University of South Florida
  • Tampa, FL, United States
Recent publications
The rapid technological advancements in video analytics and the availability of big data have made traffic conflict techniques a viable tool for road safety assessments. They can potentially overcome many major limitations of conventional road safety practices that use crash-data analyses. However, the current traffic conflict techniques flag serious concerns regarding the context-dependence of the relationship between traffic conflicts and crashes, the lack of consideration of road user and vehicle heterogeneities in their formulation , and the exclusion of crash severity estimation from the analysis process. To overcome these limitations, this study proposes a novel application of the safety field theory to estimate crash risk and severity by modeling the safety-aware interactions of various road users in a road traffic environment. The safety field theory borrows from the Physics concept of electromagnetic fields to mathematically define the safety ''buffers" that road users typically maintain around them while moving in traffic. Additionally, the model formulation explicitly accounts for exceptional circumstances (crashes and extreme conflicts) and integrates severity in the risk estimation framework to provide a holistic safety assessment framework. The proposed safety field theory application was tested by analyzing a total of 196 h of traffic movement videos collected from three signalized intersections in Brisbane, Australia and extracting the required road user trajectory information through artificial intelligence-based video analytics. Extreme value modeling of the tail distribution of the risk force generated by the interacting road user safety fields showed that it could predict the crash frequency and outcome severity more accurately than the prevalent traffic conflict indicators. Thus, the proposed approach provides a single, unified, and efficient method of accurately estimating crash risk and injury severities that can be adapted for various application contexts. The study results significantly improve the effectiveness of automated safety analysis for transport facilities and could elevate the safety prediction algorithms of real-time applications like adaptive signal control systems and Connected and Automated Vehicles.
Introduction Community centers commonly transfer patients with traumatic intracranial hemorrhage (ICH) to level 1 and 2 trauma centers for neurosurgical evaluation regardless of the degree of injury. Determining risk factors leading to neurosurgical intervention (NSI) may reduce morbidity and mortality of traumatic ICH and the transfer of patients with lower risk of NSI. Methods A retrospective chart review was performed on patients admitted or transferred to a level 1 trauma center from October 2015 to September 2019 with Glassgow Coma Scale score 13-15 and traumatic ICH on initial head computerized tomography (CTH) scan. Bivariate analyses and multivariable regression were used to identify factors associated with progression to NSI. Results Of 1542 included patients, 8.2% required NSI. A greater proportion were male (69.1% versus 52.3%, P = 0.0003), on warfarin (37.7% versus 21.6%, P = 0.0023), presented with subdural hemorrhage (98.4% versus 63.3%, P < 0.0001, larger subdural hemorrhage size (median 19 mm [interquartile range {IQR}: 14-25] versus 5 mm [IQR: 3-8], P < 0.0001), and had a worsening repeat CTH (24.4% versus 13%, P < 0.0001). On physical examination, more patients had confusion (40.5% versus 31.4%, P = 0.0495) and hemiparesis (16.2% versus 2.6%, P < 0.0001). CTH findings of midline shift (80.2% versus 10.8%, P < 0.0001) and shift size (median 8.0 mm [IQR: 5.0-12.0] versus 4 mm [IQR: 3-5], P < 0.0001) were significantly associated with NSI. Conclusions Clinical factors and patient characteristics can be used to infer a greater risk of requiring NSI. These factors could reduce unnecessary transfers and hasten the transfer of patients more likely to progress to NSI.
Background Studies exist on the association between inpatient Palliative Care Encounter (iPCE) and 30-day rehospitalization among cancer and several non-cancer conditions but limited in persons with Chronic Obstructive Pulmonary Disease (COPD). Objective To assess the association between an iPCE with the risk of 30-day rehospitalization after an index hospitalization for COPD. Methods We conducted a cross-sectional analysis of the Nationwide Readmissions Database (2010–2014). Index hospitalizations were defined as persons ≥ 18 years of age, discharge destinations of either Home/Routine, Home with Home Care, or a Facility, and an index hospitalization with Diagnosis Related Group of COPD. The International Classification of Diseases, 9th revision codes were used to extract comorbidities and a Palliative Care Encounter (V66.7). Results There were 3,163,889 index hospitalizations and iPCE occurred in 21,330 (0.67%). There were 558,059 (17.63%) with a 30-day rehospitalization. An iPCE was associated with a significantly lower adjusted odds of 30-day readmission (Odds Ratio [OR], 0.50; 95% Confidence Interval [CI], 0.46 to 0.54). By discharge destination, the odds of 30-day rehospitalization were for a discharged to a facility (OR, 0.37; 95% CI, 0.32 to 0.42), to home with home health (OR, 0.42; 95% CI, 0.37 to 0.47), and to home (OR, 0.98; 95% CI, 0.85 to 1.12) for those with relative to without iPCE. Conclusion Inpatient PCE was associated with a 50% lower relative odds of 30-day rehospitalization after an index hospitalization for COPD. This association varied by discharge destination being statistically significant among those with a discharge destination of a facility (63%) and home with home care (58%).
The availability of electronic (e-medical) homecare essentials, such as thermometers, oximeters, and oxygen concentrators during the peaks of the pandemic coronavirus disease (COVID-19), has been witnessed as critical in saving the lives of people across the world. This paper presents a supply order allocation strategy of e-medical homecare essentials (HCEs) in a multi-supplier environment by a distributor while ensuring sufficient and timely availability for emergency consumption during pandemic peaks. The results, based on the actual demand data of HCEs obtained from a regional HCE distributor during the pandemic peak of the second wave in India, i.e. April-May 2021, suggest that a minimum (maximum) average of 94% (98%) availability of e-medical HCEs respectively at pharmacies could be achieved during the peak demand period using the proposed emergency order allocation algorithm in this study. Conclusively, the analysis of this study could generate insightful implications for emergency operations decisions in the HCEs supply-distribution channel.
The digital transformation of the manufacturing industry has reshaped the collaborative innovation model of multi-agent value co-creation in the value chain. The nature and collaborative behavior of heterogeneous agents in the value chain are key factors affecting innovation performance. This study uses the structural equation model (SEM) and conducts a questionnaire survey on 381 manufacturing enterprises. It examines the different impacts of agent heterogeneity and collaborative behavior on the innovation performance of manufacturing enterprises in digital transformation under different digitization levels and enterprise sizes. The results show that highly digitized or large-scale enterprises can reduce the negative impact of agent heterogeneity on innovation performance. However, the cooperation quality of enterprises with low digitalization levels or small-scale enterprises plays an important role in improving enterprise performance. These results help manufacturing enterprises accelerate digital transformation and provide a strategic reference for transforming and upgrading the digital economy.
The COVID-19 pandemic threatened employees’ health and safety more than any event in recent years. Although millions of employees transitioned to working from home to mitigate infectious disease exposure, many worksites re-opened amid the pandemic as high infection rates persisted longer than expected. Safety guidelines were issued by the Centers for Disease Control and Prevention, the World Health Organization, and other national initiatives to improve the health and safety of employees returning to on-site work. The current work addresses predictors of infection control safety behaviors in a general working population that largely lacks infection control training and expertise. Drawing from Neal and Griffin’s model of safety behavior, we investigated organizational factors (i.e., perceived safety climate, safety-related organizational constraints, occupational risk of COVID-19 exposure) and individual factors (i.e., infection control safety attitudes, conscientiousness, and risk aversion) associated with employees’ infection control safety behaviors shortly after returning to on-site work during the pandemic. Survey results from 89 full-time employees across industries demonstrated that the organizational and individual factors accounted for 51.19 percent of the variance in employees’ infection control safety behaviors. Organizational factors accounted for 49.02 percent of the explained variance, and individual factors accounted for 50.98 percent of the explained variance. Conscientiousness, perceived safety climate, safety-related organizational constraints, and infection control safety attitudes explained significant variance in employees’ infection control safety behaviors, while the occupational risk of COVID-19 exposure and risk aversion did not. Organizations may benefit from considering employees’ conscientiousness and safety attitudes during employee selection as well as enhancing their organization’s safety climate and mitigating safety-related organizational constraints.
In this study, synergistic effects of mechanochemically synthesized metakaolinite-Ca-rich-MgO nanocomposite for effective treatment of real acid mine drainage (AMD) was meticulously reported. The obtained results were underpinned using the state-of-the-art analytical techniques and instruments, such as FTIR, HR-FIB/SEM, EDS, XRF, and XRD. The pH REdox EQuilibrium (in C language) was employed to complement experimental results. Optimum conditions were observed to be 45–60 min of mixing time, ≥10 g L⁻¹ of feedstock dosage, i.e. nanocomposite, and ambient temperature and pH. The metal content (Fe, Mn, Cr, Cu, Ni, Pb, Al, and Zn) embedded in real AMD matrices was practically depleted (>99 % removal) and sulphate greatly reduced (≥50 %). PHREEQC predicted metals to exist as mono-, di-, and-tri-valents including variety of chemical complexes, and they precipitated as metals hydroxides, (oxy)-hydroxides, carbonates, and (oxy)-hydro-sulphates. This synergistic treatment approach holds great promise for the sustainable management of real AMD effluents from coal mining activities and can provide a simple and effective solution for its management. Thenceforth, softening and filtration technologies will need to be coupled to this process to further enhance the reclamation of drinking and to explore possible recovery of valuable minerals from the generated sludge.
Based on the data of Chinese A-share listed companies from 2011 to 2019, this paper uses the multiple regression method to explore the relationship between environmental information disclosure and corporate innovation, which is divided into exploratory innovation and exploitative innovation according to heterogeneity and investigates the impact of media attention on the relationship. The results show that: (1) Environmental information disclosure significantly promotes corporate innovation. (2) Environmental information disclosure promotes exploratory innovation more significantly than exploitative innovation. (3) Media attention has an “inverted U-shaped” regulating effect on the relationship between environmental information disclosure and corporate innovation. This study is expected to guide enterprises to scientifically carry out environmental information disclosure activities and direct the media to play its role in information dissemination and external corporate governance. Additionally, it can provide new ideas for corporate innovation.
It is vital for Chinese women micro e-commerce entrepreneurs facing rapid economic transitions to improve their professional performance by responding effectively to the changing institutional environment. This study explores the influence of the institutional environment on entrepreneurial performance, specifically the mediating effect of entrepreneurial networks using the institutional theory, social network theory, and survey data from 689 female micro e-commerce entrepreneurs in China. The results demonstrate that the institutional environment, both in terms of its regulatory and cognitive dimensions, had a significant positive impact on EP; the Bootstrap test indicated that the entrepreneurial network had a partial mediating effect on this relationship. In addition to contributing to the institutional theory and social network theory in China’s specific social context, this study provides implications for improving female micro e-commerce performance by improving the institutional environment and establishing entrepreneurial networks.
A number of well-known brands are not only loved by many consumers, but also hated by a sizeable portion of the population and are thus termed polarizing brands. Because digital media offers consumers nearly unlimited opportunities to voice their hate, managers can no longer ignore vocal haters. However, the current marketing literature offers few strategies for addressing the challenge of brand hate. This paper introduces the concept of hate-acknowledging advertising (HAA), an ad technique in which polarizing brands openly admit that some segment of the population hates them. Over the course of three studies, the data indicates that, compared with supportive advertising, HAA results in higher perceptions of ad credibility and ultimately higher levels of brand trust. Moreover, the improvements in ad credibility and brand trust drive increased consumer intentions to engage in positive word of mouth on behalf of the polarizing brand.
COVID-19 has critically impacted many aspects of societies worldwide, particularly on mobility. This chapter summarizes impacts of the COVID-19 pandemic, reviews existing research, and identifies future research needs in the scope of traffic theory and modeling/optimization and traffic flow. We first review models on contagion spreading through transportation networks, including aggregated spatial metapopulation models and disaggregated individual-based models. Further research is needed to consider both intercity and intracity mobilities and leverage emerging multiple data resources for constructing individuals’ complete trip chains. Based on modeling contagion spreading, we further discuss transport operation needs in the aftermath of COVID-19. There remains a need for operating multimodal urban transport systems to satisfy basic travel demands while minimizing contagion risks. Relevant research needs are identified in optimizing transport operation via modern data acquisition technologies and advanced modeling methods. Practical intervention measures and policy implications are recommended for optimizing transport systems during the COVID-19 pandemic.
Ascaris lumbricoides is the causative agent of ascariasis in humans. It is estimated that 1.3 billion people are infected with Ascaris lumbricoides and it has affected the world's population for centuries. Transmission occurs most commonly via hand to mouth. Children are the most heavily infected with high rates of reinfection. Infected individuals are usually asymptomatic, but it can cause a high burden of disease with prolonged, severe, and fatal outcomes in few. It can result in considerable hardship in terms of food safety, security, quality of life, and negative impacts on livelihoods.
Providing the global consumer with safe foods is the priority of professionals in agriculture, food technology, nutrition, and regulatory agencies. The pioneering and initially criticized food safety efforts by German Chemist Friedrich Christian Accum (1769–1838) in London and Harvey Wiley (1844–1930) in the United States triggered significant advances to reduce dangerous substances in foods, and the promulgation of critical regulations that focused on safe foods and safe food production working conditions. Since those early days of food processing and food safety regulations, a myriad of technologies and an array of food additives have increased the safety of food products, improved the nutritional value of foods, and extended the shelf-life of a range of foods. Some of those technologies include, but are not limited to, pasteurization, aseptic processing, nonthermal processing, flash-pasteurization, edible and smart packaging, biofortification, clustered regularly interspaced short palindromic repeats (CRISPR) and biotechnology. Several of these technologies and food additives as a food ingredient category and ingredient sourcing have triggered consumer concerns, stimulated international debates, and prompted numerous reactionary food regulations and restrictions that have impacted global food trade, product availability, and consumer misunderstandings. Meaningful protection against real hazards is everyone’s business, but the challenges inherent in implementing and disseminating food safety seem complicated by consumer perceptions equating food processing and food technologies in general with health risks and poor product quality. Moreover, while seemingly privileged western food activists often focus on the availability of high-quality artisanal and locally produced organic foods, the United Nations estimates that 1 billion people live with chronic hunger and malnutrition. Communication with, and education of consumers have emerged as an overarching mandate for industry and government alike.
Digital health includes eHealth and areas such as the use of advanced computer sciences. eHealth comprises several components, including electronic health records, telehealth, and mHealth. mHealth is segmented into mHealth apps, mHealth services, and medical devices. The present paper is not intended to provide a systematic review of the current digital tools used in asthma but to provide a framework to better understand the potentials and drawbacks of some of these tools using, when available, systematic reviews.
Presently, continuously increasing the demand for synthetic polymers or plastic in daily life adversely affects the environment due to their disposal and long life span. Researchers constantly focus on the isolation of newer materials from renewable sources such as cellulose. Cellulose is the most abundant biopolymer on Earth that efficiently isolates plants, bacteria, animals, and fungi. The isolation of cellulose from fungi is most common among these organisms due to their elongated hyphae, which produce mechanical pressure on the cellulose structure, inflicting them to supply massive amounts of cellulose. Moreover, fungal strains can have higher quantities of cellulases than other organisms. This book chapter focuses on the different isolation processes of nano-cellulose from fungi. The surface functionalization of nano-cellulose might enhance its applicability. The various applications of nano-cellulose are also discussed in detail. Therefore, increasing demand for nano-cellulose is predictable due to a broader range of applicability.
Amongst a host of intracellular signaling pathways that maintain cellular homeostasis, the PI3K/PTEN/AKT/mTOR (phosphoinositide 3-kinase/phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase/Ak strain transforming kinase/mammalian target of rapamycin) pathway plays a central role in transducing signals via a myriad of receptors, each with distinct function. When a ligand binds and activates a membrane receptor, members of the PI3K enzyme family associate with the receptor and get activated. PI3K then initiates downstream signaling by activating multiple kinases that integrate signals regulating cellular growth, differentiation, cell motility, cytoskeletal remodeling, trafficking of intracellular organelles, cell shape, movement, and survival. In addition, levels of intracellular amino acids, energy needs, oxygen, and various other types of biochemical and biomechanical stressors also modulated PI3K pathway. Therefore PI3K pathway plays integral role in cellular physiology and homeostasis. However, activating PI3K mutations hyperactivate downstream PI3K pathway, leading to abnormal cell physiology and pathologies, including cardiovascular and neurological diseases, organ fibrosis, and multiple organ malignancies. While inhibition of PI3K by active site targeting is employed in the clinic, there remains challenges associated with on-target and off-target toxicities. Consequently, treatment delays, dose alterations, and/or change in inhibitors, treatment interruption or complete treatment discontinuation are not uncommon in the clinic. Keeping these challenges in mind, in this chapter, we attempt to thoroughly understand biochemistry and molecular biology of nonkinase domains and regulatory subunits of PI3Ks to provide molecular evidence that may aid in developing alternative PI3K inhibitors. Our main goal is to define intrinsically disordered regions (IDRs) involved in PI3K activation with an aim to leveraging this knowledge in inhibiting aberrant PI3K activity driving diseases. We have analyzed regulatory subunits and domains of PI3K proteins and identified IDRs that may contribute to PI3K function. While there are two major classes of PI3K enzymes, one activated by G-coupled protein receptors and the other activated by receptor tyrosine kinases, class IA PI3Ks are regulated by both mechanisms. In addition, the p85-family of regulatory subunits of class IA PI3Ks is a major influence on PI3K catalytic function. Therefore we focus on IDR in p85-family subunits that may contribute to enzyme function. It is anticipated that the detailed understanding of the mechanisms underlying modulation of PI3K enzyme function utilizing IDRs will further shed light on the normal cellular functions and may help in designing novel PI3K inhibitors for clinical treatment.
Recent developments in the field of protein science indicated that the classical “lock-and-key” and “induced fit” models cannot adequately describe protein multifunctionality and binding promiscuity, which are important phenomena defining the dramatic increase in the size of a functional proteome in comparison with the size of a corresponding genome. In fact, introduction of the proteoform concept, where a single gene produces a multitude of highly related, but chemically different protein molecules due to the various events at the DNA (genetic variations), mRNA (alternative splicing, alternative promoter usage, alternative initiation of translation, and mRNA editing), and protein levels (posttranslational modifications), provides only a partial solution to these problems. Based on the currently accumulated knowledge, a more thorough solution requires complementation of these induced proteoforms with the intrinsic conformational proteoforms generated via the presence of intrinsically disordered or structurally flexible regions in a protein and functioning proteoforms originating from the functionality-induced changes in the conformational ensembles of both intrinsic and induced proteoforms. Therefore a single gene encodes not one unique protein but a wide array of structurally and functionally different proteoforms. In other words, since a given protein exists as a dynamic conformational ensemble containing multiple structurally and functionally different proteoforms, the protein multifunctionality is rooted in the “protein structure-function continuum” model.
Biologically active proteins have broad specificity in classical enzymology. Protein promiscuity goes far beyond that, as it makes many of their molecular recognitions quite nonspecific. Enzyme promiscuity is exemplified by catalytic promiscuity, condition promiscuity, and substrate ambiguity. It is typically mediated by protein plasticity. On the other hand, promiscuity in protein–protein interactions shown by a very large number of regulatory proteins relies mostly on intrinsic disorder in at least one of the partners.
Safety Performance Functions (SPFs) can be used to predict the number of crashes for highway facilities by site characteristics, including traffic exposures and other specific site factors. The traditional approach to developing SPFs relies on factors that are observed in the data and has an unstated assumption that the relationships between safety performance and observed factors are stationary. However, there might be factors that are not captured by the data but also have significant impacts on roadway safety performance. These factors can lead to significant unobserved heterogeneity in safety performance at different sites. Failure to capture such unobserved heterogeneity in developing SPFs may result in biases and decrease the predictive accuracy. Given the interactions between highway traffic and roadway environments, the unobserved heterogeneity is likely related to the geographic space of the highway network. This study employs a spatial modeling approach, namely Geographically Weighted Negative Binomial Regression (GWNBR), to incorporate spatial heterogeneity into SPF model estimation. The GWNBR model can generate a local SPF for every site instead of a global SPF for one entire jurisdiction (e.g., a state) from the traditional approach. Local SPFs (or l-SPFs) are high-resolution and may be difficult for practitioners to use. To support the implementation of l-SPFs, this study proposes a method to aggregate l-SPFs to various geographic levels. This study first uses the 2014–2018 geo-referenced crash data from Alabama to develop l-SPFs for two-way STOP-controlled (TWST) three-leg intersections on rural two-lane two-way (TLTW) roadways in the state. The results show that l-SPFs vary substantially across Alabama. For example, the coefficients of traffic volume (AADT) on major roads range from 0.126 to 1.203 across different areas of the state. Then, an aggregation method based on K-means clustering is demonstrated to aggregate l-SPFs to various geographic levels of interest. The l-SPFs and their aggregation provide geographic flexibility in developing countermeasures and allocating funds to improve traffic safety considering local conditions.
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15,089 members
Yonggang Liu
  • College of Marine Science
Ulla Uusitalo
  • Department of Pediatrics
Ismail Kazem
  • Department of Oncologic Sciences
Ricardo Izurieta
  • College of Public Health
W. Edwin Clark
  • Department of Mathematics & Statistics
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4202 E. Fowler Ave. IDRB214, 33620, Tampa, FL, United States
Head of institution
Steve Currall
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www.usf.edu
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