Development of Key Stages 2 and 3 Teacher Resources in the Areas of Space and Flight for the Science Museum in London

The Science Museum in London\u27s overall goal is to engage students in science. This project developed prototypes of teacher resources for use in the classroom or museum on the topics of Space and Flight. The design was based on teacher interviews and the museum\u27s research about teachers\u27 wants for resources and what engages students. From the analysis of this data, we developed resource prototypes for use in the classroom or museum and created an online flight interactive game

Chemical laboratories 4.0: A two-stage machine learning system for predicting the arrival of samples

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Automated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interesting predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.This work has been supported by FCT – Funda ̧c ̃ao para a Ciˆencia e Tecnologiawithin the R&D Units Project Scope: UIDB/00319/2020. The authors also wishto thank the chemical company staff involved with this project for providing thedata and also the valuable domain feedback

Using neuroevolution for predicting mobile marketing conversion

This paper addresses user Conversion Rate (CVR) prediction within the context of Mobile Performance Marketing. Specifically, we adapt two main neuroevolution methods: Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT). First, we discuss two mechanisms for increasing execution speed (parallelism and data sampling); a strategy for preventing excessive network complexity with NEAT; and a rolling window scheme for performing an online learning. Then, we present experimental results, using distinct datasets and testing both offline and online learning environments.ThisarticleisaresultoftheprojectNORTE-01-0247-FEDER-017497,supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Mixture modeling of microarray gene expression data

About 28% of genes appear to have an expression pattern that follows a mixture distribution. We use first- and second-order partial correlation coefficients to identify trios and quartets of non-sex-linked genes that are highly associated and that are also mixtures. We identified 18 trio and 35 quartet mixtures and evaluated their mixture distribution concordance. Concordance was defined as the proportion of observations that simultaneously fall in the component with the higher mean or simultaneously in the component with the lower mean based on their Bayesian posterior probabilities. These trios and quartets have a concordance rate greater than 80%. There are 33 genes involved in these trios and quartets. A factor analysis with varimax rotation identifies three gene groups based on their factor loadings. One group of 18 genes has a concordance rate of 56.7%, another group of 8 genes has a concordance rate of 60.8%, and a third group of 7 genes has a concordance rate of 69.6%. Each of these rates is highly significant, suggesting that there may be strong biological underpinnings for the mixture mechanisms of these genes. Bayesian factor screening confirms this hypothesis by identifying six single-nucleotide polymorphisms that are significantly associated with the expression phenotypes of the five most concordant genes in the first group

Epitaxial Growth of VO2_{2} by Periodic Annealing

We report the growth of ultrathin VO$_{2}$ films on rutile TiO$_{2}$ (001) substrates via reactive molecular-beam epitaxy. The films were formed by the cyclical deposition of amorphous vanadium and its subsequent oxidation and transformation to VO$_{2}$ via solid-phase epitaxy. Significant metal-insulator transitions were observed in films as thin as 2.3 nm, where a resistance change {\Delta}R/R of 25 was measured. Low angle annular dark field scanning transmission electron microscopy was used in conjunction with electron energy loss spectroscopy to study the film/substrate interface and revealed the vanadium to be tetravalent and the titanium interdiffusion to be limited to 1.6 nm.Comment: 25 pages, 6 figure

Factors that influence the intra-articular rupture pattern of the ACL graft following single-bundle reconstruction

The number of revision anterior cruciate ligament (ACL) surgeries performed annually continues to rise. The purpose of this study was to determine the most common rupture pattern in ACL revision cases after previous single-bundle reconstruction. The second aim was to determine the relationship between rupture pattern and patient-specific factors (age, gender, time between the initial ACL reconstruction and re-injury, and etiology/mechanism of failure) and surgical factors (graft type, tunnel angle). This was a cohort study of 60 patients that underwent revision ACL surgery after previous single-bundle ACL reconstruction. Three sports medicine-trained orthopedic surgeons reviewed the arthroscopic videos and determined the rupture pattern of the grafts. The rupture pattern was then correlated to the above-mentioned factors. The inter-observer agreement had a kappa of 0.7. The most common rupture pattern after previous single-bundle ACL reconstruction is elongation of the graft. This is different from the native ACL, which displays more proximal ruptures. With the use of autograft tissue and after a longer period of time, the rupture pattern in revision surgery is more similar to that of the native ACL. The most common rupture pattern after previous single-bundle reconstruction was elongation of the graft. Factors that influenced the rupture pattern were months between ACL reconstruction and re-injury and graft type. Cohort study, Level I

ACL graft re-rupture after double-bundle reconstruction: factors that influence the intra-articular pattern of injury

To determine the most common rupture patterns of previously reconstructed DB-ACL cases, seen at the time of revision surgery, and to determine the influence of age, gender, time between the initial ACL reconstruction and re-injury, tunnel angle and etiology of failure. Forty patients who presented for revision surgery after previous double-bundle ACL reconstruction were enrolled. Three orthopedic surgeons independently reviewed the arthroscopic videos and determined the rupture pattern of both the anteromedial and posterolateral grafts. The graft rupture pattern was then correlated with the previously mentioned factors. The most common injury pattern seen at the time of revision ACL surgery was mid-substance AM and PL bundle rupture. Factors that influenced the rupture pattern (proximal vs. mid-substance and distal rupture vs. elongated, but in continuity) were months between ACL reconstruction and re-injury (P = 0.002), the etiology of failure (traumatic vs. atraumatic) (P = 0.025) and the measured graft tunnel angle (P = 0.048). The most common pattern of graft re-rupture was mid-substance AM and mid-substance PL. As the length of time from the initial DB-ACL reconstruction to revision surgery increased, the pattern of injury more closely resembled that of the native ACL. Evaluation of patients who have undergone double-bundle ACL reconstruction, with a particular focus on graft maturity, mechanism of injury and femoral tunnel angles, and graft rupture pattern assists in preoperative planning for revision surger

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower--upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage--width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.This article is a result of the project NORTE-01- 0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We would also like to thank the anonymous reviewers for their helpful suggestionsinfo:eu-repo/semantics/publishedVersio

Rotational knee laxity: Reliability of a simple measurement device in vivo

<p>Abstract</p> <p>Background</p> <p>Double bundle ACL reconstruction has been demonstrated to decrease rotational knee laxity. However, there is no simple, commercially-available device to measure knee rotation. The investigators developed a simple, non-invasive device to measure knee rotation. In conjunction with a rigid boot to rotate the tibia and a force/moment sensor to allow precise determination of torque about the knee, a magnetic tracking system measures the axial rotation of the tibia with respect to the femur. This device has been shown to have acceptable levels of test re-test reliability to measure knee rotation in cadaveric knees.</p> <p>Methods</p> <p>The objective of this study was to determine reliability of the device in measuring knee rotation of human subjects. Specifically, the intra-tester reliability within a single testing session, test-retest reliability between two testing sessions, and inter-tester reliability were assessed for 11 male subjects with normal knees.</p> <p>Results</p> <p>The 95% confidence interval for rotation was less than 5° for intra-tester, test-retest, and inter-tester reliability, and the standard error of measurement for the differences between left and right knees was found to be less than 3°.</p> <p>Conclusion</p> <p>It was found that the knee rotation measurements obtained with this device have acceptable limits of reliability for clinical use and interpretation.</p