process.

Keywords: block copolymer, dually responsive, nanostructure, photoresist

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Authors: J. N. Nackler, K. Saleh Pascha, W. Winter

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WHISCERS™ (Whole House In-Situ Carbon and Energy Reduction Solution) is an innovative process for Internal Wall Insulation (IWI) for energy-efficient retrofitting of heritage building, which uses laser measuring to determine the dimensions of a room, off-site insulation board cutting and rapid installation to complete the process. As part of a multinational investigation consortium the Austrian part adapted the WHISCERS system to local conditions of Vienna where most historical buildings have valuable stucco facades, precluding the application of an external insulation. The Austrian project contribution addresses the replacement of commonly used extruded polystyrene foam (XPS) with renewable materials such as wood and wood products to develop a more sustainable IWI system. As the timber industry is a major industry in Austria, a new innovative and more sustainable IWI solution could also open up new markets. The first approach of investigation was the Life Cycle Assessment (LCA) to define the performance of wood fibre board as insulation material in comparison to normally used XPS-boards. As one of the results the global-warming potential (GWP) of wood-fibre-board is 15 times less the equivalent to carbon dioxide while in the case of XPS it´s 72 times more. The hygrothermal simulation program WUFI was used to evaluate and simulate heat and moisture transport in multi-layer building components of the developed IWI solution. The results of the simulations prove in examined boundary conditions of selected representative brickwork constructions to be functional and usable without risk regarding vapour diffusion and liquid transport in proposed IWI. In a further stage three different solutions were developed and tested (1 - glued/mortared, 2 - with soft board, connected to wall with gypsum board as top layer, 3 - with soft board and clay board as top layer). All three solutions presents a flexible insulation layer out of wood fibre towards the existing wall, thus compensating irregularities of the wall surface. From first considerations at the beginning of the development phase, three different systems had been developed and optimized according to assembly technology and tested as small specimen in real object conditions. The built prototypes are monitored to detect performance and building physics problems and to validate the results of the computer simulation model. This paper illustrates the development and application of the Internal Wall Insulation system.

Keywords: internal insulation, wood fibre, hygrothermal simulations, monitoring, clay, condensate

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Authors: K. Kalaiarasan, B. Pandeeswari, A. Arockia John Francis

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Wireless, Multi-hop networks consist of one or more intermediate nodes along the path that receive and forward packets via wireless links. The backpressure algorithm provides throughput optimal routing and scheduling decisions for multi-hop networks with dynamic traffic. Xpress, a cross-layer backpressure architecture was designed to reach the capacity of wireless multi-hop networks and it provides well coordination between layers of network by turning a mesh network into a wireless switch. Transmission over the network is scheduled using a throughput-optimal backpressure algorithm. But this architecture operates much below their capacity due to out-of-order packet delivery and variable packet size. In this paper, we present Xpress-T, a throughput optimal backpressure architecture with TCP support designed to reach maximum throughput of wireless multi-hop networks. Xpress-T operates at the IP layer, and therefore any transport protocol, including TCP, can run on top of Xpress-T. The proposed design not only avoids bottlenecks but also handles out-of-order packet delivery and variable packet size, optimally load-balances traffic across them when needed, improving fairness among competing flows. Our simulation results shows that Xpress-T gives 65% more throughput than Xpress.

Keywords: backpressure scheduling and routing, TCP, congestion control, wireless multihop network

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Zhuang Hou, Xiaolei Cao

Abstract:

The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contactless devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contactless mini program HealthyPai’s heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) ± standard deviation was 1.4827 ± 1.7452 in the lab, 6.9231 ± 5.6426 in the real-life setting. In the SpO2 study of 24 samples, the MAE ± standard deviation of the measurement was 1.0375 ± 0.7745. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework, can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.

Keywords: remote photoplethysmography, heart rate, oxygen saturation, contactless measurement, mini program

Procedia PDF Downloads 135

Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

Abstract:

The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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Authors: Zehra Aytac, Nurdilek Gulmezoglu

Abstract:

Safflower has been grown for centuries for many purposes worldwide. Especially it is important for the orange-red dye from its petal and for its high-quality oil obtained from the seeds. The crop is high adaptable to areas with insufficient rainfall and poor soil conditions. The plant has a deep taproot that can draw moisture and plant nutrients from deep to the subsoil. The research was carried out to study the nitrogen (N) uptake of different safflower cultivars and lines at different stages of growth and different plant parts in the experimental field of Faculty of Agriculture, Eskişehir Osmangazi University under semi-arid conditions. Different safflower cultivars and lines of varied origins were used as the material. The cultivars and lines were planted in a Randomized Complete Block Design with three replications. Two different growth stages (flowering and harvest) and three different plant parts (head, stem+leaf and seed) were determined. The nitrogen concentration of different plant parts was determined by the Kjeldahl method. Statistical analysis were performed by analysis of variance for each growth stage and plant parts taking a level of p < 0.05 and p < 0.01 as significant according to the LSD test. As a result, N concentration showed significant differences among different plant parts and different growth stages for different safflower genotypes of varied origins.

Keywords: Carthamus tinctorius L., growth stages, head N, leaf N, N uptake, seed N, Safflower

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Authors: Nasser A. M. Barakat

Abstract:

Membrane technology is the most promising process for oil-water separation operation if the hydrophilicity, fouling and reusability properties could be improved. In this study, novel effective and reusable membrane for oil-water separation process is introduced based on modification of polysulfone (PSF) electrospun nanofiber mats. The modification process was achieved by incorporation of NaOH nanoparticles inside the PSF nanofibers, and formation of a thin layer from a polyamide polymer on the surface of the electrospun mat. Typically, solutions composed of PSF and NaOH (twelve solutions were prepared based on different PSF concentrations; 15, 18 and 20 wt%, and various NaOH content; 1.5, 1.7 and 2.5 wt%) have been electrospun, then the dried nanofiber mats were treated by m-phenylenediamine and 1,3,5-benzenetricarbonyl chloride to form polyamide thin layer on the surface of the mats. The results indicated that incorporation of NaOH and the formed polyamide could decrease the water contact angle from ~ 130˚ to 13˚ for the nanofiber mats obtained from 20 wt% PSF solutions containing 1.7 wt% sodium hydroxide powders. Interestingly, the membrane having the lowest contact angle could separate oil-water mixture for three successive cycles and 100% removal of the oil with relatively high water flux; 5.5 m3/m2.day. Overall, simplicity of the manufacturing technique, and effectiveness and reusability of the produced nanofiber mats open new avenue for the introduced as promising membranes for the oil-water separation process.

Keywords: electrospinning, oil-water separation, hydrophilic membrane, nanofibers

Procedia PDF Downloads 341

Authors: Sanjida Akter, Ambali Alade Odebowale, Andrey E. Miroshnichenko, Haroldo T. Hattori

Abstract:

Photodetectors (PDs) play a pivotal role in optoelectronics and optical devices, serving as fundamental components that convert light signals into electrical signals. As the field progresses, the integration of advanced materials with unique optical properties has become a focal point, paving the way for the innovation of novel PDs. This study delves into the exploration of a cutting-edge photodetector designed for deep and near ultraviolet (UV) applications. The photodetector is constructed with a composite of Zirconium Boride (ZrB2) and Chromium (Cr) alloy, deposited onto a 6H nitrogen-doped silicon carbide substrate. The determination of the optimal alloy thickness is achieved through Finite-Difference Time-Domain (FDTD) simulation, and the synthesis of the alloy is accomplished using radio frequency (RF) sputtering. Remarkably, the resulting photodetector exhibits an exceptional responsivity of 3.5 A/W under an applied voltage of -2 V, at wavelengths of 405 nm and 280 nm. This heterostructure not only exemplifies high performance but also provides a versatile platform for the development of near UV photodetectors capable of operating effectively in challenging conditions, such as environments characterized by high power and elevated temperatures. This study contributes to the expanding landscape of photodetector technology, offering a promising avenue for the advancement of optoelectronic devices in demanding applications.

Keywords: responsivity, silicon carbide, ultraviolet photodetector, zirconium boride

Procedia PDF Downloads 66

Authors: Feng Ju Chuang, Yu Wen Wang, Tai Jung Hsieh, Shyh Ming Kuo

Abstract:

Polylactic acid is a synthetic polymer with good biocompatibility and degradability, is widely used in clinical applications. In this study, we utilized Polylactic acid particles as stimulants for macrophages and the collagen synthesis of co-cultured fibroblasts was evaluated. The results indicated that Polylactic acid particles were nontoxic to cells from 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide. No obvious inflammation effect was observed (under the PLLA concentration of 1 mg/mL) after 24-h co-culture of Raw264.7 and NIH3T3 cells (from TNF-α assay). The addition of PLLA particles to the Raw264.7 and NIH3T3 co-cultures increased the synthesis of collagen, the highest collagen synthesis from the fibroblast was the 0.2 mg/mL (approximately 60% increased as compared with without addition Polylactic acid particles). Moreover, a co-axial atomization delivery device was used to percutaneously introduce Polylactic acid particles into the dermis layer and stimulating macrophages to secrete growth factors promoting fibroblasts to produce collagen. The preliminary results demonstrated the synthesis of collagen was increased mildly after the introduction of Polylactic acid particles for 28-d post implantation. The Polylactic acid particles could be successfully introduced into the dermis layer from H&E staining examination, however, the optimum concentration of Polylactic acid particles and the time-period for collagen synthesis still need to be evaluated.

Keywords: collagen synthesis, macrophage, NIH3T3 cells, polylactic acid particles

Procedia PDF Downloads 113