Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 13 Anatomy of the Andaman–Nicobar subduction system from seismic reflection data

Abstract The Andaman–Nicobar subduction system is the northwestern segment of the Sunda subduction system, where the Indian Plate subducts beneath the Sunda Plate in a nearly arc-parallel direction. The entire segment ruptured during the 2004 great Andaman–Sumatra earthquake (Mw=9.3). Using recently acquired high-resolution seismic reflection data, we characterize the shallow structure of the whole Andaman–Nicobar subduction system from west to east, starting from the nature of the subducting plate in the Bay of Bengal to back-arc spreading in the Andaman Sea. We find that the Ninety-East Ridge is overlain by thick continental margin sediments beneath the recent Bengal Fan sediments. The boundary between these two sedimentary units defines the plate interface. We observe evidence of re-activation of fracture zones on the subducting plate beneath the forearc, influencing the morphology of the upper plate. The forearc region, which includes the accretionary wedge, the forearc high and the forearc basin, is exceptionally wide (250 km). We observe an unusually large bathymetric depression within the forearc high. The forearc high is bounded in the east by a normal fault, whereas the forearc basin contains an active backthrust. The forearc basin is floored by the continental crust of Malayan Peninsula origin. The active sliver strike-slip fault lies in a deep basin, created during the rifting of the forearc continental crust and the Malayan Peninsula. The sliver fault connects with the Great Sumatra Fault in the south and with the Sagaing Fault in the north, via the Andaman Sea spreading centre and a large transform fault in the Andaman Sea.
Traffic Image Analysis with Stacked Denoising Autoencoder Neural Network

This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDA), Deep Belief Network (DBN), and Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. To enhance the reliability of experimental results, numerous experiments were conducted in this study under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to conclude. The research results showed that the SDA model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDA model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values.
Device Type Classiﬁcation Based on Two-Stage Traﬃc Behavior Analysis

SUMMARY In the beyond 5G and 6G networks, the number of connected devices and their types will greatly increase including not only user devices such as smartphones but also the Internet of Things (IoT). More-over, Non-terrestrial networks (NTN) introduce dynamic changes in the types of connected devices as base stations or access points are moving objects. Therefore, continuous network capacity design is required to fulﬁll the network requirements of each device. However, continuous optimization of network capacity design for each device within a short time span becomes diﬃcult because of the heavy calculation amount. We introduce device types as groups of devices whose traﬃc characteristics resemble and optimize network capacity per device type for eﬃcient network capacity design. This paper proposes a method to classify device types by analyzing only encrypted traﬃc behavior without using payload and packets of speciﬁc protocols. In the ﬁrst stage, general device types, such as IoT and non-IoT, are classiﬁed by analyzing packet header statistics using machine learning. Then, in the second stage, connected devices classiﬁed as IoT in the ﬁrst stage are classiﬁed into IoT device types, by analyzing a time series of traﬃc behavior using deep learning. We demonstrate that the proposed method classiﬁes device types by analyzing traﬃc datasets and outperforms the existing IoT-only device classiﬁcation methods in terms of the number of types and the accuracy. In addition, the proposed model performs comparable as a state-of-the-art model of traﬃc classiﬁcation, ResNet 1D model. The proposed method is suitable to grasp device types in terms of traﬃc characteristics toward eﬃcient network capacity design in networks where massive devices for various services are connected and the connected devices continuously change.
A Proposed Framework for the Implementation of a Business Intelligence System

A lot of focus can be attributed to the rising importance of Business Intelligence Systems (BIS), which have been viewed as a broad category of technologies, applications, and processes for analyzing data to help its users make better decisions. Although Business Intelligence (BI) can help improve a company’s performance, many companies often fail to realize the expected benefits of BI, and these projects are considered failures in themselves. Hence, it is essential to consider Critical Success Factors (CSFs) to obtain a successful implementation. However, very few existing research papers have associated CSFs with the various implementation stages and roles. This research study aims to design a framework that will embed various CSFs for BIS, categorized by the different stages, categories, priorities, and roles involved. Following the evaluation process, the study’s main results show that the proposed framework was beneficial for BI implementation as there was no such framework that interconnects multiple components such as CSFs, implementation stages, categorization of CSFs, prioritization of CSFs and implementation roles.
ON-LINE LEARNING FOR MULTIPLE MODEL ADAPTIVE CONTROL

: An on-line learning mechanism for a multiple model adaptive controller, suitable for time varying plants with sparse jump parameters changes, is presented. The learning mechanism allows to add, on-line, new nominal models and the corresponding controllers to the already existing bank of nominal plant models and controllers. Simulation results from the position control of two coupled cart, whose stiffness parameter value is subjected to abrupt changes, are presented in order to analyze and compare the performance of two adaptive control structures, based on multiple models, with and without the on-line learning mechanism.
STEAM Project Exhibition in the Metaverse for Deaf High School Students’ Affective Empowerment: The Power of Student Museum Exhibitions in Social Virtual Reality

The Metaverse is the three-dimensional iteration of the Internet, a perpetual open web of persistent, networked environments merging physical reality with digital virtuality. Science Technology Engineering Art Mathematics (STEAM) education bridges two knowledge domains often perceived as dis-jointed: science and technology with art, humanities and social studies. This paper presents the design and development of a multi-school transdisciplinary STEAM project orchestrated by the University of Patras Science and Technology Museum. The deliverables of the project were analog and digital artifacts produced by K-12 primary and secondary school students on digital literacy and future citizenship. These were presented in an innovative virtual reality exhibition that was open and accessible in a web-based 3D online environment. The study employs an exploratory case study design involving deaf high school students and teachers. Data was collected from observation and semi-structured interviews. Results showed that the exhibition of deaf student creations in a multiuser platform in the Metaverse produced a series of social ripple effects around the students themselves, school peers, with and without hearing difficulties as well as educators. Its main contribution is the practical demonstration of the social affordances of the Metaverse in educational projects for children and adolescents with hearing disabilities.
@Western Scholarship

Background: Cognitive impairment is prevalent in people with lower limb amputations (PLLA) and is associated with adverse outcomes, such as falls and worse rehabilitation outcomes. Physical function tests are essential to examine abilities; however, no research in PLLA has clarified the magnitude of cognitive demands amongst available tests in users novice at walking with a prosthesis. Research question: Is there an association between cognitive and physical function for PLLA novice at walking with a prosthesis? Methods: People from inpatient prosthetic rehabilitation were recruited. Inclusion criteria were: age ≥50 years, unilateral transtibial amputation and able to walk independently. Gait velocity and the L Test under single-task (usual) and dual-task (walking while counting backwards) conditions assessed functional mobility. The Four Square Step Test (FSST) examined dynamic balance. The Montreal Cognitive Assessment (MoCA) and the Trail Making Test (TMT-B) assessed global cognitive and executive function, respectively. Multivariable linear regressions evaluated the association of cognition on physical function. Results: Twenty-two people participated (age: 62.3 ± 8.9 years, male: 68.18%). The mean MoCA score was 26.23 ± 2.90. A 1-point MoCA increase was independently associated with faster gait velocity (cm/s) [single-task: 5.45 (95%CI: 2.35-8.54, AdjR 2 =0.46), dual-task: 5.04 (95%CI: 1.33-8.75, AdjR 2 =0.20) and a quicker L Test (s) [single-task: -4.75 (95%CI
WMM - Wireless Mesh Monitoring

This work presents a novel adaptive cluster-based monitoring scheme for wireless mesh networks. Nodes selforganize in semi-circular clusters and monitoring information from each cluster is aggregated by the cluster head and forwarded to the monitoring station. Clustering takes into account existing data-flows in the mesh network, to minimize the interference of the monitoring traffic on active streams.
The AIDS Epidemic Model 2023 for Estimating HIV Trends and Transmission Dynamics in Asian Epidemic Settings

Supplemental Digital Content is Available in the Text. Background: Thirteen Asian countries use the AIDS Epidemic Model (AEM) as their HIV model of choice. This article describes AEM, its inputs, and its application to national modeling. Setting: AEM is an incidence tool used by Spectrum for the Joint United Nations Programme on HIV/AIDS global estimates process. Methods: AEM simulates transmission of HIV among key populations (KPs) using measured trends in risk behaviors. The inputs, structure and calculations, interface, and outputs of AEM are described. The AEM process includes (1) collating and synthesizing data on KP risk behaviors, epidemiology, and size to produce model input trends; (2) calibrating the model to observed HIV prevalence; (3) extracting outputs by KP to describe epidemic dynamics and assist in improving responses; and (4) importing AEM incidence into Spectrum for global estimates. Recent changes to better align AEM mortality with Spectrum and add preexposure prophylaxis are described. Results: The application of AEM in Thailand is presented, describing the outputs and uses in-country. AEM replicated observed epidemiological trends when given observed behavioral inputs. The strengths and limitations of AEM are presented and used to inform thoughts on future directions for global models. Conclusions: AEM captures regional HIV epidemiology well and continues to evolve to meet country and global process needs. The addition of time-varying mortality and progression parameters has improved the alignment of the key population compartmental model of AEM with the age–sex-structured national model of Spectrum. Many of the features of AEM, including tracking the sources of infections over time, should be incorporated in future global efforts to build more generalizable models to guide policy and programs.
Visualizing stories of sexual harassment in the academy: Visualizing stories of sexual harassment in the academy: Community empowerment through qualitative data Community empowerment through qualitative data

: This paper presents the design report of an experimental data visualization artwork that deals with sexual harassment in academic environments. The visualization employs a qualitative dataset of stories of abuse and aims at nurturing emotional involvement by creating connections with the people behind the data. In the paper, we outline our theoretical background, considering previous research on anthropomorphic and artistic visualizations. Successively, we disclose our design approach and discuss the visualizations’ capability to nurture reflection, stimulate conversations, and empower the community of people fighting against sexual harassment in academia and beyond.
AUTOMATING REGRESSION TESTING IN A DOMAIN-SPECIFIC SAAS SETTING

This thesis aims to provide insights into how implementing an automated solution for regression testing in a domain-specific SaaS environment can address common regression testing challenges such as high cost or poor test result visualization. Additionally, it presents a practical demonstration of how to approach these challenges and the potential development of the solution. While previous studies on test automation often recommend adopting a third-party automation tool to tackle certain regression testing challenges, this thesis takes a different approach. We explore the feasibility of developing a test automation framework specifically tailored for a SaaS company operating in Sweden’s insurance and pension industry. This industry is renowned for its complex infrastructure, and the company requires efficient testing to support frequent releases and accommodate its diverse range of customers. To identify domain-specific issues and answer the feasibility of automating regression testing in a domain-specific setting, this thesis begins with a literature review and interviews to identify generic and company-specific issues related to regression testing and test automation. After identifying these issues, the thesis proceeds through the development process of a test framework, test execution, and framework analysis. It also explores the growing interest in test automation and examines the ethical considerations associated with its implementation. Finally, the thesis provides a test automation framework development process starting with identifying the specific issues related to developing an automated solution for regression testing for a SaaS system, continuing with its development, and confirming the feasibility of creating such.
Architecture for the Monitoring of Clouds and CDNs: Applications to Amazon AWS

—Clouds and CDNs are systems that tend to separate the content being requested by users from the physical servers capable of serving it. From the network point of view, monitoring and optimizing performance for the trafﬁc they generate is a challenging task, given the same resource can be located in multiple places, which can in turn change at any time. The ﬁrst step in understanding Cloud and CDN systems is thus the engineering of a monitoring platform. In this paper, we propose a novel solution which combines passive and active measurements, and whose workﬂow has been tailored to speciﬁcally characterize the trafﬁc generated by Cloud and CDN infrastructures. We validate our platform by performing a longitudinal characterization of the very well known Cloud and CDN infrastructure provider Amazon Web Services (AWS). By observing the trafﬁc generated by more than 50,000 Internet users of an Italian ISP, we explore the EC2, S3 and CloudFront AWS services, unveiling their infrastructure, the pervasiveness of web-services they host, and their trafﬁc allocation policies as seen from our vantage points. Most importantly, we observe their evolution over a two-year long period. The solution provided in this paper can be of interest for i) developers aiming at building measurement tools for Cloud Infrastructure Providers, ii) developers interested in failure and anomaly detection systems, and iii) third-party SLA certiﬁcators who can design systems to independently monitor performance. At last, we believe the results about AWS presented in this paper are interesting as they are among the ﬁrst to unveil properties of AWS as seen from the operator point of view.
Consuming Less as a Rational Choice: A Review of Applied Social Exchange Theory in Topical Research on Sustainable Consumption

Along with a fast expansion in volume, research on sustainable consumption is confounded with inconsistencies in definitions and terminology. For a better categorization and thus utilization of the knowledge already accumulated in the field, a synthetic approach outlining conceptual differences and commonalities within the multitude of research appears useful. Contributing to this conceptual approach, this article pursues the role of the paradigm of rational choice in topical (2019 – 2023) research on sustainable consumption. By way of a theory-based review, the particular areas of sustainable consumption research in which social exchange is applied are scrutinized, highlighting specific structures and inclinations in terms of the history of ideas. The findings point out a preference towards the sociological take on social exchange theory (as opposed to the socio-psychological) and demonstrate that the application of social exchange theory is significant particular in research on sharing economy. On the other hand, no application of social exchange theory in areas of sustainable consumption like minimalism, downshifting or voluntary simplicity could be documented.
Journal of Quantitative Spectroscopy and Radiative Transfer

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism.
A New Synchronous Reference Frame Based Method for Single-Phase Shunt Active Power Filters

This paper deals with the design of a novel method in the synchronous reference frame (SRF) to extract the reference compensating current for single-phase shunt active power filters (APFs). Unlike previous works in the SRF, the proposed method has an innovative feature that it does not need the fictitious current signal. Frequency-independent operation, accurate reference current extraction and relatively fast transient response are other key features of the presented strategy. The effectiveness of the proposed method is investigated by means of detailed mathematical analysis. The results confirm the excellent performance of the suggested approach. Theoretical evaluations are confirmed by experimental results.
Scholarship at UWindsor Scholarship at UWindsor

1. Globally, small-scale inshore fisheries are being recognized as highly beneficial for underdeveloped coastal communities since they directly contribute to local economies. Community coastal fisheries, however, may target species that are simultaneously harvested by large commercial vessels in adjacent offshore waters, creating uncertainty over stock units and connectivity that complicate management. 2. Greenland halibut Reinhardtius hippoglossoides , a commercially important flatfish species in the Arctic, were tagged in Scott Inlet, coastal Baffin Island, Canada, with acoustic transmitters and tracked for a 1-year period. Our aim was to measure fish movement and connectivity between inshore habitats, where Inuit fisheries are developing, and offshore waters, where an established commercial fishery operates. Four movement metrics were established, and cluster analysis and a mixed effects model were used to define movement types and identify environmental covariates of the presence/absence within the coastal environment respectively. 3. Two distinct movement patterns were characterized for Greenland halibut; the majority were transients that were no longer detected inshore by the end of November ( n = 47, 72%), and a smaller group of intermittently resident fish that moved into the offshore at the same time as transient fish, but returned to the coastal environment in the winter ( n = 8, 12%), with the remainder being undefined. The presence of Greenland halibut in the inshore was negatively correlated with ice cover, indicating that fish moved offshore as sea ice formed. 4. Synthesis and applications . Greenland halibut were previously thought to be highly resident within the coastal environment of Baffin Bay; however, our data demonstrates that this is not true for all areas. In Scott Inlet and adjacent coastal regions, Greenland halibut exhibit complex inshore-offshore connectivity, suggesting inshore and offshore fisheries require a shared quota. We recommend that in the face of developing global small-scale coastal fisheries, improved understanding of stock connectivity between environments is required to sustainably manage commercial fish species.
Smith ScholarWorks Smith ScholarWorks

Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Using an evaluation set of 20 historical maps from states and regions of the U.S., the method reduces average alignment RMSE by 12%.
The Aquila Digital Community The Aquila Digital Community

There are many transcriptional regulators found in pathogenic Staphylococcus aureus . Many of these regulators are essential for the organism’s ability to switch from commensal form to the virulent pathogenic form. One of these main regulators is CodY. This regulator has been shown to be responsive to nutrient availability during phases of growth. Additionally, we have recently found that MsaB, the only protein coding ORF of the msaABCR operon, is a putative co-regulator of capsule along with CodY. To explore these regulator interactions, we produced mutations of codY and msaABCR individually and a double mutation of msaABCR/ codY . We have observed that both MsaB and CodY bind to the cap promoter region in very close proximity. CodY binds to this region in early phases of growth in response to nutrients as a repressor of cap transcription and MsaB binds to this region in the later phase of growth as an activator of cap transcription. Using a chemically defined medium (CDM), we have shown that the msaABCR operon and ultimately MsaB is also likely involved in sensing nutrient availability. In this study, we show that the transcriptional regulator, MsaB, interacts with another known DNA binding transcriptional regulator, CodY, in S. aureus . These interactions are important in the regulation of sensing environmental nutrients and regulating capsule production in S. aureus .