Search results for: hybrid concrete anchor piles

Authors: I. R. Keke, C. S. Nwigwe, O. S. Nwanjo, A. S. Egeruoh

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An experiment was carried out at Urban Farm and Fisheries Nigeria Ltd, Owerri Imo State South East Nigeria between February and June 2014 to induce Brood stock of Heterobranchus longifilis (mean wt 1.3kg) in concrete tanks (1.0 x 2.0 x 1.5m) in dimension using a synthetic hormone (Ovaprim) and pituitary extract from Heterobranchus longifilis. Brood stock males were selected as pituitary donors and their weights matched those of females to be injected at 1ml/kg body weight of Fish. Ovaprim, was injected at 0.5ml/kg body weight of female fish. A latency period of 12 hours was allowed after injection of the Brood stock females before stripping the egg and incubation at 23 °C. While incubating the eggs, samples were drawn and the rate of fertilization was determined. Hatching occurred within 33 hours and hatchability rate (%) was determined by counting the active hatchings. The result showed that Ovaprim injected Brood stock eggs fertilized up to 80% while the pituitary from the Heterobranchus longifilis had low fertilization and hatching success 20%. Ovaprim is imported and costly, so more effort is required to enhance the procedures for homoplastic hypophysation.

Keywords: heterobranchus longifilis, ovaprim, hypophysation, latency period, pituitary

Procedia PDF Downloads 204

Authors: Andreas Gohs, Reinhold Kosfeld

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This study should be of interest for practitioners who want to predict precisely the sales numbers of vehicles equipped with an innovative propulsion technology as well as for researchers interested in applied (regional) time series analysis. The study is based on the numbers of new registrations of pure electric and hybrid cars. Methods of time series analysis like ARIMA are compared with the Bass Diffusion-model concerning their forecasting performances for new registrations in Germany at the national and federal state levels. Especially it is investigated if the additional information content from regional data increases the forecasting accuracy for the national level by adding predictions for the federal states. Results of parameters of the Bass Diffusion Model estimated for Germany and its sixteen federal states are reported. While the focus of this research is on the German market, estimation results are also provided for selected European and other countries. Concerning Bass-parameters and forecasting performances, we get very different results for Germany's federal states and the member states of the European Union. This corresponds to differences across the EU-member states in the adoption process of this innovative technology. Concerning the German market, the adoption is rather proceeded in southern Germany and stays behind in Eastern Germany except for Berlin.

Keywords: bass diffusion model, electric vehicles, forecasting performance, market diffusion

Procedia PDF Downloads 159

Authors: Sergey Kopysov, Nikita Nedozhogin, Leonid Tonkov

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The article presents a parallel iterative solver for large sparse linear systems which can be used on a heterogeneous platform. Traditionally, the problem of solving linear systems does not scale well on multi-CPU/multi-GPUs clusters. For example, most of the attempts to implement the classical conjugate gradient method were at best counted in the same amount of time as the problem was enlarged. The paper proposes the pipelined variant of the conjugate gradient method (PCG), a formulation that is potentially better suited for hybrid CPU/GPU computing since it requires only one synchronization point per one iteration instead of two for standard CG. The standard and pipelined CG methods need the vector entries generated by the current GPU and other GPUs for matrix-vector products. So the communication between GPUs becomes a major performance bottleneck on multi GPU cluster. The article presents an approach to minimize the communications between parallel parts of algorithms. Additionally, computation and communication can be overlapped to reduce the impact of data exchange. Using the pipelined version of the CG method with one synchronization point, the possibility of asynchronous calculations and communications, load balancing between the CPU and GPU for solving the large linear systems allows for scalability. The algorithm is implemented with the combined use of technologies: MPI, OpenMP, and CUDA. We show that almost optimum speed up on 8-CPU/2GPU may be reached (relatively to a one GPU execution). The parallelized solver achieves a speedup of up to 5.49 times on 16 NVIDIA Tesla GPUs, as compared to one GPU.

Keywords: conjugate gradient, GPU, parallel programming, pipelined algorithm

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Authors: Nor-Edine Abriak, Ilyas Ennahal, Abdeljalil Zri, Mahfoud Benzerzour

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Nord Pas de Calais is one of the French regions that records a large volume of dredged sediment in harbors and waterways. To ensure navigation within ports and waterways, harbor and river managers are forced to find solutions to remove sediment that contamination levels exceed levels established by regulations. Therefore, this non- submersible sediment must be managed on land and will be subject to the waste regulation. In this paper, some examples of concrete achievements and experiments of reusing dredged sediment in civil engineering and sector will be illustrated. These achievements are alternative solutions to sediment landfilling and guarantee the reuse of this material in a logic of circular economy and ecological transition. It permits to preserve the natural resources increasingly scarce and resolve issues related to the accumulation of sediments in the harbor basins, rivers, dams, and lakes, etc. Examples of beneficial use of dredged material illustrated in this paper are the result of different projects reusing harbor and waterways sediments in several applications. These projects were funded under the national SEDIMATERIAUX approach. Thus the technical and environmental feasibility of the reuse of dredged sediment is demonstrated and verified; the dredged sediment reusing would meet multiple challenges of sustainable development in relation to environmental, economic, social and societal.

Keywords: circular economy, sediment, SEDIMATERIAUX, waterways

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Authors: Zory Marantz

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Hybrid flexible (HyFlex) is a pedagogical methodology whereby an instructor delivers content in three modalities, i.e. live in-person (LIP), live online synchronous (LOS), and non-live online asynchronous (nLOaS). HyFlex is focused on providing the largest level of flexibility needed to achieve a cohesive environment across all modalities and incorporating four basic principles – learner’s choice, reusability, accessibility, and equivalency. Much literature has focused on the advantages of this methodology in providing students with the flexibility to choose their learning modality as best suits their schedules and learning styles. Initially geared toward graduate-level students, the concept has been applied to undergraduate studies, particularly during our national pedagogical response to the COVID19 pandemic. There is still little literature about the practicality and feasibility of HyFlex for hardware laboratory intensive engineering technology programs, particularly in dense, urban commuter institutions of higher learning. During a semester of engineering, a lab-based course was taught in the HyFlex modality, and students were asked to complete a survey about their experience. The data demonstrated that there is no single mode that is preferred by a majority of students and the usefulness of any modality is limited to how familiar the student and instructor are with the technology being applied. The technology is only as effective as our understanding and comfort with its functionality. For HyFlex to succeed in its implementation in an engineering technology environment within an urban commuter institution, faculty and students must be properly introduced to the technology being used.

Keywords: education, HyFlex, technology, urban, commuter, pedagogy

Procedia PDF Downloads 88

Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

Procedia PDF Downloads 137

Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Yogesh C. Sharma, A. Seetharaman

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The decision-making process in humans is a complicated system influenced by a variety of intrinsic and extrinsic factors. Human decisions have a ripple effect on subsequent decisions. In this study, the scope of human decision making is limited to employees. In an organisation, a person makes a variety of decisions from the time they are hired to the time they retire. The goal of this research is to identify various elements that influence decision-making. In addition, the environment in which a decision is made is a significant aspect of the decision-making process. Employees in today's workplace use artificial intelligence (AI) systems for automation and decision augmentation. The impact of AI systems on the decision-making process is examined in this study. This research is designed based on a systematic literature review. Based on gaps in the literature, limitations and the scope of future research have been identified. Based on these findings, a research framework has been designed to identify various factors affecting employee decision making. Employee decision making is influenced by technological advancement, data-driven culture, human trust, decision automation-augmentation, and workplace motivation. Hybrid human-AI systems require the development of new skill sets and organisational design. Employee psychological safety and supportive leadership influences overall job satisfaction.

Keywords: employee decision making, artificial intelligence (AI) environment, human trust, technology innovation, psychological safety

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Authors: Panpan Fan, Bo Ming, Niels Anten, Jochem Evers, Yaoyao Li, Shaokun Li, Ruizhi xie

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Nitrogen (N) utilization for crop production under N deficiency conditions is subject to a trade-off between maintaining specific leaf N content (SLN), important for radiation-use efficiency (RUE), versus maintaining leaf area (LA) development, important for light capture. This paper aims to explore how maize deals with this trade-off through responses in SLN, LA and their underlying traits during the vegetative and reproductive growth stages. In a ten-year N fertilization trial in Jilin province, Northeast China, three N fertilizer levels have been maintained: N-deficiency (N0), low N supply (N1), and high N supply (N2). We analyzed data from years 8 and 10 of this experiment for two common hybrids. Under N deficiency, maize plants maintained LA and decreased SLN during vegetative stages, while both LA and SLN decreased comparably during reproductive stages. Canopy-average specific leaf area (SLA) decreased sharply during vegetative stages and slightly during reproductive stages, mainly because senesced leaves in the lower canopy had a higher SLA. In the vegetative stage, maize maintained leaf area at low N by maintaining leaf biomass (albeit hence having N content/mass) and slightly increasing SLA. These responses to N deficiency were stronger in maize hybrid XY335 than in ZD958. We conclude the main strategy of maize to cope with low N is to maintain plant growth, mainly by increasing SLA throughout the plant during early growth. N was too limiting for either strategy to be followed during later growth stages.

Keywords: leaf N content per unit leaf area, N deficiency, specific leaf area, maize strateg

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Authors: Rowan Moore, Kai Scheffler, Eugen Damoc, Jennifer Sutton, Aaron Bailey, Stephane Houel, Simon Cubbon, Jonathan Josephs

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Purpose: MS analysis of monoclonal antibodies (mAbs) at the protein and peptide levels is critical during development and production of biopharmaceuticals. The compositions of current generation therapeutic proteins are often complex due to various modifications which may affect efficacy. Intact proteins analyzed by MS are detected in higher charge states that also provide more complexity in mass spectra. Protein analysis in native or native-like conditions with zero or minimal organic solvent and neutral or weakly acidic pH decreases charge state value resulting in mAb detection at higher m/z ranges with more spatial resolution. Methods: Three commercially available mAbs were used for all experiments. Intact proteins were desalted online using size exclusion chromatography (SEC) or reversed phase chromatography coupled on-line with a mass spectrometer. For streamlined use of the LC- MS platform we used a single SEC column and alternately selected specific mobile phases to perform separations in either denaturing or native-like conditions: buffer A (20 % ACN, 0.1 % FA) with Buffer B (100 mM ammonium acetate). For peptide analysis mAbs were proteolytically digested with and without prior reduction and alkylation. The mass spectrometer used for all experiments was a commercially available Thermo Scientific™ hybrid Quadrupole-Orbitrap™ mass spectrometer, equipped with the new BioPharma option which includes a new High Mass Range (HMR) mode that allows for improved high mass transmission and mass detection up to 8000 m/z. Results: We have analyzed the profiles of three mAbs under reducing and native conditions by direct infusion with offline desalting and with on-line desalting via size exclusion and reversed phase type columns. The presence of high salt under denaturing conditions was found to influence the observed charge state envelope and impact mass accuracy after spectral deconvolution. The significantly lower charge states observed under native conditions improves the spatial resolution of protein signals and has significant benefits for the analysis of antibody mixtures, e.g. lysine variants, degradants or sequence variants. This type of analysis requires the detection of masses beyond the standard mass range ranging up to 6000 m/z requiring the extended capabilities available in the new HMR mode. We have compared each antibody sample that was analyzed individually with mixtures in various relative concentrations. For this type of analysis, we observed that apparent native structures persist and ESI is benefited by the addition of low amounts of acetonitrile and formic acid in combination with the ammonium acetate-buffered mobile phase. For analyses on the peptide level we analyzed reduced/alkylated, and non-reduced proteolytic digests of the individual antibodies separated via reversed phase chromatography aiming to retrieve as much information as possible regarding sequence coverage, disulfide bridges, post-translational modifications such as various glycans, sequence variants, and their relative quantification. All data acquired were submitted to a single software package for analysis aiming to obtain a complete picture of the molecules analyzed. Here we demonstrate the capabilities of the mass spectrometer to fully characterize homogeneous and heterogeneous therapeutic proteins on one single platform. Conclusion: Full characterization of heterogeneous intact protein mixtures by improved mass separation on a quadrupole-Orbitrap™ mass spectrometer with extended capabilities has been demonstrated.

Keywords: disulfide bond analysis, intact analysis, native analysis, mass spectrometry, monoclonal antibodies, peptide mapping, post-translational modifications, sequence variants, size exclusion chromatography, therapeutic protein analysis, UHPLC

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

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The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

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Authors: Xiao Zhang, Wensheng Xiao, Junguo Cui, Hongmin Wang

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Submersible permanent magnet linear synchronous motors (SPMLSMs) are electromagnetic devices, which can directly drive plunger pump to obtain the crude oil. Those motors have been gradually applied in oil fields due to high thrust force density and high efficiency. Since the force performance closely depends on the concrete structural parameters, the seven different structural parameters are investigated in detail. This paper presents an optimum design of an SPMLSM to minimize the detent force and maximize the thrust by using design of experiment (DOE) and genetic algorithm (GA). The three significant structural parameters (air-gap length, slot width, pole-arc coefficient) are separately screened using 27 1/16 fractional factorial design (FFD) to investigate the significant effect of seven parameters used in this research on the force performance. Response surface methodology (RSM) is well adapted to make analytical model of thrust and detent force with constraints of corresponding significant parameters and enable objective function to be easily created, respectively. GA is performed as a searching tool to search for the Pareto-optimal solutions. By finite element analysis, the proposed PMLSM shows merits in improving thrust and reducing the detent force dramatically.

Keywords: optimization, force performance, design of experiment (DOE), genetic algorithm (GA)

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Authors: Abderrazak Bannari

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Acid Mine Drainage (AMD) from mine wastes and contaminations of soils and water with metals are considered as a major environmental problem in mining areas. It is produced by interactions of water, air, and sulphidic mine wastes. This environment problem results from a series of chemical and biochemical oxidation reactions of sulfide minerals e.g. pyrite and pyrrhotite. These reactions lead to acidity as well as the dissolution of toxic and heavy metals (Fe, Mn, Cu, etc.) from tailings waste rock piles, and open pits. Soil and aquatic ecosystems could be contaminated and, consequently, human health and wildlife will be affected. Furthermore, secondary minerals, typically formed during weathering of mine waste storage areas when the concentration of soluble constituents exceeds the corresponding solubility product, are also important. The most common secondary mineral compositions are hydrous iron oxide (goethite, etc.) and hydrated iron sulfate (jarosite, etc.). The objectives of this study focus on the detection and mapping of MIOP in the soil using Hyperion EO-1 (Earth Observing - 1) hyperspectral data and constrained linear spectral mixture analysis (CLSMA) algorithm. The abandoned Kettara mine, located approximately 35 km northwest of Marrakech city (Morocco) was chosen as study area. During 44 years (from 1938 to 1981) this mine was exploited for iron oxide and iron sulphide minerals. Previous studies have shown that Kettara surrounding soils are contaminated by heavy metals (Fe, Cu, etc.) as well as by secondary minerals. To achieve our objectives, several soil samples representing different MIOP classes have been resampled and located using accurate GPS ( ≤ ± 30 cm). Then, endmembers spectra were acquired over each sample using an Analytical Spectral Device (ASD) covering the spectral domain from 350 to 2500 nm. Considering each soil sample separately, the average of forty spectra was resampled and convolved using Gaussian response profiles to match the bandwidths and the band centers of the Hyperion sensor. Moreover, the MIOP content in each sample was estimated by geochemical analyses in the laboratory, and a ground truth map was generated using simple Kriging in GIS environment for validation purposes. The acquired and used Hyperion data were corrected for a spatial shift between the VNIR and SWIR detectors, striping, dead column, noise, and gain and offset errors. Then, atmospherically corrected using the MODTRAN 4.2 radiative transfer code, and transformed to surface reflectance, corrected for sensor smile (1-3 nm shift in VNIR and SWIR), and post-processed to remove residual errors. Finally, geometric distortions and relief displacement effects were corrected using a digital elevation model. The MIOP fraction map was extracted using CLSMA considering the entire spectral range (427-2355 nm), and validated by reference to the ground truth map generated by Kriging. The obtained results show the promising potential of the proposed methodology for the detection and mapping of mine iron oxide pollution in the soil.

Keywords: hyperion eo-1, hyperspectral, mine iron oxide pollution, environmental impact, unmixing

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Authors: Gabriel Sanjo Aruwajoye, E. B. Gueguim Kana

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Agricultural residues present a low-cost feedstock for bioenergy production around the world. Cassava peels waste are rich in organic molecules that can be readily converted to value added products such as biomaterials and biofuels. However, due to the presence of high proportion of structural carbohydrates and lignin, the hydrolysis of this feedstock is imperative to achieve maximum substrate utilization and energy yield. This study model and optimises the release of Fermentable Sugar (FS) from cassava peels waste using the Response Surface Methodology. The investigated pretreatment input parameters consisted of soaking temperature (oC), soaking time (hours), autoclave duration (minutes), acid concentration (% v/v), substrate solid loading (% w/v) within the range of 30 to 70, 0 to 24, 5 to 20, 0 to 5 and 2 to 10 respectively. The Box-Behnken design was used to generate 46 experimental runs which were investigated for FS release. The obtained data were used to fit a quadratic model. A coefficient of determination of 0.87 and F value of 8.73 was obtained indicating the good fitness of the model. The predicted optimum pretreatment conditions were 69.62 oC soaking temperature, 2.57 hours soaking duration, 5 minutes autoclave duration, 3.68 % v/v HCl and 9.65 % w/v solid loading corresponding to FS yield of 91.83g/l (0.92 g/g cassava peels) thus 58% improvement on the non-optimised pretreatment. Our findings demonstrate an efficient pretreatment model for fermentable sugar release from cassava peels waste for various bioprocesses.

Keywords: feedstock pretreatment, cassava peels, fermentable sugar, response surface methodology

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Authors: Ahmad Afif, Teuku Yuri M. Zagloel

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Sustainable supply chain management is a new pattern that has emerged recently in industry and companies. The procurement process is one of the key factors for efficiency in supply chain management practices. Partnership is one of the procurement strategies for strategic items. The success factors of the partnership must be determined to avoid things that endanger the financial and operational status of the company. The current supplier partnership research focuses on the selection of general criteria and sustainable supplier selection. Currently, there is still limited research on the success factors of supplier partnerships that focus on strategic items in the coal mining industry. Meanwhile, the procurement of coal mining has its own characteristics, and there are regulations related to the procurement of goods. Therefore, this research was conducted to determine the categories of goods that are included in the strategic items and to design the success factors of supplier partnerships. The main factors studied are general, financial, production, reputation, synergies, and sustainable. The research was conducted using the Kraljic method to determine the categories of goods that are included in the strategic items. To design a supplier partnership success factor using the Hybrid Multi Criteria Decision Making method. Integrated Fuzzy AHP-Fuzzy TOPSIS is used to determine the weight of the success factors of supplier partnerships and to rank suppliers on the factors used.

Keywords: supplier, partnership, strategic item, success factors, and coal mining industry

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Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

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Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

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Authors: Sinsze Koo, Benjin Luo, Matthew Henry

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In order to increase the performance of an automatic transmission, the automatic transmission fluid is required to be warm up to an optimal operating temperature. In a conventional vehicle, cold starts result in friction loss occurring in the gear box and engine. The stop and go nature of city driving dramatically affect the warm-up of engine oil and automatic transmission fluid and delay the time frame needed to reach an optimal operating temperature. This temperature phenomenon impacts both engine and transmission performance but also increases fuel consumption and CO2 emission. The aim of this study is to develop know-how of the thermal behavior in order to identify thermal impacts and functional principles in automatic transmissions. Thermal behavior was studied using models and simulations, developed using GT-Suit, on a one-dimensional thermal and flow transport. A power train of a conventional vehicle was modeled in order to emphasis the thermal phenomena occurring in the various components and how they impact the automatic transmission performance. The simulation demonstrates the thermal model of a transmission fluid cooling system and its component parts in warm-up after a cold start. The result of these analyses will support the future designs of transmission systems and components in an attempt to obtain better fuel efficiency and transmission performance. Therefore, these thermal analyses could possibly identify ways that improve existing thermal management techniques with prioritization on fuel efficiency.

Keywords: thermal management, automatic transmission, hybrid, and systematic approach

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Authors: Anna Ivanovskaya, Alexandra E. L. Overland, Swetha Chandrasekaran, Buddhinie S. Jayathilake

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Iron-based redox flow batteries (IRFB) are promising for grid-scale storage because of their low-cost and environmental safety. Earth-abundant iron can enable affordable grid-storage to meet DOE’s target material cost <$20/kWh and levelized cost for storage $0.05/kWh. In conventional redox flow batteries, energy is stored in external electrolyte tanks and electrolytes are circulated through the cell units to achieve electrochemical energy conversions. However, IRFBs are hybrid battery systems where metallic iron deposition at the negative side of the battery controls the storage capacity. This adds complexity to the design of a porous structure of 3D-electrodes to achieve a desired high storage capacity. In addition, there is a need to control parasitic hydrogen evolution reaction which accompanies the metal deposition process, increases the pH, lowers the energy efficiency, and limits the durability. To achieve sustainable operation of IRFBs, electrolyte pH, which affects the solubility of reactants and the rate of parasitic reactions, needs to be dynamically readjusted. In the present study we explore the impact of complexing agents on maintaining solubility of the reactants and find the optimal electrolyte conditions and battery operating regime, which are specific for IRFBs with additives, and demonstrate the robust operation.

Keywords: flow battery, iron-based redox flow battery, IRFB, energy storage, electrochemistry

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Erika Mae L. Valencia

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Third Culture Kids (TCKs) are children who experienced a cross-cultural upbringing – being raised and lived outside their parents’ culture. As a result, TCKs experience the difficulty of building and attaining a concrete cultural identity. However, in the context of globalization and the emergence of ICTs, the internet, and mobile technology creates better ways of constructing cultural identities. This study investigates the social and cultural impacts of the internet and mobile technology on the multi-cultural identity development among selected Filipino TCKs. Moreover, this research seeks to understand how the Filipino TCKs form their identity and address their complex issue of belonging with the use of different internet platforms and mobile technology. To explore the lived experiences of Filipino TCKs, this research employs a transcendental phenomenological design. Also, this study uses purposive and snowball sampling and conduct in-depth interviews through Skype, phone call, or face-to-face. This study utilizes Pierre Bourdieu’s social capital as a theoretical lens to gain understanding of the TCKs’ identity formation process in relation to the said ICTs. This research argues that the internet and mobile technology play a significant role in facilitating multi-cultural identity formation of Filipino TCKs, as well as potentially broadening their social network through its various technological platforms.

Keywords: identity, internet, third culture kids, mobile technology

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Authors: Saad Chahba, Rabia Sehab, Ahmad Akrad, Cristina Morel

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Nowadays, the electric machines used in urban electric vehicles are, in most cases, three-phase electric machines with or without a magnet in the rotor. Permanent Magnet Synchronous Machine (PMSM) and Induction Machine (IM) are the main components of drive trains of electric and hybrid vehicles. These machines have very good performance in healthy operation mode, but they are not redundant to ensure safety in faulty operation mode. Faced with the continued growth in the demand for electric vehicles in the automotive market, improving the reliability of electric vehicles is necessary over the lifecycle of the electric vehicle. Multiphase electric machines respond well to this constraint because, on the one hand, they have better robustness in the event of a breakdown (opening of a phase, opening of an arm of the power stage, intern-turn short circuit) and, on the other hand, better power density. In this work, a diagnosis approach using a neural network for an open circuit fault or more of a five-phase induction machine is developed. Validation on the simulator of the vehicle drivetrain, at reduced power, is carried out, creating one and more open circuit stator phases showing the efficiency and the reliability of the new approach to detect and to locate on-line one or more open phases of a five-induction machine.

Keywords: electric vehicle drivetrain, multiphase drives, induction machine, control, open circuit (OC) fault diagnosis, artificial neural network

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Authors: Tie-Gang Wang, De-Qiang Meng, Yan-Mei Liu

Abstract:

Ternary AlCrN coatings were widely used to prolong cutting tool life because of their high hardness and excellent abrasion resistance. However, the friction between the workpiece and cutter surface was increased remarkably during machining difficult-to-cut materials (such as superalloy, titanium, etc.). As a result, a lot of cutting heat was generated and cutting tool life was shortened. In this work, an appropriate amount of solid lubricant MoS₂ was added into the AlCrN coating to reduce the friction between the tool and the workpiece. A series of Al-Cr-N/MoS₂ self-lubricating coatings with different MoS₂ contents were prepared by high power impulse magnetron sputtering (HiPIMS) and pulsed direct current magnetron sputtering (Pulsed DC) compound system. The MoS₂ content in the coatings was changed by adjusting the sputtering power of the MoS₂ target. The composition, structure and mechanical properties of the Al-Cr-N/MoS2 coatings were systematically evaluated by energy dispersive spectrometer, scanning electron microscopy, X-ray photoelectron spectroscopy, X-ray diffractometer, nano-indenter tester, scratch tester, and ball-on-disk tribometer. The results indicated the lubricant content played an important role in the coating properties. As the sputtering power of the MoS₂ target was 0.1 kW, the coating possessed the highest hardness 14.1GPa, the highest critical load 44.8 N, and the lowest wear rate 4.4×10−3μm2/N.

Keywords: self-lubricating coating, Al-Cr-N/MoS₂ coating, wear rate, friction coefficient

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Authors: Galih Permana, Yuskar Lase

Abstract:

In recent years, damping device has been experimentally studied to replace diagonally reinforced coupling beams, to mitigate rebar congestion problem. This study focuses on evaluating the effectiveness of various damping devices in a high-rise building. The type of damping devices evaluated is Viscoelastic Damper (VCD) and Rotational Friction Damper (RFD), with study case of a 15-story reinforced concrete apartment building with a dual system (column-beam and shear walls). The analysis used is a nonlinear time history analysis with 11 pairs of ground motions matched to the Indonesian response spectrum based on ASCE 41-17 and ASCE 7-16. In this analysis, each damper will be varied with a different position, namely the first model, the damper will be installed on the entire floor and in the second model, the damper will be installed on the 5th floor to the 9th floor, which is the floor with the largest drift. The results show that the model using both dampers increases the level of structural performance both globally and locally in the building, which will reduce the level of damage to the structural elements. But between the two dampers, the coupling beam that uses RFD is more effective than using VCD in improving building performance. The damper on the coupling beam has a good role in dissipating earthquakes and also in terms of ease of installation.

Keywords: building, coupling beam, damper, nonlinear time history analysis

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Authors: Mannal Tariq

Abstract:

Construction industry is making progress at a high pace. The trend of the world is getting more biased towards the high rise buildings. Deep beams are one of the most common elements in modern construction having small span to depth ratio. Deep beams are mostly used as transfer girders. This experimental study consists of 16 reinforced concrete (RC) deep beams. These beams were divided into two groups; A and B. Groups A and B consist of eight beams each, having 381 mm (15 in) and 457 mm (18 in) depth respectively. Each group was further subdivided into four sub groups each consisting of two identical beams. Each subgroup was comprised of solid/control beam (without opening), opening above neutral axis (NA), at NA and below NA. Except for control beams, all beams with openings were strengthened with carbon fibre reinforced polymer (CFRP) vertical strips. These eight groups differ from each other based on depth and location of openings. For testing sake, all beams have been loaded with two symmetrical point loads. All beams have been designed based on strut and tie model concept. The outcome of experimental investigation elaborates the difference in the shear behaviour of deep beams based on depth and location of circular openings variation. 457 mm (18 in) deep beam with openings above NA show the highest strength and 381 mm (15 in) deep beam with openings below NA show the least strength. CFRP sheets played a vital role in increasing the shear capacity of beams.

Keywords: CFRP, deep beams, openings in deep beams, strut and tie modal, shear behaviour

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Authors: René Čechmánek, Vladan Prachař, Ludvík Lederer, Jiří Loskot

Abstract:

Thin-walled elements with a matrix set on a base of high-valuable Portland cement with dispersed reinforcement from alkali-resistant glass fibres are used in a range of applications as claddings of buildings and infrastructure constructions as well as various architectural elements of residential buildings. Even if their elementary thickness and therefore total weight is quite low, architects and building companies demand on even further decreasing of the bulk density of these fibre-cement elements for the reason of loading elimination of connected superstructures and easier assembling in demand conditions. By the means of various kinds of light-weight aggregates it is possible to achieve light-weighing of thin-walled fibre-cement composite elements. From the range of possible fillers with different material properties granulated expanded glass worked the best. By the means of laboratory testing an effect of two fillers based on expanded glass on the fibre reinforced cement composite was verified. Practical applicability was tested in the production of commonly manufactured glass fibre reinforced concrete elements, such as channels for electrical cable deposition, products for urban equipment and especially various cladding elements. Even if these are not structural elements, it is necessary to evaluate also strength characteristics and resistance to environment for their durability in certain applications.

Keywords: fibre-cement composite, granulated expanded glass, light-weighing

Procedia PDF Downloads 287

Authors: Ali Akyar, Osman Serdar Gedik

Abstract:

Depth estimation is a challenging computer vision task that involves estimating the distance between objects in a scene and the camera. It predicts how far each pixel in the 2D image is from the capturing point. There are some important Monocular Depth Estimation (MDE) studies that are based on Vision Transformers (ViT). We benchmark three major studies. The first work aims to build a simple and powerful foundation model that deals with any images under any condition. The second work proposes a method by mixing multiple datasets during training and a robust training objective. The third work combines generalization performance and state-of-the-art results on specific datasets. Although there are studies with thermal images too, we wanted to benchmark these three non-thermal, state-of-the-art studies with a hybrid image dataset which is taken by Multi-Spectral Dynamic Imaging (MSX) technology. MSX technology produces detailed thermal images by bringing together the thermal and visual spectrums. Using this technology, our dataset images are not blur and poorly detailed as the normal thermal images. On the other hand, they are not taken at the perfect light conditions as RGB images. We compared three methods under test with our thermal dataset which was not done before. Additionally, we propose an image enhancement deep learning model for thermal data. This model helps extract the features required for monocular depth estimation. The experimental results demonstrate that, after using our proposed model, the performance of these three methods under test increased significantly for thermal image depth prediction.

Keywords: monocular depth estimation, thermal dataset, benchmarking, vision transformers

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Authors: Bunakiye R. Japheth, Ogude U. Cyril

Abstract:

Since the process of transforming user requirements to modeling constructs are not very well supported by domain-specific frameworks, it became necessary to integrate domain requirements with the specific architectures to achieve an integrated customizable solutions space via artifact orientation. Domain-specific modeling language specifications of model-driven engineering technologies focus more on requirements within a particular domain, which can be tailored to aid the domain expert in expressing domain concepts effectively. Modeling processes through domain-specific language formalisms are highly volatile due to dependencies on domain concepts or used process models. A capable solution is given by artifact orientation that stresses on the results rather than expressing a strict dependence on complicated platforms for model creation and development. Based on this premise, domain-specific methods for producing artifacts without having to take into account the complexity and variability of platforms for model definitions can be integrated to support customizable development. In this paper, we discuss methods for the integration capabilities and necessities within a common structure and semantics that contribute a metamodel for artifact-orientation, which leads to a reusable software layer with concrete syntax capable of determining design intents from domain expert. These concepts forming the language formalism are established from models explained within the oil and gas pipelines industry.

Keywords: control process, metrics of engineering, structured abstraction, semantic model

Procedia PDF Downloads 136

Authors: Kristin Murray

Abstract:

The proposed paper exams prologues in 20th and 21st century American literature in order to show how Native American writer Tommy Orange’s Prologue in his 2018 novel There, Thereis different. In an interview about his 2018 novel There, There, explains he feels “a kind of burden to catch the general reader up with what really happened, because history has got it so wrong and still continue to” (Laubernds). Orange, thus, includes a “Prologue” in his novel to do this work, catching readers upon Native Americans and their history. Prologues are usually from the narrator’s voice, a character’s voice, or even from a fictionalized version of the author, but the tone of Orange’s “Prologue” is that of a non-fictional first-person essayist. Examining prologues in American literature posits Orange’s prologue outside the norm. This paper also examines other introductory sections, the preface, in particular. The research and examination reveal that Orange is adding his personal voice in the Prologue to the multiple narratorsof the novel, and his is the voice of a writer who knows that his audience comes to his novel with a plethora of misinformation. The truths he tells are horrifying and hopeful. He tells of Thanksgiving as a “land deal” and a “successful massacre,” but he also tellsreaders how urban Indians have found a sense of the land, even through concrete. Native American writers contributed and still contribute to the genre of autobiography in ways that have changed our understanding of this genre. This examination of Orange’s Prologue reveals the new and unexpected way to view this often under-examined introductory section, the prologue.

Keywords: native american literature, prologues, prefaces, 20th century american literature

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Authors: R. Chedid, R. Ghajar

Abstract:

Lebanon electricity crisis continues to escalate. Rationing hours still apply across the country but with different rates. The capital Beirut is subjected to 3 hours cut while other cities, town and villages may endure 9 to 14 hours of power shortage. To mitigate this situation, private diesel generators distributed illegally all over the country are being used to bridge the gap in power supply. Almost each building in large cities has its own generator and individual villages may have more than one generator supplying their loads. These generators together with their private networks form incomplete and ill-designed and managed microgrids (MG) but can be further developed to become renewable energy-based MG operating in island- or grid-connected modes. This paper will analyze the potential of introducing MG to help resolve the energy crisis in Lebanon. It will investigate the usefulness of developing MG under the prevailing situation of existing private power supply service providers and in light of the developed national energy policy that supports renewable energy development. A case study on a distribution feeder in a rural area will be analyzed using HOMER software to demonstrate the usefulness of introducing photovoltaic (PV) arrays along the existing diesel generators for all the stakeholders; namely, the developers, the customers, the utility and the community at large. Policy recommendations regarding MG development in Lebanon will be presented on the basis of the accumulated experience in private generation and the privatization and public-private partnership laws.

Keywords: decentralized systems, distributed generation, microgrids, renewable energy

Procedia PDF Downloads 128