Hasil untuk "Science (General)"

Jianguo Wu

N. L. Poff, J. Zimmerman

M. Young, K. Brookhuis, C. Wickens et al.

S. Carpenter, H. Mooney, J. Agard et al.

David Pisinger, S. Røpke

We present a unified heuristic which is able to solve five different variants of the vehicle routing problem: the vehicle routing problem with time windows (VRPTW), the capacitated vehicle routing problem (CVRP), the multi-depot vehicle routing problem (MDVRP), the site-dependent vehicle routing problem (SDVRP) and the open vehicle routing problem (OVRP). All problem variants are transformed into a rich pickup and delivery model and solved using the adaptive large neighborhood search (ALNS) framework presented in Ropke and Pisinger [An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, to appear]. The ALNS framework is an extension of the large neighborhood search framework by Shaw [Using constraint programming and local search methods to solve vehicle routing problems. In: CP-98, Fourth international conference on principles and practice of constraint programming, Lecture notes in computer science, vol. 1520, 1998. p. 417-31] with an adaptive layer. This layer adaptively chooses among a number of insertion and removal heuristics to intensify and diversify the search. The presented approach has a number of advantages: it provides solutions of very high quality, the algorithm is robust, and to some extent self-calibrating. Moreover, the unified model allows the dispatcher to mix various variants of VRP problems for individual customers or vehicles. As we believe that the ALNS framework can be applied to a large number of tightly constrained optimization problems, a general description of the framework is given, and it is discussed how the various components can be designed in a particular setting. The paper is concluded with a computational study, in which the five different variants of the vehicle routing problem are considered on standard benchmark tests from the literature. The outcome of the tests is promising as the algorithm is able to improve 183 best known solutions out of 486 benchmark tests. The heuristic has also shown promising results for a large class of vehicle routing problems with backhauls as demonstrated in Ropke and Pisinger [A unified heuristic for a large class of vehicle routing problems with backhauls. European Journal of Operational Research, 2004, to appear].

Xun Wang, Zhu Jing, Q. Peng et al.

D. Satcher

Gary King, James Honaker, Anne Joseph O'Connell et al.

D. McAdams, J. L. Pals

D. MacKenzie, J. Andrew Royle

A. Kulkarni, B. Aziz, I. Shams et al.

E. Esser, Xiaoqun Zhang, T. Chan

J. Humphreys, R. Herbert

Abstract Marine protected areas (MPAs) generate powerful interactions between social, economic and environmental interests, manifest at a circumscribed and often local scale. Consequently the designation and management of an individual MPA typically plays out in microcosm the general challenge of sustainable development in the marine environment. Some universally relevant questions relating to four commonly held defining attributes of MPAs are articulated. However, while many of the questions are universal, in practice the answers vary greatly. Consequently there are few MPAs which would not provide an informative case study elucidating the dynamics at the intersection between science, policy and management in the marine realm. The papers in this collection exemplify a range of key issues across this spectrum of disciplines. In practice most contentious issues relate to the balance within MPAs between environmental and socio-economic considerations, not least relating to fishing. In this respect greater attention in MPA management plans, to the economic benefits of MPAs for local communities is encouraged. However we also recognise that glib assertions that a secure sustainable balance between conservation and exploitation can be established in practice, typically with few resources in a largely unseen and often data-poor environment, may sometimes be politically expedient but scientifically questionable. Yet it is ultimately the work of all those involved directly with MPAs to collectively achieve the task of transforming the rhetoric of marine conservation policy into a successful reality on the ground and we commend the authors of this collection for their efforts to achieve that goal.

Caitlin Drummond, Baruch Fischhoff

L. Maier-Hein, M. Eisenmann, Duygu Sarikaya et al.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

V. Ryabinin, J. Barbière, P. Haugan et al.

Our civilization needs a clean, resilient, productive, safe, well-observed, documented and predicted ocean. “The ocean we need for the future we want” was the motto of the IOC proposal to the United Nations to consider the merit of an Ocean Science Decade. By proclaiming the Decade, the UN General Assembly offered the oceanographic community a unique, once in a life-time, opportunity to change the way we do things, make oceanography fit for purpose of effectively supporting sustainable development, and energize the ocean sciences for future generations. The Decade is the chance to put in place a more complete and sustainable observing system and feed the resulting data into a science-based informed decision-making system allowing increased reliance of our civilization on the ocean, its ecosystem services and, at the same time, preserving ocean health. Strong and proactive engagement of the oceanographic community in the design of the Decade and its observing component and subsequent energetic implementation of the ideas are sought. Participants in OceanObs’19 are invited to consider the additional possibilities and requirements associated with the Decade in their contributions to and brainstorming at the Conference. It is essential to use collective wisdom of OceanObs’19 to help developing an ambitious and also realistic implementation plan for the Decade, with a strong observational component.

D. Katz, M. Bommarito, J. Blackman

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.

H. Burgess, Lauren B. Debey, H. Froehlich et al.

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz et al.

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Anna Heffernan

LISA, the Laser Interferometer Space Antenna, due to launch mid-2035, is a large class space mission by the European Space Agency (ESA). In partnership with NASA and ESA-member states, ESA is on track to launch what is expected to be the first space-based gravitational wave detector. By hosting detectors in space, one gains access to a lower frequency band of gravitational wave sources and with them, a plethora of new science. To maximise this scientific gain, ESA and NASA selected 20 scientists for the LISA Science Team, to carry out and/or lead necessary actions on the run up to LISA launch. We give a short overview and update of the LISA mission, some of its science objectives and related waveforms, as well as the work of the LISA Science Team as of December 2025.