Hasil untuk "Science (General)"

B. B. D. Mesquita

A. N. Leont’ev

Harvey Pinney

K. Otsuka, X. Ren

M. Marshall

Rolf FaÈ rea, Shawna Grosskopfb

V. Talanquer

Vincent Hue, Bilal Benmahi, Mathieu Barthelemy et al.

Pollux is a candidate European instrument contribution to the Habitable Worlds Observatory (HWO), designed to advance our understanding of the formation and evolution of cosmic structures in the universe, and specifically search signs of life on extrasolar planets. This high-resolution spectrograph (R\,$>$\,40,000) with polarimetric capabilities offers nearly continuous and simultaneous coverage from the FUV ($\sim$100\,nm) to the NIR ($\sim$1.9\,$\micron$), making it a versatile tool for a wide range of scientific investigations from solar system studies to cosmology. Several Solar System ocean worlds have been the focal point of the scientific community to understand the conditions of their internal saline oceans, as well as the possible emergence of life beyond Earth. The ocean world science case will leverage Pollux's UV spectropolarimetric capabilities to investigate surface reflectance and composition, characterize airglow emissions in the environments of giant-planet moons, as well as constrain the microphysical properties of atmospheric aerosols.

Xiaoming Zhai, James W. Pellegrino, Matias Rojas et al.

This chapter examines the potential of generative AI in enhancing science literacy across the K-16+ grade span, including its benefits as well as the conceptual and practical challenges that doing so presents. It begins with a discussion of what defines science literacy in the era of AI, including how AI has changed science and the demand for future citizens to be scientifically literate when AI is applied in their careers and lives. The chapter further discusses why science literacy presents such a challenge in K-16+ educational settings. It then develops an argument for the type of architecture needed for AI to assist in solving the problem by bringing coherence to the teaching, learning, and assessment of science knowledge and reasoning. Components of this architecture are illustrated with respect to the AI tools and capabilities needed for design and implementation. The chapter concludes with a consideration of what has been learned regarding both science literacy and AI, as well as what remains to be learned, including the research and development (R&D) needed, and the generalizability of this science literacy case to other disciplinary learning and knowledge domains.

David Gal, Derek D. Rucker

Pengqiang Yu, Kejia Wu, Dongsheng Li et al.

This paper introduces an analytical method for passive earth pressure calculation based on a rigorous stress field analysis within the sliding wedge. Unlike traditional horizontal layer methods, this approach directly solves for the stress state at any point within the wedge by analyzing the equilibrium of 2D differential soil elements under appropriate boundary conditions, eliminating the need for a priori assumptions about the soil arch shape. The method yields the passive earth pressure distribution on the retaining structure and derives the soil arch shape analytically from major principal stress trajectories. This derived arch shape differs notably from conventional circular or parabolic assumptions, especially at higher soil–wall friction angles. Parametric studies show that the passive earth pressure coefficient increases with internal friction angle and surcharge. However, a key finding is the non-monotonic dependence of the passive earth pressure coefficient on the soil–wall friction angle, contrasting with many existing theories. Comparisons show predictions by the proposed method align well with experimental data, particularly offering a better representation of pressure distributions in the lower regions of retaining walls compared to Coulomb theory and other existing methods.

Steve Dann Camargo Hinostroza, Carmen Andrea Taza Rojas, Diana Lizet Poma Limache and Camila Jimena Poma Romero

The objective of the research was to determine the water quality index of Lake Chinchaycocha, which has faced pollution problems for several years. To do this, we worked with data from ten water quality monitoring points collected by the National Water Authority (ANA) during the period 2019-2023, after which the water quality index (ICA-PE) was calculated by analyzing a total of 12 parameters, using the Water Quality Standard (ECA) for water category 4 E1 (lagoons and lakes). The results of the physicochemical parameters indicated that the values of total nitrogen exceed the limits established in the ECA in 82% of the data obtained, pH in 13%, and phosphorus in 1%. In the evaluation of inorganic parameters, data from the LChin1S monitoring point showed that lead and zinc levels exceeded the values established in the ECA by 8% and 3%, respectively. Regarding the ICA-PE of the dry and wet seasons, it was determined that both present a good quality according to their averages and with the results obtained from the ICA-PE in a general way, it is concluded that Lake Chinchaycocha has a good water quality having total nitrogen as the main pollutant.

Nawaf Alampara, Anagha Aneesh, Martiño Ríos-García et al.

Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy datasets that are difficult to leverage in conventional machine learning approaches. A new class of models, which can be summarized under the term general-purpose models (GPMs) such as large language models, has shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss fundamental building principles of GPMs and review recent and emerging applications of those models in the chemical sciences across the entire scientific process. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years.

Na Liu, Jakub Wiktor Both, Geir Ersland et al.

Understanding processes in porous media is fundamental to a broad spectrum of environmental, energy, and geoscience applications. These processes include multiphase fluid transport, interfacial dynamics, reactive transformations, and interactions with solids or microbial components, all governed by wettability, capillarity, and reactive transport at fluid-fluid and fluid-solid interfaces. Laboratory-based multiscale imaging provides critical insights into these phenomena, enabling direct visualization and quantitative characterization from the nanometer to meter scale. It is essential for advancing predictive models and optimizing the design of subsurface and engineered porous systems. This review presents an integrated overview of imaging techniques relevant to porous media research, emphasizing the type of information each method can provide, their applicability to porous media systems, and their inherent limitations. We highlight how imaging data are combined with quantitative analyses and modeling to bridge pore-scale mechanisms with continuum-scale behavior, and we critically discuss current challenges such as limited spatio-temporal resolution, sample representativity, and restricted data accessibility. We conduct an in-depth analysis on open-science trends in experimental and computational porous media research and find that, while open-access publishing has become widespread, the availability of imaging data and analysis code remains limited, often restricted to 'upon request'. Finally, we underscore the importance of open sharing of imaging datasets to enable reproducibility, foster cross-disciplinary integration, and support the development of robust predictive frameworks for porous media systems.

Shuyin Ouyang, Dong Huang, Jingwen Guo et al.

We introduce DSCodeBench, a new benchmark designed to evaluate large language models (LLMs) on complicated and realistic data science code generation tasks. DSCodeBench consists of 1,000 carefully constructed problems sourced from realistic problems from GitHub across ten widely used Python data science libraries. DSCodeBench offers a more challenging and representative testbed, more complex code solutions, more comprehensive data science libraries, clearer and better structured problem descriptions, and stronger test suites. To construct the DSCodeBench, we develop a robust pipeline that combines task scope selection, code construction, test case generation, and problem description synthesis. The process is paired with rigorous manual editing to ensure alignment and enhance the reliability of the evaluation. Experimental result shows that DSCodeBench exhibits robust scaling behavior, where larger models systematically outperform smaller ones, validating its ability to distinguish model capabilities. The best LLM we test, GPT-4o, has a pass@1 of 0.392, indicating that LLMs still have a large room to improve for realistic data science code generation tasks. We believe DSCodeBench will serve as a rigorous and trustworthy foundation for advancing LLM-based data science programming.

P. Tyson, R. Preston-Whyte

N. Li, C. J. Somes, A. Landolfi et al.

<p>Nitrogen (N) is a crucial limiting nutrient for phytoplankton growth in the ocean. The main source of bioavailable N in the ocean is delivered by <span class="inline-formula">N₂</span>-fixing diazotrophs in the surface layer. Since field observations of <span class="inline-formula">N₂</span> fixation are spatially and temporally sparse, the fundamental processes and mechanisms controlling <span class="inline-formula">N₂</span> fixation are not well understood and constrained. Here, we implement benthic denitrification in an Earth system model (ESM) of intermediate complexity (UVic ESCM 2.9) coupled to an optimality-based plankton–ecosystem model (OPEM v1.1). Benthic denitrification occurs mostly in coastal upwelling regions and on shallow continental shelves, and it is the largest N loss process in the global ocean. We calibrate our model against three different combinations of observed <span class="inline-formula">Chl</span>, <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msup><msub><mi mathvariant="normal">NO</mi><mn mathvariant="normal">3</mn></msub><mo>-</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="30pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="e60cf2b8b1907d178ba5f85379a9361c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-21-4361-2024-ie00001.svg" width="30pt" height="15pt" src="bg-21-4361-2024-ie00001.png"/></svg:svg></span></span>, <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msup><msub><mi mathvariant="normal">PO</mi><mn mathvariant="normal">4</mn></msub><mrow><mn mathvariant="normal">3</mn><mo>-</mo></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="34pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="206537f5a0814d9a6f6694e2075cae6a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-21-4361-2024-ie00002.svg" width="34pt" height="16pt" src="bg-21-4361-2024-ie00002.png"/></svg:svg></span></span>, <span class="inline-formula">O₂</span>, and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M10" display="inline" overflow="scroll" dspmath="mathml"><mrow><mrow class="chem"><mi mathvariant="normal">N</mi></mrow><mtext>*</mtext><mo>=</mo><mrow class="chem"><msup><msub><mi mathvariant="normal">NO</mi><mn mathvariant="normal">3</mn></msub><mo>-</mo></msup></mrow><mo>-</mo><mn mathvariant="normal">16</mn><mrow class="chem"><msup><msub><mi mathvariant="normal">PO</mi><mn mathvariant="normal">4</mn></msub><mrow><mn mathvariant="normal">3</mn><mo>-</mo></mrow></msup></mrow><mo>+</mo><mn mathvariant="normal">2.9</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="135pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="462507aa747533141f7ee5f6055c20f0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-21-4361-2024-ie00003.svg" width="135pt" height="16pt" src="bg-21-4361-2024-ie00003.png"/></svg:svg></span></span>. The inclusion of N* provides a powerful constraint on biogeochemical model behavior. Our new model version including benthic denitrification simulates higher global rates of <span class="inline-formula">N₂</span> fixation with a more realistic distribution extending to higher latitudes that are supported by independent estimates based on geochemical data. The volume and water-column denitrification rates of the oxygen-deficient zone (ODZ) are reduced in the new version, indicating that including benthic denitrification may improve global biogeochemical models that commonly overestimate anoxic zones. With the improved representation of the ocean N cycle, our new model configuration also yields better global net primary production (NPP) when compared to the independent datasets not included in the calibration. Benthic denitrification plays an important role shaping <span class="inline-formula">N₂</span> fixation and NPP throughout the global ocean in our model, and it should be considered when evaluating and predicting their response to environmental change.</p>

Iosif-Alin Beti, Paul-Corneliu Herghelegiu, Constantin-Florin Caruntu

The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles.

Shangzhe Sun, Chi Chen, Bisheng Yang et al.

The global rise in electricity demand necessitates extensive transmission infrastructure, where insulators play a critical role in ensuring the safe operation of power transmission systems. However, insulators are susceptible to burst defects, which can compromise system safety. To address this issue, we propose an insulator defect detection framework, ID-Det, which comprises two main components, i.e., the Insulator Segmentation Network (ISNet) and the Insulator Burst Detector (IBD). (1) ISNet incorporates a novel Insulator Clipping Module (ICM), enhancing insulator segmentation performance. (2) IBD leverages corner extraction methods and the periodic distribution characteristics of corners, facilitating the extraction of key corners on the insulator mask and accurate localization of burst defects. Additionally, we construct an Insulator Defect Dataset (ID Dataset) consisting of 1614 insulator images. Experiments on this dataset demonstrate that ID-Det achieves an accuracy of 97.38%, a precision of 97.38%, and a recall rate of 94.56%, outperforming general defect detection methods with a 4.33% increase in accuracy, a 5.26% increase in precision, and a 2.364% increase in recall. ISNet also shows a 27.2% improvement in Average Precision (AP) compared to the baseline. These results indicate that ID-Det has significant potential for practical application in power inspection.