Digital Commons Network^™

Fatigue Crack Length Estimation Using Acoustic Emissions Technique-Based Convolutional Neural Networks, Asaad Migot, Ahmed Saaudi, Roshan Joseph, Victor Giurgiutiu

Faculty Publications

Fatigue crack propagation is a critical failure mechanism in engineering structures, requiring meticulous monitoring for timely maintenance. This research introduces a deep learning framework for estimating fatigue fracture length in metallic plates through acoustic emission (AE) signals. AE waveforms recorded during crack growth are transformed into time-frequency images using the Choi–Williams distribution. First, a clustering system is developed to analyze the distribution of the AE image-based dataset. This system employs a CNN-based model to extract features from the input images. The AE dataset is then divided into three categories according to fatigue lengths using the K-means algorithm. Principal Component Analysis …

Acknowledgement Of Reviewers

Journal of Sustainable Mining

No abstract provided.

Assessing The Student Awareness To Age Friendly Design: The Case Of Ain Shams University- Architecture Students, Shorouk Saleh Ali Kabel, Samah Mohamed Elsayed Atteia Elkhateb, Rowaida M. O. Mohamed Rashed, Wesam M. El-Bardisy

HBRC Journal

With Egypt’s rapid urbanization and projected demographic shift, where individuals aged 60 and above are expected to constitute more than 15% of the population by 2050, addressing the needs of older adults in urban design has become increasingly urgent. Despite global recognition of the importance of age-friendly design, its integration into Egyptian architectural education remains limited, particularly concerning accessibility, safety, and inclusivity.

This study evaluates the level of awareness of Age-Friendly Design (AFD) principles among architecture students at Ain Shams University using a mixed-methods approach. Quantitative data were collected through an online questionnaire assessing students’ familiarity with the concept, while …

Performance-Based Seismic Design And Retrofitting Via Drift And Strain Criteria, Tharwat A. Sakr, Hanaa E. Abd-El-Mottaleb, Shrouk S. Kamel

HBRC Journal

In recent decades, the demand for seismic code updates has significantly increased. Performance-based seismic design (PBSD) is a modern approach to earthquake-resistant building construction. By using this technique, the designers can establish performance targets that satisfy the owner. Most codes rely on strength design that utilizes a design response spectrum, highlighting serviceability without addressing performance levels. In this paper, an appraisal for the incorporation of PBSD into the Egyptian Code is proposed using nonlinear static and dynamic analysis. At first, response spectrum charts for 72, 475, and 2475 years return periods (RP) were developed based on the Egyptian seismicity information. …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Three-Dimensional Numerical Investigation Of Darrieus-Type Vertical-Axis Wind Turbine With Active Suction Cavity, Ahmed Elbaz, Nabil Mahmoud Dr, Mohamwd Akmal Ammar Dr

Mechanical Engineering

The current work aims at improving the power conversion efficiency of a widely used type of wind turbine, the vertical-axis wind turbine (VAWT). This is achieved by actively controlling the boundary layer characteristics on turbine blades through wall suction. The effect of this active control technique on the performance of a NACA0018 Darrieus vertical-axis wind turbine is investigated by means of three-dimensional numerical simulations using ANSYS-FLUENT software. Pressure-based incompressible Unsteady Reynolds-Averaged NavierStokes (URANS) equations, complemented with the Shear Stress Transport (SST) k-ω model for turbulence closure, were solved. A detailed computational methodology is adopted, including domain extent, mesh refinement, and …

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 …

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 …

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 …

Trust In Automation Causing Automation Complacency In Relation To General Aviation: A Quantitative Study, Bart Augustin Marcules

Doctoral Dissertations and Projects

This quantitative causal-comparative study examined whether a relationship exists between Trust in Automation (TIA) and the potential for automation complacency in general aviation between pilots flying automated and non-automated aircraft as single pilots, while controlling for pilot generation. Autonomation can lead to automation complacency through overreliance and trust in the automation. Automation complacency may cause pilots to lose situational awareness and be surprised by automation surprises, leading to detrimental outcomes. Although automation complacency has long challenged commercial pilots, resulting in significant research on this issue, little research has focused on this issue in general aviation. The limited research is due …

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 …

Securing U.S. Leadership In Agentic Ai Literacy And Adoption: U.S. Vs Chinese Government Policies And Initiatives, Satyadhar Joshi

Harrisburg University Other Works

This paper conducts a comparative analysis of U.S. and Chinese frameworks for AI literacy and adoption, with focus on agentic AI and Artificial General Intelligence (AGI) systems capable of autonomous reasoning and execution. We examine national policies, educational integration, governance structures, and technological roadmaps, employing both qualitative review and quantitative modeling. Mathematical formulations include multi-dimensional literacy scoring, Bass diffusion models for adoption dynamics, risk assessment functions, regulatory effectiveness indices, competitiveness metrics, and optimization frameworks for resource allocation. Our analysis reveals divergent paradigms: the U.S. Favors decentralized, innovation-driven approaches with emphasis on interoperability and public-private collaboration; while China pursues centralized, state-led …

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 …

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 …

Exploring Runtime Sparsification Of Yolo Model Weights During Inference, Tanzeel-Ur-Rehman Khan, Sanghamitra Roy, Koushik Chakraborty

Electrical and Computer Engineering Student Research

In the pursuit of real-time object detection with constrained computational resources, the optimization of neural network architectures is paramount. We introduce novel sparsity induction methods within the YOLOv4-Tiny framework to significantly improve computational efficiency while maintaining high accuracy in pedestrian detection. We present three sparsification approaches: Homogeneous, Progressive, and Layer-Adaptive, each methodically reducing the model’s complexity without compromising its detection capability. Additionally, we refine the model’s output with a memory-efficient sliding window approach and a Bounding Box Sorting Algorithm, ensuring precise Intersection over Union (IoU) calculations. Our results demonstrate a substantial reduction in computational load by zeroing out over 50% …

Molecular Survival Strategies Against Kidney Filtration: Implications For Therapeutic Protein Engineering, William Heaps, Anna Elise Packard, Kristina M. Mccammon, Tyler P. Green, Joseph P. Talley, Bradley C. Bundy, Dennis Della Corte

Faculty Publications

The glomerular filtration barrier poses a significant challenge for circulating proteins, with molecules below ~60–70 kDa facing rapid renal clearance. Endogenous proteins have evolved sophisticated evasion mechanisms including oligomerization, carrier binding, electrostatic repulsion, and FcRn-mediated recycling. Understanding these natural strategies provides blueprints for engineering therapeutic proteins with improved pharmacokinetics. This review examines how endogenous proteins resist filtration, evaluates their application in protein engineering, and discusses clinical translation including established technologies (PEGylation, Fc-fusion) and emerging strategies (albumin-binding domains, glycoengineering). We address critical challenges of balancing half-life extension with tissue penetration, biological activity, and immunogenicity—essential considerations for the rational design of next-generation …