Digital Commons Network^™

Optimisation Of Stepped Basin Solar Still With Response Surface Methodology And Machine Learning Methods, M. Balamurugan, V. Santhanam, B. Deepanraj, S. P Manikandan, M. Yuvaperiyasamy, N. Senthilkumar

Mansoura Engineering Journal

This research focuses on enhancing the distillate output of a stepped basin solar still by optimizing key operational variables using Response Surface Methodology (RSM) and advanced machine learning techniques. As water scarcity remains a critical global challenge, solar stills offer a viable, sustainable solution for desalination. These systems offer an energy-efficient approach to converting saline water intofreshwater, providing an alternative to conventional desalination methods. The primary objective was to investigate how variations in solar radiation, step height, and water inlet temperature influence the productivity of the stepped basin solar still. The solar radiation ranged from 600 to 1000 W/m², the …

Performance Of Foamed Concrete Before And After Fire Conditions: A Review, Poles Bahgat, Mohamed Kohail, A. Fathy, A. Afifi

Mansoura Engineering Journal

Foamed concrete is defined as a type of concrete distinguished by its workability, low density, and insulating properties. It is used as building material. The objective of this review is to discuss the physical and mechanical characteristics and fire sensitivity of foamed concrete. Density influences the strength of concrete. The strength of concrete is between 2 and 3.5 MPa, which increased with the addition of gold mining waste, reaching approximately 35 MPa at optimal replacement levels.  There is a 60-70% reduction in strength for lightweight concrete with a density of 400-1200 kg/m³ compared to conventional concrete. The fire sensitivity of …

Remote Sensing-Based Rice Detection: Novel Indices From Optical And Sar Synergy, Kaifi Chomani

Mansoura Engineering Journal

Accurate rice mapping is challenging due to the lack of a simple, efficient approach and the complexity and limited ability of existing methods to distinguish rice fields from non-rice fields. This study proposes two novel indices: the Multispectral Rice Detection Index (MRDI), which utilises optical imagery, and the OptiRadar Rice Index (ORRI), which incorporates both optical and Synthetic Aperture Radar (SAR) data. Ground-truth data from the Cropland Cropping Systems (CROS) were used to assess the performance of both indices. The findings indicated that MRDI achieved an overall accuracy of 97.97% with perfect recall (100%) but lower precision (78.65%). However, the …

Design, Optimization, And Techno-Economic Analysis Of A Standalone Pv-Wind-Battery Microgrid For A Remote Load In Kurdistan Region Of Iraq, Harwan Mohammed Taha, Laith Akram Mohammed

Mansoura Engineering Journal

Standalone microgrids play a crucial role in rural areas where grid electricity delivery is difficult and expensive. This research verifies the effectiveness of a standalone microgrid in a rugged area, Pers Mountain in Akre district, Duhok governorate, Iraqi Kurdistan. A first hybrid renewable energy system (HRES)-based standalone microgrid is presented to supply a remote load. Solar and wind energy are employed for their reliability, sustainability, and abundance, with battery storage ensuring continuous power when solar and wind are unavailable. Simulation results demonstrate reliable power supply during summer months (loss of power supply probability < 5%), with the system operating across three modes — PV-dominant daytime, combined wind-battery evening, and battery-discharge nighttime. The inverter achieved minimum total harmonic distortion of 1.53% for both current and voltage outputs, below the IEEE 519 limit. A full-year hourly particle swarm optimization (PSO) sizing analysis assessed annual reliability and each source's economic contribution: the original fixed sizing yields an annual loss of power supply probability (LPSP) of 26.80% (expected energy not served ~1,471 kWh/year), motivating a PSO-optimized PV-battery configuration achieving annual LPSP of 5% (~274 kWh/year unserved) at comparable cost. The site's wind resource is reasonable (13.8% capacity factor), but the fixed tower and controller cost outweighs its contribution at this small scale. The proposed HRES totals $11,960–$16,317 depending on configuration, approximately 28–38 times lower than grid extension ($455,600) to the site, while producing zero CO₂ emissions and avoiding approximately 4,189 kg/year versus an equivalent HOMER Pro-simulated diesel generator.

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 = …

An Iterative & Analytical Hybrid Deep Learning Framework For Enhanced Brain Tumor Detection And Segmentation In Mri Video Scans, Swati V. Sakhare

Mansoura Engineering Journal

The critical need for accurate and robust brain tumor detection in MRI video scans is for early diagnosis and treatment planning operations. However, the existing methods suffer from several limitations, such as not being able to capture spatio-temporal features, handling class imbalances, and generalizing across datasets with diverse characteristics. To address these challenges, I propose a hybrid deep learning framework that integrates multiple advanced techniques to enhance the detection and segmentation of brain tumors. My approach starts by using the 3D CNN-LSTM (ResNet3D-LSTM) to extract spatio-temporal feature, which utilizes ResNet3D for spatial detail extraction and LSTM for temporal coherence across …

Optimizing The Lightweight Steel Fibre Reinforced Bacterial Concrete With Nano-Silica And Coconut Shell Aggregates Using Taguchi Method, Damodaran Pooja, Mayilsamy Yuvaperiyasamy

Mansoura Engineering Journal

Concrete is essential for construction due to its high strength and durability. It supports most modern infrastructure globally. Cement production is a major source of CO₂ emissions, posing serious environmental concerns. Additionally, the rising demand for concrete is straining the limited supply of essential raw materials, such as aggregates. Researchers are examining alternative materials to improve Sustainability while maintaining structural strength to address these issues. This research aims to improve concrete performance by enhancing cement durability with nano-silica. Coconut shell, an agricultural by-product, functions as an eco-friendly alternative to conventional coarse aggregates, alleviating their scarcity. Microbiologically induced calcium carbonate precipitation …

Multi-Method Energy Optimization: Linear Programming, Hmm Modeling And Arduino-Based Simulations, A.E. El-Alfy, Eman Elayat, M. A. E. Sheta

Mansoura Engineering Journal

This paper presents the simulation and optimization of hybrid energy systems integrating renewable energy and storage technologies. Linear Programming (LP) is utilized to minimize operational costs, while a Hidden Markov Model (HMM) improves reliability by accurately predicting energy flow. Hardware-in-the-loop simulation in Proteus demonstrates efficient energy trading, significantly reducing dependence on conventional sources. The results from a 24-hour analysis show that the system achieves an 87.14% load factor, indicating high efficiency in utilizing the system's rated capacity, 26.23 system reliability, and a 20% improvement in cost efficiency and enhanced sustainability, providing a robust and practical framework for hybrid energy solutions …

Development Of Enhanced Dingo Optimization Algorithm For Distribution Network Reconfiguration, Samson Oladayo Ayanlade, Ignatius Kema Okakwu, Richard Oladayo Olarewaju, Ayodeji Akinsoji Okubanjo, Joseph Bukola Samson, Oluwadare Adepeju Adebisi, Emmanuel Idowu Ogunwole, Oluwadare Olatunde Akinrogunde

Mansoura Engineering Journal

This paper presents the Enhanced Dingo Optimization Algorithm (EDOA) to reconfigure the radial distribution network with the long-standing problem of active power loss minimization while maintaining a stable and acceptable voltage profile. EDOA enhances the standard Dingo Optimization Algorithm (DOA) through the integration of adaptive search control, elite-guided local refinement, and crossover-based diversity preservation mechanisms tailored for distribution network reconfiguration problems. The proposed method has been tested on two test systems: the IEEE 33-bus standard as well as the real-world Ayepe 34-bus distribution feeder in Ibadan, Nigeria. In the IEEE 33-bus test system, EDOA reduces active power loss by 56.00%; …

Green Hydrogen-Based Decarbonization Approach For Steel Industry With Hybrid Renewable Energy Sources, Raju Kumar Maurya, Dharmbir Prasad, Rudra Pratap Singh

Mansoura Engineering Journal

This work provides a micro-pilot techno-economic and environmental assessment of a green hydrogen-driven decarbonization scenario for low-carbon steel production at the capacity of 1 ton steel produced (i.e., requiring 50 kg of green hydrogen). In this work an integrated renewable energy (RE) scenario for the steel production site at Jamuria, West Bengal, India is planned to utilize solar photovoltaic (PV), wind turbines (WTs), battery storage and a diesel backup generator to meet the hydrogen production load from an electrolyzer using HOMER Pro software. The results show that the proposed micro-pilot system produces excess electricity with respect to the plant's operating …

Integrating Environmental Awareness In Underwater Acoustic Networks: A Comprehensive Review, Sadaf Vahabli, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi, Walid K. Hasan, Ruba Zaheer

Research outputs 2022 to 2026

Underwater Acoustic Networks (UANs) are critical for enabling long-range underwater communication, supporting a wide range of applications, such as environmental monitoring, resource exploration, disaster prevention and marine security. Unfortunately, UANs operate within a limited acoustic spectrum, where these spectrums often overlap with those used by marine animals for communication, navigation and foraging. Additionally, anthropogenic noise from industrial activities contributes to acoustic pollution, intensifying this problem. This frequency overlap poses significant risks to marine life. Therefore, this review highlights the urgent need to develop environmentally aware UANs that minimize harmful acoustic interference with marine mammals, fish and invertebrates. Further, it focuses …

Experimental Investigation And Performance Optimization Of A Double Slope Solar Still Integrated With Nanoparticles In Phase Change Materials, Pronob Das, Md Shahriar Mohtasim, Utpol K. Paul, Md Sanowar Hossain, Barun K. Das, Anik Saha, Md Golam Kibria

Research outputs 2022 to 2026

By utilizing solar energy for desalination, solar stills offer a sustainable and cost-effective means of providing fresh drinking water in remote and arid regions. Existing studies have primarily focused on improving freshwater productivity while considering economic and environmental feasibility. The present work offers an in-depth assessment of solar still (SS) systems by analyzing energy and exergy performance, exergoeconomic factors (energy-economic factor, exergoeconomic factor, and cost of water), environmental impacts (CO2 emissions, exergo-environmental factor, and carbon credit gained), and sustainability indicators (energy payback time and sustainability index). Five different cases were examined: (I) a conventional solar still (CSS), (II) CSS with …

A Universal Hybrid Model-Free Deep Quantum–Transfer Learning Controller Enhanced By Grey Wolf Optimization For Dc–Dc Boost Converters With Hardware-In-Loop Validation, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

This paper proposes a universal hybrid model-free quantum–transfer learning controller with enhanced online grey wolf optimization algorithm (GWO–QTL) for DC–DC boost converter. This system has the characteristics of non-minimum phase behavior, parasitic effects, and fractional-order dynamics because of high frequency operation. These characteristics make analytical modeling complicated and make it difficult to have a single traditional controller that will operate reliably over different converter types. This motivates the creation of a unified model-free control framework that is able to learn directly from the behavior of the converter without relying on the topology specific models. Reinforcement learning, where an agent interacts …

Multi-Scale Sensor-Aware Variational Autoencoders For Adaptive Iot Security: Integrating Drift Calibration And Cyberattack Detection, Md Kamal Hossain, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Existing approaches to Industrial Internet of Things (IoT) anomaly detection treat sensor drift calibration and cyberattack detection as separate problems, overlooking their strong interdependence and the compound failure modes that arise when drift dynamics are exploited by stealthy adversaries. Industrial Internet of IoT sensor networks therefore require a unified framework that jointly models both phenomena. This paper proposes the first unified multi-scale sensor-aware variational autoencoder (MS-VAE) framework that jointly models sensor drift and malicious activity within shared latent representations, enabling integrated calibration and security monitoring. The framework introduces three key components: (i) a multi-scale latent architecture that captures short-term anomalies …

A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller Enhanced For Speed Regulation Of Brushless Dc Motor, Seyyed Morteza Ghamari, Asma Aziz, Daryoush Habibi

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are widely used in applications that are highly-efficient, reliable, and compact, such as electric vehicles, robotics, and medical devices. However, the inherent nonlinearities and load sensitivity of BLDC motors require a robust and adaptive control strategy to ensure satisfactory performance under various operating conditions. Sliding mode control (SMC) has been widely used for the BLDC drives. However, because of its simplicity and robustness, the control effectiveness of the control is limited by the sensitivity to the disturbances and the chattering phenomenon. To remedy this, super-twisting (ST) technique has been proposed to achieve smoother response and better …

Mobility Of Hydrophilic And Hydrophobic Nanoparticles In Carbonate Reservoirs: Application To Subsurface Projects, Zain Ul Abedin Arain, Sarmad Al-Anssari, Muhammad Ali, Faisal Ur Rahman Awan, Alireza Keshavarz, Jitendra Sangwai, Mohammad Sarmadivaleh, Stefan Iglauer

Research outputs 2022 to 2026

Nanoparticles (NPs) have been suggested for subsurface projects, including enhanced oil recovery (EOR), carbon capture and storage (CCS), and hydrogen storage, due to their small size, high surface energy, controlled surface properties, and significant effects on the underground formation properties. Silica is an excellent NP that efficiently modifies the surface properties of subsurface formations for incremental oil recovery and to derisk carbon and hydrogen leaks. However, the retention of injected NPs in a narrow area near the injection inlet can limit a project’s feasibility. In the present study, hydrophobic (hybrid) NPs were produced via silanization of silica NPs, and the …

Design Of Artificial Interference Signal Waveforms For Covert Communication Aided By Multiple Friendly Nodes, Xuyang Zhao, Wei Guo, Yongchao Wang, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider a covert communication scenario with multiple friendly interference nodes. The goal is to hide a legitimate communication link from a transmitter to a receiver under a warden’s surveillance. Firstly, we propose a novel strategy for generating artificial noise (AN) signals and formulate a corresponding design problem, aiming to minimize the adverse effects of AN on the legitimate receiver while enhancing communication covertness. Specifically, we optimize the basis matrix for AN signal space using statistical information of the involved channel coefficients, when precise channel state information are unavailable. Secondly, we analyze the geometric structure of the …

Optimizing Ev Battery Charging Using Fuzzy Logic In The Presence Of Uncertainties And Unknown Parameters, Minhaz Uddin Ahmed, Md Ohirul Qays, Stefan Lachowicz, Parvez Mahmud

Research outputs 2022 to 2026

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system …

Gradient-Based, Post-Optimality Sensitivity Analysis With Respect To Parameters Of State Equations, Gene Hou, Jonathan Degroff

Mechanical & Aerospace Engineering Faculty Publications

Design optimization is a computational tool that can enable a designer to investigate the effectiveness of a design concept in an organized format. However, this design process requires the design variables, constraints, and objective function to be properly defined and expressed in mathematical forms. Post-optimality analysis thus becomes a necessary step to investigate different variations in the problem formulation and parameters to ensure that optimization produces a stable and trustworthy outcome. One efficient way to achieve this aim is to compute the local derivative of the optimized objective function with respect to the optimization problem parameters, such as bounds on …

Uncertainty-Aware Estimation, Planning, And Control For Tracking Multiple Drifting Patches In Flow Fields, Daniel O. Akanji, Krishnanand N. Kaipa, Cong Wei

Mechanical & Aerospace Engineering Faculty Publications

In this study, we present a replay-based framework for uncertainty-aware persistent tracking of multiple advected surface patches using an autonomous marine vehicle operating in spatiotemporal-varying currents. The method combines three components: local flow estimation, covariance-aware patch-boundary propagation with intermittent boundary fusion, and mission-level scheduling over multiple patches. Each patch is represented by a polygonal boundary, whose vertices are propagated through the estimated flow field while carrying per-vertex covariance, thereby quantifying uncertainty growth during advection. A flow-aware gain-scheduled linear quadratic regulator (LQR) was designed to shape the desired surge speed to take advantage of favorable currents. When the vehicle services a …

Influence Of Motion Artifacts On Instantaneous Frequency Based Heart Rate Variability Of Arterial Pulse Signals Measured By A Tactile Sensor: An Analytical Study, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Objective: Heart rate variability (HRV), quantified via the instantaneous frequency (IF) of arterial pulse signals, is a key indicator of cardiovascular (CV) regulation. This study presents a theoretical and experimental investigation of how motion artifacts (MA) influence IF-and thus HRVmeasured with a tactile sensor, emphasizing underlying mechanisms rather than population-level validation. Approach: Building on a previously developed single-degree-of-freedom (SDOF) model of the tissue-contact-sensor (TCS) stack between the sensor and the artery, MA is represented as low-frequency baseline drift and time-varying system parameters (TVSP) of the TCS stack. Analytical expressions quantify the effect of MA on the IF of each harmonic …

Mechanical-Medical Convergence In Heart Failure: Artificial Intelligence, Finite-Element Modeling, And 3d Printing For Diagnosis And Prognosis, Quazi Noor E. Sabrina, Quazi Md Zobaer Shah, Quazi Noor E. Sohela, Md Mahabub Hasan Mousum, Md. Moyeen Uddin Chisty, Quazi Md. Akbar Shah

Mechanical & Aerospace Engineering Faculty Publications

Heart failure remains a leading cause of global morbidity and mortality, yet routine clinical indices often miss the regional biomechanical disturbances that drive progression and shape treatment response. This State-of-the-Art review examines how finite-element (FE) modeling, additive manufacturing, and artificial intelligence (AI) are converging to improve the diagnosis, phenotyping, procedural planning, and prognostic assessment of heart failure (HF). Although these technologies have matured in structural heart disease and transcatheter intervention research, their greatest translational potential may lie in HF, where patient-specific ventricular remodeling, myocardial stress–strain heterogeneity, valve-ventricular coupling, and device-tissue interaction are incompletely captured by conventional clinical indices. We synthesize …

Radical-Based Oxidative Pretreatment Enhances Biofuel Production From Lignocellulosic Biomass Via Hydrothermal Liquefaction, João Vitor Dos Santos, Louis C. Bondurant, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

The sustainable production of biofuels from lignocellulosic biomass is a central goal in the transition to low-carbon energy systems. However, hydrothermal liquefaction (HTL), a promising thermochemical conversion pathway, is constrained by the high oxygen content and complex aromatic structure of lignin, which lowers bio-oil quality. Here, we used a model system of brown-rot-degraded white oak (Quercus alba) to test whether radical-based oxidative pretreatment could enhance HTL performance by converting lignin into more aliphatic intermediates. Oxidation was performed under simulated Fenton conditions using fixed Fe(II) (60 ppm) and two hydrogen peroxide concentrations (3 and 8 M), resulting in extensive lignin depolymerization …

First-Job Contract Review Cheat Sheet, Johanna Jones-Morris, Ashlee Martellacci

Teaching and Learning Resources

This cheat sheet helps first-time employees understand what to review before signing an employment contract. It highlights job duties, compensation, scheduling, employment terms, benefits, restrictive clauses, worker classification, and common red flags so that individuals can ask informed questions and recognize potentially unfair or unclear terms.

Cross-Layer Supervisory Control For Low-Altitude Uav Swarm Networks, Nitin Singh Rathore

Computer Science and Engineering Theses

Low-altitude unmanned aerial vehicle (UAV) swarms are increasingly used in applications such as aerial sensing, disaster response, and communication support, where reliable operation under dynamic and uncertain conditions is essential. In these environments, performance degradation arises from multiple sources, including external disturbances, sensing uncertainty, and communication impairments. Although these effects originate from different layers of the system, such as dynamics, observation, and networking, they often manifest as similar tracking or coordination errors. Conventional control approaches, which rely primarily on error-driven feedback, do not explicitly account for the underlying cause of these deviations, limiting their effectiveness in multi-agent settings. This thesis …

Gaussian Process Regression–Based Uncertainty Quantification For Unmanned Aircraft System Traffic Management And Advanced Air Mobility Applications, Aakarshan Khanal

Mechanical and Aerospace Engineering Dissertations

Uncertainty quantification has gained significant attention in recent years as a research area in dynamical systems. Mathematical representations of the physical system, combined with an understanding of model uncertainties, enable the propagation of uncertainty in temporal space, which allows us to make informed decisions. However, what if the true dynamics of the system is unknown or too complex to define explicitly? In such cases, the system’s behavior can instead be inferred or learned from observed input–output data rather than from an analytical or physics-based model. To this end, this dissertation focuses on developing a data-driven framework for nonparametric dynamics modeling, …

Raw Data For The Manuscript "Silicon-Stabilized Three-Dimensional Covalent Networks In High Entropy Diborides", Michael Yeung, Reza Mohammadi

Chemistry Department Faculty Scholarship

Abstract for data:

Raw data for the journal publication, Silicon-stabilized three-dimensional covalent networks in high entropy diborides. ReadMe file provided.

Abstract for Journal publication: 

High entropy ceramics offer a pathway to stabilize unconventional chemistries beyond traditional alloying rules. We report the incorporation of silicon into an AlB₂-type high entropy diboride, Cr_0.2Nb_0.2Si_0.2Ta_0.2Ti_0.2B₂, despite silicon violating classical Hume-Rothery rules for alloying. Arc melting produced a phase-pure, chemically homogeneous structure, as confirmed by powder X-ray diffraction (pXRD) and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM–EDS). Silicon occupies …

Guitar Amplifier Directivity, Rachel C. Edelman, Brian E. Anderson, Samuel D. Bellows, Timothy W. Leishman

Directivity

No abstract provided.

Uncertainty Quantification, Propagation & Conjunction Assessment In Orbital Mechanics Using Generalized Polynomial Chaos Expansion & 2-Dimensional Conjunction Plane Analysis Techniques, Monalisa Karim

Mechanical and Aerospace Engineering Theses

Uncertainties, that are inherent to dynamic models, can be associated with state initial conditions, force modelling errors, navigation and actuation errors. In system modelling stochastic differential equations are used to represent dynamic phenomena with uncertainties, for which the solutions are probability density functions of quantities of interest characterizing the realization of the stochastic processes. In Polynomial Chaos Expansion (PCE) propagation, these solutions are represented as weighted sums of multivariate spectral polynomials that are functions of the input random variables. Generalized polynomial chaos expansion (gPC) is an extension to the original homogenous PCE which projects the random solution onto a basis …

Explainable Ai-Driven Predictive Maintenance Curriculum For Smart Manufacturing, Abdullah Al Mamun, Murat Kuzlu, Katherine Smith, Vukica Jovanovic, Dalya Ismael, Adel El-Shahat, Angela Sicaja, Md. Hedayetul Islam Chy

Engineering Technology Faculty Publications

Nowadays, Industry 4.0 has transformed manufacturing industries into a data-rich system driven by IoT, automation, and Artificial Intelligence (AI). Within this context, Predictive Maintenance (PdM) provides a proactive strategy that leverages heterogeneous sensor data such as vibration, acoustic, electrical, and visual signals along with historical performance and advanced analytics to forecast equipment failures before they occur. Usually, AI-driven PdM (AI-PdM) enhances this capability by integrating AI-based sensor analytics to automate fault prediction and optimize system reliability. However, traditional AI-PdM often functions as a “black box,” providing limited interpretability of its decision-making process and posing challenges for trust, validation, and human …