Search results for: computational finance

Authors: Carlos Gonzales, Zaida Quiroz, Marcos Prates

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Several spatial variables collected at the same location that share a common spatial distribution can be modeled simultaneously through a multivariate geostatistical model that takes into account the correlation between these variables and the spatial autocorrelation. The main goal of this model is to perform spatial prediction of these variables in the region of study. Here we focus on a geostatistical multivariate formulation that relies on sharing common spatial random effect terms. In particular, the first response variable can be modeled by a mean that incorporates a shared random spatial effect, while the other response variables depend on this shared spatial term, in addition to specific random spatial effects. Each spatial random effect is defined through a Gaussian process with a valid covariance function, but in order to improve the computational efficiency when the data are large, each Gaussian process is approximated to a Gaussian random Markov field (GRMF), specifically to the block nearest neighbor Gaussian process (Block-NNGP). This approach involves dividing the spatial domain into several dependent blocks under certain constraints, where the cross blocks allow capturing the spatial dependence on a large scale, while each individual block captures the spatial dependence on a smaller scale. The multivariate geostatistical model belongs to the class of Latent Gaussian Models; thus, to achieve fast Bayesian inference, it is used the integrated nested Laplace approximation (INLA) method. The good performance of the proposed model is shown through simulations and applications for massive data.

Keywords: Block-NNGP, geostatistics, gaussian process, GRMF, INLA, multivariate models.

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Authors: H. M. Soroush

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The problem of scheduling products and services for on-time deliveries is of paramount importance in today’s competitive environments. It arises in many manufacturing and service organizations where it is desirable to complete jobs (products or services) with different weights (penalties) on or before their due dates. In such environments, schedules should frequently decide whether to schedule a job based on its processing time, due-date, and the penalty for tardy delivery to improve the system performance. For example, it is common to measure the weighted number of late jobs or the percentage of on-time shipments to evaluate the performance of a semiconductor production facility or an automobile assembly line. In this paper, we address the problem of scheduling a set of jobs on a server where processing times or due-dates of jobs are random variables and fixed weights (penalties) are imposed on the jobs’ late deliveries. The goal is to find the schedule that minimizes the expected weighted number of tardy jobs. The problem is NP-hard to solve; however, we explore three scenarios of the problem wherein: (i) both processing times and due-dates are stochastic; (ii) processing times are stochastic and due-dates are deterministic; and (iii) processing times are deterministic and due-dates are stochastic. We prove that special cases of these scenarios are solvable optimally in polynomial time, and introduce efficient heuristic methods for the general cases. Our computational results show that the heuristics perform well in yielding either optimal or near optimal sequences. The results also demonstrate that the stochasticity of processing times or due-dates can affect scheduling decisions. Moreover, the proposed problem is general in the sense that its special cases reduce to some new and some classical stochastic single machine models.

Keywords: number of late jobs, scheduling, single server, stochastic

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Authors: Taher M. Abou-deif, Esmail M. El-Bialy, Essam E. Khalil

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The paper is devoted to numerically investigating the influence of the air supply outlets geometry on human comfort inside living looms. A computational fluid dynamics model is developed to examine the air flow characteristics of a room with different supply air diffusers. The work focuses on air flow patterns, thermal behavior in the room with few number of occupants. As an input to the full-scale 3-D room model, a 2-D air supply diffuser model that supplies direction and magnitude of air flow into the room is developed. Air distribution effect on thermal comfort parameters was investigated depending on changing the air supply diffusers type, angles and velocity. Air supply diffusers locations and numbers were also investigated. The pre-processor Gambit is used to create the geometric model with parametric features. Commercially available simulation software “Fluent 6.3” is incorporated to solve the differential equations governing the conservation of mass, three momentum and energy in the processing of air flow distribution. Turbulence effects of the flow are represented by the well-developed two equation turbulence model. In this work, the so-called standard k-ε turbulence model, one of the most widespread turbulence models for industrial applications, was utilized. Basic parameters included in this work are air dry bulb temperature, air velocity, relative humidity and turbulence parameters are used for numerical predictions of indoor air distribution and thermal comfort. The thermal comfort predictions through this work were based on ADPI (Air Diffusion Performance Index),the PMV (Predicted Mean Vote) model and the PPD (Percentage People Dissatisfied) model, the PMV and PPD were estimated using Fanger’s model.

Keywords: thermal comfort, Fanger's model, ADPI, energy effeciency

Procedia PDF Downloads 396

Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: autonomous vehicles, deformable part model, dpm, pedestrian detection, real time

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Authors: W. Hamaidia, T. Zebbiche

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This paper presents the design of axisymmetric supersonic nozzles, in order to accelerate a supersonic flow to the desired Mach number and that having a small weight, in the same time gives a high thrust. The concerned nozzle gives a parallel and uniform flow at the exit section. The nozzle is divided into subsonic and supersonic regions. The supersonic portion is independent to the upstream conditions of the sonic line. The subsonic portion is used to give a sonic flow at the throat. In this case, nozzle gives a uniform and parallel flow at the exit section. It’s named by minimum length Nozzle. The study is done at high temperature, lower than the dissociation threshold of the molecules, in order to improve the aerodynamic performances. Our aim consists of improving the performances both by the increase of exit Mach number and the thrust coefficient and by reduction of the nozzle's mass. The variation of the specific heats with the temperature is considered. The design is made by the Method of Characteristics. The finite differences method with predictor-corrector algorithm is used to make the numerical resolution of the obtained nonlinear algebraic equations. The application is for air. All the obtained results depend on three parameters which are exit Mach number, the stagnation temperature, the chosen mesh in characteristics. A numerical simulation of nozzle through Computational Fluid Dynamics-FASTRAN was done to determine and to confirm the necessary design parameters.

Keywords: flux supersonic flow, axisymmetric minimum length nozzle, high temperature, method of characteristics, calorically imperfect gas, finite difference method, trust coefficient, mass of the nozzle, specific heat at constant pressure, air, error

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Usama Abdulwahab, Mohammed Wahab

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This paper presents the application of uncapacitated facility location problems (UFLP) and 1-median problems to support decision making in blood supply chain networks. A plethora of factors make blood supply-chain networks a complex, yet vital problem for the regional blood bank. These factors are rapidly increasing demand; criticality of the product; strict storage and handling requirements; and the vastness of the theater of operations. As in the UFLP, facilities can be opened at any of $m$ predefined locations with given fixed costs. Clients have to be allocated to the open facilities. In classical location models, the allocation cost is the distance between a client and an open facility. In this model, the costs are the allocation cost, transportation costs, and inventory costs. In order to address this problem the median algorithm is used to analyze inventory, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The Euclidean distance data for some Ontario cities (demand nodes) are used to test the developed algorithm. Sitation software, lagrangian relaxation algorithm, and branch and bound heuristics are used to solve this model. Computational experiments confirm the efficiency of the proposed approach. Compared to the existing modeling and solution methods, the median algorithm approach not only provides a more general modeling framework but also leads to efficient solution times in general.

Keywords: approximate dynamic programming, facility location, perishable product, inventory model, blood platelet, P-median problem

Procedia PDF Downloads 489

Authors: Sofia Sehli, Zainab El Ouafi, Casey Eddington, Soumaya Jbara, Kasambula Arthur Shem, Islam El Jaddaoui, Ayorinde Afolayan, Olaitan I. Awe, Allissa Dillman, Hassan Ghazal

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The ability to promptly sequence whole genomes at a relatively low cost has revolutionized the way we study the microbiome. Microbiologists are no longer limited to studying what can be grown in a laboratory and instead are given the opportunity to rapidly identify the makeup of microbial communities in a wide variety of environments. Analyzing whole genome sequencing (WGS) data is a complex process that involves multiple moving parts and might be rather unintuitive for scientists that don’t typically work with this type of data. Thus, to help lower the barrier for less-computationally inclined individuals, TAXAPRO was developed at the first Omics Codeathon held virtually by the African Society for Bioinformatics and Computational Biology (ASBCB) in June 2021. TAXAPRO is an advanced metagenomics pipeline that accurately assembles organelle genomes from whole-genome sequencing data. TAXAPRO seamlessly combines WGS analysis tools to create a pipeline that automatically processes raw WGS data and presents organism abundance information in both a tabular and graphical format. TAXAPRO was evaluated using COVID-19 patient gut microbiome data. Analysis performed by TAXAPRO demonstrated a high abundance of Clostridia and Bacteroidia genera and a low abundance of Proteobacteria genera relative to others in the gut microbiome of patients hospitalized with COVID-19, consistent with the original findings derived using a different analysis methodology. This provides crucial evidence that the TAXAPRO workflow dispenses reliable organism abundance information overnight without the hassle of performing the analysis manually.

Keywords: metagenomics, shotgun metagenomic sequence analysis, COVID-19, pipeline, bioinformatics

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Authors: Gaurangi Saxena, Ravindra Saxena

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Lately, cloud computing is used to enhance the ability to attain corporate goals more effectively and efficiently at lower cost. This new computing paradigm “The Cloud Computing” has emerged as a powerful tool for optimum utilization of resources and gaining competitiveness through cost reduction and achieving business goals with greater flexibility. Realizing the importance of this new technique, most of the well known companies in computer industry like Microsoft, IBM, Google and Apple are spending millions of dollars in researching cloud computing and investigating the possibility of producing interface hardware for cloud computing systems. It is believed that by using the right middleware, a cloud computing system can execute all the programs a normal computer could run. Potentially, everything from most simple generic word processing software to highly specialized and customized programs designed for specific company could work successfully on a cloud computing system. A Cloud is a pool of virtualized computer resources. Clouds are not limited to grid environments, but also support “interactive user-facing applications” such as web applications and three-tier architectures. Cloud Computing is not a fundamentally new paradigm. It draws on existing technologies and approaches, such as utility Computing, Software-as-a-service, distributed computing, and centralized data centers. Some companies rent physical space to store servers and databases because they don’t have it available on site. Cloud computing gives these companies the option of storing data on someone else’s hardware, removing the need for physical space on the front end. Prominent service providers like Amazon, Google, SUN, IBM, Oracle, Salesforce etc. are extending computing infrastructures and platforms as a core for providing top-level services for computation, storage, database and applications. Application services could be email, office applications, finance, video, audio and data processing. By using cloud computing system a company can improve its customer relationship management. A CRM cloud computing system may be highly useful in delivering a sales team a blend of unique functionalities to improve agent/customer interactions. This paper attempts to first define the cloud computing as a tool for running business activities more effectively and efficiently at a lower cost; and then it distinguishes cloud computing with grid computing. Based on exhaustive literature review, authors discuss application of cloud computing in different disciplines of management especially in the field of marketing with special reference to use of cloud computing in CRM. Study concludes that CRM cloud computing platform helps a company track any data, such as orders, discounts, references, competitors and many more. By using CRM cloud computing, companies can improve its customer interactions and by serving them more efficiently that too at a lower cost can help gaining competitive advantage.

Keywords: cloud computing, competitive advantage, customer relationship management, grid computing

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Authors: M. A. Askari, M. A. Nazari, P. Perrier, Y. Payan

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Growth and remodeling of biological structures have gained lots of attention over the past decades. Determining the response of the living tissues to the mechanical loads is necessary for a wide range of developing fields such as, designing of prosthetics and optimized surgery operations. It is a well-known fact that biological structures are never stress-free, even when externally unloaded. The exact origin of these residual stresses is not clear, but theoretically growth and remodeling is one of the main sources. Extracting body organs from medical imaging, does not produce any information regarding the existing residual stresses in that organ. The simplest cause of such stresses is the gravity since an organ grows under its influence from its birth. Ignoring such residual stresses might cause erroneous results in numerical simulations. Accounting for residual stresses due to tissue growth can improve the accuracy of mechanical analysis results. In this paper, we have implemented a computational framework based on fixed-point iteration to determine the residual stresses due to growth. Using nonlinear continuum mechanics and the concept of fictitious configuration we find the unknown stress-free reference configuration which is necessary for mechanical analysis. To illustrate the method, we apply it to a finite element model of healthy human face whose geometry has been extracted from medical images. We have computed the distribution of residual stress in facial tissues, which can overcome the effect of gravity and cause that tissues remain firm. Tissue wrinkles caused by aging could be a consequence of decreasing residual stress and not counteracting the gravity. Considering these stresses has important application in maxillofacial surgery. It helps the surgeons to predict the changes after surgical operations and their consequences.

Keywords: growth, soft tissue, residual stress, finite element method

Procedia PDF Downloads 337

Authors: Kayode A. Olaniyi, Adeola A. Ogunleye, Tola M. Osifeko

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Electric Vehicles (EV) appear to be gaining increasing patronage as a feasible alternative to Internal Combustion Engine Vehicles (ICEVs) for having low emission and high operation efficiency. The EV energy storage systems are required to handle high energy and power density capacity constrained by limited space, operating temperature, weight and cost. The choice of strategies for energy storage evaluation, monitoring and control remains a challenging task. This paper presents review of various energy storage technologies and recent researches in battery evaluation techniques used in EV applications. It also underscores strategies for the hybrid energy storage management and control schemes for the improvement of EV stability and reliability. The study reveals that despite the advances recorded in battery technologies there is still no cell which possess both the optimum power and energy densities among other requirements, for EV application. However combination of two or more energy storages as hybrid and allowing the advantageous attributes from each device to be utilized is a promising solution. The review also reveals that State-of-Charge (SoC) is the most crucial method for battery estimation. The conventional method of SoC measurement is however questioned in the literature and adaptive algorithms that include all model of disturbances are being proposed. The review further suggests that heuristic-based approach is commonly adopted in the development of strategies for hybrid energy storage system management. The alternative approach which is optimization-based is found to be more accurate but is memory and computational intensive and as such not recommended in most real-time applications.

Keywords: battery state estimation, hybrid electric vehicle, hybrid energy storage, state of charge, state of health

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Authors: Mouna Baassi, Mohamed Moussaoui, Hatim Soufi, Sanchaita RajkhowaI, Ashwani Sharma, Subrata Sinha, Said Belaaouad

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Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability, allowing a comparative study of the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of Darunavir and may be used for further experimental studies. Our study may also be used as a pipeline to search and design new potential inhibitors of HIV-1 proteases.

Keywords: QSAR, ADMET properties, molecular docking, molecular dynamics simulation.

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Authors: H. Shamoradifar, B. Teimuri, P. Parvaresh, S. Mohammadi

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One of the main characteristics of Heavy Water Moderated Reactors is their high production of plutonium. This article demonstrates the possibility of reduction of plutonium and other actinides in Heavy Water Research Reactor. Among the many ways for reducing plutonium production in a heavy water reactor, in this research, changing the fuel from natural Uranium fuel to Thorium-Uranium mixed fuel was focused. The main fissile nucleus in Thorium-Uranium fuels is U-233 which would be produced after neutron absorption by Th-232, so the Thorium-Uranium fuels have some known advantages compared to the Uranium fuels. Due to this fact, four Thorium-Uranium fuels with different compositions ratios were chosen in our simulations; a) 10% UO₂-90% THO₂ (enriched= 20%); b) 15% UO₂-85% THO₂ (enriched= 10%); c) 30% UO₂-70% THO₂ (enriched= 5%); d) 35% UO₂-65% THO₂ (enriched= 3.7%). The natural Uranium Oxide (UO₂) is considered as the reference fuel, in other words all of the calculated data are compared with the related data from Uranium fuel. Neutronic parameters were calculated and used as the comparison parameters. All calculations were performed by Monte Carol (MCNPX2.6) steady state reaction rate calculation linked to a deterministic depletion calculation (CINDER90). The obtained computational data showed that Thorium-Uranium fuels with four different fissile compositions ratios can satisfy the safety and operating requirements for Heavy Water Research Reactor. Furthermore, Thorium-Uranium fuels have a very good proliferation resistance and consume less fissile material than uranium fuels at the same reactor operation time. Using mixed Thorium-Uranium fuels reduced the long-lived α emitter, high radiotoxic wastes and the radio toxicity level of spent fuel.

Keywords: Heavy Water Reactor, Burn up, Minor Actinides, Neutronic Calculation

Procedia PDF Downloads 232

Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Ankur Mehra, M. V. Shivaani

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With increasing digitization, the world is coming closer, not only in terms of informational flow but also in terms of capital flows. And micro-finance institutions (MFIs) have perfectly leveraged this digital world by resorting to the innovative digital social peer-to-peer (P2P) lending platforms, such as, Kiva. These digital P2P platforms bring together micro-borrowers and lenders from across the world. The main objective of this study is to understand the funding preferences of social investors primarily from developed countries (such as US, UK, Australia), lending money to borrowers from rural India at zero interest rates through Kiva. Further, the objective of this study is to increase awareness about such a platform among various MFIs engaged in providing micro-loans to those in need. The sample comprises of India based micro-loan applications posted by various MFIs on Kiva lending platform over the period Sept 2012-March 2016. Out of 7,359 loans, 256 loans failed to get funded by social investors. On an average a micro-loan with 30 days to expiry gets fully funded in 7,593 minutes or 5.27 days. 62% of the loans raised on Kiva are related to livelihood, 32.5% of the loans are for funding basic necessities and balance 5.5% loans are for funding education. 47% of the loan applications have more than one borrower; while, currency exchange risk is on the social lenders for 45% of the loans. Controlling for the loan amount and loan tenure, the analyses suggest that those loan applications where the number of borrowers is more than one have a lower chance of getting funded as compared to the loan applications made by a sole borrower. Such group applications also take more time to get funded. Further, loan application by a solo woman not only has a higher chance of getting funded but as such get funded faster. The results also suggest that those loan applications which are supported by an MFI that has a religious affiliation, not only have a lower chance of getting funded, but also take longer to get funded as compared to the loan applications posted by secular MFIs. The results do not support cross-border currency risk to be a factor in explaining the determinants of loan funding. Finally, analyses suggest that loans raised for the purpose of earning livelihood and education have a higher chance of getting funded and such loans get funded faster as compared to the loans applied for purposes related to basic necessities such a clothing, housing, food, health, and personal use. The results are robust to controls for ‘MFI dummy’ and ‘year dummy’. The key implication from this study is that global social investors tend to develop an emotional connect with single woman borrowers and consequently they get funded faster Hence, MFIs should look for alternative ways for funding loans whose purpose is to meet basic needs; while, more loans related to livelihood and education should be raised via digital platforms.

Keywords: P2P lending, social investing, fintech, financial inclusion

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Authors: Daniel Panario, Daniel Santana de Freitas, Brett Stevens

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Almost Perfect Nonlinear (APN) permutations with optimal resistance against differential cryptanalysis can be found in several domains. The permutation used in the standard for symmetric cryptography (the AES), for example, is based on a special kind of inversion in GF(28). Although very close to APN (2-uniform), this permutation still contains one number 4 in its differential spectrum, which means that, rigorously, it must be classified as 4-uniform. This fact motivates the search for fully APN permutations in other domains of definition. The extremely high complexity associated to this kind of problem precludes an exhaustive search for an APN permutation with 256 elements to be performed without the support of a suitable mathematical structure. On the other hand, in principle, there is nothing to indicate which mathematically structured domains can effectively help the search, and it is necessary to test several domains. In this work, the search for APN permutations in rings ℤ2×ℤ2k is investigated. After a full, exhaustive search with k=2 and k=3, all possible APN permutations in those rings were recorded, together with their differential profiles. Some very promising heuristics in these cases were collected so that, when used as a basis to prune backtracking for the same search in ℤ2×ℤ8 (search space with size 16! ≅244), just a few tenths of a second were enough to produce an APN permutation in a single CPU. Those heuristics were empirically extrapolated so that they could be applied to a backtracking search for APNs over ℤ2×ℤ16 (search space with size 32! ≅2117). The best permutations found in this search were further refined through Simulated Annealing, with a definition of neighbors suitable to this domain. The best result produced with this scheme was a 3-uniform permutation over ℤ2×ℤ16 with only 24 values equal to 3 in the differential spectrum (all the other 968 values were less than or equal 2, as it should be the case for an APN permutation). Although far from being fully APN, this result is technically better than a 4-uniform permutation and demanded only a few seconds in a single CPU. This is a strong indication that the use of mathematically structured domains, like the rings described in this work, together with heuristics based on smaller cases, can lead to dramatic cuts in the computational resources involved in the complexity of the search for APN permutations in extremely large domains.

Keywords: APN permutations, heuristic searches, symmetric cryptography, S-box design

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Authors: Dimitra Marini, Konstantinos Bakogiannis, Spyros Polychronopoulos, Georgios Kouroupetroglou

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Archaemusicology cannot entirely depend on the study of the excavated ancient musical instruments as most of the time their condition is not ideal (i.e., missing/eroded parts) and moreover, because of the concern damaging the originals during the experiments. Researchers, in order to overcome the above obstacles, build replicas. This technique is still the most popular one, although it is rather expensive and time-consuming. Throughout the last decades, the development of physical modeling techniques has provided tools that enable the study of musical instruments through their digitally simulated models. This is not only a more cost and time-efficient technique but also provides additional flexibility as the user can easily modify parameters such as their geometrical features and materials. This paper thoroughly describes the steps to create a physical model of a woodwind ancient Greek instrument, Plagiaulos. This instrument could be considered as the ancestor of the modern flute due to the common geometry and air-jet excitation mechanism. Plagiaulos is comprised of a single resonator with an open end and a number of tone holes. The combination of closed and open tone holes produces the pitch variations. In this work, the effects of all the instrument’s components are described by means of physics and then simulated based on digital waveguides. The synthesized sound of the proposed model complies with the theory, highlighting its validity. Further, the synthesized sound of the model simulating the Plagiaulos of Koile (2nd century BCE) was compared with its replica build in our laboratory by following the scientific methodologies of archeomusicology. The aforementioned results verify that robust dynamic digital tools can be introduced in the field of computational, experimental archaemusicology.

Keywords: archaeomusicology, digital waveguides, musical acoustics, physical modeling

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Authors: Fu Jia

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The effects of soil-structure interaction (SSI) are often studied using axial-symmetric three-dimensional (3D) models to avoid the high computational cost of the more realistic, fully 3D models, which require 2-3 orders of magnitude more computer time and storage. This paper analyzes the error and presents correction factors for system frequency, system damping, and peak amplitude of structural response computed by axisymmetric models, embedded in uniform or layered half-space. The results are compared with those for fully 3D rectangular foundations of different aspect ratios. Correction factors are presented for a range of the model parameters, such as fixed-base frequency, structure mass, height and length-to-width ratio, foundation embedment, soil-layer stiffness and thickness. It is shown that the errors are larger for stiffer, taller and heavier structures, deeper foundations and deeper soil layer. For example, for a stiff structure like Millikan Library (NS response; length-to-width ratio 1), the error is 6.5% in system frequency, 49% in system damping and 180% in peak amplitude. Analysis of a case study shows that the NEHRP-2015 provisions for reduction of base shear force due to SSI effects may be unsafe for some structures and need revision. The presented correction factor diagrams can be used in practical design and other applications.

Keywords: 3D soil-structure interaction, correction factors for axisymmetric models, length-to-width ratio, NEHRP-2015 provisions for reduction of base shear force, rectangular embedded foundations, SSI system frequency, SSI system damping

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Authors: D. S. Gomes, A. T. Silva

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Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.

Keywords: logistic regression, reactivity-initiated accident, safety margins, uncertainty propagation

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Authors: Feyza Şahi̇n, Harun Deni̇zli̇, Mustafa Zabun, Hüseyi̇n OnbaşIoğli

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Heat exchangers are devices that are widely used to transfer heat between fluids due to their temperature differences. As a type of heat exchanger, air coolers are heat exchangers that cool the air as it passes through the fins of the heat exchanger by transferring heat to the refrigerant in the coil tubes of the heat exchanger. An assembled fin and tube heat exchanger consists of a coil block and a casing with a fan mounted on it. The term “Fan hood” is used to define the distance between the fan and the coil block. Air coolers play a crucial role in cooling systems, and their heat transfer performance can vary depending on design parameters. These parameters can be related to the air side or the internal fluid side. For airside efficiency, the distance between the fan and the coil block affects the performance by creating dead zones at the corners of the casing and maldistribution of airflow. Therefore, a detailed study of the effect of the fan hood on the evaporator and the optimum fan hood distance is necessary for an efficient air cooler design. This study aims to investigate the value of the fan hood in a fin and tube-type air cooler heat exchanger through computational fluid dynamics (CFD) simulations and experimental investigations. CFD simulations will be used to study the airflow within the fan hood. These simulations will provide valuable insights to optimize the design of the fan hood. In addition, experimental tests will be carried out to validate the CFD results and to measure the performance of the fan hood under real conditions. The results will help us to understand the effect of fan hood design on evaporator efficiency and contribute to the development of more efficient cooling systems. This study will provide essential information for evaporator design and improving the energy efficiency of cooling systems.

Keywords: heat exchanger, fan hood, heat exchanger performance, air flow performance

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Authors: Muhammad Nouman, Salim Gul, Karim Ullah

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Partnership has been understood as the essence of Islamic banking. However, in practice, the non-partnership paradigm dominates the operations of Islamic banks. Islamic banks adopt partnership contracts for the scheme of deposits, especially for term deposit accounts. However, they do not adopt partnership contracts (i.e., Musharakah and Mudarabah) as the main financing scheme. In practice, non-partnership contracts including Murabahah and Ijara are widely used for financing. Many authors have provided different explanations for the less utilization of the partnership contracts as a scheme of financing. However, the typology of constraints remains missing. The extant literature remains scattered, with diverse studies focused on different dimensions of the issue. Therefore, there is no unified understanding of the constraints in the application of the partnership contracts. This paper aims to highlight the major factors hindering the application of partnership contracts, and produce a coherent view by synthesizing different explanations provided in several studies conducted around the globe. The present study employs insights form the extant literature using a systematic review and provides academia, practitioners, and policy makers with a holistic framework to name and make sense of what is making partnership contracts a less attractive option for Islamic banks. A total of 84 relevant publications including 11 books, 14 chapters of edited books, 48 journal articles, 8 conference papers and 3 IMF working papers were selected using a systematic procedure. Analysis of these selected publications followed three steps: i) In the first step of analysis the constraints explicitly appearing in the literature set of 84 articles were extracted, ii) In the second step 27 factors hindering the application of partnership contracts were identified from the constraints extracted in the first step with the overlapping items either eliminated or combined, iii) In the last step the factors identified in the second step were classified into three distinct categories. Our intention was to develop the typology of constraints by connecting the rather abstract concepts into the broader sets of constraints for better conceptualization and policy implications. Our framework highlights that there are mainly three facets of lower preference for partnership contracts of financing. First, there are several factors in the contemporary business settings, prevailing social setting, and the bank’s internal environment that underpin uncertainty in the success of partnership contracts of financing. Second, partnership contracts have lower demand i.e., entrepreneurs prefer to use non-partnership contracts for financing their ventures due to the inherent restraining characteristics of the partnership contracts. Finally, there are certain factors in the regulatory framework that restraint the extensive utilization of partnership contracts of financing by Islamic banks. The present study contributes to the Islamic banking literature in many ways. It provides clarification to the heavily criticized operations of Islamic banks, integrates the scattered literature, and provides a holistic framework for better conceptualization of the key constraints in the application of the partnership contracts and policy implications. Moreover, it demonstrates an application of systematic review in Islamic banking research.

Keywords: Islamic banking, Islamic finance, Mudarabah, Musharakah, partnership, systematic review

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Authors: Machimontorn Promtong, Sherman C. P. Cheung, Guan H. Yeoh, Sara Vahaji, Jiyuan Tu

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The wide range of industrial applications involved with boiling flows promotes the necessity of establishing fundamental knowledge in boiling flow phenomena. For this purpose, a number of experimental and numerical researches have been performed to elucidate the underlying physics of this flow. In this paper, the improved wall boiling models, implemented on ANSYS CFX 14.5, were introduced to study subcooled boiling flow at elevated pressure. At the heated wall boundary, the Fractal model, Force balance approach and Mechanistic frequency model are given for predicting the nucleation site density, bubble departure diameter, and bubble departure frequency. The presented wall heat flux partitioning closures were modified to consider the influence of bubble sliding along the wall before the lift-off, which usually happens in the flow boiling. The simulation was performed based on the Two-fluid model, where the standard k-ω SST model was selected for turbulence modelling. Existing experimental data at around 5 bars were chosen to evaluate the accuracy of the presented mechanistic approach. The void fraction and Interfacial Area Concentration (IAC) are in good agreement with the experimental data. However, the predicted bubble velocity and Sauter Mean Diameter (SMD) are over-predicted. This over-prediction may be caused by consideration of only dispersed and spherical bubbles in the simulations. In the future work, the important physical mechanisms of bubbles, such as merging and shrinking during sliding on the heated wall will be incorporated into this mechanistic model to enhance its capability for a wider range of flow prediction.

Keywords: subcooled boiling flow, computational fluid dynamics (CFD), mechanistic approach, two-fluid model

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Authors: Umairah Natasya Binti Mohd Omeershffudin, Suresh Kumar

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Klebsiella pneumoniae, a Gram-negative enteric bacterium that causes nosocomial and urinary tract infections. Particular concern is the global emergence of multidrug-resistant (MDR) strains of Klebsiella pneumoniae. Characterization of antibiotic resistance determinants at the genomic level plays a critical role in understanding, and potentially controlling, the spread of multidrug-resistant (MDR) pathogens. In this study, drug-resistant Klebsiella pneumoniae strain 825795-1 was investigated with extensive computational approaches aimed at identifying novel drug targets among hypothetical proteins. We have analyzed 1099 hypothetical proteins available in genome. We have used in-silico genome subtraction methodology to design potential and pathogen-specific drug targets against Klebsiella pneumoniae. We employed bioinformatics tools to subtract the strain-specific paralogous and host-specific homologous sequences from the bacterial proteome. The sorted 645 proteins were further refined to identify the essential genes in the pathogenic bacterium using the database of essential genes (DEG). We found 135 unique essential proteins in the target proteome that could be utilized as novel targets to design newer drugs. Further, we identified 49 cytoplasmic protein as potential drug targets through sub-cellular localization prediction. Further, we investigated these proteins in the DrugBank databases, and 11 of the unique essential proteins showed druggability according to the FDA approved drug bank databases with diverse broad-spectrum property. The results of this study will facilitate discovery of new drugs against Klebsiella pneumoniae.

Keywords: pneumonia, drug target, hypothetical protein, subtractive genomics

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Authors: Md Maruf Hossain Koli

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The issue of child labor is enormous and cannot be ignored in Bangladesh. The problem of child exploitation is a socio-economic reality of Bangladesh. This paper will indicate the causes, consequences, and possibilities of using microfinance as remedies of hazardous child labor in Bangladesh. Poverty is one of the main reasons for children to become child laborers. It is an indication of economic vulnerability, inadequate law, and enforcement system and cultural and social inequities along with the inaccessible and low-quality educational system. An attempt will be made in this paper to explore and analyze child labor scenario in Bangladesh and will explain holistic intervention of BRAC, the largest nongovernmental organization in the world to address child labor through promoting incubation support to youth-led enterprises. A combination of research methods were used to write this paper. These include non-reactive observation in the form of literature review, desk studies as well as reactive observation like site visits and, semi-structured interviews. Hazardous Child labor is a multi-dimensional and complex issue. This paper was guided by the answer following research questions to better understand the current context of hazardous child labor in Bangladesh, especially in Dhaka city. The author attempted to figure out why child labor should be considered as a development issue? Further, it also encountered why child labor in Bangladesh is not being reduced at an expected pace? And finally what could be a sustainable solution to eradicate this situation. One of the most challenging characteristics of child labor is that it interrupts a child’s education and cognitive development hence limiting the building of human capital and fostering intergenerational reproduction of poverty and social exclusion. Children who are working full-time and do not attend school, cannot develop the necessary skills. This leads them and their future generation to remain in poor socio-economic condition as they do not get a better paying job. The vicious cycle of poverty will be reproduced and will slow down sustainable development. The outcome of the research suggests that most of the parents send their children to work to help them to increase family income. In addition, most of the youth engaged in hazardous work want to get training, mentoring and easy access to finance to start their own business. The intervention of BRAC that includes classroom and on the job training, tailored mentoring, health support, access to microfinance and insurance help them to establish startup. This intervention is working in developing business and management capacity through public-private partnerships and technical consulting. Supporting entrepreneurs, improving working conditions with micro, small and medium enterprises and strengthening value chains focusing on youth and children engaged with hazardous child labor.

Keywords: child labour, enterprise development, microfinance, youth entrepreneurship

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Authors: Mahmoud Amer, Ibrahim El-Sharkawy, Shinichi Ookawara, Ahmed Elwardany

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Gasification and carbonization are two of the most common ways for biomass utilization. Both processes are using part of the waste to be accomplished, either by incomplete combustion or for heating for both gasification and carbonization, respectively. The focus of this paper is to minimize the part of the waste that is used for heating biomass for gasification and carbonization. This will occur by combining both gasifiers and carbonization reactors in a single unit to utilize the heat in the product biogas to heating up the wastes in the carbonization reactors. Three different designs are proposed for the combined gasification/carbonization (CGC) reactor. These include a parallel combination of two gasifiers and carbonized syngas, carbonizer and combustion chamber, and one gasifier, carbonizer, and combustion chamber. They are tested numerically using ANSYS Fluent Computational Fluid Dynamics to ensure homogeneity of temperature distribution inside the carbonization part of the CGC reactor. 2D simulations are performed for the three cases after performing both mesh-size and time-step independent solutions. The carbonization part is common among the three different cases, and the difference among them is how this carbonization reactor is heated. The simulation results showed that the first design could provide only partial homogeneous temperature distribution, not across the whole reactor. This means that the produced carbonized biomass will be reduced as it will only fill a specified height of the reactor. To keep the carbonized product production high, a series combination is proposed. This series configuration resulted in a uniform temperature distribution across the whole reactor as it has only one source for heat with no temperature distribution on any surface of the carbonization section. The simulations provided a satisfactory result that either the first parallel combination of gasifier and carbonization reactor could be used with a reduced carbonized amount or a series configuration to keep the production rate high.

Keywords: numerical simulation, carbonization, gasification, biomass, reactor

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Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper presents a Guidance and Control (G&C) strategy to address spacecraft maneuvering problem for future Rendezvous and Docking (RVD) missions. The proposed strategy allows safe and propellant efficient trajectories for space servicing missions including tasks such as approaching, inspecting and capturing. This work provides the validation test results of the G&C laws using a Hardware-In-the-Loop (HIL) setup with two robotic mockups representing the chaser and the target spacecraft. Through this paper, the challenges of the relative motion control in space are first summarized, and in particular, the constraints imposed by the mission, spacecraft and, onboard processing capabilities. Second, the proposed algorithm is introduced by presenting the formulation of constrained Model Predictive Control (MPC) to optimize the fuel consumption and explicitly handle the physical and geometric constraints in the system, e.g. thruster or Line-Of-Sight (LOS) constraints. Additionally, the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description and accordingly explained. The resulting convex optimization problem allows real-time implementation capability based on a detailed discussion on the computational time requirements and the obtained results with respect to the onboard computer and future trends of space processors capabilities. Finally, the performance of the algorithm is presented in the scope of a potential future mission and of the available equipment. The results also cover a comparison between the proposed algorithms with Linear–quadratic regulator (LQR) based control law to highlight the clear advantages of the MPC formulation.

Keywords: autonomous vehicles, embedded optimization, real-time experiment, rendezvous and docking, space robotics

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Authors: Roberta Martino, Viviana Ventre

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Intertemporal choices are choices under conditions of uncertainty in which the consequences are distributed over time. The Discounted Utility Model is the essential reference for describing the individual in the context of intertemporal choice. The model is based on the idea that the individual selects the alternative with the highest utility, which is calculated by multiplying the cardinal utility of the outcome, as if the reception were instantaneous, by the discount function that determines a decrease in the utility value according to how the actual reception of the outcome is far away from the moment the choice is made. Initially, the discount function was assumed to have an exponential trend, whose decrease over time is constant, in line with a profile of a rational investor described by classical economics. Instead, empirical evidence called for the formulation of alternative, hyperbolic models that better represented the actual actions of the investor. Attitudes that do not comply with the principles of classical rationality are termed anomalous, i.e., difficult to rationalize and describe through normative models. The development of behavioral finance, which describes investor behavior through cognitive psychology, has shown that deviations from rationality are due to the limited rationality condition of human beings. What this means is that when a choice is made in a very difficult and information-rich environment, the brain does a compromise job between the cognitive effort required and the selection of an alternative. Moreover, the evaluation and selection phase of the alternative, the collection and processing of information, are dynamics conditioned by systematic distortions of the decision-making process that are the behavioral biases involving the individual's emotional and cognitive system. In this paper we present an original decomposition of the discount function to investigate the psychological principles of hyperbolic discounting. It is possible to decompose the curve into two components: the first component is responsible for the smaller decrease in the outcome as time increases and is related to the individual's impatience; the second component relates to the change in the direction of the tangent vector to the curve and indicates how much the individual perceives the indeterminacy of the future indicating his or her aversion to uncertainty. This decomposition allows interesting conclusions to be drawn with respect to the concept of impatience and the emotional drives involved in decision-making. The contribution that neuroscience can make to decision theory and inter-temporal choice theory is vast as it would allow the description of the decision-making process as the relationship between the individual's emotional and cognitive factors. Neurofinance is a discipline that uses a multidisciplinary approach to investigate how the brain influences decision-making. Indeed, considering that the decision-making process is linked to the activity of the prefrontal cortex and amygdala, neurofinance can help determine the extent to which abnormal attitudes respect the principles of rationality.

Keywords: impatience, intertemporal choice, neurofinance, rationality, uncertainty

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Authors: Pakawhat Khumkhreung, Yottana Khunatorn

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The flow pattern inside rectangular intake air duct of 300 MW lignite coal-fired power plant is investigated in order to analyze and reduce overall inlet system pressure drop. The system consists of the 45-degree inlet elbow, the flow instrument, the 90-degree mitered elbow and fans, respectively. The energy loss in each section can be determined by Bernoulli’s equation and ASHRAE standard table. Hence, computational fluid dynamics (CFD) is used in this study based on Navier-Stroke equation and the standard k-epsilon turbulence modeling. Input boundary condition is 175 kg/s mass flow rate inside the 11-m² cross sectional duct. According to the inlet air flow rate, the Reynolds number of airstream is 2.7x10⁶ (based on the hydraulic duct diameter), thus the flow behavior is turbulence. The numerical results are validated with the real operation data. It is found that the numerical result agrees well with the operating data, and dominant loss occurs at the flow rate measurement device. Normally, the air flow rate is measured by the airfoil and it gets high pressure drop inside the duct. To overcome this problem, the airfoil is planned to be replaced with the other type measuring instrument, such as the average pitot tube which generates low pressure drop of airstream. The numerical result in case of average pitot tube shows that the pressure drop inside the inlet airstream duct is decreased significantly. It should be noted that the energy consumption of inlet air system is reduced too.

Keywords: airfoil, average pitot tube, combustion air, CFD, pressure drop, rectangular duct

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Authors: Foad Hassaninejadafarahani, Scott Ormiston

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Reflux condensation occurs in a vertical channels and tubes when there is an upward core flow of vapor (or gas-vapor mixture) and a downward flow of the liquid film. The understanding of this condensation configuration is crucial in the design of reflux condensers, distillation columns, and in loss-of-coolant safety analyses in nuclear power plant steam generators. The unique feature of this flow is the upward flow of the vapor-gas mixture (or pure vapor) that retards the liquid flow via shear at the liquid-mixture interface. The present model solves the full, elliptic governing equations in both the film and the gas-vapor core flow. The computational mesh is non-orthogonal and adapts dynamically the phase interface, thus produces sharp and accurate interface. Shear forces and heat and mass transfer at the interface are accounted for fundamentally. This modeling is a big step ahead of current capabilities by removing the limitations of previous reflux condensation models which inherently cannot account for the detailed local balances of shear, mass, and heat transfer at the interface. Discretisation has been done based on a finite volume method and a co-located variable storage scheme. An in-house computer code was developed to implement the numerical solution scheme. Detailed results are presented for laminar reflux condensation from steam-air mixtures flowing in vertical parallel plate channels. The results include velocity and pressure profiles, as well as axial variations of film thickness, Nusselt number and interface gas mass fraction.

Keywords: Reflux, Condensation, CFD-Two Phase, Nusselt number

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Authors: Konrad Pietrykowski, Michał Gęca, Michał Bialy

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The paper presents the results of the crankshaft strength simulation. The crankshaft was taken from the opposed piston engine. Calculations were made using finite element method (FEM) in Abaqus software. This program allows to perform strength tests of individual machine parts as well as their assemblies. The crankshaft that was used in the calculations will be used in the two-stroke aviation research aircraft engine. The assumptions for the calculations were obtained from the AVL Boost software, from one-dimensional engine cycle model and from the multibody model using the method developed in the MSC Adams software. The research engine will be equipped with 3 combustion chambers and two crankshafts. In order to shorten the calculation time, only one crankcase analysis was performed. The cut of the shaft has been selected with the greatest forces resulting from the engine operation. Calculations were made for two cases. For maximum piston force when maximum bending load occurs and for the maximum torque. Cast iron material was adopted. For this material, Poisson's number, density, and Young's modulus were determined. The computational grid contained of 1,977,473 Tet elements. This type of elements was chosen because of the complex design of the crankshaft. Results are presented in the form of stress distributions maps and displacements on the surface and inside the geometry of the shaft. The results show the places of tension stresses, however, no stresses are exceeded at any place. The shaft can thus be applied to the engine in its present form. Acknowledgement: This work has been realized in the cooperation with The Construction Office of WSK 'PZL-KALISZ’ S.A. and is part of Grant Agreement No. POIR.01.02.00-00-0002/15 financed by the Polish National Centre for Research and Development.

Keywords: aircraft diesel engine, crankshaft, finite element method, two-stroke engine

Procedia PDF Downloads 171