998 research outputs found

    Undergraduate Catalog of Studies, 2022-2023

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    Data-Driven Evaluation of In-Vehicle Information Systems

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    Today’s In-Vehicle Information Systems (IVISs) are featurerich systems that provide the driver with numerous options for entertainment, information, comfort, and communication. Drivers can stream their favorite songs, read reviews of nearby restaurants, or change the ambient lighting to their liking. To do so, they interact with large center stack touchscreens that have become the main interface between the driver and IVISs. To interact with these systems, drivers must take their eyes off the road which can impair their driving performance. This makes IVIS evaluation critical not only to meet customer needs but also to ensure road safety. The growing number of features, the distraction caused by large touchscreens, and the impact of driving automation on driver behavior pose significant challenges for the design and evaluation of IVISs. Traditionally, IVISs are evaluated qualitatively or through small-scale user studies using driving simulators. However, these methods are not scalable to the growing number of features and the variety of driving scenarios that influence driver interaction behavior. We argue that data-driven methods can be a viable solution to these challenges and can assist automotive User Experience (UX) experts in evaluating IVISs. Therefore, we need to understand how data-driven methods can facilitate the design and evaluation of IVISs, how large amounts of usage data need to be visualized, and how drivers allocate their visual attention when interacting with center stack touchscreens. In Part I, we present the results of two empirical studies and create a comprehensive understanding of the role that data-driven methods currently play in the automotive UX design process. We found that automotive UX experts face two main conflicts: First, results from qualitative or small-scale empirical studies are often not valued in the decision-making process. Second, UX experts often do not have access to customer data and lack the means and tools to analyze it appropriately. As a result, design decisions are often not user-centered and are based on subjective judgments rather than evidence-based customer insights. Our results show that automotive UX experts need data-driven methods that leverage large amounts of telematics data collected from customer vehicles. They need tools to help them visualize and analyze customer usage data and computational methods to automatically evaluate IVIS designs. In Part II, we present ICEBOAT, an interactive user behavior analysis tool for automotive user interfaces. ICEBOAT processes interaction data, driving data, and glance data, collected over-the-air from customer vehicles and visualizes it on different levels of granularity. Leveraging our multi-level user behavior analysis framework, it enables UX experts to effectively and efficiently evaluate driver interactions with touchscreen-based IVISs concerning performance and safety-related metrics. In Part III, we investigate drivers’ multitasking behavior and visual attention allocation when interacting with center stack touchscreens while driving. We present the first naturalistic driving study to assess drivers’ tactical and operational self-regulation with center stack touchscreens. Our results show significant differences in drivers’ interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. These results emphasize the importance of context-dependent driver distraction assessment of driver interactions with IVISs. Motivated by this we present a machine learning-based approach to predict and explain the visual demand of in-vehicle touchscreen interactions based on customer data. By predicting the visual demand of yet unseen touchscreen interactions, our method lays the foundation for automated data-driven evaluation of early-stage IVIS prototypes. The local and global explanations provide additional insights into how design artifacts and driving context affect drivers’ glance behavior. Overall, this thesis identifies current shortcomings in the evaluation of IVISs and proposes novel solutions based on visual analytics and statistical and computational modeling that generate insights into driver interaction behavior and assist UX experts in making user-centered design decisions

    Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

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    This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

    State of Art of Plithogeny Multi Criteria Decision Making Methods

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    Plithogenic sets coined by Smarandache in the year 2018 has unveiled new research opportunities in the field of Multi criteria decision making (MCDM). The contributions and developments of new decision making approaches based on plithogeny is gaining high momentum presently. The theoretical conceptualization of different phenomenon with plithogenic sets are also applied in designing optimal solutions to the decision making problems. This review paper presents the applications of plithogenic MCDM from the year 2018 to till date in almost all the spheres of decision making scenario. The literature works of the researchers presented in this paper will certainly portray the compatibility and flexibility of plithogenic sets, operators and other decision making tools. Though the time span considered for counting on the plithogeny based works is short, the applications of plithogenic sets are growing many in number and also plithogeny based theories are amplifying in a speedy manner. This has motivated the authors to investigate on the proliferation of plithogeny applications in decision making. This review paper has focused on the dimensions of different fields to which plithogeny is applied, new plithogeny based theories, extension of plithogeny, plithogenic based operators and measures. In addition to it the data on the publications of plithogeny based articles and interests of researchers are also presented as a part of this review work. The overall impact of plithogeny in the arena of decision making science and on the researchers of the same field is well sketched in this paper with the intention and hope of inspiring plithogenic researchers

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    The Investigation of Novel Bovine Oocyte-Specific Long Non-coding RNAs and Their Roles in Oocyte Maturation and Early Embryonic Development

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    Early embryonic loss is a significant factor in livestock species\u27 infertility, resulting in an economic deficit. In cattle, the in vivo fertilization rate is ~90%, with an average calving rate of about 55%, indicating an embryonic-fetal mortality rate of roughly 35%. Further, 70-80% of total embryonic loss in cattle occurs during the first three weeks after insemination, particularly between days 7-16. Growing evidence indicates that the oocyte plays an active role in regulating critical aspects of the reproductive process required for successful fertilization, embryo development, and pregnancy. However, defining oocyte quality remains enigmatic. Recently, many have abandoned the notion that one transcript or gene network modulates oocyte competence. Instead, it is speculated that a vast network of transcripts regulates gene expression. With the advent of deep sequencing technology, it was discovered that roughly 1.2% of the human genome represents protein-coding exons, whereas the remaining classifies as non-coding RNA. What once was thought of as “genetic noise” from leaky transcriptional machinery has more recently come to the foreground of modern research in molecular biology due to its broad versatility in regulating gene expression. Specifically, long non-coding RNAs (lncRNAs) have been reported to play critical roles in various biological processes. Despite their gaining popularity, most lncRNA studies focus on identifying differentially expressed lncRNAs throughout bodily systems and are left to predict their functional roles using bioinformatic and comparative analyses. Recently, lncRNAs have been identified as critical regulators of embryonic genome activation in humans, mice, pigs, goats, and rabbits. Further investigations of lncRNAs in mouse oocytes and early embryos have revealed essential roles in regulating oocyte maturation and early embryonic development. However, the functional role of lncRNAs in bovine oocytes remains to be elucidated. Previously, using RNA sequencing, our laboratory identified 1,535 lncRNAs present in bovine oocytes. The top three candidate genes, OOSNCR1, OOSNCR2, and OOSNCR3, were characterized in bovine somatic tissues, the cells within the ovarian follicle, and throughout early embryonic development. Our data revealed that OOSNCR1 and OOSNCR2 are oocyte-specific, with OOSNCR3 being highly abundant in the fetal ovary and detected at low levels in the spleen. Follicular cell expression revealed that all three lncRNAs were detected throughout the follicle. Further, all three lncRNAs were expressed highest in the oocyte, decreasing expression as the distance from the oocyte increased. Moreover, expression throughout oocyte maturation and early embryonic development revealed that OOSNCR1, OOSNCR2, and OOSNCR3 were highest during oocyte maturation, decreased at fertilization, and ceased altogether by the 16-cell stage. Collectively, the expression data suggested all three transcripts were maternal effect genes. Maternal origin was confirmed using an RNA polymerase II inhibitor, α-amanitin. The functional role of OOSNCR1, OOSNCR2, and OOSNCR3 during oocyte maturation and early embryonic development was evaluated using siRNA-mediated knockdown. Injection of the cumulus-enclosed germinal vesicle (GV) oocyte did not affect cumulus expansion; however, oocyte survival at 12 hours post-insemination was significantly reduced following the microinjection procedure. Additionally, lncRNA knockdown decreased the relative abundance of maternal effect genes NPM2, GDF9, BMP15, and JY-1 and resulted in blastocyst rates close to zero. Using siRNA-mediated knockdown in the presumptive zygote, the percentage of embryos reaching the blastocysts stage was decreased by roughly half for all three lncRNAs. The potential relationship between lncRNA expression and oocyte quality was investigated. In addition to OOSNCR1, OOSNCR2, and OOSNCR3, OOSNCR4 and OOSNCR5 were selected from the RNA sequencing dataset as highly abundant lncRNAs in bovine oocytes. All lncRNAs were quantified in oocytes of various qualities. Specifically, lncRNA expression was examined in oocytes (1) collected from small and large follicles before and after maturation, (2) differentially stained using brilliant cresyl blue (BCB), and (3) exposed to heat stress (410C) during oocyte maturation. Data revealed that OOSNCR1 and OOSNCR3 were accumulated during maturation, whereas OOSNCR2 and OOSNCR4 were degraded. Further, OOSNCR1, OOSNCR2, and OOSNCR4 were more abundant in oocytes collected from small follicles. Specifically, OOSNCR2 and OOSNCR4 were expressed highest in immature oocytes. Conversely, OOSNCR3 was more abundant in mature oocytes collected from large follicles. Following BCB staining, OOSNCR3 was expressed lower in BCB+ oocytes. Finally, maturation in a heat-stressed environment decreased cumulus cell expansion. Heat stress during maturation also caused OOSNCR1 to decrease expression, whereas OOSNCR3, OOSNCR4, and OOSNCR5 expression increased. Overall, the data herein revealed dynamic expression profiles of novel lncRNAs and suggests a functional requirement of OOSNCR1, OOSNCR2, and OOSNCR3 during bovine oocyte maturation and early embryogenesis

    Year of the Golden Jubilee: Culture Change in the Past, Present and Future

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    Part 1 of the IACCP Proceedings contains the abstracts and links to the recordings of the XXVI Congress of the International Association for Cross-Cultural Psychology, 2022. (c) 2023, International Association for Cross-Cultural Psychologyhttps://scholarworks.gvsu.edu/iaccp_proceedings/1011/thumbnail.jp

    Integrated Immunity-based Methodology for UAV Monitoring and Control

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    A general integrated and comprehensive health management framework based on the artificial immune system (AIS) paradigm is formulated and an automated system is developed and tested through simulation for the detection, identification, evaluation, and accommodation (DIEA) of abnormal conditions (ACs) on an unmanned aerial vehicle (UAV). The proposed methodology involves the establishment of a body of data to represent the function of the vehicle under nominal conditions, called the self, and differentiating this operation from that of the vehicle under an abnormal condition, referred to as the non-self. Data collected from simulations of the selected UAV autonomously flying a set of prescribed trajectories were used to develop and test novel schemes that are capable of addressing the AC-DIEA of sensor and actuator faults on a UAV. While the specific dynamic system used here is a UAV, the proposed framework and methodology is general enough to be adapted and applied to any complex dynamic system. The ACs considered within this effort included aerodynamic control surface locks and damage and angular rate sensor biases. The general framework for the comprehensive health management system comprises a novel complete integration of the AC-DIEA process with focus on the transition between the four different phases. The hierarchical multiself (HMS) strategy is used in conjunction with several biomimetic mechanisms to address the various steps in each phase. The partition of the universe approach is used as the basis of the AIS generation and the binary detection phase. The HMS approach is augmented by a mechanism inspired by the antigen presenting cells of the adaptive immune system for performing AC identification. The evaluation and accommodation phases are the most challenging phases of the AC-DIEA process due to the complexity and diversity of the ACs and the multidimensionality of the AIS. Therefore, the evaluation phase is divided into three separate steps: the qualitative evaluation, direct quantitative evaluation, and the indirect quantitative evaluation, where the type, severity, and effects of the AC are determined, respectively. The integration of the accommodation phase is based on a modular process, namely the strategic decision making, tactical decision marking, and execution modules. These modules are designed by the testing of several approaches for integrating the accommodation phase, which are specialized based on the type of AC being addressed. These approaches include redefining of the mission, adjustment or shifting of the control laws, or adjusting the sensor outputs. Adjustments of the mission include redefining of the trajectory to remove maneuvers which are no longer possible, while adjusting of the control laws includes modifying gains involved in determination of commanded control surface deflections. Analysis of the transition between phases includes a discussion of results for integrated example cases where the proposed AC-DIEA process is applied. The cases considered show the validity of the integrated AC-DIEA system and specific accommodation approaches by an improvement in flight performance through metrics that capture trajectory tracking errors and control activity differences between nominal, abnormal, and accommodated cases

    Benchmarking electric power companies’ sustainability and circular economy behaviors : using a hybrid PLS-SEM and MCDM approach

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    This research examines the impact of firms’ decision-making, crisis management, and risk-taking behaviors on their sustainability and circular economy behaviors through the mediating role of their eco-innovation behavior in the energy industry in Iraq. Firms are exploring applicable mechanisms to increase green practices. This requires the industry to possess the essential skills to overcome the challenges that reduce sustainable activities. We applied a dual-stage structural equation modeling (PLS-SEM) and a multi-criteria decision-making (MCDM) approach to explore the linear relationships between variables, determine the weight of the criteria, and rank energy companies based on a circular economy. The online questionnaire was sent to 549 managers and heads of departments of Iraqi electric power companies. Out of these, 384 questionnaires were collected. The results indicate that firms’ crisis management, decision-making, and risk-taking behaviors are significantly and positively linked to their eco-innovation behavior. This study confirms the significant and positive impact of firms’ eco-innovation behavior on their sustainability and circular economy behaviors. Likewise, eco-innovation behavior has a fully mediating role. For the MCDM methods, ranking energy companies according to the circular economy can support policymakers’ decisions to renew contracts with leading companies in the ranking. Practitioners can also impose government regulations on low-ranked companies. Thus, governments can reduce the problems of greenhouse gas emissions and other environmental pollution.peer-reviewe
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