Digital Commons Network^™

Survivor Bond Models For Securitizing Longevity Risk, Priscilla Mansah Codjoe

Doctoral Dissertations

"Longevity risk is the risk that a reference population’s mortality rates deviate from what is projected from prior life tables. This is due to discoveries in biological sciences, improved public health measures, and nutrition, which have dramatically increased life expectancy. Longevity risk raises life insurers’ liability, increasing product costs and reserves. Securitization through longevity derivatives is a way of dealing with this risk.

To enhance the pricing of life contingent products, we present an additive type mortality model in the style of the Lee-Carter. This model incorporates policyholder covariates. By using counting processes and martingale machinery, we obtain close form …

Hydroclimate Variability In Central America During The Holocene Inferred From Lacustrine Sediments In Lake Izabal, Eastern Guatemala, Edward Fernando Duarte

Doctoral Dissertations

"Holocene hydroclimate reconstructions have contributed to our understanding of the mechanisms controlling precipitation in Central America. Recent hydroclimate proxy records from the region, however, have revealed considerable spatiotemporal complexity in precipitation variability. This complexity is hypothesized to result from the interaction between multiple oceanic-atmospheric processes that converge in the region. This project analyzed three sediment cores from Lake Izabal, eastern lowland Guatemala, with the goal of understanding changes in precipitation, lake productivity, and lake water chemistry during the Holocene. Our proxy results indicate that precipitation in the region increased from the early to the middle Holocene, when Lake Izabal became …

Studies Of Multiphase Distribution And Flow In Bubble/ Slurry Bubble Columns With /Without Internals And Advanced Sustainable Construction Materials Using Sophisticated Measurement Techniques, Omar Farid

Doctoral Dissertations

"Bubble/slurry bubble columns have been widely studied and employed as multiphase flow reactors with and without vertical heat-exchanging tubes. In order to understand more deeply the effect of the dense internals on the hydrodynamics of bubble/slurry bubble column, systematic experiments were performed to visualize and quantify using the radioactive particle tracking (RPT) technique. A new methodology for implementing the radioactive particle tracking (RPT) technique has been developed to investigate the effect of the dense internals on the hydrodynamics of the bubble/slurry column reactor. Monte Carlo N-Particle (MCNP) simulation has been implemented to simulate the system to generate a large number …

Social Media Analytics With Applications In Disaster Management And Covid-19 Events, Md Yasin Kabir

Doctoral Dissertations

"Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled …

Secured Information Dissemination And Misbehavior Detection In Vanets, Ayan Roy

Doctoral Dissertations

"In a connected vehicle environment, the vehicles in a region can form a distributed network (Vehicular Ad-hoc Network or VANETs) where they can share traffic-related information such as congestion or no-congestion with other vehicles within its proximity, or with a centralized entity via. the roadside units (RSUs). However, false or fabricated information injected by an attacker (or a malicious vehicle) within the network can disrupt the decision-making process of surrounding vehicles or any traffic-monitoring system. Since in VANETs the size of the distributed network constituting the vehicles can be small, it is not difficult for an attacker to propagate an …

Semiparametric Estimation With Clustered Right Censored Data Via Multivariate Gaussian Random Fields, Fathima Zahra Sainul Abdeen

Doctoral Dissertations

Consider a fixed number of clustered areas identified by their geographical coordinate that are monitored for the occurrences of an event such as pandemic, epidemic, migration to name a few. Data collected on units at all areas include time varying covariates and other environmental factors that may affect event occurrences. The event times in every area can be independent. They can also be correlated with correlation between two units induced by an unobservable frailty. In both cases, the collected data is considered pairwise to account for spatial correlation between all pair of areas. The pairwise right censored data is probit-transformed …

Fully Kinetic Particle-In-Cell Simulations Of Plasma-Surface-Dust Interactions For Lunar Exploration, Jianxun Zhao

Doctoral Dissertations

"The studies involving lunar surface explorations have drawn attentions in recent years. A better understanding of possible potential hazards to astronauts and electronic equipment has become a necessity for future lunar explorations. The lunar surface, lacking an atmosphere and global magnetic field therefore directly exposed to solar radiation and solar wind plasma, is electrically charged by the bombardment of solar wind plasma and emission/collection of photoelectrons. Additionally, lunar dust grains can also get charged and levitated from the surface under the influence of the electric field as well as gravity within the plasma sheath. Since the plasma sheath formed near …

Development Of Flood Prediction Models Using Machine Learning Techniques, Bhanu Kanwar

Doctoral Dissertations

"Flooding and flash flooding events damage infrastructure elements and pose a significant threat to the safety of the people residing in susceptible regions. There are some methods that government authorities rely on to assist in predicting these events in advance to provide warning, but such methodologies have not kept pace with modern machine learning. To leverage these algorithms, new models must be developed to efficiently capture the relationships among the variables that influence these events in a given region. These models can be used by emergency management personnel to develop more robust flood management plans for susceptible areas. The research …

Maximising Social Welfare In Selfish Multi-Modal Routing Using Strategic Information Design For Quantal Response Travelers, Sainath Sanga

Masters Theses

"Traditional selfish routing literature quantifies inefficiency in transportation systems with single-attribute costs using price-of-anarchy (PoA), and provides various technical approaches (e.g. marginal cost pricing) to improve PoA of the overall network. Unfortunately, practical transportation systems have dynamic, multi-attribute costs and the state-of-the-art technical approaches proposed in the literature are infeasible for practical deployment. In this paper, we offer a paradigm shift to selfish routing via characterizing idiosyncratic, multiattribute costs at boundedly-rational travelers, as well as improving network efficiency using strategic information design. Specifically, we model the interaction between the system and travelers as a Stackelberg game, where travelers adopt multi-attribute …

Two-Path Interference In Resonance-Enhanced Few-Photon Ionization Of Li Atoms, B. P. Acharya, S. Dubey, K. L. Romans, A. H.N.C. De Silva, K. Foster, O. Russ, K. Bartschat, N. Douguet, Daniel Fischer

Physics Faculty Research & Creative Works

We investigate the resonance-enhanced few-photon ionization of atomic lithium by linearly polarized light whose frequency is tuned near the 2s-2p transition. Considering the direction of light polarization orthogonal to the quantization axis, the process can be viewed as an atomic "double-slit experiment "where the 2p states with magnetic quantum numbers mℓ=±1 act as the slits. In our experiment, we can virtually close one of the two slits by preparing lithium in one of the two circularly polarized 2p states before subjecting it to the ionizing radiation. This allows us to extract the interference term between the two pathways and obtain …

Study Of The Very High Energy Emission Of M87 Through Its Broadband Spectral Energy Distribution, R. Alfaro, C. Alvarez, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, E. Belmont-Moreno, T. Capistrán, A. Carramiñana, S. Casanova, U. Cotti, J. Cotzomi, S. Coutiño De León, E. De La Fuente, C. De León, R. Diaz Hernandez, M. A. Duvernois, M. Durocher, J. C. Díaz-Vélez, C. Espinoza, K. L. Fan, M. Fernandez Alonso, N. Fraija, J. A. Garcia-Gonzalez, F. Garfias, M. M. Gonzalez, J. A. Goodman, J. P. Harding, G. Huang, F. Hueyotl-Zahuantitla, P. Huntemeyer, V. Joshi, H. Leon Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martinez-Castro, J. A. Matthews, P. Miranda-Romagnoli, E. Moreno, M. Mostafa, A. Nayerhoda, L. Nellen, R. Noriega-Papaqui, N. Omodei, A. Peisker, E. G. Perez-Perez, C. D. Rho, D. Rosa-Gonzalez, H. Salazar, D. Salazar-Gallegos, F. Salesa Greus, A. Sandoval, J. Serna-Franco, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, F- Urena-Mena, L. Villasenor, Xiaojie Wang, E. Willox, A. Zepeda

Physics Faculty Research & Creative Works

The radio galaxy M87 is the central dominant galaxy of the Virgo Cluster. Very high-energy (VHE, ≳0.1 TeV) emission from M87 has been detected by imaging air Cherenkov telescopes. Recently, marginal evidence for VHE long-term emission has also been observed by the High-Altitude Water Cherenkov Observatory, a gamma-ray and cosmic-ray detector array located in Puebla, Mexico. The mechanism that produces VHE emission in M87 remains unclear. This emission originates in its prominent jet, which has been spatially resolved from radio to X-rays. In this paper, we construct a spectral energy distribution from radio to gamma rays that is representative of …

Dynamic Path Planning For Unmanned Aerial Vehicles Under Deadline And Sector Capacity Constraints, Sudharsan Vaidhun, Zhishan Guo, Jiang Bian, Haoyi Xiong, Sajal K. Das

Computer Science Faculty Research & Creative Works

The US National Airspace System is currently operating at a level close to its maximum potential. The limitation comes from the workload demand on the air traffic controllers. Currently, the air traffic flow management is based on the flight path requests by the airline operators, whereas the minimum separation assurance between flights is handled strategically by air traffic control personnel. In this paper, we propose a scalable framework that allows path planning for a large number of unmanned aerial vehicles (UAVs) taking into account the deadline and weather constraints. Our proposed solution has a polynomial-time computational complexity that is also …

Temporal Lidar Scanning In Quantifying Cumulative Rockfall Volume And Hazard Assessment: A Case Study At Southwestern Saudi Arabia, Abdullah A. Alotaibi, Norbert H. Maerz, Kenneth J. Boyko, Ahmed M. Youssef, Biswajeet Pradhan

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Rockfalls and unstable slopes pose a serious threat to people and property along roads/highways in the southwestern mountainous regions of Saudi Arabia. In this study, the application of terrestrial light detection and ranging (LiDAR) technology was applied aiming to propose a strategy to analyze and accurately depict the detection of rockfall changes, calculation of rockfall volume, and evaluate rockfall hazards along the Habs Road, Jazan Region, Saudi Arabia. A series of temporal LiDAR scans were acquired at three selected sites. Our results show that these three sites have different degrees of hazard due to their geological differences. The mean volume …

Advances In Energy Hybridization For Resilient Supply: A Sustainable Approach To The Growing World Demand, Haider Al-Rubaye, Joseph D. Smith, Mohammed H.S. Zangana, Prashant Nagapurkar, Yishu Zhou, Gregory M. Gelles

Chemical and Biochemical Engineering Faculty Research & Creative Works

Energy poverty, defined as a lack of access to reliable electricity and reliance on traditional biomass resources for cooking, affects over a billion people daily. The World Health Organization estimates that household air pollution from inefficient stoves causes more premature deaths than malaria, tuberculosis, and HIV/AIDS. Increasing demand for energy has led to dramatic increases in emissions. The need for reliable electricity and limiting emissions drives research on Resilient Hybrid Energy Systems (RHESs), which provide cleaner energy through combining wind, solar, and biomass energy with traditional fossil energy, increasing production efficiency and reliability and reducing generating costs and emissions. Microgrids …

High-Performance Catalytic Four-Channel Hollow Fibers With Highly Dispersed Nickel Nanoparticles Prepared By Atomic Layer Deposition For Dry Reforming Of Methane, Baitang Jin, Shiguang Li, Xinhua Liang

Chemical and Biochemical Engineering Faculty Research & Creative Works

Highly dispersed nickel (Ni) nanoparticles (NPs) with an average particle size of 4.3 nm were uniformly deposited on the outer surface, the inner channel surface, and inside the pores of 20 cm long four-channel α-Al₂O₃hollow fibers (HFs) by atomic layer deposition (ALD) for dry reforming of methane (DRM). Cerium oxide (CeO₂) was added to promote the catalytic performance of Ni/Al₂O₃-HF catalysts. Rationally designed filling methods, by tuning the reactor size and inert fillings, can reduce the catalyst bed voidage in a fixed bed reactor for better reactant gas distribution, effectively …

Drone-Truck Cooperated Delivery Under Time Varying Dynamics, Arindam Khanda, Federico Corò, Sajal K. Das

Computer Science Faculty Research & Creative Works

Rapid technological developments in autonomous unmanned aerial vehicles (or drones) could soon lead to their large-scale implementation in the last-mile delivery of products. However, drones have a number of problems such as limited energy budget, limited carrying capacity, etc. On the other hand, trucks have a larger carrying capacity, but they cannot reach all the places easily. Intriguingly, last-mile delivery cooperation between drones and trucks can synergistically improve delivery efficiency. In this paper, we present a drone-truck co-operated delivery framework under time-varying dynamics. Our framework minimizes the total delivery time while considering low energy consumption as the secondary objective. The …

Ds-Menet For The Classification Of Citrus Disease, Xuyao Liu, Yaowen Hu, Guoxiong Zhou, Weiwei Cai, Mingfang He, Jialei Zhan, Yahui Hu, Liujun Li

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish …

Ds-Menet For The Classification Of Citrus Disease, Xuyao Liu, Yaowen Hu, Guoxiong Zhou, Weiwei Cai, Mingfang He, Jialei Zhan, Yahui Hu, Liujun Li

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish …

Region-Adaptive, Error-Controlled Scientific Data Compression Using Multilevel Decomposition, Qian Gong, Ben Whitney, Chengzhu Zhang, Xin Liang, Anand Rangarajan, Jieyang Chen, Lipeng Wan, Paul Ullrich, Qing Liu, Robert Jacob, Sanjay Ranka, Scott Klasky

Computer Science Faculty Research & Creative Works

The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed …

Bert-Er: Query-Specific Bert Entity Representations For Entity Ranking, Shubham Chatterjee, Laura Dietz

Computer Science Faculty Research & Creative Works

Entity-oriented search systems often learn vector representations of entities via the introductory paragraph from the Wikipedia page of the entity. As such representations are the same for every query, our hypothesis is that the representations are not ideal for IR tasks. In this work, we present BERT Entity Representations (BERT-ER) which are query-specific vector representations of entities obtained from text that describes how an entity is relevant for a query. Using BERT-ER in a downstream entity ranking system, we achieve a performance improvement of 13-42% (Mean Average Precision) over a system that uses the BERT embedding of the introductory paragraph …

Wikimarks: Harvesting Relevance Benchmarks From Wikipedia, Laura Dietz, Shubham Chatterjee, Connor Lennox, Sumanta Kashyapi, Pooja Oza, Ben Gamari

Computer Science Faculty Research & Creative Works

We provide a resource for automatically harvesting relevance benchmarks from Wikipedia - which we refer to as "Wikimarks"to differentiate them from manually created benchmarks. Unlike simulated benchmarks, they are based on manual annotations of Wikipedia authors. Studies on the TREC Complex Answer Retrieval track demonstrated that leaderboards under Wikimarks and manually annotated benchmarks are very similar. Because of their availability, Wikimarks can fill an important need for Information Retrieval research. We provide a meta-resource to harvest Wikimarks for several information retrieval tasks across different languages: paragraph retrieval, entity ranking, query-specific clustering, outline prediction, and relevant entity linking and many more. …

Machine Learning Boosts Performance Of Optical Fiber Sensors: A Case Study For Vector Bending Sensing, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

The Spectral Response Produced When a High-Sensitivity Optical Fiber Sensor (OFS) is Subject to an External Perturbation Has Recently Been Shown to Contain Rich Information that Can Be Potentially Exploited for Multi-Dimensional Sensing. in This Article, We Propose the Use of Machine Learning to Directly and Statistically Learn the Relation between the Complex Spectral Response from an OFS and a Measurand of Interest, Without Knowing If There Are Distinct and Tractable Features in the Spectrum. as a Proof-Of-Concept Demonstration, It is Shown that a Simple Heterostructure-Based Device with a Capillary Tube Sandwiched between Two Single-Mode Fibers Without Any Fiber Modification …

Combination Of High Ph And An Antioxidant Improves Chemical Stability Of Two-Dimensional Transition-Metal Carbides And Carbonitrides (Mxenes) In Aqueous Colloidal Solutions, Shuohan Huang, Vadym Mochalin

Chemistry Faculty Research & Creative Works

MXenes, a large family of two-dimensional (2D) transition-metal carbides/nitrides, have attracted increased attention in recent years because of their excellent electronic, mechanical, thermal, and optical properties. Studying chemical properties of MXenes is important to prolong the shelf life of their colloids and provide robust performance of MXenes in devices and applications. While the role of MXene reactivity with the environment, including water and components of air, is becoming more recognized, less is known about the role of parameters influencing the reactivity. In this work, we investigate the individual and combined effects of the pH and antioxidant on chemical stability of …

Derivation And Analysis Of A Discrete Predator–Prey Model, Sabrina H. Streipert, Gail S.K. Wolkowicz, Martin Bohner

Mathematics and Statistics Faculty Research & Creative Works

We derive a discrete predator–prey model from first principles, assuming that the prey population grows to carrying capacity in the absence of predators and that the predator population requires prey in order to grow. The proposed derivation method exploits a technique known from economics that describes the relationship between continuous and discrete compounding of bonds. We extend standard phase plane analysis by introducing the next iterate root-curve associated with the nontrivial prey nullcline. Using this curve in combination with the nullclines and direction field, we show that the prey-only equilibrium is globally asymptotic stability if the prey consumption-energy rate of …

Quantum Oscillations Of The J=3/2 Fermi Surface In The Topological Semimetal Yptbi, Hyunsoo Kim, Junhyun Lee, Halyna Hodovanets, Kefeng Wang, Jay D. Sau, Johnpierre Paglione

Physics Faculty Research & Creative Works

The bismuth-based half-Heusler materials host a nontrivial topological band structure, unconventional superconductivity, and large spin-orbit coupling in a system with very low electron density. In particular, the inversion of p-orbital-derived bands with an effective angular momentum j of up to 3/2 is thought to play a central role in anomalous Cooper pairing in the cubic half-Heusler semimetal YPtBi, which is thought to be the first "high-spin" superconductor. Here, we report an extensive study of the angular dependence of quantum oscillations (QOs) in the electrical conductivity of YPtBi, revealing an anomalous Shubnikov-de Haas effect consistent with the presence of a coherent …

An Explicit Time-Domain Finite-Element Boundary Integral Method For Analysis Of Electromagnetic Scattering, Ming Dong, Liang Chen, Lijun Jiang, Ping Li, Hakan Bagci

Electrical and Computer Engineering Faculty Research & Creative Works

A numerical scheme, which hybridizes the element-level dual-field time-domain finite-element domain decomposition method (ELDDM) and the time-domain boundary integral (TDBI) method to accurately and efficiently analyze open-region transient electromagnetic scattering problems, is proposed. Element-level decomposition decouples Maxwell equations on a discretization element from those on its neighboring elements using equivalent currents defined on their faces. For any element inside the computation domain, the equivalent currents are obtained from fields in the neighboring elements. For any element on the boundary of the computation domain, the equivalent currents are obtained using the fields generated by TDBI. To generate these fields, TDBI 'radiates' …

Foldy-Wouthuysen Transformation In Strong Magnetic Fields And Relativistic Corrections For Quantum Cyclotron Energy Levels, Albert Wienczek, Christopher Moore, Ulrich D. Jentschura

Physics Faculty Research & Creative Works

We carry out a direct iterative Foldy-Wouthuysen transformation of a general Dirac Hamiltonian coupled to an electromagnetic field, including the anomalous magnetic moment. The transformation is carried out through an iterative disentangling of the particle and antiparticle Hamiltonians in the expansion for higher orders of the momenta. The time-derivative term from the unitary transformation is found to be crucial in supplementing the transverse component of the electric field in higher orders. Final expressions are obtained for general combined electric and magnetic fields, including strong magnetic fields. The time derivative of the electric field is shown to enter only in the …

Snow Parameters Inversion From Passive Microwave Remote Sensing Measurements By Deep Convolutional Neural Networks, Heming Yao, Yanming Zhang, Lijun Jiang, Hong Tat Ewe, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow's layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared with the traditional inverse method, the trained ConvNet can predict the result with higher accuracy. Besides, the proposed method has a strong tolerance for noise. The proposed ConvNet composes three pairs of convolutional and activation layers with one additional fully connected layer to realize regression, i.e., the inversion of snow parameters. The feasibility of the proposed method …

Mgard+: Optimizing Multilevel Methods For Error-Bounded Scientific Data Reduction, Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, Scott Klasky

Computer Science Faculty Research & Creative Works

Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which …

Error Estimate Of A Decoupled Numerical Scheme For The Cahn-Hilliard-Stokes-Darcy System, Wenbin Chen, Shufen Wang, Yichao Zhang, Daozhi Han, Cheng Wang, Xiaoming Wang

Mathematics and Statistics Faculty Research & Creative Works

We analyze a fully discrete finite element numerical scheme for the Cahn-Hilliard-Stokes-Darcy system that models two-phase flows in coupled free flow and porous media. To avoid a well-known difficulty associated with the coupling between the Cahn-Hilliard equation and the fluid motion, we make use of the operator-splitting in the numerical scheme, so that these two solvers are decoupled, which in turn would greatly improve the computational efficiency. The unique solvability and the energy stability have been proved in Chen et al. (2017, Uniquely solvable and energy stable decoupled numerical schemes for the Cahn-Hilliard-Stokes-Darcy system for two-phase flows in karstic geometry. …