University of Central Florida
  • Orlando, Florida, United States
Recent publications
The disability, mortality and costs due to ionizing radiation (IR)-induced osteoporotic bone fractures are substantial and no effective therapy exists. Ionizing radiation increases cellular oxidative damage, causing an imbalance in bone turnover that is primarily driven via heightened activity of the bone-resorbing osteoclast. We demonstrate that rats exposed to sublethal levels of IR develop fragile, osteoporotic bone. At reactive surface sites, cerium ions have the ability to easily undergo redox cycling: drastically adjusting their electronic configurations and versatile catalytic activities. These properties make cerium oxide nanomaterials fascinating. We show that an engineered artificial nanozyme composed of cerium oxide, and designed to possess a higher fraction of trivalent (Ce³⁺) surface sites, mitigates the IR-induced loss in bone area, bone architecture, and strength. These investigations also demonstrate that our nanozyme furnishes several mechanistic avenues of protection and selectively targets highly damaging reactive oxygen species, protecting the rats against IR-induced DNA damage, cellular senescence, and elevated osteoclastic activity in vitro and in vivo. Further, we reveal that our nanozyme is a previously unreported key regulator of osteoclast formation derived from macrophages while also directly targeting bone progenitor cells, favoring new bone formation despite its exposure to harmful levels of IR in vitro. These findings open a new approach for the specific prevention of IR-induced bone loss using synthesis-mediated designer multifunctional nanomaterials.
The current study contributes to safety literature by incorporating the influence of temporal factors (observed and unobserved) within a multivariate model system for medical professional generated body region specific injury severity score. For this purpose, we adopt a hybrid econometric modeling approach that accommodates for the unobserved factors using two mechanisms. First, we parameterize unobserved temporal factor variation through the customization of the variance by time cohort (heteroscedasticity). Second, the common unobserved factors affecting severity across various body regions is accommodated through traditional random parameter consideration process. The proposed model system is estimated using data drawn from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) database for the time cohorts 2003, 2006, 2009, 2012, and 2015. For the current analysis, we consider 6-point Abbreviated Injury Scale (AIS) for eight body regions (head, face, neck, abdomen, thorax, spine, lower extremity, and upper extremity). The proposed model system offers interesting insights on body region severity evolution over time. The model estimation is augmented with post-estimation exercises including hold-out sample validation analysis, illustrative policy analysis and extensive elasticity effect computation.
The current study aims to examine the longitudinal effects of emotional labor on the mental health of hotel employees based on the Allostatic Load and Conservation of Resources theories. Four waves of data were collected from 534 hotel interns in an eight-month period. Latent growth modeling and lagged path analysis were used to analyze the time-series data. The study results indicated that hotel employees experienced increased anxiety and depression within the first three months of their new jobs. Surface acting increased employees' anxiety and depression. Interestingly, deep acting decreased employees' anxiety and depression in the short run but increased their anxiety and depression in the long run. Emotional exhaustion explained the double-edged effect of deep acting on mental health. The study results provide meaningful implications for hotel managers in workplace stress management and employees’ mental health improvement.
The simply supported slab bridge is a typical perfricated reinforced concrete bridge. Under the influence of increasing vehicle loads and natural environmental erosion, the hinge joints between slabs suffer from damage that cannot be easily evaluated, which brings negative effects on the load carrying capacity of bridges. In the present study, a hybrid method for damage detection and condition assessment of hinge joints in hollow slab bridges using physical models and vision-based measurements was proposed. The stiffness reduction of hinge joints is taken as the damage degree and condition level of the inspected hinge joints. An analytical model of a simplified spring-mass system was firstly built to demonstrate the applicability of using the relative displacement ratio as the damage index of hinge joints. The relationship between the relative displacement ratio and the stiffness reduction of hinge joints was then studied thoroughly through a parametric study on finite element models considering different damage levels of hinge joints. Thresholds of the relative displacement ratio were defined to classify the damage states of hinge joints. The damage index of target hinge joints can be calculated from the actual data provided by using computer vision-based multi-camera and multi-point displacement measurements. Lastly, the application of a real-life bridge under normal traffic was demonstrated to verify the feasibility of the quantitative evaluation of the service status of joints in hinged-slab bridges. It indicated that the proposed method could evaluate the damage degree of joints quantitatively, effectively and economically.
Target detection in infrared imagery is a particularly challenging problem due to the presence of terrain clutter. The TCRNet-2 CNN architecture was introduced to combat this issue and has been shown to perform better than conventional networks such as faster RCNN and YOLOv3. In this paper, we evaluate the performance of the Boosted 2-Stream TCRNet in detail (including robustness to range variations, performance under day and night conditions) and compare it with that of YOLOv5. A MWIR dataset released by DSIAC is used for training and testing the network. We also propose the MWIR target classifier that recognizes the 10 classes in the NVESD dataset and achieves an accuracy of 65.72% which is state-of-the-art to date.
As Structural Health Monitoring (SHM) being implemented more over the years, the use of operational modal analysis of civil structures has become more significant for the assessment and evaluation of engineering structures. Machine Learning (ML) and Deep Learning (DL) algorithms have been in use for structural damage diagnostics of civil structures in the last couple of decades. While collecting vibration data from civil structures is a challenging and expensive task for both undamaged and damaged cases, in this paper, the authors are introducing Generative Adversarial Networks (GAN) that is built on the Deep Convolutional Neural Network (DCNN) and using Wasserstein Distance for generating artificial labelled data to be used for structural damage diagnostic purposes. The authors named the developed model “1D W-DCGAN” and successfully generated vibration data which is very similar to the input. The methodology presented in this paper will pave the way for vibration data generation for numerous future applications in the SHM domain.
A Gallai coloring is a coloring of the edges of a complete graph without rainbow triangles, and a Gallai k-coloring is a Gallai coloring that uses at most k colors. For an integer k≥1, the Gallai–Ramsey number GRk(H) of a given graph H is the least positive integer N such that every Gallai k-coloring of the complete graph KN contains a monochromatic copy of H. Let Cm denote the cycle on m≥4 vertices and let Θm denote the family of graphs obtained from Cm by adding an additional edge joining two non-consecutive vertices. We prove that GRk(Θ2n+1)=n⋅2k+1 for all k≥1 and n≥3. This implies that GRk(C2n+1)=n⋅2k+1 all k≥1 and n≥3. Our result yields a unified proof for the Gallai–Ramsey number of all odd cycles on at least five vertices.
In this paper, we expand functions of specific q-exponential growth in terms of its even (odd) Askey-Wilson q-derivatives at 0 and η=(q1/4+q−1/4)/2. This expansion is a q-version of the celebrated Lidstone expansion theorem, where we expand the function in q-analogs of Lidstone polynomials, i.e., q-Bernoulli and q-Euler polynomials as in the classical case. We also raise and solve a q-extension of the problem of representing an entire function of the form f(z)=g(z+1)−g(z), where g(z) is also an entire function of the same order as f(z).
This article presents the development and assessment of the Multidimensional Dispositional Greed Assessment (MDGA) scores, designed to measure adults’ dispositional greed. We present two studies detailing (a) the construction and administration of the MDGA to an initial sample of adults (study 1, exploratory factor analysis [EFA]; N = 875), and (b) the administration of the MDGA to a validating sample of adults (confirmatory factor analysis [CFA]; N = 922) and examining evidence of convergent validity (study 2). The EFA results identified a 21-item MDGA exploratory model, accounting for 73.97% of the variance and encompassing three factors, including Insatiable Pursuit for More at all Costs, Desire for More, and Retention Motivation. The CFA results validated a three-factor oblique 20-item MDGA model, accounting for 59.1% of the variance, and evidence of convergent validity. The MDGA is a promising self-report measure for scholars investigating the construct of dispositional greed.
Background: The evidence-based practice (EBP) process was challenged during the early phase of the COVID-19 pandemic by factors such as a novel disease, rapidly changing guidelines, shortage of personal protective equipment, and other health care supplies. Objectives: Our aims were to (1) explore sources of evidence sought by critical care nurses during a pandemic and (2) explore nurses' perceptions of EBP. Methods: A qualitative exploratory study was conducted using deidentified data from the American Association of Critical-Care Nurses (ACCN) open-access Facebook page, January 28 to April 30, 2020. Results: Two major themes were identified: (1) "sharing and seeking evidence," that is, nurses used both formal and informal sources to explore evidence supporting evolving clinical practices, and (2) "concerns about evidence," that is, nurses expressed concerns about lack of evidence and mistrust of evolving evidence. Discussion: Initially, there was a mismatch in nurses' expectations of the American Association of Critical-Care Nurses Facebook page. A major limitation of Facebook is the lack of a repository for quick retrieval of information. Despite these limitations, and fear and mistrust of changing guidelines, social media was used to communicate, collaborate, and share evidence to support clinical practice. Critical care nurses seemed to value evidence to support patient management and their personal safety during this evolving health crisis. Conclusions: Social media played a large role in dissemination of timely evidence-based information during the early pandemic. Our results show that current EBP models should be revised to prepare for future crises and include direction for dealing with limited health care resources, and lack of and/or rapidly changing evidence.
Introduction Thrombolysis for acute ischemic stroke (AIS) with alteplase is the currently approved therapy for patients who present within 4.5 h of symptom onset and meet criteria. Recently, there has been interest in the thrombolytic tenecteplase, a modified version of alteplase, due to its lower cost, ease of administration, and studies reporting better outcomes when compared to alteplase. This systematic review compares the efficacy of tenecteplase vs. alteplase with regard to three outcomes: (1) rate of symptomatic hemorrhage, (2) functional outcome at 90 days, and (3) reperfusion grade after thrombectomy to compare the efficacy of both thrombolytics in AIS Methods The search was conducted in August 2021 in PubMed, filtered for randomized controlled trials, and studies in English. The main search term was “tenecteplase for acute stroke.” Results A total of 6 randomized clinical trials including 1675 patients with AIS was included. No one’s study compared alteplase to tenecteplase with all three outcomes after acute ischemic stroke; however, by using a combination of the results, this systematic review summarizes whether tenecteplase outperforms alteplase. Conclusions The available evidence suggests that tenecteplase appears to be a better thrombolytic agent for acute ischemic stroke when compared to alteplase.
Background: There is an alarming shortage of addiction psychiatrists in the United States. To promote interest in addiction psychiatry (ADP), it is essential to maximize resources available through ADP fellowship websites. The aim of this study was to investigate the perceived adequacy and accessibility of content on ADP fellowship websites and discover what further information is considered important among trainees interested in becoming addiction specialists. Methods: Three virtual focus groups were conducted between January and February 2021 among medical students and residents in diverse geographic regions. Participants were asked about the availability of information on ADP fellowship program websites and other material they would like to see available. Focus groups were recorded, with data transcribed and coded using NVivo 11 and Dedoose. A coding scheme was deductively developed based on the core research questions. Results: The majority of participants (N = 27) identified areas of dissatisfaction with the content currently available on ADP websites. The sample was highly representative of racial and ethnic minoritized trainees (n = 12) and genderqueer/non-binary participants (n = 3). Three major themes were identified and durable across all focus groups: lack of emphasis on diversity/health equity, lack of portrayal of everyday life and activities of fellows, and inadequate representation of curricula. Overwhelmingly, participants identified a dedication to health equity (for example, working with minoritized populations) as a key deciding factor in whether to apply to a particular ADP fellowship. Conclusions: ADP fellowship websites are perceived to have considerable variability in the amount and quality of information. Many do not appear to provide the full spectrum of content desired by diverse potential applicants, such as information regarding current fellows and community-centered initiatives. This is concerning, as it suggests ADP fellowships may be interfacing poorly with burgeoning leaders, especially those from race and gender minoritized backgrounds, neglecting potential opportunities to develop future addiction specialists.
Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS 2 sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS 2 , Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.
Liquid crystal displays (LCDs) and photonic devices play a pivotal role to augmented reality (AR) and virtual reality (VR). The recently emerging high-dynamic-range (HDR) mini-LED backlit LCDs significantly boost the image quality and brightness and reduce the power consumption for VR displays. Such a light engine is particularly attractive for compensating the optical loss of pancake structure to achieve compact and lightweight VR headsets. On the other hand, high-resolution-density, and high-brightness liquid-crystal-on-silicon (LCoS) is a promising image source for the see-through AR displays, especially under high ambient lighting conditions. Meanwhile, the high-speed LCoS spatial light modulators open a new door for holographic displays and focal surface displays. Finally, the ultrathin planar diffractive LC optical elements, such as geometric phase LC grating and lens, have found useful applications in AR and VR for enhancing resolution, widening field-of-view, suppressing chromatic aberrations, creating multiplanes to overcome the vergence-accommodation conflict, and dynamic pupil steering to achieve gaze-matched Maxwellian displays, just to name a few. The operation principles, potential applications, and future challenges of these advanced LC devices will be discussed.
Public-private partnerships (PPP) have many critical socio-economic concession variables that need to be determined during the negotiation of the PPP contracts. However, their determination presents complexities to decision-makers due to these components’ interdependencies. Assessing the dynamic and interdependent relationships between the socio-economic concession components can enhance the development of PPP concessions. System dynamics (SD) techniques have provided a holistic system understanding of several complex structures from a holistic perspective. This paper aims to build a novel socio-economic SD model to facilitate the decision-making process for PPP projects via determining and assessing the adequate concession period, concession price (user-payment), government subsidy, and the capital structure (in the form of equity). A case study for a PPP toll-road project (I-4 Ultimate) is utilized to validate the proposed model’s results. Higher concession prices increased net present value (NPV) levels and PPP effectiveness. Simulation results showed that the variables are interdependent, and a change in the value of one variable will lead to a change in the values of the other variables. The results also showed that the concession price (user-payment) has a major influence on the concession variables. The model proposed in this study gives a holistic perspective of the complex interplay between PPP effectiveness and several socio-economic variables and is potentially valuable in facilitating and enhancing the decision-making process for PPP projects. While many scholarly discussions have been fronted on the use of system dynamics modeling in PPPs, the specific and unique combination of concession variables is the ultimate contribution of this study to the existing body of knowledge.
Negative online reviews can drastically influence consumer behavior and business strategies. Recent attention on the subject demonstrates its importance in the consumer and marketing literature. Even so, no study quantitatively investigates the corpus of the literature. This study quantitatively and systematically investigates the foundational research streams of negative online reviews to identify influential sources and main areas of knowledge in the domain. The study employs an integration of text mining and co-citation analysis, recognizing that firms’ responses to negative online reviews cannot be analyzed without understanding the role of customers. Accordingly, this study generates insight into customers and firms in each negative online review stage, furnishing a conceptual framework that synthesizes the previous literature and highlights the most important research gaps requiring attention. Ultimately, the conceptual framework can guide future researchers in unfolding new and novel directions to expand the boundaries of the negative online review literature.
Dendritic mesoporous silica nanoparticles (DMSNs) are a new generation of porous materials that have gained great attention compared to other mesoporous silicas due to attractive properties, including straightforward synthesis methods, modular surface chemistry, high surface area, tunable pore size, chemical inertness, particle size distribution, excellent biocompatibility, biodegradability, and high pore volume compared with conventional mesoporous materials. The last years have witnessed a blooming growth of the extensive utilization of DMSNs as an efficient platform in a broad spectrum of biomedical and industrial applications, such as catalysis, energy harvesting, biosensing, drug/gene delivery, imaging, theranostics, and tissue engineering. DMSNs are considered great candidates for nanomedicine applications due to their ease of surface functionalization for targeted and controlled therapeutic delivery, high therapeutic loading capacity, minimizing adverse effects, and enhancing biocompatibility. In this review, we will extensively detail state-of-the-art studies on recent advances in synthesis methods, structure, properties, and applications of DMSNs in the biomedical field with an emphasis on the different delivery routes, cargos, and targeting approaches and a wide range of therapeutic, diagnostic, tissue engineering, vaccination applications and challenges and future implications of DMSNs as cutting-edge technology in medicine.
Generally, freeway tunnels are built to overcome the complex driving environments in mountainous terrains. However, crashes in these tunnels can be more severe than those on the open road sections due to their closed driving environment. Despite the higher crash severity, very few studies have attempted to investigate the severity of injuries in freeway tunnel crashes. Also, the existing studies on the injury severity analysis of tunnels did not fully consider the unobserved heterogeneity and its interactive effects. To address these issues, the present study first collected a comprehensive dataset containing five-year of police-reported tunnel crashes from Hunan province, China. A random parameters model with heterogeneity in means and variances was then developed to explore the influence of different variables related to the environment, drivers, crashes, vehicles, and tunnels. The study observed that the presence of curves and speeding indicators produce random parameters with heterogeneity in means and variances for freeway tunnels, which is influenced by the young drivers and outside exit zone variables. Also, the results reveal that factors, including weekdays, daytime, speeding, fatigue driving, rear-end collisions, collisions with fixtures, large passenger vehicles, and downgrades increase, while rain reduces the probability of severe injury outcomes in freeway tunnel crashes. More importantly, considering the unique tunnel driving environment, the summer, young drivers, novice drivers, presence of curves, and different tunnel sections (access, entrance, and outside exit zones) also significantly affect the risk of severe injury outcomes. Finally, the study’s findings could be used as a basis for developing plans and technologies to minimize the severity of crash injuries in freeway tunnels.
Rigorous coupled wave analysis (RCWA) is conducted on in situ spectroscopic ellipsometry data to understand profile evolution during film deposition inside nanotrenches. Lithographically patterned SiO 2 nanotrenches are used as test structures. The nanotrenches are 170 nm wide at the top with a taper angle of 4.5° and are 300 nm in depth. Atomic layer deposition of ZnO is used as a model process where the thickness (cycles) of the film is varied from 0 (0 cycles) to 46 nm (300 cycles). The analysis predicts transient behavior in deposition affecting film conformality and changes to the trench taper angle. In the process, the aspect ratio varies from 2.05 at the start of the process to 6.67 at the end. The model predicts changes in the refractive index of the ZnO film as a function of thickness. The real and imaginary parts of the refractive index at a wavelength of 350 nm change from 1.81 to 2.37 and 0.25 to 0.87, respectively. Scanning electron microscopy cross sections confirm thickness at the top and bottom of the trench to within 13% of those predicted by RCWA. The experimentally measured conformality degrades as film deposition proceeds from 97.3% at 100 cycles to 91.1% at 300 cycles. These results demonstrate the potential of using RCWA for continuous and in situ monitoring of growth inside 3D nanostructures.
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12,199 members
Frederick Carrick
  • College of Medicine
Jorge Ridderstaat
  • Rosen College of Hospitality Management
Su-I Hou
  • Department of Health Management and Informatics
Deepak Balasubramanian
  • Burnett School of Biomedical Sciences
Lee Chow
  • Department of Physics
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