There has never been a more pressing and opportune time for science and practice to collaborate towards restoration of the world's forests. Multiple uncertainties remain for achieving successful, long-term forest landscape restoration (FLR). In this article, we use expert knowledge and literature review to identify knowledge gaps that need closing to advance restoration practice, as an introduction to a landmark theme issue on FLR and the UN Decade on Ecosystem Restoration. Aligned with an Adaptive Management Cycle for FLR, we identify 15 essential science advances required to facilitate FLR success for nature and people. They highlight that the greatest science challenges lie in the conceptualization, planning and assessment stages of restoration, which require an evidence base for why, where and how to restore, at realistic scales. FLR and underlying sciences are complex, requiring spatially explicit approaches across disciplines and sectors, considering multiple objectives, drivers and trade-offs critical for decision-making and financing. The developing tropics are a priority region, where scientists must work with stakeholders across the Adaptive Management Cycle. Clearly communicated scientific evidence for action at the outset of restoration planning will enable donors, decision makers and implementers to develop informed objectives, realistic targets and processes for accountability. This article paves the way for 19 further articles in this theme issue, with author contributions from across the world. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.
Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.
The Yen–Mullins model, also known as the modified Yen model, specifies the predominant molecular and colloidal structure of asphaltenes in crude oils and laboratory solvents and consists of the following: The most probable asphaltene molecular weight is ∼750 g/mol, with the island molecular architecture dominant. At sufficient concentration, asphaltene molecules form nanoaggregates with an aggregation number less than 10. At higher concentrations, nanoaggregates form clusters again with small aggregation numbers. The Yen–Mullins model is consistent with numerous molecular and colloidal studies employing a broad array of methodologies. Moreover, the Yen–Mullins model provides a foundation for the development of the first asphaltene equation of state for predicting asphaltene gradients in oil reservoirs, the Flory–Huggins–Zuo equation of state (FHZ EoS). In turn, the FHZ EoS has proven applicability in oil reservoirs containing condensates, black oils, and heavy oils. While the development of the Yen–Mullins model was founded on a very large number of studies, it nevertheless remains essential to validate consistency of this model with important new data streams in asphaltene science. In this paper, we review recent advances in asphaltene science that address all critical aspects of the Yen–Mullins model, especially molecular architecture and characteristics of asphaltene nanoaggregates and clusters. Important new studies are shown to be consistent with the Yen–Mullins model. Wide ranging studies with direct interrogation of the Yen–Mullins model include detailed molecular decomposition analyses, optical measurements coupled with molecular orbital calculations, nuclear magnetic resonance (NMR) spectroscopy, centrifugation, direct-current (DC) conductivity, interfacial studies, small-angle neutron scattering (SANS), and small-angle X-ray scattering (SAXS), as well as oilfield studies. In all cases, the Yen–Mullins model is proven to be at least consistent if not valid. In addition, several studies previously viewed as potentially inconsistent with the Yen–Mullins model are now largely resolved. Moreover, oilfield studies using the Yen–Mullins model in the FHZ EoS are greatly improving the understanding of many reservoir concerns, such as reservoir connectivity, heavy oil gradients, tar mat formation, and disequilibrium. The simple yet powerful advances codified in the Yen–Mullins model especially with the FHZ EoS provide a framework for future studies in asphaltene science, petroleum science, and reservoir studies.
Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.
Abstract One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.
Attosecond science offers formidable tools for the investigation of electronic processes at the heart of important physical processes in atomic, molecular and solid-state physics. In the last 15 years impressive advances have been obtained from both the experimental and theoretical points of view. Attosecond pulses, in the form of isolated pulses or of trains of pulses, are now routinely available in various laboratories. In this review recent advances in attosecond science are reported and important applications are discussed. After a brief presentation of various techniques that can be employed for the generation and diagnosis of sub-femtosecond pulses, various applications are reported in atomic, molecular and condensed-matter physics.
Recent advances in the neurosciences offer a wealth of new information about how the brain works, and how the body and mind interact. These findings offer important and surprising implications for work in political science. Specifically, emotion exerts an impact on political decisions in decisive and significant ways. While its importance in political science has frequently been either dismissed or ignored in favor of theories that privilege rational reasoning, emotion can provide an alternate basis for explaining and predicting political choice and action. In this article, I posit a view of decision making that rests on an integrated notion of emotional rationality.Rose McDermott is the author of Risk Taking in International Relations (1998) and Political Psychology in International Relations (2004) and works largely in the areas of political psychology, experimentation, and American foreign policy. The author is grateful to Jennifer Hochschild, Robert Jervis, and Stephen Rosen for generous and constructive advice and encouragement; and to Gerald Clore, Jonathan Cowden, Thomas Kozachek, Jonathan Mercer, Joanne Miller, Philip Zimbardo, the members of the Political Psychology and Behavior Workshop at Harvard, and anonymous reviewers for useful guidance and suggestions.Passion is a sort of fever in the mind, which ever leaves us weaker than it found us.—William Penn, Fruits of Solitude (1693)We consider affective processing to be an evolutionary antecedent to more complex forms of information processing; but higher cognition requires the guidance provided by affective processing.—Ralph Adolphs and Antonio Damasio, “The Interaction of Affect and Cognition” (2001)
Dinosaur DNA may still be out of reach, but scientists are uncovering something almost as exciting—ancient blood vessels hidden inside fossilized bones。 In a massive Tyrannosaurus rex nicknamed Scotty, researchers discovered a network of preserved vessels within a rib that once fractured and began healing 66 million years ago。 Using powerful synchr
Over the last several decades, several factors have contributed to a major transformation in heat pipe science and technology applications. The first major contribution was the development and advances of new heat pipes, such as loop heat pipes (LHPs), micro and miniature heat pipes, and pulsating heat pipes (PHPs). In addition, there are now many commercial applications that have helped contribute to the recent interest in heat pipes. For example, several million heat pipes are manufactured each month for applications in CPU cooling and laptop computers. Numerical modeling, analysis, and experimental simulation of heat pipes have significantly progressed due to a much greater understanding of various physical phenomena in heat pipes as well as advances in computational and experimental methodologies. A review is presented hereafter concerning the types of heat pipes, heat pipe analysis, and simulations.
At WIRED Health, pioneering Alzheimer's researcher John Hardy outlined the stakes—and next steps—of where treatment is headed next
A key protein involved in fat metabolism has been found to do more than scientists once thought。 Instead of just releasing fat, it helps maintain healthy fat tissue and balance in the body。 When it’s missing or disrupted, the results can be surprisingly harmful
The goal of The Science of Prevention: Methodological Advances From Alcohol and Substance Abuse Research is to promote critical thinking among new and established investigators about how to design research and analyze research findings. Although the substantive focus of many chapters is on applications to the prevention of alcohol and substance abuse, nearly all of the methodological principles and statistical models are general and have potential application to the full range of areas in which prevention research takes place. The contributors to this book share their knowledge from an informed, applied perspective. Most are active researchers in the field of substance abuse prevention who are also methodological experts. They have a firsthand knowledge not only of the methodological, statistical, and measurement issues but also of the substantive issues of their field. [publisher description]
Abstract The Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission represents the National Aeronautics and Space Administration’s (NASA) next investment in satellite ocean color and the study of Earth’s ocean–atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth’s changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles, food-web processes, and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and air and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument is a spectrometer that spans the ultraviolet to shortwave-infrared wavelengths, with a ground sample distance of 1 km at nadir. This payload is complemented by two multiangle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022 to early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean–atmosphere–land system. Here, we present an overview of the PACE mission, including its developmental history, science objectives, instrument payload, observatory characteristics, and data products.
Advances in Pharmacological and Pharmaceutical Sciences publishes original research articles and review articles in all areas of experimental and clinical pharmacology, pharmaceutics, medicinal chemistry and drug delivery.
The topics addressed in this volume of a continuing series on the soil sciences are soil-water repellency, nutrient transformations in soils amended by green manures, and the physical fractionation of soil and organic matter in primary particle size and density separates.
Subjective well-being (SWB) is an extremely active area of research with about 170,000 articles and books published on the topic in the past 15 years. Methodological and theoretical advances have been notable in this period of time, with the increasing use of longitudinal and experimental designs allowing for a greater understanding of the predictors and outcomes that relate to SWB, along with the process that underlie these associations. In addition, theories about these processes have become more intricate, as findings reveal that many associations with SWB depend on people's culture and values and the context in which they live. This review provides an overview of many major areas of research, including the measurement of SWB, the demographic and personality-based predictors of SWB, and process-oriented accounts of individual differences in SWB. In addition, because a major new focus in recent years has been the development of national accounts of subjective well-being, we also review attempts to use SWB measures to guide policy decisions.
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In this book, leading methodologists address the issue of how effectively to apply the latest developments in social network analysis to behavioural and social science disciplines