Physical Sciences and Mathematics Commons^™

The Coral Bleaching Automated Stress System (Cbass): A Low-Cost, Portable System For Standardized Empirical Assessments Of Coral Thermal Limits, Nicholas R. Evensen, Katherine E. Parker, Thomas A. Oliver, Stephen R. Palumbi, Cheryl A. Logan, James S. Ryan, Courtney N. Klepac, Gabriela Perna, Mark E. Warner, Christian R. Voolstra, Daniel J. Barshis

Biological Sciences Faculty Publications

Ocean warming is increasingly affecting marine ecosystems across the globe. Reef-building corals are particularly affected by warming, with mass bleaching events increasing in frequency and leading to widespread coral mortality. Yet, some corals can resist or recover from bleaching better than others. Such variability in thermal resilience could be critical to reef persistence; however, the scientific community lacks standardized diagnostic approaches to rapidly and comparatively assess coral thermal vulnerability prior to bleaching events. We present the Coral Bleaching Automated Stress System (CBASS) as a low-cost, open-source, field-portable experimental system for rapid empirical assessment of coral thermal thresholds using standardized temperature …

A Multi-Taxon Analysis Of European Red Lists Reveal Major Threats To Biodiversity, Axel Hochkirch, Melanie Bilz, Catarina C. Ferreira, Anja Danielczak, David Allen, Ana Nieto, Carlo Rondinini, Kate Harding, Craig Hilton-Taylor, Caroline M. Pollock, Mary Seddon, Jean-Christophe Vié, Keith N. A. Alexander, Emily Beech, Manuel Biscoito, Yoan Braud, Ian J. Burfield, Filippo Maria Buzzetti, Marta Cálix, Kent E. Carpenter, Ning Labbish Chao, Dragan Chobanov, Maarten J. M. Christenhusz, Bruce B. Collette, Mia T. Comeros-Raynal, Neil Cox, Matthew Craig, Annabelle Cuttelod, William R. T. Darwall, Benoit Dodelin, Nicholas K. Dulvy, Eve Englefield, Michael F. Fay, Nicholas Fettes, Jörg Freyhof, Silvia García, Mariana García Criado, Michael Harvey, Nick Hodgetts, Christina Ieronymidou, Vincent J. Kalkman, Shelagh P. Kell, James Kemp, Sonia Khela, Richard V. Lansdown, Julia M. Lawson, Danna J. Leaman, Joana Magos Brehm, Nigel Maxted, Rebecca M. Miller, Eike Neubert, Baudewijn Odé, David Pollard, Riley Pollom, Rob Pople, Juan José Presa Asensio, Gina M. Ralph, Hassan Rankou, Malin Rivers, Stuart P. M. Roberts, Barry Russell, Alexander Sennikov, Fabien Soldati, Anna Staneva, Emilie Stump, Andy Symes, Dmitry Telnov, Helen Temple, Andrew Terry, Anastasiya Timoshyna, Chris Van Swaay, Henry Väre, Rachel H. L. Walls, Luc Willemse, Brett Wilson, Jemma Window, Emma G. E. Wright, Thomas Zuna-Kratky

Biological Sciences Faculty Publications

Biodiversity loss is a major global challenge and minimizing extinction rates is the goal of several multilateral environmental agreements. Policy decisions require comprehensive, spatially explicit information on species’ distributions and threats. We present an analysis of the conservation status of 14,669 European terrestrial, freshwater and marine species (ca. 10% of the continental fauna and flora), including all vertebrates and selected groups of invertebrates and plants. Our results reveal that 19% of European species are threatened with extinction, with higher extinction risks for plants (27%) and invertebrates (24%) compared to vertebrates (18%). These numbers exceed recent IPBES (Intergovernmental Platform on Biodiversity …

Mechanical Devices For Census And Detection Of Off-Host Larval Ticks (Acari: Ixodidae), Donald B. Thomas, Holly D. Gaff, Brenda Leal-Galvan

Biological Sciences Faculty Publications

Standard off-host tick census techniques are generally inefficient for larval stages. Also, such techniques expose the investigator to environmental hazards including tick bites. Mechanical devices are an option which were tested under field conditions for proof of concept and in side-by-side comparison to the standard techniques. The tick-vac collected more ticks than the legging technique, but with lower frequency of positive detections. It may have utility for sampling small areas, such as weedy corrals suspected of harboring ticks. Different configurations of tick-bots, robotic vehicles with flags, depended on intensive operator control to avoid obstacles, limiting operation to line of sight. …

Comparative Study On Development Of Novel Catalytic Oxidation For Removing Emerging Contaminants, Abdul H. Asif

Theses: Doctorates and Masters

‘Wastewater treatment’ is not itself a new concept, where global water bodies have been polluted since the evolution of mankind. As a result of human activities including domestic, industrial and commercial practices, levels of water pollution through aqueous contaminants are increasing every day. Moreover, rapid industrialisation and recent human progress has given rise to a new classification of pollution, that of emerging contaminants (ECs). Further, these chemicals are highly resistant to biodegradation and are capable of travelling, where they can penetrate from one part of the world to another. Whilst traditional wastewater treatment techniques, such as adsorption, might be effective …

Probabilistic Solutions Of Fractional Differential And Partial Differential Equations And Their Monte Carlo Simulations, Tamer Oraby, Erwin Suazo, Harrinson Arrubla

School of Mathematical & Statistical Sciences Faculty Publications

The work in this paper is four-fold. Firstly, we introduce an alternative approach to solve fractional ordinary differential equations as an expected value of a random time process. Using the latter, we present an interesting numerical approach based on Monte Carlo integration to simulate solutions of fractional ordinary and partial differential equations. Thirdly, we show that this approach allows us to find the fundamental solutions for fractional partial differential equations (PDEs), in which the fractional derivative in time is in the Caputo sense and the fractional in space one is in the Riesz–Feller sense. Lastly, using Riccati equation, we …

Quantization Dimension For Inhomogeneous Bi-Lipschitz Ifs, Amit Priyadarshi, Mrinal Kanti Roychowdhury, Manuj Verma

School of Mathematical & Statistical Sciences Faculty Publications

Let ν be a Borel probability measure on a d-dimensional Euclidean space Rd, d≥1, with a compact support, and let (p0,p1,p2,…,pN) be a probability vector with pj>0 for 1≤j≤N. Let {Sj:1≤j≤N} be a set of contractive mappings on Rd. Then, a Borel probability measure μ on Rd such that μ=∑Nj=1pjμ∘S−1j+p0ν is called an inhomogeneous measure, also known as a condensation measure on Rd. For a given r∈(0,+∞), the quantization dimension of order r, if it exists, denoted by Dr(μ), of a Borel probability measure μ on Rd represents the speed at which the nth quantization error of order r …

Constrained Quantization For Probability Distributions, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, for a Borel probability measure P on a Euclidean space Rk, we extend the definitions of nth unconstrained quantization error, unconstrained quantization dimension, and unconstrained quantization coefficient, which traditionally in the literature known as nth quantization error, quantization dimension, and quantization coefficient, to the definitions of nth constrained quantization error, constrained quantization dimension, and constrained quantization coefficient. The work in this paper extends the theory of quantization and opens a new area of research. In unconstrained quantization, the elements in an optimal set are the conditional expectations in their own Voronoi regions, and it is not true …

Numerical Study Of Sediment Suspension Affected By Rigid Cylinders Under Unidirectional And Combined Wave-Current Flows, Sha Lou, Xiaolan Chen, Shengyu Zhou, Gangfeng Ma, Shuguang Liu, Larisa Dorzhievna Radnaeva, Elena Nikitina, Irina Viktorovna Fedorova

Civil & Environmental Engineering Faculty Publications

Sediment transport modeling for flows with cylinders is very challenging owing to the complicated flow–cylinder–sediment interactions, especially under the combined wave-current flows. In this paper, an improved formulation for incipient sediment suspension considering the effect of cylinder density (i.e., solid volume fraction) is employed to simulate the bottom sediment flux in the flow with cylinders. The proposed model is calibrated and validated using laboratory measurements under unidirectional and combined wave-current flows in previous studies. It is proved that the effects of cylinders on sediment suspension can be accounted for through a modified critical Shields number, and the proposed model is …

Automating Intersection Marking Data Collection And Condition Assessment At Scale With An Artificial Intelligence-Powered System, Kun Xie, Huiming Sun, Xiaomeng Dong, Hong Yang, Hongkai Yu

Civil & Environmental Engineering Faculty Publications

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with …

Patch-Wise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal

Mechanical & Aerospace Engineering Faculty Publications

This paper expands the authors’ prior work[1], which focuses on developing a convolutional neural network (CNN) model capable of mapping time-averaged, unsteady Reynold’s-averaged Navier-Stokes (URANS) simulations to higher resolution results informed by time-averaged detached eddy simulations (DES). The authors present improvements over the prior CNN autoencoder model that result from hyperparameter optimization, increased data set augmentation through the adoption of a patch-wise training approach, and the predictions of primitive variables rather than vorticity magnitude. The training of the CNN model developed in this study uses the same URANS and DES simulations of a transonic flow around several NACA 4-digit airfoils …

Model Based Systems Engineering With A Docs-As-Code Approach For The Sealion Cubesat Project, Kevin Chiu, Sean Marquez, Sharanabasaweshwara Asundi

Mechanical & Aerospace Engineering Faculty Publications

The SeaLion mission architecture team sought to create a model-based systems engineering approach to assist improving CubeSat success rates as well as for the SeaLion CubeSat project to guide an implementation for the flight software. This is important because university CubeSat teams are growing in number but often have untrained students as their core personnel. This was done using a document-as-code, or docs-as-code, approach. With this the team created tools for the systems architecture with the Mach 30 Modeling Language to create an architecture that is easy to learn and use even for newly admitted team members with little to …

Implementation Of Course Structure In Stem Courses For Student Motivation And Learning, And Lab Innovation, Muzammil Arshad, Mamoona Muzammil

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

The present study is an extension of implementation of the course structure which was initially designed, developed and implemented at Texas A&M University for engineering courses. This study extends its implementation to other STEM courses to assess its applicability and effectiveness in science related courses. The course structure is employed at the Chemistry department at University of Texas Rio Grande Valley (UTRGV). The present study is an autoethnography of the implementation of the course structure and its effectiveness assessment. This study highlights the implementation of the course structure considering student motivation and learning since student motivation is an important research …

Mofx-Db: An Online Database Of Computational Adsorption Data For Nanoporous Materials, N. Scott Bobbitt, Kaihang Shi, Benjamin J. Bucior, Haoyuan Chen, Nathaniel Tracy-Amoroso, Zhao Li

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

Machine learning and data mining coupled with molecular modeling have become powerful tools for materials discovery. Metal–organic frameworks (MOFs) are a rich area for this due to their modular construction and numerous applications. Here, we make data from several previous large-scale studies in MOFs and zeolites from our groups (and new data for N2 and Ar adsorption in MOFs) easily accessible in one place. The database includes over three million simulated adsorption data points for H2, CH4, CO2, Xe, Kr, Ar, and N2 in over 160 000 MOFs and 286 zeolites, textural properties like pore sizes and surface areas, and …

The Impact Of Climate Change On Environmental Sustainability And Human Mortality, Xingzhi Mara Chen, Andrew Sharma, Hua Liu

Political Science & Geography Faculty Publications

Climate dictates the critical aspects of human environmental conditions. The frequency and intensity of extreme weather conditions due to human-induced climate change have alarmingly increased. Consequently, climate change directly affects environmental sustainability and human mortality in the short term and creates prolonged and complicated long-term indirect grave risks. This paper examines three-level environmental impact risks associated with climate change on human mortality. It proposes a conceptual framework for developing an empirical event-based human mortality database related to climate change and communication strategies to enhance global environmental adaptation, resilience, and sustainability.

Optical Phase Transition Characteristics In Germanium-Antimony-Selenium-Tellurium And Graphene Oxide Thin Film For Application In Photonics Memory., Gan Soon Xin

Student Works (2020-2029)

This thesis aims to study the phase change characteristic of Ge2Sb2Se4Te1 (GSST) using 1550 nm pulsed laser as excitation source, and to explore its potential in multilevel nonvolatile photonic memory application. Firstly, the amorphous to crystalline state phase transition of GSST using 1550 nm pulsed laser as optical excitation source is investigated. The threshold laser pulse duration to achieve crystallization are 190 ns, 140 ns, 100 ns and 70 ns for laser intensities of 0.46 GW/cm2, 0.61 GW/cm2, 0.76 GW/cm2 and 0.85 GW/cm2, respectively. However, it is found that the phase change tolerance of GSST is very stringent (phase transition …

Liquid Waste Treatment For Sustainable Tapioca Industry: A Comparison Between Biogas And Liquid Sugar, Lilik Sulistyowati, Novi Andareswari, Rifqi Rahmat Hidayatullah, Nurul Amri Komarudin, Yuni Yolanda

ASEAN Journal on Science and Technology for Development

There have been many studies examining the processing of tapioca wastewater. However, most studies have only researched at a laboratory scale without considering whether the treatment is appropriate. The study's purpose is to compare the processing technology of biogas and liquid sugar using technical, environmental, social, and cost-benefit analysis criteria to determine the appropriate technology to be applied. Literature study, observation, and interviews were used to determine the characteristics of each criterion. Cost-benefit analysis is calculated using the R/C Ratio. Furthermore, priority determination is carried out using the Exponential Comparison Method (MPE) using assessments from environmental and industrial experts on …

S-400s, Disinformation, And Anti-American Sentiment In Turkey, Russell "Alex" Korb, Saltuk Karahan, Gowri Prathap, Ekrem Kaya, Luke Palmieri, Hamdi Kavak, Richard L. Wilson (Ed.), Major Brendan Curran (Ed.)

Political Science & Geography Faculty Publications

As social and political discourse in most countries becomes more polarized, anti-Americanism has risen not only in the Middle East and Latin America but also among the U.S. allies in Europe. Social media is one platform used to disseminate anti-American views in NATO countries, and its effectiveness can be magnified when mass media, public officials, and popular figures adopt these views. Disinformation, in particular, has gained recognition as a cybersecurity issue from 2016 onward, but disinformation can be manufactured domestically in addition to being part of a foreign influence campaign. In this paper, we analyze Turkish tweets using sentiment analysis …

Advancing Estuarine Shoreline Change Analysis Using Small Uncrewed Autonomous Systems, Thomas R. Allen, Devon Eulie, Mariko Polk, George Mcleod, Robert Stuart, Alexandra Garnand, A. J. Manning (Editor)

Political Science & Geography Faculty Publications

Estuarine shorelines face the threats of accelerating sea-level rise, recurrent storms, and disruptions of natural sediment and ecological adjustments owing to historic human interventions. The growing availability and technical capability of uncrewed systems (UxS), including remote or autonomous aerial and surface vessels, provide new opportunities to study and understand estuarine shoreline changes. This chapter assesses the state of the technology, interdisciplinary science and engineering literature, and presents case studies from the Chesapeake Bay, Virginia, and coastal North Carolina, USA, that demonstrate new insights into coastal geomorphic processes and applications to managing complex and dynamic estuarine shorelines. These technologies enhance the …

The Behavior Of Partially Coherent Twisted Space-Time Beams In Atmospheric Turbulence, Milo W. Hyde Iv

Faculty Publications

We study how atmospheric turbulence affects twisted space-time beams, which are non-stationary random optical fields whose space and time dimensions are coupled with a stochastic twist. Applying the extended Huygens–Fresnel principle, we derive the mutual coherence function of a twisted space-time beam after propagating a distance z through atmospheric turbulence of arbitrary strength. We specialize the result to derive the ensemble-averaged irradiance and discuss how turbulence affects the beam’s spatial size, pulse width, and space-time twist. Lastly, we generate, in simulation, twisted space-time beam field realizations and propagate them through atmospheric phase screens to validate our analysis.

Organic Chemistry Ii, Layne Morsch, Steven Farmer, Krista Cunningham, Zachery Sharrett, Kevin M. Shea

Open Educational Resources: Textbooks

This text has many contributors. It was adapted for use in CHM 223 at Smith College by Kevin Shea with the assistance of a Smith College Library OER Incentive Grant.

Light Auditor: Power Measurement Can Tell Private Data Leakage Through Iot Covert Channels, Woosub Jung, Kailai Cui, Kenneth Koltermann, Junjie Wang, Chunsheng Xin, Gang Zhou

Electrical & Computer Engineering Faculty Publications

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users' private data by exploiting the smart bulb's capability of infrared emission.

In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process …

Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …

Mwirgan: Unsupervised Visible-To Mwir Image Translation With Generative Adversarial Network, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Unsupervised image-to-image translation techniques have been used in many applications, including visible-to-Long-Wave Infrared (visible-to-LWIR) image translation, but very few papers have explored visible-to-Mid-Wave Infrared (visible-to-MWIR) image translation. In this paper, we investigated unsupervised visible-to-MWIR image translation using generative adversarial networks (GANs). We proposed a new model named MWIRGAN for visible-to-MWIR image translation in a fully unsupervised manner. We utilized a perceptual loss to leverage shape identification and location changes of the objects in the translation. The experimental results showed that MWIRGAN was capable of visible-to-MWIR image translation while preserving the object’s shape with proper enhancement in the translated images and …

View Synthesis With Scene Recognition For Cross-View Image Localization, Uddom Lee, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with …

The Effect Of The Width Of The Incident Pulse To The Dielectric Transition Layer In The Scattering Of An Electromagnetic Pulse — A Qubit Lattice Algorithm Simulation, George Vahala, Linda Vahala, Abhay K. Ram, Min Soe

Electrical & Computer Engineering Faculty Publications

The effect of the thickness of the dielectric boundary layer that connects a material of refractive index n₁ to another of index n₂is considered for the propagation of an electromagnetic pulse. A qubit lattice algorithm (QLA), which consists of a specially chosen non-commuting sequence of collision and streaming operators acting on a basis set of qubits, is theoretically determined that recovers the Maxwell equations to second-order in a small parameter ϵ. For very thin boundary layer the scattering properties of the pulse mimics that found from the Fresnel jump conditions for a plane wave - except that …

Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty

Electrical & Computer Engineering Faculty Publications

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Adaptive Critic Network For Person Tracking Using 3d Skeleton Data, Joseph G. Zalameda, Alex Glandon, Khan M. Iftekharuddin, Mohammad S. Alam (Ed.), Vijayan K. Asari (Ed.)

Electrical & Computer Engineering Faculty Publications

Analysis of human gait using 3-dimensional co-occurrence skeleton joints extracted from Lidar sensor data has been shown a viable method for predicting person identity. The co-occurrence based networks rely on the spatial changes between frames of each joint in the skeleton data sequence. Normally, this data is obtained using a Lidar skeleton extraction method to estimate these co-occurrence features from raw Lidar frames, which can be prone to incorrect joint estimations when part of the body is occluded. These datasets can also be time consuming and expensive to collect and typically offer a small number of samples for training and …

Class Activation Mapping And Uncertainty Estimation In Multi-Organ Segmentation, Md. Shibly Sadique, Walia Farzana, Ahmed Temtam, Khan Iftekharuddin, Khan Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

Deep learning (DL)-based medical imaging and image segmentation algorithms achieve impressive performance on many benchmarks. Yet the efficacy of deep learning methods for future clinical applications may become questionable due to the lack of ability to reason with uncertainty and interpret probable areas of failures in prediction decisions. Therefore, it is desired that such a deep learning model for segmentation classification is able to reliably predict its confidence measure and map back to the original imaging cases to interpret the prediction decisions. In this work, uncertainty estimation for multiorgan segmentation task is evaluated to interpret the predictive modeling in DL …

Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …