Search results for: pair programming

Authors: V. Batishchev, V. Getman

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We study the self-similar fluid flows in the Marangoni layers with the axial symmetry. Such flows are induced by the radial gradients of the temperatures whose distributions along the free boundary obey some power law. The self-similar solutions describe thermo-capillar flows both in the thin layers and in the case of infinite thickness. We consider both positive and negative temperature gradients. In the former case the cooling of free boundary nearby the axis of symmetry gives rise to the rotation of fluid. The rotating flow concentrates itself inside the Marangoni layer while outside of it the fluid does not revolve. In the latter case we observe no rotating flows at all. In the layers of infinite thickness the separation of the rotating flow creates two zones where the flows are directed oppositely. Both the longitudinal velocity and the temperature have exactly one critical point inside the boundary layer. It is worth to note that the profiles are monotonic in the case of non-swirling flows. We describe the flow outside the boundary layer with the use of self-similar solution of the Euler equations. This flow is slow and non-swirling. The introducing of an outer flow gives rise to the branching of swirling flows from the non-swirling ones. There is such the critical velocity of the outer flow that a non-swirling flow exists for supercritical velocities and cannot be extended to the sub-critical velocities. For the positive temperature gradients there are two non-swirling flows. For the negative temperature gradients the non-swirling flow is unique. We determine the critical velocity of the outer flow for which the branching of the swirling flows happens. In the case of a thin layer confined within free boundaries we show that the cooling of the free boundaries near the axis of symmetry leads to the separating of the layer and creates two sub-layers with opposite rotations inside. This makes sharp contrast with the case of infinite thickness. We show that such rotation arises provided the thickness of the layer exceed some critical value. In the case of a thin layer confined within free and rigid boundaries we construct the branching equation and the asymptotic approximation for the secondary swirling flows near the bifurcation point. It turns out that the bifurcation gives rise to one pair of the secondary swirling flows with different directions of swirl.

Keywords: free surface, rotation, fluid flow, bifurcation, boundary layer, Marangoni layer

Procedia PDF Downloads 338

Authors: Omar A. Abuobidalla, Mingyuan Chen, Satyaveer S. Chauhan

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Railroad transportation of hazardous materials freights is important to the North America economics that supports the national’s supply chain. This paper introduces various extensions of the dynamic hazardous materials trip plan problems. The problem captures most of the operational features of a real-world railroad transportations systems that dynamically initiates a set of blocks and assigns each shipment to a single block path or multiple block paths. The dynamic hazardous materials trip plan policies have distinguishing features that are integrating the blocking plan, and the block activation decisions. We also present a non-linear mixed integer programming formulation for each variant and present managerial insights based on a hypothetical railroad network. The computation results reveal that the dynamic car scheduling policies are not only able to take advantage of the capacity of the network but also capable of diminishing the population, and environment risks by rerouting the active blocks along the least risky train services without sacrificing the cost advantage of the railroad. The empirical results of this research illustrate that the issue of integrating the blocking plan, and the train makeup of the hazardous materials freights must receive closer attentions.

Keywords: dynamic car scheduling, planning and scheduling hazardous materials freights, airborne hazardous materials, gaussian plume model, integrated blocking and routing plans, box model

Procedia PDF Downloads 198

Authors: Azam Zare

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Separation flow control for performance enhancement over airfoils at high incidence angle has become an increasingly important topic. This work details the characteristics of an efficient feedback control of the turbulent subsonic flow over NACA23012 airfoil using forced reduced‐order model based on the proper orthogonal decomposition/Galerkin projection and perturbation method on the compressible Reynolds Averaged Navier‐Stokes equations. The forced reduced‐order model is used in the optimal control of the turbulent separated flow over a NACA23012 airfoil at Mach number of 0.2, Reynolds number of 5×106, and high incidence angle of 24° using blowing/suction controlling jets. The Spallart-Almaras turbulence model is implemented for high Reynolds number calculations. The main shortcoming of the POD/Galerkin projection on flow equations for controlling purposes is that the blowing/suction controlling jet velocity does not show up explicitly in the resulting reduced order model. Combining perturbation method and POD/Galerkin projection on flow equations introduce a forced reduced‐order model that can predict the time-varying influence of the blowing/suction controlling jet velocity. An optimal control theory based on forced reduced‐order system is used to design a control law for a nonlinear reduced‐order model, which attempts to minimize the vorticity content in the turbulent flow field over NACA23012 airfoil. Numerical simulations were performed to help understand the behavior of the controlled suction jet at 12% to 18% chord from leading edge and a pair of blowing/suction jets at 15% to 18% and 24% to 30% chord from leading edge, respectively. Analysis of streamline profiles indicates that the blowing/suction jets are efficient in removing separation bubbles and increasing the lift coefficient up to 22%, while the perturbation method can predict the flow field in an accurate Manner.

Keywords: flow control, POD, Galerkin projection, separation

Procedia PDF Downloads 142

Authors: Amirhossein Chambari

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This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

Procedia PDF Downloads 259

Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Tobias Strübing, Fatemeh Ziaeetabar

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Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction.

Keywords: enriched semantic event chain, semantic action representation, spatial relations, statistical analysis

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Authors: Jonas Gradauskas, Oleksandr Masalskyi, Ihor Zharchenko

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The advancement in improving the efficiency of conventional solar cells toward the Shockley-Queisser limit seems to be slowing down or reaching a point of saturation. The challenges hindering the reduction of this efficiency gap can be categorized into extrinsic and intrinsic losses, with the former being theoretically avoidable. Among the five intrinsic losses, two — the below-Eg loss (resulting from non-absorption of photons with energy below the semiconductor bandgap) and thermalization loss —contribute to approximately 55% of the overall lost fraction of solar radiation at energy bandgap values corresponding to silicon and gallium arsenide. Efforts to minimize the disparity between theoretically predicted and experimentally achieved efficiencies in solar cells necessitate the integration of innovative physical concepts. Hot carriers (HC) present a contemporary approach to addressing this challenge. The significance of hot carriers in photovoltaics is not fully understood. Although their excessive energy is thought to indirectly impact a cell's performance through thermalization loss — where the excess energy heats the lattice, leading to efficiency loss — evidence suggests the presence of hot carriers in solar cells. Despite their exceptionally brief lifespan, tangible benefits arise from their existence. The study highlights direct experimental evidence of hot carrier effect induced by both below- and above-bandgap radiation in a singlejunction solar cell. Photocurrent flowing across silicon and GaAs p-n junctions is analyzed. The photoresponse consists, on the whole, of three components caused by electron-hole pair generation, hot carriers, and lattice heating. The last two components counteract the conventional electron-hole generation-caused current required for successful solar cell operation. Also, a model of the temperature coefficient of the voltage change of the current–voltage characteristic is used to obtain the hot carrier temperature. The distribution of cold and hot carriers is analyzed with regard to the potential barrier height of the p-n junction. These discoveries contribute to a better understanding of hot carrier phenomena in photovoltaic devices and are likely to prompt a reevaluation of intrinsic losses in solar cells.

Keywords: solar cell, hot carriers, intrinsic losses, efficiency, photocurrent

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Authors: Andriy Didenko, Zanin Kavazovic

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Engineering students often have certain difficulties in mastering major theoretical concepts in mathematical courses such as differential equations. Student projects were proposed to motivate students’ learning and can be used as a tool to promote students’ interest in the material. Authors propose a student project that includes the use of Microsoft Excel. This instructional tool is often overlooked by both educators and students. An integral component of the experimental part of such a project is the exploration of an interactive spreadsheet. The aim is to assist engineering students in better understanding of Euler’s method. This method is employed to numerically solve first order differential equations. At first, students are invited to select classic equations from a list presented in a form of a drop-down menu. For each of these equations, students can select and modify certain key parameters and observe the influence of initial condition on the solution. This will give students an insight into the behavior of the method in different configurations as solutions to equations are given in numerical and graphical forms. Further, students could also create their own equations by providing functions of their own choice and a variety of initial conditions. Moreover, they can visualize and explore the impact of the length of the time step on the convergence of a sequence of numerical solutions to the exact solution of the equation. As a final stage of the project, students are encouraged to develop their own spreadsheets for other numerical methods and other types of equations. Such projects promote students’ interest in mathematical applications and further improve their mathematical and programming skills.

Keywords: student project, Euler's method, spreadsheet, engineering education

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Authors: Zekang Lan, Yan Xu, Yingkun Huang, Dian Huang, Shengzhong Feng

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Jobs on high-performance computing (HPC) clusters can suffer significant performance degradation due to inter-job network interference. Topology-aware job allocation problem (TJAP) is such a problem that decides how to dedicate nodes to specific applications to mitigate inter-job network interference. In this paper, we study the window-based TJAP on a fat-tree network aiming at minimizing the cost of communication hop, a defined inter-job interference metric. The window-based approach for scheduling repeats periodically, taking the jobs in the queue and solving an assignment problem that maps jobs to the available nodes. Two special allocation strategies are considered, i.e., static continuity assignment strategy (SCAS) and dynamic continuity assignment strategy (DCAS). For the SCAS, a 0-1 integer programming is developed. For the DCAS, an approach called neural simulated algorithm (NSA), which is an extension to simulated algorithm (SA) that learns a repair operator and employs them in a guided heuristic search, is proposed. The efficacy of NSA is demonstrated with a computational study against SA and SCIP. The results of numerical experiments indicate that both the model and algorithm proposed in this paper are effective.

Keywords: high-performance computing, job allocation, neural simulated annealing, topology-aware

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Authors: Linyu Wang, Jiahui Ma, Jianhong Xiang, Hanyu Jiang

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For the pilot design of the sparse channel estimation model in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, the observation matrix constructed according to the matrix cross-correlation criterion, total correlation criterion and other optimization criteria are not optimal, resulting in inaccurate channel estimation and high bit error rate at the receiver. This paper proposes a pilot design method combining high-power sum and high-power variance criteria, which can more accurately estimate the channel. First, the pilot insertion position is designed according to the high-power variance criterion under the condition of equal power. Then, according to the high power sum criterion, the pilot power allocation is converted into a cone programming problem, and the power allocation is carried out. Finally, the optimal pilot is determined by calculating the weighted sum of the high power sum and the high power variance. Compared with the traditional pilot frequency, under the same conditions, the constructed MIMO-OFDM system uses the optimal pilot frequency for channel estimation, and the communication bit error rate performance obtains a gain of 6~7dB.

Keywords: MIMO-OFDM, pilot optimization, compressed sensing, channel estimation

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Authors: Hiripurage Mallika Sandamali Dissanayaka

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Butterflies are found everywhere on Earth, helping flowering plants reproduce through pollination. Wetlands perform many valuable functions such as providing wildlife habitat. Diyasaru Wetland Park was chosen as the study site. It is located in a highly urbanized area of Sri Jayawardenepura Kotte, Sri Lanka. A distribution map was prepared to increase butterfly habitat in the urbanized area, and research was conducted to determine the most suitable sections for using it. As this wetland has footpaths for walking, line transect surveys were used to mark species within the sampling area, and directly observed species were recorded. All data collection was done from 0900 to 1200 hours and 1300 to 1600 hours and fieldwork was done from 11 February 2020 to 20 January 2021. ED binoculars (10.5x45), DSLR cameras (Canon EOS/EFS5 mm 3.5-5.6), and Garmin GPS (Etrex 10) were used to observe butterfly species, identify locations, and take photographs as evidence. Analyzing their habitats using GIS (ArcGIS Pro) to identify their distribution within the park premises, the distribution density of the known size of the population was calculated for each point by kernel density, and local similarity values were calculated for each pair of corresponding features through hotspot analysis, and cell values were determined by inverse distance weighting (IDW) using a linearly weighted combination of a set of sample points. According to the maps prepared to predict the distribution of butterflies in this park, the high level of distribution or favorable areas were near flower gardens and meadows, but some individual species prefer habitats that are more suitable for their life activities, so they live in other areas. Sixty-six (66) species belonging to six (6) families have been recorded in the premises. Sixty (60) species of least concern (LC), two (2) near threatened (NT), and four (4) vulnerable (VU) species have been recorded, and several new species, such as Plum Judy (Abisara echerius), were reported. The outcome of the study will form the basis for decision-making by the Sri Lanka Land Development (SLLD) Corporation for the future development and maintenance of the park.

Keywords: wetland, Lepidoptera, habitat, urban, west

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Authors: A. Sivakumar, S. S. Darun Prakash, P. Navaneethakrishnan

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In and around Erode District, it is estimated that more than 1250 chemical and allied textile processing fabric industries are affected, partially closed and shut off for various reasons such as poor management, poor supplier performance, lack of planning for productivity, fluctuation of output, poor investment, waste analysis, labor problems, capital/labor ratio, accumulation of stocks, poor maintenance of resources, deficiencies in the quality of fabric, low capacity utilization, age of plant and equipment, high investment and input but low throughput, poor research and development, lack of energy, workers’ fear of loss of jobs, work force mix and work ethic. The main objective of this work is to analyze the existing conditions in textile fabric sector, validate the break even of Total Productivity (TP), analyze, design and implement fuzzy sets and mathematical programming for improvement of productivity and quality dimensions in the fabric processing industry. It needs to be compatible with the reality of textile and fabric processing industries. The highly risk events from productivity and quality dimension were found by fuzzy systems and results are wrapped up among the textile fabric processing industry.

Keywords: break even point, fuzzy crisp data, fuzzy sets, productivity, productivity cycle, total productive maintenance

Procedia PDF Downloads 323

Authors: Sergey Kopysov, Nikita Nedozhogin, Leonid Tonkov

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

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

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Authors: Amauri Josafat Gomez Aguilar, Jean Pierre Kenné

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This research presents the development of a self-sustainable manufacturing system from a circular economy perspective, structured by a multi-stage serial production system consisting of a series of machines under deterioration in charge of producing a single product and a reverse remanufacturing system constituted by the same productive systems of the first scheme and different tooling, fed by-products collected at the end of their life cycle, and non-conforming elements of the first productive scheme. Since the advanced production manufacturing system is unable to satisfy the customer's quality expectations completely, we propose the development of a mixed integer linear mathematical model focused on the optimal search and assignment of quality stations and preventive maintenance operation to the machines over a time horizon, intending to segregate the correct number of non-conforming parts for reuse in the remanufacturing system and thereby minimizing production, quality, maintenance, and customer non-conformance penalties. Numerical experiments are performed to analyze the solutions found by the model under different scenarios. The results showed that the correct implementation of a closed manufacturing system and allocation of quality inspection and preventive maintenance operations generate better levels of customer satisfaction and an efficient manufacturing system.

Keywords: closed loop, mixed integer linear programming, preventive maintenance, quality inspection

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Authors: Isaac Owusu Asante, Yushi Jiang, Hailin Tao

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Live streaming marketing, the new electronic commerce element, became an optional marketing channel following the COVID-19 pandemic. Many sellers have leveraged the features presented by live streaming to increase sales. Studies on live streaming have focused on gaming and consumers’ loyalty to brands through live streaming, using interview questionnaires. This study, however, was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during live streaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study introduces a new way of measuring interactions in live streaming commerce and proposes a way to manually gather data on consumer behaviors in live streaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness

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Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

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Authors: Juhan Kim, Jinsoo Kim

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South Korea cannot import natural gas in any form other than LNG because of the division of South and North Korea. Further, the high proportion of natural gas in the national energy mix makes this resource crucial for energy security in Korea. Expansion of Panama Canal will allow for reducing the cost of shipping between the Far East and U.S East. Panama Canal expansion can have significant impacts on South Korea. Due to this situation, we review the natural gas optimal portfolio by considering the uniqueness of the Korean Natural gas market and expansion of Panama Canal. In order to assess Korea’s natural gas optimal portfolio, we developed natural gas portfolio model. The model comprises two steps. First, to obtain the optimal long-term spot contract ratio, the study examines the price level and the correlation between spot and long-term contracts by using the Markowitz, portfolio model. The optimal long-term spot contract ratio follows the efficient frontier of the cost/risk level related to this price level and degree of correlation. Second, by applying the obtained long-term contract purchase ratio as the constraint in the linear programming portfolio model, we determined the natural gas optimal import portfolio that minimizes total intangible and tangible costs. Using this model, we derived the optimal natural gas portfolio considering the expansion of Panama Canal. Based on these results, we assess the portfolio for natural gas import to Korea from the perspective of energy security and present some relevant policy proposals.

Keywords: natural gas, Panama Canal, portfolio analysis, South Korea

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Authors: Pranav Gulati, Isha Sharma

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Hypertension and hypotension are known to have repercussions on the health of an individual, with hypertension contributing to an increased probability of risk to cardiovascular diseases and hypotension resulting in syncope. This prompts the development of a non-invasive, non-intrusive, continuous and cuff-less blood pressure monitoring system to detect blood pressure variations and to identify individuals with acute and chronic heart ailments, but due to the unavailability of such devices for practical daily use, it becomes difficult to screen and subsequently regulate blood pressure. The complexities which hamper the steady monitoring of blood pressure comprises of the variations in physical characteristics from individual to individual and the postural differences at the site of monitoring. We propose to develop a continuous, comprehensive cardio-analysis tool, based on reflective photoplethysmography (PPG). The proposed device, in the form of an eyewear captures the PPG signal and estimates the systolic and diastolic blood pressure using a sensor positioned near the temporal artery. This system relies on regression models which are based on extraction of key points from a pair of PPG wavelets. The proposed system provides an edge over the existing wearables considering that it allows for uniform contact and pressure with the temporal site, in addition to minimal disturbance by movement. Additionally, the feature extraction algorithms enhance the integrity and quality of the extracted features by reducing unreliable data sets. We tested the system with 12 subjects of which 6 served as the training dataset. For this, we measured the blood pressure using a cuff based BP monitor (Omron HEM-8712) and at the same time recorded the PPG signal from our cardio-analysis tool. The complete test was conducted by using the cuff based blood pressure monitor on the left arm while the PPG signal was acquired from the temporal site on the left side of the head. This acquisition served as the training input for the regression model on the selected features. The other 6 subjects were used to validate the model by conducting the same test on them. Results show that the developed prototype can robustly acquire the PPG signal and can therefore be used to reliably predict blood pressure levels.

Keywords: blood pressure, photoplethysmograph, eyewear, physiological monitoring

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Authors: Susanta Kumar Gachhayat, S. K. Dash

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Multi objective non-convex economic dispatch problems of a thermal power plant are of grave concern for deciding the cost of generation and reduction of emission level for diminishing the global warming level for improving green-house effect. This paper deals with ramp rate constraints for achieving better inequality constraints so as to incorporate valve point loading for cost of generation in thermal power plant through ramp rate biogeography based optimization involving mutation and migration. Through 50 out of 100 trials, the cost function and emission objective function were found to have outperformed other classical methods such as lambda iteration method, quadratic programming method and many heuristic methods like particle swarm optimization method, weight improved particle swarm optimization method, constriction factor based particle swarm optimization method, moderate random particle swarm optimization method etc. Ramp rate biogeography based optimization applications prove quite advantageous in solving non convex multi objective economic dispatch problems subjected to nonlinear loads that pollute the source giving rise to third harmonic distortions and other such disturbances.

Keywords: economic load dispatch, ELD, biogeography-based optimization, BBO, ramp rate biogeography-based optimization, RRBBO, valve-point loading, VPL

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Authors: Mona Misfer Alshardan, Djamal Ziani

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Enterprise resource planning (ERP) systems are the organizations tickets to the global market. With the implementation of ERP, organizations can manage and coordinate all functions, processes, resources and data from different departments by a single software. However, many organizations consider the cost of traditional ERP to be expensive and look for alternative affordable solutions within their budget. One of these alternative solutions is providing ERP over a software as a service (SaaS) model. This alternative could be considered as a cost effective solution compared to the traditional ERP system. A key feature of any SaaS system is the multi-tenancy architecture where multiple customers (tenants) share the system software. However, different organizations have different requirements. Thus, the SaaS developers accommodate each tenant’s unique requirements by allowing tenant-level customization or configuration. While customization requires source code changes and in most cases a programming experience, the configuration process allows users to change many features within a predefined scope in an easy and controlled manner. The literature provides many techniques to accomplish the configuration process in different SaaS systems. However, the nature and complexity of SaaS ERP needs more attention to the details regarding the configuration process which is merely described in previous researches. Thus, this research is built on strong knowledge regarding the configuration in SaaS to define specifically the configuration borders in SaaS ERP and to design a configuration service with the consideration of the different configuration aspects. The proposed architecture will ensure the easiness of the configuration process by using wizard technology. Also, the privacy and performance are guaranteed by adopting the databases isolation technique.

Keywords: configuration, software as a service, multi-tenancy, ERP

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Vinayak Bassi, Rajpreet Singh

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Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: Abdelbaky Abouzeid

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The study aimed at determining the effect of using the active learning on achievement and attitudes toward studying the human rights course for the Bahrain Teachers College students and the extent to which any differences of statistical significance according to gender and section can exist. To achieve the objectives of the study, the researcher developed and implemented research tools such as academic achievement test and the scale of attitudes towards the study of the Human Rights Course. The scale of attitudes towards Human Rights was constructed of 40 items investigating four dimensions; the cognitive dimension, the behavioral dimension, the affective dimension, and course quality dimension. The researcher then applied some of the active learning strategies in teaching this course to all students of the first year of the Bahrain Teachers College (102 male and female students) after excluding two students who did not complete the course requirements. Students were divided into five groups. These strategies included interactive lecturing, presentations, role playing, group projects, simulation, brainstorming, concept maps and mind maps, reflection and think-pair-share. The course was introduced to students during the second semester of the academic year 2016-2017. The study findings revealed that the use of active learning strategies affected the achievement of students of Bahrain Teachers College in the Human Rights course. The results of the T-test showed statistically significant differences on the pre-test and post-test in favor of the post-test. No statistically significant differences in the achievement of students according to the section and gender were found. The results also indicated that the use of active learning strategies had a positive effect on students' attitudes towards the study of the Human Rights Course on all the scale’s items. The general average reached (4.26) and the percentage reached (85.19%). Regarding the effect of using active learning strategies on students’ attitudes towards all the four dimensions of the scale, the study concluded that the behavioral dimension came first; the quality of the course came second, the cognitive dimension came third and in the fourth place came the affective dimension. No statistically significant differences in the attitude towards studying the Human Rights Course for the students according to their sections or gender were found. Based on the findings of the study, the researchers suggested some recommendations that can contribute to the development of teaching Human Rights Course at the University of Bahrain.

Keywords: attitudes, academic achievement, human rights, behavioral dimension, cognitive dimension, affective dimension, quality of the course

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Authors: Nia McRae, Robert Wright, Ghalib Bello

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Introduction: Postpartum depression is a prevalent mood disorder that is detrimental to the mental and physical health of mothers and their newborns. Lead (Pb) is a toxic metal that is associated with hormonal imbalance and mental impairments. The hormone changes that accompany pregnancy and childbirth may be exacerbated by Pb and increase new mothers’ susceptibility to postpartum depression. To the best of the author’s knowledge, this is the only study that investigates the association between prenatal Pb exposure and postpartum depression. Identifying risk factors can contribute to improved prevention and treatment strategies for postpartum depression. Methods: Data was derived from the Programming Research in Obesity, Growth, Environment and Social Stress (PROGRESS) study which is an ongoing longitudinal birth cohort. Postpartum depression was identified by a score of 13 or above on the 10-Item Edinburg Postnatal Depression Scale (EPDS) 6-months and 12-months postpartum. Pb was measured in the blood (BPb) in the second and third trimester and in the tibia and patella 1-month postpartum. Quantile regression models were used to assess the relationship between BPb and postpartum depression. Results: BPb in the second trimester was negatively associated with the 80th percentile of depression 6-months postpartum (β: -0.26; 95% CI: -0.51, -0.01). No significant association was found between BPb in the third trimester and depression 6-months postpartum. BPb in the third trimester exhibited an inverse relationship with the 60th percentile (β: -0.23; 95% CI: -0.41, -0.06), 70th percentile (β: -0.31; 95% CI: -0.52, -0.10), and 90th percentile of depression 12-months postpartum (β: -0.36; 95% CI: -0.69, -0.03). There was no significant association between BPb in the second trimester and depression 12-months postpartum. Bone Pb concentrations were not significantly associated with postpartum depression. Conclusion: The negative association between BPb and postpartum depression may support research which demonstrates lead is a nontherapeutic stimulant. Further research is needed to verify these results and identify effect modifiers.

Keywords: depression, lead, postpartum, prenatal

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Authors: Daniela Ribeiro, Silvia Lenyra Meirelles Campos Titotto

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Nature has long served as inspiration for humans, since various technologies present in society are a mirror of the natural world. This is due to the fact that nature has adapted for millions of years to possess the characteristics they have today. In this sense, man takes advantage of this situation and uses it to produce his own objects and solve his problems. This concept, which is known as biomimetics, is something relatively new, once it was only denominated in 1957. Nature, in turn, responds directly and consistently to environmental conditions. For example, plants that have touch sensitivity contract with this stimulus. Such a situation resembles a technology that has been gaining ground in the contemporary world of scientific innovation: 4D printing. 4D printing technology emerged in 2012 as a complement to 3D printing and presents numerous benefits since it provides a deficiency in the second kind of printing mentioned. This type of technology reaches several areas, since it is capable of producing materials that change over time, be it in its composition, form or properties and is such a characteristic that determines the additional dimension of the material. Precisely because of these factors, this type of impression resembles nature and is related to biomimetics. However, only certain types of ‘intelligent’ materials are generally employed in this type of impression, since only they will respond well to such stimuli, one of which is the hydrogel. The hydrogel is a biocompatible polymer that presents several applications, these in turn will be briefly mentioned in this article to exemplify its importance and the reason for choosing this material as object of study. In addition, aspects that configure 4D printing will be treated here, such as the importance of architecture, programming language and the reversibility of printed materials.

Keywords: 4D printing, biomimetic, hydrogel, materials

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Authors: Sina Keyhanian, Abbas Ahmadi, Behrooz Karimi

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We present a multi-objective binary programming model which considers surgical cases are scheduling among operating rooms and the configuration of surgical instruments in limited capacity hospital trays, simultaneously. Many mathematical models have been developed previously in the literature addressing different challenges in health-care logistics such as assigning operating rooms, leveling beds, etc. But what happens inside the operating rooms along with the inventory management of required instruments for various operations, and also their integration with surgical scheduling have been poorly discussed. Our model considers the minimization of movements between trays during a surgery which recalls the famous cell formation problem in group technology. This assumption can also provide a major potential contribution to robotic surgeries. The tray configuration problem which consumes surgical instruments requirement plan (SIRP) and sequence of surgical procedures based on required instruments (SIRO) is nested inside the bin packing problem. This modeling approach helps us understand that most of the same-output solutions will not be necessarily identical when it comes to the rearrangement of surgeries among rooms. A numerical example has been dealt with via a proposed nested simulated annealing (SA) optimization approach which provides insights about how various configurations inside a solution can alter the optimal condition.

Keywords: health-care logistics, hospital tray configuration, off-line bin packing, simulated annealing optimization, surgical case scheduling

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Authors: Mukhtiar Singh, Sumeet Nagar

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Vehicle routing problem (VRP) is a combinatorial optimization and nonlinear programming problem aiming to optimize decisions regarding given set of routes for a fleet of vehicles in order to provide cost-effective and efficient delivery of both services and goods to the intended customers. This paper proposes the application of integrated particle swarm optimization (PSO) and genetic optimization algorithm (GA) to address the Vehicle routing problem in sugarcane industry in India. Suger industry is very prominent agro-based industry in India due to its impacts on rural livelihood and estimated to be employing around 5 lakhs workers directly in sugar mills. Due to various inadequacies, inefficiencies and inappropriateness associated with the current vehicle routing model it costs huge money loss to the industry which needs to be addressed in proper context. The proposed algorithm utilizes the crossover operation that originally appears in genetic algorithm (GA) to improve its flexibility and manipulation more readily and avoid being trapped in local optimum, and simultaneously for improving the convergence speed of the algorithm, level set theory is also added to it. We employ the hybrid approach to an example of VRP and compare its result with those generated by PSO, GA, and parallel PSO algorithms. The experimental comparison results indicate that the performance of hybrid algorithm is superior to others, and it will become an effective approach for solving discrete combinatory problems.

Keywords: fuzzy logic, genetic algorithm, particle swarm optimization, vehicle routing problem

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Authors: Rong Li, Brian D. Wirth, Bing Liu

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Graphite is the matrix, and structural material in the high temperature gas-cooled reactor exhibits an irradiation response. It is of significant importance to analyze the defect production and evaluate the role of graphite under irradiation. A vast experimental literature exists for graphite on the dimensional change, mechanical properties, and thermal behavior. However, simulations have not been applied to the atomistic perspective. Remarkably few molecular dynamics simulations have been performed to study the irradiation response in graphite. In this paper, irradiation-induced damage cascades in graphite were investigated with molecular dynamics simulation. Statistical results of the graphite defects were obtained by sampling a wide energy range (1–30 KeV) and 10 different runs for every cascade simulation with different random number generator seeds to the velocity scaling thermostat function. The chemical bonding in carbon was described using the adaptive intermolecular reactive empirical bond-order potential (AIREBO) potential coupled with the standard Ziegler–Biersack–Littmack (ZBL) potential to describe close-range pair interactions. This study focused on analyzing the number of defects, the final cascade morphology and the distribution of defect clusters in space, the length-scale cascade properties such as the cascade length and the range of primary knock-on atom (PKA), and graphite mechanical properties’ variation. It can be concluded that the number of surviving Frenkel pairs increased remarkably with the increasing initial PKA energy but did not exhibit a thermal spike at slightly lower energies in this paper. The PKA range and cascade length approximately linearly with energy which indicated that increasing the PKA initial energy will come at expensive computation cost such as 30KeV in this study. The cascade morphology and the distribution of defect clusters in space mainly related to the PKA energy meanwhile the temperature effect was relatively negligible. The simulations are in agreement with known experimental results and the Kinchin-Pease model, which can help to understand the graphite damage cascades and lifetime span under irradiation and provide a direction to the designs of these kinds of structural materials in the future reactors.

Keywords: graphite damage cascade, molecular dynamics, cascade morphology, cascade distribution

Procedia PDF Downloads 144