Showing papers by "University of Wisconsin–Eau Claire published in 2021"

Connecting Land–Atmosphere Interactions to Surface Heterogeneity in CHEESEHEAD19

Abstract: The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

Digital Dating Abuse Among a National Sample of U.S. Youth

Abstract: Digital dating abuse is a term used to describe physical, sexual, or psychological/emotional violence that occurs between romantic partners through the use of texting, social media, and related online mediums. Survey data were obtained from a nationally representative sample of 2,218 American middle and high school students (12-17 years old) who have been in a romantic relationship. About 28% of students in a relationship in the previous year had been the victim of digital dating abuse. Males were more likely to report having experienced it (32% compared to 24%), though no other demographic differences emerged. Several covariates did emerge as significantly related to experience with digital dating abuse, including depressive symptoms, sexual intercourse, sexting, and being the victim of cyberbullying. Experiencing offline dating abuse was by far the strongest correlate. Implications for prevention and policy within schools and the community are discussed, along with considerations for future research in this important area.

A literature review on one-class classification and its potential applications in big data

Abstract: In severely imbalanced datasets, using traditional binary or multi-class classification typically leads to bias towards the class(es) with the much larger number of instances. Under such conditions, modeling and detecting instances of the minority class is very difficult. One-class classification (OCC) is an approach to detect abnormal data points compared to the instances of the known class and can serve to address issues related to severely imbalanced datasets, which are especially very common in big data. We present a detailed survey of OCC-related literature works published over the last decade, approximately. We group the different works into three categories: outlier detection, novelty detection, and deep learning and OCC. We closely examine and evaluate selected works on OCC such that a good cross section of approaches, methods, and application domains is represented in the survey. Commonly used techniques in OCC for outlier detection and for novelty detection, respectively, are discussed. We observed one area that has been largely omitted in OCC-related literature is its application context for big data and its inherently associated problems, such as severe class imbalance, class rarity, noisy data, feature selection, and data reduction. We feel the survey will be appreciated by researchers working in these areas of big data.

Clinical applications of Infrared and Raman spectroscopy in the fields of cancer and infectious diseases

Abstract: Analytical technologies that can improve disease diagnosis are highly sought after. Current screening/diagnostic tests for several diseases are limited by their moderate diagnostic performance, invasiveness, costly and laborious methodologies or the need for multiple tests before a definitive diagnosis. Spectroscopic techniques, including infrared (IR) and Raman, have attracted great interest in the medical field, with applications expanding from early disease detection to monitoring and real-time diagnosis. This review highlights applications of IR and Raman spectroscopy, with a focus on cancer and infectious diseases since 2015, and underscores the diverse sample types that can be analyzed, such as biofluids, cells and tissues. Studies involving more than 25 participants per group (disease and control group; if no control group >25 in disease group) were considered eligible, to retain the clinical focus of the paper. Following literature searches, we identified 94 spectroscopic studies on different cancers and 30 studies on infectious diseases. The review suggests that such technologies have the potential to develop into an objective, inexpensive, point-of-care test or facilitate disease diagnosis and monitoring. Up-to-date considerations for the implementation of spectroscopic techniques into a clinical setting, health economics and successful applications of vibrational spectroscopic tests in the clinical arena are also discussed.

User Authentication Schemes Using Machine Learning Methods—A Review

Abstract: With the recent advancements in technology, more and more people rely on their personal devices to store their sensitive information. Concurrently, the environment in which these devices are connected have grown to become more dynamic and complex. This opens the discussion of if the current authentication methods being used in these devices are reliable enough to keep these user’s information safe. This paper examines the different user authentication schemes proposed to increase the security of different devices. This article is split into two different avenues discussing authentication schemes that use either behavioral biometrics or physical layer authentication. This survey will discuss both the advantages and challenges that arise with the accuracy, usability, and overall security of machine learning methods in these authentication systems. This article aims to improve further research in this field by exhibiting the various current authentication models, their schematics, and their results.

An Analysis of IoT Cyber Security Driven by Machine Learning

Abstract: Since the beginning of the Internet of Things (IoT), the number of IoT devices connected to the Internet has grown rapidly. However, many IoT devices lack the security standards that non-IoT devices have. This means that billions of smart devices could be used as part of a botnet attack or point of entry into a secured network. The potential to exploit an IoT device makes the search to find suitable IoT security measures extremely important. In order to fill this need, this study explores the use of machine learning in IoT security measures. Upon reviewing recent developments of machine learning in IoT security, it was found that the methods with the highest threat detection accuracy utilized the random forest and K-nearest neighbor algorithms and the most efficient methods utilized software-defined networks (SDN) and the fog layer of networks. In addition, the methods which determine the type of IoT device one is when it connects to a network primarily used the random forest algorithm. This study will take an in-depth look at the use of machine learning algorithms to detect malicious and anomalous data within IoT systems.

Overview of the Lake Michigan Ozone Study 2017

Abstract: The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multi-agency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes, the role of lake breezes, contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management, and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.

Effects of Pokémon GO on Physical Activity and Psychological and Social Outcomes: A Systematic Review

Abstract: Augmented reality (AR) mobile game, Pokemon GO, leverages gamification and location tracking technology to encourage players to walk in different places to catch Pokemon characters in real-world settings. The systematic review sought to explore the impact Pokemon GO has on players’ physical activity (PA), and psychological and social outcomes. Six research databases (PubMed, SPORTDiscus, PsycInfo, Web of Science, Science Direct, and Scopus) were used. Study inclusion criteria were: (1) quantitative research published in English; (2) examined the relationships between or impact of Pokemon GO on PA, psychological, and/or social outcomes; and (3) included participants played or exposed to Pokemon GO. Thirty-six studies were included with a total sample of 38,724 participants. Players had significantly greater PA than non-players in terms of daily steps and number of days spent in moderate PA. Pokemon GO game also improved players’ social interactions and their mood/affects. Selective attention and concentration improved in adolescents and memory improved in young adults after playing the game. Findings suggest playing Pokemon GO could promote meaningful improvements in walking behavior, as well as psychological and social well-being. More multidimensional research with randomized controlled trial design is needed to identify factors that influence adoption and sustainability of Pokemon GO playing.

Customers’ Online Purchase Intentions and Customer Segmentation During the Period of COVID-19 Pandemic

Abstract: Researchers have investigated the impact of various factors on customers’ online shopping behaviors. What is not yet clear is the influence of customers’ pandemic concerns on their purchase intenti...

Cyberbullying Among Tweens in the United States: Prevalence, Impact, and Helping Behaviors:

Abstract: The goal of this study was to provide key prevalence rates for experiences with cyberbullying among tweens (children between the ages of 9 and 12 years old). We were also interested in the extent t...

Characterization of ground-based atmospheric pollution and meteorology sampling stations during the Lake Michigan Ozone Study 2017

Abstract: The Lake Michigan Ozone Study 2017 (LMOS 2017) in May and June 2017 enabled study of transport, emissions, and chemical evolution related to ozone air pollution in the Lake Michigan airshed. Two hi...

A Moral Disengagement Investigation of How and When Supervisor Psychological Entitlement Instigates Abusive Supervision

Abstract: Building on the emerging research on antecedents of abusive supervision, the current research offers an empirical investigation concerning how and when supervisor psychological entitlement instigates abusive supervision in the workplace. Specifically, drawing on social cognitive theory, we develop and test a moderated-mediation model delineating the process that prompts psychologically entitled supervisors to become abusive towards subordinates. We argue that supervisor psychological entitlement facilitates supervisor moral disengagement, which subsequently incites supervisory abusive behaviors. We also argue that supervisor moral identity (MI) and core self-evaluation (CSE) are likely to play an essential role in predicting the relationship between supervisor psychological entitlement and abusive supervision. We argue that supervisor psychological entitlement is more likely to instigate abusive supervision through moral disengagement when the magnitudes of supervisor MI and CSE are weaker. We test our theoretical model utilizing time-lagged, multisource (i.e., subordinate–supervisor dyads, N = 181) data from a variety of organizations in the United States. We find general support for our hypotheses. We discuss implications for theory and practice as well as future research avenues.

Earliest record of transfer cells in Lower Devonian plants

Abstract: Key sources of information on the nature of early terrestrial ecosystems are the fossilized remains of plants and associated organic encrustations, which are interpreted as either biofilms, biological soil crusts or lichens. The hypothesis that some of these encrustations might be the remains of the thalloid gametophytes of embryophytes provided the stimulus for this investigation. Fossils preserved in charcoal were extracted from Devonian Period (Lochkovian Stage, c. 410-419 Myr old) sediments at a geological site in Shropshire (UK). Scanning electron micrographs (SEMs) of the fossils were compared with new and published SEMs of extant bryophytes and tracheophytes, respectively. One specimen was further prepared and imaged by transmission electron microscopy. Fossils of thalloid morphology were composed almost entirely of cells with labyrinthine ingrowths; these also were present in fossils of axial morphology where they were associated with putative food-conducting cells. Comparison with modern embryophytes demonstrates that these distinctive cells are transfer cells (TCs). Our fossils provide by far the earliest geological evidence of TCs. They also show that some organic encrustations are the remains of thalloid land plants and that these are possibly part of the life cycle of a newly recognized group of plants called the eophytes.

Deep Learning optimization in remote sensing image segmentation using dilated convolutions and ShuffleNet

Abstract: Semantic segmentation of land use land cover data using deep learning networks have gained significant importance in the remote sensing domain. However, deep learning architectures are computation-intensive. In this research, we propose an Atrous Shuffle-UNet network, which is designed to be lightweight. The network comprises of modified ShuffleNet units which are arranged in a similar network structure as the UNet. Atrous convolution in the proposed network increases the receptive field of the network enabling faster convergence. We compare the proposed network to state of the art deep learning architectures such as UNet, UNet with ResNet modules and a UNet with standard ShuffleNet modules. The proposed changes in the ShuffleNet units enable the network to outperform these architectures and do so with significantly less parameters.

Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review

Abstract: The Internet of things (IoT) is a rapidly advancing area of technology that has quickly become more widespread in recent years. With greater numbers of everyday objects being connected to the Internet, many different innovations have been presented to make our everyday lives more straightforward. Pattern recognition is extremely prevalent in IoT devices because of the many applications and benefits that can come from it. A multitude of studies has been conducted with the intention of improving speed and accuracy, decreasing complexity, and reducing the overall required processing power of pattern recognition algorithms in IoT devices. After reviewing the applications of different machine learning algorithms, results vary from case to case, but a general conclusion can be drawn that the optimal machine learning-based pattern recognition algorithms to be used with IoT devices are support vector machine, k-nearest neighbor, and random forest.

Recalibrating Rodinian rifting in the northwestern United States

Abstract: A lack of precise age constraints for Neoproterozoic strata in the northwestern United States (Washington State), including the Buffalo Hump Formation (BHF), has resulted in conflicting interpretations of Rodinia amalgamation and breakup processes. Previous detrital zircon (DZ) studies identified a youngest ca. 1.1 Ga DZ age population in the BHF, interpreted to reflect mostly first-cycle sourcing of unidentified but proximal magmatic rocks intruded during the amalgamation of Rodinia at ca. 1.0 Ga. Alternatively, the ca. 1.1 Ga DZ population has been suggested to represent a distal source with deposition occurring during the early phases of Rodinia rifting, more than 250 m.y. after zircon crystallization. We combined conventional laser-ablation split-stream analyses of U-Pb/Lu-Hf isotopes in zircon with a method of rapid (8 s per spot) U-Pb analysis to evaluate these opposing models. Our study of ~2000 DZ grains from the BHF identified for the first time a minor (~1%) yet significant ca. 760 Ma population, which constrains the maximum depositional age. This new geochronology implies that the BHF records early rift deposition during the breakup of Rodinia and correlates with sedimentary rocks found in other late Tonian basins of southwestern Laurentia.

What changes in where?: an empirical study of bug-fixing change patterns

Abstract: A deep understanding of the common patterns of bug-fixing changes is useful in several ways: (a) such knowledge can help developers in proactively avoiding coding patterns that lead to bugs and (b) bug-fixing patterns are exploited in devising techniques for automatic bug localization and program repair. This work includes an in-depth quantitative and qualitative analysis over 4,653 buggy revisions of five software systems. Our study identifies 38 bug-fixing edit patterns and discovers 37 new patterns of nested code structures, which frequently host the bug-fixing edits. While some of the edit patterns were reported in earlier studies, these nesting patterns are new and were never targeted before.

Leveraging the NEON Airborne Observation Platform for socio-environmental systems research

Abstract: SESYNC under National Science Foundation [DBI-1639145]; National Science FoundationNational Science Foundation (NSF); National Science Foundation through the NEON Program

Assessing Survey Satisficing: The Impact of Unmotivated Questionnaire Responding on Data Quality:

Abstract: Education researchers use surveys widely. Yet, critics question respondents’ ability to provide high-quality responses. As schools increasingly use student surveys to drive local policy making, res...

Vitamin D and COVID-19: A review on the role of vitamin D in preventing and reducing the severity of COVID-19 infection.

Abstract: Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a pathogenic coronavirus causing COVID-19 infection. The interaction between the SARS-CoV-2 spike protein and the human receptor angiotensin-converting enzyme 2, both of which contain several cysteine residues, is impacted by the disulfide-thiol balance in the host cell. The host cell redox status is affected by oxidative stress due to the imbalance between the reactive oxygen/nitrogen species and antioxidants. Recent studies have shown that Vitamin D supplementation could reduce oxidative stress. It has also been proposed that vitamin D at physiological concentration has preventive effects on many viral infections, including COVID-19. However, the molecular-level picture of the interplay of vitamin D deficiency, oxidative stress, and the severity of COVID-19 has remained unclear. Herein, we present a thorough review focusing on the possible molecular mechanism by which vitamin D could alter host cell redox status and block viral entry, thereby preventing COVID-19 infection or reducing the severity of the disease.

Standing variation rather than recent adaptive introgression probably underlies differentiation of the texanus subspecies of Helianthus annuus.

Abstract: The origins of geographic races in wide-ranging species are poorly understood. In Texas, the texanus subspecies of Helianthus annuus has long been thought to have acquired its defining phenotypic traits via introgression from a local congener, H. debilis, but previous tests of this hypothesis were inconclusive. Here, we explore the origins of H. a. texanus using whole genome sequencing data from across the entire range of H. annuus and possible donor species, as well as phenotypic data from a common garden study. We found that although it is morphologically convergent with H. debilis, H. a. texanus has conflicting signals of introgression. Genome wide tests (Patterson's D and TreeMix) only found evidence of introgression from H. argophyllus (sister species to H. annuus and also sympatric), but not H. debilis, with the exception of one individual of 109 analysed. We further scanned the genome for localized signals of introgression using PCAdmix and found minimal but nonzero introgression from H. debilis and significant introgression from H. argophyllus in some populations. Given the paucity of introgression from H. debilis, we argue that the morphological convergence observed in Texas is probably from standing genetic variation. We also found that genomic differentiation in H. a. texanus is mostly driven by large segregating inversions, several of which have signatures of natural selection based on haplotype frequencies.

Belief Propagation Decoding of Short Graph-Based Channel Codes via Reinforcement Learning

Abstract: In this work, we consider the decoding of short sparse graph-based channel codes via reinforcement learning (RL). Specifically, we focus on low-density parity-check (LDPC) codes, which for example have been standardized in the context of 5G cellular communication systems due to their excellent error correcting performance. LDPC codes are typically decoded via belief propagation on the corresponding bipartite (Tanner) graph of the code via flooding, i.e., all check and variable nodes in the Tanner graph are updated at once. We model the node-wise sequential LDPC scheduling scheme as a Markov decision process (MDP), and obtain optimized check node (CN) scheduling policies via RL to improve sequential decoding performance as compared to flooding. In each RL step, an agent decides which CN to schedule next by observing a reward associated with each choice. Repeated scheduling enables the agent to discover the optimized CN scheduling policy which is later incorporated in our RL-based sequential LDPC decoder. In order to reduce RL complexity, we propose a novel graph-induced CN clustering approach to partition the state space of the MDP in such a way that dependencies between clusters are minimized. Compared to standard decoding approaches from the literature, some of our proposed RL schemes not only improve the decoding performance, but also reduce the decoding complexity dramatically once the scheduling policy is learned. By concatenating an outer Hamming code with an inner LDPC code which is decoded based on our learned policy, we demonstrate significant improvements in the decoding performance compared to other LDPC decoding policies.

Collaboration between Law Enforcement Agencies in Combating Cybercrime: Implications of a Taiwanese Case Study about ATM Hacking.

Abstract: An increasing number of cybercrimes has presented new global challenges to law enforcement agencies that traditionally operate within designated geographical jurisdictions and patrol territories. T...

Leadership Perceptions and Practices of Hospitality in Senior Care.

Abstract: Senior care providers are increasingly focused on hospitality to distinguish themselves from competitors. A mixed-methods approach was used to examine perceptions of hospitality from leadership (i....

Machine Learning Algorithms In User Authentication Schemes.

Abstract: In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different Machine Learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.