Digital Commons Network^™

Effect Of Changing Properties Of In-Pipe Inspection Robot And Payload Capacity On Its Performance, Sarra Gismelseed, Amur Al-Yehmadi, Riadh Zaier, Mojtaba Ghodsi

The Journal of Engineering Research

The performance of screw-drive in-pipe inspection robots is significantly influenced by factors such as wheel tilting angle, coefficient of friction, and spring constant. A comprehensive understanding of these parameters is critical for the effective design, optimization, and control of the robot, particularly to enhance energy efficiency and payload capacity during vertical pipeline inspections. In this study, we developed a mathematical model based on discrete Lagrangian mechanics to formulate an optimization problem aimed at minimizing power consumption and maximizing payload capacity under various dynamic constraints and design conditions. Our results show that increasing the wheel tilt angle allows the robot to …

The Online Spsg Parameters Prediction With Endorsement Based On Fem And The D-Q Model, Ahmed H. Ahmed, Basil M. Saied

The Journal of Engineering Research

The aim of this research is to present a method of online parameter estimation for a salient-pole, three-phase synchronous generator. Two main approaches have been used, based on different theories, to prove the validity of parameter estimation results. The first approach is based on the representation of synchronous machines using the d-q model with the sequence of the Hartley Series function and the finite element model (FEM). Therefore, two sets of parameter estimation results have been predicted. In the second approach, where the synchronous machine has been represented in real form, and the obtained results have been analysed to obtain …

Papr Reduction For Ofdm-Over-Noma Using Hybrid Partial Selective Mapping And Artificial Neural Networks, Tamadhar Al-Hashmi, Naser Tarhuni, Mostefa Mesbah, Hafiz Asif

The Journal of Engineering Research

Orthogonal Frequency Division Multiple Access (OFDMA) combined with Non-Orthogonal Multiple Access (NOMA) represents a promising hybrid approach for next-generation wireless communication systems. By leveraging the strengths of both OFDM and NOMA, this integration aims to improve system capacity and spectral efficiency while supporting Ultra-Reliable Low-Latency Communications (URLLC) for a wide range of devices. Despite its potential, one major limitation of this technology is the high peak-to-average power ratio (PAPR), which can negatively impact system performance. Traditional methods for PAPR reduction, such as Selective Mapping (SLM), rely on searching for optimal phase rotations across all subcarriers, but these methods are computationally …

Experimental Investigation On The Hydraulic Conductivity Variation In Coastal And Inland Sabkha Soils In Kuwait Under Continuous Leaching, Fahad A. Al-Otaibi, Yahia E.-A. Mohamedzein, Mohammed Y. Al-Aghbari

The Journal of Engineering Research

The existence of high salt content in sabkha soils is highly affecting their geotechnical properties and leaching behaviour. In the current study, the variation of the hydraulic conductivity of two types of sabkha soils in Kuwait, namely coastal and inland sabkha, was experimentally investigated. For this purpose, leaching tests were conducted on soil samples under a pressure of 21 kN/m2 to increase water percolation. More than 55 pore volumes of water in more than 20 days were collected for measuring total dissolved solids (TDS). The variation in the hydraulic conductivity and TDS were analyzed periodically for the outflow at 5 …

Efficient Fault Detection In Nonlinear Industrial Processes: A Reduced Kernel Pca-Based Spectral Clustering Approach, Khadija Attouri, Majdi Mansouri, Abdelmalek Kouadri

The Journal of Engineering Research

Kernel Principal Component Analysis (KPCA) is a powerful tool for nonlinear process monitoring, yet its quadratic computational complexity (O( N² )) and high memory demands limit its applicability to large-scale industrial systems. This paper proposes a novel Reduced Kernel Principal Component Analysis based on Spectral Clustering (RKPCA_SpC) paradigm to efficiently address these limitations. For comprehensive comparison and to demonstrate the general efficacy of data reduction, an RKPCA_Kmeans variant is also developed and evaluated. The proposed and comparative methods are rigorously assessed through extensive case studies on two complex industrial processes: the Tennessee Eastman Process (TEP) …

Steady-State Performance Analysis Of Grid-Connected Windfarm Arrays With Mvdc Collection Networks, Esam A. Saafan, Ibrahim A. Metwally, Mohamed Eladawy

The Journal of Engineering Research

Recently, there has been an increased impetus worldwide to harness wind power to generate electricity on a large scale and integrate such systems into the existing electrical networks. In this paper, a mathematical model of a wind turbine with a horizontal axis, considering the drive train, is investigated to describe the wind turbine aerodynamics and the processes of mechanical energy conversion. The wind turbine characteristics are studied in two states, namely, a standalone turbine and a generator-connected turbine. The modelling of both states is performed using PSCAD/EMTDC. Steady-state analysis for a grid-connected wind system is performed at fixed and variable …

Development Of An Algorithm For Daylight Simulation And Prediction In Buildings Using A Deep Learning Approach, Tolulope David Makanju, Matthew Olayinka Oyeleye, Oluwole John Famoriji

The Journal of Engineering Research

This paper presents an algorithm for estimating daylight in buildings based on the weather parameters. The algorithm was used to simulate daylight in buildings. The simulated result was used to develop a deep-learning predictive model that can help engineers estimate the daylight that will be appropriate during the design stage of a building. The deep learning approach for the prediction of daylight is based on the recurrent neural network Batch Normalisation-long short-term memory (BN-LSTM) model because daylight is a time-series occurrence. The accuracy of the model is less than 10% for the daylight lux level in the building, indicating that …

Integrated Decision-Support Framework For Enhancing Coastal Community Resilience Against Hurricane-Induced Hazards, Kooshan Amini, Tanmoy Palit, Abigail L. Beck, Jayant Patil, Tao Lu, Himadri Sen Gupta, Yousef Darestani, Et. Al

Michigan Tech Publications

Coastal communities face escalating threats from hurricanes, necessitating a shift from descriptive risk assessment to prescriptive, optimized portfolio planning to enhance resilience. This study introduces an integrated decision-support framework that couples high-fidelity, multi-hazard socio-physical system modeling with a multi-objective optimization model. The framework quantifies the cascading impacts of hurricane-induced wind, storm surge, and waves on a community’s interdependent buildings, transportation network, and detailed distribution-level electric power network (EPN), along with the resulting population dislocation and long-term recovery. It then identifies Pareto-optimal portfolios of mitigation strategies by simultaneously minimizing three conflicting objectives—economic loss, population dislocation, and recovery time—under various budget constraints. …

Ultrasound-Enabled Refreshable Electrochemical Sensors For Cardiac Biomarker Monitoring, Mayank Garg, Heng Guo, Myeong Namkoong, Elizabeth Zhanov, Connor Malone, Md Saifur Rahman, Ayse Akcan-Arikan, Limei Tian

Faculty, Staff and Students Publications

Cardiac troponin I (cTnI) is the gold-standard biomarker for acute myocardial infarction, yet current laboratory-based high-sensitivity assays require centralized infrastructure, trained personnel, and serial testing, limiting their suitability for real-time or continuous monitoring in resource-limited settings. Point-of-care electrochemical sensors have shown promise for rapid cTnI detection, but their reliance on antibodies as biorecognition elements and redox mediator solutions, and single-use operation restricts practical translation. Here, we report an ultrasound-enabled refreshable electrochemical biosensor for sensitive, stable, and repeatable cTnI monitoring. The device employs a cTnI-specific peptide as the biorecognition element, providing enhanced stability during storage, transport, and operation. Label-free electrochemical impedance …

Bias Before Generation: Attention-Based Preemptive Fairness Signals In Large Language Models, Aniket Das

Master’s Dissertations

Warning: This paper includes examples of language that may be perceived as inappropriate or offensive. Large language models (LLMs) are known to propagate social biases embedded in their training corpora, producing outputs that disproportionately disadvantage individuals based on sensitive attributes such as gender, religion, race, sexual orientation and nationality. Existing mitigation strategies are either computationally prohibitive, require access to model parameters, or apply corrections only after biased content has already been generated. This work addresses a different question: can the model’s own internal attention dynamics, observed at inference time, serve as a reliable early-warning signal for bias, enabling intervention before …

Literature Landscape Of Government, Academic, And Industry Efforts In Responsible, Secure, And Trustworthy Ai, Jae C. Oh, Shiu-Kai Chin, Garrett Katz, William E. Young, Matt Clark

Electrical Engineering and Computer Science - All Scholarship

This report presents a systematic analysis of 54,628 AI research papers published between 2021 and 2025, examining the extent to which current government, academic, and industry efforts address the trustworthiness requirements of mission-critical AI systems. Papers were classified along two dimensions: organizational tier (from AI model to mission/business pro- cess) using NIST SP 800-39, and systems engineering lifecycle phase using ISO/IEC/IEEE 15288. Classification combined automated LLM-assisted screening with human validation, achieving inter-rater reliability measured by Krippendorff’s α and Gwet’s AC1. Only 16.8% of papers examined addressed any aspect of AI trustworthiness, reliability, or security, and virtually all were concentrated at …

Rethinking Interpreter Training: The Role Of Microlearning In Boosting Knowledge Retention, Hicham Boughaba

Al-Kīmiyā

This study examines the effectiveness of microlearning interventions in enhancing knowledge retention and interpreting performance among trainee interpreters. A quasi-experimental design was employed, with interpreting students divided into two groups: an experimental group receiving targeted microlearning modules on specialized terminology and a control group relying on general knowledge only. Their performance was evaluated according to four criteria: accuracy, clarity and fluency, terminology usage, and cultural and contextual understanding. Results indicate that participants who were exposed to microlearning modules achieved significantly higher scores across all evaluation criteria, with notable gains in accuracy and terminology usage. These findings suggest that microlearning constitutes …

Heterogeneity Mechanism And More Precise Prediction Of Seafarer Fatigue Based On Group Modelling, Zhiwei Zhao, Anqi Quan, Shuxin He, Lijun Tang, Xinjian Wang, Wessel M.A. Van Leeuwen

Plymouth Business School

Traditional research often treats seafarers as a homogeneous group when exploring the causes of fatigue, neglecting the potential heterogeneity in fatigue mechanisms arising from differences in rank and department. This study systematically analyzes the fatigue driving patterns of four subgroups – Deck Department vs. Engine Department, and Officers vs. Ratings – and constructs high-precision prediction models. Based on questionnaire survey data from 450 seafarers from two Chinese shipping companies, Exploratory Factor Analysis was conducted to extract group-specific factors. Subsequently, Multiple Linear Regression and Backpropagation Neural Network models were established to identify key influencing factors and compare predictive performance. The results …

Detection Of C60 Combination Bands In The Near-Ir Spectrum Of Tc 1, Morgan M. Giese, Vincent J. Esposito, Simon Van Schuylenbergh, Jan Cami, Els Peeters, Charmi Bhatt, Dries Van De Putte, A. G. G. M. Tielens, Michael J. Barlow, Jeronimo Bernard-Salas, Alessandra Candian, Bryan Changala, Nick L. J. Cox, Harriet L. Dinerstein, D. A. García-Hernández, Marco A. Gómez-Muñoz, Kay Justtanont, Kathleen E. Kraemer, Eric Lagadec, Arturo Manchado, Ana Monreal Ibero, Raghvendra Sahai, Ameek Sidhu, G. C. Sloan, N. C. Sterling, Jeremy R. Walsh, Roger Wesson, Joshua Cole Whitman, Albert Zijlstra

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

We report the detection of a set of new near-infrared emission features between 3.5 and 5.2 μm in JWST/NIRSpec observations of Tc 1, the planetary nebula known for displaying the cleanest and most prominent mid-infrared cosmic fullerene spectrum. These broad features share the same spatial distribution as the well-known C₆₀ and C₇₀ mid-infrared emission bands, peaking in an asymmetric ring approximately 5″–6″ from the central star. Through comparison with new anharmonic quantum chemical calculations, we demonstrate that these features arise from C₆₀ combination bands, marking their first detection in an astrophysical environment. The total energy radiated …

Phonetic Morphology: Its Concept, Justifications For Its Emergence, And Foundations Of Analysis, Nawal Saleh Mahdi, Omar Rashid Shaker Al-Samarrai

Alustath Journal for Human and Social Sciences

This research examines the attempts of modern scholars to establish a new branch of linguistics concerned with the methodological overlap between the phonological and morphological levels, which they have termed phonological morphology. What prompted researchers to pursue this line of thinking was their realization that a rigorous scientific methodology must transcend the idea of separating the linguistic analysis levels. This separation was merely for educational purposes and should not persist in analytical scientific research. It appears that phonological morphology has acquired the status of an independent science, as it now has its established concept documented in modern linguistic literature, both …

Extending Unibreak: Semantic Retrieval And Harmful-Intent Direction Suppression For Token-Level Llm Jailbreaking, Sanket Saha

Master’s Dissertations

Token-level adversarial perturbations remain one of the most efficient known attacks against the safety alignment of instruction-tuned large language models (LLMs). Among recent works, the UniBreak framework (You et al., 2026) stands out for unifying gradient-based optimization with an evolutionary perturbation repository. However, its repository relies solely on accumulated success frequency without utilizing query content, and its fitness function implicitly assumes that suppressing refusal tokens is sufficient to elicit harmful responses. In this dissertation, we extend UniBreak along both axes and re-evaluates the framework under stricter generalization and judgment protocols. Specifically, we introduce a semantic perturbation repository that replaces frequency-only …

International Law Situations: Internment Of Belligerent Forces, Raul (Pete) Pedrozo

International Law Studies

This Situation examines the duty of neutral States to detain belligerent warships, their officers, and crews during an international armed conflict. At the outset of the armed conflict between the United States and Iran, Iranian warships sought refuge in India and Sri Lanka following the sinking of the Iranian frigate IRIS Dena. India and Sri Lanka, as neutrals, have a duty to detain those warships and their officers and crews for the duration of the armed conflict and take appropriate measures to render the ships and their personnel incapable of taking to sea during the conflict.

The Kashmir Conflict After The Abrogation Of Articles 370 And 35a Of The Indian Constitution: Geopolitical Dynamics And Human Security Implications, Rizwan Ali

Dissertations and Theses

The abrogation of Article 370 of the Indian Constitution on August 5, 2019, revoked the special autonomous status of Jammu and Kashmir (J&K) under the Indian administration. It further bifurcated it into two Union Territories under central Indian control. The move came amid a severe security lockdown and communication blackout that lasted for months. It was followed by instrumental legal, social, and economic developments that directly affected the area’s disputed status and the human rights of its residents. This research examines the geopolitical and human security implications of the abrogation and subsequent developments through a qualitative, analytical design, integrating Edward …

Air-Writing Recognition, Gaurang Shukla

Master’s Dissertations

Air-writing is the act of tracing characters or words in free space with a fingertip, recorded by a camera, giving a touch-free input modality for smart displays, augmented and virtual reality, and assistive interfaces. It is difficult because the finger never lifts: connecting strokes join adjacent letters with no pen-up signal to mark boundaries, and the same word varies widely in scale, position, and slant across writers. The WiTA benchmark of Kim et al. provides a large, person-disjoint dataset and a baseline that treats each clip as RGB video, recognised by a spatio-temporal 3D residual network trained with a CTC …

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 …

Community Optometrist–Led Monitoring Of Quiescent Neovascular Age-Related Macular Degeneration: The Fenetre Randomized Clinical Trial, Anitta Sharma, Aljazy Jaber, Bishwanath Pal, John G. Lawrenson, Robert A. Harper, Adnan Tufail, Annastazia E. Learoyd, Emily Robinson, Abdel Douiri, Rachel Burman, Ashleigh Kernohan, Sofia Vougioukalou, Simon Read, Judit Csontos, Aled Jones, Sajjad Mahmood, Martin Mckibbin, Janet L. Peacock, Richard Gale, Praveen J. Patel, Pearse A. Keane, Robin Hamilton, Luke Vale, Catey Bunce, Konstantinos Balaskas

School of Nursing and Midwifery

Importance Hospital-based ophthalmology faces increasing demand for long-term monitoring of neovascular age-related macular degeneration (nAMD). Safe redistribution of routine monitoring to community clinicians is relevant to integrated community (primary)–secondary care models.   Objective To examine whether community optometrist–led monitoring of nAMD is noninferior to hospital-based monitoring for detecting disease activity requiring treatment.   Design, Setting, and Participants This multicenter, noninferiority randomized clinical trial was conducted from October 8, 2019, to January 31, 2024, at secondary centers (17 hospitals) and primary centers (60 community optometry practices) with 12-month follow-up. Statisticians were masked to patient grouping. Adults 55 years or older with quiescent AMD …

The Flight Of The Purdue Owl: An Institutional History, Harry C. Denny, Vicki R. Kennell, Garrett I. Colón

The Founders Series

The Flight of the Purdue OWL: An Institutional History chronicles both the widely known Purdue OWL website and the lesser-known but much-celebrated on-campus writing center. Purdue University is renowned for its world-class STEM curriculum as well as its intercollegiate athletics. The university also receives considerable attention, however, for the OWL website, which is one of the most used educational resources in the world. The on-campus OWL is also a highly regarded collegiate writing center. This book tells the history of how the Purdue OWL grew out of decades of faculty working with students and their desire for a laboratory where …

Building Bridges: The Reference Interview As A Mechanism For Social Capital In School Libraries, Jessica M. Lilly

Learning Hub

Building Bridges: The Reference Interview as a Mechanism for Social Capital in School Libraries

Abstract

School librarians occupy a unique position within schools, interacting across all stakeholder groups and serving as a conduit between students, teachers, administrators, and the broader learning community. This article examines the reference interview not merely as a transactional information-retrieval tool but as a relational practice through which school librarians generate, sustain, and distribute social capital. Drawing on the foundational theories of Putnam (2000), Coleman (1988), and Bourdieu (1986), and on the reference transaction model grounded in Jesse H. Shera's foundational work (Coleman et al., …