Hasil untuk "Science"

Kazuhiko Takeuchi

Sustainability Science probes interactions between global, social, and human systems, the complex mechanisms that lead to degradation of these systems, and concomitant risks to human well-being. The journal provides a platform for building sustainability science as an evolving academic discipline which can point the way to a sustainable global society by facing challenges that existing disciplines have not addressed. These include endeavors to simultaneously understand phenomena and solve problems, uncertainty and application of the precautionary principle, the co-evolution of knowledge and recognition of problems, and trade-offs between global and local problem solving. The journal promotes science-based predictions and impact assessments of global change, and seeks ways to ensure that these can be understood and accepted by society. Sustainability Science creates a transdisciplinary academic structure and discovery process that fuses the natural sciences, social sciences, and humanities.

Colin Camerer, Anna Dreber, Felix Holzmeister et al.

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

Shifa Zhong, Kai Zhang, M. Bagheri et al.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

David Weintrop, Elham Beheshti, Michael S. Horn et al.

M. Brongersma, N. Halas, P. Nordlander

P. Veers, K. Dykes, E. Lantz et al.

A multifaceted future for wind power Modern wind turbines already represent a tightly optimized confluence of materials science and aerodynamic engineering. Veers et al. review the challenges and opportunities for further expanding this technology, with an emphasis on the need for interdisciplinary collaboration. They highlight the need to better understand atmospheric physics in the regions where taller turbines will operate as well as the materials constraints associated with the scale-up. The mutual interaction of turbine sites with one another and with the evolving features of the overall electricity grid will furthermore necessitate a systems approach to future development. Science, this issue p. eaau2027 BACKGROUND A growing global population and an increasing demand for energy services are expected to result in substantially greater deployment of clean energy sources. Wind energy is already playing a role as a mainstream source of electricity, driven by decades of scientific discovery and technology development. Additional research and exploration of design options are needed to drive innovation to meet future demand and functionality. The growing scale and deployment expansion will, however, push the technology into areas of both scientific and engineering uncertainty. This Review explores grand challenges in wind energy research that must be addressed to enable wind energy to supply one-third to one-half, or even more, of the world’s electricity needs. ADVANCES Drawing from a recent international workshop, we identify three grand challenges in wind energy research that require further progress from the scientific community: (i) improved understanding of the physics of atmospheric flow in the critical zone of wind power plant operation, (ii) materials and system dynamics of individual wind turbines, and (iii) optimization and control of fleets of wind plants comprising hundreds of individual generators working synergistically within the larger electric grid system. These grand challenges are interrelated, so progress in each domain must build on concurrent advances in the other two. Characterizing the wind power plant operating zone in the atmosphere will be essential to designing the next generation of even-larger wind turbines and achieving dynamic control of the machines. Enhanced forecasting of the nature of the atmospheric inflow will subsequently enable control of the plant in the manner necessary for grid support. These wind energy science challenges bridge previously separable geospatial and temporal scales that extend from the physics of the atmosphere to flexible aeroelastic and mechanical systems more than 200 m in diameter and, ultimately, to the electrical integration with and support for a continent-sized grid system. OUTLOOK Meeting the grand research challenges in wind energy science will enable the wind power plant of the future to supply many of the anticipated electricity system needs at a low cost. The interdependence of the grand challenges requires expansion of integrated and cross-disciplinary research efforts. Methods for handling and streamlining exchange of vast quantities of information across many disciplines (both experimental and computational) will also be crucial to enabling successful integrated research. Moreover, research in fields related to computational and data science will support the research community in seeking to further integrate models and data across scales and disciplines. The cascade of scales underlying wind energy scientific grand challenges. Length scales from weather systems at a global level down the boundary layer of a wind turbine airfoil and time scales from seasonal fluctuations in weather to subsecond dynamic control and balancing of electrical generation and demand must be understood and managed. ILLUSTRATION: JOSH BAUER AND BESIKI KAZAISHVILI, NREL Harvested by advanced technical systems honed over decades of research and development, wind energy has become a mainstream energy resource. However, continued innovation is needed to realize the potential of wind to serve the global demand for clean energy. Here, we outline three interdependent, cross-disciplinary grand challenges underpinning this research endeavor. The first is the need for a deeper understanding of the physics of atmospheric flow in the critical zone of plant operation. The second involves science and engineering of the largest dynamic, rotating machines in the world. The third encompasses optimization and control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs and perhaps beyond.

Ngss Lead States

D. Kahan, E. Peters, M. Wittlin et al.

Alice J. Sophia Fox, A. Bedi, S. Rodeo

Articular cartilage is the highly specialized connective tissue of diarthrodial joints. Its principal function is to provide a smooth, lubricated surface for articulation and to facilitate the transmission of loads with a low frictional coefficient (Figure 1). Articular cartilage is devoid of blood vessels, lymphatics, and nerves and is subject to a harsh biomechanical environment. Most important, articular cartilage has a limited capacity for intrinsic healing and repair. In this regard, the preservation and health of articular cartilage are paramount to joint health. Figure 1. Gross photograph of healthy articular cartilage in an adult human knee. Injury to articular cartilage is recognized as a cause of significant musculoskeletal morbidity. The unique and complex structure of articular cartilage makes treatment and repair or restoration of the defects challenging for the patient, the surgeon, and the physical therapist. The preservation of articular cartilage is highly dependent on maintaining its organized architecture.

R. Bonney, C. Cooper, J. Dickinson et al.

R. Duschl, H. Schweingruber, Andrew Shouse

J. Reynolds, D. Smith, E. Lambin et al.

B. Skinner

T. Cech, M. Waldrop

Avi Hofstein, Vincent N. Lunetta

S. Schultz

Dasgupta Partha, P. David

D. Dennett, F. Varela, E. Thompson et al.

A. Gennaro