Hasil untuk "Science (General)"

Qiming Sun, Xing Zhang, S. Banerjee et al.

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.

Xiaoxiang Zhu, D. Tuia, Lichao Mou et al.

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.

David Adger

Syntax is the cognitive capacity of human beings that allows us to connect linguistic meaning with linguistic form. The study of syntax is a huge field that has generated a great deal of empirical and theoretical work over the decades. This article outlines why understanding our syntactic capacity is important to cognitive science in general and why the data of syntactic research is to be taken seriously. It then provides an overview of a number of broad findings about the character of the syntax of human language, including evidence for abstract constituent structure, core properties of constituents, the importance of functional categories, the link between syntactic structure and meaning, and the range of types of syntactic dependencies, including dependencies of form, dependencies of position, and dependencies that create new meanings. WIREs Cogn Sci 2015, 6:131–147. doi: 10.1002/wcs.1332

J. Busemeyer, P. Bruza

Quantum Models of Cognition and Decision, Second Edition presents a fully updated and expanded version of this innovative and path-breaking text. It offers an accessible introduction to the intersection of quantum theory and cognitive science, covering new insights, modelling techniques, and applications for understanding human cognition and decision making. In it, Busemeyer and Bruza delve into such topics as the non-commutative nature of judgments, quantum interference as a general principle governing human decision making, contextuality in modelling human cognition, and thought-provoking speculation about what a quantum approach to cognition might reveal about the ultimate nature of the human mind. Additions include new material on measurement, open systems, and applications to computer science. Requiring no prior background in quantum physics, this book comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

F. Witte

M. Jamshidian, E. Tehrany, Muhammad Imran et al.

J. Frieman, M. Turner, D. Huterer

Ten years ago, the discovery that the expansion of the universe is accelerating put in place the last major building block of the present cosmological model, in which the universe is composed of 4% baryons, 20% dark matter, and 76% dark energy. At the same time, it posed one of the most profound mysteries in all of science, with deep connections to both astrophysics and particle physics. Cosmic acceleration could arise from the repulsive gravity of dark energy—for example, the quantum energy of the vacuum—or it may signal that general relativity (GR) breaks down on cosmological scales and must be replaced. We review the present observational evidence for cosmic acceleration and what it has revealed about dark energy, discuss the various theoretical ideas that have been proposed to explain acceleration, and describe the key observational probes that will shed light on this enigma in the coming years.

B. Domon, R. Aebersold

D. Svergun, L. Feigin, G. Taylor

R. Little, D. Rubin

R. Ghanea-Hercock

P. Checkland

A. Savin, R. Nesper, S. Wengert et al.

F. Schreiber

J. Sanders, Edward Kandrot

A. Gambardella, A. McGahan

H. Riesch, C. Potter

Nghia Nhu Nguyen, Bao The Nguyen, Huyen Thi Ngoc Le et al.

Background Liver fibrosis is a significant health burden in Vietnamese male adults, driven by high rates of hepatitis B and hepatitis C, excessive alcohol consumption, and genetic and environmental factors. Despite progress in diagnostic tools, there is a pressing need for cost-effective screening methods tailored to this high-risk group, particularly in resource-limited settings. Methods This study enrolled 952 Vietnamese male adults over 40 years old undergoing FibroScan, excluding those with conditions affecting test accuracy. Data on demographics, clinical history, and anthropometrics were collected, and fibrosis stages were classified using the METAVIR system. Model development combined Bayesian model averaging and forward stepwise methods, with predictive performance validated via receiver operating characteristic (ROC) analysis and area under the curve (AUC) estimation in the R environment. Results Among 952 male participants, the prevalence of liver fibrosis was 19.9%, with most cases classified as mild (F1). Multivariate analysis identified significant risk factors, including advanced age (odds ratio (OR) = 1.6; 95% confidence interval (CI) [1.02–2.51]), alcohol abuse (OR = 4.44; 95% CI [2.65–7.42]), hepatitis B (OR = 6.76; 95% CI [3.14–14.54], hepatitis C (OR = 33.04; 95% CI [5.26–207.42]), family history of cirrhosis (OR = 16.14; 95% CI [3.28–79.55]), and hepatic steatosis (OR = 4.02; 95% CI [2.57–6.28]). The predictive model demonstrated good discriminative performance with an AUC of 0.769 (95% CI [0.734–0.800]) and showed satisfactory calibration through bootstrap resampling, indicating close agreement between predicted and observed risks. Conclusion The current prevalence of liver fibrosis among Vietnamese male adults was found to be 19.9%, and the developed risk prediction model effectively identifies high-risk individuals, enabling early diagnosis and targeted prevention, particularly in resource-limited settings. However, the lack of external validation and the sample restricted to Vietnamese male adults limit the generalizability of the model, which should be further evaluated in other populations.

Meryem Mojtahid, Magali Schweizer, Damien Le Moigne et al.

This paper presents the results of a dual approach for assessing fossil benthic foraminiferal communities using both traditional morphology‐based analyses and sedimentary ancient DNA (sedaDNA) metabarcoding. The main objectives are to test the feasibility of sedaDNA analyses to assess foraminiferal biodiversity in temperate shelf sediments (Le Croisic, France) off a major river system through time (Mid‐ to Late Holocene), and to point out the similarities and differences between classical and molecular methods. Our results show that, in contrast to the high foraminiferal diversity obtained from classic morphological analysis (over 140 taxa), the sedaDNA analysis yielded only 20 operational taxonomic units (OTUs), which can be considered as equivalent to species. This strongly suggests a bad preservation of foraminiferal DNA downcore, likely due to the relatively ‘high’ temperature of the study site (14 °C) and/or to a methodological bias (e.g. insufficient amount of extracted sediment). In the total sedaDNA, more than 90% of the reads were assigned to monothalamids (organic‐shelled foraminifera). In contrast, only a small number of mineralized taxa, highly dominant when identified using the morphological approach, were detected. This could be due to the naturally higher abundance of monothalamids compared to hard‐shelled foraminifera. While this abundance is reflected in sedaDNA, it is not preserved in fossil morphological assemblages. In addition, the sedaDNA of monothalamids might be easier to extract and their barcode to amplify than hard‐shelled foraminifera. The discrepancies between the microfossil data and sedaDNA also include several species (e.g. Ammonia confertitesta (T6), Elphidium oceanense (S3), Nonionella sp. T4 and Nonionella sp. T6) that were rarely or not at all found in the fossil material which might be an indication of the presence of propagules, morphologically undetected in the >63 μm size fraction used. Finally, the presence of sequences of A. confertitesta and fossil specimens in the deep layers of the study cores suggests that this species, considered until now as recently invasive on the European coast, could have been present in the Atlantic coast several thousand years ago, much before any anthropogenic activity involving international shipping and commercial trades.

Russell Beale

Generative AI tools - most notably large language models (LLMs) like ChatGPT and Codex - are rapidly revolutionizing computer science education. These tools can generate, debug, and explain code, thereby transforming the landscape of programming instruction. This paper examines the profound opportunities that AI offers for enhancing computer science education in general, from coding assistance to fostering innovative pedagogical practices and streamlining assessments. At the same time, it highlights challenges including academic integrity concerns, the risk of over-reliance on AI, and difficulties in verifying originality. We discuss what computer science educators should teach in the AI era, how to best integrate these technologies into curricula, and the best practices for assessing student learning in an environment where AI can generate code, prototypes and user feedback. Finally, we propose a set of policy recommendations designed to harness the potential of generative AI while preserving the integrity and rigour of computer science education. Empirical data and emerging studies are used throughout to support our arguments.