Criterion Validity of an Automated Method of Detecting Live Play Periods in Basketball

This study aimed to develop an automated method to detect live play periods from accelerometry-derived relative exercise intensity in basketball, and to assess the criterion validity of this method. Relative exercise intensity (% oxygen uptake reserve) was quantified for two men's semi-professional basketball matches. Live play period durations were automatically determined using a moving average sample window and relative exercise intensity threshold, and manually determined using annotation of video footage. The sample window duration and intensity threshold were optimised to determine the input parameters for the automated method that would result in the most similarity to the manual method. These input parameters were used to compare the automated and manual active play period durations in another men's semi-professional match and a women's professional match to assess the criterion validity of the automated method. The optimal input parameters were a 9-s sample window and relative exercise intensity threshold of 31% oxygen uptake reserve. The automated method showed good relative (ρ = 0.95–0.96 and ICC = 0.96–0.98, p < 0.01) and absolute (median bias = 0 s) agreement with the manual method. These findings support the use of an automated method using accelerometry-derived relative exercise intensity and a moving average sample window to detect live play periods in basketball.

The Majority of Inpatient Short Synacthen Tests Are Performed Incorrectly, Due to Imprecise Timing, Incorrect Sampling and Failure to Interrupt Steroid Administration

Introduction: The short synacthen test (SST) is commonly used to assess adrenal function. Accurate timing and appropriate holding of exogenous steroids are essential to ensure correct interpretation of results. Aims & Methods: We reviewed all SSTs performed on inpatients in our hospital over a 1-year period, in order to determine accuracy of testing. Results: 42 patients (Male 15, Female 27), with mean age 68 years (range 43–90), underwent SST. The majority (39/42; 93%) of tests were requested by internal medicine physicians. The indications for testing were; suspected adrenal insufficiency (18), HPA axis suppression (9), fatigue (7), hyponatremia (5), suspected pituitary disease (2) and vomiting (1). 7 (44%) of the 16 patients taking steroids did not have medication appropriately held. 31 (74%) patients did not have serum ACTH measured prior to the test. 28 (66%) tests were not started at the correct time. Only 10 (24%) of the 30 minute samples were completed within the 25-35min sample window. The mean time between the 0min and 30min samples was 42mins (median 62mins; range 0-209mins). 12 (29%) tests involved an unnecessary 60min sample. 8 (19%) tests had no interpretation of results documented in the medical notes. 4 (10%) patients underwent repeat testing, necessitated by an incorrect first test. Discussion: The vast majority of inpatient SSTs (33/42;79%) were performed suboptimally, with the most common errors pertaining to incorrect timing of the test, inaccurate sampling and inappropriate pre-test steroid administration. Considering these errors, some results may have been interpreted incorrectly. Repeat tests were recognised as required in 10% of patients, with associated inconvenience, cost and discomfort. Improved training and guidelines for performing SSTs should be available to hospital staff to ensure more accurate application of the test.

Improved Prototypical Network Model for Forest Species Classification in Complex Stand

Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to the application of deep learning in airborne image classification. Prototypical networks are practical deep learning networks that have demonstrated effectiveness in handling small-sample classification. In this study, an improved prototypical network is proposed (by adding L2 regularization to the convolutional layer and dropout to the maximum pooling layer) to address the problem of overfitting in small-sample classification. The proposed network has an optimal sample window for classification, and the window size is related to the area and distribution of the study area. After performing dimensionality reduction using principal component analysis, the time required for training using hyperspectral images shortened significantly, and the test accuracy increased drastically. Furthermore, when the size of the sample window was 27 × 27 after dimensionality reduction, the overall accuracy of forest species classification was 98.53%, and the Kappa coefficient was 0.9838. Therefore, by using an improved prototypical network with a sample window of an appropriate size, the network yielded desirable classification results, thereby demonstrating its suitability for the fine classification and mapping of tree species.

Optimization of Railroad Bearing Health Monitoring System for Wireless Utilization

In the railroad industry, systematic health inspections of freight railcar bearings are required. Bearings are subjected to high loads and run at high speeds, so over time the bearings may develop a defect that can potentially cause a derailment if left in service operation. Current bearing condition monitoring systems include Hot-Box Detectors (HBDs) and Trackside Acoustic Detection Systems (TADS™). The commonly used HBDs use non-contact infrared sensors to detect abnormal temperatures of bearings as they pass over the detector. Bearing temperatures that are about 94°C above ambient conditions will trigger an alarm indicating that the bearing must be removed from field service and inspected for defects. However, HBDs can be inconsistent, where 138 severely defective bearings from 2010 to 2019 were not detected. And from 2001 to 2007, Amsted Rail concluded that about 40% of presumably defective bearings detected by HBDs did not have any significant defects upon teardown and inspection. TADS™ use microphones to detect high-risk bearings by listening to their acoustic sound vibrations. Still, TADS™ are not very reliable since there are less than 30 active systems in the U.S. and Canada, and derailments may occur before bearings encounter any of these systems. Researchers from the University Transportation Center for Railway Safety (UTCRS) have developed an advanced algorithm that can accurately and reliably monitor the condition of the bearings via temperature and vibration measurements. This algorithm uses the vibration measurements collected from accelerometers on the bearing adapters to determine if there is a defect, where the defect is within the bearing, and the approximate size of the defect. Laboratory testing is performed on the single bearing and four bearing test rigs housed at the University of Texas Rio Grande Valley (UTRGV). The algorithm uses a four second sample window of the recorded vibration data and can reliably identify the defective component inside the bearing with up to a 100% confidence level. However, about 20,000 data points are used for this analysis, which requires substantial computational power. This can limit the battery life of the wireless onboard condition monitoring system. So, reducing the vibration sample window to conduct an accurate analysis should result in a more optimal power-efficient algorithm. A wireless onboard condition monitoring module that collects one second of vibration data (5,200 samples) was manufactured and tested to compare its efficacy against a wired setup that uses a four second sample window. This study investigates the root-mean-square values of the bearing vibration and its power spectral density plots to create an optimized and accurate algorithm for wireless utilization.

Effects of Sampling Frequency and Sample Window Size on Median Frequency of Surface EMG

Muscle fatigue can be evaluated through the assessment of the downward shift in the median frequency (MDF) of the electromyographic (EMG) signal collected through surface electromyography. Previous research has shown that the value of MDF may be affected by sampling parameters. The purpose of this study was to quantify the combined effect of different sampling frequencies and window sizes on the calculated MDF. A sample of 24 participants performed a simple static elbow flexion exertion (15% MVC) and the EMG activity of the biceps brachii was periodically sampled using surface electrodes for four seconds at a frequency of 4096 Hz as the biceps brachii became fatigued. These collected data were then down-sampled to create a dataset of four window sizes (1s, 2s, 3s, and 4s) and five sampling frequencies (256 Hz, 512 Hz, 1024 Hz, 2048 Hz, and 4096 Hz). Median frequencies were calculated for each combination of sampling frequency and window size and then compared with the 4096 Hz / 4 s condition (considered gold standard) and the errors were calculated. Results suggest the use of a minimum sampling frequency of 512 Hz and a window size of 4s.

Research on phase transition and strength under ramp compression with graded density impactor

In our work, graded density impactors fabricated from 8 to 40 layers, are specifically designed to generate desired strain rates (on the order of 105~106 s-1) and thermodynamic path(shock loading-ramp loading-release). And experiments on phase transition and strength for metals (bismuth, LY12 Al) have been performed with light gas gun to peak pressure between 30 GPa and 50 GPa. Particle velocity at sample/window interface in these experiments are simultaneously traced by a distance interferometer system for any reflector, and a wave profile analysis is employed to explore the solidification transition and strength behaviour along elevated isentrope.

Language Samples From Children Who Use Speech-Generating Devices: Making Sense of Small Samples and Utterance Length

Purpose Mean length of utterance in morphemes (MLUm) is underreported in people who use augmentative and alternative communication (AAC). MLUm is difficult to measure in people who use AAC because of 2 challenges described in literature: the challenge of small language samples (difficulty collecting representative samples) and the challenge of transcribing short utterances (difficulty transcribing 1-morpheme utterances). We tested solutions to both challenges in a corpus of language samples from children who use speech-generating devices. Method The first challenge was addressed by adjusting the length of the sampling window to obtain representative language samples. The second challenge was addressed by using mean syntactic length (MSL) as an alternative to MLUm. Results A 24-hour sample window consistently failed to yield representative samples. An extended 1-month sample window consistently yielded representative samples. A significant positive prediction of MLUm by MSL was found in a normative sample. Observed measures of MSL were used to predict MLUm in representative language samples from children who use AAC. Conclusions Valid measures of utterance length in people who use AAC can be obtained using extended sampling windows and MSL. Research is needed to characterize the strengths and limitations of both solutions.

Sustainability of Brazilian fiscal policy, once again: corrective policy response over time

<title>Abstract</title><p>This paper estimates a fiscal reaction function for Brazil and investigates how the government's fiscal reaction has changed over time when controlling for cyclical variations in output and the relative participation of indexed debt. Using monthly data since 1991, we estimate a rolling reaction function with a one observation step and a sample-window of 12 observations. Our results indicate that the government's fiscal response has been such that a one percent increase in the debt-GDP ratio can be associated to an average increase in the primary surplus of approximately 0.096% over GDP or 9.6 basis points; the government's fiscal reaction has become more stable but less responsive to the debt-income level after 2000 and assumed a declining trend after 2006.</p>

Accounting Comparability and the Accuracy of Peer-Based Valuation Models

ABSTRACT We examine the link between enhanced accounting comparability and the valuation performance of pricing multiples. Using the warranted multiple method proposed by Bhojraj and Lee (2002), we demonstrate how enhanced accounting comparability leads to better peer-based valuation performance. Empirical tests using firms from 15 European Union (EU) countries over the period 1997–2011 (with comparable peers selected from the entire cross-section of foreign firms) document significant improvement in valuation performance measured as pricing accuracy, the ability of value estimates to explain cross-sectional variation in observed price, and the ability of the pricing multiple to predict future market-to-book multiples. Findings for a series of identification tests suggest that enhanced valuation performance is the consequence of improvements in the degree of cross-border accounting comparability that occurred during the sample window, and that a significant fraction of comparability gain operates through improved peer selection.