Central Florida Research Articles

PARTNER project 1, low-frequency noise, and FAA's noise policy review

To improve upon current FAA noise policy, it seems reasonable to consider supplemental metrics. One of these is to include the annoyance impacts of low-frequency noise (LFN). It is established that LFN can cause rattle in homes near airports, causing annoyance. Current DNL 65 does not address LFN. In fact, most acoustical measurements do not include low frequencies due to the limitations of inexpensive measurement equipment. Hence, the FAA is genuinely missing these impacts from a physics point of view. The FAA has already paid for a very high quality LFN study through Project 1 in the PARTNER Center of Excellence (predecessor of the current ASCENT center). That project completed in 2007. The present author was not a participant in PARTNER Project 1, but he closely observed the amazing work done by the investigators from The Pennsylvania State University, Purdue University, and the University of Central Florida. If sufficient LFN data is regularly obtained at larger airports with improved noise monitoring equipment, then FAA could include supplemental metrics, such as the number of low-frequency noise events in a given period. [Work supported by the Penn State College of Engineering and its United Technologies Corporation Professorship.]

Ground-Based Pyrethroid Adulticides Reduce Mosquitoes But Not Nontarget Insects in Central Florida.

As stewards of public and environmental health, mosquito control agencies are rightfully concerned about impacts on nontarget organisms. This study examined the impact of a modern, pyrethroid based ground adulticide program using ultra-low volume applications in a metropolitan county in central Florida. Nontarget insects and mosquitoes were collected in a before-after control-impact design at 21 sites over 1.5 years. While mosquitoes were reduced, we found no evidence for reduction of nontarget insects, regardless of taxon. Night-flying Lepidoptera may experience greater risk than other nontarget taxa, but overall effects of adulticide missions on this group were low and inconsistent. Instead, meteorology, habitat, and phenology dominate patterns of nontarget abundance. Mosquito reduction was more clearly observed and corrected post-mission reduction was consistent with results expected in complex urban and suburban treatment zones.

First record of Mimogonus fumator (Fauvel, 1889) (Coleoptera: Staphylinidae: Osoriinae) in the USA, with a synopsis of the Osoriinae of the Southeastern USA

The discovery of Mimogonus fumator (Fauvel, 1889) in a suburban area in Central Florida marks the first record of this pantropical species in the United States of America. This finding emphasizes the critical significance of insect monitoring in urban environments, as it unveils the potential for detecting non-native and potentially invasive species accidentally transported through soil and ornamental plants. A taxonomic key and distributional checklist for the known species of the subfamily Osoriinae in the Southeastern United States are also provided. These resources aim to enhance our understanding of the regional insect fauna and provide useful tools for future detection.

Unsupervised deep domain adaptation algorithm for video based human activity recognition via recurrent neural networks

The integration of cyber-physical systems and artificial intelligent human activity recognition (HAR) applications enables intelligent interactions within a physical environment. In real-world HAR applications, the domain shift between training (source) and testing (target) images captured in different scenarios leads to low classification accuracy. Existing unsupervised domain adaptation (UDA) methods often require some labeled target samples for model adaptation, which limits their practicality. This study proposes a novel unsupervised deep domain adaptation algorithm (UDDAA) for HAR using recurrent neural networks. UDDAA introduces a maximum mean class discrepancy (MMCD) metric, accounting for both inter-class and intra-class differences within each domain. MMCD extends the maximum mean discrepancy to measure the class-level distribution discrepancy across source and target domains, aligning these distributions to enhance domain adaptation performance. Without relying on labeled target data, UDDAA predicts pseudo-labels for the unlabeled target dataset, combining these with labeled source data to train the model on domain-invariant representations. This approach makes UDDAA highly practical for scenarios where labeled target data is difficult or expensive to obtain, enabling human-computer interaction (HCI) systems to function effectively across varied environments and user behaviors. Extensive experiments on benchmark datasets demonstrate UDDAA's superior classification accuracy over existing baselines. Notably, UDDAA achieved 92% and 99% accuracy for University of Central Florida database (UCF) to Human Motion Database (HMDB) and HMDB to UCF transfers, respectively. Additionally, on personal recorded videos with complex backgrounds, it achieved high classification accuracies of 95% for basketball and 90% for football activities, underscoring its generalization ability, robustness, and effectiveness.

Tick Island Incised: an early ceramic skeuomorph in Late Archaic Florida

ABSTRACT Tick Island Incised (TII) is a relatively rare variety of Late Archaic fiber-tempered pottery found across sections of central and northern peninsular Florida. The striking stylistic divergences of TII from other Orange pottery types have led multiple researchers to invoke foreign influences or migrations to account for its appearance. This paper presents newly collected data on TII vessels from the Silver Glen complex (8LA1/8MR123) and related sites along the St. Johns River and northern Gulf Coast. These data, which represent the most detailed information yet collected on the technology, style, composition, and spatiotemporal distribution of TII pottery, are used to address numerous published speculations regarding the type's timing, origin, and cultural meaning. This research supports the idea that TII's aberrant style, which features symbolically powerful sinistral or left-opening spirals, can be explained as an example of skeuomorphism linked to preexisting lightning whelk shell cups. It further indicates that TII appeared during a particularly volatile time during the Late Archaic period and that its use and deposition in ritualized contexts may have been part of a deliberate effort to intervene in ongoing historical processes and mediate their impacts.

Diurnal variation of indoor air pollutants and their influencing factors in educational buildings: A case study using LASSO-based ANNs

This study explores the diurnal variations and influencing factors of PM2.5, NO2, and ozone concentrations in educational buildings. Utilizing an integrated system of indoor and outdoor sensors, building automation control networks, and walk-through inspections, air quality data along with relevant characteristics were collected from ten educational buildings in Central Florida. Advanced Neural Network models (RNNs and CNNs), including the Long Short-Term Memory (LSTM) and the Attention Temporal Convolutional Network (ATCN) algorithms based on the Least Absolute Shrinkage and Selection Operator (LASSO), were developed to accurately identify diurnal patterns in indoor air quality (IAQ) and the differences in influencing factors. The findings indicate greater variability in diurnal differences and factors influencing indoor NO2 and ozone concentrations compared to PM2.5. Although the factors influencing day and night PM2.5 levels were similar, there were significant differences in the contribution weights of these factors. Optimized RNNs and CNNs significantly outperformed standard Artificial Neural Network (ANN) models in dynamically simulating and predicting target pollutants. Comparative analysis of the root-mean-square error (RMSE) demonstrated that LASSO-LSTM models comprehensively outperformed LASSO-ATCN models by averaging 13.4% (p < 0.05). These results can be referenced in studies concerning Indoor Air Quality (IAQ) control conducted in similar environmental settings.

Use of a modified SCORFAD scale to define localised reaction patterns associated with a flea burden in cats.

The SCORing Feline Allergic Dermatitis scale (SCORFAD) is a validated dermatological scale assessing reaction patterns associated with allergic dermatitis in cats. While no pathognomonic reaction pattern is recognised with flea allergy dermatitis (FAD), miliary dermatitis is more common. This study aimed to document regional cutaneous reaction patterns of cats with fleas using a modified SCORFAD (mSCORFAD). In total, 142 owned cats from West Central Florida enrolled over three study periods. To be included, each cat and household environment had five or more fleas on Day (D)0. Cats were examined on D0, D7, D14, D21, D28, D44, D56 and D80. On D0 they were treated with an isoxazoline and treatment continued through the study. Ten body regions were assessed using mSCORFAD to give a regional score for each of four reaction patterns. These were compared using the Wilcoxon rank sum test. At D80 there was an 80% reduction in total mSCORFAD representing clinical response to ectoparasite control only. The most commonly affected region was the ventral abdomen with a mean mSCORFAD score of 1.8 (95% confidence interval = 1.5-2.1) compared with other body regions (p < 0.01). The most common reaction patterns were self-induced alopecia on the ventral abdomen and miliary dermatitis in the cervical region (p < 0.0001). The mSCORFAD suggests that self-induced alopecia on the ventral abdomen and miliary dermatitis in the cervical region is more strongly associated with a flea burden in cats.

Monitoring framework development for a network of multiple laboratory structures

The stadium structures have unique structural features increasing the significance of structural monitoring systems specifically designed for them. Aside from vibrations serviceability concerns and human-induced excitations, the development and propagation of structural damage under all possible atmospheric and seismic conditions need to be closely monitored for structural resiliency and integrity of the stadia. As such, Structural Health Monitoring (SHM) methods combined with effective data evaluation methodologies need to be deployed to monitor the structural performance of stadiums. Even though stadia monitoring has been performed at multiple locations in the world, a web based and real-time SHM network of stadia is not known to authors. As a preliminary study for the network implementation of stadia monitoring with acceleration measurements, the presented work focuses on the fundamental steps to accomplish this goal, with a collaborative research effort between Qatar University, the University of Central Florida, and University of Alberta. The authors performed analytical investigations and experimental testing on stadium-type structures built in laboratory environments for the development of the SHM framework. Specialized signal processing algorithms, sensing suites and approaches considering multi-scale monitoring were used on collected acceleration measurements. The novelty of the work presented in this manuscript are the following items which exist simultaneously in the developed SHM framework. The developed framework is a web-based monitoring application where structural damage is detected in real-time. The proposed methodology operates directly on raw acceleration signals and runs at a network level. With that, the damage detection, damage localization, and damage quantification tasks are performed simultaneously, while the feature extraction and classification stages are combined in one learning body.

A Methodology to Measure Pedestrian Crash Rates against Statewide Averages for Roadways and Intersections in Central Florida

A Methodology to Measure Pedestrian Crash Rates against Statewide Averages for Roadways and Intersections in Central Florida

An efficient convolutional neural network for detecting the crime of stealing in videos

In the current age of technological progress, surveillance cameras are extensively placed in densely populated areas to encourage public safety. However, detecting stealing activities within surveillance footage remains a challenge. The global issue of stealing leads to significant casualties and financial losses annually. There has been limited research on efficient techniques to identify this anomaly. In this manuscript, an efficient multi-scale attention-based convolutional neural network with skill optimization algorithm (MSAC-SOA) is proposed for detecting theft during day and night in surveillance videos. This approach integrates with the enhanced single-shot multibook detector (ESSD) for object detection in video footage, facilitating the precise identification and localization of objects within each frame. Subsequently, the proposed MSAC-SOA effectively detects and classifies instances of stealing. Finally, a theft detection alert message is sent to the control room. This approach is assessed with two datasets: University of Central Florida (UCF) crime dataset for daytime theft detection and real-time video data for nighttime theft detection. The proposed method demonstrates faster computation times compared to previous techniques, highlighting its efficiency and suitability in terms of various merits for real-life videos of diverse scenarios. It exhibits robust performance across various metrics, including 99.88% accuracy, 99.66% precision, 100% recall, 99.89% F1-score, 99.83% area under the ROC curve (AUC), and 99.83% specificity.

Asian swamp eels (Synbranchidae, Monopterus) in Florida: distribution, spread, and range of hydrologic tolerance over twenty-seven years (1997–2023)

Asian swamp eels (Monopterus albus/javanensis) were first reported as introduced to Florida waterbodies in 1997 near Tampa and Miami; a third population was recorded by 1999 in Homestead. Initial assessments, published soon after swamp eels in southern Florida were first recorded in wetlands beyond canals and ponds (in 2007), concluded there was little threat to Florida’s aquatic ecosystems. Long-term data now suggest they precipitated population crashes of crayfishes and small fishes in the eastern Everglades. We used records from continuous long-term monitoring programs, sporadic monitoring studies, and online databases to reconstruct swamp eel presence across Florida. Monitoring studies provided wetland hydrologic variables to assess limits for swamp eels. From 1997–2007, populations in southern Florida remained restricted to canals; initial spread from 2007–2017 across southern Everglades National Park proceeded slowly and the two populations covered ~1500 km2 of southern Florida. From 2017–2022, the rate of spread increased as they spread west and north (~5800 km2 range). Through 2014, the Tampa population occurred only along southern/eastern Tampa Bay (~60 km2) but has since spread south along the Gulf Coast, east into central Florida, and south along the Lake Wales Ridge (~11,000 km2). We found evidence of two potentially new introductions, in Palm Beach County and Orlando. There was no clear evidence of limitation of wetland drying on swamp eel occurrence in the Everglades; they were captured in marshes that dried for 1–5 months during the previous dry season, but short-hydroperiod wetlands may have slowed spread. In the Everglades, evidence suggests swamp eels may have been inadvertently spread into marshes from canals used to deliver water for flood control and hydrologic restoration. Swamp eels are currently spreading unchecked across Florida, and there should be great concern about continued spread in this region and their establishment and spread elsewhere.

Unexpected effect of geographic origin on post‐translocation survival in a long‐lived reptile, the gopher tortoise

AbstractMitigation translocations move wildlife from specific areas due to conflict with humans over land use at the site. A critical decision when carrying out mitigation translocation is the acceptable distance across which animals can be moved. This decision trades off logistical expediency of unrestricted translocation with the risk of reducing translocation success due to environmental mismatch between origin and translocation site conditions. In this study, we used a large dataset of 502 individually identifiable carcasses to examine the role of geographic origin and translocation distance in the relative survival of 2822 translocated subadult and adult gopher tortoises (Gopherus polyphemus), a species experiencing large‐scale mitigation translocation, at a recipient site in the Florida panhandle, USA. We hypothesized that if climate or habitat differences between the origin and translocation site influenced survival, tortoises translocated from within the Florida panhandle would have the highest survival. To the contrary, we found that survival slightly increased with increasing climatic difference between origin and recipient site, driven by higher survival of tortoises coming from central Florida sites compared to those from the panhandle and north Florida. This suggests that environmental mismatch due to long‐distance translocation is not a main driver of mortality. These models also indicated an effect of season, with a survival advantage to tortoises translocated in the spring and late fall, relative to summer translocations, and a negative effect of initial density on survival. Finally, we also estimated the upper bound on annual survival in three well‐monitored groups to be quite low (92–95%) for several years following release, suggesting caution when considering large translocated populations to be viable without first assessing adult survival. Our unexpected results highlight the importance of investigating species‐specific sensitivities to translocation distances and indicate the limitations of assumed linear effects of translocation distance on outcomes.

Shock-Tube Measurements of Carbon Monoxide During Combustion of Hydroxyl-Terminated Polybutadiene Products/Air

Hydroxyl-terminated polybutadiene (HTPB) is a fuel proposed for use in hypersonic rockets, specifically in solid fuel ramjets (SFRJs), where it combines with air and undergoes combustion processes that range from fuel pyrolysis to fuel lean- combustion. A better understanding and the improvement of SFRJ combustion can be accomplished through fundamental experiments and computational fluid dynamics modeling. The current chemical-kinetic models need data to capture the reaction kinetics at the conditions under which SFRJs operate. This work focuses on providing combustion data to assess the predictive capabilities of state-of-the-art mechanisms at conditions relevant to SFRJs. The University of Central Florida shock-tube facility was used to conduct experiments of various combinations of HTPB products in air near 5 atm for temperatures ranging from 1100 to 1800 K and Φ=0.5, 1.0, 4.76. Nonintrusive, time-resolved laser absorption spectroscopy was used to record carbon-monoxide-concentration time histories and compared against the predictions of AramcoMech3.0 and HyChem mechanisms. For each mixture, the mid- and high-temperature conditions matched the models well, whereas there was a significant discrepancy for the low-temperature condition. A sensitivity analysis was then conducted to determine which reactions warranted further investigation to address the discrepancy at low temperatures.

Pterygodermatites (Mesopectines) whartoni (Nematoda: Rictulariidae) encysted larvae in invasive Cuban treefrogs (Osteopilus septentrionalis) from Florida, United States.

Species of Pterygodermatites are spirurid nematodes that have expanded their geographic distribution worldwide. They infect a variety of mammalian definitive hosts with few reports of potential paratenic infections in amphibian and reptile hosts. In this study, we report Pterygodermatites sp. larvae identified in free-ranging, invasive Cuban treefrogs (Osteopilus septentrionalis), from central Florida, United States. Encysted larvae were recovered from the skeletal muscle and/or the coelomic cavity of three frogs; molecular characterization of the small subunit (18S) ribosomal RNA and cytochrome oxidase I genes of the parasites matched reported sequences of Pterygodermatites (Mesopectines) whartoni (Tubangui, 1931). This is a parasite native to Southeastern Asia and to the best of the authors' knowledge, it is the first report of the species in the New World. The recovery of invasive Pterygodermatites from invasive Cuban treefrogs in North America highlights the growing concern regarding the potential impact non-native parasites and invasive species may have on native wildlife populations.

Steering Flow Sensitivity in Forecast Models for Hurricane Ian (2022)

Abstract Hurricane Ian made landfall along Florida’s west coast at 1905 UTC 28 September 2022 near the Fort Myers area as a high-impact storm. Here, we examine the potential link between track forecast errors near the time of landfall and errors in both the synoptic-scale upper-level flow and a shortwave moving within that flow. Five days before the actual landfall (0000 UTC 23 September), most model guidance indicated landfall would occur close to where Ian eventually came ashore. But by 0000 UTC 25 September, model forecasts were all forecasting landfall in the Florida Panhandle. One day later, the models again agreed with each other but for a landfall 100–200 km north of Tampa, Florida. By 0000 UTC 27 September, forecast models indicated landfall would occur near Tampa. Model forecasts continued shifting to the right and finally converged on Punta Gorda, Florida, as the landfall location, less than 24 h before landfall. In this short article, we hypothesize that the track of Ian depended on subtle interactions with an extratropical wave in the middle and upper atmosphere. Deterministic and ensemble model forecasts reveal that the interactions were very sensitive to the characteristics of this wave and the synoptic-scale flow in which the wave was embedded. A 1–2-dam difference in the geopotential heights played a major role in whether Ian moved north into the Panhandle or toward the east, making landfall in central Florida. Significance Statement The purpose of this work is to look at possible causes of error in forecasts of Hurricane Ian’s landfall with the intent of saving lives and reducing damage, which can run to the billions of dollars. The path that hurricanes take is strongly influenced by the larger-scale weather patterns around them. We find that small errors in the upper-level flow patterns north of the hurricane appear to have played an important role in the forecast. These errors were present in both the general flow and a wave moving through that flow, resulting in small but significant changes to the forecast path. Understanding the cause of forecast errors is a key step both in improving forecasts and in identifying types of events that are more difficult to forecast.

Preliminary experimental studies into the storage capacity of cryogenic hydrogen in aerogel blanket materials

The abundance and diversity of hydrogen applications necessitates continued and accelerated research into advanced storage technologies. Traditionally, hydrogen has been stored as either a high-pressure, warm gas; or a low-pressure, cryogenic liquid. Methods such as cryo-supercritical and cryo-adsorbed have been explored, but are not yet mainstream. Cryo-adsorbed is attractive because higher storage densities at higher temperatures than liquid may be achieved. Recently NASA, in partnership with Eta Space, Southwest Research Institute, the University of Central Florida, and Air Liquide, have been exploring the use of inexpensive, commercially available silica aerogel blanket materials for cryo-adsorbed hydrogen storage. Unlike most adsorbents, aerogel blanket is not a powder, but a robust, composite material that can be formed into complex shapes to aid in more efficient storage system designs, and has already been proven to uptake large quantities of fluids such as nitrogen and oxygen. Recent experimental efforts into the uptake of low-pressure hydrogen gas at 77 K, and liquid hydrogen at normal boiling point (NBP) will be discussed. Although preliminary in nature, the test results are promising, showing up to a 49% increase in storage density at 77 K over the gas alone, and greater than a one-to-one volume equivalency with NBP LH2.

Blended Learning: A Disruptive Innovation" by Tony Bates, which Explores the Impact and Effectiveness of Blended Learning in Educational Settings

Background and Aims: The impact and effectiveness of blended learning are critical because they show how combining online and in-person instruction can improve student engagement, academic performance, and flexibility. This approach provides a comprehensive educational experience that adapts to a variety of learning needs and environments, ultimately improving overall educational outcomes. The study's goal was to investigate how blended learning disrupts traditional education models and what benefits it may offer. Methodology: Bates' research integrates academic literature, case studies, and empirical data to assess the impact of blended learning, with surveys, interviews, and case study protocols used to collect comprehensive data. The analysis brings these findings together to reveal key themes and gaps, providing a thorough understanding of blended learning's effectiveness and implementation. Results: The finding found that Blended learning has been shown to improve student engagement, academic performance, and flexibility through successful implementations at institutions such as the University of Central Florida and Georgia State University. Addressing technical and pedagogical challenges, as well as incorporating emerging technologies, is critical for reaping the benefits. Continued research and support systems will be critical for optimizing blended learning and meeting the changing needs of students and teachers. Conclusion: Blended learning improves student engagement, performance, and flexibility, as demonstrated by successful models at UCF and Georgia State University. To fully realize its potential, it must address technical and pedagogical challenges while also embracing emerging technologies, with ongoing research and strong support systems critical to future success.

Social-media-based crisis communication: Assessing the engagement of local agencies in Twitter during Hurricane Irma

As social media platforms have become vital means of communication, it has become imperative for emergency managers and policy makers to understand how people are interacting with different agencies on these platforms for enhancing community response coordination during disasters. Although many public agencies have already adopted social media platforms for crisis communication purposes, empirical evidence on whether and how these agencies are effectively engaged on these platforms is lacking. This research aims to examine crisis communication activities of a variety of agencies in Twitter (now X) in response to Hurricane Irma in 2017. The study analyzed 13,353 hurricane-related tweets posted by the local agencies from eight counties in East Central Florida as well as federal and state agencies and other nonprofit and private organizations during the hurricane response period. An engagement metric was applied on these tweets to determine which agencies were most active in disseminating information during Hurricane Irma. The results revealed the most engaged local agencies in Twitter during Hurricane Irma for crisis communication and the Emergency Support Functions (ESFs) associated with them in coordinating disaster response activities. The study further investigated Twitter profiles and relevant attributes of these actively engaged agencies, and the contents of the hurricane-related tweets during Hurricane Irma. The study found that local agencies remain active and engaged in Twitter during a disaster compared to other levels of agencies. The ESF representing communication (ESF #2) was one of the most frequent ESFs associated with these active agencies; however, ESF #6 (Mass Care and Sheltering) was also found to be highly utilized. The results also provided insights on crisis communication performance of these agencies in terms of the three dimensions of social media engagement including popularity, commitment, and virality, which are correlated to the counts of like, reply, and retweet of each post, respectively. The study includes recommendations to local government and partner organizations and emergency managers to improve crisis communication in social media and suggests future research directions.

Judge rules against former medical student

Case name: Rusovici v. The University of Central Florida Board of Trustees, et al., No. 6:22‐cv‐2172 (M.D. Fla. 12/09/23).

Unveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis.

While existing research has identified diverse fall risk factors in adults aged 60 and older across various areas, comprehensively examining the interrelationships between all factors can enhance our knowledge of complex mechanisms and ultimately prevent falls. This study employs a novel approach-a mixed undirected graphical model (MUGM)-to unravel the interplay between sociodemographics, mental well-being, body composition, self-assessed and performance-based fall risk assessments, and physical activity patterns. Using a parameterized joint probability density, MUGMs specify the higher-order dependence structure and reveals the underlying graphical structure of heterogeneous variables. The MUGM consisting of mixed types of variables (continuous and categorical) has versatile applications that provide innovative and practical insights, as it is equipped to transcend the limitations of traditional correlation analysis and uncover sophisticated interactions within a high-dimensional data set. Our study included 120 elders from central Florida whose 37 fall risk factors were analyzed using an MUGM. Among the identified features, 34 exhibited pairwise relationships, while COVID-19-related factors and housing composition remained conditionally independent from all others. The results from our study serve as a foundational exploration, and future research investigating the longitudinal aspects of these features plays a pivotal role in enhancing our knowledge of the dynamics contributing to fall prevention in this population.