83 research outputs found
Application and Research Progress of Heat Pipe in Thermal Management of Lithium-Ion Battery
Lithium-ion batteries have the advantages of high energy density, high average output voltage, long service life, and environmental protection, and are widely used in the power system of new energy vehicles. However, during the working process of the battery, the working temperature is too high or too low, which will affect the charging and discharging performance, battery capacity and battery safety. As a result, a battery thermal management system (BTMS) is essential to maintain the proper ambient temperature of the working battery. Thermal management of power batteries is a key technology to ensure maximum battery safety and efficiency. This paper discusses the significance of thermal management technology in the development of new energy vehicles, introduces the main technical means of thermal management of lithium-ion batteries for vehicle, and focuses on the current state of research on the use of various types of heat pipes in lithium-ion batteries. Finally, the use of heat pipes in the thermal control of lithium-ion batteries is promising.Citation:Ā Ning, Y., Tao, R., Luo, J., and Hu, Q. (2022). Application and Research Progress of Heat Pipe in Thermal Management of Lithium-Ion Battery. Trends in Renewable Energy, 8, 130-144. DOI: 10.17737/tre.2022.8.2.0014
A roadmap to fair and trustworthy prediction model validation in healthcare
A prediction model is most useful if it generalizes beyond the development
data with external validations, but to what extent should it generalize remains
unclear. In practice, prediction models are externally validated using data
from very different settings, including populations from other health systems
or countries, with predictably poor results. This may not be a fair reflection
of the performance of the model which was designed for a specific target
population or setting, and may be stretching the expected model
generalizability. To address this, we suggest to externally validate a model
using new data from the target population to ensure clear implications of
validation performance on model reliability, whereas model generalizability to
broader settings should be carefully investigated during model development
instead of explored post-hoc. Based on this perspective, we propose a roadmap
that facilitates the development and application of reliable, fair, and
trustworthy artificial intelligence prediction models.Comment: 12 pages, 2 figure
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
Survival analysis is essential for studying time-to-event outcomes and
providing a dynamic understanding of the probability of an event occurring over
time. Various survival analysis techniques, from traditional statistical models
to state-of-the-art machine learning algorithms, support healthcare
intervention and policy decisions. However, there remains ongoing discussion
about their comparative performance. We conducted a comparative study of
several survival analysis methods, including Cox proportional hazards (CoxPH),
stepwise CoxPH, elastic net penalized Cox model, Random Survival Forests (RSF),
Gradient Boosting machine (GBM) learning, AutoScore-Survival, DeepSurv,
time-dependent Cox model based on neural network (CoxTime), and DeepHit
survival neural network. We applied the concordance index (C-index) for model
goodness-of-fit, and integral Brier scores (IBS) for calibration, and
considered the model interpretability. As a case study, we performed a
retrospective analysis of patients admitted through the emergency department of
a tertiary hospital from 2017 to 2019, predicting 90-day all-cause mortality
based on patient demographics, clinicopathological features, and historical
data. The results of the C-index indicate that deep learning achieved
comparable performance, with DeepSurv producing the best discrimination
(DeepSurv: 0.893; CoxTime: 0.892; DeepHit: 0.891). The calibration of DeepSurv
(IBS: 0.041) performed the best, followed by RSF (IBS: 0.042) and GBM (IBS:
0.0421), all using the full variables. Moreover, AutoScore-Survival, using a
minimal variable subset, is easy to interpret, and can achieve good
discrimination and calibration (C-index: 0.867; IBS: 0.044). While all models
were satisfactory, DeepSurv exhibited the best discrimination and calibration.
In addition, AutoScore-Survival offers a more parsimonious model and excellent
interpretability
Hepatitis E Virus Genotype Diversity in Eastern China
We studied 47 hepatitis E virus (HEV) isolates from hospitalized patients in Nanjing and Taizhou, eastern China. Genotypes 1, 3, and 4 were prevalent; genotype 3 and subgenotype 4b showed a close relationship with the swine strains in eastern China, thus indicating that HEV genotype 3 had infected humans in China
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Federated learning (FL) has gained popularity in clinical research in recent
years to facilitate privacy-preserving collaboration. Structured data, one of
the most prevalent forms of clinical data, has experienced significant growth
in volume concurrently, notably with the widespread adoption of electronic
health records in clinical practice. This review examines FL applications on
structured medical data, identifies contemporary limitations and discusses
potential innovations. We searched five databases, SCOPUS, MEDLINE, Web of
Science, Embase, and CINAHL, to identify articles that applied FL to structured
medical data and reported results following the PRISMA guidelines. Each
selected publication was evaluated from three primary perspectives, including
data quality, modeling strategies, and FL frameworks. Out of the 1160 papers
screened, 34 met the inclusion criteria, with each article consisting of one or
more studies that used FL to handle structured clinical/medical data. Of these,
24 utilized data acquired from electronic health records, with clinical
predictions and association studies being the most common clinical research
tasks that FL was applied to. Only one article exclusively explored the
vertical FL setting, while the remaining 33 explored the horizontal FL setting,
with only 14 discussing comparisons between single-site (local) and FL (global)
analysis. The existing FL applications on structured medical data lack
sufficient evaluations of clinically meaningful benefits, particularly when
compared to single-site analyses. Therefore, it is crucial for future FL
applications to prioritize clinical motivations and develop designs and
methodologies that can effectively support and aid clinical practice and
research
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