Digital Commons Network^™

Explainable Multi-Horizon Wind Power Forecasting Via Aquila-Optimized Machine Learning Models, Mostafa A. Abdelnaby, Nahla B. Abdel-Hamid, Eman M. El-Gendy, Mahmoud M. Saafan

Mansoura Engineering Journal

The inherently unpredictable nature of wind energy necessitates the development of sophisticated forecasting models to ensure grid stability and optimal distribution. In this research, we propose a novel and systematic approach to wind power forecasting (WPF) across diverse timescales. This approach leverages the power of various machine learning (ML) models, metaheuristic hyperparameter optimization, and utilizes explainable artificial intelligence (XAI). The developed methodology is based on the Aquila optimizer (AO), capable of automatically adjusting different ML models for four different time periods (30 minutes, 6 hours, 24 hours, and 36 hours) on the data collected from the Gabal El-Zayt wind power …

A Novel Superaug Adaptive Data Augmentation Strategy For Metabolic Syndrome Prediction: The First Egyptian National Cohort Study With Multi-National Comparison, Nihal Elsonny, Ashraf Elsharkawy, Abeer Tawakol, Amira Y. Haikal

Mansoura Engineering Journal

Metabolic Syndrome (MetS) is a multifactorial disorder associated with an increased risk of cardiovascular disease, type 2 diabetes, and obesity-related complications. Early and accurate prediction of MetS remains challenging due to heterogeneous data sources, class imbalance, limited sample sizes in regional studies, and poor generalization across populations.

This study introduces a comprehensive machine learning framework for predicting MetS and its prognostic factors using two heterogeneous datasets: a newly collected Egyptian clinical cohort and the U.S.-based (NHANES) [CDC, 2023] dataset. For each dataset, multiple experimental pipelines are developed, integrating preprocessing, feature selection, data augmentation, hyperparameter tuning, and ensemble modeling. …

Warm Mix Asphalt: Key Issues, Performance Challenges, And Modeling Advances, Mahmoud Owais, Marwa Awad

Mansoura Engineering Journal

Warm Mix Asphalt (WMA) has emerged as a sustainable alternative to conventional Hot Mix Asphalt by enabling asphalt production and compaction at reduced temperatures, thereby lowering energy consumption, emissions, and worker exposure to heat and fumes. Despite these advantages, the field performance of WMA remains influenced by technology-specific mechanisms, additive–binder compatibility, aggregate characteristics, moisture susceptibility, compaction quality, recycled material content, and climatic conditions. This review provides a critical synthesis of major WMA technologies, including foamed systems, chemical additives, organic waxes, hybrid additives, bio-based modifiers, and WMA mixtures incorporating reclaimed asphalt pavement. Particular attention is given to how these technologies affect …

State-Dependent Queueing For Adaptive Signal Control: A Simulation-Based Performance Evaluation, Shaimaa Alseddiek, Usama Elrawy Shahdah, Hala B. Nafea, Hossam El-Din Moustafa, El-Said Ahmed Marzouk, Mohamed M. Ashour

Mansoura Engineering Journal

Urban traffic congestion persists as a critical challenge to transportation system efficiency, sustainability, and safety. Traditional queuing models utilizing fixed service rates inadequately represent the dynamic feedback between congestion and capacity in real vehicular flow. State-Dependent Queuing Models (SDQMs) address this limitation by modelling service rate as a function of queue length or density. This research advances SDQM application for adaptive traffic signal control through development of a calibrated state-dependent departure rate implemented within a microscopic simulation environment using SUMO and TraCI. Six control strategies including fixed-time, actuated, and two SDQM variants were evaluated across traffic demands ranging from undersaturated …

Intelligent Torque Control Of Switched Reluctance Motors Based On Ditc And Wavelet Neural Network, Ameer L. Saleh, László Számel

Mansoura Engineering Journal

Switched Reluctance Motors (SRMs) have been considered a high-performance and environmentally friendly solution for Electric Vehicle (EV) applications owing to their simpler construction, robust design, and high efficiency. However, it suffers from high torque ripple and acoustic noise due to its highly nonlinear magnetic characteristics and double-salient structure.  This paper introduces a nonlinear torque control based on the Direct Instantaneous Torque Control (DITC) scheme and a Wavelet Neural Network (WNN)  to achieve better dynamic response and mitigate torque ripple.  The proposed WNN is employed as a nonlinear torque controller, inserted between the torque error and the hysteresis torque controller within …

Reliability-Oriented Spatiotemporal Machine Learning For High-Impact Power Outage Event Prediction, Marwa Gamal

Mansoura Engineering Journal

Power outages have become an increasing concern for modern power systems due to their impact on infrastructure reliability, economic activities, and public safety. The growing frequency of extreme weather events and the rising demand for electricity have made it more difficult to anticipate high-impact outage events. One of the main challenges in this context is the complex interaction between temporal patterns and geographic variations, which traditional methods often fail to capture effectively. This study develops a machine learning framework that combines temporal characteristics with geographic information to improve the prediction of high-impact power outages. Temporal features such as seasonal patterns, …

A Hybrid Geospatial And Remote Sensing Methodology For Drought Vulnerability Assessment In Semi-Arid Ecosystems, Kaifi Fakhir Chomani

Mansoura Engineering Journal

The Kurdistan Region of Iraq (KRI) faced significant drought challenges due to global and environmental changes, necessitating drought assessments. Advanced techniques of remote sensing, Geographic Information Systems (GIS), and Analytic Hierarchy Process (AHP) were combined in this research to perform drought vulnerability zonation for KRI. Average annual rainfall, Average number of rainy days, Average annual temperature, slope, elevation, normalised difference water index (NDWI), normalised difference vegetation index (NDVI), land surface temperature (LST), and temperature condition index (TCI) were selected as contributing parameters for drought vulnerability assessments. The considered parameters were weighted using pairwise comparison, and thematic maps were created to …

Psychological Impact Of Cultural Heritage Vr Experience Using Well-Mind Framework. (Case Study: Mahmoud Sami Palace In Mansoura)., Sara Ahmed El-Bayoumi, Hager Ahmed El-Sayed El-Ayouti, Mohamed Mohamed Shawki Abo Leila

Mansoura Engineering Journal

This study examines the psychological effects of immersive virtual reality (VR) experiences designed to preserve and disseminate intangible cultural heritage. It addresses the growing challenge of declining cultural engagement and the need for innovative tools that support psychological well-being, emotional engagement, and cultural connection. A VR-based reconstruction was developed within the historic Mahmoud Sami Al-Baroudi Palace in Mansoura, Egypt, focusing on traditional Egyptian practices and oral storytelling. The evaluation employed the WELL-MIND framework alongside participant feedback to assess both structured well-being indicators and subjective user experiences. The WELL-MIND-based assessment indicated improvements in cognitive stimulation, emotional engagement, and environmental perception. In …

Control Of Steel Corrosion Using Natural Corrosion Inhibitor, Rasha A. Nasef, O. Hamed, M. M. El-Halwany, M. H. Mahmoud

Mansoura Engineering Journal

This study investigates corrosion control of AISI 430 stainless steel in 1M HCl medium using an eco-friendly inhibitor extracted from pomegranate peel (PPE). The inhibition efficiency was evaluated at different PPE concentrations, with the highest applied concentration of 13g/L. Corrosion behavior was evaluated through weight-loss measurements and potentiodynamic polarization techniques conducted at room temperature.

The experimental results showed that corrosion resistance improved as the inhibitor  concentration increased. Lower corrosion rates were observed in the presence of PPE, along with reduced anodic and cathodic activities, indicating that the extract functions as a mixed-type inhibitor. Surface analysis confirmed the formation of …

The Multiwalled Carbon Nanotube-Incorporated Cfrp Laminates Interlaminar Fracture Toughness, Nikhil Janardan Rathod, Markala Karthik, Harshvardhan P. Ghongade, Javed Sikandar Shaikh, Rahul Soma Deshmukh

Mansoura Engineering Journal

The structural integrity of carbon fibre reinforced polymers (CFRPs) suffers from damage caused by interlaminar cracking and delamination which diminishes their performance capabilities. This study provides an experimental analysis of the impact of carboxyl-functionalized multi-walled carbon nanotubes (MWCNTs) on the Mode I interlaminar fracture toughness of CFRP laminates. The researchers created composite specimens by using epoxy systems which included MWCNT loadings of 0.1 wt% 0.2 wt% and 0.3 wt% together with an unmodified control. The researchers conducted Double Cantilever Beam (DCB) testing according to ASTM standards to ascertain the rate of critical strain energy release (G_IC) of the material. The …

Preserving The Identity Of Historic Coptic Orthodox Monasteries In Egypt Under Contemporary Pressures: Case Studies Of St. Bishoy And St. Anthony Monasteries, George Medhat Adeeb, Mohamed Khairy Amin, Mohamed Ahmed Al-Sherbiny

Mansoura Engineering Journal

Historic Coptic Orthodox monasteries in Egypt (HCOMEs) represent an important component of Christian heritage, integrating architectural value with enduring spiritual traditions that reflect both tangible and intangible dimensions. However, contemporary development and changing functional demands are generating transformations that may affect their identity if not carefully managed. Despite extensive scholarship, limited research has systematically examined these transformations through a structured evaluative approach. This study addresses this gap by developing and applying an analytical and evaluation framework to examine how contemporary pressures influence the historical, architectural-spatial, and spiritual identity of HCOMEs, and to assess the effectiveness of monastic responses. The framework …

Optimal Design Of Concrete Canal Section For Minimizing Overall Costs Using Artificial Ecosystem Optimization, Aya M. Elkhouly, Hamdy A. El-Ghandour, Tharwat Sarhan, Mahmoud E. Abd-Elmaboud

Mansoura Engineering Journal

The optimal design of concrete-lined canals represents a critical challenge in hydraulic engineering, requiring simultaneous minimization of construction and operational costs while satisfying hydraulic performance constraints. This study presents a novel optimization framework that utilizes the Artificial Ecosystem-based Optimization (AEO) algorithm for determining optimal canal cross-sectional dimensions. The proposed methodology comprehensively considers excavation costs, lining expenses, and water losses due to seepage and evaporation while ensuring hydraulic efficiency through velocity and Froude number constraints. The AEO algorithm mimics natural ecosystem processes through production, consumption, and decomposition phases to efficiently explore the design space and converge to global optima. The framework …

Optimization Of Mechanical Properties Of Copper Nanofiller Reinforced Banana Fiber Polyester Composites Using Grey–Taguchi Method, Nikhil Janardan Rathod, Mayur Jayant Gitay, Markala Karthik, Harshwardhan Ghongade, Upendra Rajak, Rajendra Sopan Narkhede

Mansoura Engineering Journal

Mechanical performance of banana fiber-reinforced polyester composites maximized using a composite Taguchi and a Grey Relational Analysis (GRA) process. This will aim at enhancing dispensation limits to enhance better tensile, flexural and impact belongings. The hand lay-up method was used to create composites by variable three key limits and they included banana fiber content (30 wt%), copper nanoparticle content (15 wt%), and curing temperature (80 o C). Trial design and mechanical properties were being measured in form of ASTM values on a L9 orthogonal array.

The multi-response optimization was performed with the help of the Grey Relational Analysis, which was …

Self-Lubricating Nano-Coatings For Tribological Applications: Materials, Mechanisms, And Ai-Driven Design Approaches, Viral Panara, Vikas Panchal, Satayu Travadi, Harmish Bhatt, Madhav Oza

Mansoura Engineering Journal

Self-lubricating coatings with nanostructured structures are becoming crucial in order to decrease friction and wear in critical tribological systems like precision tools, biomedical implants, aerospace actuators, and engines. In this review, the most representative nano-engineered solid lubricants such as transition metal dichalcogenides, diamond-like carbon, hexagonal boron nitride, graphene, MXenes and hybrid multilayer architectures are discussed. The focus of the discussion is on the most important tribological mechanisms like lamellar sliding, oxidative adaptation, tribofilm formation, transfer-film development, and nano-asperity contact evolution. It also summarizes the most significant deposition techniques, such as cathodic arc physical vapor deposition, chemical vapor deposition, plasma-based deposition, …

Data Collection Algorithm Based On Regression Analysis, Aiming To Minimize Data Processing Delays In 6g-Enabled Iot Networks, Archana Rajendra Mane, Sachin Vasant Chaudhari

Mansoura Engineering Journal

Researchers are currently investigating 6G wireless communication technologies because they can offer ultra-low latency, high data rates, and smarter network management than 5G. IoT services have spurred the development of cutting-edge technologies such as quantum communications, terahertz (THz), and artificial intelligence (AI), all of which are expected to be included into the subsequent generation of 6G networks. This paper presents a regression-based data collection algorithm for 6G-enabled IoT networks that dynamically prioritizes data through Random Forest regression. With a Mean Absolute Error (MAE) of 0.62 ms and a Root Mean Squared Error (RMSE) of 0.83 ms, the proposed method can …

Frequent Pattern Mining With Optimized Gated Recurrent Units For Automated Waste Classification Systems In Smart Cities, Anupriya Garg, Ratish Kumar

Mansoura Engineering Journal

Efficient waste segregation into biodegradable and non-biodegradable categories is a crucial requirement for sustainable smart city development. While Convolutional Neural Network (CNN)-based approaches have demonstrated promising performance, their high architectural complexity and limited adaptability often restrict real-time deployment. This paper proposes BnBCNet, a lightweight waste classification framework that reformulates image-based waste segregation as a sequential learning problem using Gated Recurrent Units (GRUs). To further enhance classification performance, Grey Wolf Optimization (GWO) is employed for hyperparameter optimization, while Adam optimizer is used for network weight updates. Experiments conducted on a publicly available waste segregation image dataset demonstrate that the proposed BnBCNet …

Harmonic Mitigation And Reactive Power Support Using Dpfc And Upfc In Grid‑Connected Solar Pv Systems: A Comparative Performance Evaluation, Abhishek Vashistha, Dharmbir Prasad, Rudra Pratap Singh

Mansoura Engineering Journal

Harmonic distortion, reactive power imbalance, and voltage instability represent major challenges regarding the power quality of grid-connected solar photovoltaic (PV) systems. Various recently developed flexible AC transmission system (FACTS) devices such as the unified power flow controller (UPFC) and distributed power flow controller (DPFC), however, have made them possible solutions but still do not provide a quantitative comparison in similar dynamic conditions. In this paper, comparative performance analysis of UPFC and DPFC in 3 MW grid connected solar PV system using Matlab Simulink is presented. The two controllers are affected by same disturbances, namely irradiance step change at t = …

Dielectric And Thermal Ageing Characteristics Of Al₂O₃ Nanofluid-Impregnated Cellulose For Power Transformer Applications, Shaymaa A. Qenawy, Basma Eltlhawy, Hussein M. Waly

Mansoura Engineering Journal

As nanotechnology progresses, nanofluids have emerged as promising candidates for transformer insulation due to their enhanced dielectric and thermal properties. This study evaluates the degradation behavior of a mineral oil-cellulose system compared to an alumina (Al₂O₃) nanofluid-cellulose system. Accelerated thermal ageing tests were conducted at 120°C for a 20-day duration, simulating approximately 20 years of field service. Key findings reveal that the nanofluid-impregnated paper exhibited superior mechanical longevity, characterized by a 3% reduction in the tensile strength deterioration rate relative to mineral oil samples. Initially, the AC breakdown voltage (BDV) of the nanofluid was 10.85% higher …

Effect Of Graphene Oxide On The Upconversion Photoluminescence Behavior Of Er/Yb Co-Doped Pvdf-Go Composite Nanofibers, Saptasree Bose, Jack Ryan Summers, Bhupendra B. Srivastava, Karen Lozano, Victoria Padilla-Gainza

Mechanical Engineering Faculty Publications

Upconversion photoluminescence (UCPL) materials, particularly rare-earth (RE) doped nanoparticles, have garnered significant attention due to their ability to convert near-infrared (NIR) excitation into visible emission, offering benefits such as high photostability, long lifetimes, low autofluorescence, and deep tissue penetration. Among various platforms, polymer-based one-dimensional (1D) nanofibers with in-situ lanthanide doping remain relatively unexplored, despite their superior mechanical flexibility, processability, and potential for improved luminescence performance. In this study, we report the fabrication and UCPL quenching behavior of Er3+/Yb3+ co-doped polyvinylidene difluoride (PVDF) nanofibers incorporated with graphene oxide (GO), synthesized for the first time using the scalable Forcespinning® technique. PVDF, a …

Forcing A Molecule To Switch: Quantifying Mechanical Control At The Atomic Scale, A. M. Shashika D. Wijerathna, Markus Zirnheld, Michael L. Hildebrand, Myles Perry, Marjorie Cenese, Yuan Zhang

Physics Faculty Publications

Mechanically induced conformational switching at the single-molecule level represents a fundamental mechanism for molecular functionality, yet quantitative characterization of the underlying force and energy landscape remains limited. Here, we study individual TBrPP-Co(II) molecules on Au(111) using qPlus atomic force microscopy. By reconstructing interaction potentials from 3D Δf(x,y,z) data, we determine a threshold force of ∼96 ± 8 pN and a tip-induced switching interaction energy of ∼38 ± 4 meV associate with the conformational transition. The isolated tip-molecule force follows a power law (exponent ∼6), indicating dominance of long-range van der Waals interactions. …

Reaching The Intrinsic Performance Limits Of Superconducting Nanowire Single-Photon Detectors Up To 0.1 Mm Wide, Kristen M. Parzuchowski, Eli Mueller, Bakhrom G. Oripov, Benedikt Hampel, Ravin A. Chowdhury, Sahil R. Patel, Daniel Kuznesof, Emma K. Batson, Ryan Morgenstern, Robert H. Hadfield, Varun B. Verma, Matthew D. Shaw, Jason P. Allmaras, Martina J. Stevens, Alex Gurevich, Adam N. Mccaughan

Physics Faculty Publications

Superconducting nanowire single-photon detectors combine high detection efficiency, low noise, and excellent timing resolution, making them a leading platform for photon-counting applications. However, despite decades of materials and fabrication research, detector performance has never been shown to match theoretical performance expectations. Here, we demonstrate in situ tuning of a detector from its typical, suboptimal operation, to a regime limited only by material quality, allowing the device to reach its intrinsic performance limit. Our approach is based on current-biased superconducting “rails” placed on either side of the detector that redistribute current across its width to achieve peak performance. This technique reduces …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Towards Supporting Real-Time Estimation Of Vehicle Fuel Consumption And Co2 Emissions In Smart City Applications, Abrar Alali, Stephan Olariu

Computer Science Faculty Publications

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

Exploring Drone Technology For The Survey And Documentation Of Aerospace Archaeology Sites, David G. Morgan, Thomas R. Allen

Political Science & Geography Faculty Publications

This research examines the implementation of Unmanned Aerial Systems (UAS) during a pilot mission in Nike Park to support its aerospace preservation. Located in Carrollton, Virginia, it was a Cold War missile installation that formed part of the area’s air-defense network. This project aims to use a drone to capture high-resolution video and imagery of Nike Park’s Administrative Office Supply and PX building and the Nike Ajax missile to create virtual 3D models for the Isle of Wight County Museum’s exhibit. For this mission, the DJI Mini 4 Pro drone manually flew a circular flight pattern around the missile and …

Autonomous Weapons And Strategic Stability, Chick Edmond

Political Science & Geography Faculty Publications

The increasing number of artificial intelligence (AI) elements within military systems has introduced new forms of security dilemmas related to speed, level of secrecy, and transfer of responsibility from humans to machines. This article addresses the question of whether or how AI enabled autonomous weapons can lead to greater levels of strategic instability. Three causal mechanisms were determined by this study to potentially create destabilizing effects due to the introduction of autonomy; the first mechanism is a reduction in time available for decision making. The second mechanism involves the creation of multiple pathways of escalation. The third mechanism is the …

Harnessing Mono-2-(Methacryloyloxy) Ethyl Succinate Grafting For Robust Micro/Nanoplastic-Resistant Ultrafiltration Membranes, Mohadeseh Najafi, Javad Farahbakhsh, Mitra Golgoli, Michael Johns, Masoumeh Zargar

Research outputs 2022 to 2026

The growing presence of microplastics (MPs) and nanoplastics (NPs) in water systems poses a serious threat to conventional treatment processes, particularly membrane filtration. This study focuses on a surface modification strategy for commercial ultrafiltration (UF) membranes via plasma-induced grafting of mono-2-(methacryloyloxy) ethyl succinate (MMES) to enhance resistance against MP/NPs fouling. The membranes were characterised using advanced analytical techniques, including high-resolution scanning electron microscopy (HRSEM), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM), confirming successful grafting, increased hydrophilicity, and a more negative surface charge. At optimal conditions of grafting (2 min pretreatment, 0.5 M monomer concentration, and 3 h grafting …