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Articles 1381 - 1410 of 119540
Full-Text Articles in Entire DC Network
Design And Evaluation Of A Code-Switching-Aware Multilingual Conversational Ai System Using Advanced Rag Architectures, Ashutosh Juvale
Design And Evaluation Of A Code-Switching-Aware Multilingual Conversational Ai System Using Advanced Rag Architectures, Ashutosh Juvale
Master’s Dissertations
Conversational artificial intelligence has become the primary interface through which hundreds of millions of users in India seek information and customer support. Yet the way these users actually write and speak is fundamentally at odds with the monolingual assumptions baked into most retrieval and generation systems: they code-switch, fluidly mixing one or more of the twenty-two scheduled languages of India with English, frequently typing Indic words in the Roman script ("mera refund kab tak aayega"). Standard Retrieval-Augmented Generation (RAG) pipelines silently fail on such input — the retriever returns off-topic passages because the query and the knowledge base live in …
Data-Driven Electrochemistry Reveals The Impact Of Hydrophobicity On Aptamer Cross-Reactivity, Emily Carroll, Michael A. Pence, Elizabeth Winterholler, Taylor D. Sparks, Shelley D. Minteer
Data-Driven Electrochemistry Reveals The Impact Of Hydrophobicity On Aptamer Cross-Reactivity, Emily Carroll, Michael A. Pence, Elizabeth Winterholler, Taylor D. Sparks, Shelley D. Minteer
Chemistry Faculty Research & Creative Works
Electrochemical aptamer-based (E-AB) biosensors offer a promising platform for reagentless detection of molecular targets, yet aptamer recognition can be limited by cross-reactivity, particularly for hydrophobic analytes such as steroid hormones. To investigate how cross-reactivity influences E-AB sensor performance, we use automation and machine learning to screen a library of possible interferent molecules against a steroid-binding aptamer, with progesterone serving as a physiologically relevant test case. Here, we develop a label-free E-AB sensor for progesterone detection using a methylene blue-modified aptamer anchored with a hexanethiol linker. We then used an automated electrochemistry platform to perform reproducible and high-throughput characterization of our …
Morphodynamic Evolution Of Midnight Pass, West-Central Florida, Sydney C. Scott
Morphodynamic Evolution Of Midnight Pass, West-Central Florida, Sydney C. Scott
USF Tampa Graduate Theses and Dissertations
This study takes advantage of a rare opportunity provided by the reopening of Midnight Pass due to two consecutive hurricanes in 2024, to examine the morphologic evolution of a newly reopened inlet and subsequent beach-inlet interactions. Historical aerial photos since the 1940s were collected and analyzed to understand the last closure of Midnight Pass, and to gain knowledge on the future of the newly reopened inlet. Time-series topography and bathymetry survey of the beach-inlet system were conducted bimonthly to quarterly for the first year and a half after the reopening, with the goals of quantifying the morphologic changes and understanding …
Innovative Approach To Converting Wastewater Brine Into Sustainable Pla-Based Composite Materials, Fatima Abdulaziz Al Jaberi
Innovative Approach To Converting Wastewater Brine Into Sustainable Pla-Based Composite Materials, Fatima Abdulaziz Al Jaberi
Thesis/ Dissertation Defenses
The increasing environmental concerns associated with desalination reject brine and date palm wood waste have created a need for sustainable waste valorization strategies. This research investigates the development of biodegradable polylactic acid (PLA)-based composite materials reinforced with a filler derived from desalination reject brine (EPC-RB) and date palm wood (DPW) waste. The study was conducted in two phases. First, PLA composites containing different concentrations of brine-derived filler were fabricated and evaluated to determine the optimum composition based on mechanical performance. Second, date palm wood waste was incorporated into the selected formulation to produce hybrid composites. Mechanical characterization was performed through …
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.
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
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
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
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
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
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
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, …
Efficiency Improvement Of Rag Based Slm For Edge Devices, Pavan Prashanth Avanigadda
Efficiency Improvement Of Rag Based Slm For Edge Devices, Pavan Prashanth Avanigadda
Master’s Dissertations
The increasing need to deploy language models on constrained devices has given rise to efficiency issues in retrieval-augmented generation (RAG) approaches. Although RAGs boost answers’ quality by retrieving knowledge from external sources, current methods utilize static retrieval mechanisms, resulting in unnecessary computation, higher latencies, and inefficiency in resource usage. In this work, an efficient RAG approach based on small language models (SLMs) is presented, which uses a efficient and adaptive retrieval scheme. This method dynamically changes the retrieval depth and context constrution based on the complexity of the query, using a trained MLP router whose routing decisions are learned from …
Dynamic Property Ordering For Efficient Multi-Property Bounded Model Checking, Vivek Kumar
Dynamic Property Ordering For Efficient Multi-Property Bounded Model Checking, Vivek Kumar
Master’s Dissertations
Formal verification plays a critical role in ensuring the correctness of modern hardware designs. As the complexity of digital systems increases, designs are often associated with a large number of verification properties that must be analyzed within limited computational resources. In conventional multi-property bounded model checking (BMC), all properties are verified simultaneously. While this approach enables parallel analysis, difficult properties can consume a disproportionate amount of resources, causing simpler properties to be delayed and reducing the overall efficiency of bug detection. This thesis presents dynamic property ordering techniques for efficient multi-property verification using SAT-based bounded model checking in the ABC …
Pinnlab: An Interactive Dashboard For Teaching Data-Driven Parameter Estimation In Differential Equations Using Physics-Informed Neural Networks, Mohan J. Parthasarathy, Padmanabhan Seshaiyer
Pinnlab: An Interactive Dashboard For Teaching Data-Driven Parameter Estimation In Differential Equations Using Physics-Informed Neural Networks, Mohan J. Parthasarathy, Padmanabhan Seshaiyer
CODEE Journal
Undergraduate instruction in ordinary differential equations (ODEs) is typically organized around the forward problem: finding solution trajectories when the governing equation and its parameters are known. In scientific practice, however, inverse problems are often more relevant, requiring unknown parameters to be inferred from noisy observations while assessing whether a proposed model is consistent with the data. We introduce PINNLab, an open-source MATLAB dashboard designed to help undergraduate students explore inverse modeling through physics-informed neural networks (PINNs). PINNLab presents PINNs as a complementary data-driven framework that connects differential equations, optimization, empirical data, and scientific machine learning. The instructional sequence is organized …
A Study Of Prompt Tuning On Small Language Models(Slms): A Controlled Benchmark And A Lightweight Instance-Aware Method, Narkadamilli Sahith
A Study Of Prompt Tuning On Small Language Models(Slms): A Controlled Benchmark And A Lightweight Instance-Aware Method, Narkadamilli Sahith
Master’s Dissertations
Parameter-efficient fine-tuning (PEFT) adapts a frozen pre-trained language model by training only a small number of additional parameters. Among PEFT approaches, prompt tuning prepends trainable continuous vectors (soft prompts) to the input. A recurring finding in the literature is that prompt tuning is strongly scale dependent: it rivals full fine-tuning on very large models but lags on smaller ones. This dissertation studies prompt tuning specifically in the small-language-model (SLM) regime. We (i) re-implement a representative set of prompt-tuning methods—Prompt Tuning, P-Tuning v2, LoPT, DPT, DePT, ACCEPT, Residual Prompt Tuning, and PARA—within a single controlled harness, enabling a fair head-to-head comparison …
Learning To See Lesions, Not Skin Tone: Counterfactual Multimodal Learning For Fair, Trustworthy, And Text-Free Dermatology Ai, Shivam Jangid
Learning To See Lesions, Not Skin Tone: Counterfactual Multimodal Learning For Fair, Trustworthy, And Text-Free Dermatology Ai, Shivam Jangid
Master’s Dissertations
Recent advances in deep learning have significantly improved the performance of automated skin lesion classification systems, enabling accurate detection of various dermatological conditions from medical images. Despite these achievements, concerns regarding fairness and generalization remain a major challenge for the deployment of such systems in real-world clinical settings. A key factor contributing to these challenges is the presence of bias in training datasets, particularly with respect to skin-tone representation. Most publicly available skin lesion datasets contain a disproportionate number of samples from individuals with lighter skin tones. As a result, deep learning models trained on these datasets often learn representations …
Adaptive Spectral Trust Gate For Physics- Constrained Operator Learning, Soham Chakraborty
Adaptive Spectral Trust Gate For Physics- Constrained Operator Learning, Soham Chakraborty
Master’s Dissertations
Physics-informed machine learning improves the plausibility, data-efficiency and generalization of surrogate models by injecting prior physical knowledge into the learning process. The current approaches can be broadly divided into two main categories: soft constraints, which add a physics residual to the training loss but guarantee nothing at inference time, and hard constraints, which project the model output onto the constraint set exactly but apply the projection uniformly to every part of the signal — including parts that are dominated by noise, discretization error, or model mismatch, where the idealized physics is not actually trustworthy. This dissertation proposes the Adaptive Spectral …
Interseismic Creep Along The Enriquillo–Plantain Garden Fault, Haiti, Estimated From Insar, Rishabh Dutta, Jeremy L. Maurer, Yi Chieh Lee
Interseismic Creep Along The Enriquillo–Plantain Garden Fault, Haiti, Estimated From Insar, Rishabh Dutta, Jeremy L. Maurer, Yi Chieh Lee
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
The Enriquillo–Plantain Garden fault zone (EPGFZ) is a major left-lateral strike-slip fault in southern Haiti hosting several recent destructive earthquakes, including the 2021 MW 7.1 Nippes event with primary thrust-slip. To investigate how strain is accommodated in the vicinity of the 2021 event, we analyzed Sentinel-1 interferometric synthetic aperture radar (InSAR) data from 2017 to 2021 using a PS + DS (persistent + distributed scatterers) InSAR time-series approach to overcome decorrelation in this highly vegetated region. We reveal interseismic creep along the EPGF and adjacent Grand'Anse–Sud border faults, with pure strike-slip mechanism and rates up to 9 mm/yr. We suggest …
Load Profile Analysis And Forecasting For Rural Mini Grids In Uganda, Prossy Mutesi, Santos L. Kihwele, Emmanuel S. Matee
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 …
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
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
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. …
Direct Minimization Of Hartree-Fock-Roothaan Energy Functionals Using Derivative-Free Optimization Algorithms: A Case Study On The Use Of Noninteger Slater-Type Orbitals, Ali̇ Bağci
Turkish Journal of Physics
This study presents an evaluation of derivative-free optimization algorithms for the direct minimization of Hartree−Fock−Roothaan energy functionals involving nonlinear orbital parameters and noninteger-order quantum numbers. The analysis focuses on atomic calculations employing noninteger Slater-type orbitals. Analytic derivatives of the energy functional are not readily available for these orbitals. Four methods are investigated under identical numerical conditions: Powell’s conjugate−direction method, the Nelder−Mead simplex algorithm, coordinate-based pattern search, and a model-based algorithm utilizing radial basis functions for surrogate-model construction. Performance analysis is first carried out using the Powell singular function, a well-established test case exhibiting challenging properties, including Hessian singularity at the …
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
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
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 …
Extending Unibreak: Semantic Retrieval And Harmful-Intent Direction Suppression For Token-Level Llm Jailbreaking, Sanket Saha
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 …
Ontology-Based Decision Support System For Improving Aquaculture Management, Thuy Thi Thu Pham, Phuong Thi Can, Nga Thi Thanh Ha
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
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
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
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
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 …