Computational Engineering Commons^™

Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler

SMU Data Science Review

This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …

Vowel Harmony Viewed As Error-Correcting Code, Yvo Meeres, Tommi A. Pirinen

Proceedings of the Society for Computation in Linguistics

Robustness reduces the risk of information loss. At present the notion of error-correcting codes (ECCs) is used to achieve robustness in technical fields only. Viewing fault-tolerant natural systems as systems equipped with error-correcting codes permits a formal comparison of natural and technical robustness.

Instancing natural language (NL), we show differences in technical and natural error-correcting approaches. By picking a specific grammar phenomenon which some NLs exhibit – vowel harmony (VH) – we show that (1) VH can be formalized as an ECC as well as (2) VH adds to the robustness of its NL. We provide empirical as well as …

Renewable Energy Production By Solar Chimney: The Influence Of Curved Guide Vanes On The Performance Of A Solar Chimney Using Cfd Simulation, Haokun Xue

Theses, Dissertations and Capstones

The aim of this study is to investigate the effect of the guide vanes on the efficiency of the turbine of solar chimney power plant using Computational Fluid Dynamics (CFD). In this study, a 3-Dimentional CFD simulation of solar chimney power plant based on the Manzanares prototype is performed. The CFD simulation is validated by comparing the experimental data from the Manzanares prototype and simulation data with both 2D and 3D cases. To capture turbulent flow inside the chimney, the SST 𝑘 − 𝜔 turbulence model is used. The first object is to investigate the flow performance under the influence …

The Diffuse Bounce Back Lattice Boltzmann Method And Its Applications On The Study Of Fluid-Particle Interactions, Geng Liu

Dissertations and Theses

Fluid-structure interaction is very broadly seen and widely used in many industrial, engineering and environmental processes. The lattice Boltzmann method has been preferred for simulating particulate flows due to its advantages of easy implementation, micro- and mesoscopic physical insights and parallel algorithm. Both sharp and diffuse boundary treatments are studied to recover curved and moving boundaries on structured orthogonal grids for the lattice Boltzmann method. These methods can describe curved boundaries more accurately and more smoothly than the naive staircase approximation. However, to improve the order of velocity accuracy and to reduce the fluctuation of force, either interpolation or additional …

Novel Methods In Computational Imaging With Applications In Remote Sensing, Adam Webb

Dissertations, Master's Theses and Master's Reports

This dissertation is devoted to novel computational imaging methods with applications in remote sensing. Computational imaging methods are applied to three distinct applications including imaging and detection of buried explosive hazards utilizing array radar, high resolution imaging of satellites in geosynchronous orbit utilizing optical hypertelescope arrays, and characterization of atmospheric turbulence through multi-frame blind deconvolution utilizing conventional optical digital sensors.

The first application considered utilizes a radar array employed as a forward looking ground penetrating radar system with applications in explosive hazard detection. A penalized least squares technique with sparsity-inducing regularization is applied to produce imagery, which is consistent with …

Evaluación Analítica De Parámetros No Considerados En La Formulación De Resistencia Nominal De Conectores Tipo Espigo, José David Ovalle Fernández, William Oswaldo Ramírez Patiño

Ingeniería Civil

Existe una gran variedad de sistemas estructurales, cada uno con características específicas para el soporte de cargas verticales y horizontales. Uno de ellos, el sistema compuesto, el cual es desarrollado buscando ventajas como: la optimización del uso de los materiales combinando ambos en una unidad estructural, el uso de mayores luces entre columnas; la posibilidad de reutilización de la estructura; reducción de los costos de construcción debido a la disminución de tiempos en obra; de tamaño de columnas y cimentación; además, del aumento de protección contra el fuego y corrosión.

Con la aplicación de tecnologías como la soldadura, fue posible …

Desarrollo De Una Herramienta Computacional Como Apoyo A Pequeños Y Grandes Productores A Través De La Orientación Y Seguimiento De Cultivos., Angie Paola Botello Espitia, Juan Pablo Moya Cárdenas

Ingeniería en Automatización

El presente trabajo de grado se enmarca en el desarrollo de una herramienta computacional enfocada a las necesidades de los estudiantes de la Universidad de La Salle de la sede Utopía. Para establecer las necesidades o requerimientos de la herramienta, se realizó una investigación en el sector de las TIC y el papel que se desempeñan en el plan de Extensión Rural del Ministerio de Agricultura. Además, se realizaron entrevistas con docentes e ingenieros del programa de Ingeniería Agronómica, encargados del área de viveros de la sede; la información recolectada sirvió como base para realizar el levantamiento de requerimientos de …

Energy Considerations In Blockchain-Enabled Applications, Cesar Enrique Castellon Escobar

UNF Graduate Theses and Dissertations

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while mitigating significant cybersecurity risks pertaining to the main application. Associated tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory, computation) required by each blockchain-enabled host. This thesis applies an energy-reducing algorithmic engineering technique for Merkle Tree root and Proof of Work calculations, two principal elements of blockchain computations, as a means to preserve the promised security benefits but with less compromise to system availability. Using pyRAPL, a python library to measure computational energy, we experiment with both the …

Bibliometric Analysis Of Plant Disease Prediction Using Climatic Condition, Shivali Amit Wagle, Harikrishnan R

Library Philosophy and Practice (e-journal)

The changes in the climatic conditions are having beneficial as well as harmful effects on crop yields depending on the drastic changes. There can be a yield loss due to the occurrence of disease in crops. Apart from severe yield losses, infected yield can be harmful and threatening to living being’s health as that is the source of food. This also affects the economy of the agricultural depended country. Disease prediction tools advance in the management of exertions for diseases in plants. Machine learning techniques help in elucidating complex associations between hosts and pathogens without invoking difficult-to-satisfy expectations. For the …

Hybrid Modelling For Stroke Care: Review And Suggestions Of New Approaches For Risk Assessment And Simulation Of Scenarios, Tilda Herrgårdh, Vince I. Madai, John Kelleher, Rasmus Magnusson, Mika Gustafsson, Lili Milani, Peter Gennemark, Gunnar Cedersund

Articles

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose …

Inclusive Access For All, Marcia Dority Baker, Jaci Lindburg

Publications from Information Technology Services

Inclusive Access provides a framework for digital course material delivered via the learning management system (LMS) day-one to students. This platform assists instructors with selecting current, quality, affordable material, and supports learning analytics by providing engagement data in Canvas. The University of Nebraska Provost office has funded an initial series of grants to support open educational resources (OER) initiatives at the Lincoln, Kearney, and Omaha campuses for several years. The vast majority of these dollars went to incentivize faculty in the adoption of OER. The OER and Inclusive Access pilots are ready to mature into a service supported by Academic …

Computational Analysis And Prediction Of Intrinsic Disorder And Intrinsic Disorder Functions In Proteins, Akila I. Katuwawala

Theses and Dissertations

COMPUTATIONAL ANALYSIS AND PREDICTION OF INTRINSIC DISORDER AND INTRINSIC DISORDER FUNCTIONS IN PROTEINS

By Akila Imesha Katuwawala

A dissertation submitted in partial fulfillment of the requirements for the degree of Engineering, Doctor of Philosophy with a concentration in Computer Science at Virginia Commonwealth University.

Virginia Commonwealth University, 2021

Director: Lukasz Kurgan, Professor, Department of Computer Science

Proteins, as a fundamental class of biomolecules, have been studied from various perspectives over the past two centuries. The traditional notion is that proteins require fixed and stable three-dimensional structures to carry out biological functions. However, there is mounting evidence regarding a “special” class …

Impacts Of Using Tubular Sections In Open Web Steel Joists, Hollis (Cas) L. Caswell V

Honors Theses

Open web steel joists are lightweight structural trusses used in place of I-beams to support long-span floors and roofs of open space buildings. Their slender geometry makes them highly efficient in resisting flexure, but susceptible to out-of-plane buckling in a failure mode known as lateral-torsional buckling. This failure can be avoided by running lateral bracing between joists called bridging or potentially by using tubular sections to build up the joists rather than angle sections.

It is possible that a joist design using tubular cross-sections could require less bridging and prevent the need to use erection bridging for initial joist construction. …

Support To Design For Air Traffic Management: An Approach With Agent-Based Modelling And Evolutionary Search, Gabriella Gigante, Roberto Palumbo, Domenico Pascarella, Alessandro Pellegrini, Gabriella Duca, Miquel Angel Piera, Juan José Ramos

International Journal of Aviation, Aeronautics, and Aerospace

This paper presents a methodology to manage the support to design in ATM operations. We propose a workflow for the design of ATM solutions in a performance-based setting. The methodology includes the evaluation of the impact on human behaviour and exploits a combination of different paradigms, such as Agent-Based Modelling and Simulation, and Agent-Based Evolutionary Search. We prove the soundness of the methodology by carrying out a real case study, which is the transition from Direct Routing to Free Routing in the Italian airspace. The validation results exhibit limited errors for the assessment of the performance metrics under evaluation. Furthermore, …

Preference-Aware Task Assignment In Mobile Crowdsensing, Fatih Yucel

Theses and Dissertations

Mobile crowdsensing (MCS) is an emerging form of crowdsourcing, which facilitates the sensing data collection with the help of mobile participants (workers). A central problem in MCS is the assignment of sensing tasks to workers. Existing work in the field mostly seek a system-level optimization of task assignments (e.g., maximize the number of completed tasks, minimize the total distance traveled by workers) without considering individual preferences of task requesters and workers. However, users may be reluctant to participate in MCS campaigns that disregard their preferences. In this dissertation, we argue that user preferences should be a primary concern in the …

Self && Self, Shuang Cai

Senior Projects Spring 2021

Seldom before the COVID-19 pandemic have so many people simultaneously had their lifestyle drastically changed in the same way. The forced physical isolation is, ironically, a communal experience. The sickening quarantine left everyone nothing but time to confront and reconnect with themselves. Another inevitable result of corporal isolation is the predominant awakening awareness of digital existences and connections. Evoking the shared sensitivity and delicacy, studying the tectonic activity of the digital world, the project documents the endured contemplation in the upcoming resurgence.

Towards A Holistic Risk Model For Safeguarding The Pharmaceutical Supply Chain: Capturing The Human-Induced Risk To Drug Quality, Heather R. Campbell

Theses and Dissertations--Pharmacy

Counterfeit, adulterated, and misbranded medicines in the pharmaceutical supply chain (PSC) are a critical problem. Regulators charged with safeguarding the supply chain are facing shrinking resources for inspections while concurrently facing increasing demands posed by new drug products being manufactured at more sites in the US and abroad. To mitigate risk, the University of Kentucky (UK) Central Pharmacy Drug Quality Study (DQS) tests injectable drugs dispensed within the UK hospital. Using FT-NIR spectrometry coupled with machine learning techniques the team identifies and flags potentially contaminated drugs for further testing and possible removal from the pharmacy. Teams like the DQS are …

Review Of Forecasting Univariate Time-Series Data With Application To Water-Energy Nexus Studies & Proposal Of Parallel Hybrid Sarima-Ann Model, Cory Sumner Yarrington

Graduate Theses, Dissertations, and Problem Reports

The necessary materials for most human activities are water and energy. Integrated analysis to accurately forecast water and energy consumption enables the implementation of efficient short and long-term resource management planning as well as expanding policy and research possibilities for the supportive infrastructure. However, the integral relationship between water and energy (water-energy nexus) poses a difficult problem for modeling. The accessibility and physical overlay of data sets related to water-energy nexus is another main issue for a reliable water-energy consumption forecast. The framework of urban metabolism (UM) uses several types of data to build a global view and highlight issues …

Water Surfaces Detection From Sentinel-1 Sar Images Using Deep Learning, Chao Huang Lin

All Master's Theses

Nowadays, Synthetic Aperture Radar (SAR) images have been widely used in the industry and the scientific community for different remote sensing applications. The main advantage of SAR technology is that it can acquire images from nighttime since it does not require sunlight. Additionally, it can capture images under the cloud where the traditional optical sensor is limited. It is very convenient to use SAR image for surface water detection because the flatness of the calm water surface reflects off all the energy from the radar and this makes the surface water appears in a SAR image as dark pixels. The …

Interactive Visual Self-Service Data Classification Approach To Democratize Machine Learning, Sridevi Narayana Wagle

All Master's Theses

Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end users to design a model and classify data with more confidence and without having to compromise on the accuracy. Such technique is especially helpful when dealing with sensitive and crucial data like cancer data in the medical domain with high cost of errors. With the help of the proposed interactive and …