Intrusion Detection Latency: The Neglected Metric,
2026
Portland State University
Intrusion Detection Latency: The Neglected Metric, Sandhyarani Dash, John M. Acken
Electrical and Computer Engineering Faculty Publications and Presentations
Accuracy remains the dominant evaluation metric in Intrusion Detection System (IDS) research, yet an IDS that detects attacks too late is functionally equivalent to one that fails—particularly in Internet of Things (IoT) environments. In operational settings, the timing of detection shapes both the scope of adversarial activity and the feasibility of effective response. To the best of our knowledge, latency (the speed at which intrusions are identified) has received no systematic attention. Our analysis of published IDS papers reveals that latency is defined inconsistently—often referring to inference, communication, computation time, or combinations of these-leading to incomparable performance claims. To close …
Axial Sulfur-Coordination Engineering Boosting Fe–N–C Catalysts For High-Performance Proton Exchange Membrane Fuel Cells,
2026
State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China; School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
Axial Sulfur-Coordination Engineering Boosting Fe–N–C Catalysts For High-Performance Proton Exchange Membrane Fuel Cells, Lin Lin, Xiu-Xuan Hou, Zhe-Chen Fan, Yi-Xuan Yin, Wei-Yi Zhao, Kai Wei, Yu-Die Zhou, Li-Na Hou, Ying Wang, Hao Wan, Jun-Jie Ge
Journal of Electrochemistry
Fe-N-C catalysts have long suffered from kinetically sluggish oxygen reduction reaction (ORR) due to excessive adsorption strength toward oxygen intermediates and low site utilization. Heteroatom doping effectively accelerates ORR reaction kinetics through electronic structure modulation of metal sites for optimal intermediate adsorption, while chemical vapor deposition (CVD) enhances the turnover frequency (TOF) of active sites. Herein, we developed an FeSNC catalyst featuring abundant FeS1N4 sites via a dual-precursor CVD strategy. Experimental and theoretical analyses revealed that S incorporation disrupts the symmetric coordination of active sites, which optimizes OH* adsorption energies from 0.212 eV to 1.194 eV. Moreover, …
Deciphering The Role Of Binder Reaction Exothermicity In Thermal Runaway Of Lithium-Ion Cells,
2026
State Key Laboratory of Chemical Engineering and Low-carbon Technology, School of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China
Deciphering The Role Of Binder Reaction Exothermicity In Thermal Runaway Of Lithium-Ion Cells, Wen Wen, Jing-Hong Zhou, Hao-Tian Lu, Xing-Gui Zhou
Journal of Electrochemistry
Thermal safety associated with lithium-ion cells as power sources remains a critical industry concern. A comprehensive understanding of how internal exothermic side reactions contribute to temperature rise is fundamental for accurately analyzing thermal runaway processes and predicting the thermal safety of lithium-ion cells. While various side-reactions, such as decomposition of solid electrolyte interphase layer, reaction between anode materials and electrolyte, reaction between cathode materials and electrolyte, and electrolyte decomposition, have been identified as heat generation sources in previous studies, the quantification of these reactions remains insufficiently standardized. Particularly, the impact of heat generation from binder decomposition (most commonly polyvinylidene difluoride) …
A Pretraining-Based Framework For On-Device Training Of Imu-Based Locomotion Mode Detection For Wearable Active Exoskeletons,
2026
Louisiana State University and Agricultural and Mechanical College
A Pretraining-Based Framework For On-Device Training Of Imu-Based Locomotion Mode Detection For Wearable Active Exoskeletons, Muhammad Tahir Khan
LSU Master's Theses
Active exoskeletons are being developed to support human movement in physically demanding industries such as construction. For these systems to work effectively, they must be able to correctly identify the user’s current activity. This process is known as locomotion mode detection and plays an important role in selecting the appropriate control parameters for exoskeletons. Many existing approaches use inertial measurement units (IMUs) to recognize these activities and have shown strong performance. However, most of these methods depend on large amounts of labeled data collected under specific conditions. As a result, they often do not perform well when applied to new …
Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review,
2026
UIN Sunan Ampel of Surabaya, Indonesia
Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti
Library Philosophy and Practice (e-journal)
This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …
Deep Learning Based Control System For Multi-Zone Hvac Residential Buildings,
2026
University of Texas at Tyler
Deep Learning Based Control System For Multi-Zone Hvac Residential Buildings, Mohammad H. Z. Alghalayeeni
Mechanical Engineering Theses
Heating, ventilation, and air conditioning (HVAC) systems are major contributors to residential energy consumption. However, most homes continue to rely on single zone thermostat control, which regulates temperature based on a single sensor and cannot account for thermal variations across multiple rooms. This often leads to uneven thermal conditions and inefficient energy use in multi-zone residential buildings. This thesis presents a deep reinforcement learning based approach for improving HVAC zoning control through dynamic airflow distribution. A physics based multi zone thermal model of a residential house was developed to simulate heat transfer processes including conduction, convection, solar and internal heat …
Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management,
2026
Dakota State University
Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park
Annual Research Symposium
Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …
Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition,
2026
University of California - San Diego
Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition, Mason Li, Tiffany Le, Jiajing Huang, Yuxin Wen
Engineering Faculty Articles and Research
Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual …
Arid: Agglomerative Regionalization Via Information Divergence, A Novel Clustering Algorithm For Geo-Spatial Data,
2026
Portland State University
Arid: Agglomerative Regionalization Via Information Divergence, A Novel Clustering Algorithm For Geo-Spatial Data, Joshua David Sills
Dissertations and Theses
Regionalization is a clustering problem that seeks to partition geospatial data into geographically contiguous regions while remaining internally homogeneous in their attributes. It has been successfully applied towards the development of urban planning, natural resource discovery, and ecological analysis. Existing popular approaches optimize homogeneity using Euclidean or variance-based criteria, which ignore distributional differences such as variance shifts, multimodality, and higher-order dependence. This thesis introduces ARID (Agglomerative Regionalization via Information Divergence), a spatially constrained agglomerative clustering framework that replaces distance-based merging with an information-theoretic, Ward-like criterion that utilizes the Kullback-Leibler (KL) divergence. ARID constructs a neighborhood graph from coordinate space and …
Enhanced Cuckoo Search-Based Optimization For Single Distributed Generation Placement And Sizing In Radial Distribution Systems,
2026
Department of Electrical and Electronics Engineering, College of Engineering and Environmental Studies, Olabisi Onabanjo University, Ago-Iwoye, Ogun State, Nigeria
Enhanced Cuckoo Search-Based Optimization For Single Distributed Generation Placement And Sizing In Radial Distribution Systems, Samson Oladayo Ayanlade, Abdulrasaq Jimoh, Richard Oladayo Olarewaju, Ignatius Kema Okakwu, Israel O. Adejumobi, Joseph B. Samson, Oluwadare A. Adebisi, Oluwadare O. Akinrogunde
Al-Bahir
This paper presents an Enhanced Cuckoo Search Algorithm (ECSA) to optimally place and size Distributed Generation (DG) in radial distribution systems to minimize real power loss within operating constraints. The proposed ECSA has exponentially decaying adaptive Lévy flights, constraint-aware solution repair with dynamic penalty coefficients, and diversity-directed stochastic replacement to enhance search robustness and convergence speed. It was tested with 30 independent runs on the IEEE 33-bus, IEEE 69-bus, and a practical Nigerian 32-bus distribution network. The simulations show that the ECSA lowers the active power loss of the IEEE 33-bus system from 201.58 kW to 102.75 kW (49.03%), and …
Voltage-Mode Driver With Sar-Based Termination Calibration,
2026
Department of Electronics and Communication Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Voltage-Mode Driver With Sar-Based Termination Calibration, Moustafa M. Elsayed, Abeer T. Khalil, Sameh A. Ibrahim, Mohy Eldin A. Abo-Elsoud
Mansoura Engineering Journal
This paper introduces a DAC-based four-level pulse-amplitude modulation (PAM-4) driver capable of operating at data rates up to 80 Gb/s in 65-nm CMOS technology. A replica-based termination calibration loop is proposed to preserve driver linearity across process, voltage, and temperature (PVT) variations. The proposed driver achieves a relative level mismatch (RLM) of 99.3%. In addition, the transmitter demonstrates a vertical eye opening of 134.8 mV and a horizontal eye opening of 0.44 UI under worst-case conditions.
Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks,
2026
Louisiana State University and Agricultural and Mechanical College
Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks, Donovan Gegg
LSU Master's Theses
Autonomous Underwater Vehicles (AUVs) are untethered robotic platforms used for tasks such as seafloor mapping, infrastructure inspection, and environmental monitoring. Recent technological advances have produced smaller, more affordable platforms, broadening access to research teams and small companies alike. This miniaturization comes at the cost of them handling drawbacks associated with a more compact machine such as reduced battery capacity as well as limited processing and sensing capabilities. These constraints make small-sized marine vehicle’s reliability critical as they can cause malfunctions, making the loss of a vehicle more likely. Actuator faults are particularly consequential as unintended and unstable control in an …
Polymer Microstructures For Advanced Biomanufacturing,
2026
Louisiana State University and Agricultural and Mechanical College
Polymer Microstructures For Advanced Biomanufacturing, Tongyao Wu
LSU Doctoral Dissertations
With the continued growth of the biopharmaceutical industry, the demand for scalable, robust, and resource-efficient platforms for large-scale mammalian cell culture is amplified. Recent developments in microfluidic technology, such as precise control of the microenvironment, showed the potential to improve the performance of cell culture systems. However, constrained by scalability and operational efficiency, applying such approaches to large-scale cell culture and biopharmaceutical production presents challenges. This dissertation addresses these challenges through three independent but conceptually related technological developments. First, a roll-to-roll (R2R) fabrication process was developed for the scalable production of hollow microcarriers (HMCs). HMCs provide three-dimensional microenvironments suitable for …
Graph-Based And Uncertainty-Aware Machine Learning For Academic Performance Prediction,
2026
Florida Atlantic University
Graph-Based And Uncertainty-Aware Machine Learning For Academic Performance Prediction, Anca Olivia Muresan
Electronic Theses and Dissertations
Early identification of students at risk of academic failure is essential for timely pedagogical interventions and reducing dropout rates. While Artificial Intelligence (AI) has significantly advanced predictive modeling in education, two primary challenges persist: effectively modeling the complex, evolving relationships within heterogeneous educational data, and ensuring the reliability of model outputs for high-stakes decision-making. This dissertation addresses these challenges by proposing a comprehensive framework for early and continuous student performance prediction applied to the Open University Learning Analytics (OULA) dataset. First, we introduce a Heterogeneous Graph Neural Network (HGNN) approach that utilizes metapath structures to capture latent interactions between diverse …
Sonochemically Synthesized Al-Doped Zno Nanorods-Based Flexible Piezoelectric Nanogenerators For Durable Energy Harvesting,
2026
The University of Texas Rio Grande Valley
Sonochemically Synthesized Al-Doped Zno Nanorods-Based Flexible Piezoelectric Nanogenerators For Durable Energy Harvesting, Tiham Fayaz, Ahmed Hasnain Jalal, Fahmida Alam
Electrical and Computer Engineering Faculty Publications
This study presents a high-performance piezoelectric nanogenerator (PENG) based on aluminum-doped zinc oxide (Al:ZnO) nanorods synthesized via a novel sonochemical method. This rapid, cost-effective, and reproducible approach enables the synthesis of ZnO nanorods (ZnO NRs) under ambient conditions. The PENG, fabricated on a 177 µm flexible Indium Tin Oxide (ITO) coated polyethylene terephthalate (PET) substrate, benefits from optimized aluminum doping, enhancing the output voltage. Structural analyses conducted using atomic force microscopy (AFM), scanning electron microscopy (SEM), and X-ray diffraction (XRD) demonstrated that the synthesized ZnO nanorods possessed a high degree of crystallinity. The synthesis process was carefully fine-tuned to avoid …
Best Practices For Electrical Safety At Home,
2026
Embry-Riddle Aeronautical University
Best Practices For Electrical Safety At Home, Joshua Henderson, Derek Centrone
Student Research Symposium (SRS)
The household, although it’s a comfortable and relaxing environment, can in fact be riddled with everyday hazards that homeowners and residents overlook or are too complacent in acknowledging or even recognizing their presence. One such common oversight is electrical hazards. This may be due to the high usage of electricity in people’s day-to-day lives, especially since the perceived risk of electrical hazards by the public can be very low. In a household, there are many hazards present each and every day. One such hidden deadly hazard is electricity. While on the surface it doesn’t seem that dangerous, in reality, it …
Enhanced Antenna Selection Techniques For Energy-Efficient Code Index Modulation Aided Spatial Modulated Wireless Communication Systems, Fati̇h Çögen, Burak Ahmet Özden, Erdoğan Aydin
Turkish Journal of Electrical Engineering and Computer Sciences
This study proposes an integrated multiple-input multiple-output (MIMO) transceiver framework, termed CIM-HQAM-SM, which combines code index modulation (CIM) and spatial modulation (SM) with energy-efficient hexagonal quadrature amplitude modulation (HQAM). In the proposed bit mapping, the information bits jointly select (i) the active transmit-antenna index, (ii) the Walsh–Hadamard spreading-code indices for the in-phase and quadrature branches, and (iii) an HQAM symbol. Hence, the payload is conveyed through the constellation symbol as well as through antenna and code indices. For the considered Rayleigh-fading scenarios and matched spectral-efficiency settings, the proposed framework offers BER improvements over conventional SM and quadrature SM (QSM), while …
An Ensembled Two-Phase Deep Learning Approach For A Psychiatric Disorder Detection,
2026
C. V. RAMAN COLLEGE OF ENGINEERING, BHUBANESWAR, INDIA
An Ensembled Two-Phase Deep Learning Approach For A Psychiatric Disorder Detection, Prajna Paramita Debata, Midhun Chakkaravarthy, Brojo Kishore Mishra
Turkish Journal of Electrical Engineering and Computer Sciences
Computational Psychiatry represents a burgeoning realm within scientific inquiry, delving into the intricate interplay of neurobiology within the brain. The escalating prevalence of mental illness underscores the urgency to confront this challenge. Among the prevalent disorders, Schizophrenia and Bipolar Disorder loom large, affecting a significant portion of the population at some point in their lives. However, pinpointing psychiatric disorders poses a formidable challenge. Genetic predispositions significantly influence the development of mental illnesses, with intriguing overlaps observed among certain disorders. This convergence complicates accurate diagnosis. Here, a deep learning approach is considered for significant gene biomarker identification and classification of Schizophrenia …
Sentisec: Combining Keyword Heuristics And Sentiment Modeling For Ai-Powered Threat Detection,
2026
TÜBİTAK
Sentisec: Combining Keyword Heuristics And Sentiment Modeling For Ai-Powered Threat Detection, Ridho Surya Kusuma, Erum Ashraf, Selvakumar Manickam, Shankar Karuppayah
Turkish Journal of Electrical Engineering and Computer Sciences
This work presents SENTISEC, a hybrid LLM-based threat detection framework designed to classify security logs by integrating keyword heuristics, domain-adapted sentiment scoring, and Retrieval-Augmented Generation (RAG). The system achieves an overall accuracy of 93.67%, with 91.46% macro recall, 89.07% macro F1, and 95.15% threat recall, while maintaining a low false-positive rate of 1.68%. Its methodology incorporates strict keyword and IOC matching, a domain-tuned DistilBERT sentiment module, hybrid BM25–MiniLM retrieval enhanced with BGE reranking, adaptive quantile-based threshold calibration, and SHAP-based explainability. Comparative evaluations against keyword-only, sentiment-only, classical machine-learning models, and DistilBERT-only baselines show that SENTISEC consistently improves both true-positive and true-negative …
Optimal Network Reconfiguration Based On Discrete Metaheuristic Techniques For Reduction Of Power Loss And Carbon Emission In Distribution Networks,
2026
Universiti Malaya
Optimal Network Reconfiguration Based On Discrete Metaheuristic Techniques For Reduction Of Power Loss And Carbon Emission In Distribution Networks, Asad Ali, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hussain Shareef, Hasmaini Mohamad, Munir Azam Muhammad
Turkish Journal of Electrical Engineering and Computer Sciences
Power distribution systems play a crucial role in transmitting electrical power from generation sources to end users. During transmission, significant power losses occur in the form of heat as the current flowing along the lines/cables has resistance. To minimize power losses, distribution network reconfiguration (DNR) has been widely adopted. This paper proposes optimal DNR based on metaheuristic techniques with discrete mutation feature targeting active power loss reduction, which subsequently lowers carbon emissions and operational costs. Through the discrete mutation feature, computational time to find optimal solution has been reduced significantly with fewer iterations compared to conventional mutation techniques. The proposed …
