Digital Commons Network^™

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 …

A Storm-Ai Primer With An Operationally Representative Example, Shiu-Kai Chin, Matthew Clark, Garrett Katz, Jae C. Oh, William Young

Electrical Engineering and Computer Science - All Scholarship

A STORM-AI Primer with an Operationally Representative Example presents the Systems-Theoretic Technical and Operational Risk Management with Artificial Intelligence (STORM-AI) methodology, a transdisciplinary framework for establishing the trustworthiness of mission systems that may incorporate AI components. The core value proposition of STORM-AI is mission assurance—ensuring that mission systems behave with the predictability and proportionality required by senior leadership and demanded by international humanitarian law. STORM-AI achieves this by faithfully preserving commander's intent from high-level mission descriptions down to hardware-level behavioral specifications through a structured sequence of four formally grounded models: a Mission Model, a Protection (Loss Control) Model, a Security …

Calibration And Validation Of An Ogden Material Model For Thermoplastic Polyurethane In 3d-Printed Tensegrity Structures, Caleb C. Swain

Theses and Dissertations

This work is motivated by ongoing efforts within the Smart Materials, Adaptronics, and Shock Laboratory (SMASH Lab) at Brigham Young University (BYU) to develop variable-stiffness, external fixators using 3D-printed tensegrity structures. In the process of designing and evaluating these systems, the need arose to accurately model the nonlinear mechanical behavior of thermoplastic polyurethane (TPU), the primary material used in prototype fabrication. This thesis investigates how accurately a calibrated Ogden material model can predict the nonlinear mechanical response of TPU in 3D-printed tensegrity structures. Experimental data were used to calibrate the parameters of the hyperelastic constitutive model through nonlinear optimization. An …

Rheological Protocol To Assess And Optimize Latex Coagulation Dynamics For The Thin Glove Coagulant Dipping Process, Monday U. Okoronkwo, Kok Kong Ng, Wolfram Franke, Gaurav Sant

Chemical and Biochemical Engineering Faculty Research & Creative Works

Many products that directly impact the quality of human life today — gloves, catheters, condoms, and baby bottle teats — are made through the latex-dipping technology. While a variety of methods have been developed – e.g., particle counting, turbidimetry, microscopy, and light scattering – which are suitable for studying the coagulation of latex at very low concentrations, much less work has focused on methods suitable for in-situ characterization of latex coagulation in concentrated solutions (e.g., as relevant to the dipping process). This paper presents a process-relevant rheological protocol for assessing and optimizing latex coagulation dynamics for the thin glove coagulant …

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 …

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 …

Operational Feasibility Of Reinforcement Learning For Vehicle Routing Under Heterogeneous Fleet Capacity Constraints, Freddy Giovanny Aviles Moreno

Electronic Theses and Dissertations 2020 - Present

Reinforcement learning methods have demonstrated strong performance on vehicle routing benchmarks, yet their behavior under severe capacity constraints remains unexplored. This dissertation investigates whether PPO-based neural routing policies maintain operational viability when vehicle capacity is severely constrained, as occurs in resource-limited rural logistics settings.

Through controlled experiments on synthetic instances and validation on real-world rural healthcare networks in Florida, this research reveals a critical capacity threshold effect. Moderate capacity reductions from 40 to 20 produce negligible performance loss (4.1%), while severe reductions to capacity 10 trigger catastrophic failure with 243% degradation, manifested through degenerate single-customer routing patterns. Convergence analysis identifies …

Development Of A Coupled Hydrological-Nitrogen Modeling Framework For Urban Stormwater Pond Analysis, Tione Grant

USF Tampa Graduate Theses and Dissertations

Urbanization impacts the hydrological and biogeochemical processes that regulate water quality in downstream ecosystems, leading to increased stormwater volume and nitrogen loads in receiving aquatic environments. Although stormwater ponds are designed as simple hydraulic storage basins, they function more like managed ecosystems within urban drainage systems, offering both hydraulic attenuation and passive nutrient treatment. However, nitrogen removal efficiency varies significantly and depends on interactions between hydrological and biogeochemical factors. This thesis details the development, calibration, validation, and sensitivity analysis of a coupled hydrological-nitrogen model framework for Aaran’s Pond, an urban stormwater pond in Hillsborough County, Florida.

A process-based water budget …

A Portable Device To Elicit Sensory Trick And Monitor Forearm Muscle Activity In Pianists With Musician’S Dystonia, Josef Franczak

USF Tampa Graduate Theses and Dissertations

Musician’s Dystonia (MD) is a condition that can be acquired in professional musicians,causing involuntary contractions and the loss of fine motor control. The most common pharmaceutical treatment, botulinum toxin injections, has been shown to have side effects of muscle weakness and atrophy. The sensory trick, shown to temporarily relieve symptoms of dystonia, has potential to be used as an alternative or alongside current therapies, allowing patients with MD to return to playing music professionally. To systematically deploy the sensory trick and monitor forearm muscle activity, the first prototype of the Wearable Sensorimotor Integrator (wSMI) has been developed.

The wSMI is …

Boiling Flow Estimation For Aero-Optic Phase Screen Generation, Jeffrey W. Utley, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz

Faculty Publications

Aero-optic effects due to turbulence can reduce the effectiveness of transmitting light waves to a distant target. Methods to compensate for turbulence typically rely on realistic turbulence data, which can be generated by i) experiment, ii) high-fidelity computational fluid dynamics (CFD), iii) low-fidelity CFD, and iv) autoregressive methods. However, each of these methods has significant drawbacks, including monetary and/or computational expense, limited quantity, inaccurate statistics, and overall complexity. By contrast, the boiling flow algorithm is a simple, computationally efficient model that can generate atmospheric phase screen data with only a handful of parameters. However, boiling flow has not been widely …

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 …

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 …

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 …

Amending Planting Media With Typha-Derived Biochar For Sustainable Horticulture, Elissa Touma

USF Tampa Graduate Theses and Dissertations

Constructed wetlands are widely used to improve water quality by removing excess nutrients from wastewater and stormwater. Nutrients are stored within wetland vegetation, particularly in fast-growing plants such as Typha. Harvesting vegetation provides an opportunity to permanently remove these nutrients while generating biomass suitable for beneficial reuse. Converting harvested biomass into value-added products such as compost and biochar can support nutrient recovery and sustainable biomass management. In addition, increasing concerns regarding the environmental impacts of peat extraction have created interest in renewable alternatives for horticultural growing media. Therefore, the objective of this study was to evaluate the potential of harvested …

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 …

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 …

Beneficial Investigation Of Sustainable Recycled Waste Plastic In Treating The Consolidation Behavior Of Fine Soil, Aram Mohammed Raheem, Cumaraswamy Vipulanandan, Mohammed Hiwa Abdullrahman, Ali Tariq Omer

Journal of Sustainable Construction Materials and Technologies

Recycled waste plastic is one of the regular garbage utilized as a sustainable material in soil remediation. This study examines the efficacy of utilizing local recycled waste plastic as an ecological quantifiable along with soil containing fine particles to analyze the consolidation behavior. Various recycled waste plastic contents ranging from 2% to 10% were employed experimentally in two patterns where in the first pattern the recycled waste plastic were mixed as dry material with the fine soil particles while in the second pattern a sandwich layer of 10% of the recycled waste plastic was placed in the middle of the …

Modeling Of Tensile Stress-Strain Response For Steel Fiber Reinforced Self-Compacting Concretes, Anıl Niş, Nilufer Özyurt, Turan Özturan

Journal of Sustainable Construction Materials and Technologies

Main aim of the research was to further understand self-compacting concrete reinforced with steel fibers (SFR-SCC) behavior and to contribute modeling of the tensile stress-strain diagrams for the standardization of the fiber reinforced materials for structural utilization. Fiber reinforced materials has not been used without structural rebar because of the absence of stress-strain curve in tension required for the structural design codes. Hence, an inverse analysis (multi-layer model) was further modified for various steel fibers to predict tensile stress-strain behaviour. For this purpose, 5 different mixes; control, reinforced with long fibers with 0.5% and 1%, short fiber with 0.5% and …

Investigating The Optical Qualities Of Aluminum Nanoparticles With Nd:Yag Pulsed Laser Ablation In Liquid Technology, Ban A. Bader, Muna Y. Slewa, Zaid T. Aldabbagh

AUIQ Technical Engineering Science

This research investigated the preparation of aluminum nanoparticles using pulsed laser ablation in liquid (PLAL). Ablation was performed on a metallic target in distilled water using a 532 nm Nd:YAG laser at different laser energies (400, 600, and 800 mJ) with a repetition frequency of 1 Hz and 300 pulses. A change in the solution's color is one of the first indicators of nanoparticle formation. We observed a change in the color of the deionized distilled water from light white to dark white, suggesting the production of aluminum nanoparticles. Furthermore, UV-VIS measurements showed an increase in the material concentration with …