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Articles 151 - 180 of 294497
Full-Text Articles in Physical Sciences and Mathematics
A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi
A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi
All Works
As vehicular applications become increasingly complex, their computational demands often exceed the capabilities of individual vehicles. Vehicular Edge Computing (VEC) alleviates this limitation by enabling task delegation to nearby edge resources; however, high mobility, dynamic topology, and fluctuating vehicle density make real-time offloading decisions challenging. To address these issues, we propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic Vehicular Ad-hoc Networks (VANETs). The framework follows a two-stage design: (i) context-aware edge-node selection based on live topology capture via periodic beaconing, and (ii) cumulative score-based dynamic priority queuing at the selected edge node. The priority score jointly …
Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi
Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi
All Works
Skin cancer is among the most prevalent and life-threatening dermatological diseases worldwide, with melanoma responsible for a substantial proportion of skin cancer–related deaths due to delayed and unreliable diagnosis. Conventional clinical screening based on visual inspection and expert interpretation is inherently subjective and often affected by inter-observer variability, lesion heterogeneity, and imaging artifacts, highlighting the need for accurate and generalizable automated diagnostic systems. This study proposes a novel hybrid deep learning architecture for skin cancer classification that integrates an attention-guided autoencoder with a transformer-inspired global context modeling module, forming a unified and robust representation learning framework. The encoder–decoder structure is …
Temporally Rigorous And Traceable Predictive Maintenance Via Joint Labeler-Model Optimization, Maytha Al-Ali, Ahmad Alharbi
Temporally Rigorous And Traceable Predictive Maintenance Via Joint Labeler-Model Optimization, Maytha Al-Ali, Ahmad Alharbi
All Works
Predictive maintenance (PdM) is a critical enabler of intelligent asset management in Industry 4.0, yet many existing frameworks remain difficult to operationalize due to methodological fragmentation. Common limitations include sacrificing temporal realism and class granularity for computational expediency, decoupling labeling strategy design from model hyperparameter optimization, and insufficient support for reproducibility and deployment traceability; particularly in rare-failure regimes. To address these challenges, we propose a unified, end-to-end, and fully traceable PdM framework that jointly optimizes labeling and model parameters while enforcing strict temporal fidelity. The proposed pipeline co-optimizes the failure lookahead window () and LightGBM hyperparameters within a single Bayesian …
Understanding The Out-Of-Distribution Generalization Of Chain-Of-Thought Reasoning In Llms, Nuojing Liang, Xiaotong Yuan, Pan Zhou
Understanding The Out-Of-Distribution Generalization Of Chain-Of-Thought Reasoning In Llms, Nuojing Liang, Xiaotong Yuan, Pan Zhou
Research Collection School Of Computing and Information Systems
Chain-of-Thought (CoT) reasoning substantially improves Large Language Models (LLMs), yet its out-of-distribution (OOD) generalization mechanism remains theoretically underexplored. We study CoT as compositional OOD generalization: the target task lacks complete reasoning trajectories or direct input-output pairs, while training provides only in-distribution (ID) atomic step data. We show that CoT generalizes by repeatedly applying a shared single-step predictor to compose reusable ID transitions. Theoretically, the OOD task risk is controlled by the sum of ID subtask risks, scaled by a depth-independent hard-decoding coefficient Bℓ b = O(k) that is independent of the reasoning depth T. A Rademachercomplexity analysis further gives an …
Drag As Evidence: Motion-Grounded Latent Recomposition For Drag-Based Editing, Xinyu Pu, Hongsong Wang, Jie Gui, Pan Zhou
Drag As Evidence: Motion-Grounded Latent Recomposition For Drag-Based Editing, Xinyu Pu, Hongsong Wang, Jie Gui, Pan Zhou
Research Collection School Of Computing and Information Systems
Modern image editors excel at semantic manipulation and visual synthesis, yet remain limited in precise spatial control, motivating the development of drag-based editing. However, existing drag-based methods often struggle to balance drag accuracy with natural, plausible, and intent-aligned generation. We propose MoReDrag, a motion-grounded drag-based editing method. Our key insight is to treat pixel-space warping as coarse motion evidence, and to inject this evidence into the generative sampling trajectory. Specifically, MoRe-Drag performs region-aware latent recomposition over refinement, inpainting, and anchor regions, coupled with stage-adaptive conditioning that progressively shifts from motion-grounded structure formation to semantic refinement. We further support an instruction-free …
Llm-Enabled Applications Require System-Level Threat Monitoring, Yedi Zhang, Haoyu Wang, Xianglin Yang, Jin Song Dong, Jun Sun
Llm-Enabled Applications Require System-Level Threat Monitoring, Yedi Zhang, Haoyu Wang, Xianglin Yang, Jin Song Dong, Jun Sun
Research Collection School Of Computing and Information Systems
LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task execution. This paradigm shift, however, introduces fundamentally new reliability challenges and significantly expands the security attack surface, due to the non-deterministic, learning-driven, and difficult-to-verify nature of LLM behavior. In light of these emerging and unavoidable safety challenges, we argue that such risks should be treated as expected operational conditions rather than exceptional events, necessitating a dedicated incident-response perspective. Consequently, the primary barrier to trustworthy deployment is not further improving model capability but establishing system-level threat monitoring mechanisms that can detect …
A Deep Learning Ensemble Framework For Multi-Subtype Renal Tumor Classification Using Contrast-Enhanced Ct, Hisham Abdeltawab, Ahmed Alksas, Mohamed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Rasha T. Abouelkheir, Ahmed Elmahdy, Mohamed Abou El-Ghar, Sohail Contractor, Ayman El–Baz
A Deep Learning Ensemble Framework For Multi-Subtype Renal Tumor Classification Using Contrast-Enhanced Ct, Hisham Abdeltawab, Ahmed Alksas, Mohamed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Rasha T. Abouelkheir, Ahmed Elmahdy, Mohamed Abou El-Ghar, Sohail Contractor, Ayman El–Baz
All Works
Renal cell carcinoma (RCC) is considered the most aggressive and common form of renal cancer. Therefore, early detection is crucial to ensure appropriate and effective treatment planning. In our study, we propose a novel computer-aided diagnostic (CAD) approach which incorporates a deep learning ensemble to differentiate between five renal tumor subtypes, utilising the modality of contrast-enhanced computed tomography (CE-CT). The addressed renal lesions are malignant tumors (chromophobe RCC (chRCC), papillary RCC (pRCC), and clear cell RCC (ccRCC)) and benign tumors (renal oncocytoma (RO) and angiomyolipoma (AML)). Our study includes 280 patients who underwent renal biopsy, 112 patients were diagnosed with …
A Robust Approach For Olive Leaf Disease Detection In Uncontrolled Environments, Rima Grati, Khouloud Boukadi, Emna Ben Abdallah, Ahmed Seffah
A Robust Approach For Olive Leaf Disease Detection In Uncontrolled Environments, Rima Grati, Khouloud Boukadi, Emna Ben Abdallah, Ahmed Seffah
All Works
Detecting diseases in olive leaves is crucial for maintaining tree health and ensuring stable olive production. Early signs of infection often appear on the leaves, making them a key indicator for timely disease detection and intervention. Traditionally, farmers rely on visual inspection or laboratory tests to diagnose plant diseases. However, recent advancements in deep learning (DL) have significantly improved the accuracy and efficiency of olive leaf disease diagnosis. Numerous studies in the literature have explored this task using CNN-based architectures and, more recently, Vision Transformers. While these models have shown promising performance on benchmark datasets, they are often trained and …
Ai-Driven Collision Risk Prediction And Debris Optimization In Low Earth Orbit, Emad Alkhazraji, Mohamed E. Yahia, R. E. Tolba, N. S. Alharthi
Ai-Driven Collision Risk Prediction And Debris Optimization In Low Earth Orbit, Emad Alkhazraji, Mohamed E. Yahia, R. E. Tolba, N. S. Alharthi
All Works
Debris congestion and collision in low Earth orbit have become a major point of concern due to the rapid expansion of activities in that region. In this work, an AI-driven framework is developed and deployed to predict, analyze, and optimize collision risk across the altitude inclination space. Local debris object population, mass density distribution, and conjunction derived risk are computed. Then, an AI surrogate model, viz., gradient-boosted decision trees, was trained to predict collision conjunction relative probabilities and risk. Thereafter, data analysis and explain ability techniques are utilized to uncover underlying risk drivers and reveal how altitude, inclination, mass loading, …
Stimuli-Responsive Proton Transfer: Photoacid Design, Co2 Sorbent Regeneration, And Polymer Applications, Melyse Ava Laud
Stimuli-Responsive Proton Transfer: Photoacid Design, Co2 Sorbent Regeneration, And Polymer Applications, Melyse Ava Laud
Theses and Dissertations
Control over proton transfer enables the regulation of many pH-dependent chemical and material processes. Stimuli-responsive functional materials provide a means of achieving this control. Functional materials such as Metastable-state photoacids (mPAHs) reversibly release large amounts of protons when stimulated by light from a weakly acidic site within their structure. However, the dark acidities of previously reported mPAHs limit their use under basic conditions. To address this limitation, a benzimidazole-type mPAH was developed with an improved ground-state pKa of 11.9 and a metastable-state pKa of 9.8, enabling reversible proton transfer under mildly basic conditions. Additional mPAHs were examined to determine how …
The Hidden Heatwave: Thermal Hazards In The New York City Subway System, Abdou Rachid Bah, Maimuna Muntaha, Lenny L. Metlitsky, Cesar Pascal Vasquez, Tyler Ayala, Isaac Morel Lopez, Shaquan A. Larose, Kingston Ditsch, Kip Nielsen, Shaunak Sharma, Reginald U. Avila-Ortega Blake, Hamidreza I. Norouzi
The Hidden Heatwave: Thermal Hazards In The New York City Subway System, Abdou Rachid Bah, Maimuna Muntaha, Lenny L. Metlitsky, Cesar Pascal Vasquez, Tyler Ayala, Isaac Morel Lopez, Shaquan A. Larose, Kingston Ditsch, Kip Nielsen, Shaunak Sharma, Reginald U. Avila-Ortega Blake, Hamidreza I. Norouzi
Publications and Research
Underground transit systems (Subway) are susceptible to frequent heat waves, which pose serious health and safety risks to commuters and transit workers. Yet, the patterns and extent of temperature changes within the subway system remain insufficiently understood. To address this gap, we conducted a study of thermal variability across 112 subway stations in New York City. Temperature and relative humidity were measured at both platform and street levels. Results show that during the summer, 70% of subway stations had platform heat index higher than street level heat index. We also developed a machine learning forecasting model to predict platform-level temperatures. …
Efficient Removal Of Methyl Orange Dye Via A Mnfe₂O₄/Go Nanocomposite With A Ctab Dual-Layer Surfactant Coating, Taher Karami, Soleiman Bahar, Reza Mostafazadeh
Efficient Removal Of Methyl Orange Dye Via A Mnfe₂O₄/Go Nanocomposite With A Ctab Dual-Layer Surfactant Coating, Taher Karami, Soleiman Bahar, Reza Mostafazadeh
Research outputs 2022 to 2026
This study investigated the adsorption of methyl orange (MO) from aqueous solutions using a novel MnFe₂O₄/GO nanocomposite coated with cetyltrimethylammonium bromide (CTAB). The dual-layer surfactant modification facilitates both electrostatic and lipophilic interactions. This enhancement significantly improves dye removal efficiency. The adsorption process was monitored using spectrophotometry at 464 nm, with various characterization techniques confirming the structural and magnetic properties of the nanocomposite. The optimized parameters for maximum adsorption include a 2-minute ultrasonic dispersion, pH 6.8, and a surfactant-to-adsorbent ratio of 1, achieving a maximum adsorption capacity of 285.7 mg/g. The kinetic data followed a pseudo-second-order model, whereas the adsorption isotherm …
Structural And Electrochemical Characterization Of Na2co3/ Γ- Al2o3 Electrodes For Supercapacitor, Wiwin Dwiana, Jeniva Dinta Nanda Fahreza, Anissa Chairani Alfin Nadhira, Fatimatuz Zahra, Erma Surya Yuliana, Muhammad Safwan Abd Aziz, Nandang Mufti
Structural And Electrochemical Characterization Of Na2co3/ Γ- Al2o3 Electrodes For Supercapacitor, Wiwin Dwiana, Jeniva Dinta Nanda Fahreza, Anissa Chairani Alfin Nadhira, Fatimatuz Zahra, Erma Surya Yuliana, Muhammad Safwan Abd Aziz, Nandang Mufti
Journal of Mechanical Engineering Science and Technology (JMEST)
The development of electrode materials with suitable structural and surface properties is crucial for improving electrochemical energy storage systems. Gamma alumina (𝛾-Al2O3) has attracted considerable interest in electrochemical research due to its unique structural and surface properties. However, its charge-storage capacity is still limited, highlighting the need for structural and compositional modifications to improve its electrochemical performance. In this study, Na2CO3 was introduced into 𝛾-Al2O3 as a sodium precursor to induce phase modification and enhance the electrochemical activity. The systematic incorporation of Na2CO3 also provides an opportunity …
Mgrre_Thinsections_Mgrre_11_26, Mgrre
Mgrre_Thinsections_Mgrre_11_25, Mgrre
Mgrre_Thinsections_Mgrre_11_24, Mgrre
Mgrre_Thinsections_Mgrre_11_23, Mgrre
Mgrre_Thinsections_Mgrre_11_22, Mgrre
Mgrre_Thinsections_Mgrre_11_21, Mgrre
Mgrre_Thinsections_Mgrre_11_20, Mgrre
Mgrre_Thinsections_Mgrre_11_19, Mgrre
Mgrre_Thinsections_Mgrre_11_18, Mgrre
Mgrre_Thinsections_Mgrre_11_17, Mgrre
Mgrre_Thinsections_Mgrre_11_16, Mgrre
Peakon Solutions And Analytical Properties For The Camassa–Holm-Type Equations With Quadratic Nonlinearities, Yonghong Chen, Zhijun Qiao, Mingxuan Zhu
Peakon Solutions And Analytical Properties For The Camassa–Holm-Type Equations With Quadratic Nonlinearities, Yonghong Chen, Zhijun Qiao, Mingxuan Zhu
School of Mathematical & Statistical Sciences Faculty Publications
In this paper, we derive the multi-peakon dynamical system of a class of Camassa-Holm-type equations with quadratic nonlinearities. We also consider the analytical properties for the Cauchy problem. Firstly, we establish local well-posedness of solutions in Besov spaces and then provide the blow-up criteria. Subsequently, we impose appropriate sufficient conditions on the initial data to guarantee that the corresponding solution either exists globally or blows up in a finite time. Finally, we prove the ill-posedness in the Besov space B2,∞3/2 by utilizing the non-traveling wave solutions.
Mgrre_Thinsections_Mgrre_11_15, Mgrre
Mgrre_Thinsections_Mgrre_11_14, Mgrre
Mgrre_Thinsections_Mgrre_11_13, Mgrre