Correlation of throwing velocity to the results of lower body field tests in male college baseball players

Baseball-specific athleticism, potential, and performance have been difficult to predict. Increased muscle strength and power can increase throwing velocity but the majority of research has focused on the upper body. The present study sought to determine if bilateral or unilateral lower-body field testing correlates with throwing velocity. Baseball throwing velocity scores were correlated to the following tests: medicine ball (MB) scoop toss and squat throw, bilateral and unilateral vertical jumps, single and triple broad jumps, hop and stop in both directions, lateral to medial jumps, 10- and 60-yd sprints, and both left and right single-leg 10-yd hop for speed in 42 college baseball players. A multiple regression analysis (forward method), assessing the relationship between shuffle and stretch throwing velocities and lower-body field test results determined that right-handed throwing velocity from the stretch position was most strongly predicted by lateral to medial jump right (LMJR) and body weight (BW; R2 = 0.322), whereas lateral to medial jump left (LMJL; R2 = 0.688) predicted left stretch throw. Right-handed shuffle throw was most strongly predicted by LMJR and MB scoop (R2 = 0.338), whereas LMJL, BW, and LMJR all contributed to left-handed shuffle throw (R2 = 0.982). Overall, this study found that lateral to medial jumps were consistently correlated with high throwing velocity in each of the throwing techniques, in both left-handed and right-handed throwers. This is the first study to correlate throwing velocity with a unilateral jump in the frontal plane, mimicking the action of the throwing stride

Development of improved semi-organic structural adhesives for elevated temperature applications Technical summary report, 1 ~JUL. 1964 - 29 ~FEB. 1968

Titanium chelate polymer adhesive formulation for aluminum joint curing in high temperature application

Transport properties of anyons in random topological environments

The quasi one-dimensional transport of Abelian and non-Abelian anyons is studied in the presence of a random topological background. In particular, we consider the quantum walk of an anyon that braids around islands of randomly filled static anyons of the same type. Two distinct behaviours are identified. We analytically demonstrate that all types of Abelian anyons localise purely due to the statistical phases induced by their random anyonic environment. In contrast, we numerically show that non-Abelian Ising anyons do not localise. This is due to their entanglement with the anyonic environment that effectively induces dephasing. Our study demonstrates that localisation properties strongly depend on non-local topological interactions and it provides a clear distinction in the transport properties of Abelian and non-Abelian statistics.Comment: 9 pages, 5 figure

Dynamical tunneling in mushroom billiards

We study the fundamental question of dynamical tunneling in generic two-dimensional Hamiltonian systems by considering regular-to-chaotic tunneling rates. Experimentally, we use microwave spectra to investigate a mushroom billiard with adjustable foot height. Numerically, we obtain tunneling rates from high precision eigenvalues using the improved method of particular solutions. Analytically, a prediction is given by extending an approach using a fictitious integrable system to billiards. In contrast to previous approaches for billiards, we find agreement with experimental and numerical data without any free parameter.Comment: 4 pages, 4 figure

Human phosphodiesterase 4D7 (PDE4D7) expression is increased in TMPRSS2-ERG positive primary prostate cancer and independently adds to a reduced risk of post-surgical disease progression

background: There is an acute need to uncover biomarkers that reflect the molecular pathologies, underpinning prostate cancer progression and poor patient outcome. We have previously demonstrated that in prostate cancer cell lines PDE4D7 is downregulated in advanced cases of the disease. To investigate further the prognostic power of PDE4D7 expression during prostate cancer progression and assess how downregulation of this PDE isoform may affect disease outcome, we have examined PDE4D7 expression in physiologically relevant primary human samples. methods: About 1405 patient samples across 8 publically available qPCR, Affymetrix Exon 1.0 ST arrays and RNA sequencing data sets were screened for PDE4D7 expression. The TMPRSS2-ERG gene rearrangement status of patient samples was determined by transformation of the exon array and RNA seq expression data to robust z-scores followed by the application of a threshold >3 to define a positive TMPRSS2-ERG gene fusion event in a tumour sample. results: We demonstrate that PDE4D7 expression positively correlates with primary tumour development. We also show a positive association with the highly prostate cancer-specific gene rearrangement between TMPRSS2 and the ETS transcription factor family member ERG. In addition, we find that in primary TMPRSS2-ERG-positive tumours PDE4D7 expression is significantly positively correlated with low-grade disease and a reduced likelihood of progression after primary treatment. Conversely, PDE4D7 transcript levels become significantly decreased in castration resistant prostate cancer (CRPC). conclusions: We further characterise and add physiological relevance to PDE4D7 as a novel marker that is associated with the development and progression of prostate tumours. We propose that the assessment of PDE4D7 levels may provide a novel, independent predictor of post-surgical disease progression

Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

Autonomous Light Management in Flexible Photoelectrochromic Films Integrating High Performance Silicon Solar Microcells

Commercial smart window technologies for dynamic light and heat management in building and automotive environments traditionally rely on electrochromic (EC) materials powered by an external source. This design complicates building-scale installation requirements and substantially increases costs for applications in retrofit construction. Self-powered photoelectrochromic (PEC) windows are an intuitive alternative wherein a photovoltaic (PV) material is used to power the electrochromic device, which modulates the transmission of the incident solar flux. The PV component in this application must be sufficiently transparent and produce enough power to efficiently modulate the EC device transmission. Here, we propose Si solar microcells (μ-cells) that are i) small enough to be visually transparent to the eye, and ii) thin enough to enable flexible PEC devices. Visual transparency is achieved when Si μ-cells are arranged in high pitch (i.e. low-integration density) form factors while maintaining the advantages of a single-crystalline PV material (i.e., long lifetime and high performance). Additionally, the thin dimensions of these Si μ-cells enable fabrication on flexible substrates to realize these flexible PEC devices. The current work demonstrates this concept using WO₃ as the EC material and V₂O₅ as the ion storage layer, where each component is fabricated via sol-gel methods that afford improved prospects for scalability and tunability in comparison to thermal evaporation methods. The EC devices display fast switching times, as low as 8 seconds, with a modulation in transmission as high as 33%. Integration with two Si μ-cells in series (affording a 1.12 V output) demonstrates an integrated PEC module design with switching times of less than 3 minutes, and a modulation in transmission of 32% with an unprecedented EC:PV areal ratio

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

Phenotyping hypotensive patients in critical care using hospital discharge summaries

Among critically-ill patients, hypotension represents a failure in compensatory mechanisms and may lead to organ hypoperfusion and failure. In this work, we adopt a datadriven approach for phenotype discovery and visualization of patient similarity and cohort structure in the intensive care unit (ICU). We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically learn a d-dimensional feature representation of patients that captures the latent 'topic' structure of diseases, symptoms, medications, and findings documented in hospital discharge summaries. We then used the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to convert the d-dimensional latent structure learned from HDP into a matrix of pairwise similarities for visualizing patient similarity and cohort structure. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluated the clinical utility of the discovered topic structure in phenotyping critically-ill patients who experienced hypotensive episodes. Our results indicate that the approach is able to reveal clinically interpretable clustering structure within our cohort and may potentially provide valuable insights to better understand the association between disease phenotypes and outcomes.National Institutes of Health (U.S.) (Grant R01-EB017205)National Institutes of Health (U.S.) (Grant R01-EB001659)National Institutes of Health (U.S.) (Grant R01GM104987