Engineering Commons^™

Place Personality And User Behaviour: Insights From Cairo’S Streets, Sara Sabahy, Zeina Elzein, Doaa Abouelmagd

HBRC Journal

A place’s personality is beyond the sum of its characteristics. It’s how people perceive it on a human level, hoping for a better understanding and seeking connection. This study aims to understand how people connect with public spaces to enhance the quality of urban life.

A place’s personality extends beyond its physical attributes—it reflects how people emotionally perceive and connect with it. This study investigates the relationship between the perceived personality of urban streets and users’ behaviour to enhance the quality of urban life in Cairo. Focusing on Downtown Cairo and New Cairo, it addresses a gap in destination personality …

Evaluating The Fate And Variability Of Soil Organic Carbon And Nitrogen Species Under Conservation Practices In The Raccoon River Watershed, Zhonglong Zhang, May Wu

Civil and Environmental Engineering Faculty Publications and Presentations

This study evaluates the fate and variability of soil organic carbon (SOC) stocks and nitrogen species using the latest version of the Soil and Water Assessment Tool– Carbon (SWAT-C) and assesses how conservation practices influence their dynamics in the Raccoon River Watershed (RRW). Dominated by intensive agricultural pro- duction, the RRW is a significant contributor of sediment and nutrient loads to local rivers and the Mississippi River. This SWAT-C model simulates the export of SOC and nitrogen species and evaluates their responses under varying management scenarios. Model calibration was performed for streamflow, sediment, nitrate, total nitrogen, and organic carbon with …

Theoretical Analysis Of Mir-Based Differential Photoacoustic Spectroscopy For Noninvasive Glucose Sensing, Tasnim Ahmed, Khan Mahmud, Md Rejvi Kaysir, Shazzad Rassel, Dayan Ban

Electrical and Computer Engineering Faculty Research

Diabetes is a developing global health concern that cannot be cured, necessitating frequent blood glucose monitoring and dietary management. Photoacoustic Spectroscopy (PAS) in the mid-infrared (MIR) region has recently emerged as a viable noninvasive blood glucose monitoring technique. However, MIR-PAS confronts significant challenges: (i) Water absorption, which reduces light penetration, and (ii) interference from other blood components. This paper systematically analyzes the background of photoacoustic signal generation and proposes a differential PAS (DPAS) in the MIR region for removing the background signals arising from water and other interfering components of blood, which improves the overall detection sensitivity. A detailed mathematical …

Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond, Sadiq Ur Rehman, Jawwad Ahmed, Muhammad Zubair, Syed Sajjad Hussain Rizvi

Turkish Journal of Electrical Engineering and Computer Sciences

Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission …

Polystyrene Nanoplastics As Pfas Carriers And Their Interactions With Zwitterionic Phospholipid Membranes, Jiahuiyu Fang, Tongxuan Qiao, Pranab Sarker, Xiaoxue Qin, Size Zheng, Mark J. Uline, Tao Wei

Faculty Publications

The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and nanoplastics (NPs) poses a synergistic threat to environmental and human health, yet the molecular mechanisms governing PFAS–NP complexation and membrane interactions remain unclear. Using atomistic molecular dynamics simulations, we investigated the adsorption of neutral polytetrafluoroethylene (PTFE) and anionic perfluorinated compounds (perfluorooctanoic acid, PFOA, and perfluorooctanesulfonic acid, PFOS) on polystyrene NPs (3.1 and 6.7 nm) and their interactions with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membranes. Polystyrene NPs act as carriers, transporting PFAS molecules to the lipid/water interface, where PFAS attachment modifies NP interfacial behavior. PFAS adsorption on the NP surface is driven by …

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

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 …

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control, Zülal Hi̇lal Yildiz Budak, Seyi̇t Alperen Çeltek, Aki̇f Durdu

Turkish Journal of Electrical Engineering and Computer Sciences

Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

Improved Performance Of Wave Energy Converters And Arrays For Wave-To-Onshore Power Grid Integration, Madelyn Veurink, David Wilson, Rush Robinett, Wayne Weaver

Michigan Tech Publications

This paper focuses on power grid integration of wave energy converter (WEC) arrays that minimize added energy storage for maximizing power capture as well as smoothing the oscillatory power inputs into the grid. In particular, a linear right circular cylinder WEC array that implements complex conjugate control is compared and contrasted to a nonlinear WEC array that implements an hourglass buoy shape while both are integrated into the grid utilizing phase control (i.e., relative spacing of the WEC array) on the input powers to the grid. The Hamiltonians of the two WEC systems are derived, enabling a direct comparison of …

Energetics Analysis Of Solitary Waves Using A Multi-Layer Model, Hunter Boswell, Frank D. Han, Guirong Yan, Wouter Mostert

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study investigates the performance of a vertically-Lagrangian multi-layer model on numerically simulating shoaling and breaking two-dimensional solitary waves during both the breaking and post-breaking processes. The energy dissipation of the breaking event for the multi-layer waves is analyzed and compared to prior direct numerical simulation work with the same bathymetric and wave cases. It shows very similar data collapse to shallow-water inertial theory. For post-breaking behavior, bore characteristics are compared to an experimental study of bores formed from breaking solitary waves and similar results are found. While the multi-layer method was not found to behave sufficiently well for direct …

Why Agtech Startups Fail? Evidence From Global Shutdown Patterns In 2025 And The Cost-Adoption Mismatch Effect, Ankit Chandra, Ishani Lal

Department of Agricultural and Biological Systems Engineering: Presentations and White Papers

Agricultural technologies are widely seen as a driver for sustainability, resilience, and food-system transformation. Yet in 2025, agtech startup across North America, Europe, Asia, and Africa experienced multiple shutdowns. Although each company closed for its own reasons, the failures highlight consistent underlying patterns. We analyze 18 publicly reported shutdowns across controlled-environment agriculture (CEA), robotics, insect protein, digital platforms, sensors, and ag-biotechnology. We find that most ventures struggled not with scientific feasibility but with economic and adoption dynamics at the farm level. We identify a Cost-Adoption Mismatch Effect, in which the capital and operational burden of a technology exceeds farmers’ capacity …

Spectroscopic Investigations On Polyvinylidene Fluoride Nanofibers, Parinaz Amaniabdolmalaki, Jui Vitthal Kharade, Alexandro Trevino, Lydia Morales, Karen Lozano, Victoria Padilla, Karen S. Martirosyan, Horacio Vasquez, Mircea Chipara

Physics & Astronomy Faculty Publications

The production of polyvinylidene fluoride nanofibers by force-spinning from polymer solutions was confirmed by electron microscopy. The structural and phase characteristics of the resulting nanofiber mats were examined using Fourier Transform Infrared Spectroscopy in Attenuated Total Reflectance mode, Raman spectroscopy, and X-ray Diffraction. Results from all these techniques consistently indicated that both the powder and the mats of polyvinylidene fluoride predominantly contain the α phase, with a small admixture of the β phase. Within experimental errors, no other phases were noticed both in the powder and in the as-obtained mats. The ratios of the areas of the Raman lines at …

Single-Valued Neutrosophic Pessimistic Multi-Granulation Rough Set Model Based On (A,B,C)-Cut Relations And Its Applications, Xu-Xi Wu, Hu Zhao, Qiao-Ling Song, Xiong-Wei Zhang

Neutrosophic Systems with Applications

To address uncertainty in multi-source data, this paper proposes a single-valued neutrosophic pessimistic multi-granulation rough set (P-SVN-MGRS) model based on (a,b,c)-cut relations. In this framework, each neutrosophic relation is characterized by three membership-degree functions: T(x,y), I(x,y), and F(x,y). These functions correspond to truth-membership, indeterminacy, and falsity, respectively. The (α,β,γ)-cut relation employs parameters α,β,γ∈(0,1] as thresholds for the three functions. A pair (x,y) belongs to …

Pythagorean Neutrosophic Mathematical Modelling, M. Kavitha, R. Irene Hepzibah

Neutrosophic Systems with Applications

Survey-based assessments often suffer from ambiguity, inconsistency, and uncertainty, which weaken the reliability of decision-making outcomes. To address these challenges, this study proposes a novel decision-support framework for data fuzzification, ranking, and agility measurement using Pythagorean Neutrosophic Fuzzy Sets (PNFS). The proposed method offers three major advantages: (i) enhanced ability to capture high levels of indeterminacy compared with classical fuzzy and intuitionistic models, (ii) improved ranking accuracy through a newly developed score function and ranking algorithm, and (iii) greater robustness in scenarios involving conflicting, incomplete, or imprecise expert judgments. The framework includes a refined Pythagorean Neutrosophic fuzzification technique, mathematically supported …

Neutrosophic Hankel Transforms And Their Application To Cross-Domain Legislative Integration, Mona Gharib, Mehboob Ali, Ishtiaq Hussain

Neutrosophic Systems with Applications

This paper introduces the Neutrosophic Hankel Transform (NHT) as a novel mathematical framework for modeling systems with radial structure under uncertainty, indeterminacy, and inconsistency. Building upon classical Hankel transforms and neutrosophic logic, we define two complementary realizations: a componentwise transform (NHT–C) that transports uncertainty with the signal, and a kernel-weighted transform (NHT–K) that embeds neutrosophic weights into the integral kernel. We establish linearity, inversion, and Parseval-type relations, and derive operational rules that diagonalize the Bessel radial operator.

To demonstrate utility, we formulate a radial diffusion–reaction model for pollutant concentration in a radialized river cross-section and solve it in closed form …

A Study Of Regular And Irregular Complex Neutrosophic Vague Graphs, Suriyakumar G, V. J. Sudhakar

Neutrosophic Systems with Applications

In this paper, We define the regular complex neutrosophic vague graph and the irregular complex neutrosophic vague graph for this purpose. We specify a node’s degree and total degree in a normal complex neutrosophic vague graph. A few features and theorems of those regular and irregular complex neutrosophic vague graphs are presented. This article defines busy and free nodes in a normal complex neutrosophic vague graph. Also, we describe a regular and irregular complex neutrosophic vague graph with a cycle as the underlying crisp graph.

From Uncertainty To Lucidity: Awareness Neutrosophic Kähler-Einstein Innovative Evaluator Methodological In Era Of Green Artificial Intelligence, Mona Mohamed, Ahmed M. Ali

Neutrosophic Systems with Applications

The rapid development of generative artificial intelligence (Gen AI) is a double-edged sword. On the positive side, Large Language Models (LLMs) of Gen AI as chatbot considered intelligent friend. Due to its potential to stimulate the maturation of ideas and cultivate fundamental general abilities like problem-solving and critical thinking. The advancement of Gen AI continued after that, moving from “chatbots” to “AI agents” that carry out multi-step activities in addition to responding to queries.

Regarding the downside, the terminology of “Red AI” era brought about by generative AI is marked by a performance at any expense that puts pressure on …

Comparing And Optimizing Hollow Fibre And Flat Sheet Vacuum Membrane Distillation, Hamed Kariman, Mehdi Khiadani, Hussein A. Mohammed

Research outputs 2022 to 2026

Membrane Distillation (MD) systems have the potential to tackle the growing global water demand. Within various MD systems, Vacuum Membrane Distillation (VMD) systems are particularly important in addressing this issue. Hollow Fibre VMD (HF-VMD) and Flat Sheet VMD (FS-VMD) are the two main configurations of VMD systems, where limited studies are available on their comparison and optimisation under identical operational conditions. This study firstly charts the mathematical modelling of HF-VMD and FS-VMD systems that have been developed, accounting for the amount of flux, Gain Output Ratio (GOR), Temperature Polarisation Coefficient (TPC), and Specific Thermal Energy Consumption (STEC) of both configurations …

Applications For The Quasi-Atomic Orbital And Constrained Density Functional Theory Methods On Catalytic Reactions, Alvaro David Loaiza Orduz

LSU Doctoral Dissertations

Selective activation of C–O, C–H, and C–C bonds underpins biomass upgrading, alkane functionalization, and CO₂ conversion. Despite their importance, the electronic factors governing catalytic performance remain incompletely understood, and many industrial processes still rely on empirical trends or material-specific observations. This dissertation addresses this gap by applying density functional theory (DFT), thermodynamic decomposition, and electronic-structure analysis to identify unifying principles of reactivity across transition-metal phosphides, vanadate oxides, copper-based electrocatalysts, and mixed IrO₂–RuO₂ layers. This work examines how charge transfer, orbital localization, and ligand-induced perturbations control reaction pathways in diverse catalytic systems. C–O bond scission in 2-methyltetrahydrofuran (MTHF) and methanol was …

Reproducible Semantic Data Management Workflow For Materials Data Science: Generating Knowledge Graphs With Robust Fairifcation Pipelines, Kyle R. Henrikson, Van D. Tran, Meredith Francis, Isabella Giammattei, Quynh D. Tran, Laura S. Bruckman, Erika I. Barcelos, Roger H. French

Student Scholarship

Combining data from multiple sources is crucial for efficient knowledge aggregation in materials data science. FAIR data from ontology and Linked Data principles enable this. Semantic data management streamlines data exchange and aggregation, ensuring information is available and extractable. FAIRLinked and GraphDB provide solutions for consolidating, hosting, and extracting meaningful insight from multimodal data.

Methods For Radiation-Sparing Navigation In Endovascular And Spine Surgery, William Ross Warner

Dartmouth College Ph.D Dissertations

Modern surgical navigation systems improve procedural accuracy, limit complications, and can reduce reliance on intraoperative ionizing radiation, yet their adoption remains uneven across surgical domains. Informed by clinical immersion, this thesis identifies and addresses two unmet needs revealed through contrasting image-guidance practices in endovascular and spine surgery, where existing guidance techniques are costly, intermittent, or fundamentally limited. Specifically, these needs include (1) the absence of radiation-sparing navigation techniques for endovascular procedures, which rely heavily on 2D x-ray angiography, and (2) the need for dynamic registration methods in open spine surgery, where navigation accuracy can degrade after initial registration and intraoperative …

Simulation Study On Nh3 Combustion And NoX Emissions Under Gas Turbine-Relevant Conditions, Kumeesha Arumawadu, Braxton Wiggins, Ziyu Wang

Mechanical and Aerospace Engineering Student Publications and Presentations

Ammonia (NH₃) is a zero-carbon fuel and an attractive hydrogen (H₂) carrier for gas turbine power generation due to its high energy density, ease of storage, and transportation. This study numerically investigates NH₃/air combustion using a hybrid Well-Stirred Reactor (WSR) and Plug Flow Reactor (PFR) model in Cantera at pressures of 1–20 atm, temperatures of 1850–2150 K, and equivalence ratios (ϕ) of 0.7–1.2. The effects of pressure, equivalence ratio, and temperature on NH₃ conversion and NO formation are examined. Results show that NH₃ exhibits a non-monotonic conversion curve with pressure after the …

Volumetric Wear Analysis Of Polyethylene Orthopedic Liners Using Point Clouds, Gabriel Landi

Dartmouth College Master’s Theses

In vivo wear of ultra-high molecular weight polyethylene (UHMWPE) orthopedic bearings represents a major problem in shoulder and knee arthroplasty, with significant amounts of devices failing due to the complication itself, and even more due to associated reasons for retrieval, including aseptic loosening. However, quantification of UHMWPE wear has largely been limited to linear and in vitro volumetric analyses, obscuring the understanding of the amount of clinically relevant material loss. This study proposes new methods to measure articular, rim, and backside surfaces of retrieved shoulder and knee UHMWPE liners, centered around the coordinate measuring machine (CMM)-based collection of high density …

Distributed Model Predictive Control-Based Power Management Scheme For Grid-Integrated Microgrids, Sergio Escareno, Sijo Augustine, Liang Sun, Sathishkumar J. Ranade, Olga Lavrava, Enrico Pontelli, John Hedengren

Faculty Publications

Transitioning from traditional electrical grids to smart grids is currently an ongoing process that many nations are striving for due to their access to renewable resources. Energy management is one of the key parameters that decides the performance of such complex systems. Distributed Model Predictive Control (DMPC) is a promising technique that can be used to improve the energy management of grid-connected systems. This paper analyzes a grid-connected inverter system with DMPC that exchanges key operating parameters with the grid to optimize coordinated power sharing between its respective loads. The state-space model for the inverter is derived and verified to …