Ultrastructure of Diplophrys parva, a New Small Freshwater Species, and a Revised Analysis of Labyrinthulea (Heterokonta)

We describe Diplophrys parva n. sp., a freshwater heterotroph, using fine structural and sequence evidence. Cells are small (L = 6.5 ± 0.08, W = 5.5 ± 0.06 µm; mean ± SE) enclosed by an envelope/theca of overlapping scales, slightly oval to elongated-oval with rounded ends, (1.0 × 0.5–0.7 µm), one to several intracellular refractive granules (~ 1.0–2.0 µm), smaller hyaline peripheral vacuoles, a nucleus with central nucleolus, tubulo-cristate mitochondria, and a prominent Golgi apparatus with multiple stacked saccules (~ 10). It is smaller than published sizes of Diplophrys archeri (~ 10–20 µm), modestly less than Diplophrys marina (~ 5–9 µm), and differs in scale size and morphology from D. marina. No cysts were observed. We transfer D. marina to a new genus Amphifila as it falls within a molecular phylogenetic clade extremely distant from that including D. parva. Based on morphological and molecular phylogenetic evidence, Labyrinthulea are revised to include six new families, including Diplophryidae for Diplophrys and Amphifilidae containing Amphifila. The other new families have distinctive morphology: Oblongichytriidae and Aplanochytriidae are distinct clades on the rDNA tree, but Sorodiplophryidae and Althorniidae lack sequence data. Aplanochytriidae is in Labyrinthulida; the rest are in Thraustochytrida; Labyrinthomyxa is excluded

RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node

Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is not available inside of buildings. Also other existing methods for indoor localization have low accuracy. In addition, they use fixed infrastructure support. In this paper, we present a novel system for indoor localization, which also works well outside. We have developed a mathematical model for estimating location (distance and direction) of a mobile device using wireless technology. Our experimental results on Smartphones (Android and iOS) show good accuracy (an error less than 2.5 meters). We have also used our developed system in asset tracking and complex activity recognition

Ultrastructure of Diplophrys parva, a New Small Freshwater Species, and a Revised Analysis of Labyrinthulea (Heterokonta)

We describe Diplophrys parva n. sp., a freshwater heterotroph, using fine structural and sequence evidence. Cells are small (L = 6.5 ± 0.08, W = 5.5 ± 0.06 µm; mean ± SE) enclosed by an envelope/theca of overlapping scales, slightly oval to elongated-oval with rounded ends, (1.0 × 0.5–0.7 µm), one to several intracellular refractive granules (~ 1.0–2.0 µm), smaller hyaline peripheral vacuoles, a nucleus with central nucleolus, tubulo-cristate mitochondria, and a prominent Golgi apparatus with multiple stacked saccules (~ 10). It is smaller than published sizes of Diplophrys archeri (~ 10–20 µm), modestly less than Diplophrys marina (~ 5–9 µm), and differs in scale size and morphology from D. marina. No cysts were observed. We transfer D. marina to a new genus Amphifila as it falls within a mo-lecular phylogenetic clade extremely distant from that including D. parva. Based on morphological and molecular phylogenetic evidence, Labyrinthulea are revised to include six new families, including Diplophryidae for Diplophrys and Amphifilidae containing Amphifila. The other new families have distinctive morphology: Oblongichytriidae and Aplanochytriidae are distinct clades on the rDNA tree, but Sorodiplophryidae and Althorniidae lack sequence data. Aplanochytriidae is in Labyrinthulida; the rest are in Thraustochytrida; Laby-rinthomyxa is excluded

Raising a Small Flock of Sheep in Ohio

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Feasibility of a Self-Paced Educational Intervention Protocol on Standardized Assessment of Public Building Accessibility

Limited research informs the implementation of web-based and mobile learning (mLearning) protocols for the assessment of public building accessibility in occupational therapy graduate students. This study tested the feasibility of a self-paced protocol designed to teach students how to evaluate community environment accessibility. Students across five sites completed an online learning module and community building evaluations. Students were randomized into lecture or lab educational groups and then crossed over to receive the second experience. Outcomes were student satisfaction, self-perceived learning, and knowledge on a researcher-developed measure. Data were analyzed using descriptive statistics. Two hundred and twelve students completed the study. The students were satisfied with their education and their community accessibility knowledge significantly increased from approximately 60% to 85%. Site and order of the learning components did not impact student ability to achieve competence. This multi-site approach is feasible and effective in instructing students in this highly protocolized and specialized area of practice

A Light Weight Smartphone Based Human Activity Recognition System with High Accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works

Mathematical justification of the hydrostatic approximation in the primitive equations of geophysical fluid dynamics

Geophysical fluids all exhibit a common feature: their aspect ratio (depth to horizontal width) is very small. This leads to an asymptotic model widely used in meteorology, oceanography, and limnology, namely the hydrostatic approximation of the time-dependent incompressible Navier–Stokes equations. It relies on the hypothesis that pressure increases linearly in the vertical direction. In the following, we prove a convergence and existence theorem for this model by means of anisotropic estimates and a new time-compactness criterium.Fonds Franco-Espagnol D.R.E.I.FMinisterio de Educación y Cienci