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Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch Jan 2026

Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article studies the practical predefined-time synchronization (PPTS) for complex networks (CNs) under deception attacks based on the asynchronously intermittent event-triggered control (AIE-TC). Notably, AIE-TC effectively integrates the advantages of asynchronously intermittent control (AIC) and event-triggered control, where AIC provides each subsystem node with independent control and rest intervals. Besides, all synchronization errors of the CNs converge to an adjustable neighborhood within the predefined time by designing a bounded time-varying function into the controller. Moreover, this article considers that the transmission network is subjected to stochastic deception attacks modeled by a Markov process, which captures the state-driven dynamic transition characteristics …

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Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei Jan 2026

Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Controlling CO2 channeling in heterogeneous reservoirs remains a major challenge for both enhanced oil recovery (EOR) and secure geological storage. AMPS-HPAM copolymers exhibit high-temperature resistance and brine tolerance compared with conventional HPAM gels, making them well suited for the harsh environments associated with CO2 injection. Chromium-based crosslinkers (CrAc and CrCl3) were investigated because sulfonic acid groups in AMPS can coordinate with trivalent chromium ions, enabling dual ionic crosslinking and the formation of a robust gel network. While organic crosslinked AMPS-HPAM gels have been widely studied, the behavior of chromium-crosslinked AMPS-containing systems, particularly their gelation kinetics under …

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Effect Of Ionic Strength On Gelation Time And Strength Of Amps-Based Polymer Gels, Maryam Sharifi Paroushi, Xuyang Tian, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei Jan 2026

Effect Of Ionic Strength On Gelation Time And Strength Of Amps-Based Polymer Gels, Maryam Sharifi Paroushi, Xuyang Tian, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Polymer gel treatment has been widely applied for improving sweep efficiency and controlling excessive water and gas production. Their performance depends on gelation time and final gel strength. In most studies, brine salinity is used to describe the effect of formation water on gel behavior. However, changing salinity also changes ionic strength and ion composition at the same time. Because of this coupling, it is difficult to identify the mechanisms controlling gelation, which has led to inconsistent trends in the literature. Increasing salinity has been reported to either slow or accelerate gelation and to weaken or strengthen gels depending on …

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Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao Jan 2026

Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address the limited solubility and applicability of conventional hydrocarbon surfactants in supercritical CO2, a series of multi-ester headgroup surfactants were designed and synthesized by leveraging the CO2-philic properties of ester groups. The molecular structures were characterized using Fourier transform infrared (FT-IR) spectroscopy and 1H NMR. A custom-designed laser-based apparatus was developed to quantify surfactant solubility and systematically investigate phase behavior in CO2. Molecular dynamics (MD) simulations were employed to elucidate structure–solubility relationships across multiple scales, including solubility parameters, interaction energies, radial distribution functions (RDFs), and free volume fractions. Results indicate that, at 323.15 K, …

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Pahina: Precision-Aware Hierarchical In-Network Aggregation For Edge Distributed Training, Yingpu Nian, Bo Yi, Qiang He, Xingwei Wang, Geyong Min, Keqin Li, Sajal K. Das Jan 2026

Pahina: Precision-Aware Hierarchical In-Network Aggregation For Edge Distributed Training, Yingpu Nian, Bo Yi, Qiang He, Xingwei Wang, Geyong Min, Keqin Li, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To …

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Fairrfl: Fair And Robust Federated Learning In The Presence Of Selfish Clients, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das Jan 2026

Fairrfl: Fair And Robust Federated Learning In The Presence Of Selfish Clients, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a paradigm that enables collaborative machine learning without disclosing the local data of the participants. However, in real-world FL deployment scenarios, some unscrupolous clients may alter the training process to skew the global model towards their local optimum, unfairly prioritizing their data distribution. Their influence can degrade overall model performance for normal clients and reduce fairness in the system. We call this novel category of misbehaving clients 'selfish'. This work proposes a Fair and Robust strategy for aggregation in the Federated Learning (FL) server to mitigate the effect of Selfish clients (FairRFL). FairRFL incorporates a novel …

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Future Mining: Learning For Safety And Security, Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria Jan 2026

Future Mining: Learning For Safety And Security, Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria

Computer Science Faculty Research & Creative Works

Mining industry is rapidly transforming into an AI-driven cyber-physical ecosystem where safety and operational reliability depend on robust perception, resilient communication, trustworthy distributed intelligence and continuous monitoring of miners and equipment. Real-world mining environments impose severe constraints like poor illumination, dust, occlusion, GPS-denied conditions, irregular underground topologies, and intermittent connectivity. These factors degrade perception quality, disrupt situational awareness, impair trajectory prediction and weaken the reliability of distributed learning systems. Emerging cyber-physical threats, including backdoor triggers, sensor spoofing, label-flip attacks and poisoned model updates, further jeopardize operational safety, particularly as mines increasingly adopt autonomous vehicles, humanoid assistance, and federated learning for …

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Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman Jan 2026

Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which …

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Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman Jan 2026

Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network …

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Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das Jan 2026

Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the identities of the participants, FL may attract adversaries in order to hamper the underlying model. In this paper, we propose an FL framework, FedDOT, to defend against adversaries performing targeted attacks. FedDOT incorporates two powerful defense algorithms, Maximum Spanning Tree based attacker detection (MSTAD) and Densest graph-based attacker detection (Density-AD), which leverage correlation between weight updates and graph …

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You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin Jan 2026

You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image …

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Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das Jan 2026

Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes …

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Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma Jan 2026

Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma

Computer Science Faculty Research & Creative Works

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive …

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Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang Jan 2026

Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang

Computer Science Faculty Research & Creative Works

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …

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Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong Jan 2026

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …

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Aim5b: Ai Integrated Semantic Framework For 5g And Beyond Network Management, Thanveer Sulthana, Ava Sharif Jourabchi, Venkat Rao Manavarthi, Jayadithya Nalajala, Ankitha Srirama Reddy, Baek Young Choi, Sejun Song Jan 2026

Aim5b: Ai Integrated Semantic Framework For 5g And Beyond Network Management, Thanveer Sulthana, Ava Sharif Jourabchi, Venkat Rao Manavarthi, Jayadithya Nalajala, Ankitha Srirama Reddy, Baek Young Choi, Sejun Song

Computer Science Faculty Research & Creative Works

Scalable, interpretable, and intelligent network monitoring and management are critical for 5 G and future networks. This paper introduces Aim5B, an AI-integrated semantic framework for 5 G and beyond network management to address these challenges. Aim5B processes unstructured logs from key 5G core network functions, and transforms them into a knowledge graph aligned with the semantic structure of control-plane events. Leveraging a large language model (LLM), Aim5B enables natural language queries to be translated into Cypher graph queries, facilitating precise log retrieval, event analysis, temporal correlation, and statistical summarization-without relying on static parsing rules or predefined dashboards. Integrated on a …

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Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das Jan 2026

Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das

Computer Science Faculty Research & Creative Works

Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In realworld scenarios, these networks are often large and dynamic, introducing significant computational challenges. Due to the absence of specialized software packages and data structures, the analysis of large dynamic hypergraphs remains largely unexplored. Motivated by this gap, we propose ESCHER, a GPU-centric parallel data structure for Efficient and Scalable Hypergraph Evolution Representation, designed to manage largescale hypergraph dynamics efficiently. We also design …

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Evaluating The Use Of Machine Learning For Road Maintenance Cost Estimation, Shahla Shirinzad, David Enke Jan 2026

Evaluating The Use Of Machine Learning For Road Maintenance Cost Estimation, Shahla Shirinzad, David Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Accurate estimation of highway maintenance costs is essential for efficient resource allocation and long-term infrastructure sustainability. This study evaluates the effectiveness of advanced machine learning models, including ResNet, Transformer, and other neural network architectures, for forecasting maintenance costs using historical data from the Highway Maintenance Improvement Program (HMIP) provided by the North Carolina Department of Transportation (NC DOT). A comprehensive comparative analysis is conducted across multiple models using standard performance metrics, including R2, MAE, RMSE, and MSE. The results demonstrate that advanced architectures, particularly ResNet and Transformer, consistently outperform traditional statistical approaches and baseline machine learning models, achieving …

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Digital Twin Freshness Maximization In Edge Computing, Jing Li, Jianping Wang, Weifa Liang, Quan Chen, Sajal K. Das, Xiaohua Jia Jan 2026

Digital Twin Freshness Maximization In Edge Computing, Jing Li, Jianping Wang, Weifa Liang, Quan Chen, Sajal K. Das, Xiaohua Jia

Computer Science Faculty Research & Creative Works

Mobile Edge Computing (MEC) shifts powerful computing resource provisioning from remote powerful data centers to the edge of core networks. Meanwhile, Digital Twin (DT) has surfaced as a promising technology to provide comprehensive and dynamic descriptions of physical objects in cyberspace with bidirectional and real-time interactions. Moreover, Internet of Things (IoT) devices have contributed abundant, heterogeneous and continuous data from interconnected devices to the explosion of DTs. With technologies evolution, there is an increasing necessity to address the freshness of both DT states and DT data, through timely synchronizations between DTs and their objects in a highly dynamic IoT environment. …

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On-Device Artificial Intelligence Solutions With Applications To Smart Environments, Fabrizio De Vita, Dario Bruneo, Sajal K. Das Jan 2026

On-Device Artificial Intelligence Solutions With Applications To Smart Environments, Fabrizio De Vita, Dario Bruneo, Sajal K. Das

Computer Science Faculty Research & Creative Works

Recent advances in Artificial Intelligence (AI) and the increasing availability of computational power have accelerated the diffusion of Intelligent Cyber-Physical Systems (ICPSs), enabling smart applications with reasoning capabilities. However, the limited resources of embedded and Edge devices significantly constrain the complexity of deep learning models that can be effectively deployed. Traditional approaches rely on cloud-based training and edge-only inference, a paradigm that becomes inadequate when low latency, privacy, security, and high customization are required. In this context, On-device AI is emerging as a new paradigm in which both training and inference are performed directly on the device, avoiding data transfer …

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Concerted Proton Transfer In Homogeneous And Heterogeneous Cyclic Hydrogen-Bonded Clusters Of H2o, Hf, And Hcl, Max R. Tucker, Yuan Xue, Nathan R. Speake, Eden Nickolson, Jeremy M. Carr, Gregory S. Tschumper Jan 2026

Concerted Proton Transfer In Homogeneous And Heterogeneous Cyclic Hydrogen-Bonded Clusters Of H2o, Hf, And Hcl, Max R. Tucker, Yuan Xue, Nathan R. Speake, Eden Nickolson, Jeremy M. Carr, Gregory S. Tschumper

Chemistry Faculty Research & Creative Works

This work examines various homogeneous and heterogeneous clusters of hydrogen-bonded (HF)x(H2O)y(HCl)z complexes, where x + y + z = 3 or 4. For each unique cyclic structure associated with the xyz permutations, the geometries of the reactant, product, and transition state (TS) for concerted proton transfer (CPT) were fully optimized using second-order Møller-Plesset perturbation theory (MP2) with a mixed basis set consisting of cc-pVTZ for H atoms and aug-cc-pVTZ for O, Cl, and F atoms (denoted haTZ). Harmonic vibrational frequencies were also computed at the same level of theory to verify the nature of the stationary points. Intrinsic …

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Volatile Organic Compound Analysis Of Humboldt Penguin (Spheniscus Humboldti) Preen Oil: A Pilot Study, Dante E. Rojas, Mitchell M. Mccartney, Eva Borras, Michael O. Eze, Abigail Pietrow, Jennifer J. Valvo, Cristina E. Davis Jan 2026

Volatile Organic Compound Analysis Of Humboldt Penguin (Spheniscus Humboldti) Preen Oil: A Pilot Study, Dante E. Rojas, Mitchell M. Mccartney, Eva Borras, Michael O. Eze, Abigail Pietrow, Jennifer J. Valvo, Cristina E. Davis

Chemistry Faculty Research & Creative Works

Avian olfaction has gained prominence in recent decades for its roles in social communication and behavior. In penguins, the chemical characterization of preen oil remains limited. In this exploratory study, we characterized the volatile organic compound (VOC) profile of preen oil from Humboldt Penguins (Spheniscus humboldti). Preen oil from 12 captive individuals was analyzed using headspace sorptive extraction (HSSE) coupled to gas chromatography–mass spectrometry (GC–MS). Chemometric analyses examined variation in VOC profiles by sex, age class, and breeding activity. We detected 54 compounds (20 tentatively identified), including linear alcohols, methyl ketones, carboxylic acids, saturated hydrocarbons, oxygenated and nitrogen-containing organics, prenolipids, …

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Synthesis Of The Naga(S1−Xsex)2 Solid Solution From Mechanically Activated Precursors, Louisiane Verger, Julien Trébosc, Santhoshkumar Sundaramoorthy, Amitava Choudhury, Olivier Hernandez, Eric Furet, Sébastien Chenu, David Le Coq, Laurent Calvez, Olivier Lafon Jan 2026

Synthesis Of The Naga(S1−Xsex)2 Solid Solution From Mechanically Activated Precursors, Louisiane Verger, Julien Trébosc, Santhoshkumar Sundaramoorthy, Amitava Choudhury, Olivier Hernandez, Eric Furet, Sébastien Chenu, David Le Coq, Laurent Calvez, Olivier Lafon

Chemistry Faculty Research & Creative Works

NaGaS2 and NaGaSe2 are two recently discovered compounds that crystallize in the same structure type. In this work, NaGa(S1−xSex)2 (x = 0.5, 0.75 and 1) are prepared using an alternative synthesis route, mechanochemistry followed by heat treatment. Na2S, Na2Se, Ga2S3 and Ga2Se3 are milled in stoichiometric proportions. Differential scanning calorimetry, X-ray diffraction and solid-state nuclear magnetic resonance (23Na and 71Ga) show that the compounds after milling are composed of crystalline NaGa(S1−xSex)2 with an amorphous part. …

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Soft-Chemical Scalable One-Pot Aqueous-Medium Synthesis Of Na3(Vo)2(Po4)2f-Based Cathodes: Compositional Tuning And Electrochemical Performance In Na- And Li-Ion Batteries, Prashanth Sandineni, Subal Chandra Manna, Sutapa Bhattacharya, Santhoshkumar Sundaramoorthy, Ramesh Deokate, Milad Aghayi-Anaraki, George E. Sterbinsky, Kartik Ghosh, Amitava Choudhury Jan 2026

Soft-Chemical Scalable One-Pot Aqueous-Medium Synthesis Of Na3(Vo)2(Po4)2f-Based Cathodes: Compositional Tuning And Electrochemical Performance In Na- And Li-Ion Batteries, Prashanth Sandineni, Subal Chandra Manna, Sutapa Bhattacharya, Santhoshkumar Sundaramoorthy, Ramesh Deokate, Milad Aghayi-Anaraki, George E. Sterbinsky, Kartik Ghosh, Amitava Choudhury

Chemistry Faculty Research & Creative Works

One of the most important cathode materials for Na-ion batteries, Na3(VO)2(PO4)2F, and its compositional variants have been synthesized using four different and facile one-step soft chemical routes. The as-synthesized compounds, Na2.95(VO)2(PO4)2F (I), Na2.93(VO)2(PO4)2F (II), and Na2.58(VO)2(PO4)2F (IV), crystallize in the tetragonal crystal system in the P42/mnm space group, while Na3(VO)2(PO4)2F (III) crystallizes in the orthorhombic crystal system in …

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Real-Time Likelihood Map Generation To Localize Short-Duration Gamma-Ray Transients, Jeremy Buhler, Marion Sudvarg Dec 2025

Real-Time Likelihood Map Generation To Localize Short-Duration Gamma-Ray Transients, Jeremy Buhler, Marion Sudvarg

Computer Science Faculty Research & Creative Works

High-energy transient astrophysical phenomena, such as supernovae and binary neutron star mergers, benefit from a multi-wavelength investigation in which a space- or balloon-based omnidirectional telescope detects and localizes early high-energy emissions (such as a gamma-ray burst), then alerts a narrow-field follow-up instrument to observe the source. The high-energy telescope must provide a map that assigns to each sky location a likelihood that the source appears there. To issue prompt alerts despite limits on communication bandwidth and latency, it is desirable to compute this map aboard the high-energy telescope, but doing so requires rapid response while computing under stringent size, weight, …

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Instrument Response Functions Of The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Wenlei Chen, James H. Buckley, Marion Sudvarg Dec 2025

Instrument Response Functions Of The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Wenlei Chen, James H. Buckley, Marion Sudvarg

Computer Science Faculty Research & Creative Works

The Antarctic Demonstrator for the Advanced Particle-astrophysics Telescope (ADAPT) gamma-ray/cosmic-ray instrument serves as a precursor to the proposed APT mission. The APT mission is designed to improve sensitivity in the MeV-TeV gamma-ray range by an order of magnitude compared to current missions and is optimized for dark-matter and multimessenger research. The ADAPT instrument uses scintillating fibers for particle tracking and sodium-doped cesium iodide (CsI:Na) tiles read out with wavelength shifting (WLS) fibers for imaging, with solid-state silicon photomultipliers (SiPMs) for calorimetry. It includes four layers of imaging calorimeter detectors and scintillating-fiber trackers, functioning both as a Compton and Pair telescope …

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Performance Modeling And Improvements On The Grb Source Localization Streaming Pipeline Aboard The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Ye Htet, Ye Htet, Marion Sudvarg, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, Wenlei Chen, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler, Eric Burns Dec 2025

Performance Modeling And Improvements On The Grb Source Localization Streaming Pipeline Aboard The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Ye Htet, Ye Htet, Marion Sudvarg, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, Wenlei Chen, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler, Eric Burns

Computer Science Faculty Research & Creative Works

The Advanced Particle-astrophysics Telescope (APT) is a mission concept for a space-based gamma-ray telescope whose capabilities include prompt localization of gamma-ray bursts (GRBs) to support multi-wavelength and multi-messenger astrophysics. ADAPT — APT's balloon-borne prototype — can localize GRBs in well under a second using on-board computing hardware. ADAPT will partner with ground-based, fast-slewing optical telescopes, rapidly providing alerts that enable the partner to observe a short-duration burst within a few seconds of detection. In this work, we investigate the utility of having ADAPT issue progressively more accurate location estimates for a GRB as detected Compton events from the burst accumulate …

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Fpga-Based Data Processing Using High-Level Synthesis On The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Marion Sudvarg, Marion Sudvarg, Longhao Huang, Boran Yang, Blake Bal, Roger Chamberlain, Jeremy Buhler, Leonardo Di Venere, Leonardo Di Venere, Davide Serini, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler Dec 2025

Fpga-Based Data Processing Using High-Level Synthesis On The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Marion Sudvarg, Marion Sudvarg, Longhao Huang, Boran Yang, Blake Bal, Roger Chamberlain, Jeremy Buhler, Leonardo Di Venere, Leonardo Di Venere, Davide Serini, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler

Computer Science Faculty Research & Creative Works

FPGAs are widely deployed on high-energy astrophysics telescopes to read out sensor data from front-end electronics. To support continuous data streams or high trigger rates, FPGA logic may be employed to process raw sensor readout values, reducing the volume of data transmitted, processed, and stored by downstream CPU-based computational platforms. Across instruments, these FPGA-based processing pipelines often have similar semantics and share common stages. However, diverse telescope designs require unique implementations of the constituent algorithms, and the logic is often rewritten from scratch for a new instrument. Writing, simulating, and debugging firmware is difficult and time consuming. However, High-Level Synthesis …

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Polyminhash: Efficient Area-Based Minhashing Of Polygons For Approximate Nearest Neighbor Search, Alima Subedi, Sankalpa Pokharel, Satish Puri Dec 2025

Polyminhash: Efficient Area-Based Minhashing Of Polygons For Approximate Nearest Neighbor Search, Alima Subedi, Sankalpa Pokharel, Satish Puri

Computer Science Faculty Research & Creative Works

Similarity searches are a critical task in data mining. As datasets grow larger, exact nearest neighbor searches quickly become unfeasible, leading to the adoption of approximate nearest neighbor (ANN) searches. ANN has been studied for text data, images, and trajectories. However, there has been little effort to develop ANN systems for polygons in spatial database systems and geographic information systems. We present PolyMinHash, a system for approximate polygon similarity search that adapts MinHashing into a novel 2D polygon-hashing scheme to generate short, similarity-preserving signatures of input polygons. Minhash is generated by counting the number of randomly sampled points needed before …

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Accessing Cation And Anion Redox In Mixed-Valent One-Dimensional Iron-Chalcogenides, Na1.5fe1s2–Xsex(X = 0, 1, And 2), For Na-Ion Batteries, Santhoshkumar Sundaramoorthy, Milad Aghayi-Anaraki, Sutapa Bhattacharya, Amitava Choudhury Dec 2025

Accessing Cation And Anion Redox In Mixed-Valent One-Dimensional Iron-Chalcogenides, Na1.5fe1s2–Xsex(X = 0, 1, And 2), For Na-Ion Batteries, Santhoshkumar Sundaramoorthy, Milad Aghayi-Anaraki, Sutapa Bhattacharya, Amitava Choudhury

Chemistry Faculty Research & Creative Works

Sodium-ion batteries (NIBs) have attracted considerable attention as a cost-effective and sustainable alternative to lithium-ion batteries (LIBs), owing to the abundance and low cost of sodium resources. Here, we investigate previously reported mixed-valent (Fe3+/2+) one-dimensional (1D) iron chalcogenides: Na1.5FeS2, its selenide analogue Na1.5FeSe2, and newly developed solid solution Na1.5FeSSe, focusing on their synthesis, structural characterization, and electrochemical performance in NIBs. While the full Fe2+sulfide version (NaFeS2) of the compound is explored in solid-state cells for its dual cation–anion redox mechanism, we reveal the effect of anion substitution on the electrochemical behavior of Na1.5FeS2–xSex(x = 0, 1, …

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