459 research outputs found
Bibliometric analysis on the research of offshore wind power based on web of science
As renewable energy expands rapidly in installed capacity and in
built-over area, constructors and researchers are shifting their
sights from the lands to the seas. Offshore wind power (OWP), or
offshore wind farm, is a typical source of the renewable energy
constructed on the offshore islands or in the oceans. Since the
installed capacity of OWP has become booming since 2000, its
relevant researches also grow substantially. The objective of this
paper is to quantify the research works of OWP and to analyze
their focuses, main producers and high impact literature using
bibliometric method, where the OWP-related core literature in
recent 40 years are sorted out and a visualized analysis closely
concerned terms, contributors on national/regional basis, and
highly cited articles. The results show that researchers have been
largely followed on the grid-connection operations, the frameworks
and the ambient environment change of offshore wind
power. Moreover, the UK has taken the leading position on the
study of OWP at present
Building Relationships at The U School: Refining and Enhancing Possi Circles
The U School is a district high school in the Innovation Network of Philadelphia Schools dedicated to preparing students from low-income and underserved communities for college and career. One of the primary structures that has been developed to provide personalized support for students is its advisory system, Possi Circles, representing the Possibilities for students at the U School. These non-academic advisory/support groups of 10-15 students and one faculty leader form during freshman year and remain together for the duration of high school. Research supports the central importance of relationships in helping students, particularly at-risk youth like those at the U School, find success in school and develop lifelong capacities for well-being and achievement. This paper presents a set of recommendations, rooted in the science of positive psychology, for optimizing the form, content, and implementation of Possi Circles, including: a defined pathway to successful Possi Circles with a progression of measurable milestones, additional curriculum for year one Circles, and suggestions for successful implementation
Knowledge Graph Driven Recommendation System Algorithm
In this paper, we propose a novel graph neural network-based recommendation
model called KGLN, which leverages Knowledge Graph (KG) information to enhance
the accuracy and effectiveness of personalized recommendations. We first use a
single-layer neural network to merge individual node features in the graph, and
then adjust the aggregation weights of neighboring entities by incorporating
influence factors. The model evolves from a single layer to multiple layers
through iteration, enabling entities to access extensive multi-order associated
entity information. The final step involves integrating features of entities
and users to produce a recommendation score. The model performance was
evaluated by comparing its effects on various aggregation methods and influence
factors. In tests over the MovieLen-1M and Book-Crossing datasets, KGLN shows
an Area Under the ROC curve (AUC) improvement of 0.3% to 5.9% and 1.1% to 8.2%,
respectively, which is better than existing benchmark methods like LibFM,
DeepFM, Wide&Deep, and RippleNet
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Five-S-isotope evidence of two distinct mass-independent sulfur isotope effects and implications for the modern and Archean atmospheres.
The signature of mass-independent fractionation of quadruple sulfur stable isotopes (S-MIF) in Archean rocks, ice cores, and Martian meteorites provides a unique probe of the oxygen and sulfur cycles in the terrestrial and Martian paleoatmospheres. Its mechanistic origin, however, contains some uncertainties. Even for the modern atmosphere, the primary mechanism responsible for the S-MIF observed in nearly all tropospheric sulfates has not been identified. Here we present high-sensitivity measurements of a fifth sulfur isotope, stratospherically produced radiosulfur, along with all four stable sulfur isotopes in the same sulfate aerosols and a suite of chemical species to define sources and mechanisms on a field observational basis. The five-sulfur-isotope and multiple chemical species analysis approach provides strong evidence that S-MIF signatures in tropospheric sulfates are concomitantly affected by two distinct processes: an altitude-dependent positive 33S anomaly, likely linked to stratospheric SO2 photolysis, and a negative 36S anomaly mainly associated with combustion. Our quadruple stable sulfur isotopic measurements in varying coal samples (formed in the Carboniferous, Permian, and Triassic periods) and in SO2 emitted from combustion display normal 33S and 36S, indicating that the observed negative 36S anomalies originate from a previously unknown S-MIF mechanism during combustion (likely recombination reactions) instead of coal itself. The basic chemical physics of S-MIF in both photolytic and thermal reactions and their interplay, which were not explored together in the past, may be another ingredient for providing deeper understanding of the evolution of Earth's atmosphere and life's origin
A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture
Instance detection (InsDet) is a long-lasting problem in robotics and
computer vision, aiming to detect object instances (predefined by some visual
examples) in a cluttered scene. Despite its practical significance, its
advancement is overshadowed by Object Detection, which aims to detect objects
belonging to some predefined classes. One major reason is that current InsDet
datasets are too small in scale by today's standards. For example, the popular
InsDet dataset GMU (published in 2016) has only 23 instances, far less than
COCO (80 classes), a well-known object detection dataset published in 2014. We
are motivated to introduce a new InsDet dataset and protocol. First, we define
a realistic setup for InsDet: training data consists of multi-view instance
captures, along with diverse scene images allowing synthesizing training images
by pasting instance images on them with free box annotations. Second, we
release a real-world database, which contains multi-view capture of 100 object
instances, and high-resolution (6k x 8k) testing images. Third, we extensively
study baseline methods for InsDet on our dataset, analyze their performance and
suggest future work. Somewhat surprisingly, using the off-the-shelf
class-agnostic segmentation model (Segment Anything Model, SAM) and the
self-supervised feature representation DINOv2 performs the best, achieving >10
AP better than end-to-end trained InsDet models that repurpose object detectors
(e.g., FasterRCNN and RetinaNet).Comment: Accepted by NeurIPS 2023, Datasets and Benchmarks Trac
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