Hasil untuk "Science (General)"

Ia.B. Zel'dovich, G. I. Barenblatt, V. B. Librovich et al.

J. Hyde, S. Lindberg, M. Linn et al.

M. Owe, R. Jeu, T. Holmes

B. C. Grau, Ian Horrocks, B. Motik et al.

R. Imhoff, P. Lamberty

During the coronavirus disease pandemic rising in 2020, governments and nongovernmental organizations across the globe have taken great efforts to curb the infection rate by promoting or legally prescribing behavior that can reduce the spread of the virus. At the same time, this pandemic has given rise to speculations and conspiracy theories. Conspiracy worldviews have been connected to refusal to trust science, the biomedical model of disease, and legal means of political engagement in previous research. In three studies from the United States (N = 220; N = 288) and the UK (N = 298), we went beyond this focus on a general conspiracy worldview and tested the idea that different forms of conspiracy beliefs despite being positively correlated have distinct behavioral implications. Whereas conspiracy beliefs describing the pandemic as a hoax were more strongly associated with reduced containment-related behavior, conspiracy beliefs about sinister forces purposefully creating the virus related to an increase in self-centered prepping behavior.

Alexander L. George, E. Miller, David Braybrooke et al.

G. Norman, H. Schmidt

N. Grimm, J. Grove, S. Pickett et al.

J. Shoven, J. Whalley

H. Pretzsch

Amanpreet Singh, Narina Thakur, A. Sharma

Yizhou Qian, James Lehman

V. Hoorens, A. Stone, J. Turkkan et al.

H. Small

Meenakshi Sudarvizhi Seenipeyathevar, Prasath Palaniappan, Vijayakumar Arumugam et al.

ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the radial Modeling technique. The primary hurdle that is being addressed is the accurate prediction of wear behavior in spatially graded magnesium matrix composites, while simultaneously avoiding extensive experimental testing. Under varying degrees of applied loads (4.4 to 39 N), sliding speeds (0.45 to 4.5 m/s), and sliding distances (500 to 4500 m), the wear performance was experimentally assessed. Results demonstrate a hardness increment of 26.26% in the outer region compared to the inner region, while resistance to wear was enhanced by 19.8% in the outer zone due to the grading of ceramic fibers. A limited experimental dataset consisting of wear measurements from the inner, middle, and outer zones of the composite was utilized in developing and validating four machine‐learning models for wear rate prediction. The tree‐based ensemble methods significantly outperformed deep‐learning strategies, with the LightGBM model providing the best prediction performance across all zones and achieving optimization with a maximum tree depth of 5, 480 leaves, and a feature fraction of 0.05. Moreover, zone‐specific XGBoost models were also developed, employing customized learning rates and minimal loss reduction parameters in order to elevate prediction accuracy. The proposed machine‐learning framework thus provides a pathway for rapid and reliable wear rate estimation for ceramic fiber‐reinforced magnesium composites, significantly lessening experimental burden. Results highlight that recycled magnesium waste, when combined with ceramic reinforcement, can be effectively employed to produce sustainable and economically viable materials with improved wear resistance, particularly for automotive and industrial applications.

Xiaoyang Liu, Xupeng Huang, Rongjin Zhu et al.

To improve the counting accuracy in dense rice seed scenarios, this study proposes a YOLOv5-based dense rice seed counting method that integrates C3CBAM and Soft-NMS. This method integrates the CBAM attention module into the shallow C3 modules of the backbone network to enhance image features. Additionally, it removes the original large and medium-sized object detection heads of YOLOv5 and adds a dedicated detection head for tiny rice seeds. For post-processing of model prediction data, the Soft-NMS algorithm is employed to replace standard Non-Maximum Suppression (NMS) and reduce missed detections. Finally, image acquisition, seed counting, and a user interface are integrated into a single system, enabling rice breeders to conduct seed counting tasks more intuitively and efficiently. Compared with the baseline YOLOv5 model, the recall and mAP@[0.5:0.95] of the improved model increase by 6.4 % and 5.7 %, respectively. Furthermore, this study designs experiments with three levels of seed density. In the intermediate-type rice seed samples, the detection accuracy reaches 100 % under light and moderate density conditions, while it maintains stable counting performance under heavy density conditions with an accuracy above 99.7 %. This work significantly enhances rice seed counting efficiency for researchers and facilitates rice variety improvement studies.

John W. Moffat

An approach is presented to resolve key paradoxes in black hole physics through the application of complex Riemannian spacetime. We extend the Schwarzschild metric into the complex domain, employing contour integration techniques to remove singularities while preserving the essential features of the original solution. A new regularized radial coordinate is introduced, leading to a singularity-free description of black hole interiors. Crucially, we demonstrate how this complex extension resolves the long-standing paradox of event horizon formation occurring only in the infinite future of distant observers. By analyzing trajectories in complex spacetime, we show that the horizon can form in finite complex time, reconciling the apparent contradiction between proper and coordinate time descriptions. This approach also provides a framework for the analytic continuation of information across event horizons, resolving the Hawking information paradox. We explore the physical interpretation of the complex extension versus its projection onto real spacetime. The gravitational collapse of a dust sphere with negligible dust is explored in the complex spacetime extension. The approach offers a mathematically rigorous framework for exploring quantum gravity effects within the context of classical general relativity.

Michał Ledwosiński

Artykuł podejmuje problematykę kompetencji miękkich w kontekście przekładoznawstwa oraz ich znaczenia w pracy tłumacza, zwłaszcza w środowiskach zawodowych, kształtowanych przez nowe technologie. Analizie poddano relacje między kompetencjami twardymi a miękkimi oraz wpływ kompetencji interpersonalnych na efektywność pracy tłumaczeniowej w warunkach współczesnej współpracy zespołowej. Celem tekstu jest ukazanie, że rozwój technologiczny nie redukuje znaczenia umiejętności społecznych, lecz wręcz potęguje potrzebę ich obecności w zawodzie tłumacza.

Eric Burns, Christopher L. Fryer, Ivan Agullo et al.

Astrophysical observations of the cosmos allow us to probe extreme physics and answer foundational questions on our universe. Modern astronomy is increasingly operating under a holistic approach, probing the same question with multiple diagnostics including how sources vary over time, how they appear across the electromagnetic spectrum, and through their other signatures, including gravitational waves, neutrinos, cosmic rays, and dust on Earth. Astrophysical observations are now reaching the point where approximate physics models are insufficient. Key sources of interest are explosive transients, whose understanding requires multidisciplinary studies at the intersection of astrophysics, gravity, nuclear science, plasma physics, fluid dynamics and turbulence, computation, particle physics, atomic, molecular, and optical science, condensed matter and materials science, radiation transport, and high energy density physics. This white paper provides an overview of the major scientific advances that lay at the intersection of physics and astronomy and are best probed through time-domain and multimessenger astrophysics, an exploration of how multidisciplinary science can be fostered, and introductory descriptions of the relevant scientific disciplines and key astrophysical sources of interest.

Bastian Breiner, Daniel M. Kainz, Stefan Wagner et al.

Food allergies are a severe burden for affected individuals and healthcare systems. To tackle the need for simple food allergen detection, we developed a system for the detection of the soy protein glycinin via a centrifugal microfluidics-assisted lateral flow immunoassay (LFIA). Glycinin is a complex allergen requiring extensive sample preparation. The presented workflow includes a manual denaturing extraction, followed by automated centrifugal microfluidic desalting, metering and detection via LFIA. The functionality of the microfluidic cassettes was tested on prototypes produced via microthermoforming before an injection molding tool was designed, which added a cylindrical lens to improve the readout. Overall, this system aims to aid in food allergen detection with high sensitivity and minimized manual steps.