Physical Sciences and Mathematics Commons^™

An Analytical Review Of Preprocessing Techniques In Bengali Natural Language Processing, Sovon Chakraborty, Protiva Das, Shakib Mahmud Dipto, Md Aktaruzzaman Pramanik, Jannatun Noor

Computer Science Faculty Publications

Research in Bengali Natural Language Processing (BNLP) is rapidly expanding. Despite being one of the most widely spoken languages in the world, BNLP research remains insufficient, particularly in Bengali speech recognition. The languages rich morphology, agglutinative structure, and diverse dialects make text and speech processing especially challenging. However, these challenges can be addressed with effective preprocessing techniques. Various organizations in Bangladesh and West Bengal are integrating Natural Language Processing (NLP) into their services, but without a thorough understanding of preprocessing, these implementations remain incomplete. Applying proper preprocessing techniques to the Bengali language will serve as a foundation for developing robust …

Position: Benchmarking Is Broken - Don't Let Ai Be Its Own Judge, Zerui Cheng, Stella Wohnig, Ruchika Gupta, Samiul Alam, Tassallah Abdullahi, João Alves Ribeiro, Christian Nielsen-Garcia, Saif Mir, Siran Li, Jason Orender, Seyed Ali Bahrainian, Daniel Kirste, Aaron Gokaslan, Carsten Eickhoff, Pramod Viswanath, Ruben Wolff

Computer Science Faculty Publications

The meteoric rise of Artificial Intelligence (AI), with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals …

Not Here, Go There: Analyzing Redirection Patterns On The Web, Kritika Garg, Sawood Alam, Dietrich Ayala, Michele C. Weigle, Michael L. Nelson

Computer Science Faculty Publications

URI redirections are integral to web management, supporting structural changes, SEO optimization, and security. However, their complexities affect usability, SEO performance, and digital preservation. This study analyzed 11 million unique redirecting URIs, following redirections up to 10 hops per URI, to uncover patterns and implications of redirection practices. Our findings revealed that 50% of the URIs terminated successfully, while 50% resulted in errors, including 0.06% exceeding 10 hops. Canonical redirects, such as HTTP to HTTPS transitions, were prevalent, reflecting adherence to SEO best practices. Non-canonical redirects, often involving domain or path changes, highlighted significant web migrations, rebranding, and security risks. …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

A Real-Time Approach To Capture Ambient And Focal Attention In Visual Search, Gavindya Jayawardena, Yasith Jayawardana, Yasasi Abeysinghe, Bhanuka Mahanama, Sampath Jayarathna, Jacek Gwizdka

Computer Science Faculty Publications

During visual search, individuals’ attention shifts between ambient and focal states in response to task demands and stimuli. The ambient/focal coefficient K is a statistically validated measure of these states, computed offline from fixation duration and saccade amplitude data. While current methods compute K offline, real-time computation could enable applications such as monitoring user attention, creating attention-adaptive user interfaces, and optimizing graphics rendering. However, real-time computation of K requires stable estimates for the parameters of fixation duration and saccade amplitude distributions. Since these distributions are heavy-tailed, the real-time estimates exhibit high variance and slow convergence. To overcome this, we propose …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Ai For Nuclear Physics: The Exclaim Project, S. Liuti, D. Adams, M. Boër, G. W. Chern, M. Cuic, M. Engelhardt, G. R. Goldstein, B. Kriesten, Y. Li, H. W. Lin, M. Sievert, D. Sivers

Computer Science Faculty Publications

An overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Geometric Gnns For Charged Particle Tracking At Gluex, Ahmed Hossam Mohammed, Kishansingh Rajput, Simon Taylor, Denis Furletov, Sergey Furletov, Malachi Schram

Computer Science Faculty Publications

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this …

Quickque: Enabling Quick Access To Information In User Reviews For Screen Reader Users, Mohan Sunkara, Akshay Kolgar Nayak, Sandeep Kalari, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Efficiently perusing online customer reviews is presently challenging for blind users, who rely on a screen reader that supports predominantly one-dimensional narration of content via keyboard shortcuts. To address this, with restaurant reviews as seminal case study, we developed QuickCue, a browser extension prototype that enables screen reader users to quickly obtain the positives and negatives regarding different aspects of a restaurant (e.g., food quality, hygiene, ambiance), without having to sift through numerous reviews containing redundant information. At its core, QuickCue utilizes a large language model to perform aspect and sentiment-based joint classification of reviews to group them based on …

Accessmenu: Enhancing Usability Of Online Restaurant Menus For Screen Reader Users, Nithiya Venkatraman, Akshay Kolgar Nayak, Suyog Dahal, Yash Prakash, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online food ordering has become commonplace due to its convenience. The wide variety of culinary choices, combined with fast and economical door-delivery services, encourages more people to order food online. To facilitate this process, food vendors, including restaurants, often provide full menus on their websites, typically in visual formats such as images or PDFs. While this is convenient for sighted users, blind and visually impaired (BVI) individuals face significant challenges accessing these visual menus with their screen reader assistive technology. An interview study with 12 BVI screen reader users revealed that present assistive tools do not adequately satisfy the needs …

Adapting Online Customer Reviews For Blind Users: A Case Study Of Restaurant Reviews, Mohan Sunkara, Akshay Kolgar Nayak, Sandeep Kalari, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online reviews have become an integral aspect of consumer decision-making on e-commerce websites, especially in the restaurant industry. Unlike sighted users who can visually skim through the reviews, perusing reviews remains challenging for blind users, who rely on screen reader assistive technology that supports predominantly one-dimensional narration of content via keyboard shortcuts. In an interview study, we uncovered numerous pain points of blind screen reader users with online restaurant reviews, notably, the listening fatigue and frustration after going through only the first few reviews. To address these issues, we developed QuickCue assistive tool that performs aspect-focused sentiment-driven summarization to reorganize …

Decode The Workload: Training Deep Learning Models For Efficient Compute Cluster Representation, Ahmed Hossam Mohammed, Mark Jones, Diana Mcspadden, Malachi Schram, Bryan Hess, Kishansingh Rajput

Computer Science Faculty Publications

In this study, we address the mounting challenge of monitoring high throughput computing clusters running computationally intensive jobs, which increasingly strains system administrators. We develop autoencoders that analyze traces of Linux kernel CPU metrics to capture salient system features by producing robust compressed embeddings for various downstream tasks. In addition, we employ graph neural networks to incorporate contextual information from surrounding CPUs and assess their performance. We also demonstrate the enhanced job differentiation achieved by increasing the sampling rate of these traces. Our models are evaluated based on their ability to generate meaningful latent representations, detect anomalies, and distinguish between …

Adversarially Attacking Graph Properties And Sparsification In Graph Learning, Chunjiang Zhu, Blake Gaines, Jing Deng, Jinbo Bi

Computer Science Faculty Publications

Graph neural networks and graph transformers explicitly or implicitly rely on fundamental properties of the underlying graph, such as spectral properties and shortest-path distances. However, it is still not clear how these graph properties are vulnerable to adversarial attacks and what impacts this has on the downstream graph learning. Moreover, while graph sparsification has been used to improve computational cost of learning over graphs, its susceptibility to adversarial attacks has not been studied. In this paper, we study adversarial attacks on graph properties and graph sparsification and their impacts on downstream graph learning, paving the way for how to protect …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Effective Pii Extraction From Llms Through Augmented Few-Shot Learning, Shuai Cheng, Shu Meng, Haitao Xu, Haoran Zhang, Shuai Hao, Chuan Yue, Wenrui Ma, Meng Han, Fang Zhang, Zhao Li

Computer Science Faculty Publications

Large Language Models (LLMs) exhibit strong natural language processing capabilities but also pose significant privacy risks, particularly regarding the leakage of Personally Identifiable Information (PII) embedded in their training data. Existing PII extraction methods suffer from the limitations of low success rates or impracticality for large-scale PII extraction. In this study, we propose a novel PII extraction approach based on enhanced few-shot learning techniques, which achieves efficient and cost-effective PII retrieval without relying on fine-tuning or jailbreaking. We evaluated our approach on both open-source and closed-source LLMs. The experimental results demonstrate that, for non-targeted PII extraction, the attack success rate …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

Characterizing Language Use In Online Accessibility Discussion Forums, Nithiya Venkatraman, Anand Ravi Aiyer, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Discussion forums are one of the favored platforms for knowledge sharing. Given their popularity, copious research exists on understanding the linguistic and behavioral characteristics of forum conversations, so as to inform the design of many downstream applications including discourse visualization, sentiment analysis, and question answering. However, prior investigations have mainly focused on general forums designed primarily for sighted users, and as such the applicability of their findings to dedicated accessibility discussion forums frequented by blind screen reader users remains unanswered. To bridge this knowledge gap and facilitate the development of better-informed assistive technologies for blind people, we investigated language use …

Nexus: Network Exploration For Exploiting Unsafe Sequences In Multi-Turn Llm Jailbreaks, Javad Rafiei Asl, Sidhant Narula, Mohammad Ghasemigol, Eduardo Blanco, Daniel Takabi

Computer Science Faculty Publications

Large Language Models (LLMs) have revolutionized natural language processing, yet remain vulnerable to jailbreak attacks—particularly multi-turn jailbreaks that distribute malicious intent across benign exchanges, thereby bypassing alignment mechanisms. Existing approaches often suffer from limited exploration of the adversarial space, rely on hand-crafted heuristics, or lack systematic query refinement. We propose NEXUS (Network Exploration for eXploiting Unsafe Sequences), a modular framework for constructing, refining, and executing optimized multi-turn attacks. NEXUS comprises: (1) ThoughtNet, which hierarchically expands a harmful intent into a structured semantic network of topics, entities, and query chains; (2) a feedback-driven Simulator that iteratively refines and prunes these chains …

Contextual Memory Recall: A Novel Metric For Class Incremental Learning, Balasubramanian S, Sai Subramaniam M., Sai Sriram Talasu, Yedu Krishna P., Pranav Phanindra Sai M., Darshan Gera, Ravi Mukkamala

Computer Science Faculty Publications

We propose a novel metric for class incremental learning (CIL) called Contextual Memory Recall (CMR), which evaluates how well a CIL model recalls previously learned classes when given relevant past cues. Inspired by human memory, CMR offers newer insights into continual aspects of a CIL model that were not addressed by previously proposed metrics for CIL. Specifically, the standard metric, average incremental accuracy (AIA), overlooks the quality of evolving feature representations, whereas our proposed CMR accounts for it. As a result, methods using feature distillation perform well under AIA but poorly under CMR, while those without feature distillation excel under …

Applying The Matching Law To Major League Baseball (Mlb), Christopher Watkins, Vincent Berardi

Psychology Faculty Articles and Research

The application of the generalized matching equation (GME) has been detailed in a variety of sports, including football, basketball, and others. However, only a limited number of studies have focused on Major League Baseball (MLB), and they typically have examined ≤ 5 players and/or focused on a single behavior. This paper increases the generalizability of such work by using newly available, state-of-the-art data from thousands of players to explore the GME in several scenarios within three aspects of a baseball game - defense, pitching and batting. We found that the GME accurately summarized response allocation in most scenarios, with r …

Machine Learning Predicting Acute Pain And Opioid Dose In Radiation Treated Oropharyngeal Cancer Patients, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Kareem A Wahid, Dina M Elhabashy, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Ariana J Sahli, Katherine A Hutcheson, Gary Brandon Gunn, David I Rosenthal, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random …

Generalizations Of Some Q-Product Identities Of Ramanujan And Others, Timothy J. Huber, James Mclaughlin, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

By considering certain limiting cases of a WP-Bailey chain discovered by Andrews, and also limiting cases of certain classical summation formulae for basic hypergeometric series, we derive new expressions for certain Lambert series in terms of basic hypergeometric series. In some cases, the resulting series involve an arbitrary Bailey pair. This allows for the derivation of new basic hypergeometric expansions for some q-products and series that Ramanujan expressed in terms of Lambert series. Some of Ramanujan’s identities are extended to more general relations containing one or more free parameters.

Artificial Intelligence In Science And Society: The Vision Of Usern, Tommaso Dorigo, Gary D. Brown, Carlo Casonato, Artemi Cerda, Joseph Ciarrochi, Mauro Da Lio, Nicole D'Souza, Nicolas R. Gauger, Steven C. Hayes, Stefan G. Hofmann, Robert Johansson, Marcus Liwicki, Fabien Lotte, Juan J. Nieto, Giulia Olivato, Peter Parnes, George Perry, Alice Plebe, Idupulapati M. Rao, Nima Rezaei, Fredrik Sandin, Andrey Ustyuzhanin, Giorgio Vallortigara, Pietro Vischia, Niloufar Yazdanpanah

All Peer-Reviewed Publications

The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in …

Global Strategies To Manage Huanglongbing (Hlb) And Its Vectors: Insights And Implications For The Mediterranean Region, Meritxell Pérez-Hedo, Mark S. Hoddle, Fernando Alferez, Lochy Batista, George A.C. Beattie, Shourish Chakravarty, Paul Holford, Fathiya Mbarak Khamis, Inusa Jacob Ajene, Aruna Manrakhan, Paul H. Fourie, Maritza Luis, Eduardo Osorio-Hernández, Mamoudou Sétamou, Alejandro Tena, Pedro Takao Yamamoto, Tara Wade, Changyong Zhou, Nian Wang, Lukasz L. Stelinski, Alberto Urbaneja

All Peer-Reviewed Publications

Huanglongbing (HLB), also known as citrus greening disease, poses a severe threat to the global citrus industry. This disease is caused by three species of bacteria, ‘Candidatus Liberibacter asiaticus’, ‘C. L. africanus’, and ‘C. L. ameri-canus’. The primary pathogen vectors are the Asian citrus psyllid (Diaphorina citri Kuwayama, Hemiptera: Psyllidae) and the African citrus psyllid (Trioza erytreae Del Guercio, Hemiptera: Triozidae). The disease is incurable, and its management relies on reducing vector densities and the adverse effects of bacterial infections on host trees. Managing psyllid vec-tors, especially D. citri, has proven exceptionally challenging due to its rapid spread and subsequent …

Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach, Franck B.N. Tonle, Henri E.Z. Tonnang, Milliam M.Z. Ndadji, Maurice Tchoupe Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy

All Peer-Reviewed Publications

Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key …

Morphometric Characterization Of Invasive Diaphorina Citri Infesting Citrus And Orange Jasmine In Ghana, Kodwo Dadzie Ninsin, Maxwell Kelvin Billah, Bernard Agyeman Boateng, Jonathan Osei-Owusu, Fred Kormla Ablormeti, Samuel Opuni-Basoa, Aboagye Kwarteng Dofuor, George Edusei, Steve B.S. Baleba, Owusu Fordjour Aidoo

All Peer-Reviewed Publications

Morphometric analysis plays a crucial role in the accurate identification and classification of insect pests, which is essential for implementing effective management strategies. The Asian citrus psyllid (Diaphorina citri Kuwayama; Hemiptera: Liviidae), an invasive species and a known vector of ‘Candidatus Liberibacter asiaticus’ and ‘Ca. Liberibacter americanus,’ the bacterial pathogens associated with Huanglongbing (HLB), has recently emerged as a pest of concern in Ghana. Despite its significance, little is known about the biological traits of field populations in the region. In this study, we examined morphometric variation in D. citri populations infesting citrus and orange jasmine (Murraya paniculata) across different …