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Instructions To Authors, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Keynote Address And Meeting Program, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Business Meeting Report (Secretary's And Treasurer's Report), Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

A Review Of Herpetofaunal Distributions In Arkansas With Special Emphasis On Contributions From Citizen Science: Part Ii – Turtles, Lizards, And Alligators, L. Hobbs, E. Kelley, J. Jackson, J.D. Willson, K. G. Roberts, R. Kannan

Journal of the Arkansas Academy of Science

We update and present a comprehensive review of the distributions of the species of lizards (14), alligators (1), and turtles (16) in Arkansas. We compare historical and current distributions with citizen science data uploaded and verified in iNaturalist, a leading online citizen science biodiversity monitoring database. The Arkansas Herpetological Atlas, an online resource maintaining species occurrence records, is also used and compared. We highlight the role played by iNaturalist in enhancing our knowledge of the distributions of these species in Arkansas. As of 13 February 2025, verified and vetted Research Grade (RG) iNaturalist observations from Arkansas include 7,462 observations of …

Analysis Of Coal Seam Water Sources Based On Hydrochemistry And Water Isotopes, Wei Dong, Wang Hao, Wang Chungang, Wang Xiaodong, Liu Wei, Wang Qiangmin, Liu Quanhui, Zhou Zhenfang, Wang Yutong

Coal Geology & Exploration

Background The northern Yuheng mining area exhibits the widespread occurrence of water-bearing coal seams. This unique hydrogeological phenomenon poses a serious challenge to efficient mine roadway construction and safe coal mining in the mining area. The recharge sources of the coal seam water remain poorly understood, representing a primary bottleneck in the prevention and control of coal seam water hazards. Methods This study aims to reveal the recharge sources and evolutionary mechanisms of coal seam water in the northern Yuheng mining area. By integrating methods including hydrochemical analysis, isotope analysis, and inverse hydrogeochemical simulation, this study systematically investigated the hydrogeochemical …

Advances In Research On Methods For Intelligent Identification Of Seismic Facies, Liu Xingye, Yu Peilin, He Hengjun

Coal Geology & Exploration

Background The intelligent identification of seismic facies can significantly improve the efficiency of sedimentary system characterization and hydrocarbon reservoir interpretation. However, influenced by factors such as non-stationary geological bodies, high costs of sample labeling, and limited training samples, conventional methods for intelligent identification are generally insufficient to achieve high identification accuracy and widespread application concurrently. Advances This study presents a systematic review of three types of technologies for the intelligent identification of seismic facies, namely unsupervised, supervised, and semi-supervised learning, with each type including deep learning methods. The three technological types are comparatively verified using 3D seismic data from a …

A Method For 3d Model Reconstruction Of Large-Diameter Rescue Wells Based On Multiple Cameras, Gu Hairong, Wang Boyang, Yang Wenjuan, Tong Yubo, Wang Jiaxi, Sun Lishun

Coal Geology & Exploration

Objective Surface drilling for mine rescue represents a critical technique in the emergency rescue system against mine disasters. To accurately assess the passability of large-diameter rescue wells during rescue operations, it is essential to build precise 3D models of the rescue wells for rapid reconstruction of rescue scenarios. Methods Based on the theory of multi-camera-based 3D model reconstruction, this study investigated the method of arranging four Intel D435i cameras within a large-diameter rescue well. Accordingly, a multi-camera-based 3D model reconstruction system for a large-diameter rescue well was established, involving the joint calibration of the camera array and the selection of …

Methods For Assessing The Circulation Depth Of Geothermal Water In A Moderate- To Low-Temperature Convective Geothermal System And Their Applications, Li Yiman, Xia Haoran, Gao Binbin, Sun Wenjie, Pang Zhonghe

Coal Geology & Exploration

Background The circulation depth of geothermal water is an important parameter for investigating geothermal systems or fields. It determines the thermal energy intensity and mineral composition of geothermal water, emerging as a critical factor influencing the efficient and sustainable development and utilization of geothermal resources.Progress This study presents a summary of the characteristics of typical moderate- to low-temperature convective geothermal systems from the perspective of sources (heat and fluid sources), pathways, geothermal reservoirs, cap rocks, and the geothermal water upwelling process and its impacts. Furthermore, this study systematically reviews suitable methods for assessing the circulation depth of geothermal water, …

Algorithmic Monocultures In Hiring, Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Saul Jurafsky, Percy Liang

Economics Faculty Articles and Research

Many employers screen job applicants with algorithms built by the same few algorithm vendors. We hypothesize that algorithmic monoculture leads to the same individuals and members of the same racial groups facing rejection. We acquire and analyze a novel dataset of 3 million applicants submitting 4 million applications where all the applications are screened by algorithms built by the same vendor. We find clear racial disparities in applicant outcomes. Of all applications submitted by Asian and Black applicants, 14.74% and 25.87% are submitted to positions that adversely impact Asian and Black applicants, respectively, according to U.S. employment discrimination standards. Individuals …

Research On Optimization Modeling Method For Eye Tracking In Solfeggio Cognitive Simulation, Kun Zhang, Jiajie Qian, Shuhong Ma, Zengxu Zhao, Yuchen Pan, Yaoqi Tang

Journal of System Simulation

To address the fixation offset problem caused by head movement in music solfeggio teaching simulation and the lack of system-level simulation validation in existing methods, this paper proposed a fixation accuracy optimization method integrating image semantic understanding, temporal trajectory modeling, and solfeggio cognitive simulation. With Vision Transformer as the core, after preprocessing via Mahalanobis distance, sliding window, and region of interest, position offset perception, offset residual regression, and dual-pathway fusion were introduced to achieve offset modeling and correction under unlabeled conditions. Simulation results indicate that the error of this method decreases by 43.9% compared with the original value error; removing …

Recent Advances In Research On Intelligent Technologies And Equipment For Underground Geological Exploration Of Coal Mines In China, Zhang Jianming, Li Quanxin, Liu Fei, Fang Jun

Coal Geology & Exploration

Background Intelligent coal mine construction represents an important measure to promote the high-quality development of the coal industry. Developing intelligent technologies and equipment for geological exploration is the key to both transparent geological guarantee and the efficient prevention and control of major disasters in coal mines. Advances This study presents a systematic analysis of the latest advances in intelligent technologies and equipment for underground geological exploration of coal mines in China, focusing on intelligent drilling and geophysical prospecting. In terms of intelligent drilling, this study introduces intelligent drilling rigs equipped with multiple functions, including adaptive control, remote monitoring, and fault …

Lafe0.925ti0.075o3 Perovskite Coated With Ethyl Cellulose: A High-Stability, Low-Hysteresis Platform For Advanced Humidity Sensing, Akhmad Futukhillah Fataba Alaih, Djoko Triyono, Rifqi Almusawi Rafsanjani

Makara Journal of Science

Humidity sensors are pivotal in numerous sectors, such as environmental monitoring, industrial automation, and health-care. This study presents the fabrication and detailed evaluation of an advanced humidity sensor using ethyl cellulose (EC)-coated LaFe_0.925Ti_0.075O₃ (LFTO) perovskite nanoparticles synthesized via the sol-gel method. The LFTO nanopar-ticles exhibited a mesoporous structure with an enhanced surface area, which significantly improved their moisture adsorption capabilities. The innovative application of EC coating addresses critical substrate adhesion issues, enhancing mechanical stability without compromising sensor sensitivity. Comprehensive performance evaluations revealed minimal hysteresis (

Building Trustworthy Information Systems: A Unified Framework For Comparative Risk Detection, Parisa Momeni

USF Tampa Graduate Theses and Dissertations

Risk detection in large scale information systems increasingly depends on heterogeneous data generatedby both centralized and distributed ecosystems. While centralized systems provide curated and validated reports, distributed environments produce large-scale and real-time observational evidence. Existing computational approaches analyze these ecosystems in isolation, limiting systematic comparison of risk repre-sentations across heterogeneous sources.

This dissertation presents a unified computational framework for comparative risk detection across centralized and distributed information systems. The framework provides a domain independent methodology for transforming heterogeneous risk reporting data into comparable multidimensional representations. To enable interpretable comparison of heterogeneous risk distributions, this work introduces the Geometric Overlap Score …

Seeing Around Corners: An Indirect Light Transport Decomposition Framework For Fusing Physics And Learned Priors, Fadlullah Raji

USF Tampa Graduate Theses and Dissertations

Non-line-of-sight (NLOS) imaging, or seeing around corners, is the ability to recover information about objects hidden from direct view and remains one of the most compelling challenges in computational imaging. Existing approaches fall broadly into two categories: active and passive. Active time-resolved methods achieve impressive performance but require specialized pulsed laser hardware and ultrafast detectors, making them expensive and limited by slow measurement acquisition. Passive NLOS methods, which instead exploit the subtle soft shadows (penumbrae) cast by a hidden scene onto a visible matte surface, have recently emerged as a practical alternative. However, existing passive approaches are largely limited to …

Portable Trace Gas Analysis With Multipass Cavity Raman Scattering, Charuka Muktha Arachchige

USF Tampa Graduate Theses and Dissertations

The continual increase in the production and utilization of chemicals across a growing range of industrial, environmental, and medical applications necessitates accurate and efficient measurement of molecular composition. Trace gas detection is particularly critical in industrialized settings, where the identification of colorless and odorless gases is essential for ensuring operational safety and regulatory compliance. Recent advances in optical instrumentation have positioned spontaneous Raman scattering as a promising analytical technique for trace gas analysis, with multipass cavity enhancement providing a cost-effective pathway to high detection sensitivity. However, existing implementations remain confined to laboratory-scale optical table setups, limiting their applicability in field …

Non-Euclidean Geometries And Fairness Constraints In Advanced Clustering, Arnab Seal

Master’s Dissertations

A fundamental challenge in modern unsupervised learning is adapting classical clustering algorithms to handle complex, real-world data constraints. Traditional models often assume data resides in a flat, Euclidean space and optimize strictly for cluster cohesion, thereby failing to capture intrinsic hierarchical structures and ignoring sociotechnical demographic biases. This thesis addresses these critical limitations by extending generalized mean-shift dynamics into two novel clustering frameworks. First, to natively accommodate data with tree-like structures (e.g., taxonomies and social networks), we propose Hyperbolic Gaussian Blurring Mean Shift (HypeGBMS). By projecting data into the Poincar´e ball model and utilizing M¨obius vector space operations, HypeGBMS successfully …

Leveraging Spatial Statistics For Domain Adaptation Of Vision Language Models In Medical Vqa, Himanshu Raj

Master’s Dissertations

Recent advances in Vision–Language Models (VLMs) have demonstrated strong performance in Medical Visual Question Answering (Medical VQA) task. Although they perform very well within their domains, these models often experience issues with their generalization ability on unknown clinical distribution data because of different imaging technologies and patient groups used in various medical facilities. Generalization problems faced by these models make their practical application in the field of VLM-based medical VQA systems rather difficult. To overcome this limitation we proposed our method named Spatial Semantics Aware Domain Adaptation (SSADA), which is an integrated framework that combines both finetuning and prompt-based in-context …

Risk-Aware Deep Reinforcement Learning In Portfolio Optimization, Shimin Zhang

USF Tampa Graduate Theses and Dissertations

Portfolio optimization is important in investment, with the purpose of maximizing profit whilemanaging risk. The traditional portfolio optimization method, Modern Portfolio Theory, is based on the assumption that returns are normally distributed, which is always violated in practical financial market settings. In addition, traditional approaches to portfolio optimization are mostly designed as static optimizations and fail to account for the nonlinearity and non-stationarity of contemporary financial markets. Recent developments in deep reinforcement learning have attracted considerable attention for treating portfolio optimization as a sequential decision-making process. Traditional portfolio optimization methods usually evaluate risk based on the variance of the returns. …

Movement Ecology And Population Dynamics Of Box-Nesting Black-Bellied Whistling-Ducks (Dendrocygna Autumnalis) In Louisiana, Katie E. Miranda

LSU Master's Theses

Understanding demographic and behavioral processes underlying population dynamics is essential for effective wildlife management, especially for species subject to harvest pressure or experiencing population shifts. Black-bellied Whistling-Ducks (Dendrocygna autumnalis) are currently undergoing a notable northward range expansion and are increasing in abundance across the southeastern United States, raising management concerns for both ecological and economic reasons. As a result, there is growing public support for a special Black-bellied Whistling-Duck-specific harvest season, yet little research has been conducted on this species, particularly within newly-established populations. To address this gap, I conducted this study with the goal of evaluating seasonal …

Learning Trajectories Of Online Batch Selection Methods, Luke Green

Theses and Dissertations

Modern deep neural networks achieve strong performance on large-scale datasets, but often require substantial training time. Online batch selection methods seek to reduce this cost by updating models on informative subsets of each batch rather than on all available examples. Recently introduced methods leverage teacher models and report substantial speedups, particularly in noisy-label settings. However, comparisons are often based on the number of epochs required to reach a target test accuracy, a coarse metric that is sensitive to implementation details and may obscure important differences in learning dynamics. In this thesis, we implement several online batch selection methods in a …

Beyond Single Images: A Comprehensive Benchmark For Album-Level Vision-Language Understanding, Shawn Huang

Theses and Dissertations

Automatic album organization has been studied extensively over the past decades due to significant progress in digital photography. Recent Vision-Language Models (VLMs) have shown strong performance on multi-image understanding, making them natural candidates for automating album organization workflows. While VLMs’ abilities in multi-image understanding have been widely studied, their performance on album organization remains underexplored. To bridge this gap, we introduce AlbumBench, the first comprehensive benchmark for automatic album organization. Specifically, we (1) define album organization tasks as photo selection for album-specific user objectives, photo rating according to how well user intents are fulfilled, and album-specific photo grouping given a …

To What Extent Could Quantum Computing Pose A Threat To Global Modern Data Security?, Aniket Maheshwari

Journal of Cybersecurity Education, Research and Practice

Quantum computing has emerged as a transformative technology with the potential to fundamentally disrupt modern cryptographic systems that underpin global data security. This paper examines the extent to which quantum computing could pose a threat to modern global data security by synthesising existing technical, institutional, and policy-oriented literature. Drawing on a narrative review of scholarly research, industry reports, and government frameworks, the analysis focuses on the implications of quantum algorithms such as Shor’s and Grover’s, which challenge the mathematical foundations of widely used cryptographic schemes. The findings suggest that while quantum computing presents a credible long-term threat to asymmetric encryption …

Label-Flip Attack Detection Via Trust-Weighted Aggregation In Federated Learning For Underground Mine Security, Md Sazedur Rahman, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining operations are increasingly dependent on autonomous vehicles, robotic drilling systems, and intelligent inspection platforms operating in confined, GPS-denied tunnel environments. These systems rely on distributed perception models to interpret navigation cues, hazard warnings, and environmental signals in real time. While centralized deep learning can enhance model performance, transferring raw operational data across mining sites introduces serious confidentiality and security risks. Federated Learning (FL) offers a privacy-preserving alternative by enabling collaborative model training without sharing local datasets. However, deploying FL in underground mining introduces several critical challenges: (i) Training labels may be modified either maliciously by compromised clients or …

Van Der Waals Interactions Through The Lens Of 2d Materials With Emphasis On Many-Body Effects, Thi Xuan Diem Dang

USF Tampa Graduate Theses and Dissertations

This dissertation addresses the long-standing challenge of accurately and efficiently modeling long-range electron correlations, with a particular focus on van der Waals (vdW) interactions. These interactions play an essential role in predicting the structure, stability, and functionality of molecular systems and condensed matter materials. Although Density Functional Theory (DFT) has become a robust cornerstone of computational materials science, traditional formulations of the theory frequently fail to capture dispersive interactions, leading to significant errors in determining interlayer distances, binding energies, and stacking configurations. These limitations become particularly pronounced in layered materials and vdW heterostructures (HSTs), where weak long-range correlations are sensitive …

Re: Conditional Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Clark Tailings Consolidated Waste Management Area (Ctcwma) Site Investigation Quality Assurance Project Plan (Qapp) (Dated June 1, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Integration Of Intraoperative Data In Interpretable Machine Learning Models To Predict Postoperative Aki In Noncardiac Surgery Patients, Justin Do, Karan H. Shah, Melissa Xu, Andrew Hyunwoo Kim, Vivaswat Suresh, Nidhir Guggilla, Michael Li, Rishi Kothari

Department of Anesthesiology Faculty Papers

OBJECTIVES: We aimed to (1) quantify changes in discrimination when adding intraoperative data to preoperative data and (2) compare tabular machine learning with feature engineering against a time-aware LSTM-based model.

MATERIALS AND METHODS: Retrospective cohort of 46 204 adults undergoing 57 055 eligible noncardiac surgery in the INSPIRE database. We extracted 38 preoperative and 49 intraoperative variables; acute kidney injury (AKI) was defined by KDIGO serum creatinine criteria and modeled as stage 2/3 postoperative AKI. Models were trained on preoperative-only and combined pre- and intraoperative data. Intraoperative series were summarized using eight statistical features for tabular models or integrated directly …

Vibrations Of Tapered Beam Via The Exterior Matrix Method, Simranjit Kaur

Student Theses and Dissertations

Cell phone towers, utility poles, and traffic signal poles all use hollow, tapered beams as their main structural element. Because these structures are tall and exposed to wind forces, understanding their vibration behavior is important for ensuring stability and safety. We will use the Exterior Matrix Method to analyse a single beam, which can be used to analyse compound structures involving tapered beams. First, we find the system of four equations satisfied by the tapered beam, which can be converted to a 4 x 4 matrix. Then we find the exterior matrix, which is a 6 x 6 matrix, corresponding …

American Sign Language Recognition And Analysis Using Deep Learning, Saurabh Kumar Soni

Master’s Dissertations

In this work I build a system that recognizes isolated American Sign Language (ASL) words, and I use it to ask one fairly direct question: when training data is scarce, is it better to look at the video pixels or at the geometry of the signer’s body? To find out, I train two very different models on exactly the same clips. The first is appearance-based. Every frame is run through standard preprocessing and a ResNet50 backbone pre-trained on ImageNet, which turns it into a 2048-dimensional feature vector, and a Bidirectional LSTM then reads that sequence over time. The second model …

Enhanced Embedding For Multimodal Medical Visual Question And Answering, Akash Suna

Master’s Dissertations

Visual Answering of questions in the field of Medical which is called as (VqA) has grown as a dominant area of research that fuse processing of natural language and vision of computer often known as CV or NLP to assist in medical decision-making. However, effective multimodal fusion between medical images and clinical questions remains a significant challenge. This thesis examines the application of the Perceiver IO architecture as an efficient multimodal aggregator for medical VQA. The work has been carried out in multiple directions. First, a classification-based framework is developed by combining Vision Transformer (ViT) and ClinicalBERT alongside a Perceiver …

An Empirical Study Of Rlvr Fine-Tuning For Mathematical Problem Solving In Llms, Rashmi Konnur

Master’s Dissertations

Large language models have shown immense improvement in coding and math performances thanks to reinforcement learning boosted algorithms. However, its true impact on broadening the reasoning and analytical capacities of an LLM is still contended. In this dissertation, we outline the foundations of Large Language Models, and delve into Reinforcement Learning with Verifiable Rewards (RLVR). We discuss various strategies to efficiently manipulate memory during a fine tuning update. We finally perform RLVR fine-tuning techniques on different models with varied use cases and compare their performances, which corroborate the efficiency of RLVR.