Digital Commons Network^™

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

First Results On The Search For Lepton Number Violating Neutrinoless Double-Β Decay With The Legend-200 Experiment, H. Acharya, N. Ackermann, M. Agostini, A. Alexander, C. Andreoiu, G. R. Araujo, Frank Avignone Iii, M. Babicz, W. Bae, A. M. Bakalyarov, M. Balata, A. S. Barabash, C. J. Barton, L. Baudis, C. Bauer, E. Bernieri, L. Bezrukov, K. H. Bhimani, V. Biancaccu, E. Blalock, Et. Al.

Faculty Publications

The LEGEND Collaboration is searching for neutrinoless double-beta (0νββ) decay by operating high-purity germanium detectors enriched in ⁷⁶Ge in a low-background liquid argon environment. Building on key technological innovations from the GERmanium Detector Array (GERDA) experiment and the MAJORANA DEMONSTRATOR experiment, LEGEND-200 has performed a first 0νββ decay search based on 61.0 kg yr of data. Over half of this exposure comes from our highest performing detectors, including newly developed inverted-coaxial detectors, and is characterized by an estimated background level of 0.5^+0.3_−0.2 cts/(keV ton yr) in the 0νββ decay signal region. A combined …

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

A Robust Deep Learning Ensemble Framework For Waterbody Detection Using High-Resolution X-Band Sar Under Data-Constrained Conditions, Soyeon Choi, Seung Hee Kim, Son V. Nghiem, Menas Kafatos, Minha Choi, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, …

Analysis Of A Few Quantum Algorithms And Circuits Related To Boolean Functions, Suman Dutta

Doctoral Theses

Boolean functions are fundamental to computation and presently play a crucial role in quantum information processing. This thesis presents two facets of Boolean functions in the context of quantum computing: (I) extending and applying the theoretical framework of Forrelation to cryptographic analysis, and (II) designing efficient quantum circuits for multi-controlled Toffoli gates and Boolean circuit implementations. Given two Boolean functions $f$ and $g$, Forrelation, introduced by Aaronson (2010), measures the correlation between the truth table of $f$ and the Walsh-Hadamard transform of $g$ at the corresponding points. Here, we revisit the Forrelation framework to study several cryptographically significant spectra of …

Forest Aboveground Biomass In The Southwestern U.S. From Misr And Gedi: Assessment With Nasa Carbon Monitoring System Data, Mark J. Chopping, Zhousen Wang, Crystal B. Schaaf, Michael Bull

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Forest aboveground biomass (AGB) density mapping initiatives generally use one of three remote sensing approaches: lidar, radar, or near-nadir multispectral imaging leveraging machine learning methods, or a combination thereof. However, the active instrument record is limited and near-nadir multispectral imaging data are relatively insensitive to canopy physical structure. Multiangle imaging enables annual wall-to-wall mapping with a global record that extends back to 2000 as these data are highly sensitive to forest AGB. This paper describes work to validate estimates in a published annual, wall-to-wall record of forest AGB on a 250 m grid, derived using 672 nm imagery from the …

Spectroscopic Investigations On Polyvinylidene Fluoride Nanofibers, Parinaz Amaniabdolmalaki, Jui Vitthal Kharade, Alexandro Trevino, Lydia Morales, Karen Lozano, Victoria Padilla, Karen S. Martirosyan, Horacio Vasquez, Mircea Chipara

Physics & Astronomy Faculty Publications

The production of polyvinylidene fluoride nanofibers by force-spinning from polymer solutions was confirmed by electron microscopy. The structural and phase characteristics of the resulting nanofiber mats were examined using Fourier Transform Infrared Spectroscopy in Attenuated Total Reflectance mode, Raman spectroscopy, and X-ray Diffraction. Results from all these techniques consistently indicated that both the powder and the mats of polyvinylidene fluoride predominantly contain the α phase, with a small admixture of the β phase. Within experimental errors, no other phases were noticed both in the powder and in the as-obtained mats. The ratios of the areas of the Raman lines at …

Improving Stem Assessments: Strategies For Increasing The Accessibility And Adoption Of Two-Stage Exams, Kristina Callaghan

LSU Doctoral Dissertations

Over three decades of research have shown that students taught using active learning strategies achieve better learning outcomes compared to those taught using traditional methods. Two-stage exams, which pair a traditional exam with a group component, are an increasingly popular method for incorporating active learning within the assessment component of a course. Previous studies have shown this assessment strategy provides a variety of benefits for students, including increased engagement and collaboration, immediate feedback, and potentially increased content retention. However, the unique circumstances of each instructor can pose a non-trivial barrier to the implementation of two-stage exams. In this thesis, we …

Single-Valued Neutrosophic Pessimistic Multi-Granulation Rough Set Model Based On (A,B,C)-Cut Relations And Its Applications, Xu-Xi Wu, Hu Zhao, Qiao-Ling Song, Xiong-Wei Zhang

Neutrosophic Systems with Applications

To address uncertainty in multi-source data, this paper proposes a single-valued neutrosophic pessimistic multi-granulation rough set (P-SVN-MGRS) model based on (a,b,c)-cut relations. In this framework, each neutrosophic relation is characterized by three membership-degree functions: T(x,y), I(x,y), and F(x,y). These functions correspond to truth-membership, indeterminacy, and falsity, respectively. The (α,β,γ)-cut relation employs parameters α,β,γ∈(0,1] as thresholds for the three functions. A pair (x,y) belongs to …

Developing Pattern Recognition And Interpretable Convolutional Neural Network Based Frameworks For Identifying Drug Resistant And Pan Cancer Mirnas From Expression Data, Joginder Jsingh

Doctoral Theses

Micro Ribonucleic Acids (miRNAs) are short length (∼24) non-coding RNAs and are considered as key biomarkers in cancer diagnosis and treatment. They play a vital role in classifying cancer patients from normal ones and drug resistant patients from control ones. The control patients are those who have not received any drug for cancer treatment. The objective is to identify a subset of miRNAs those help in the classification of the patients using expression data. The thesis is comprised of four contributory chapters in addition to an introduction and conclusion. In the first two contributory chapters, computational methods for ranking and …

Stylespade: Realistic Image Augmentation For Robust Infrastructure Crack Segmentation Via Ensemble Learning, Jaeung Sim, Menas Kafatos, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

The rapid deterioration of global infrastructure necessitates precise and automated crack detection technologies for proactive maintenance. However, deep learning-based segmentation models often suffer from a scarcity of diverse, high-quality labeled datasets. This study proposes StyleSPADE, a novel conditional image generation model that integrates semantic masks and style images to synthesize realistic crack data with diverse background textures while preserving precise geometric morphology. To validate the effectiveness of the generated data, we conducted extensive semantic segmentation tasks using Transformer-based (Mask2Former, Swin-UPerNet) and CNN-based (K-Net) models. Experimental results demonstrate that StyleSPADE-based augmentation significantly outperforms baseline models, achieving a Crack IoU of 0.6376 …

Cospectrality Results For Signed Graphs With Two Eigenvalues Unequal To ±1, Willem H. Haemers, Hati̇ce Topcu

Turkish Journal of Mathematics

Recently the collection G of all signed graphs for which the adjacency matrix has all but at most two eigenvalues equal to ±1 has been determined. Here we investigate G for cospectral pairs, and for signed graphs determined by their spectrum (up to switching). If the order is at most 20, the outcome is presented in a clear table. If the spectrum is symmetric we find all signed graphs in G determined by their spectrum, and we obtain all signed graphs cospectral with the bipartite double of the complete graph. In addition we determine all signed graphs cospectral with the …

Protocol For The Development Of The Who Gestational Weight Gain Charts, Thais Rangel Bousquet Carrilho, Olufemi Taiwo Oladapo, Jennifer A. Hutcheon, Giovanna Gatica-Domínquez, Kathleen M. Rasmussen, Monica C. Flores-Urrutia, Richard Kumapley, Ӧzge Tunçalp, Dang Bahya-Batinda, Amel A. Fayed, Annick Bogaerts, Aris T. Papageorghiou, Cinthya Muñoz-Manrique, Dayana Rodrigues Farias, Eric Ohuma, Harshpal Sachdev, Hayfaa A. Wahabi, Helena J. Teede, Molin Wang, Nandita Perumal Phd

Faculty Publications

Introduction Gestational weight gain (GWG) is an important indicator of maternal nutrition to be monitored during pregnancy. However, there is no evidence-based tool that can be used to monitor it across all geographic locations and pre-pregnancy body mass index (BMI) categories. The WHO is undertaking a project to develop GWG charts by pre-pregnancy BMI category, and to identify GWG ranges associated with the lowest risks of adverse maternal and infant outcomes. This protocol describes all the steps that will be used to accomplish the development of these GWG charts.

Methods and analysis This project will involve the analysis of individual …

Design Of Nanomaterial-Based Sensors For Enhanced Halogen Bonding, Ben H. Edelman, Charles W. Sheppard, Lucas A. Chuidian, Arielle Vinnikov, Felix Bevc, Lillian B. Hughes, Carol A. Parish, Michael C. Leopold

Chemistry Faculty Publications

Halogen bonding is a highly directional, noncovalent, intermolecular interaction which has been harnessed for a variety of applications, including sensor design. A halogen bond (XB) is formed between a region of positive electrostatic potential on a halogen atom (X) and electron rich portions of target molecules. The strength of XB interactions relies on shorter XB bond distances and more linear R–X···B bond angles, which facilitate stronger, more negative binding energies. While prior studies have sought to maximize interactions, few have explored or experimentally demonstrated how geometries and bond angles can enhance XB interactions. Herein, fundamental studies are conducted at self-assembled …

Interview With Orca Award Winner Reuben Rieke, Unl Chemistry; Discoverer Of Rieke Metals, Mark Griep, Reuben Rieke

UNL ORCA Interview Series

Emeritus Prof. Reuben Rieke was awarded the ORCA in 1995. Reuben was born in Fairfax, Minnesota. Something in his youth must have sparked an interest in science because he traveled 100 miles east to The University of Minnesota where he earned a bachelor’s in chemistry. Next, he earned a doctorate from the University of Wisconsin, and did postdoctoral work at the University of California Los Angeles. In 1977, after about 11 years as a faculty member in the chemistry department at the University of North Carolina, he joined UNL as a full professor of chemistry. I’ll share two remarkable facts …

Optimized Photoemission From Organic Molecules In 2d Layered Halide Perovskites, Muhammad S. Muhammad, Dilruba A. Popy, Hamza Shoukat, John M. Lane, Neeraj Rai, Vojtěch Vaněček, Zdeněk Remeš, Romana Kučerková, Vladimir Babin, Chenjia Mi, Yitong Dong, Mark D. Smith, Novruz G. Akhmedov, Daniel T. Glatzhofer, Bayram Saparov

Faculty Publications

In recent years, hybrid organic–inorganic metal halides have been at the forefront of materials research. Typically, the functional (e.g., optoelectronic) properties of hybrid halides are derived from the inorganic structural part, whereas the organic structural units can add extra advantages in terms of stability, rigidity, and processability. Here, we report the design, synthesis, and characterization of two new hybrid materials in which the outstanding photophysical properties originate from the organic structural part. The new compounds, (C₁₅H16N)₂CdCl₄ and((Br)C₁₅H₁₅N)₂CdCl₄, have 2D layered Ruddlesden–Popper-type perovskite structures. These hybrids are blue-white …

Contact Binary Stars: A Roadmap To Their Nature And Evolution, Larry A. Molnar, M. Chalov, R. Deaton, R. Reynolds

University Faculty Publications and Creative Works

A complete life cycle for W UMa systems

• Mass transfer from secondary driven by primary
• Sign of flow essential to stability criteria 
• Consistent with observations of distributions of M, T, P, q, dP/dt, (P,q)merger

Ongoing

• THEORY 
• Understand f distribution 
• Compute grid of models to compare with populations of various ages • 

OBSERVATION:Rubin observatory data

• Establish parameter distributions vs. location or kinematics 
• Find the next red nova before it explodes

Statistics For Social Scientists Who Are A Little Afraid Of Them, Kristie L. Campana

MSU Authors Collection

This is a free textbook aimed at helping advanced undergraduate/beginner graduate students navigate statistics in the social sciences. All resources for this book are released under a creative commons license CC BY-SA 4.0. which means you are able to share, copy, and distribute the material in any medium or format, and that you can adapt and transform upon this content for any purpose. However, this can occur only under the following terms:

You must give appropriate attributions to the text, provide a link to the license, and indicate if any alterations to the text have been made. This can be …

Environmental Degradations In Images: Analysis, Restoration, And Applications, Harsh Bhandari

Doctoral Theses

This thesis investigates computational models and methodologies for restoring images degraded by challenging environmental conditions such as haze and underwater environments. It further explores the analysis and estimation of particulate matter (PM) concentration from both day and night scenes under varying weather conditions, using degraded visual data as a primary input. Each environment introduces distinct forms of visual degradation, making image restoration a critical challenge that directly impacts applications including visibility enhancement, weather analysis, particulate concentration estimation, and object detection. By addressing these challenges, this thesis aims to develop adaptable, data-driven solutions that enhance image clarity and improve information extraction …

Confluence, Vol. 4, Iss. 2, Full Issue

The Confluence

No abstract provided.

A Review Of Routing Attacks In Routing Protocol Over Low-Power And Lossy-Based Iot Networks, Lanka Chris Sejaphala Mr., Vusimuzi Malele Prof, Francis Lugayizi Prof.

Journal of Cybersecurity Education, Research and Practice

Low-power and Lossy IoT Networks (LLNs) comprise physical sensors, processing capability, power, and other technologies to exchange information between systems and devices over the internet. However, these networks are susceptible to routing attacks affecting resources, traffic flow, and topology formation. In the related work, it has been discovered that many previous studies do not consider algorithms and implementation approaches for routing attacks. This research study provides a comprehensive in-depth synthesis insight into the description, effects, and algorithms & implementation of four routing attacks in LLNs i.e., rank, sinkhole, DIS-flooding, and worst parent attacks. The findings of this research study highlight …

A Comparison Of Modeled Daytime E Regions From E-Probed And Pyiri With Ionosonde Observations, Daniel J. Emmons, Cornelius C. J. H. Salinas, Dong L. Wu, Nimalan Swarnalingam, Eugene V. Dao, Jorge L. Chau, Yosuke Yamazaki, Kyle E. Fitch, Victoriya V. Forsythe

Faculty Publications

While the F region is the primary focus of many ionospheric models because it contains the peak electron density, the E region is an important region for ionospheric conductivities and high-frequency radio propagation. This study analyzes modeled E regions from the newly developed PyIRI and E-PROBED models. A long-term comparison of E region predictions from E-PROBED and PyIRI with ionosonde observations is performed for three sites spanning low- (Fortaleza, Brazil), mid- (El Arenosillo, Spain), and high-latitudes (Gakona, Alaska). Modeled foE and hmE trends are compared against a combination of manually-scaled and automatically-scaled ionograms using ARTIST-5 for the period 2009–2024 for …

Densest Subgraph Discovery On The Cpu, Hunter Gerard Gareau

Theses and Dissertations

The Densest Subgraph Discovery (DSD) problem is a prevalent problem in the field of graph mining, aiming to find the cohesive subgraph. Given a graph �� = (��,��) and an integer �� ≥ 2, the goal is to find a vertex sub- set �� ⊆ �� whose induced subgraph �� (��) maximizes the ��-clique density, defined as the number of ��-cliques per vertex. Larger val- ues of �� capture higher-order connectivity patterns beyond edges, enabling the discovery of more cohesive structures. There have been many solutions to this problem. However, one avenue that other graph mining problems have gone down …

Reformulation Of The Protein Databank For Real-Time Search Of Geometrical Attributes Of Protein Structures, Musa Azeem, Christopher Lee, Aaron Hein, Christopher Ott, Homayoun Valafar

Faculty Publications

Introduction:

In this study, we introduce the design and implementation of PDBMine, a large-scale, queryable platform for mining sequence-structure statistics from the Protein Data Bank (PDB). PDBMine enables rapid analysis of local conformational trends across proteins by extracting dihedral angles and sequence patterns at scale. In addition to the design and implementation of PDBMine, we also present results validating its ability to return structurally meaningful information.

Methods:

We first assess the accuracy of its dihedral angle distributions by comparing them to established Ramachandran space and verifying expected behaviors of residues such as glycine and proline. We then use PDBMine to …

Wild@Fire2025: Overview Of Word-Level Code-Mixed Language Identification In Dravidian Languages, Ameeta Agrawal, Asha Hegde, Sharal Coelho, Sabur Butt, Fazlourrahman Balouchzahi, Sudha V, Shashirekha Hosahalli Lakshmaiah

Computer Science Faculty Publications and Presentations

Code-mixing is considered as a linguistic phenomenon that combines several languages into one text. It has now become very common in multilingual societies, especially in digital communication. Word-Level Identification of Languages in Dravidian Languages (WILD) - a Code-mixed Language Identification (CoLI) in Dravidian languages shared task, organized as a part of Forum for Information Retrieval and Evaluation (FIRE) 2025, put forward these challenges to the researchers by asking them to develop models capable of classifying words in code-mixed texts involving Dravidian languages - Tamil, Telugu, Malayalam, Kannada, and Tulu, which are interwoven with English. It poses significant challenges due to …

Interactive Effects Of Hyposalinity And Nitrate Loading On Growth, Physiology, And Nitrogen Status Of The Seagrass, Halodule Wrightii, Joseph L. Kowalski, Hudson R. Deyoe, Kirk Cammarata, Kristina P. Vatcheva

School of Earth, Environmental, & Marine Sciences Faculty Publications

The study goal was to examine the interactive physiological effects of two freshwater inflow stressors, nitrate pulses coupled with salinity decrease, on the seagrass Halodule wrightii. A microcosm experiment was designed to approximate an observed freshwater inflow event. Over a 13-day period plants were subjected to three sequential salinity drops (S35→ S23→ S15→ S5) with nitrate-nitrogen added simultaneously at 0, 30 or 60 µM. For comparisons, the Control was no salinity change and no nitrate added denoted by S35/No N. Measurements of H. wrightii shoot production, photosynthesis, respiration, quantum efficiency, %N, C:N ratios and δ¹⁵N …

The Pure Yang Mills Field V2, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

We construct a pure Yang-Mills field based on a non-Abelian simple gauge group.

Axioms and the mass gap are satisfied.

Measurement Of 18f(Alpha,P)21ne With The Array For Nuclear Astrophysics And Structure With Exotic Nuclei (Anasen), Keilah S. Davis

LSU Doctoral Dissertations

Certain (α,p) reactions on proton-rich nuclei are among the most important nuclear reactions occurring during Type I X-ray bursts, as well as being impactful in stellar explosions during the Asymptotic Giant Branch (AGB) phase and on accreting white dwarfs (WD). However, large uncertainties remain for reaction rates on some radioactive nuclei due to the lack of direct measurements. The Array for Nuclear Astrophysics and Structure with Exotic Nuclei (ANASEN) is a gas target and charged-particle detector designed for direct measurements of (α,p) reactions. ANASEN was redesigned with two hexagonal barrels of 100-µm-thick silicon strip surrounding the beam axis over a …

Jiving With Llms: Assessing First Year Students’ Computer Programming Self Efficacy After Reading Code With Llms, Michelle Jarvie-Eggart, Joseph Roy Teahen, Daniel T. Masker, Jose Padilla, Leo C. Ureel Ii, Laura E. Brown, Scott Pomerville, Jon Sticken

Michigan Tech Publications

This study investigated the impact of leveraging generative artificial intelligence (GenAI) to assist 1st-year engineering and computer science (CS) students in reading code in a new (to them) language. Students were asked to comment code in FORTRAN. They were then asked to run the code through ChatGPT-4.0 for its comments and reflect on what they learned from the experience. Participants completed survey items from Ramalingam and Wiedenbeck’s Computer Programming Self-Efficacy Scale (CPSES) prior to and after the intervention. Additional open-ended reflective (qualitative) questions were added to the quantitative questions in the postintervention questionnaire. This study documents increases in self-efficacy for …