Engineering Commons^™

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, …

Optimization Of Bio-Briquettes Production From Cassava Stems Co-Blended With Sisal Bole Bagasse Using Response Surface Methodology, Agnes Nshoma Msobi, Neema O. Msuya, Kando Khalifa Janga

Tanzania Journal of Science

This research aimed at producing bio-briquettes from cassava stems and sisal bole bagasse as an alternative source of cooking energy. A Response Surface Methodology (RSM) with Face - Centered Central Composite Design (FCCCD) featured a multi-objective optimization of the effect of the production parameters, focusing on maximizing char yield and Higher Heating Value (HHV) of the produced briquette while minimizing carbonization temperature and residence time. The findings showed that bio-briquettes produced at the lowest temperature (250 ℃) and residence time (30 min) and the blending ratio of 75% by 25% had the highest char yield of 76.92% and HHV of …

Extraction Of High-Quality Cashew Nutshell Liquid Using Carbon Dioxide-Expanded Hexane, Frank N. Jacob, Neema Msuya, Joseph Y.N Philip, Kando Khalifa Janga

Tanzania Journal of Science

Cashew nutshell liquid (CNSL) is a by–product from cashew processing industries containing four naturally occurring phenolic compounds. This study investigated an extraction approach using CO2–expanded hexane. The central composite design (CCD) was employed in experimental design to investigate the effects and interaction of CO 2 mole fractions and extraction temperature on the CNSL yield and quality (Colour) from Steamed CNSL. The dissolution power of the CO2–expanded hexane was assessed by determining the solubility of CNSL during the initial stages of extraction at different extraction conditions. Findings indicated that higher CO2 mole fractions reduced CNSL solubility, while higher temperatures enhanced it. …

Load Profile Analysis And Forecasting For Rural Mini Grids In Uganda, Prossy Mutesi, Santos L. Kihwele, Emmanuel S. Matee

Tanzania Journal of Science

Accurate load forecasting is essential for the reliable and cost-effective operation of rural mini grids, where constrained generation capacity and high penetration of renewable energy resources require well-informed operational decisions. This study examines electricity demand characteristics and forecasting performance for the Buzaami and Ssenyondo mini grids in Uganda, with particular focus on diurnal load profiles, peak demand behavior, and seasonal variability. 2022 operational data show extended peak demand from early morning to late evening, driven by socio-economic activities that strain resource scheduling and reliability management. To address these challenges, the study evaluates and compares Long Short-Term Memory (LSTM) networks, fuzzy …

Robust Real-Time Uav Target Tracking With Onboard Vision-Based Yaw Control, Rylan Malarchick, Jose Castelblanco, Enrique Amaya, Carmen Dimario, Graysen Brinkman, Chirag Kumar, Kiwon Yoon, Sajid Berhane

Beyond: Undergraduate Research Journal

Autonomous tracking of agile unmanned aerial vehicles (UAVs) presents significant challenges for real-time perception and control systems. This work presents AIRHOUND (Autonomous Intelligent Rotorcraft for Hostile Object Unified Navigation and Detection), a UAV platform implementing vision-based yaw tracking through a modular ROS2 software architecture. The system employs YOLOv8 object detection optimized with NVIDIA TensorRT for embedded deployment on an NVIDIA Jetson Orin companion computer. Detected targets are processed through a geometric tracking module that converts pixel coordinates to angular yaw errors using pinhole camera intrinsics, with a proportional controller generating rate-limited yaw commands. These commands are streamed to a PX4 …

The Dynamics Of Educational Change: A Complex Systems Perspective, Preethi Nanjundan, Lijo Thomas, Abith K. Sunil

Northeast Journal of Complex Systems (NEJCS)

The perception of university teachers toward educational reforms plays an important role in determining the success of changes introduced in the education sector. This study focuses on teachers’ attitudes toward change, their emotional responses, and their overall views on educational reforms. Across the world, many educational reforms have failed to achieve their expected outcomes in improving teaching practices and student learning. As education systems are highly complex, the approach toward implementing reforms has also changed over time. Some reforms are introduced gradually, while others involve major innovations within the system. Complexity theory provides useful insights and tools that help educators …

Engineering Nanoelectrocatalytic Systems For Co2 And Nitrate Conversion Into Value-Added Chemicals, Abdelrahman Mohamed Abdelmohsen

Theses and Dissertations

The thesis addresses two major related environmental crises: nitrate pollution of water systems and the ever-increasing levels of atmospheric carbon dioxide. Both challenges are closely linked to anthropogenic disturbance of nitrogen and carbon cycles and require sustainable, energy efficient mitigation techniques. In this study, we investigate electrochemical conversion routes as a unified approach to convert these pollutants into value-added compounds: ammonia (NH3) via nitrate reduction (NO3-RR) and ethylene (C2H4) via carbon dioxide reduction (CO2RR). The first half of this work deals with the design and engineering of Cu-Zn alloy electrocatalysts for efficient NO3-RR. Tuning the alloy composition and surface nanostructure …

Tuning Ion Mobility And Molecular Confinement In High-Performance Polymer Electrolytes For Energy Storage, Ezzeldien Yousef Muhammed Yousef

Theses and Dissertations

This research addresses the critical energy density limitations of aqueous supercapacitors, which are traditionally constrained by the narrow electrochemical stability window (ESW) of water 1.23 V. By employing two distinct molecular engineering strategies, this study developed high-performance electrolyte systems that significantly extend voltage stability and thermal resilience.

The first system, CsBr@PAM/HA, utilizes a polyacrylamide and hyaluronic acid hydrogel matrix. This system exploits the chaotropic nature of Cs⁺ ions to disrupt the aqueous hydrogen-bonding network, enhancing ionic conductivity to 104 mS cm^-1. Through systematic salt screening, CsBr was identified as the optimal electrolyte, enabling a stable 2.0 V …

Ontology-Based Decision Support System For Improving Aquaculture Management, Thuy Thi Thu Pham, Phuong Thi Can, Nga Thi Thanh Ha

Journal of Marine Science and Technology–Taiwan

Early detection of shrimp diseases is vital for reducing losses and ensuring sustainable aquaculture. In this study, a novel ontology-driven framework for shrimp disease diagnosis that integrates environmental knowledge with machine learning-based image analysis is proposed. An ontology was constructed from a structured dataset of environmental parameters and farm conditions related to shrimp diseases, which formally represents concepts such as farm characteristics, environmental factors, symptoms, and disease types. Two public shrimp image datasets were subsequently used to train convolutional neural networks (ResNet50 and MobileNetV2) for automated disease classification. The predicted disease labels and confidence scores were mapped onto the ontology. …

Case Study On Operational Performance Improvement By Turbocharger Nozzle-Ring Modification In Shipboard Generators, Jong-Hun Im, Mukyeong Kang, Sang-Deuk Lee

Journal of Marine Science and Technology–Taiwan

Owing to increasingly stringent emission regulations imposed by the International Maritime Organization and rising fuel prices, the demand for enhanced energy efficiency and emission control in marine power systems is increasing. This study presents a case study in which the performance of an aging auxiliary generator is restored by modifying the turbocharger nozzle ring. The vessel, a 12-year-old crude-oil tanker, experienced repeated operational issues with its No. 3 generator. This included elevated exhaust gas temperatures, reduced intake air pressure, and frequent turbocharger surges. To address these challenges, the nozzle-ring blade angle was reduced by approximately 2° using spare components, while …

Towards Sustainable Marine Education: Assessing High School Students’ Understanding Of Conservation Concepts, Cheng-Chieh Chang, Tsuyoshi Sasaki, Ya-Yin Liao, Ling-Ya Hung, Liang-Ting Tsai

Journal of Marine Science and Technology–Taiwan

This study assesses the concepts of marine resource conservation and sustainability among Taiwanese high school students by developing a Chinese version of the Marine Resources Conservation and Sustainability Scale. Grounded in the cognitive, socio-emotional, and behavioral learning objectives outlined in UNESCO’s Education for Sustainable Development Goals (ESDGs), the scale targets students aged 16 to 18 to examine gender differences in marine knowledge and conservation concepts. The scale comprises 31 items across four dimensions: the first section evaluates marine knowledge, while the remaining dimensions utilize a four-point Likert scale to assess cognitive, socio-emotional, and behavioral learning objectives, respectively. The results indicate …

Dynamic Governance Of Transboundary Fisheries Through Deep Learning And High-Frequency Vessel Tracking Data For Climate-Resilient Management: An Application To The East China Sea, Qin Lin, Yaqing Shu, Manel Grifoll, Hongxiang Feng

Journal of Marine Science and Technology–Taiwan

Transboundary fisheries governance is increasingly challenged by jurisdictional conflicts, regulatory asymmetries, and climate-induced stock shifts in fish stocks, especially in contested regions like the East China Sea (ECS). This study capitalizes on China’s high-frequency BeiDou Navigation Satellite System (BDS) data (June 2021–May 2022) to pioneer a CNN-BiLSTM deep learning model to dynamically identify fishing hotspots and evaluate policy effectiveness. By integrating vessel trajectories, environmental variables, and regulatory events, we examine spatiotemporal patterns of fishing effort relative to seasonal bans, extreme weather, and quota systems. Key findings reveal: (1) post-moratorium (September–December) concentration of fishing intensity, peaking in November; (2) significant weather-driven …

Lock Scheduling Decisions And Strategy Optimization Under Operation Interruption Scenarios, Junhe Li, Qiang Zhang, Junpeng Jiang, Jiangchao Bao

Journal of Marine Science and Technology–Taiwan

Locks are critical nodes in inland waterway transportation systems that concentrate vessel traffic between upstream and downstream reaches, making their operations highly sensitive to hydrological conditions. Therefore, disruptions caused by droughts, floods, or routine maintenance can easily trigger congestion. To address this issue, this study systematically examines the interrelationships among the number of ships awaiting passage, scheduling strategies, the trade-off between lock chamber utilization and ship waiting time, the influence of ship entry sequences on user satisfaction, and the combined effects of these factors on overall lock scheduling performance. Based on these analyses, an integrated decision-making model for two-stage ship …

Corrosion Protection Assessment Of Offshore Wind Turbine Substructures Using Beasy Cp Simulation Analysis, Chuan-Yang Wan, Hung-Hua Sheu, Sheng-Yu Wang, Yao-Ming Wang, Wei-De Chen, Ming-Yuan Lin, Hung-Bin Lee

Journal of Marine Science and Technology–Taiwan

This study investigates the corrosion protection assessment of offshore wind turbine substructures established in the waters off Keelung, Taiwan. First, a cathodic protection system for the monopile foundation is designed and established. The Boundary Element Method (BEM) is applied using the BEASY simulation software to develop a 3D geometric model for cathodic protection. In the steel monopile simulation analysis, the exposed marine environment is set based on the sea conditions of the Keelung small boat marina, including parameters such as salinity, resistance, and polarization curves, to establish an effective cathodic protection system for underwater structures. By simulating these parameters, a …

Face-To-Face Versus Distance Education In Competence-Based Maritime Education And Training, Clara Borén, Marcella Castells-Sanabra, Manel Grifoll

Journal of Marine Science and Technology–Taiwan

The COVID-19 pandemic created significant challenges for higher education, with universities being shut down and face-to-face teaching and assessment shifting to an online format. This presented an opportunity to focus on the continuity of learning through distance education, especially in the maritime context, due to the on-board training requirements, and even though five years have passed, this shift has reshaped the education landscape, proving that remote learning has come to stay.

At the Barcelona School of Nautical Studies, we have developed a weather routing software in the framework of distance education and teaching innovation. We conducted a teaching trial alternatively …

A Bibliometric And Visualization Analysis Of Global Giant Clam Research, Chia-Hsiang Chen, Chih-Hsien Chang

Journal of Marine Science and Technology–Taiwan

In this study, bibliometric analysis and visualization tools were used to investigate the academic network and knowledge structure surrounding global giant clam research. A total of 551 relevant publications from the Web of Science database, published from 1900 to 2024, were analyzed. The findings indicate that research on giant clams has experienced considerable growth since 2012, peaking in publication output in 2021. Sixty-six countries were represented in the study sample, with the United States, Australia, China, Singapore, and Japan being the most represented ones. Notably, the rate of international collaboration was only 29%, highlighting a need for additional integrated cross-border …

Design Framework For Polymer Gel Treatments From A Database Of Field Projects, Baojun Bai, Munqith Aldhaheri, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study compiles and analyzes data from over 125 published field projects around the world, covering nearly 900 treated wells. Building on those detailed studies, this paper distills the most important findings into a concise, practical, and actionable framework to guide where in-situ gel treatments should be applied, how they should be designed, and how their outcomes can be evaluated. The proposed workflow guides engineers through five integrated stages: screening appropriate gel types, designing formulation and injection strategies, predicting field response, evaluating treatment performance, and conducting iterative refinement with feedback loops to adjust designs based on observed outcomes. The framework …

Rapid-Prototyping Nanofabrication: Lcd-Based Projection Lithography And Physical Vapor Deposition, Sabeel Saleem Mohammmed

University Honors Theses

The semiconductor industry's continued growth, driven in large part by demand for artificial intelligence hardware, has highlighted the need for greater workforce development in regions adjacent to major fabrication centers like Oregon's Silicon Forest. This capstone project lays the groundwork for a small scale and student led semiconductor fabrication lab at Portland State University by demonstrating two of the core steps in chip manufacturing: photolithography and thin film deposition. Rather than relying on conventional fixed reticles, this work explores a unique, low cost approach to patterning that uses an ultraviolet-compatible liquid crystal display (LCD) as a programmable reticle, allowing arbitrary …

Enhanced Uav Surveillance With Rf-Based Drone Identification Using Transfer Learning, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

With the development of technology and the decrease in costs, drones are now becoming easily accessible to the public. As the accessibility of this technology continues to grow, the concerns of security and surveillance increase, and to ensure a sense of security, the need to have reliable drone detection and identification systems is more urgent than ever. Besides, many civilian applications have been found for drones, which play a huge role in modern security and warfare. Unauthorized drones can be very dangerous regarding security issues, as they can be used for spying, smuggling, or even attacks against critical infrastructure. We …

Comparative Analysis Of Leaflet Materials, Stent Materials, And Stent Cell Density For Bileaflet Transcatheter Mitral Valve Design, Joseph Chibuike Nwokeafor, Joshua D. Hofmeister, Breandan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

Michigan Tech Publications

Background: The biomechanical performance of bileaflet transcatheter mitral valves (TMVs) depends on complex interactions between leaflet material behavior and stent design. However, the contributions of leaflet materials and constitutive models, stent materials, and stent geometry to valve function and durability remain poorly understood. Methods: A parametric finite element study was conducted using a CAD model of a bileaflet TMV subjected to physiological pressure loading. Five leaflet material models were evaluated: 3 glutaraldehyde-fixed tissues—bovine pericardium (BP; FBP1, FBP2) and porcine pericardium (PP; FPP)—and 2 unfixed tissues—bovine (UBP) and porcine pericardium (UPP). BP was modeled as a linear elastic (FBP1) and Ogden …

Optimization Of Cyanide Leaching Process Based On Grain Size And Slurry Density In Artisanal Small-Scale Gold Mining In Tatelu Area, North Sulawesi, Indonesia, Dadan Mohamad Nurjaman, Harmin Sulistiyaning Titah, Adji Kawigraha, Ipung Fitri Purwanti, Wahyu Hidayat

Journal of Sustainable Mining

Artisanal and small-scale gold mining (ASGM) in Tatelu Village, North Sulawesi, has shifted from mercury amalgamation to cyanide leaching. However, gold recovery remains inefficient, requiring process optimization. This study uses multiple linear regression analysis to evaluate the influence of grain size and slurry density on gold dissolution. The results indicate that grain size plays a dominant role in leaching efficiency, while slurry density, pH, temperature, dissolved oxygen (DO), and cyanide concentration have statistically insignificant effects (p > 0.05). Laboratory tests show that finer grain sizes enhance dissolution, with the highest recovery rate of 89.08% achieved at 75% passing through 44 …

Investigation Of Portland Limestone Cement Blended With Biochar From Downdraft Gasification: Hydration Kinetics, Microstructure, Rheology, And Performance Mechanisms, Ugochukwu Ewuzie, Paul C. Ani, Abdulkareem O. Yusuf, Joseph D. Smith, Monday Uchenna Okoronkwo

Chemical and Biochemical Engineering Faculty Research & Creative Works

The widespread adoption of Portland Limestone Cement (PLC) in U.S. concrete production has necessitated research into commercially available supplementary cementitious materials (SCMs) for formulating high-performance, low-carbon blended binders with PLC. This study investigated the influence of biochar (BC) in biochar-PLC composites (CBCs), focusing on hydration kinetics, microstructure, rheology, pore-solution chemistry, compressive strength, and the underlying mechanisms. Biochar was produced via downdraft gasification at 850 °C and incorporated into PLC at 5, 7, and 10% substitution by mass. Isothermal calorimetry and hydration data modeled using the Knudsen equation revealed that BC retarded early hydration but improved hydration degree and later-age heat …

Rape Pregnancies And Consequent Ptsd Resulting From The Overturn Of Roe V. Wade, Emma Lindenfelser, Yulia Garaeva, Nenette Nti-Agyemang, Ellen Davis, Anna Kabwa, Alexandra M Calaman

Binghamton University Undergraduate Journal

Prior to the 1973 Roe v. Wade ruling, access to safe legal abortions was limited and often restricted along racial, economic, and geographic lines. Nearly fifty years later, women’s health policy evolved again following the 2022 Dobbs v. Jackson Women’s Health Organization decision overturning Roe v. Wade. This court ruling decimated federal protections of abortion. This paper presents the research that the 2022 Dobbs judicial decision may contribute to and exacerbate rape-related PTSD on 1 out of 20 American who are unable to access a safe abortion after a rape-related pregnancy. Pregnant women living in conservative states that have outlawed …

Matching Two Long Interferometric Pathlengths Using Low Temporal Coherence Light For Finding Hong–Ou–Mandel Dip, Keith A. Wyman, Noah S. Everett, Anil K. Patnaik

Faculty Publications

Hong–Ou–Mandel (HOM) dip from a biphoton source in a two-photon interferometer provides a myriad of quantum tools for quantum communication and sensing. But the stringent requirements for spatial coherence between the photon pair makes it prohibitively difficult to observe high-fidelity HOM dip in long-distance free-space implementations, e.g., for the photon pairs involved in quantum communication need to match the two path lengths within a few 10 s of micron because of the short coherence width of the two-photon wave-packet. While many techniques for the pathlength balancing of two interferometric arms have been studied and applied extensively, such balancing is further …

Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz

Faculty Publications

Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive three-dimensional (3D) density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a …

A Comprehensive Survey Of Artificial Intelligence Applications In Predicting Mining-Induced Subsidence, Deformation, And Landslides: Strengths, Limitations, And Future Trends, Long Quoc Nguyen, Dung Ba Nguyen, Minh Tuyet Dang

Journal of Sustainable Mining

Mining activities often cause mining-induced ground deformation, including subsidence and landslides, and related geo-environmental impacts, posing significant risks to infrastructure and safety. This study conducts a systematic assessment to identify, categorize, and evaluate AI-based methods (machine learning, deep learning, and hybrid models) for predicting and monitoring mining-induced ground deformation. The literature search was performed across major scientific databases, using predefined keywords and selection criteria, resulting in a final dataset of relevant peer-reviewed studies. The reviewed works were classified into three methodological groups: traditional machine learning, deep learning-based approaches, and hybrid methods. The results show that ML still dominates in terms …

Decoding Stock Market Movements: The Role Of Unemployment And Volatility, Bipllab Roy, Suparna Bhattacharjee

Northeast Journal of Complex Systems (NEJCS)

This study investigates how unemployment and market volatility interact with stock prices in the Indian context, framing the stock–labour–volatility nexus as a complex adaptive system (CAS) rather than a set of linear, time-invariant relationships. Using verified secondary data on unemployment, India VIX, and NSE stock indices for 2013–2023, we first apply simple and multiple regression as a descriptive baseline. Results show a strong negative association between unemployment and stock prices (R ≈ 0.824, R² ≈ 0.68, p < 0.05), consistent with Keynesian demand-side channels, while the linear VIX–stock relationship is weak and statistically insignificant (R² ≈ 0.07, p > 0.05), consistent with the expectation that volatility operates through non-linear, regime-dependent mechanisms not captured by OLS.

Importantly, we document and transparently disclose critical …