813 research outputs found
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics
No full textIn shaping the Internet of Money, the application of blockchain and
distributed ledger technologies (DLTs) to the financial sector triggered
regulatory concerns. Notably, while the user anonymity enabled in this field
may safeguard privacy and data protection, the lack of identifiability hinders
accountability and challenges the fight against money laundering and the
financing of terrorism and proliferation (AML/CFT). As law enforcement agencies
and the private sector apply forensics to track crypto transfers across
ecosystems that are socio-technical in nature, this paper focuses on the
growing relevance of these techniques in a domain where their deployment
impacts the traits and evolution of the sphere. In particular, this work offers
contextualized insights into the application of methods of machine learning and
transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin
transactions represented as a directed graph network through various
techniques. The modeling of blockchain transactions as a complex network
suggests that the use of graph-based data analysis methods can help classify
transactions and identify illicit ones. Indeed, this work shows that the neural
network types known as Graph Convolutional Networks (GCN) and Graph Attention
Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN
outperform other classic approaches and GAT are applied for the first time to
detect anomalies in Bitcoin. Ultimately, the paper upholds the value of
public-private synergies to devise forensic strategies conscious of the spirit
of explainability and data openness
Digital Workflow in Complete Denture Manufacturing
No full textDigital workflow is popular for replacing conventional analog procedures for manufacturing complete dentures. A complete digital denture corresponds to a prosthesis that is fabricated through automation using computer-aided design/computer-aided manufacturing (CAD/CAM) technology, a modern system that allows for manufacturing with high efficiency in daily clinical practice. The CAD/CAM technique offers numerous advantages for complete dentures manufacturing in the preprocessing step, including ease of use, faster speed, reduction of manual labor, and preservation of the digital record. It also assists in providing a prosthetic device with improved retention, adequate mechanical, and surface properties that inhibit excessive biofilm formation. The digital workflow can be of great help to an older population with limited access to dental care, giving them a better quality of life with predictable oral rehabilitation. In this chapter, we present the status of the fabrication of a complete denture using digital technology. Information concerning digital impressions, designing, and processing, as well as an overview of biomaterial applications and prospects in this field, is summarized. Moreover, a comparison of conventional and digital denture fabrication workflows, advantages and disadvantages, current concepts, and different fabrication methods of digital complete dentures is presented.</p
A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts
No full textCryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop’s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin’s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research
Vehicle-assisted bridge damage assessment using deep learning
No full textThis thesis introduces innovative methodologies for vehicle-assisted bridge health monitoring, aiming to improve maintenance procedures of ageing infrastructure, a critical concern for transport network owners. By taking advantage of advancements in sensing technology and the increasing interconnectivity between vehicles and infrastructure, these methodologies focus on developing an automated bridge assessment method that efficiently evaluates the current condition of bridge structures. This approach enables more accurate and timely maintenance decisions.
The primary objective of this thesis is to create an automated bridge assessment framework for existing bridges by harnessing the synergy between sensors installed on structures and signals transmitted by passing vehicles. By gathering comprehensive information from various sources, including vehicles and the bridge itself, and fusing this data using deep learning techniques, the framework efficiently evaluates the current condition of bridge structures, facilitating more precise and prompt maintenance decisions.
The thesis comprises several studies investigating deep learning techniques, such as deep autoencoders (DAE) and probabilistic temporal autoencoders (PTAE), for extracting features and capturing temporal relationships in the data. This enables accurate identification and quantification of potential damage in bridge structures.
The first study (Paper IA IB) examines an indirect bridge monitoring system using vertical acceleration responses from a fleet of vehicles passing over a healthy bridge. This study’s findings reveal that the error in signal reconstruction from the trained DAE is sensitive to damage, considering the distribution of results from multiple separate vehicle-crossing events. The proposed method proves effective in detecting damage under operational conditions and demonstrates potential as a new tool for cost-effective bridge health monitoring.
The second study introduces a methodology for assessing bridge conditions using a PTAE and multi-sensor data from a fixed sensing framework, collected during train crossings. The study’s results indicate that the proposed method can detect damage with a limited number of sensors, making it a valuable approach to enhance bridge safety. An Exponentially Weighted Moving Average (EWMA) filter and a control chartbased threshold mechanism are applied to further refine the damage assessment process, distinguishing between healthy and progressively deteriorating damage cases.
The third study proposes a Probabilistic Deep Neural Network framework for damage assessment, combining vehicle and bridge responses to extract damage-sensitive features for classifying different damage states. The findings of this study demonstrate that incorporating multiple sensor information reduces uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions.
In conclusion, this thesis advances knowledge in the field of structural assessment through structural health monitoring by providing insights and improvements in techniques and methodologies. By taking advantage of the combined strengths of sensors mounted on structures and signals transmitted by moving vehicles, the developed methodologies provide reliable and precise damage evaluation capabilities. These valuable insights enhance bridge safety, improve resource allocation, and contribute to the overall performance of transport networks. Ultimately, this approach leads to more sustainable and resilient infrastructure, better equipped to handle modern society’s growing demands
The Change in Growth, Osmolyte Production and Antioxidant Enzymes Activity Explains the Cadmium Tolerance in Four Tree Species at the Saplings Stage
No full textPhytoremediation is a green technology; however, very few species of arid environments have been identified as hyperaccumulators and fast growers. Therefore, a greenhouse experiment was performed to evidence the phytoaccumulation potential of Conocarpus erectus, Syzygium cumini, Populus deltoides and Morus alba at the sapling stage. Six-month-old plant saplings were subjected to control (CK; 0 µM) and cadmium treatments (Cd; CdCl2; 200 µM). The results depicted that plant growth, dry biomass production (leaf and stem) and chl a, b and carotenoid contents decreased significantly in all four species under Cd treatment; however, the lowest decrease was evidenced in Conocarpus erectus. The concentration of hydrogen peroxide and superoxide radical increased significantly in all four species, with the highest increase observed in Morus alba. Osmolytes production, antioxidant enzymes activity (superoxide dismutase, peroxidase, catalase and ascorbate peroxidase) and Cd accumulation in the leaves, stem and root increased significantly in all four species under Cd treatment, with the highest increase observed in Conocarpus erectus. The translocation factor was >1 in Conocarpus erectus, Syzyngoim cumini and Populus deltoides and was <1 in Morus alba. The study revealed a better Cd tolerance in Conocarpus erectus, which was driven by the effective osmolyte balance and antioxidant enzymes mechanism
Estimating Passenger Car Equivalent Factors for Heterogeneous Traffic Using Occupancy-Density Linear Regression Model
No full textA variety of methods have been proposed in the existing literature for the estimation of passenger car equivalent (PCE) factors. These methods are based on the comparison of selected attributes of different vehicles. This research, for the first time, utilizes the basic notion of the linear relationship between road area occupancy and density for the estimation of PCE factors for different vehicle types in heterogeneous traffic. Aerial photographs obtained from an unmanned aerial vehicle (UAV) were analyzed to estimate the road area occupancy and the number of vehicles classified in seven selected groups. A linear least-squares regression model was developed between road area occupancy and classified vehicle count. The coefficients of the occupancy-density linear regression model were used to estimate PCE and motorcycle equivalent (MCE) factors. The comparison of the estimated set of PCE values with the values reported in the literature shows that PCE factors estimated using the proposed method are reasonable and produce a better occupancy-density relationship than the other studies. In comparison with the existing methods that rely on lane-based measurements, the proposed method is well suited for traffic with weak/no lane discipline, as it considers the entire road width and the dynamics of lateral movement of different types of vehicles. The proposed method does not need extensive traffic data of speeds, headways, flow rates, and so forth, and is applicable on aerial photographs obtained from other sources, such as satellites.Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported with funding from Exascale Open Data Analytics Lab, National Center for Big Data and Cloud Computing (NCBC) and the Higher Education Commission of Pakistan.
Acknowledgments
The authors are thankful to research students Syed Hassan Ali, Haseeb Ahmed, Zohaib Ahmed, Aqib Abbasi, Asad Rehan, Mirza Ali Haider, Syed Abbas Hasan Zaidi, and Omema for their help in this research
Novel Sepiolite Reinforced Emerging Composite Polymer Electrolyte Membranes for high Performance Direct Methanol Fuel Cells
No full textMethanol permeation is the main issue of Nafion membranes when they are used as a polymer
electrolyte membrane (PEM) in direct methanol fuel cells (DMFCs). In the current study, novel
nanocomposite polymer membranes are prepared by the integration of surface-modified sepiolite
(MS) in polyvinylidene fluoride grafted polystyrene (PVDF-g-PS) copolymer as PEM in DMFCs.
Sepiolite surface is chemically modified using vinyltriethoxysilane and analyzed by fouriertransform infrared (FTIR) spectroscopy, X-ray diffraction (XRD) and scanning electron
microscopy (SEM). Nanocomposite PVDF-g-PS/MS membranes are prepared by phase inversion
technique and subsequently treated with chlorosulfonic acid to induce sulfonic acid (SO3H) active
sites at the membrane surface. The prepared nanocomposite membranes (S-PPMS) are analyzed
for their physicochemical characteristics in terms of water uptake percentage, cation exchange
capacity (CEC), proton conductivity (σ), and methanol permeability. MS dispersion in the
copolymer matrix is proved through morphological SEM examination. The S-PPMS membranes
exhibit increased proton conductivity due to the presence of well-dispersed MS and surface
functional –SO3H groups. A peak power density of 210 mWcm−2 is recorded for S-PPMS10 at
110 °C which is higher than the output obtained from Nafion-117. These promising results indicate
the potential utilization of prepared nanocomposite PEMs for DMFC application
Research NEXUS : Volume 6 - 2023
No full text• Researcher Spotlight: Dr Marleen Temmerman, Director CoEWCH• Insights from the Bi-annual Research Townhalls• GoalKeepers 2030: Dr Fyezah Jehan Advances Global Goals Through Innovation• Strengthening Biosafety in Research: Insights from Dr Muhammad Zohaib • Navigating Grant & Funding Opportunities with Funding Institutionalhttps://ecommons.aku.edu/research_outlook/1016/thumbnail.jp
Algorithms for passive dynamical modeling and passive circuit realizations
No full textThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 163-174).The design of modern electronic systems is based on extensive numerical simulations, aimed at predicting the overall system performance and compliance since early design stages. Such simulations rely on accurate dynamical models. Linear passive components are described by their frequency response in the form of admittance, impedance or scattering parameters which are obtained by physical measurements or electromagnetic field simulations. Numerical dynamical models for these components are constructed by a fitting to frequency response samples. In order to guarantee stable system level simulations, the dynamical models of the passive components need to preserve the passivity property (or inability to generate power), in addition to being causal and stable. A direct formulation results into a non-convex nonlinear optimization problem which is difficult to solve. In this thesis, we propose multiple algorithms that fit linear passive multiport dynamical models to given frequency response samples. The algorithms are based on convex relaxations of the original non-convex problem. The proposed techniques improve accuracy and computational complexity compared to the existing approaches. Compared to sub-optimal schemes based on singular value or Hamiltonian eigenvalue perturbation, we are able to guarantee convergence to the optimal solution within the given relaxation. Compared to convex formulations based on direct Bounded-Real (or Positive-Real) Lemma constraints, we are able to reduce both memory and time requirements by orders of magnitude. We show how these models can be extended to include geometrical and design parameters. We have applied our passive modeling algorithms and developed new strategies to realize passive multiport circuits to decouple multichannel radio frequency (RF) arrays, specifically for magnetic resonance imaging (MRI) applications. In a coupled parallel transmit array, because of the coupling, the power delivered to a channel is partially distributed to other channels and is dissipated in the circulators. This dissipated power causes a significant reduction in the power efficiency of the overall system. In this work, we propose an automated eigen-decomposition based approach to designing a passive decoupling matrix interfaced between the RF amplifiers and the coils. The decoupling matrix, implemented via hybrid couplers and reactive elements, is optimized to ensure that all forward power is delivered to the load. The results show that our decoupling matrix achieves nearly ideal decoupling. The methods presented in this work scale to any arbitrary number of channels and can be readily applied to other coupled systems such as antenna arrays.by Zohaib Mahmood.Ph. D
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