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Articles 961 - 990 of 33676
Full-Text Articles in Entire DC Network
Using Structural Similarity And Kolmogorov-Arnold Networks For Anatomical Embedding Of Cortical Folding Patterns, Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu
Using Structural Similarity And Kolmogorov-Arnold Networks For Anatomical Embedding Of Cortical Folding Patterns, Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populations. However, 3HGs are identified and validated within individual spaces, making it difficult to directly serve as the brain network nodes due to the absence of cross-subject correspondence. The 3HG correspondences represent the intrinsic regulation of brain organizational architecture, traditional image-based registration methods tend to fail because individual anatomical properties …
Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu
Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge. The major idea is to …
Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang
Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang
Computer Science Faculty Research & Creative Works
Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) to this task. In this study, we propose a novel Feature Fusion Transferability Aware Transformer (FFTAT) to enhance ViT performance in UDA tasks. Our method introduces two key innovations: First, we introduce a patch discriminator to evaluate the transferability of patches, generating a transferability matrix. We integrate this matrix into self-attention, directing the model to focus …
Echopulse: Ecg Controlled Echocardiograms Video Generation, Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li
Echopulse: Ecg Controlled Echocardiograms Video Generation, Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li
Computer Science Faculty Research & Creative Works
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily rely on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose ECHOPulse, an ECG-conditioned ECHO video generation model. ECHOPulse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token …
Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
Computer Science Faculty Research & Creative Works
Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain specific. To this end, in …
Pregnancy And Postpartum Effects Of Electronic Cigarettes On Maternal Health And Vascular Function In The Fourth Trimester, Amber Mills, Duaa Dakhlallah, Anand Ranpara, W. Travis Goldsmith, Paul D. Chantler, Yue-Wern Huang, Jonathan Boyd, I. Mark Olfert
Pregnancy And Postpartum Effects Of Electronic Cigarettes On Maternal Health And Vascular Function In The Fourth Trimester, Amber Mills, Duaa Dakhlallah, Anand Ranpara, W. Travis Goldsmith, Paul D. Chantler, Yue-Wern Huang, Jonathan Boyd, I. Mark Olfert
Biological Sciences Faculty Research & Creative Works
Pregnancy is a vulnerable time with significant cardiovascular changes that can lead to adverse outcomes, which can extend into the postpartum window. Exposure to emissions from electronic cigarettes (Ecig), commonly known as "vaping," has an adverse impact on cardiovascular function during pregnancy and post-natal life of offspring, but the postpartum effects on maternal health are poorly understood. We used a Sprague Dawley rat model, where pregnant dams are exposed to Ecigs between gestational day (GD)2-GD21 to examine postpartum consequences. Litter and dam health were monitored during the weaning period, and maternal vascular and endocrine function were assessed after weaning. Exposure …
Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das
Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform's long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall …
On The Sharpness Of A Korn’S Inequality For Piecewise H1 Space And Its Applications, Qingguo Hong, Young Ju Lee, Jinchao Xu
On The Sharpness Of A Korn’S Inequality For Piecewise H1 Space And Its Applications, Qingguo Hong, Young Ju Lee, Jinchao Xu
Mathematics and Statistics Faculty Research & Creative Works
In this paper, we investigate the sharpness of a Korn's inequality for piecewise H1 space and its applications. We first revisit a Korn's inequality for the piecewise H1 space based on general polygonal or polyhedral decompositions of the domain. We express the Korn's inequality with minimal jump terms. Then we prove that such minimal jump conditions are sharp for achieving the Korn's inequality. The sharpness of the Korn's inequality and explicitly given minimal conditions can be used to test whether any given finite element spaces satisfy Korn's inequality, immediately as well as to build or modify nonconforming finite elements for …
Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework For Enhanced Nuclear Reactor Power Prediction, James Daniell, Kazuma Kobayashi, Ayodeji Alajo, Syed Bahauddin Alam
Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework For Enhanced Nuclear Reactor Power Prediction, James Daniell, Kazuma Kobayashi, Ayodeji Alajo, Syed Bahauddin Alam
Nuclear Engineering and Radiation Science Faculty Research & Creative Works
The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. By leveraging a combination of feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state …
Estimation And Model Misspecification For Recurrent Event Data With Covariates Under Measurement Errors, Ravinath Alahakoon, Gideon K.D. Zamba, Xuerong Meggie Wen, Akim Adekpedjou
Estimation And Model Misspecification For Recurrent Event Data With Covariates Under Measurement Errors, Ravinath Alahakoon, Gideon K.D. Zamba, Xuerong Meggie Wen, Akim Adekpedjou
Mathematics and Statistics Faculty Research & Creative Works
For subject i, we monitor an event that can occur multiple times over a random observation window [0, (Formula presented.)). At each recurrence, p concomitant variables, (Formula presented.), associated to the event recurrence are recorded—a subset ((Formula presented.)) of which is measured with errors. To circumvent the problem of bias and consistency associated with parameter estimation in the presence of measurement errors, we propose inference for corrected estimating equations with well-behaved roots under an additive measurement errors model. We show that estimation is essentially unbiased under the corrected profile likelihood for recurrent events, in comparison to biased estimations under a …
Multi-Valued Variational Inequalities For Variable Exponent Double Phase Problems: Comparison And Extremality Results, Siegfried Carl, Vy Khoi Le, Patrick Winkert
Multi-Valued Variational Inequalities For Variable Exponent Double Phase Problems: Comparison And Extremality Results, Siegfried Carl, Vy Khoi Le, Patrick Winkert
Mathematics and Statistics Faculty Research & Creative Works
We prove existence and comparison results for multi-valued variational inequalities in a bounded domain Ω of the form (Formula presented.) where A:W1,H(Ω)→W1,H(Ω)∗ given by (Formula presented.) for u∈W1,H(Ω), is the double phase operator with variable exponents and W1,H(Ω) is the associated Musielak–Orlicz Sobolev space. First, an existence result is proved under some weak coercivity condition. Our main focus aims at the treatment of the problem under consideration when coercivity fails. To this end we establish the method of sub–super-solution for the multi-valued variational inequality in the space W1, H(Ω) based on appropriately defined sub- and super-solutions, which yields the existence …
Performance-Based Approach To Characterize External Sulfate Attack For Reactive Powder Concrete, Umut Bakhbergen, Chang Seon Shon, Dichuan Zhang, Jong Ryeol Kim, Jenny Liu
Performance-Based Approach To Characterize External Sulfate Attack For Reactive Powder Concrete, Umut Bakhbergen, Chang Seon Shon, Dichuan Zhang, Jong Ryeol Kim, Jenny Liu
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Reactive powder concrete (RPC) is a relatively new type of high-performance concrete, offering enhanced load-bearing capacity, mechanical strength, and durability. The enhanced microstructural density of RPC with the incorporation of reinforcing fibers significantly increases its resistance to durability challenges, particularly against external sulfate attack (ESA). However, conventional laboratory testing methods for evaluating the resistance of RPC to ESA are limited. Hence, a new performance-based approach was developed to evaluate the durability of RPC exposed to ESA. Expansion of nine RPC mixtures designed by Taguchi L9 orthogonal array method with four factors (steel fiber content, water-to-binder ratio (w/b), silica fume content, …
Pervasive Sensing To Correlate Vehicle Driving Behavior With City-Scale Traffic Dynamics, Debasree Das, Shameek Bhattacharjee, Sandip Chakraborty, Bivas Mitra, Sajal K. Das
Pervasive Sensing To Correlate Vehicle Driving Behavior With City-Scale Traffic Dynamics, Debasree Das, Shameek Bhattacharjee, Sandip Chakraborty, Bivas Mitra, Sajal K. Das
Computer Science Faculty Research & Creative Works
Individual driving behavior is a pivotal element that shapes the overall traffic dynamics in a city. In this work, we study and analyze the complex web of relationships between individual driving behaviors and their impact on the overall traffic dynamics of a smart city with two primary objectives: first, understanding the spatial interaction between individual vehicles and their impact on each other, and second, finding anomalous driving behaviors, which lead to congestion and traffic incidents. Specifically, we introduce an overarching modular framework investigating human factors of driver characteristics, vehicle attributes, geographical terrain surrounding the road infrastructure, and environmental conditions. Analyzing …
Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria
Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria
Computer Science Faculty Research & Creative Works
The significant advancement in Internet of Things (IoT) adoption has enabled Multi-access Edge Computing (MEC) to mitigate IoT sensors limited computational, transmission power constraints, and data distribution overhead. However, integrating MEC with the IoT ecosystem poses several challenges, resulting in integrity issues with the MECs, impacting their capacity to effectively serve users seeking data generated by IoT sensors. To address this, we propose SmartSLA, a blockchain based solution to ensure Quality of Service (QoS) from third party IoT devices. SmartSLA leverages the decentralized and immutable nature of blockchain to combat the shortcomings of MECs. Using smart contracts, we develop a …
Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das
Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das
Computer Science Faculty Research & Creative Works
The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building …
J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of …
Performance And Economic Evaluation Of Asphalt-Based High Friction Surface Treatment (Hfst) Applications, Alireza Roshan, Magdy Abdelrahman
Performance And Economic Evaluation Of Asphalt-Based High Friction Surface Treatment (Hfst) Applications, Alireza Roshan, Magdy Abdelrahman
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
High Friction Surface Treatments (HFSTs) are recognized for enhancing friction between tires and road surfaces, with reduced road accidents being a key benefit. Epoxy-based HFSTs, though widely used, come with challenges like compatibility issues with existing pavements, higher installation and removal costs, and reduced durability tied to substrate quality. Recently, state agencies have increasingly focused on developing asphalt-based alternative binders for HFST applications as highlighted in the National Cooperative Highway Research Program (NCHRP) RFP #NCHRP 10-145. This study introduces asphalt-based HFSTs as an alternative to traditional epoxy-based treatments. Various aggregate types were examined for friction performance and the effect of …
Existence Results For A Discrete Fractional Boundary Value Problem, David Barilla, Martin Bohner, Giuseppe Caristi, Shapour Heidarkhani, Shahin Moradi
Existence Results For A Discrete Fractional Boundary Value Problem, David Barilla, Martin Bohner, Giuseppe Caristi, Shapour Heidarkhani, Shahin Moradi
Mathematics and Statistics Faculty Research & Creative Works
In this study, we investigate the existence of at least one solution and the existence of an infinite number of solutions for a discrete fractional boundary value problem. Requiring an algebraic condition on the nonlinear term for small values of the parameter and requiring an additional asymptotical behavior of the potential at zero, we investigate the existence of at least one nontrivial solution for the problem. Moreover, under suitable assumptions on the oscillatory behavior of the nonlinearity at infinity, for exact collections of the parameter, we discuss the existence of a sequence of solutions for the problem. We also present …
Analyzing Micrometeoroid And Orbital Debris Shield Performance For Sample Return Spacecraft, William P. Schonberg, Michael Squire
Analyzing Micrometeoroid And Orbital Debris Shield Performance For Sample Return Spacecraft, William P. Schonberg, Michael Squire
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Sample-return missions typically seek to collect and return samples from extraterrestrial locations to Earth for analysis. The return of samples from certain locations in the solar system represents a potential hazard to Earth's biosphere from foreign microorganisms. One way this could occur would be a break-up of the returning capsule during entry (which would disperse the samples through the air) due to undetected damage to the capsule thermal protection system (TPS). As a result, missions that plan to return payloads from some destinations are likely to have certain design requirements for the TPS surrounding their returning capsules, which may include …
Class-Wise Histogram Matching-Based Domain Adaptation In Deep Learning-Based Bridge Element Segmentation, Tarutal Ghosh Mondal, Zhenhua Shi, Haibin Zhang, Genda Chen
Class-Wise Histogram Matching-Based Domain Adaptation In Deep Learning-Based Bridge Element Segmentation, Tarutal Ghosh Mondal, Zhenhua Shi, Haibin Zhang, Genda Chen
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
This study focused on the problem of domain shift in deep learning-based bridge element segmentation. The impracticability of accounting for all possible variabilities vis-à-vis structural shape, size, color, texture, illumination, and other operational conditions in the training process leads to the deterioration in the model performance when applied to test data from novel unseen domains. In such situations, rebuilding the model with labeled training data from the target domain becomes prohibitively expensive and time-consuming in many practical cases. Recent advancements in unsupervised domain adaptation techniques are known to provide viable solutions to this problem. However, it was observed in this …
Challenges In Numerical Simulation Of Frost Heave, Antai Dong, Xiong Zhang
Challenges In Numerical Simulation Of Frost Heave, Antai Dong, Xiong Zhang
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Frost heave of soil extensively exists in northern regions and poses a significant threat to infrastructure in cold regions. Despite over a century of research, challenges persist in numerically simulating frost heave. This study addresses two key issues: (1) What is the primary driving force for liquid water transfer during the freezing process? (2) How can we correctly represent unfrozen water content? Critical insights are derived from the theoretical analysis of coupled hydrothermal migration during soil freezing processes, followed by a case simulation using COMSOL Multiphysics. It concludes that of the water content gradient, suction gradient, and hydraulic gradient, only …
Using Preformed Particle Gels To Control Transport In Geothermal Reservoirs: Mathematical Modeling, Philip Winterfeld, Baojun Bai, Yu Shu Wu
Using Preformed Particle Gels To Control Transport In Geothermal Reservoirs: Mathematical Modeling, Philip Winterfeld, Baojun Bai, Yu Shu Wu
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
We are developing swellable Preformed Particle Gels (PPG), which can control preferential fluid and heat flow through fracture networks to increase the performance of EGS reservoirs. Part of this development is a mathematical model and numerical simulator to simulate PPG treatments by considering coupled thermal-hydraulic-mechanical effects, and gel swelling kinetics and plugging efficiency, from which an optimized gel treatment design and operation can be achieved. The starting point for our mathematical model is the TOUGH2-CSM formulation and code. The TOUGH2-CSM fluid and heat flow formulation is based on the TOUGH2 one for multiphase, multicomponent, and multi-porosity systems, with the latter …
Icrop+: An Edge-Boosted Crop Disease Detection System Via Tinyml And Lora Communication, Xu Tao, Jackson Butcher, Simone Silvestri, Sajal K. Das
Icrop+: An Edge-Boosted Crop Disease Detection System Via Tinyml And Lora Communication, Xu Tao, Jackson Butcher, Simone Silvestri, Sajal K. Das
Computer Science Faculty Research & Creative Works
Crop disease detection is essential for controlling dis-ease spread and minimizing agricultural losses. In this demo, we present an implementation of iCrop+, an end-to-end autonomous crop disease detection system that integrates on-device AI, low-power long-range communication (LoRa), and server-based deep learning to create a hybrid architecture suitable for real-world deployment. The prototype efficiently balances local processing and remote inference through category-based optimization, adaptive classification, and intelligent data transmission, ensuring that only the most informative segments are transmitted to the server. Built on low-cost devices such as Raspberry Pi, LoRa transceiver modules, and a laptop, the demo showcases its potential for …
Message From The Phd Dissertation Showcase Chairs, Sanjay Kumar Madria, Anita Graser
Message From The Phd Dissertation Showcase Chairs, Sanjay Kumar Madria, Anita Graser
Computer Science Faculty Research & Creative Works
No abstract provided.
Dynamic Resource Allocation In Cloud-To- Things Continuum For Real-Time Iot Applications, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Dynamic Resource Allocation In Cloud-To- Things Continuum For Real-Time Iot Applications, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
The proliferation of loT devices and the growing demand for real-time applications have driven a shift in the computation paradigm, from Cloud computing to Edge computing, creating the Cloud-to-Things Continuum (C2TC). Many real-time loT applications involve Mobile Nodes (MNs), which may dynamically join or leave. In addition, in future reconfigurable loT systems, applications with different requirements will coexist, and will be dynamically introduced or removed. All this asks for dynamic management mechanisms to ensure the requirements of different real-time applications, even when the system configuration changes over time. In this paper, we propose DJ-NECORA, an online algorithm for the joint …
Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, And Challenges, Anurag Satpathy, Manmath Narayan Sahoo, Chittaranjan Swain, Paolo Bellavista, Mohsen Guizani, Khan Muhammad, Sambit Bakshi
Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, And Challenges, Anurag Satpathy, Manmath Narayan Sahoo, Chittaranjan Swain, Paolo Bellavista, Mohsen Guizani, Khan Muhammad, Sambit Bakshi
Computer Science Faculty Research & Creative Works
Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In …
Floquet Theory For First-Order Delay Equations And An Application To Height Stabilization Of A Drone’S Flight, Martin Bohner, Alexander Domoshnitsky, Oleg Kupervasser, Alex Sitkin
Floquet Theory For First-Order Delay Equations And An Application To Height Stabilization Of A Drone’S Flight, Martin Bohner, Alexander Domoshnitsky, Oleg Kupervasser, Alex Sitkin
Mathematics and Statistics Faculty Research & Creative Works
In this paper, we proposed a version of the Floquet theory for delay differential equations. We demonstrated that very natural assumptions for control in technical applications can lead us to a one-dimensional fundamental system. This approach allowed researchers to work with classical methods used in the case of ordinary differential equations. On this basis, new original unexpected results on the exponential stability were proposed. For example, in the equation x' (4)+a(t)x(t—-T(7)) = 0, t € [0, co), we avoided the assumption on the smallness of the product sup,j9,.) 41 SUP;< {9,00) TD) < 3/2 for asymptotic stability. We obtained that in the case of w-periodic coefficient and delay, the fact that the period w was situated in a corresponding interval can lead to exponential stability. We then applied our new tests of stability to the stabilization of a drone's flight, where smallness of the noted above product could not be achieved from a technical point of view. For an equation with periodic coefficient and delay, we got a formula of the solution's representation on the semiaxis.
A Unified Concept Of Periodicity On Any Time Scale And Applications, Martin Bohner, Jaqueline G. Mesquita, Sabrina H. Streipert
A Unified Concept Of Periodicity On Any Time Scale And Applications, Martin Bohner, Jaqueline G. Mesquita, Sabrina H. Streipert
Mathematics and Statistics Faculty Research & Creative Works
We introduce a novel definition of periodicity on arbitrary time scales, dependent on a strictly increasing and differentiable function. This removes the commonly used and restrictive assumption of a periodic time scale to define periodic functions. Our new definition furthermore allows for a wider class of functions to be studied using the theory of periodic systems. After providing crucial properties of these periodic functions, such as the translation invariance of integrals of periodic functions, we apply the concept of this new periodicity to linear dynamic equations. We provide necessary and sufficient conditions for a linear dynamic equation to have such …
The Discrete Generalized Proportional Fractional Derivative, Martin Bohner, Rajrani Gupta
The Discrete Generalized Proportional Fractional Derivative, Martin Bohner, Rajrani Gupta
Mathematics and Statistics Faculty Research & Creative Works
In this paper, we have introduced a discrete generalized proportional fractional derivative and generated Riemann-Liouville and Caputo discrete generalized proportional fractional derivatives. The Laplace transforms of the discrete generalized proportional fractional derivatives and integrals are also calculated.
Material Suppliers’ Perspective On Collaboration In Industrial Construction Projects, Seogjae Choi, William J. O'Brien
Material Suppliers’ Perspective On Collaboration In Industrial Construction Projects, Seogjae Choi, William J. O'Brien
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Material suppliers' involvement in construction project planning has been recommended as an effective measure to improve project performance by using their expertise. However, the recommendation is not universal, and some researchers disputed the value of early involvement of suppliers depending on material type. Moreover, prior studies considered main contractors' and owners' perspectives mostly in examining how to increase the involvement of suppliers. To add material suppliers' perspective, this study conducted interviews with experienced professionals from 16 material suppliers including standardized, make-to-order, and custom products. The results are specified by separating the type of materials and sub-phases of planning. The results …