Search results for: intelligent computational techniques

Authors: Imane Benghalia, Mohammed Zamoum, Rachid Boucetta

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Hydrodynamic cavitation is a complex physical phenomenon that appears in hydraulic systems (pumps, turbines, valves, Venturi tubes, etc.) when the fluid pressure decreases below the saturated vapor pressure. The works carried out in this study aimed to get a better understanding of the cavitating flow phenomena. For this, we have numerically studied a cavitating bubbly flow through a Venturi nozzle. The cavitation model is selected and solved using a commercial computational fluid dynamics (CFD) code. The obtained results show the effect of the inlet pressure (10, 7, 5, and 2 bars) of the Venturi on pressure, the velocity of the fluid flow, and the vapor fraction. We found that the inlet pressure of the Venturi strongly affects the evolution of the pressure, velocity, and vapor fraction formation in the cavitating flow.

Keywords: cavitating flow, CFD, phase change, venturi

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Authors: Norhashidah Hj. Mohd Ali, Teng Wai Ping

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In this paper, we present a high order group explicit method in solving the two dimensional Helmholtz equation. The presented method is derived from a nine-point fourth order finite difference approximation formula obtained from a 45-degree rotation of the standard grid which makes it possible for the construction of iterative procedure with reduced complexity. The developed method will be compared with the existing group iterative schemes available in literature in terms of computational time, iteration counts, and computational complexity. The comparative performances of the methods will be discussed and reported.

Keywords: explicit group method, finite difference, helmholtz equation, rotated grid, standard grid

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Authors: M. H. Alhajeri, Hamad M. Alhajeri, A. H. Alenezi

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This paper reports a Computational Fluid Dynamics (CFD) investigation for a high-temperature high-pressure filtration (ceramic candle filter). However, parallel flow to the filter is considered in this study. Different face (filtration) velocities are examined using the CFD code, FLUENT. Different sizes of particles are tracked through the domain to find the height at which the particles will impinge on the filter surface. Furthermore, particle distribution around the filter (or filter cake) is studied to design efficient cleaning mechanisms. Gravity effect to the particles with various inlet velocities and pressure drop are both considered. In the CFD study, it is found that the gravity influence should not be ignored if the particle sizes exceed 1 micron.

Keywords: fluid flow, CFD, filtration, HTHP

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Authors: K. N. Dinesh Babu, P. K. Gargava

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Energization of a transformer results in sudden flow of current which is an effect of core magnetization. This current will be dominated by the presence of second harmonic, which in turn is used to segregate fault and inrush current, thus guaranteeing proper operation of the relay. This additional security in the relay sometimes obstructs or delays differential protection in a specific scenario, when the 2^nd harmonic content was present during a genuine fault. This kind of scenario can result in isolation of the transformer by Buchholz and pressure release valve (PRV) protection, which is acted when fault creates more damage in transformer. Such delays involve a huge impact on the insulation failure, and chances of repairing or rectifying fault of problem at site become very dismal. Sometimes this delay can cause fire in the transformer, and this situation becomes havoc for a sub-station. Such occurrences have been observed in field also when differential relay operation was delayed by 10-15 ms by second harmonic blocking in some specific conditions. These incidences have led to the need for an alternative solution to eradicate such unwarranted delay in operation in future. Modern numerical relay, called as intelligent electronic device (IED), is embedded with advanced protection features which permit higher flexibility and better provisions for tuning of protection logic and settings. Such flexibility in transformer protection IEDs, enables incorporation of alternative methods such as dynamic switching of second harmonic feature for blocking the differential protection with additional security. The analysis and precautionary measures carried out in this case, have been simulated and discussed in this paper to ensure that similar solutions can be adopted to inhibit analogous issues in future.

Keywords: differential protection, intelligent electronic device (IED), 2nd harmonic inhibit, inrush inhibit

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Authors: Lin Xie, Yang Wang

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An extensive body of research studies suggest that students, teachers, and supervisors can enhance the likelihood of reaching their goals by improving their communication skills. It is highly important to learn how and when to provide different kinds of feedback, e.g. anticipatory, corrective and positive) will gain better result and higher morale. The purpose of this mixed methods research is twofold: 1) To find out what factors affect effective communication among different stakeholders and how these factors affect student learning 2) What are the good practices for improving communication among different stakeholders and improve student achievement. This presentation first begins with an introduction to the recent research on Marshall’s Nonviolent Communication Techniques (NVC), including four important components: observations, feelings, needs, requests. These techniques can be effectively applied at all levels of communication. To develop an in-depth understanding of the relationship among different techniques within, this research collected, compared, and combined qualitative and quantitative data to better improve communication and support student learning.

Keywords: communication, education, language learning, goal achievement, academic success

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Authors: Duc V. Nguyen

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Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.

Keywords: fault detection, FFT, induction motor, predictive maintenance

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Ashish Kumar Agrahari, Amit Kumar

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Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired clonal blood disorder that manifests with hemolytic anemia, thrombosis, and peripheral blood cytopenias. The disease is caused by the deficiency of two glycosylphosphatidylinositols (GPI)-anchored proteins (CD55 and CD59) in the hemopoietic stem cells. The deficiency of GPI-anchored proteins has been associated with the somatic mutations in phosphatidylinositol glycan class A (PIGA). However, the mutations that do not cause PNH is associated with the multiple congenital anomalies-hypotonia-seizures syndrome 2 (MCAHS2). To best of our knowledge, no computational study has been performed to explore the atomistic level impact of PIGA mutations on the structure and dynamics of the protein. In the current work, we are mainly interested to get insights into the molecular mechanism of PIGA mutations. In the initial step, we screened the most pathogenic mutations from the pool of publicly available mutations. Further, to get a better understanding, pathogenic mutations were mapped to the modeled structure and subjected to 50ns molecular dynamics simulation. Our computational study suggests that four mutations are highly vulnerable to altering the structural conformation and stability of the PIGA protein, which illustrates its association with PNH and MCAHS2 phenotype.

Keywords: homology modeling, molecular dynamics simulation, missense mutations PNH, MCAHS2, PIGA

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: S. H. Borghei, E. Teymourian, M. Mobin, G. M. Komaki, S. Sheikh

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Imperialist competitive algorithm (ICA) is a recent meta-heuristic method that is inspired by the social evolutions for solving NP-Hard problems. The ICA is a population based algorithm which has achieved a great performance in comparison to other meta-heuristics. This study is about developing enhanced ICA approach to solve the cell formation problem (CFP) using sequence data. In addition to the conventional ICA, an enhanced version of ICA, namely EICA, applies local search techniques to add more intensification aptitude and embed the features of exploration and intensification more successfully. Suitable performance measures are used to compare the proposed algorithms with some other powerful solution approaches in the literature. In the same way, for checking the proficiency of algorithms, forty test problems are presented. Five benchmark problems have sequence data, and other ones are based on 0-1 matrices modified to sequence based problems. Computational results elucidate the efficiency of the EICA in solving CFP problems.

Keywords: cell formation problem, group technology, imperialist competitive algorithm, sequence data

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Authors: Hubert Feiglstorfer

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The technique of making terrazzo has been known since ancient times. During the Roman Empire, known as opus signinum, at the time of the Renaissance, known as composto terrazzo marmorino or at the turn of the 19th and 20th centuries, the use of terrazzo experienced a common use in Europe. In Asia, especially in the Himalayas and the Tibetan highlands, a particular floor and roof manufacturing technique is commonly used for about 1500 years, known as arga. The research question in this contribution asks for technical and cultural-historical synergies of these floor-making techniques. The making process of an arga floor shows constructive parallels to the European terrazzo. Surface processing by grinding, burnishing and sealing, in particular, reveals technological similarities. The floor structure itself, on the other hand, shows differences, for example in the use of hydraulic aggregate in the terrazzo, while the arga floor is used without hydraulic material, but the result of both techniques is a tight, water-repellent and shiny surface. As part of this comparative study, the materials, processing techniques and quality features of the two techniques are compared and parallels and differences are analysed. In addition to text and archive research, the methods used are results of material analyses and ethnographic research such as participant observation. Major findings of the study are the investigation of the mineralogical composition of arga floors and its comparison with terrazzo floors. The study of the cultural-historical context in which both techniques are embedded will give insight into technical developments in Europe and Asia, parallels and differences. Synergies from this comparison let possible technological developments in the production, conservation and renovation of European terrazzo floors appear in a new light. By making arga floors without cement-based aggregates, the renovation of historical floors from purely natural products and without using energy by means of a burning process can be considered.

Keywords: European and Asian crafts, material culture, floor making technology, terrazzo, arga, Tibetan building traditions

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Authors: Azene Zenebe

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

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

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Authors: Armando A. Rodríguez, Emilio Salceda, Anoland Garateix, André J. Zaharenko, Steve Peigneur, Omar López, Tirso Pons, Michael Richardson, Maylín Díaz, Yasnay Hernández, Ludger Ständker, Jan Tytgat, Enrique Soto

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Acid-sensing ion channels are cation (Na+) channels activated by a pH drop. These proteins belong to the ENaC/degenerin superfamily of sodium channels. ASICs are involved in sensory perception, synaptic plasticity, learning, memory formation, cell migration and proliferation, nociception, and neurodegenerative disorders, among other processes; therefore those molecules that specifically target these channels are of growing pharmacological and biomedical interest. Sea anemones produce a large variety of ion channels peptide toxins; however, those acting on ligand-gated ion channels, such as Glu-gated, Ach-gated ion channels, and acid-sensing ion channels (ASICs), remain barely explored. The peptide PhcrTx1 is the first compound characterized from the sea anemone Phymanthus crucifer, and it constitutes a novel ASIC inhibitor. This peptide was purified by chromatographic techniques and pharmacologically characterized on acid-sensing ion channels of mammalian neurons using patch-clamp techniques. PhcrTx1 inhibited ASIC currents with an IC50 of 100 nM. Edman degradation yielded a sequence of 32 amino acids residues, with a molecular mass of 3477 Da by MALDI-TOF. No similarity to known sea anemone peptides was found in protein databases. The computational analysis of Cys-pattern and secondary structure arrangement suggested that this is a structurally ICK (Inhibitor Cystine Knot)-type peptide, a scaffold that had not been found in sea anemones but in other venomous organisms. These results show that PhcrTx1 represents the first member of a new structural group of sea anemones toxins acting on ASICs. Also, this peptide constitutes a novel template for the development of drugs against pathologies related to ASICs function.

Keywords: animal toxin, inhibitor cystine knot, ion channel, sea anemone

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Authors: Adnan Baig, Ishteeaq Naeem, Saad Mansoor

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The Internet of Things (IoT) gadgets have advanced quickly in recent years, and their use is steadily rising daily. However, cyber-attackers can target these gadgets due to their distributed nature. Additionally, many IoT devices have significant security flaws in their implementation and design, making them vulnerable to security threats. Hence, these threats can cause important data security and privacy loss from a single attack on network devices or systems. Botnets are a significant security risk that can harm the IoT network; hence, sophisticated techniques are required to mitigate the risk. This work uses a machine learning-based method to identify IoT orchestrated by botnets. The proposed technique identifies the net attack by distinguishing between legitimate and malicious traffic. This article proposes a hyperparameter tuning model to improvise the method to improve the accuracy of existing processes. The results demonstrated an improved and more accurate indication of botnet-based cyber-attacks.

Keywords: Internet of Things, Botnet, BoT-IoT dataset, ML techniques

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Authors: Troy M. Houser

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The successor representation balances flexible and efficient reinforcement learning by learning to predict the future, given the present. As such, the successor representation models stimuli as what future states they lead to. Therefore, two stimuli that are perceptually dissimilar but lead to the same future state will come to be represented more similarly. This is very similar to an older behavioral paradigm -the acquired equivalence paradigm, which measures the generalization of learned associations. Here, we test via computational modeling the plausibility that the successor representation is the mechanism by which people generalize knowledge learned in the acquired equivalence paradigm. Computational evidence suggests that this is a plausible mechanism for acquired equivalence and thus can guide future empirical work on individual differences in associative-based generalization.

Keywords: acquired equivalence, successor representation, generalization, decision-making

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Authors: Y. Laib Dit Leksir, S. Bouhouche

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Analysis and processing of data bases resulting from infrared thermal measurements made on the electrical installation requires the development of new tools in order to obtain correct and additional information to the visual inspections. Consequently, the methods based on the capture of infrared digital images show a great potential and are employed increasingly in various fields. Although, there is an enormous need for the development of effective techniques to analyse these data base in order to extract relevant information relating to the state of the equipments. Our goal consists in introducing recent techniques of modeling based on new methods, image and signal processing to develop mathematical models in this field. The aim of this work is to capture the anomalies existing in electrical equipments during an inspection of some machines using A40 Flir camera. After, we use binarisation techniques in order to select the region of interest and we make comparison between these methods of thermal images obtained to choose the best one.

Keywords: infrared thermography, defect detection, troubleshooting, electrical equipment

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Authors: Ya-Li Tsai

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Nitrogen is a vital element crucial for crop growth, significantly influencing crop yield. In rice cultivation, farmers often apply substantial nitrogen fertilizer to maximize yields. However, excessive nitrogen application increases the risk of lodging and pest infestation, leading to yield losses. Additionally, conventional flooded irrigation methods consume significant water resources, necessitating precise agricultural and intelligent water management systems. In this study, it leveraged physiological data and field images captured by unmanned aerial vehicles, considering fertilizer treatment and irrigation as key factors. Statistical models incorporating rice physiological data, yield, and vegetation indices from image data were developed. Missing physiological data were addressed using multiple imputation and regression methods, and regression models were established using principal component analysis and stepwise regression. Target nitrogen accumulation at key growth stages was identified to optimize fertilizer application, with the difference between actual and target nitrogen accumulation guiding recommendations for ear dressing dosage. Field experiments conducted in 2022 validated the recommended ear dressing dosage, demonstrating no significant difference in final yield compared to traditional fertilizer levels under alternate wetting and drying irrigation. These findings highlight the efficacy of applying recommended dosages based on fertilizer decision models, offering the potential for reduced fertilizer use while maintaining yield in rice cultivation.

Keywords: intelligent fertilizer management, nitrogen top and ear dressing fertilizer, rice, yield optimization

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Authors: Nagaratna Rajur, J. D. Mallapur, Y. B. Kirankumar

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The security in mobile communication is very different from the internet or telecommunication, because of its poor user interface and limited processing capacity, as well as combination of complex network protocols. Hence, it poses a challenge for less memory usage and low computation speed based security system. Security involves all the activities that are undertaken to protect the value and on-going usability of assets and the integrity and continuity of operations. An effective network security strategies requires identifying threats and then choosing the most effective set of tools to combat them. Cryptography is a simple and efficient way to provide security in communication. RSA is an asymmetric key approach that is highly reliable and widely used in internet communication. However, it has not been efficiently implemented in mobile communication due its computational complexity and large memory utilization. The proposed algorithm modifies the current RSA to be useful in mobile communication by reducing its computational complexity and memory utilization.

Keywords: M-RSA, sensor networks, sensor applications, security

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Authors: A. Kaidal, M. M. Abdllahi, O. L. Badaki

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The study was conducted to determine the relationship between Quetelet Equation and Skin fold measurement in determining obesity among adolescent male students of University of Maiduguri Demonstration Secondary School, Borno State, Nigeria. A total of 66 students participated in the study, their age ranges from 15-18 years. The ex-post-facto research design was used for this study. Anthropometric measurements were taken at three sites (thigh, abdomen and chest) using accu–measure Skin fold caliper. The values of the three measurements were used to determine the percentage body fat of the participants using the 3-Point Skin Fold Bodyfat calculator of Jackson-Pollock. Body mass index (BMI) was determined using weight (kg) divided by height in (m2). The data obtained was analyzed using descriptive statistics (mean and standard deviation) and inferential statistics of Pearson product moment correlation coefficient to determine the relationship between the two techniques. The result showed a significant positive relationship r=0.673 p<0.05 between body mass index and skin fold measurement techniques. It was however observed that BMI techniques of determining body fat tend to overestimate the actual percent body fat of adolescents studied. Based on this result, it is recommended that the use of BMI as a technique for determining obesity should be used with caution.

Keywords: body max index, skin fold, quetelet, techniques

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Authors: Seyed Vahid Kamal Alavi

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Italy has experienced a notable quantity and impact of disasters due to natural hazards and technological accidents caused by diverse risk sources on its physical, technological, and human/sociological infrastructures during past decade. This study discusses the frequency and impacts of the most three physical devastating natural hazards in Italy for the period 2000–2013. The approach examines the reliability of a range of open source WebGIS techniques versus a proposed multi-hazard risk management methodology. Spatial and attribute data which include USGS publically available hazard data and thirteen years Munich RE recorded data for Italy with different severities have been processed, visualized in a GIS (Geographic Information System) framework. Comparison of results from the study showed that the multi-hazard risk maps generated using open source techniques do not provide a reliable system to analyze the infrastructures losses in respect to national risk sources while they can be adopted for general international risk management purposes. Additionally, this study establishes the possibility to critically examine and calibrate different integrated techniques in evaluating what better protection measures can be taken in an area.

Keywords: multi-hazard risk mapping, risk management, GIS, Italy

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Authors: P. Deepak Kumar, P. R. Maiti

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In recent years, sediment scour near bridge piers and abutment is a serious problem which causes nationwide concern because it has resulted in more bridge failures than other causes. Scour is the formation of scour hole around the structure mounted on and embedded in erodible channel bed due to the erosion of soil by flowing water. The formation of scour hole around the structures depends upon shape and size of the pier, depth of flow as well as angle of attack of flow and sediment characteristics. The flow characteristics around these structures change due to man-made obstruction in the natural flow path which changes the kinetic energy of the flow around these structures. Excessive scour affects the stability of the foundation of the structure by the removal of the bed material. The accurate estimation of scour depth around bridge pier is very difficult. The foundation of bridge piers have to be taken deeper and to provide sufficient anchorage length required for stability of the foundation. In this study, computational model simulations using a 3D Computational Fluid Dynamics (CFD) model were conducted to examine the mechanism of scour around a cylindrical pier. Subsequently, the flow characteristics around these structures are presented for different flow conditions. Mechanism of scouring phenomenon, the formation of vortex and its consequent effect is discussed for a straight channel. Effort was made towards estimation of scour depth around bridge piers under different flow conditions.

Keywords: bridge pier, computational fluid dynamics, multigrid, pier shape, scour

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Authors: Abdulmohsen Alghadeer, Fahad Al Mahashir, Loai Al Owa, Najim Alshahrani

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Electrical continuity of steel structure members is an essential condition to ensure equipotential and ultimately to protect personnel and assets in industrial facilities. The steel structure is electrically connected to provide a low resistance path to earth through equipotential bonding to prevent sparks and fires in the event of fault currents and avoid malfunction of the plant with detrimental consequences to the local and global environment. The oil and gas industry is commonly establishing steel structure electrical continuity by bare surface connection of coated steel members. This paper presents information pertaining to a real case of exploring and applying different techniques to achieve the electrical continuity in erecting steel structures at a gas plant facility. A project was supplied with fully coated steel members even at the surface connection members that cause electrical discontinuity. This was observed while a considerable number of steel members had already been received at the job site and erected. This made the resolution of the case to use different techniques such as bolt tightening and torqueing, chemical paint stripping and single point jumpers. These techniques are studied with comparative analysis related to their applicability, workability, time and cost advantages and disadvantages.

Keywords: coated Steel, electrical continuity, equipotential bonding, galvanized steel, gas plant facility, lightning protection, steel structure

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Authors: Salima Smiti, Ines Gasmi, Makram Soui

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Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

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Authors: Salvatore Luongo, Carlo Luongo

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This paper discusses the implementation solutions to reduce the computational load for the Traffic Situational Awareness with Alerts (TSAA) application, based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology. In 2008, there were 23 total mid-air collisions involving general aviation fixed-wing aircraft, 6 of which were fatal leading to 21 fatalities. These collisions occurred during visual meteorological conditions, indicating the limitations of the see-and-avoid concept for mid-air collision avoidance as defined in the Federal Aviation Administration’s (FAA). The commercial aviation aircraft are already equipped with collision avoidance system called TCAS, which is based on classic transponder technology. This system dramatically reduced the number of mid-air collisions involving air transport aircraft. In general aviation, the same reduction in mid-air collisions has not occurred, so this reduction is the main objective of the TSAA application. The major difference between the original conflict detection application and the TSAA application is that the conflict detection is focused on preventing loss of separation in en-route environments. Instead TSAA is devoted to reducing the probability of mid-air collision in all phases of flight. The TSAA application increases the flight crew traffic situation awareness providing alerts of traffic that are detected in conflict with ownship in support of the see-and-avoid responsibility. The relevant effort has been spent in the design process and the code generation in order to maximize the efficiency and performances in terms of computational load and memory consumption reduction. The TSAA architecture is divided into two high-level systems: the “Threats database” and the “Conflict detector”. The first one receives the traffic data from ADS-B device and provides the memorization of the target’s data history. Conflict detector module estimates ownship and targets trajectories in order to perform the detection of possible future loss of separation between ownship and each target. Finally, the alerts are verified by additional conflict verification logic, in order to prevent possible undesirable behaviors of the alert flag. In order to reduce the computational load, a pre-check evaluation module is used. This pre-check is only a computational optimization, so the performances of the conflict detector system are not modified in terms of number of alerts detected. The pre-check module uses analytical trajectories propagation for both target and ownship. This allows major accuracy and avoids the step-by-step propagation, which requests major computational load. Furthermore, the pre-check permits to exclude the target that is certainly not a threat, using an analytical and efficient geometrical approach, in order to decrease the computational load for the following modules. This software improvement is not suggested by FAA documents, and so it is the main innovation of this work. The efficiency and efficacy of this enhancement are verified using fast-time and real-time simulations and by the execution on a real device in several FAA scenarios. The final implementation also permits the FAA software certification in compliance with DO-178B standard. The computational load reduction allows the installation of TSAA application also on devices with multiple applications and/or low capacity in terms of available memory and computational capabilities

Keywords: traffic situation awareness, general aviation, aircraft conflict detection, computational load reduction, implementation solutions, software certification

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Authors: Rajat Mittal, Jung Hee Seo, Jacob Turner, Harshal Raut

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The perpetual advancement in computational capabilities, coupled with the continuous evolution of software tools and numerical algorithms, is creating novel avenues for research, exploration, and application at the nexus of computational fluid and structural mechanics. Fish leverage their remarkably flexible bodies and fins to harness energy from vortices, propelling themselves with an elegance and efficiency that captivates engineers. Bats fly with unparalleled agility and speed by using their flexible membrane wings. Wave-assisted propulsion (WAP) systems, utilizing elastically mounted hydrofoils, convert wave energy into thrust. Each of these problems involves a complex and elegant interplay between fluid dynamics and structural mechanics. Historically, investigations into such phenomena were constrained by available tools, but modern computational advancements now facilitate exploration of these multi-physics challenges with an unprecedented level of fidelity, precision, and realism. In this work, the author will discuss projects that harness the capabilities of high-fidelity sharp-interface immersed boundary methods to address a spectrum of engineering and biological challenges involving fluid-structure interaction.

Keywords: immersed boundary methods, CFD, bioflight, fluid structure interaction

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: Emanuele Bonamente, Andrea Aquino, Franco Cotana

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The exploitation of the low-temperature geothermal resource via ground-source heat pumps is often limited by the high investment cost mainly due to borehole drilling. From the monitoring of a prototypal system currently used by a commercial building, it was found that a simple upgrade of the conventional layout, obtained including a thermal storage between the ground-source heat exchangers and the heat pump, can optimize the ground energy exploitation requiring for shorter/fewer boreholes. For typical applications, a reduction of up to 66% with respect to the conventional layout can be easily achieved. Results from the monitoring campaign of the prototype are presented in this paper, and upgrades of the thermal storage using phase change materials (PCMs) are proposed using computational fluid dynamics simulations. The PCM thermal storage guarantees an improvement of the system coefficient of performance both for summer cooling and winter heating (up to 25%). A drastic reduction of the storage volume (approx. 1/10 of the original size) is also achieved, making it possible to easily place it within the technical room, avoiding extra costs for underground displacement. A preliminary optimization of the PCM geometry is finally proposed.

Keywords: computational fluid dynamics (CFD), geothermal energy, ground-source heat pumps, phase change materials (PCM)

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Authors: Janneth Gonzalez, Marco Avila, George Barreto

Abstract:

GRP78 participates in multiple functions in the cell during normal and pathological conditions, controlling calcium homeostasis, protein folding and unfolded protein response. GRP78 is located in the endoplasmic reticulum, but it can change its location under stress, hypoxic and apoptotic conditions. NF-κB represents the keystone of the inflammatory process and regulates the transcription of several genes related with apoptosis, differentiation, and cell growth. The possible relationship between GRP78-NF-κB could support and explain several mechanisms that may regulate a variety of cell functions, especially following brain injuries. Although several reports show interactions between NF-κB and heat shock proteins family members, there is a lack of information on how GRP78 may be interacting with NF-κB, and possibly regulating its downstream activation. Therefore, we assessed the computational predictions of the GRP78 (Chain A) and NF-κB complex (IkB alpha and p65) protein-protein interactions. The interaction interface of the docking model showed that the amino acids ASN 47, GLU 215, GLY 403 of GRP78 and THR 54, ASN 182 and HIS 184 of NF-κB are key residues involved in the docking. The electrostatic field between GRP78-NF-κB interfaces and molecular dynamic simulations support the possible interaction between the proteins. In conclusion, this work shed some light in the possible GRP78-NF-κB complex indicating key residues in this crosstalk, which may be used as an input for better drug design strategy targeting NF-κB downstream signaling as a new therapeutic approach following brain injuries.

Keywords: computational biology, protein interactions, Grp78, bioinformatics, molecular dynamics

Procedia PDF Downloads 342

Authors: Sachin Aggarwal, Sarah Kassinger, Nicholas Hoffman

Abstract:

Geometry simplification is a key step in performing conjugate heat transfer analysis using CFD. This paper proposes a standard methodology for the geometry simplification of rotating machines, such as electrical generators and electrical motors (both air and liquid-cooled). These machines are extensively deployed throughout the aerospace and automotive industries, where optimization of weight, volume, and performance is paramount -especially given the current global transition to renewable energy sources and vehicle hybridization and electrification. Conjugate heat transfer analysis is an essential step in optimizing their complex design. This methodology will help in reducing convergence issues due to poor mesh quality, thus decreasing computational cost and overall analysis time.

Keywords: CFD, electrical machines, Geometry simplification, heat transfer

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Authors: André Smith, Amr Abdel-Dayem

Abstract:

This paper presents an overview of the history and development of stereo matching algorithms. Details from its inception, up to relatively recent techniques are described, noting challenges that have been surmounted across these past decades. Different components of these are explored, though focus is directed towards the local matching techniques. While global approaches have existed for some time, and demonstrated greater accuracy than their counterparts, they are generally quite slow. Many strides have been made more recently, allowing local methods to catch up in terms of accuracy, without sacrificing the overall performance.

Keywords: developmental survey, local stereo matching, rectification, stereo correspondence

Procedia PDF Downloads 293