Effect of Anticipatory Shooting Strategy on Performance Consistency in Skilled Elite Archer

PURPOSE This study examined the effect of anticipatory control strategies on stable upright posture and consistency in archery performance among skilled elite archers. METHODS Nine skilled archery players participated in this study and performed repeated shooting trials under two different shooting conditions: clicker and non-clicker. In the clicker condition, archers shot in response to clicker signals, whereas in the non-clicker condition, they used an anticipatory strategy to determine shooting time in a self-paced manner without using the clicker. A motion capture system with six infrared cameras was used to measure the coordinates of the bow and archers’ hands, which were then used to calculate the aiming precision index and draw-related variables. Electromyography of the lower leg muscles and the center of pressure (COP) were also analyzed for a short period immediately before release to determine the differences in anticipatory postural adjustments (APAs) between the two shooting conditions. RESULTS The non-clicker condition resulted in a relatively short drawing duration and better precision index. The COP speed rapidly increased immediately before the release (i.e., APAs), and the rate of increase was lower in the non-clicker condition than in the clicker shooting condition. Furthermore, smaller APAs were significantly correlated with better-aiming precision in the non-clicker condition. CONCLUSION These findings suggest that using an anticipatory strategy rather than reacting to a clicker can improve archery performance consistency by reducing APA and ensuring a stable shooting posture. This strategy can be used in archery training to predict clicker signals during the aim-release stage

Stock Prices of Renewable Energy Firms: Are There Asymmetric Responses to Oil Price Changes?

This article revisits the question of whether crude oil prices have a positive effect on stock the prices of renewable energy firms. To examine this question carefully, we allow for the asymmetric effects of oil price changes in our modeling process, using the nonlinear autoregressive distributed lag (ARDL) approach. We find that changes in oil prices indeed have a significant, positive short-run effect on renewable energy stock prices in an asymmetric manner. However, this short-run effect does not appear to last in the long-run

Linearly Replaceable Filters for Deep Network Channel Pruning

Convolutional neural networks (CNNs) have achieved remarkable results; however, despite the development of deep learning, practical user applications are fairly limited because heavy networks can be used solely with the latest hardware and software supports. Therefore, network pruning is gaining attention for general applications in various fields. This paper proposes a novel channel pruning method, Linearly Replaceable Filter (LRF), which suggests that a filter that can be approximated by the linear combination of other filters is replaceable. Moreover, an additional method called Weights Compensation is proposed to support the LRF method. This is a technique that effectively reduces the output difference caused by removing filters via direct weight modification. Through various experiments, we have confirmed that our method achieves state-of-the-art performance in several benchmarks. In particular, on ImageNet, LRF-60 reduces approximately 56% of FLOPs on ResNet-50 without top-5 accuracy drop. Further, through extensive analyses, we proved the effectiveness of our approaches

Dual‐logic‐in‐memory implementation with orthogonal polarization of van der Waals ferroelectric heterostructure

Abstract The rapid advancement of AI‐enabled applications has resulted in an increasing need for energy‐efficient computing hardware. Logic‐in‐memory is a promising approach for processing the data stored in memory, wherein fast and efficient computations are possible owing to the parallel execution of reconfigurable logic operations. In this study, a dual‐logic‐in‐memory device, which can simultaneously perform two logic operations in four states, is demonstrated using van der Waals ferroelectric field‐effect transistors (vdW FeFETs). The proposed dual‐logic‐in‐memory device, which also acts as a two‐bit storage device, is a single bidirectional polarization‐integrated ferroelectric field‐effect transistor (BPI‐FeFET). It is fabricated by integrating an in‐plane vdW ferroelectric semiconductor SnS and an out‐of‐plane vdW ferroelectric gate dielectric material—CuInP2S6. Four reliable resistance states with excellent endurance and retention characteristics were achieved. The two‐bit storage mechanism in a BPI‐FeFET was analyzed from two perspectives: carrier density and carrier injection controls, which originated from the out‐of‐plane polarization of the gate dielectric and in‐plane polarization of the semiconductor, respectively. Unlike conventional multilevel FeFETs, the proposed BPI‐FeFET does not require additional pre‐examination or erasing steps to switch from/to an intermediate polarization, enabling direct switching between the four memory states. To utilize the fabricated BPI‐FeFET as a dual‐logic‐in‐memory device, two logical operations were selected (XOR and AND), and their parallel execution was demonstrated. Different types of logic operations could be implemented by selecting different initial states, demonstrating various types of functions required for numerous neural network operations. The flexibility and efficiency of the proposed dual‐logic‐in‐memory device appear promising in the realization of next‐generation low‐power computing systems