Physical Sciences and Mathematics Commons^™

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) …

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 …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

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 …

Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid, Dushmanta Kumar Das, Samaniba Imchen

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining smart grid stability is crucial for the reliable operation of decentralized electricity networks, especially as the energy sector becomes more complex. The process of ensuring grid stability begins with collecting consumer data and comparing it to power supply requirements. Ultimately, consumers receive a report showing their energy use and pricing details. However, this process is time-consuming and can be improved by leveraging artificial intelligence to predict smart grid stability more efficiently. Specifically, an optimized Long Short-Term Memory (LSTM) network is proposed to predict smart grid stability, addressing the challenges associated with traditional data collection and evaluation methods. Simulations from …

A Joint Optimization-Based Novel Attack For Genomic Beacon Reconstruction, Kousar Saleem, Si̇nem Sav

Turkish Journal of Electrical Engineering and Computer Sciences

Genomic data sharing has become an essential component of biomedical research, enabling large-scale collaborations and accelerating discoveries in human genetics. To balance the need for accessibility with privacy concerns, several controlled-access mechanisms have been proposed, including genomic beacons. Genomic beacons answer simple presence/absence queries about specific genetic variants. However, prior work has demonstrated that beacons remain vulnerable to genome reconstruction attacks, where an adversary can recover large portions of participants’ genomes using summary statistics. Building on insights from prior reconstruction attacks, we introduce an approach that unifies SNP correlation and allele frequency alignment objectives within a single-stage joint optimization framework. …

Automated Software Size Measurement Using Multilingual Domain-Adapted Language Models, Samet Tenekeci̇, Hüseyi̇n Ünlü, Burak Keçeci̇, Muhammed Efe İnci̇r, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

Software Size Measurement (SSM) is crucial for estimating required project effort as well as budget and schedule. However, many small and medium-sized companies struggle to apply objective SSM due to limited resources and lack of expertise. This often leads to inaccurate estimates and project overruns. There is a need for practical, low-resource solutions that support these tasks without requiring expert involvement. Motivated by this challenge, this study proposes an automated software size measurement approach that formulates the measurement task as supervised regression over natural language requirements, using domain-adapted transformer models. We construct large-scale Turkish and English software engineering corpora to …

Reducing Complexity In Versatile Video Coding Intra-Coding Through Machine Learning-Based Optimization Of Partitioning And Prediction, Amina Kessentini, Amna Maraoui, Imen Werda, Fatma Ezahra Sayadi

Turkish Journal of Electrical Engineering and Computer Sciences

The escalating demand for high-resolution multimedia content has necessitated more efficient video compression solutions. The Versatile Video Coding (VVC) standard, despite achieving remarkable compression gains, introduces significant computational complexity, primarily due to its exhaustive Rate-Distortion Optimization (RDO) process. To address this, we propose an intelligent approach leveraging supervised machine learning techniques to streamline the VVC encoding process. Specifically, we introduce a Lightweight Neural Network (LNN) for efficient coding unit partitioning decisions and a Decision Tree (DT) classifier for optimizing the intra prediction process. This dual-method framework, tailored for All Intra coding configuration, significantly reduces encoder complexity while maintaining compression performance …

Designing Risk-Aware Mixed-Mode Evacuation Strategies For Tsunamis: Insights From İstanbul, Vedat Bayram, Doruk Ergez, Ada Arikanoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

​Tsunamis pose severe and time-critical risks to densely populated coastal cities, where limited warning times and infrastructure constraints demand carefully coordinated evacuation strategies. This study develops an integrated, risk-aware optimization framework that jointly considers vertical and horizontal sheltering options together with mixed pedestrian-vehicular evacuation dynamics. The proposed mixed-integer second-order cone programming (MISOCP) model simultaneously determines vertical shelter location, evacuee assignment, road-use designation for pedestrians and vehicles, and route selection under congestion, capacity, and budget constraints. Vehicle travel times incorporate congestion effects through a convex flow-dependent function, while pedestrian routing ensures convergent and conflict free evacuation paths. A risk-minimization objective accounts …

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 …

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, 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 …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Reducing Range Anxiety Through Predictive Modeling Of Ev Battery Degradation, Caleb Thornsbury, Christian Castro, Bivin Sadler

SMU Data Science Review

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery …

Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain

Faculty Publications

Most methods that astronomers use to characterize the strength of atmospheric turbulence in and around their observatories use differential image motion monitors observing a star to provide the necessary data for the measurement. With the Moon becoming a greater national priority, the need to characterize atmospheric paths between observatories on Earth and the Moon is potentially going to grow in the future. To this end, the differential image motion monitor is not an ideal instrument for characterizing turbulence along paths between observatories and the Moon as the bright Moon makes it difficult to detect and locate stars in its vicinity. …

Sensing Negative-Cone Rotational Diffusion Of Dipole-Like Emitters, Yuanxin Qiu, Kaizhi A. Nie, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Fluorescence anisotropy and single-molecule orientation-localization microscopy (SMOLM) are powerful techniques that quantify the rotational diffusion of dipole-like emitters, which is important for sensing molecular interactions and chemical environments at the nanoscale. Numerous theoretical and experimental studies have thoroughly characterized single-molecule rotations even when those rotations are much faster than the detector integration time. Here, we extend the theory of measuring rotational diffusion to situations where a single dipole rotates uniformly everywhere outside of an isotropic cone of a certain size, termed a negative cone. This scenario corresponds to negative fluorescence anisotropy 𝑟 and has been observed in emitters exhibiting strong …

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

Current Research Progress On Electrode Materials For All-Vanadium Redox Flow Batteries, Wen-Qi Wang, Jie Jin, Li-Min Wang, Xin-Yue Liu, Tao Cheng, Yong Hou, Han Xue, Zhi-Yu Wang, Bo Liu, Jia-Bao Liu, Xu-Bin Lu

Journal of Electrochemistry

The redox active species in all-vanadium redox flow batteries (VRFBs) reside in the electrolyte, while the heterogeneous reactions occur on the electrode surface; the electrode is therefore the decisive platform for dynamic adsorption, electron transfer, and ion conversion, especially for the VO²⁺/VO₂⁺ and V²⁺/V³⁺ couples. One of the major challenges for VRFBs is the slow charge transfer in VO²⁺/VO₂⁺ and V²⁺/V³⁺ reactions, mainly caused by poor catalytic performance of electrodes and weak adhesion of catalysts to electrodes. This review focuses on the key challenges and recent …

Contrasting Coastal Dune Environments In Chile, Aurora Christianson

Student Research Symposium (SRS)

Emerging coastalization and urbanization threats to the prehistoric Concón Dunes and Humedal de Mantagua coastal area of Chile is being investigated by researchers via uncrewed aircraft systems (UAS). Four UAS were utilized: Anzu Raptor T, DJI Mavic 3E, DJI Mavic 3M, and DJI Air 3 to collect various images of the coastal dunes. Multispectral and RGB cameras gather images by photogrammetry to create orthomosaics and monitor vegetation indexes in Pix4Dmapper. Thermal cameras provided images in rainbow, white hot infrared, and black hot infrared schemes to monitor wildlife and vegetation. The normalized difference vegetation index (NDVI) was calculated to visualize overall …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …

Carbon Supported Octahedral Ptni Nanoparticles (Oct-Ptni/C) As A Cathode Catalyst For Proton Exchange Membrane Fuel Cells (Pemfcs) With Improved Activity And Durability, Zi-Wei Feng, Hai-Zhong Chen, Xiao Duan, Ling Tang, Yun-Kun Zhao, Long Huang

Journal of Electrochemistry

Proton exchange membrane fuel cells (PEMFCs) are considered as a promising renewable power source. However, the massive commercial application of PEMFCs has been greatly hindered by their high expense and less-satisfied performance mainly due to the sluggish oxygen reduction reaction (ORR) kinetics even on state-of-the-art Pt catalyst. Octahedral PtNi nanoparticles (oct-PtNi NPs) with excellent ORR activity in a half-cell have been widely studied, while their performance in membrane electrode assembly (MEA) has much less reported. Herein, we investigated the MEA performance using the carbon supported oct-PtNi NPs (oct-PtNi/C) as the cathode catalyst. Under the mild acid washing condition, the surface …

Development Status And Existing Problems Of Ion-Solvation Membranes For Electrolysis Of Water, Zheng-Yuan Zhou, Yu-Tao Sun, Zheng-Bang Liu, Chuan-Zheng Wang, Yong-Nan Zhou, Xi Luo, Tian-Chi Zhou, Jin-Li Qiao

Journal of Electrochemistry

Ion-solvaing membranes (ISMs) have received extensive attention in recent years as a key component in electrochemical energy conversion and storage devices. This article provides an overview of structural composition, performance advantages, research progress, ion conduction mechanism and existing issues of ISMs, primarily classifying them according to the matrix structure. A detailed analysis of performance enhancement methods, key performance indicators of ISMs and performance influencing factors is also presented. The article contributes to further optimizing the design and application of ion-solvation membranes, providing theoretical support for the development of fields such as hydrogen production through electrolysis of water and electrochemical energy …

The Ntp Anode For Aqueous Sodium Ion Batteries: Recent Advances And Future Perspectives, Ming-Li Wang, Xue-Ying Su, Zheng-Xiang Shan, Shu-Zhe Yang, Heng-Rui Guo, Hao Luo, Dong-Liang Chao

Journal of Electrochemistry

Aqueous sodium-ion batteries (ASIBs) have attracted great attention in aqueous batteries due to their merit of high safety. However, the constrained work potential and insufficient chemical stability of anode materials in aqueous electrolytes hinder the large-scale application of ASIBs. Sodium titanium phosphate, NaTi₂(PO₄)₃ (NTP), is considered one of the most promising anode materials for ASIBs due to its excellent electrochemical performance and tunable structure. Recently, great achievements have been made in the development of NTP, however, a comprehensive review of existing studies is still lacking. This article firstly introduces the basic properties of NTP and …

Iron-Involved Orr Electrocatalysts Under The Lens Of In-Situ/Operando Mössbauer Spectroscopy, Sumbal Farid, Jun-Hu Wang

Journal of Electrochemistry

Exploring cost-effective and efficient catalysts for oxygen reduction reaction (ORR) poses a significant challenge, especially in the pursuit of alternatives to precious metals like platinum. Significant advancements have driven electrochemists to develop efficient ORR catalysts using abundant materials, particularly iron (Fe)-based, known for their exceptional performance in ORR. While the crucial function of Fe in boosting ORR catalytic activity is recognized, the connection between material attributes and catalytic performance remains enigmatic. Understanding the dynamic processes involved in oxygen electrocatalysis is paramount for designing precious-metals-free ORR electrocatalysts. Mössbauer spectroscopy stands out as a powerful technique for deciphering the structural characteristics of …

Developing A Collaborative Tool To Foster Communication In Sustainability Research, Nina Hunter, Noëlle-Laetitia Perret, Martin Klepal

Electrical & Electronic Engineering

Climate change necessitates urgent responses based on knowledge produced by teams that transcend disciplinary boundaries, and with members whose work focuses on the generation of knowledge and on the application of knowledge, with some integrating both. Almost a third of Europe’s building stock consists of heritage buildings requiring renovation that is ideally sustainable as part of an energy transition response. The European transdisciplinary CALECHE study team with use-cases in France, Italy, Sweden and Switzerland aims to support decision-making on the sustainable renovation of heritage buildings, via research that employs co-design. However, team members are from various disciplines, with different skillsets …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang

Turkish Journal of Electrical Engineering and Computer Sciences

Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …

A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation, Mengjuan Zhao, Yitao Liang, Weiya Shi, Juan Xia

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, grayscale images are preferred as input data for some specific vision tasks. Decolorization is the transformation of a color image into a grayscale image. Efficient decolorization algorithms can improve the overall task efficiency, while perceptual preservation in decolorization can provide more information for further processing. In recent research, traditional methods focus on preserving contrast or detail information with little attention to perceptual features. Deep-learning methods are beginning to consider perceptual preservation, but they run inefficiently. In addition, the decolorization methods lack the optimal target grayscale images for reference. Therefore, we propose a new deep learning-based real-time no-reference decolorization network …