Engineering Commons^™

Optimizing Human Capital In Ai-Enabled Architectures: A Systems Constraint And Capability Analysis, Jeremy A. Schlegel, Jennifer Daffinee

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Artificial intelligence (AI) comprises not only models, but full socio-technical systems involving data pipelines, instrumentation, human-machine interfaces, deployment architectures, and organizational processes for design, monitoring, and evaluation. Using a systems-oriented analytical framework, this paper argues that despite accelerating advances in AI capabilities, human capital remains the enduring and dominant system constraint. Human interfaces define throughput limits in areas such as prompt engineering, data-stream curation, adjudication of model outputs, and the orchestration of hybrid automation workflows including robotics, scraping, and digitization. Synthesizing emerging research across human-AI interaction, machine-learning lifecycle management, organizational adoption, and adult learning theory, we present a socio-technical evaluation …

Law Or Flaw: A Double – Blind Study Comparing Student Comprehension Of Real And Ai Generated Legal Case Briefs, Grant Shostak, Nick Wintz, Melissa A. Petkovsek

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

In recent years, the U.S. legal system has seen an increase of legal filings using fictitious court cases or legal propositions generated by artificial intelligence (AI) (Stokel-Walker, 2026). Rules of professional responsibility require that lawyers review filings in which AI was used to ensure their accuracy (Missouri Bar, Office of Legal Ethics Counsel, 2024, Opinion No. 2024‑11) Despite this mandate, filings with fictitious cases and incorrect statements of law are being filed. Besides posing a threat to the parties to an action, such filings may set an unwarranted precedent for future cases. They also tie up court resources searching for …

A Smart Contract Framework For Project Financing In Gold Mining Operations: Implications For Improved Financial Forecasting, Silvia Lydia Priskilla, Mohammed Ali Berawi, Mustika Sari Dr.

Smart City

Gold mining projects are characterized by high capital intensity and significant operational variability, making financial forecasting particularly sensitive to the quality and timing of cash-flow data. In practice, conventional financing systems rely on multi-stage administrative processes that create delays between work completion and payment realization, reducing the reliability of financial records used for investment evaluation.

This study aims to develop a smart contract-based financing framework to improve the accuracy of profitability projections in gold mining projects. A case study approach is employed using operational and financial data from a mining project in Sumatra over the period 2023–2026. The analysis combines …

Integrasi Metodologi Hazop Dalam Pengendalian Risiko Dan Keberlanjutan Operasional Pada Unit Pemulihan Urea: Studi Kasus Pada Industri Pupuk, Riny Yolandha Parapat, Arin Nur'aini Putri, Aryasatya Ramadhan Sukresno

National Journal of Occupational Health and Safety

The fertilizer industry is one of the chemical sectors with high-risk potential due to its operational processes involving hazardous materials as well as extreme pressure and temperature conditions. This study aims to identify and analyze hazards in the urea recovery unit using the Hazard and Operability Study (HAZOP) method, while also formulating effective risk control strategies to support the operational sustainability of the plant. The study was conducted directly at a commercial fertilizer plant in West Java using a semi-quantitative approach, which included field observations, review of technical documents such as Process Flow Diagrams (PFD), Piping and Instrumentation Diagrams (P&ID), …

Discrete Fracture Network Application To Rock Slope Stability In An Open Pit Mine, Elvis Karikari Mensah, Erzah Ackah, Reginald Hammah, Hani Mitri

Journal of Sustainable Mining

The stability of rock slopes in open pit mines is crucial for the safety and efficiency of the mining operation. Conventional stability analysis methods, such as kinematic and limit equilibrium analyses, primarily focus on identifying structural failure mechanisms and evaluating their factors of safety. Although insightful, these approaches do not accurately estimate failure volumes and block locations due to their limited consideration of joint frequency and persistence, which are key parameters in understanding block geometries. Discrete fracture network (DFN) modelling addresses these limitations by explicitly simulating rock mass discontinuities in 3D, which automatically incorporates joint spacing and persistence.

This paper …

Upholding Long-Termism In Basic Research To Shape Leading Advantage Of Automotive Industry, Yubo Lian

Bulletin of Chinese Academy of Sciences (Chinese Version)

General Secretary Xi Jinping has pointed out that basic research is the origin of the entire scientific system and the master switch for all technological issues, emphasizing that it must be advanced with greater efforts and more concrete measures. Upholding long-termism in basic research means placing it at the foundation of the innovation chain and maintaining continuous investment with strategic patience characterized by a long-term horizon and high tolerance for failure. Drawing on the author’s industrial practice, this study systematically reviews the characteristics and roles of basic research throughout the development of China’s new energy vehicle industry. In light of …

Analysis On Thermo-Viscous Steady Fluid Motion Over An Impermeable Infinitely Stretched Horizontal Moving Rectangular Surface, P. Krishna, N. Pothanna, K. Spandana

Mansoura Engineering Journal

This study presents analysis on thermo-viscous steady fluid motion over an impermeable infinitely stretched horizontal moving rectangular surface. The numerical results have been found employing R-K method of order 6 shooting techniques developed in Mathematica software ND solve for the flow adaptable equations comprising temperature and velocity. The flow behavior and the impacts of material constraints on the flow region have been analyzed and deliberated taking the help from the generated graphs. The variations of these flow fields have been studied for wide spectrum of physical characteristics which influences the nature of thermo-viscous fluid. The impact of viscosity, constant pressure …

A Conceptual Model Of The External Costs Of Truck Overloading On Road Damage, Emissions, And Traffic Congestion: A Systematic Literature Review, Aleksius Sitepu, Sutanto Soehodho, Andyka Kusuma

Smart City

Freight transport services cannot be separated from the infrastructure and facilities involved, each of which faces its own challenges in delivering optimal performance in the logistics sector. A common challenge in Indonesia’s road sector is the presence of overloaded freight vehicles, which has become a national issue. This phenomenon has a negative impact not only on road infrastructure but also on the environment and traffic. This study aims to develop a conceptual model of the external costs caused by truck overloading practices in terms of road infrastructure damage, increased exhaust emissions, and traffic congestion through a systematic literature review. This …

Leveraging A Centralized Fleet Assignment Management For An Aviation Resilience, Inof Seno Acton Mr., Sutanto Soehodho, Nahry Yusuf

Smart City

Since deregulation in the aviation industry, competition among airlines has intensified. This competition is shown by the increasing number of routes, service times, aircraft, and airports served. This competition causes not all flight services to meet their targets, resulting in aircraft operating less efficiently. Passenger seats are not filled, and flight delays are becoming more frequent. Obviously, this will negatively impact consumers and the airline's finances. The study focused on assigning the fleet used to serve flights to maintain existing aviation services and improve aircraft operational efficiency. This study aims to maximize profits by optimizing fleet assignments through the Centralized …

Decarbonizing Electrical Substations: An Integrative Review Of Green Technologies And Sustainable Practices Across The Infrastructure Life Cycle, Sulthan Syah Ali, Mohammed Ali Berawi, Mustika Sari

Smart City

Decarbonization of electrical infrastructure s becoming increasingly important in efforts to meet climate change mitigation targets, particularly in reducing emissions from substations. However, conventional substations continue to release significant quantities of GHG emissions throughout their life cycle As an example, a typical 500 kV substation emits around 150,000 tCO2e from construction to decommissioning. In addition, the development of green substation concepts remains limited, particularly in Indonesia where standardized approaches are not yet established. In response to the need for more sustainable power infrastructure, this study aims to identify, classify, and review all types of green technologies, practices, and innovations that …

Optimization Of Sea Transportation Services In The Kepulauan Seribu Using The Vehicle Routing Problem (Vrp) Model, Darmadi Darmadi, Sutanto Soehodho, Nahry Nahry

Smart City

The Kepulauan Seribu regency relies heavily on sea transportation for passenger mobility and goods distribution. However, current systems face efficiency challenges, high operational costs, and potential imbalances between demand and service capacity. This study proposes a framework to optimize sea transportation services in the Kepulauan Seribu using the Vehicle Routing Problem (VRP) method, especially the Capacitated Vehicle Routing Problem – Many Single Depot (CVRP–MSD) model with heterogeneous fleets and mixed cargo (passenger and goods). The main objective is to minimize total operating costs, which include fixed costs of using the vessel and variable travel costs, and unmet demand, both passenger …

En-Feat: An Effective Feature Selection Method Using Ensemble Approach, Sasank Nath, Dhruba Kumar Bhattacharyya

Mansoura Engineering Journal

Feature selection is a crucial step in machine learning and data preprocessing, significantly influencing model performance and interpretability. This paper presents a comprehensive study and contributions in the domain of feature selection by integrating traditional learning techniques with ensemble-based, proposing an effective approach. We propose a Mutual Information-based feature aggregation approach applied to union sets of features, aiming to derive an optimal subset of features that maximizes accuracy. Then, we employ an ensemble method that utilizes forward selection over union sets to identify the optimal feature subsets through sequential feature selection. Our ensemble-based feature selection method called En-feat, is evaluated …

Some New Oscillatory Behavior Of Higher-Order Elliptic Partial Differential Equations, S. Priyadharshini, V. Sadhasivam, Samrajesh Mault, K. K. Viswanathan

Mansoura Engineering Journal

The main objective of this study is to investigate the new adequate conditions for oscillation of higher-order elliptic partial differential equations by using the Riccati transformation and integral average method. The Riccati transformation converts a nonlinear first order Riccati differential equation into a second order linear ordinary differential equation, enabling solution via standard linear methods followed by inversion. Our plan of action is to reduce the multidimensional problem to an ordinary differential problem by using Jensen's inequality. Elliptic partial differential equations are used in almost every field of mathematics and physics, including Lie theory, geometry, and harmonic analysis. An elliptic …

Adaptative And Structured Optimization Stockwell Transform For Robust Power-Quality Disturbances Detection, Moudjibatou Afoda, Séna Apeke, Filippo Gatti, Alessio Iovine, Léonce W. Tokam, Guy M. Toche Tchio, Ouro-Djobo S. Sanoussi

Mansoura Engineering Journal

The monitoring of electrical power quality requires advanced analysis techniques capable of accurately detecting and localizing non-stationary disturbances. This work proposes a structured and adaptive optimization of the Stockwell Transform (ST), referred to as Two-Power Optimization (TPO). A multiparametric Gaussian window governed by four parameters (m, p, k, r), is introduced, and a genetic algorithm is applied independently to each signal to optimize these parameters. The optimization process adapts the analysis scale to the local characteristics of the signal while preserving the physical consistency of the window function. Simulation results on synthetic power quality signals demonstrate that the proposed method …

Effect Of Rotating Cylinder On Boundary Layer Behavior And Aerodynamic Performance Of Naca 0015 Airfoil, K. Suresh, Inamul Hasan

Mansoura Engineering Journal

Flow separation over airfoils at moderate and high angles of attack leads to a significant degradation in aerodynamic performance. Active boundary layer control using moving surfaces provides an effective approach to delay separation and enhance lift. In this study, the aerodynamic efficiency of a NACA 0015 airfoil equipped with a rotating leading edge cylinder is numerically investigated using computational fluid dynamics. Simulations are carried out for a range of angles of attack and cylinder surface speed ratios at low Reynolds numbers like Re = 1.2 x105. The rotating cylinder injects momentum into the boundary layer, promoting flow attachment on the …

Finite Element Modelling On A Non-Linear Radiative Hybrid Nanofluid Flowing Through A Rotating Disk, N. Janaki Phani Madhuri, Md. Shamshuddin

Mansoura Engineering Journal

This study develops a finite element model to investigate the MHD flow and heat transfer phenomena of Cu–H₂O and Cu–Fe₃O₄–H₂O hybrid nanofluids flow across a spinning disk modelling by the Tiwari–Das model. The governing non-dimensional momentum and energy equations account for the effects of buoyancy, nonlinear radiation, and ohmic heating. The nonlinear coupled system is solved with the element method (FEM) to find the velocity and temperature fields. Parametric studies will investigate the effects of the magnetic field, buoyancy, volume fraction of nanoparticles, and radiation. Skin friction and Nusselt number are also reported. The results show that with the hybrid …

Real-Time Iot-Enabled Dissolved Oxygen Monitoring And Automated Wastewater Recirculation In Constructed Wetlands, Sivasankar Pandiarajan, Vanitha Sankararajan, Devarinti Chandu

Mansoura Engineering Journal

Wastewater treatment is necessary for environmental conservation and combating water pollution. This study demonstrates real-time dissolved oxygen (DO) monitoring in constructed wetland treatment. The system would ensure an adequate supply of oxygen through automated water circulation whenever the level is below 4 mg/L, and shut down when it reaches the normal level. From experiments conducted, it was observed that within three automated circulation processes, it is possible to increase DO concentrations from 0.5 mg/L to 5.0 mg/L. Additionally, Total Dissolved Solids reduced from 1,380 ppm to 920 ppm. pH also stabilized from 8.38 to 7.9. In this system, data is …

Bridging Machine Learning And Climate Futures: A Framework For Explainable, Self-Directed (Agentic) Ai Models In Monitoring, Mitigation, And Governance Of Anthropogenic Climate Impacts, Vijayanandh Rajamanickam, Ketaki Kulkarni, Vishwanadham Mandala, Ozgur Kisi

Mansoura Engineering Journal

Environmental and societal damage due to anthropogenic climate change demands detection and mitigation strategies that are both effective and accountable. Machine Learning (ML) appears to offer a powerful set of tools but remains unexploited in these areas. A key barrier remains its poor ability to draw causal inferences about external systems, an essential requirement for meaningful continued monitoring, acting on, and shaping of the climate. Explainable, self-directed learned models operate by attributing environmental changes and deciding how best to model the resulting dynamics—they offer a natural solution. Recent developments in computational ecology, meteorology, and ML are combined to propose a …

Optimizing Graphene Oxide In Self-Compacting Concrete: A Sustainable Nano-Material Strategy For Enhanced Durability And Structural Performance, Mohammed Shakeebulla Khan, Vijaya G. S

Journal of Sustainable Construction Materials and Technologies

In order to reduce the environmental impact of the constructed world, advanced and sustainable construction materials are needed. This work is a summary of the comprehensive study on a novel self-compacting concrete with nano-engineered material (graphene oxide) modified self-compacting concrete (GO-SCC). The optimum dosage of GO was determined using Taguchi method, Analysis of Variance (ANOVA) and Response Surface Methodology (RSM) multi-objective statistical optimization method, and was found to be 0.10% by weight of cement (bwoc). This dosage of 0.10% GO led to the overall desirability score of 0.87, which confirmed that this was the optimum dosage for six response variables …

Deep Q Network For Adaptive Pelican Crossing Signal Optimization Balancing Pedestrian Safety And Vehicular Efficiency, Amalia Yasmin Chairunnisa, Andyka Kusuma, Jachrizal R. Sumabrata

Smart City

Static pelican crossing systems often fail to accommodate the stochastic arrival patterns prevalent in high-density transit-oriented development (TOD) zones, creating operational inefficiencies between pedestrian clearance and vehicular flow. At the Cikini Station transit hub in Jakarta, existing fixed signal cycles contribute to pedestrian delays and peak-hour congestion. This study evaluates an adaptive signal control model for mid-block pelican crossings using a Deep Q-Network (DQN) algorithm. To calibrate the simulation, empirical data were extracted from field CCTV footage using the YOLOv8 algorithm, accurately capturing 15-minute peak flow fluctuations and commuter surges. The system is formulated as a Markov Decision Process (MDP) …

Development Of An Adaptive Pelican Crossing Model Using Fuzzy Logic In Mixed Traffic Conditions, Manazil Adam, Andyka Kusuma, R. Jachrizal Sumabrata

Smart City

Traffic management at at-grade pedestrian crossing facilities (pelican crossings) in highly populated areas, such as the Universitas Indonesia Station, faces significant inefficiency challenges. During peak hours, the fixed-time system is frequently disabled and replaced with subjective manual control by security personnel, thereby triggering irregular stop-and-go cycles and a high accumulation of vehicle delays. This study aims to develop a hybrid adaptive control model integrating Computer Vision, Genetic Algorithm (GA), and Fuzzy Logic to optimize intersection performance under mixed traffic conditions. The research methodology begins with the extraction of traffic and pedestrian characteristic data, calculated manually through recorded field observations. This …

Processing Mechanism And Efficacy Maintenance Strategy Of Ginseng Active Ingredients Based On Spectroscopy Technology, Yang Hantong, Kong Linghui, Jiang Mengmeng, Wang Yingxiu, Li Xiangguo

Food and Machinery

Ginseng, as a medicinal and edible plant, has ginsenosides as its core active components, which are widely applied due to their physiological functions such as antioxidant and anti-inflammatory activities; however, their structures and activities are easily affected by food processing. During processing steps such as cleaning, steaming/boiling, and fermentation, temperature, pH, and microbial enzyme systems alter the types, contents, and stability of active components through reactions such as hydrolysis and oxidation. The intrinsic linkage mechanism of "component transformation-activity-bioavailability" constitutes a core focus and challenge in current research. This study, based on spectroscopic techniques including high-performance liquid chromatography (HPLC), nuclear magnetic …

Adaptive Intervention Strategies In Co-Evolving Multiplex Networks: A Reinforcement Learning Approach To Real-Time Misinformation Containment, Anjali Ashokrao Bhadre Dr., Harshvardhan Prabhakar Ghongade Dr.

Northeast Journal of Complex Systems (NEJCS)

The growing transmission of misinformation via social media creates serious challenges to public health, democracy and social cohesion. To date, methods used to contain misinformation rely upon static representations of networks and set rules for interventions. In contrast, this study presents the first Multiplex Adaptive Reinforcement Intervention Network (MARIN), a framework for real-time adaptive intervention in the context of dynamic misinformation transmission using co-evolving multiplex networks and deep reinforcement learning. Unlike past studies that have assumed static network structures, MARIN has the ability to allow for dynamic changes in network topology as a result of both misinformation transmission and intervention …

Tinyvgg-Based Real-Time Degradation Classification For Adverse Driving Scenes Using A Newly Collected Iraqi Driving Dataset, Yousif N. Abbas, Matheel E. Abdulmunim, Nada H. Ali, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Environmental conditions such as low-light at night, fog scattering, glare artifacts, rain streaks, and rain smear distortions are significant issues of camera-based perception in Autonomous Vehicles (AVs). These degradations alter the statistics of the scene, mask structure, introduce non-uniform noise, and adversely affect downstream vision processes, including detection and tracking. To overcome this shortcoming, this paper presents a lightweight TinyVGG-based degradation classification system that runs in real time. The network extracts discriminative spatial features with hierarchical convolutional encoding and projects them to a lower-dimensional semantic representation with fully connected layers and a multi-class predictor based on SoftMax. In addition, a …

Improving Approach Of Evolutionary Strategies For Clustering Technique Enhancement, Duaa Mahde Saleh, Hasanen S. Abdullah, Ahmad Zamsuri

Journal of Soft Computing and Computer Applications

The existence of the information has been the essential aspect of the whole society. Information is concentrated in all forms to be effectively utilized. Clustering — an unsupervised learning technique. It is based on data similarity that gives rise to issues in collection, challenges and instability in data structure. It proposes an advanced evolutionary method by combining two approaches. Firstly, it adopts the evolutionary approach and integrates the advantages between two methods to design one. Among them are Differential Evolution (DE) and Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP), and Evolutionary Programming (EP) and Particle Swarm Optimization …

A Comprehensive Review Of 1d Deep Learning Approaches In Facial Analysis: Face Recognition, Landmark Detection, And Mesh Modeling, Duaa J. Al Hammami, Rehab F. Hassan

Journal of Soft Computing and Computer Applications

Facial Analysis has progressed rapidly with deep learning and its 2D image-based models, especially Convolutional Neural Networks (CNNs), which have been the most popular methods. In recent years, 1D deep learning models have gained traction in the search for efficient solutions for face recognition, facial landmark detection, and 3D face mesh modeling. 1D models encode the facial structure as sequences, curves, or temporal signals, resulting in high computational efficiency, a small memory footprint, and good interpretability, making them well-suited for real-time and edge devices. This review is a step-by-step, organized exploration of 1D deep learning analysis of the face, its …

Comparative Study On Throughput Optimization In Nfv: Traditional Dissemination Techniques Vs. Swarm Intelligence Approaches, Sanaa Salih Alwan, Asia Ali Salman, Wulfrano Arturo Luna Ramírez

Journal of Soft Computing and Computer Applications

Network Functions Virtualization (NFV) modernizes networks by replacing hardware with software, creating a more flexible network architecture and offering flexibility in dynamic network environments. This foundational technology is essential for creating the networks of the future, including the Internet of Things (IoT) and cellular services. NFV does provide flexibility, but it struggles to maintain system throughput during high traffic loads while achieving high resource utilization efficiency and dynamic packet routing. The problem lies in the fact that traditional request distribution mechanisms, such as flooding and gossip, fail to operate efficiently in complex network topologies (scale-free networks), leading to: (a) random …

A Comprehensive Review Of The A* Algorithm: Evolution, Applications, And Future Trends In Path Planning, Saleel H. Abood, Hussein M. H. Al-Khafaji, Mohanned M. H. Al-Khafaji

Journal of Soft Computing and Computer Applications

Despite being a fundamental problem to autonomous robotics and intelligent navigation systems, path planning is still a challenge. The A^* algorithm is often used among search-based techniques for optimal search performance, as it's a tradeoff of computation. The above techniques have been developed for various applications as many versions of A^* Dynamic A^* (D^*), D^* Lite, Hybrid A^*, and Anytime A^* are suggested to deal with dynamic environments, real-time constraints, and kinematic restrictions. This paper comprehensively and structurally reviews the A^* algorithm and its major extensions, encompassing historical development, methodological …

Skin Lesion Classification Using Cnn Model And Augmented Dataset, Mohammed Nawzad Mohammed-Ramzi, Aso M. Aladdin

Journal of Soft Computing and Computer Applications

Skin cancer is a deadly disease. Skin lesion classification is a critical challenge due to its prevalent and deadly nature. Skin lesions are difficult for dermatologists to detect using eye examination, which is time-consuming and variable. A deep learning model of skin lesions classification has been proposed using a Convolutional Neural Network (CNN) trained on the HAM10000 dataset of 10,015 dermatoscopies. To improve resilience and address the dataset's extreme class imbalance, data augmentation techniques such as geometric transformations, brightness/contrast adjustments, blurring, noise addition, histogram equalization, color space alterations, and elastic deformations are used. With a carefully balanced 10% test set, …

Comparative Analysis Of Random Forest And Artificial Neural Networks For Predicting In-Situ Soil Density, Eng. Jinan Ali Abd Al-Kareem Al-Maliki, Dr Ammar Salman Dawood, Dr. Ihsan Al-Abboodi

HBRC Journal

This study suggests that RF and ANN are proven to be robust algorithms in predicting in-situ soil density, which is considered a significant geotechnical parameter. The research is based on 86 soil samples and focuses on five main input parameters: Gravel Percentage (G%), Plastic Limit (PL%), Sand Percentage (S%), Fines Percentage (F%), and Liquid Limit (LL%). The models developed here utilize five commonly recorded index properties (G%, S%, F%, LL, and PL) for all field samples taken from the Basra-Faw Road project. The influence of moisture content and compressive energy was ignored, as all field samples acquired the same moisture …