Physical Sciences and Mathematics Commons^™

Scaling Isolated-Attosecond-Pulse Duration By Controlling A Trajectory Parameter, Dian Peng, Jean Marcel Ngoko Djiokap

Department of Physics and Astronomy: Faculty Publications

Reaching an ever shorter duration of isolated attosecond pulses (IAPs) is an ongoing mission in attosecond science. Wide usage of long-wavelength driving lasers has greatly broadened bandwidths for generating IAPs. We propose a strategy to further exploit the ability of a long-wavelength laser in producing short IAPs. We introduce a scaling relationship between IAP duration and a trajectory parameter that is associated with the classical cutoff trajectory of high-order harmonic generation (HHG) in gases. This trajectory parameter can facilitate in shortening IAP duration by shaping laser waveform. We demonstrate the effectiveness of our methods with calculations of HHG from Ne …

A Generalized Solution Method To Undamped Constant-Coefficient Second-Order Odes Using Laplace Transforms And Fourier Series, Laurie A. Florio, Ryan D. Hanc

CODEE Journal

A generalized method for solving an undamped second order, linear ordinary differential equation with constant coefficients is presented where the non-homogeneous term of the differential equation is represented by Fourier series and a solution is found through Laplace transforms. This method makes use of a particular partial fraction expansion form for finding the inverse Laplace transform. If a non-homogeneous function meets certain criteria for a Fourier series representation, then this technique can be used as a more automated means to solve the differential equation as transforms for specific functions need not be determined. The combined use of the Fourier series …

Exploring Parameter Sensitivity Analysis In Mathematical Modeling With Ordinary Differential Equations, Viktoria Savatorova

CODEE Journal

This paper presents an exploration into parameter sensitivity analysis in mathematical modeling using ordinary differential equations (ODEs). Taking the first steps in understanding local sensitivity analysis through the direct differential method and global sensitivity analysis using metrics like Pearson, Spearman, PRCC, and Sobol’, we provide readers with a basic understanding of parameter sensitivity analysis for mathematical modeling using ODEs. As an illustrative application, the system of differential equations modeling population dynamics of several fish species with harvest considerations is utilized. The results of employing local and global sensitivity analysis are compared, shedding light on the strengths and limitations of each …

Phytochemical Constituent And Antioxidant Activity Evaluation Of Red Seaweed Eucheuma Sp., Ade Arsianti

Indonesian Journal of Medical Chemistry and Bioinformatics

Oxidative stress is a condition in which there is an imbalance between production of free radicals and protective response via antioxidant system. There are endogenous and exogenous antioxidants, however as age increases, there is a reduction in endogenous antioxidant, thus the search for potential exogenous antioxidants which could be derived from natural resources are needed. Indonesia is a megabiodiversity country which has more than 30,000 species of plants and animals. Red seaweed Eucheuma sp. is one of marine macroalgae species which shows potent biological activities. This study aims to determine the phytochemical constituent and to evaluate the antioxidant activity of …

Adjusting For Berkson Error In Exposure In Ordinary And Conditional Logistic Regression And In Poisson Regression, Tamer Oraby, Santanu Chakraborty, Siva Sivaganesan, Laurel Kincl, Lesley Richardson, Mary Mcbride, Jack Siemiatycki, Elisabeth Cardis, Daniel Krewski

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Background

INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers.

Methods

In this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson …

The Accumulation And Growth Of Pseudomonas Aeruginosa On Surfaces Is Modulated By Surface Mechanics Via Cyclic-Di-Gmp Signaling, Liyun Wang, Yu-Chern Wong, Joshua M. Correira, Megan Wancura, Chris J. Geiger, Shanice S. Webster, Ahmed Touhami, Benjamin J. Butler, George A. O’Toole, Richard M. Langford

Physics and Astronomy Faculty Publications and Presentations

Attachment of bacteria onto a surface, consequent signaling, and accumulation and growth of the surface-bound bacterial population are key initial steps in the formation of pathogenic biofilms. While recent reports have hinted that surface mechanics may affect the accumulation of bacteria on that surface, the processes that underlie bacterial perception of surface mechanics and modulation of accumulation in response to surface mechanics remain largely unknown. We use thin and thick hydrogels coated on glass to create composite materials with different mechanics (higher elasticity for thin composites; lower elasticity for thick composites) but with the same surface adhesivity and chemistry. The …

Service-Oriented Framework For Developing Interoperable E-Health Systems In A Low-Income Country, Bonface Abima, Agnes Nakakawa, Geoffrey Mayoka Kituyi

The African Journal of Information Systems

e-Health solutions in low-income countries are fragmented, address institution-specific needs, and do little to address the strategic need for inter-institutional exchange of health data. Although various e-health interoperability frameworks exist, contextual factors often hinder their effective adoption in low-income countries. This underlines the need to investigate such factors and to use findings to adapt existing e-health interoperability models. Following a design science approach, this research involved conducting an exploratory survey among 90 medical and Information Technology personnel from 67 health facilities in Uganda. Findings were used to derive requirements for e-health interoperability, and to orchestrate elements of a service oriented …

10.9.2023, Liz Williamson

ORSP Newsletter

No abstract provided.

Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras

Computer Science Faculty and Staff Publications

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible …

Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter

All Works

The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have turned to machine learning to combat this surge in malware attacks and enhance detection and prevention methods, they often encounter limitations when it comes to sourcing malware binaries. This limitation places the burden on malware researchers to create context-specific datasets and detection mechanisms, a time-consuming and intricate process that involves a series of experiments. The lack of accessible analysis reports and a centralized platform for sharing and verifying findings has resulted …

Influences On Participation In The National Flood Insurance Program’S Community Rating System In Coastal Counties In Louisiana, Mississippi, Alabama, And Florida, Jennifer E. Argote

LSU Doctoral Dissertations

The National Flood Insurance Program provides an incentive-based program, the Community Rating System (“CRS”), to encourage communities to improve their hazard mitigation protocols to better protect against and prevent flood-related hazards. This dissertation analyzes factors that influence participation and points scored within the CRS to gain an understanding of the conditions under which communities are willing and able to take advantage of an incentive-based flood hazard mitigation program. It also includes an analysis of survey responses from 41 coastal county floodplain and CRS managers to gauge their opinions on the CRS and how it can be improved to better serve …

Ab Initio Quantum Scattering Calculations And A New Potential Energy Surface For The Hcl(X¹Σ+)-O2( X 3 Σ G − ) System: Collision-Induced Line Shape Parameters For O2-Perturbed R(0) 0-0 Line In H35cl, Artur Olejnik, Hubert Jóźwiak, Maciej Gancewski, Ernesto Quintas-Sánchez, Richard Dawes, Piotr Wcisło

Chemistry Faculty Research & Creative Works

The Remote Sensing of Abundance and Properties of HCl—the Main Atmospheric Reservoir of Cl Atoms that Directly Participate in Ozone Depletion—is Important for Monitoring the Partitioning of Chlorine between "Ozone-Depleting" and "Reservoir" Species. Such Remote Studies Require Knowledge of the Shapes of Molecular Resonances of HCl, Which Are Perturbed by Collisions with the Molecules of the Surrounding Air. in This Work, We Report the First Fully Quantum Calculations of Collisional Perturbations of the Shape of a Pure Rotational Line in H35Cl Perturbed by an Air-Relevant Molecule [As the First Model System We Choose the R(0) Line in HCl Perturbed by …

Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram

Turkish Journal of Electrical Engineering and Computer Sciences

Hyperspectral imaging has emerged as a prominent area of research in the field of remote sensing science. However, hyperspectral images (HSIs) pose a notable challenge due to the presence of numerous irrelevant and redundant spectral bands exhibiting high correlation. Therefore, it is necessary to enhance the classification performance for HSI processing by selecting the most relevant discriminative spectral bands. To this end, this paper introduces a metaheuristic search method called enhancing exploration-exploitation in harmony search (E3HS). The standard harmony search suffers from many weaknesses, such as premature convergence and falling easily into the local optimum. Consequently, E3HS was proposed to …

Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi

Turkish Journal of Electrical Engineering and Computer Sciences

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …

Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth

Turkish Journal of Electrical Engineering and Computer Sciences

The duty of monitoring traffic during rush hour is difficult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traffic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traffic density in …

Conserving Nature's Chorus: Local And Landscape Features Promoting Frog Species Richness In Farm Dams, Martino E. Malerba, Jodi J L Rowley, Peter I. Macreadie, James Frazer, Nicholas J. Wright, Nayyar Zaidi, Asef Nazari, Dhananjay Thiruvady, Don A. Driscoll

Climate Science Research Articles

Habitat loss is a key factor in the ongoing freshwater biodiversity crisis. A promising way to help tackle the rapid decline in freshwater biodiversity is to improve the potential for artificial wetlands to provide habitat for aquatic wildlife. Farm dams (also known as agricultural ponds) are among the most abundant waterbodies in agricultural landscapes and can act as “oases” against droughts for many species. Despite their prominent role in agriculture, predictive models to evaluate their ecological potential are yet to emerge. Here we use a continental-scale data set of 104,013 audio recordings from citizen scientists to identify and locate 107 …

Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …

Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images; concatenation, which concatenates the extracted deep features to form a robust feature vector; dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension; and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, …

Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy

Turkish Journal of Electrical Engineering and Computer Sciences

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with …

Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima

Turkish Journal of Electrical Engineering and Computer Sciences

The conventional approach to creating 3D surfaces from 2D medical images is the marching cube algorithm, but it often results in rough surfaces. On the other hand, B-spline curves and nonuniform rational B-splines (NURBSs) offer a smoother alternative for 3D surface reconstruction. However, NURBSs use control points (CTPs) to define the object shape and corners play an important role in defining the boundary shape as well. Thus, in order to fill the research gap in applying corner detection (CD) methods to generate the most favorable CTPs, in this paper corner points are identified to predict organ shape. However, CTPs must …

A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj

Turkish Journal of Electrical Engineering and Computer Sciences

Early diagnosis of colorectal cancer lengthens human life and is helpful in efforts to cure the illness. Histopathological inspection is a routinely utilized technique to diagnose it. Visual assessment of histopathological images takes more investigation time, and the decision is based on the individual perceptions of clinicians. The existing methods for colorectal cancer classification use only spatial information. However, studies on the spectral domains of information are lacking in the literature. Therefore, the performance of the existing techniques is moderate. To improve the performance of colorectal cancer classification, this work proposes a unique hybrid domain hand-crafted feature formulated using scale-invariant …

Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur

Turkish Journal of Electrical Engineering and Computer Sciences

Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants …

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli

Turkish Journal of Electrical Engineering and Computer Sciences

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, …

Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan

Turkish Journal of Electrical Engineering and Computer Sciences

The training of supervised machine learning approaches is critically dependent on annotating large-scale datasets. Semisupervised learning approaches aim to achieve compatible performance with supervised methods using relatively less annotation without sacrificing good generalization capacity. In line with this objective, ways of leveraging unlabeled data have been the subject of intense research. However, semisupervised video action recognition has received relatively less attention compared to image domain implementations. Existing semisupervised video action recognition methods trained from scratch rely heavily on augmentation techniques, complex architectures, and/or the use of other modalities while distillation-based methods use models that have only been trained for 2D …

Addressing The Challenges Of Establishing Quality Wastewater Or Non- Sewered Sanitation-Based Surveillance, Including Laboratory And Epidemiological Considerations, In Malawi, Rochelle H. Holm, Ruth Nyirenda, Ted Smith, Petros Chigwechokha

Faculty and Staff Scholarship

Learning from clinical laboratories, wastewater or environmental (including non-sewered sanitation) environmental microbiology laboratories can be established in resource-limited settings that focus on pathogen detection and pandemic prevention. Transparent discussions on the laboratory challenges and adaptations required for this can help meet the future requirements of health research and surveillance. This report aims to describe the challenges encountered when setting up a wastewater or environmental laboratory for multipathogen surveillance in Malawi, a resource-limited setting, as well as the lessons learnt. We identified nine unifying themes: what to monitor, human resource capacity, indicators of data quality, equipment availability, supply of consumable goods, …

Complex Impacts Of Wars On Global Sustainable Development In A Metacoupled World, Qutu Jiang, Zhenci Xu, Yuanzheng Cui, Jianguo Liu

I-GUIDE Forum

Wars and armed conflicts have had profound impacts on local and global sustainable development in an interconnected world. However, evidence on the impacts of wars is fragmented and little attention has been paid to the impacts on the 17 UN’s Sustainable Development Goals (SDGs), a unifying framework for achieving global sustainable development. This perspective synthesizes the scattered information to provide a holistic analysis and highlight the applications of remote sensing in assessing the impacts of wars on global sustainable development in a metacoupling world. Wars have complex impacts on all 17 SDGs, which cascade beyond conflict zones and spillover to …

Geospatial Data Integration Middleware For Exploratory Analytics Addressing Regional Natural Resource Grand Challenges In The Us Mountain West, Shannon Albeke, Nicholas Case, Samantha Ewers, Jeffrey Hamerlinck, William Kirkpatrick, Jerod Merkle, Luke Todd

I-GUIDE Forum

This paper describes CyberGIS-based research and development aimed at improving geospatial data integration and visual analytics to better understand the impact of regional climate change on water availability in the U.S. Rocky Mountains. Two Web computing applications are presented. DEVISE - Derived Environmental Variability Indices Spatial Extractor, streamlines utilization of environmental data for better-informed wildlife decisions by biologists and game managers. The WY-Adapt platform aims to enhance predictive understanding of climate change impacts on water availability through two modules: “Current Conditions” and “Future Scenarios”. It integrates high-resolution models of the biophysical environment and human interactions, providing a robust framework for …

Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang

I-GUIDE Forum

Urbanization has entered a new phase characterized by urban changes occurring at a micro-scale and “under the roof”, as opposed to external modifications. These changes, known as urban retrofitting, involve the incorporation of novel technologies or features into pre-existing systems to promote sustainability. Given the limitations of remote sensing images in identifying such urban changes, novel tools need to be developed for detecting urban retrofitting. In this study, we first build a pipeline to collect large-scale time-series urban street view images from Google Street View in Mecklenburg County, North Carolina. And we examine the feasibility of utilizing the acquired dataset …