Search results for: Neural Processing Element (NPE)

Authors: Sung-Sik Park, Han Chang, Kyung-Hun Chae, Sae-Byeok Lee

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In this study, a three-dimensional (3D) Particle method without using grid was developed for analyzing large deformation of soils instead of using ordinary finite element method (FEM) or finite difference method (FDM). In the 3D Particle method, the governing equations were discretized by various particle interaction models corresponding to differential operators such as gradient, divergence, and Laplacian. The Mohr-Coulomb failure criterion was incorporated into the 3D Particle method to determine soil failure. The yielding and hardening behavior of soil before failure was also considered by varying viscosity of soil. First of all, an unconfined compression test was carried out and the large deformation following soil yielding or failure was simulated by the developed 3D Particle method. The results were also compared with those of a commercial FEM software PLAXIS 3D. The developed 3D Particle method was able to simulate the 3D large deformation of soils due to soil yielding and calculate the variation of normal and shear stresses following clay deformation.

Keywords: particle method, large deformation, soil column, confined compressive stress

Procedia PDF Downloads 561

Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: J.-Y. Lee, H.-S. Lim, S.-E. Kim

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Recently, the use of high-strength materials is increasing as the construction of large structures and high-rise structures increases. This paper presents an analysis of the shear behavior of prestressed concrete members with various types of materials by simulating a finite element (FE) analysis. The analytical results indicated that the shear strength and shear failure mode were strongly influenced by not only the shear reinforcement ratio but also the yield strength of shear reinforcement and the compressive strength of concrete. Though the yield strength of shear reinforcement increased the shear strength of prestressed concrete members, there was a limit to the increase in strength because of the change of shear failure modes. According to the results of FE analysis on various parameters, the maximum yield strength of the steel stirrup that can be applied to prestressed concrete members was about 860 MPa.

Keywords: prestressed concrete members, high strength reinforcing bars, high strength concrete, shear behavior

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Authors: Faruk Firat Çalim, Nurullah Karaca, Hakan Tacettin Türker

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In this study, out-of-plane free vibrations of a circular rods is investigated theoretically. The governing equations for naturally twisted and curved spatial rods are obtained using Timoshenko beam theory and rewritten for circular rods. Effects of the axial and shear deformations are considered in the formulations. Ordinary differential equations in scalar form are solved analytically by using transfer matrix method. The circular rods of the mass matrix are obtained by using straight rod of consistent mass matrix. Free vibrations frequencies obtained by solving eigenvalue problem. A computer program coded in MATHEMATICA language is prepared. Circular beams are analyzed through various examples for free vibrations analysis. Results are compared with ANSYS results based on finite element method and available in the literature.

Keywords: circular rod, out-of-plane free vibration analysis, transfer matrix method

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Authors: Thomas Paris, Vincent Bruyere, Patrick Namy

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A numerical model is developed to simulate gas blowdowns through a thin tube and a filter (porous media), separating a high pressure gas filled reservoir to low pressure ones. Based on a previous work, a one-dimensional approach is developed by using the finite element method to solve the transient compressible flow and to predict the pressure and temperature evolution in space and time. Mass, momentum, and energy conservation equations are solved in a fully coupled way in the reservoirs, the pipes and the porous media. Numerical results, such as pressure and temperature evolutions, are firstly compared with experimental data to validate the model for different configurations. Couplings between porous media and pipe flow are then validated by checking mass balance. The influence of the porous media and the nature of the gas is then studied for different initial high pressure values.

Keywords: compressible flow, fluid mechanics, heat transfer, porous media

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Authors: Jonas De Kerf, Rein De Cooman, Sara De Gieter

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For many decades, scholars have aimed to understand how work can become more meaningful by maximizing both potential and enhancing feelings of satisfaction. One of the largest contributions towards such positive psychology was made with the introduction of the concept of ‘flow,’ which refers to a condition in which people feel intense engagement and effortless action. Since then, valuable research on work-related flow has indicated that this state of mind is related to positive outcomes for both organizations (e.g., social, supportive climates) and workers (e.g., job satisfaction). Yet, scholars still do not fully comprehend how such deep involvement at work is obtained, given the notion that flow is considered a short-term, complex, and dynamic experience. Most research neglects that people who experience flow ought to be optimally challenged so that intense concentration is required. Because attention is at the core of this enjoyable state of mind, this study aims to comprehend how elements that affect workers’ cognitive functioning impact flow at work. Research on cognitive performance suggests that working on mentally demanding tasks (e.g., information processing tasks) requires workers to concentrate deeply, as a result leading to flow experiences. Based on social facilitation theory, working on such tasks in an isolated environment eases concentration. Prior research has indicated that working at home (instead of working at the office) or in a closed office (rather than in an open-plan office) impacts employees’ overall functioning in terms of concentration and productivity. Consequently, we advance such knowledge and propose an interaction by combining cognitive task characteristics and work environments among part-time teleworkers. Hence, we not only aim to shed light on the relation between cognitive tasks and flow but also provide empirical evidence that workers performing such tasks achieve the highest states of flow while working either at home or in closed offices. In July 2022, an experience-sampling study will be conducted that uses a semi-random signal schedule to understand how task and environment predictors together impact part-time teleworkers’ flow. More precisely, about 150 knowledge workers will fill in multiple surveys a day for two consecutive workweeks to report their flow experiences, cognitive tasks, and work environments. Preliminary results from a pilot study indicate that on a between level, tasks high in information processing go along with high self-reported fluent productivity (i.e., making progress). As expected, evidence was found for higher fluency in productivity for workers performing information processing tasks both at home and in a closed office, compared to those performing the same tasks at the office or in open-plan offices. This study expands the current knowledge on work-related flow by looking at a task and environmental predictors that enable workers to obtain such a peak state. While doing so, our findings suggest that practitioners should strive for ideal alignments between tasks and work locations to work with both deep involvement and gratification.

Keywords: cognitive work, office lay-out, work location, work-related flow

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Authors: Mohammad Moezzibadi, Isabelle Charpentier, Adrien Wanko, Robert Mosé

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The calibration of hydrodynamic parameters for subsurface constructed wetlands (CWs) is a sensitive process since highly non-linear equations are involved in unsaturated flow modeling. CW systems are engineered systems designed to favour natural treatment processes involving wetland vegetation, soil, and their microbial flora. Their significant efficiency at reducing the ecological impact of urban runoff has been recently proved in the field. Numerical flow modeling in a vertical variably saturated CW is here carried out by implementing the Richards model by means of a mixed hybrid finite element method (MHFEM), particularly well adapted to the simulation of heterogeneous media, and the van Genuchten-Mualem parametrization. For validation purposes, MHFEM results were compared to those of HYDRUS (a software based on a finite element discretization). As van Genuchten-Mualem soil hydrodynamic parameters depend on water content, their estimation is subject to considerable experimental and numerical studies. In particular, the sensitivity analysis performed with respect to the van Genuchten-Mualem parameters reveals a predominant influence of the shape parameters α, n and the saturated conductivity of the filter on the piezometric heads, during saturation and desaturation. Modeling issues arise when the soil reaches oven-dry conditions. A particular attention should also be brought to boundary condition modeling (surface ponding or evaporation) to be able to tackle different sequences of rainfall-runoff events. For proper parameter identification, large field datasets would be needed. As these are usually not available, notably due to the randomness of the storm events, we thus propose a simple, robust and low-cost numerical method for the inverse modeling of the soil hydrodynamic properties. Among the methods, the variational data assimilation technique introduced by Le Dimet and Talagrand is applied. To that end, a variational data assimilation technique is implemented by applying automatic differentiation (AD) to augment computer codes with derivative computations. Note that very little effort is needed to obtain the differentiated code using the on-line Tapenade AD engine. Field data are collected for a three-layered CW located in Strasbourg (Alsace, France) at the water edge of the urban water stream Ostwaldergraben, during several months. Identification experiments are conducted by comparing measured and computed piezometric head by means of the least square objective function. The temporal variability of hydrodynamic parameter is then assessed and analyzed.

Keywords: automatic differentiation, constructed wetland, inverse method, mixed hybrid FEM, sensitivity analysis

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Authors: Mohammad Esmaeilpour

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One of the important open problems in the field of medical image processing is detection and quantification of small changes. In this poster, we try to investigate that, how the algebraic decomposition techniques can be used for semiautomatically detecting and quantifying subtle changes in Magnetic Resonance (MR) neuroimaging volumes. We mostly focus on the low-rank values of the matrices achieved from decomposing MR image pairs during a period of time. Besides, a skillful neuroradiologist will help the algorithm to distinguish between noises and small changes.

Keywords: magnetic resonance neuroimaging, subtle change detection and quantification, algebraic decomposition, basis functions

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Authors: Anne Sørebø

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The present study was designed to test how fulfilment of three basic psychological needs influence students development of perceived usefulness (PU) and ease of use (EOU) in connection with use of a spreadsheet. Both PU and EOU are assumed to be critical for development of students' willingness to utilize spreadsheet in future work within business administration. A questionnaire was completed by 196 business students in Norway. We found that satisfying the need for competence and autonomy is most critical for willingness to utilize the software package. The results also indicate that satisfying the need for relatedness, surprisingly, has no influence on students’ willingness to utilize the software package. A key implication of the present research is that teachers mainly should focus on fulfilling students need for competence and self-determination when the purpose is to motivate them to utilize new software. That students’ should develop their own competence when using a new technology is somewhat obvious, but that the feeling of being self-determined needs to be a complementary element in this connection is not necessary seen as obvious.

Keywords: spreadsheet, business students, technology acceptance, basic psychological needs

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Authors: Fani Sakellariadou, Danae Antivachis

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Purpose of this paper is to evaluate the environmental status of a coastal Mediterranean lake, named Koumoundourou, located in the northeastern coast of Elefsis Bay, in the western region of Attiki in Greece, 15 km far from Athens. It is preserved from ancient times having an important archaeological interest. Koumoundourou lake is also considered as a valuable wetland accommodating an abundant flora and fauna, with a variety of bird species including a few world’s threatened ones. Furthermore, it is a heavily modified lake, affected by various anthropogenic pollutant sources which provide industrial, urban and agricultural contaminants. The adjacent oil refineries and the military depot are the major pollution providers furnishing with crude oil spills and leaks. Moreover, the lake accepts a quantity of groundwater leachates from the major landfill of Athens. The environmental status of the lake results from the intensive land uses combined with the permeable lithology of the surrounding area and the existence of karstic springs which discharge calcareous mountains. Sediment samples were collected along the shoreline of the lake using a Van Veen grab stainless steel sampler. They were studied for the determination of the total metal content and the metal fractionation in geochemical phases as well as the characterization of the dissolved organic matter (DOM). These constituents have a significant role in the ecological consideration of the lake. Metals may be responsible for harmful environmental impacts. The metal partitioning offers comprehensive information for the origin, mode of occurrence, biological and physicochemical availability, mobilization and transport of metals. Moreover, DOM has a multifunctional importance interacting with inorganic and organic contaminants leading to biogeochemical and ecological effects. The samples were digested using microwave heating with a suitable laboratory microwave unit. For the total metal content, the samples were treated with a mixture of strong acids. Then, a sequential extraction procedure was applied for the removal of exchangeable, carbonate hosted, reducible, organic/sulphides and residual fractions. Metal content was determined by an ICP-MS (Perkin Elmer, ICP MASS Spectrophotometer NexION 350D). Furthermore, the DOM was removed via a gentle extraction procedure and then it was characterized by fluorescence spectroscopy using a Perkin-Elmer LS 55 luminescence spectrophotometer equipped with the WinLab 4.00.02 software for data processing (Agilent, Cary Eclipse Fluorescence). Mono dimensional emission, excitation, synchronous-scan excitation and total luminescence spectra were recorded for the classification of chromophoric units present in the aqueous extracts. Total metal concentrations were determined and compared with those of the Elefsis gulf sediments. Element partitioning showed the anthropogenic sources and the contaminant bioavailability. All fluorescence spectra, as well as humification indices, were evaluated in detail to find out the nature and origin of DOM. All the results were compared and interpreted to evaluate the environmental quality of Koumoundourou lake and the need for environmental management and protection.

Keywords: anthropogenic contaminant, dissolved organic matter, lake, metal, pollution

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Authors: A. Chiker, A. Benamor, A. Haddad, Y. Hadji, M. Hadji

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Metal matrix composites (MMCs) have been of big interest for a few decades. Their major drawback lies in their enhanced mechanical performance over unreinforced alloys. They found ground in many engineering fields, such as aeronautics, aerospace, automotive, and other structural applications. One of the most used alloys as a matrix is nickel alloys, which meet the need for high-temperature mechanical properties; some attempts have been made to develop nickel base composites reinforced by high melt point and high modulus particulates. Among the carbides used as reinforcing particulates, chromium carbide is interesting for wear applications; it is widely used as a tribological coating material in high-temperature applications requiring high wear resistance and hardness. Moreover, a set of properties make it suitable for use in MMCs, such as toughness, the good corrosion and oxidation resistance of its three polymorphs -the cubic (Cr23C6), the hexagonal (Cr7C3), and the orthorhombic (Cr3C2)-, and it’s coefficient of thermal expansion that is almost equal to that of metals. The in-situ synthesis of CrC-reinforced Ni matrix composites could be achieved by the powder metallurgy route. To ensure the in-situ reactions during the sintering process, the use of phase precursors is necessary. Recently, new precursor materials have been proposed; these materials are called MAX phases. The MAX phases are thermodynamically stable nano-laminated materials displaying unusual and sometimes unique properties. These novel phases possess Mn+1AXn chemistry, where n is 1, 2, or 3, M is an early transition metal element, A is an A-group element, and X is C or N. Herein, the pressureless sintering method is used to elaborate Ni/Cr2AlC composites. Four composites were elaborated from 5, 10, 15 and 20 wt% of Cr2AlC MAX phase precursor which fully reacted with Ni-matrix at 1100 °C sintering temperature for 4 h in argon atmosphere. XRD results showed that Cr2AlC MAX phase was totally decomposed forming chromium carbide Cr7C3, and the released Al and Cr atoms diffused in Ni matrix giving rise to γ-Ni(Al,Cr) solid solution and γ’-Ni3(Al,Cr) intermetallic. Scanning Electron Microscopy (SEM) of the elaborated samples showed the presence of nanosized Cr7C3 reinforcing particles embedded in the Ni metal matrix, which have a direct impact on the tribological properties of the composites and their hardness. All the composites exhibited higher hardness than pure Ni; whereas adding 15 wt% of Cr2AlC gives the highest hardness (1.85 GPa). Using a ball-on-disc tribometer, dry sliding tests for the elaborated composites against 100Cr6 steel ball were studied under different applied loads. The microstructures and worn surface characteristics were then analyzed using SEM and Raman spectroscopy. The results show that all the composites exhibited better wear resistance compared to pure Ni, which could be explained by the formation of a lubricious tribo-layer during sliding and the good bonding between the Ni matrix and the reinforcing phases.

Keywords: composites, microscopy, sintering, wear

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Authors: Anjalika Fernando, Banuka Athuraliya

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This paper presents a comprehensive approach for converting Sinhala Sign Language (SSL) into grammatically correct sentences using Natural Language Processing (NLP) techniques in real-time. While previous studies have explored various aspects of SSL translation, the research gap lies in the absence of grammar checking for SSL. This work aims to bridge this gap by proposing a two-stage methodology that leverages deep learning models to detect signs and translate them into coherent sentences, ensuring grammatical accuracy. The first stage of the approach involves the utilization of a Long Short-Term Memory (LSTM) deep learning model to recognize and interpret SSL signs. By training the LSTM model on a dataset of SSL gestures, it learns to accurately classify and translate these signs into textual representations. The LSTM model achieves a commendable accuracy rate of 94%, demonstrating its effectiveness in accurately recognizing and translating SSL gestures. Building upon the successful recognition and translation of SSL signs, the second stage of the methodology focuses on improving the grammatical correctness of the translated sentences. The project employs a Neural Machine Translation (NMT) architecture, consisting of an encoder and decoder with LSTM components, to enhance the syntactical structure of the generated sentences. By training the NMT model on a parallel corpus of Sinhala wrong sentences and their corresponding grammatically correct translations, it learns to generate coherent and grammatically accurate sentences. The NMT model achieves an impressive accuracy rate of 98%, affirming its capability to produce linguistically sound translations. The proposed approach offers significant contributions to the field of SSL translation and grammar correction. Addressing the critical issue of grammar checking, it enhances the usability and reliability of SSL translation systems, facilitating effective communication between hearing-impaired and non-sign language users. Furthermore, the integration of deep learning techniques, such as LSTM and NMT, ensures the accuracy and robustness of the translation process. This research holds great potential for practical applications, including educational platforms, accessibility tools, and communication aids for the hearing-impaired. Furthermore, it lays the foundation for future advancements in SSL translation systems, fostering inclusive and equal opportunities for the deaf community. Future work includes expanding the existing datasets to further improve the accuracy and generalization of the SSL translation system. Additionally, the development of a dedicated mobile application would enhance the accessibility and convenience of SSL translation on handheld devices. Furthermore, efforts will be made to enhance the current application for educational purposes, enabling individuals to learn and practice SSL more effectively. Another area of future exploration involves enabling two-way communication, allowing seamless interaction between sign-language users and non-sign-language users.In conclusion, this paper presents a novel approach for converting Sinhala Sign Language gestures into grammatically correct sentences using NLP techniques in real time. The two-stage methodology, comprising an LSTM model for sign detection and translation and an NMT model for grammar correction, achieves high accuracy rates of 94% and 98%, respectively. By addressing the lack of grammar checking in existing SSL translation research, this work contributes significantly to the development of more accurate and reliable SSL translation systems, thereby fostering effective communication and inclusivity for the hearing-impaired community

Keywords: Sinhala sign language, sign Language, NLP, LSTM, NMT

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Authors: Supachai Supmak, Yachai Limpiyakorn

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Mutation testing can be applied for the quality assessment of test cases. Prioritization of mutation test generation has been a critical element of the industry practice that would contribute to the evaluation of test cases. The industry generally delivers the product under the condition of time to the market and thus, inevitably sacrifices software testing tasks, even though many test cases are required for software verification. This paper presents an approach of applying a social network centrality measure, PageRank, to prioritize mutation test generation. The source code with the highest values of PageRank will be focused first when developing their test cases as these modules are vulnerable to defects or anomalies which may cause the consequent defects in many other associated modules. Moreover, the approach would help identify the reducible test cases in the test suite, still maintaining the same criteria as the original number of test cases.

Keywords: software testing, mutation test, network centrality measure, test case prioritization

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Authors: M. Masroor Akhtar Khan, Moin Uddin, Lalit Varshney

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Recently, there has been a rapidly expanding interest in finding applications of natural polymers in view of value addition to agriculture. It is now being realized that radiation processing of natural polysaccharides can be beneficially utilized either to improve the existing methodologies used for processing the natural polymers or to impart value addition to agriculture by converting them into more useful form. Gamma-ray irradiation is employed to degrade and lower the molecular weight of some of the natural polysaccharides like alginates, chitosan and carrageenan into small sized oligomers. When these oligomers are applied to plants as foliar sprays, they elicit various kinds of biological and physiological activities, including promotion of plant growth, seed germination, shoot elongation, root growth, flower production, suppression of heavy metal stress, etc. Furthermore, application of these oligomers can shorten the harvesting period of various crops and help in reducing the use of insecticides and chemical fertilizers. In recent years, the oligomers of sodium alginate obtained by irradiating the latter with gamma-rays at 520 kGy dose are being employed. It was noticed that the oligomers derived from the natural polysaccharides could induce growth, photosynthetic efficiency, enzyme activities and most importantly the production of secondary metabolite in the plants like Artemisia annua, Beta vulgaris, Catharanthus roseus, Chrysopogon zizanioides, Cymbopogon flexuosus, Eucalyptus citriodora, Foeniculum vulgare, Geranium sp., Mentha arvensis, Mentha citrata, Mentha piperita, Mentha virdis, Papaver somniferum and Trigonella foenum-graecum. As a result of the application of these oligomers, the yield and/or contents of the active constituents of the aforesaid plants were significantly enhanced. The productivity, as well as quality of medicinal and aromatic plants, may be ameliorated by this novel technique in an economical way as a very little quantity of these irradiated (depolymerised) polysaccharides is needed. Further, this is a very safe technique, as we did not expose the plants directly to radiation. The radiation was used to depolymerize the polysaccharides into oligomers.

Keywords: essential oil, medicinal and aromatic plants, plant production, radiation processed polysaccharides, active constituents

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Authors: Eugene Stepanov, Arkadi Ponossov

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Many mathematical models used in biological and other applications are ill-posed. The reason for that is the nature of differential equations, where the nonlinearities are assumed to be step functions, which is done to simplify the analysis. Prominent examples are switched systems arising from gene regulatory networks and neural field equations. This simplification leads, however, to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid feedback controls to regularize the problem. Roughly speaking, one attaches a finite state control (‘automaton’), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. This ‘hybridization’ is shown to regularize the original switched system and gives rise to efficient hybrid numerical schemes. Several examples are provided in the presentation, which supports the suggested analysis. The method can be of interest in other applied fields, where differential equations contain step-like nonlinearities.

Keywords: hybrid feedback control, ill-posed problems, singular perturbation analysis, step-like nonlinearities

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Authors: Hyun-Kyu Cho, Jun-Su Kim, Choon-Geun Huh, Geon Lee Young-Chul Park

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In this study, the relationship between flow properties and fluid projection distance look into connection for shape variations of foam monitor. A numerical analysis technique for fluid analysis of a foam monitor was developed for the prediction. Shape of foam monitor the flow path of fluid flow according to the shape, The fluid losses were calculated from flow analysis result.. The modified model used the length increase model of the flow path, and straight line of the model. Inlet pressure was 7 [bar] and external was atmosphere codition. am. The results showed that the length increase model of the flow path and straight line of the model was improved in the nozzle projection distance.

Keywords: injection performance, finite element method, foam monitor, Projection distance

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Authors: F. Noto, F. De Persio, V. Bellini, G. Costa. F. Mammoliti, F. Meddi, C. Sutera, G. M. Urcioli

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One plane X and one plane Y of silicon microstrip detectors will constitute the front part of the Super Bigbite Spectrometer that is under construction and that will be installed in the experimental Hall A of the Thomas Jefferson National Accelerator Facility (Jefferson Laboratory), located in Newport News, Virgina, USA. Each plane will be made up by two nearly identical, 300 μm thick, 10 cm x 10.3 cm wide silicon microstrip detectors with 50 um pitch, whose electronic signals will be transferred to the front-end electronic based on APV25 chips through C-shaped FR4 Printed Circuit Boards (PCB). A total of about 10000 strips are read-out. This paper treats the optimization of the detector support structure, the materials used through a finite element simulation. A very important aspect of the study will also cover the optimization of the bonding parameters between detector and electronics.

Keywords: FEM analysis, bonding, SBS tracker, mechanical structure

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Sari Aouatef, Larabi Amina

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First-principles calculations within density functional theory based are used to investigate the influence of doped rare earth elements on some properties of perovskite systems Dy₀.₀₆₂₅Y₀.₉₃₇₅FeO₃ and Dy₀.₁₂₅ Y₀.₈₇₅ FeO₃. The electronic and magnetic properties are studied by means of the full-potential linearized augmented plane wave method with Vasp code. The calculated densities of states presented in this work identify the semiconducting behavior for Dy₀.₁₂₅ Y₀.₈₇₅ FeO₃, and the semi-metallic behavior for Dy₀.₀₆₂₅Y₀.₉₃₇₅ FeO₃. Besides, to investigate magnetic properties of several compounds, four magnetic configurations are considered (ferromagnetic (FM), antiferromagnetic type A (A-AFM), antiferromagnetic type C (C-AFM) and antiferromagnetic type G (G-AFM). By doping the Dy element, the system shows different changes in the magnetic order and electronic structure. It is found that Dy₀.₀₆₂₅Y₀.₉₃₇₅ FeO₃ exhibits the strongest magnetic change corresponding to the transition to the ferromagnetic order with the largest magnetic moment of 4.997.

Keywords: DFT, Perovskites, multiferroic, magnetic properties

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Authors: Mohammed Eltayeb Salih Elamin

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This study discusses the concept of the artificial heart using engineering concepts, of the fluid mechanics and the characteristics of the non-Newtonian fluid. For the purpose to serve heart patients and improve aspects of their lives and since the Statistics review according to world health organization (WHO) says that heart disease and blood vessels are the first cause of death in the world. Statistics shows that 30% of the death cases in the world by the heart disease, so simply we can consider it as the number one leading cause of death in the entire world is heart failure. And since the heart implantation become a very difficult and not always available, the idea of the artificial heart become very essential. So it’s important that we participate in the developing this idea by searching and finding the weakness point in the earlier designs and hoping for improving it for the best of humanity. In this study a pump was designed in order to pump blood to the human body and taking into account all the factors that allows it to replace the human heart, in order to work at the same characteristics and the efficiency of the human heart. The pump was designed on the idea of the diaphragm pump. Three models of blood obtained from the blood real characteristics and all of these models were simulated in order to study the effect of the pumping work on the fluid. After that, we study the properties of this pump by using Ansys15 software to simulate blood flow inside the pump and the amount of stress that it will go under. The 3D geometries modeling was done using SOLID WORKS and the geometries then imported to Ansys design modeler which is used during the pre-processing procedure. The solver used throughout the study is Ansys FLUENT. This is a tool used to analysis the fluid flow troubles and the general well-known term used for this branch of science is known as Computational Fluid Dynamics (CFD). Basically, Design Modeler used during the pre-processing procedure which is a crucial step before the start of the fluid flow problem. Some of the key operations are the geometry creations which specify the domain of the fluid flow problem. Next is mesh generation which means discretization of the domain to solve governing equations at each cell and later, specify the boundary zones to apply boundary conditions for the problem. Finally, the pre–processed work will be saved at the Ansys workbench for future work continuation.

Keywords: Artificial heart, computational fluid dynamic heart chamber, design, pump

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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Authors: C. Tezara, H. Y. Lim, M. H. Yazdi, J. W. Lim, J. P. Siregar

Abstract:

Natural fibre has become an element in human lives. A lot of researchers have conducted research about natural fibre reinforced polymer. Malaysian government has spent a lot of money on the research funding for researchers and academician especially research on kenaf fibre due to exclusion of tobacco from AFTA (Asean Free Trade Area) list. This work is to investigate the mechanical properties of kenaf fiber reinforced epoxy composite where short kenaf fibre was applied and the mechanical properties of 5%, 10% and 15% wt. of kenaf fibre were added into the mixture of epoxy resin. Hand lay-up process was selected in the fabrication of the specimen for testing. The tensile, flexural and impact test were conducted following ASTM D3039, ASTM D790 and ASTM D256 accordingly. From the experiment result, the effect of different fiber loading of the specimen on its mechanical properties would be analyzed and compared in the result and discussion.

Keywords: Kenaf fibre, epoxy, composite, fibre

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Authors: Ali Soroush, Mehdi Mirzaei Alavijeh, Touraj Ahmadi Jouybari, Fazel Zinat-Motlagh, Abbas Aghaei, Mari Ataee

Abstract:

Diabetes is a disease with long cardiovascular, renal, ophthalmic and neural complications. It is prevalent all around the world including Iran, and its prevalence is increasing. The aim of this study was to determine the factors related to self-care behavior based on health belief model among sample of Iranian diabetic patients. This cross-sectional study was conducted among 301 type 2 diabetic patients in Gachsaran, Iran. Data collection was based on an interview and the data were analyzed by SPSS version 20 using ANOVA, t-tests, Pearson correlation, and linear regression statistical tests at 95% significant level. Linear regression analyses showed the health belief model variables accounted for 29% of the variation in self-care behavior; and perceived severity and perceived self-efficacy are more influential predictors on self-care behavior among diabetic patients.

Keywords: diabetes, patients, self-care behaviors, health belief model

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Authors: Javad Shamsi Soosahab, Reza Ziaie Moayed

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The compressive and tensile bearing capacity of helical piles in sand is investigated by means of numerical modeling. The analyses are carried out using two-dimensional finite-element software, Optum G2. The load–displacement behavior under compression and tension is compared in different relative densities for constant and various elastic modulus. The criterion used to find the ultimate axial load is the load corresponding to 5% of the helical diameter. The results show that relative density of sand plays an essential role in the response of ultimate capacities towards various condition. Increase in elastic modulus with depth is found to play a relatively more significant role to the increase in ultimate compressive load capacities, however tension bearing capacity decreases.

Keywords: helical piles, Optum G2, relative density, constant and various elastic modulus

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Authors: Marcin Michalak

Abstract:

Biclustering is the way of two-dimensional data analysis. For several years it became possible to express such issue in terms of Boolean reasoning, for processing continuous, discrete and binary data. The mathematical backgrounds of such approach — proved ability of induction of exact and inclusion–maximal biclusters fulfilling assumed criteria — are strong advantages of the method. Unfortunately, the core of the method has quite high computational complexity. In the paper the basics of Boolean reasoning approach for biclustering are presented. In such context the problems of computation parallelization are risen.

Keywords: Boolean reasoning, biclustering, parallelization, prime implicant

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Authors: Anita Kushwaha

Abstract:

We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: Enrique Castañeda, Tomas Hernadez, Mario Ulloa

Abstract:

Recent studies related to submarine pipelines under high pressure, temperature and buried, forces us to make bibliographical and documentary research to make us of references applicable to our problem. This paper presents an experimental methodology to the implementation of results obtained in a scale model, bibliography soil mechanics and finite element simulation. The model consists of a tank of 0.60 x 0.90 x 0.60 basis equipped high side windows, tires and digital hardware devices for measuring different variables to be applied to the model, where the mechanical properties of the soil are determined, simulation of drag a pipeline buried in a non-cohesive seafloor of the Gulf of Mexico, estimate the failure surface and application of each of the variables for the determination of mechanical elements.

Keywords: static friction coefficient, maximum passive force resistant soil, normal, tangential stress

Procedia PDF Downloads 345

Authors: Adriana Estokova, Lenka Palascakova, Martina Kovalcikova

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Cement is a basic material used for building construction. Chromium as an indelible non-volatile trace element of raw materials occurs in cement clinker in the trivalent or hexavalent form. Hexavalent form of chromium is harmful and allergenic having very high water solubility and thus can easily come into contact with the human skin. The paper is aimed at analyzing the content of total chromium in Portland cements and leaching rate of hexavalent chromium in various leachants: Deionized water, Britton-Robinson buffer, used to simulate the natural environment, and hydrochloric acid (HCl). The concentration of total chromium in Portland cement samples was in a range from 173.2 to 218.5 mg/kg. The content of dissolved hexavalent chromium ranged 0.23-3.19, 2.0-5.78 and 8.88-16.25 mg/kg in deionized water, Britton-Robinson solution and hydrochloric acid, respectively. The calculated leachable fraction of Cr(VI) from cement samples was observed in the range 0.1--7.58 %.

Keywords: environment, cement, chromium, leaching

Procedia PDF Downloads 255

Authors: Jongho Park, Jinwoong Choi, Sungnam Hong, Seung-Kyung Kye, Sun-Kyu Park

Abstract:

Recently, to cope with the rapidly changing construction trend because of aging infrastructures, modular bridge technology has been studied actively. Modular bridge is easily constructed by assembling standardized precast structure members in the field. It will be possible to construct rapidly and reduce construction cost efficiently. However, the shape examination of the transverse connection of T-type girder newly developed between the segmented modules is not performed. Therefore, the investigation of the connection shape is needed. In this study, shape of the modular T-girder bridge transverse connection was analyzed by finite element model that was verified in study which was verification of model for transverse connection using Abaqus. Connection angle was chosen as the parameter. The result of analyses showed that optimal value of angle is 130 degree.

Keywords: modular bridge, optimal transverse shape, parameter, FEM

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Authors: Gabriele Barrocas, Habon Issa

Abstract:

The amygdala is one of various brain regions that tend to be pathological in individuals with autism spectrum disorder (ASD). ASD is a prevalent and heterogeneous developmental disorder affecting all ethnic and socioeconomic groups and consists of a broad range of severity, etiology, and behavioral symptoms. Common features of ASD include but are not limited to repetitive behaviors, obsessive interests, and anxiety. Neuroscientists view the amygdala as the core of the neural system that regulates behavioral responses to anxiogenic and threatening stimuli. Despite this consensus, many previous studies and literature reviews on the amygdala’s alterations in individuals with ASD have reported inconsistent findings. In this review, we will address these conflicts by highlighting recent studies which reveal that anatomical and related socio-emotional differences detected between individuals with and without ASD are highly age-dependent. We will specifically discuss studies using functional magnetic resonance imaging (fMRI), structural MRI, and diffusion tensor imaging (DTI) to provide insights into the neuroanatomical substrates of ASD across development, with a focus on amygdala volumes, cell densities, and connectivity.

Keywords: autism, amygdala, development, abnormalities

Procedia PDF Downloads 114