1,177 research outputs found
Nursing-sensitive indicators: A concept analysis
AIM: To report a concept analysis of nursing-sensitive indicators within the applied context of the acute care setting. BACKGROUND: The concept of ānursing sensitive indicatorsā is valuable to elaborate nursing care performance. The conceptual foundation, theoretical role, meaning, use and interpretation of the concept tend to differ. The elusiveness of the concept and the ambiguity of its attributes may have hindered research efforts to advance its application in practice. DESIGN: Concept analysis. DATA SOURCES: Using āclinical indicatorsā or āquality of nursing careā as subject headings and incorporating keyword combinations of āacute careā and ānurs*ā, CINAHL and MEDLINE with full text in EBSCOhost databases were searched for English language journal articles published between 2000ā2012. Only primary research articles were selected. METHODS: A hybrid approach was undertaken, incorporating traditional strategies as per Walker and Avant and a conceptual matrix based on Holzemer's Outcomes Model for Health Care Research. RESULTS: The analysis revealed two main attributes of nursing-sensitive indicators. Structural attributes related to health service operation included: hours of nursing care per patient day, nurse staffing. Outcome attributes related to patient care included: the prevalence of pressure ulcer, falls and falls with injury, nosocomial selective infection and patient/family satisfaction with nursing care. CONCLUSION: This concept analysis may be used as a basis to advance understandings of the theoretical structures that underpin both research and practical application of quality dimensions of nursing care performance
Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling
Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and time-of-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77% and 6.64% for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed
Interaction of a symmetrical Ī±,Ī±',Ī“,Ī“'-Tetramethyl-cucurbit[6]uril with LnĀ³āŗ : potential applications for isolation of lanthanides
The interaction of a symmetrical Ī±,Ī±ā²,Ī“,Ī“ā²-tetramethyl-cucurbit[6]uril (TMeQ[6]) with a series of lanthanide cations (LnĀ³āŗ) was investigated in neutral water and in acidic solution. Analysis by single crystal X-ray diffraction revealed that different isomorphous families formed under different synthetic conditions. Such differences in the interaction between TMeQ[6] and LnĀ³āŗ could potentially be used for isolating heavier LnĀ³āŗ from their lighter counterparts in neutral solution, and lighter lanthanide cations from their heavier counterparts in acidic solution
Federated Conformal Predictors for Distributed Uncertainty Quantification
Conformal prediction is emerging as a popular paradigm for providing rigorous
uncertainty quantification in machine learning since it can be easily applied
as a post-processing step to already trained models.
In this paper, we extend conformal prediction to the federated learning
setting.
The main challenge we face is data heterogeneity across the clients -- this
violates the fundamental tenet of \emph{exchangeability} required for conformal
prediction.
We propose a weaker notion of \emph{partial exchangeability}, better suited
to the FL setting, and use it to develop the Federated Conformal Prediction
(FCP) framework.
We show FCP enjoys rigorous theoretical guarantees and excellent empirical
performance on several computer vision and medical imaging datasets.
Our results demonstrate a practical approach to incorporating meaningful
uncertainty quantification in distributed and heterogeneous environments.
We provide code used in our experiments
\url{https://github.com/clu5/federated-conformal}.Comment: 23 pages, 18 figures, accepted to International Conference on Machine
Learning (ICML) 202
Nanotube Piezoelectricity
We combine ab initio, tight-binding methods and analytical theory to study
piezoelectric effect of boron nitride nanotubes. We find that piezoelectricity
of a heteropolar nanotube depends on its chirality and diameter and can be
understood starting from the piezoelectric response of an isolated planar
sheet, along with a structure specific mapping from the sheet onto the tube
surface. We demonstrate that coupling between the uniaxial and shear
deformation are only allowed in the nanotubes with lower chiral symmetry. Our
study shows that piezoelectricity of nanotubes is fundamentally different from
its counterpart in three dimensional (3D) bulk materials.Comment: 4 pages, with 3 postscript figures embedded. Uses REVTEX4 macros.
Also available at
http://www.physics.upenn.edu/~nsai/preprints/bn_piezo/index.htm
ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval
The huge domain gap between sketches and photos and the highly abstract
sketch representations pose challenges for sketch-based image retrieval
(\underline{SBIR}). The zero-shot sketch-based image retrieval
(\underline{ZS-SBIR}) is more generic and practical but poses an even greater
challenge because of the additional knowledge gap between the seen and unseen
categories. To simultaneously mitigate both gaps, we propose an
\textbf{A}pproaching-and-\textbf{C}entralizing \textbf{Net}work (termed
"\textbf{ACNet}") to jointly optimize sketch-to-photo synthesis and the image
retrieval. The retrieval module guides the synthesis module to generate large
amounts of diverse photo-like images which gradually approach the photo domain,
and thus better serve the retrieval module than ever to learn domain-agnostic
representations and category-agnostic common knowledge for generalizing to
unseen categories. These diverse images generated with retrieval guidance can
effectively alleviate the overfitting problem troubling concrete
category-specific training samples with high gradients. We also discover the
use of proxy-based NormSoftmax loss is effective in the zero-shot setting
because its centralizing effect can stabilize our joint training and promote
the generalization ability to unseen categories. Our approach is simple yet
effective, which achieves state-of-the-art performance on two widely used
ZS-SBIR datasets and surpasses previous methods by a large margin.Comment: the paper is under consideration at IEEE Transactions on Circuits and
Systems for Video Technolog
CT-BERT: Learning Better Tabular Representations Through Cross-Table Pre-training
Tabular data -- also known as structured data -- is one of the most common
data forms in existence, thanks to the stable development and scaled deployment
of database systems in the last few decades. At present however, despite the
blast brought by large pre-trained models in other domains such as ChatGPT or
SAM, how can we extract common knowledge across tables at a scale that may
eventually lead to generalizable representation for tabular data remains a full
blank. Indeed, there have been a few works around this topic. Most (if not all)
of them are limited in the scope of a single table or fixed form of a schema.
In this work, we first identify the crucial research challenges behind tabular
data pre-training, particularly towards the cross-table scenario. We position
the contribution of this work in two folds: (i)-we collect and curate nearly 2k
high-quality tabular datasets, each of which is guaranteed to possess clear
semantics, clean labels, and other necessary meta information. (ii)-we propose
a novel framework that allows cross-table pre-training dubbed as CT-BERT.
Noticeably, in light of pioneering the scaled cross-table training, CT-BERT is
fully compatible with both supervised and self-supervised schemes, where the
specific instantiation of CT-BERT is very much dependent on the downstream
tasks. We further propose and implement a contrastive-learning-based and masked
table modeling (MTM) objective into CT-BERT, that is inspired from computer
vision and natural language processing communities but sophistically tailored
to tables. The extensive empirical results on 15 datasets demonstrate CT-BERT's
state-of-the-art performance, where both its supervised and self-supervised
setups significantly outperform the prior approaches
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