Hasil untuk "Science (General)"

J. Gurevitch, J. Koricheva, Shinichi Nakagawa et al.

C. Nesshöver, T. Assmuth, K. Irvine et al.

In this paper, we reflect on the implications for science, policy and practice of the recently introduced concept of Nature-Based Solutions (NBS), with a focus on the European context. First, we analyse NBS in relation to similar concepts, and reflect on its relationship to sustainability as an overarching framework. From this, we derive a set of questions to be addressed and propose a general framework for how these might be addressed in NBS projects by funders, researchers, policy-makers and practitioners. We conclude that: To realise their full potential, NBS must be developed by including the experience of all relevant stakeholders such that 'solutions' contribute to achieving all dimensions of sustainability. As NBS are developed, we must also moderate the expectations placed on them since the precedent provided by other initiatives whose aim was to manage nature sustainably demonstrates that we should not expect NBS to be cheap and easy, at least not in the short-term.

C. Wannous, German Velásquez

The Sendai Framework for Disaster Risk Reduction 2015–2030 was agreed at the Third UN World Conference on Disaster Risk Reduction in Sendai, Japan in March 2015 and endorsed by the UN General Assembly in June 2015. The goal of the Sendai Framework is to prevent new and reduce existing disaster risk. UNISDR coordinates and ensures synergies among the disaster reduction activities of the United Nations system and regional organizations and stakeholders The role of science and technology in providing the evidence and knowledge on risk features prominently in the Sendai Framework. Expanding the interface between science, technology and policy is therefore essential for effective disaster risk reduction. In January 2016, UNISDR hosted the Science and Technology Conference on the Implementation of the Sendai Framework. The main outcome of the conference was the launching of the Science and Technology Partnership and the endorsement of the science and technology roadmap that outlines expected outcomes, actions, and deliverables under each of the four priority actions of the Sendai Framework. Over the last twenty years, the majority of disasters have been caused by floods, storms, heatwaves and other weather-related events. Most of these disasters can cause landslides, which in turn cause hundreds of billions of dollars in damage and hundreds of thousands of deaths and injuries each year The International Consortium on Landslides (ICL) 2015–2025 and The Sendai Partnerships promotes global understanding and reduction of landslide disaster risk. They will contribute significantly to the implementation of the science and technology roadmap by providing practical solutions and tools, education and capacity building, and communication and public outreach to reduce landslides risks. UNISDR fully supports the work of the Sendai Partnerships and the community of practice on landslides risks

N. Brewer, G. Chapman, Alexander J. Rothman et al.

K. Abazajian, Peter Adshead, Z. Ahmed et al.

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales.

B. Husic, V. Pande

Mostafa Salari Rad, Alison Jane Martingano, Jeremy Ginges

Two primary goals of psychological science should be to understand what aspects of human psychology are universal and the way that context and culture produce variability. This requires that we take into account the importance of culture and context in the way that we write our papers and in the types of populations that we sample. However, most research published in our leading journals has relied on sampling WEIRD (Western, educated, industrialized, rich, and democratic) populations. One might expect that our scholarly work and editorial choices would by now reflect the knowledge that Western populations may not be representative of humans generally with respect to any given psychological phenomenon. However, as we show here, almost all research published by one of our leading journals, Psychological Science, relies on Western samples and uses these data in an unreflective way to make inferences about humans in general. To take us forward, we offer a set of concrete proposals for authors, journal editors, and reviewers that may lead to a psychological science that is more representative of the human condition.

P. Pintrich

M. S. Morris, K. Thorne

J. Merigó, J. Merigó, Jianbo Yang

Valentin J. M. Manès, HyungSeok Han, Choongwoo Han et al.

Among the many software testing techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.

M. Alloghani, D. Al-Jumeily, Jamila Mustafina et al.

Tian Qin, Josep Cornella, Chao Li et al.

A. Bartók, J. Kermode, N. Bernstein et al.

The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length and time scales due to computational cost and its scaling. Techniques based on machine learning ideas for interpolating the Born-Oppenheimer potential energy surface without explicitly describing electrons have recently shown great promise, but accurately and efficiently fitting the physically relevant space of configurations has remained a challenging goal. Here we present a Gaussian Approximation Potential for silicon that achieves this milestone, accurately reproducing density functional theory reference results for a wide range of observable properties, including crystal, liquid, and amorphous bulk phases, as well as point, line, and plane defects. We demonstrate that this new potential enables calculations that would be extremely expensive with a first principles electronic structure method, such as finite temperature phase boundary lines, self-diffusivity in the liquid, formation of the amorphous by slow quench, and dynamic brittle fracture. We show that the uncertainty quantification inherent to the Gaussian process regression framework gives a qualitative estimate of the potential's accuracy for a given atomic configuration. The success of this model shows that it is indeed possible to create a useful machine-learning-based interatomic potential that comprehensively describes a material, and serves as a template for the development of such models in the future.

S. Franconeri, Lace M. K. Padilla, P. Shah et al.

Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.

F. Giorgi, Carmine Ceraolo, D. Mercatelli

The R programming language is approaching its 30th birthday, and in the last three decades it has achieved a prominent role in statistics, bioinformatics, and data science in general. It currently ranks among the top 10 most popular languages worldwide, and its community has produced tens of thousands of extensions and packages, with scopes ranging from machine learning to transcriptome data analysis. In this review, we provide an historical chronicle of how R became what it is today, describing all its current features and capabilities. We also illustrate the major tools of R, such as the current R editors and integrated development environments (IDEs), the R Shiny web server, the R methods for machine learning, and its relationship with other programming languages. We also discuss the role of R in science in general as a driver for reproducibility. Overall, we hope to provide both a complete snapshot of R today and a practical compendium of the major features and applications of this programming language.

F. Bergaya, G. Lagaly

Yunhan Zhang, Ying Zhang, Jiaoyan Song et al.

Abstract Piezo1 plays a key role in the immune response during sepsis. To date, our understanding of the role of Piezo1 in inflammatory diseases has mostly been limited to influencing vasomotor function and regulating inflammatory infiltration. Whether and how Piezo1 in macrophages is involved in developing septic cardiac dysfunction has never been explored. Here, we have successfully established a mouse model with myeloid cell-specific knockdown of Piezo1. The intraperitoneal injection of lipopolysaccharide (LPS) resulted in a significant increase in cardiac macrophage infiltration, as well as an increase in the expression of inflammatory factors and the inflammatory response. However, myeloid cell-specific knockdown of Piezo1 impaired this response, leading to an increase in macrophage polarization towards the M2 type and the decreased inflammatory response. As a result, myocardial injury caused by sepsis was attenuated. We have also demonstrated that the PI3K/AKT pathway is significantly activated after Piezo1 knockdown, resulting in reduced myocardial dysfunction. Our data indicate that myeloid cell-specific knockdown of Piezo1 can influence macrophage polarization and thus exert cardioprotective effects in a murine model of sepsis, providing potential ideas and targets for the treatment of infectious cardiac dysfunction.

Yong Wang, Hongwen He, Yuankai Wu et al.

An effective energy management strategy (EMS) is essential to optimize the energy efficiency of electric vehicles (EVs). With the advent of advanced machine learning techniques, the focus on developing sophisticated EMS for EVs is increasing. Here, we introduce LearningEMS: a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS. LearningEMS is distinguished by its ability to support a variety of EV configurations, including hybrid EVs, fuel cell EVs, and plug-in EVs, offering a general platform for the development of EMS. The framework enables detailed comparisons of several EMS algorithms, encompassing imitation learning, deep reinforcement learning (RL), offline RL, model predictive control, and dynamic programming. We rigorously evaluated these algorithms across multiple perspectives: energy efficiency, consistency, adaptability, and practicability. Furthermore, we discuss state, reward, and action settings for RL in EV energy management, introduce a policy extraction and reconstruction method for learning-based EMS deployment, and conduct hardware-in-the-loop experiments. In summary, we offer a unified and comprehensive framework that comes with three distinct EV platforms, over 10  000 km of EMS policy data set, ten state-of-the-art algorithms, and over 160 benchmark tasks, along with three learning libraries. Its flexible design allows easy expansion for additional tasks and applications. The open-source algorithms, models, data sets, and deployment processes foster additional research and innovation in EV and broader engineering domains.

Daniel Apai, Rory Barnes, Matthew M. Murphy et al.

The search for extraterrestrial life in the Solar System and beyond is a key science driver in astrobiology, planetary science, and astrophysics. A critical step is the identification and characterization of potential habitats, both to guide the search and to interpret its results. However, a well-accepted, self-consistent, flexible, and quantitative terminology and method of assessment of habitability are lacking. Our paper fills this gap based on a three year-long study by the NExSS Quantitative Habitability Science Working Group. We reviewed past studies of habitability, but find that the lack of a universally valid definition of life prohibits a universally applicable definition of habitability. A more nuanced approach is needed. We introduce a quantitative habitability assessment framework (QHF) that enables self-consistent, probabilistic assessment of the compatibility of two models: First, a habitat model, which describes the probability distributions of key conditions in the habitat. Second, a viability model, which describes the probability that a metabolism is viable given a set of environmental conditions. We provide an open-source implementation of this framework and four examples as a proof of concept: (a) Comparison of two exoplanets for observational target prioritization; (b) Interpretation of atmospheric O2 detection in two exoplanets; (c) Subsurface habitability of Mars; and (d) Ocean habitability in Europa. These examples demonstrate that our framework can self-consistently inform astrobiology research over a broad range of questions. The proposed framework is modular so that future work can expand the range and complexity of models available, both for habitats and for metabolisms.