University of Central Florida

About: University of Central Florida is a education organization based out in Orlando, Florida, United States. It is known for research contribution in the topics: Laser & Population. The organization has 18822 authors who have published 48679 publications receiving 1234422 citations. The organization is also known as: UCF.

Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity-Score-Matching Estimator

Abstract: This study examines the effects of air quality regulation on economic activity. Anecdotal evidence and some recent empirical studies suggest that an inverse relationship exists between the stringency of environmental regulations and new plant formations. Using a unique county-level data set for New York State from 1980 to 1990, we revisit this conjecture using a seminonparametric method based on propensity score matching. Our empirical estimates suggest that pollution-intensive plants are responding to environmental regulations; more importantly, we find that traditional parametric methods used in previous studies may dramatically understate the impact of more stringent regulations.

Plastid-expressed betaine aldehyde dehydrogenase gene in carrot cultured cells, roots, and leaves confers enhanced salt tolerance

Abstract: Salinity is one of the major factors that limits geographical distribution of plants and adversely affects crop productivity and quality We report here high-level expression of betaine aldehyde dehydrogenase (BADH) in cultured cells, roots, and leaves of carrot (Daucus carota) via plastid genetic engineering Homoplasmic transgenic plants exhibiting high levels of salt tolerance were regenerated from bombarded cell cultures via somatic embryogenesis Transformation efficiency of carrot somatic embryos was very high, with one transgenic event per approximately seven bombarded plates under optimal conditions In vitro transgenic carrot cells transformed with the badh transgene were visually green in color when compared to untransformed carrot cells, and this offered a visual selection for transgenic lines BADH enzyme activity was enhanced 8-fold in transgenic carrot cell cultures, grew 7-fold more, and accumulated 50- to 54-fold more betaine (93-101 micromol g(-1) dry weight of beta-Ala betaine and Gly betaine) than untransformed cells grown in liquid medium containing 100 mm NaCl Transgenic carrot plants expressing BADH grew in the presence of high concentrations of NaCl (up to 400 mm), the highest level of salt tolerance reported so far among genetically modified crop plants BADH expression was 748% in non-green edible parts (carrots) containing chromoplasts, and 53% in proplastids of cultured cells when compared to chloroplasts (100%) in leaves Demonstration of plastid transformation via somatic embryogenesis utilizing non-green tissues as recipients of foreign DNA for the first time overcomes two of the major obstacles in extending this technology to important crop plants

Attention-deficit/hyperactivity disorder and behavioral inhibition: a meta-analytic review of the stop-signal paradigm.

Abstract: Deficient behavioral inhibition (BI) processes are considered a core feature of attention deficit/hyperactivity disorder (ADHD). This meta-analytic review is the first to examine the potential influence of a wide range of subject and task variable moderator effects on BI processes—assessed by the stop-signal paradigm—in children with ADHD relative to typically developing children. Results revealed significantly slower mean reaction time (MRT), greater reaction time variability (SDRT), and slower stop-signal reaction time (SSRT) in children with ADHD relative to controls. The non-significant between-group stop-signal delay (SSD) metric, however, suggests that stop-signal reaction time differences reflect a more generalized deficit in attention/cognitive processing rather than behavioral inhibition. Several subject and task variables served as significant moderators for children’s mean reaction time.

Blockchain technology innovations

Abstract: Digital world has produced efficiencies, new innovative products, and close customer relationships globally by the effective use of mobile, IoT (Internet of Things), social media, analytics and cloud technology to generate models for better decisions. Blockchain is recently introduced and revolutionizing the digital world bringing a new perspective to security, resiliency and efficiency of systems. While initially popularized by Bitcoin, Blockchain is much more than a foundation for crypto currency. It offers a secure way to exchange any kind of good, service, or transaction. Industrial growth increasingly depends on trusted partnerships; but increasing regulation, cybercrime and fraud are inhibiting expansion. To address these challenges, Blockchain will enable more agile value chains, faster product innovations, closer customer relationships, and quicker integration with the IoT and cloud technology. Further Blockchain provides a lower cost of trade with a trusted contract monitored without intervention from third parties who may not add direct value. It facilitates smart contracts, engagements, and agreements with inherent, robust cyber security features. This paper is an effort to break the ground for presenting and demonstrating the use of Blockchain technology in multiple industrial applications. A healthcare industry application, Healthchain, is formalized and developed on the foundation of Blockchain using IBM Blockchain initiative. The concepts are transferable to a wide range of industries as finance, government and manufacturing where security, scalability and efficiency must meet.

Task Agnostic Meta-Learning for Few-Shot Learning

Abstract: Meta-learning approaches have been proposed to tackle the few-shot learning problem. Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile when it is over-trained on existing tasks during meta-training phase. In other words, the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks, especially when only very few examples are available to update the model. To avoid a biased meta-learner and improve its generalizability, we propose a novel paradigm of Task-Agnostic Meta-Learning (TAML) algorithms. Specifically, we present an entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks. Alternatively, a more general inequality-minimization TAML is presented for more ubiquitous scenarios by directly minimizing the inequality of initial losses beyond the classification tasks wherever a suitable loss can be defined. Experiments on benchmarked datasets demonstrate that the proposed approaches outperform compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.