147 research outputs found
Magnetoresistance in Thin Permalloy Film (10nm-thick and 30-200nm-wide) Nanocontacts Fabricated by e-Beam Lithography
In this paper we show spin dependent transport experiments in
nanoconstrictions ranging from 30 to 200nm. These nanoconstrictions were
fabricated combining electron beam lithography and thin film deposition
techniques. Two types of geometries have been fabricated and investigated. We
compare the experimental results with the theoretical estimation of the
electrical resistance. Finally we show that the magnetoresistance for the
different geometries does not scale with the resistance of the structure and
obtain drops in voltage of 20mV at 20Oe.Comment: 15 pages, 4 figures. Accepted by AP
γ-Secretase Functions through Notch Signaling to Maintain Skin Appendages but Is Not Required for Their Patterning or Initial Morphogenesis
AbstractThe role of Notch signaling during skin development was analyzed using Msx2-Cre to create mosaic loss-of-function alleles with precise temporal and spatial resolution. We find that γ-secretase is not involved in skin patterning or cell fate acquisition within the hair follicle. In its absence, however, inner root sheath cells fail to maintain their fates and by the end of the first growth phase, the epidermal differentiation program is activated in outer root sheath cells. This results in complete conversion of hair follicles to epidermal cysts that bears a striking resemblance to Nevus Comedonicus. Sebaceous glands also fail to form in γ-secretase-deficient mice. Importantly, mice with compound loss of Notch genes in their skin phenocopy loss of γ-secretase in all three lineages, demonstrating that Notch proteolysis accounts for the major signaling function of this enzyme in this organ and that both autonomous and nonautonomous Notch-dependent signals are involved
Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation
The high acquisition cost and the significant demand for disruptive
discharges for data-driven disruption prediction models in future tokamaks pose
an inherent contradiction in disruption prediction research. In this paper, we
demonstrated a novel approach to predict disruption in a future tokamak only
using a few discharges based on a domain adaptation algorithm called CORAL. It
is the first attempt at applying domain adaptation in the disruption prediction
task. In this paper, this disruption prediction approach aligns a few data from
the future tokamak (target domain) and a large amount of data from the existing
tokamak (source domain) to train a machine learning model in the existing
tokamak. To simulate the existing and future tokamak case, we selected J-TEXT
as the existing tokamak and EAST as the future tokamak. To simulate the lack of
disruptive data in future tokamak, we only selected 100 non-disruptive
discharges and 10 disruptive discharges from EAST as the target domain training
data. We have improved CORAL to make it more suitable for the disruption
prediction task, called supervised CORAL. Compared to the model trained by
mixing data from the two tokamaks, the supervised CORAL model can enhance the
disruption prediction performance for future tokamaks (AUC value from 0.764 to
0.890). Through interpretable analysis, we discovered that using the supervised
CORAL enables the transformation of data distribution to be more similar to
future tokamak. An assessment method for evaluating whether a model has learned
a trend of similar features is designed based on SHAP analysis. It demonstrates
that the supervised CORAL model exhibits more similarities to the model trained
on large data sizes of EAST. FTDP provides a light, interpretable, and
few-data-required way by aligning features to predict disruption using small
data sizes from the future tokamak.Comment: 15 pages, 9 figure
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
The full understanding of plasma disruption in tokamaks is currently lacking,
and data-driven methods are extensively used for disruption prediction.
However, most existing data-driven disruption predictors employ supervised
learning techniques, which require labeled training data. The manual labeling
of disruption precursors is a tedious and challenging task, as some precursors
are difficult to accurately identify, limiting the potential of machine
learning models. To address this issue, commonly used labeling methods assume
that the precursor onset occurs at a fixed time before the disruption, which
may not be consistent for different types of disruptions or even the same type
of disruption, due to the different speeds at which plasma instabilities
escalate. This leads to mislabeled samples and suboptimal performance of the
supervised learning predictor. In this paper, we present a disruption
prediction method based on anomaly detection that overcomes the drawbacks of
unbalanced positive and negative data samples and inaccurately labeled
disruption precursor samples. We demonstrate the effectiveness and reliability
of anomaly detection predictors based on different algorithms on J-TEXT and
EAST to evaluate the reliability of the precursor onset time inferred by the
anomaly detection predictor. The precursor onset times inferred by these
predictors reveal that the labeling methods have room for improvement as the
onset times of different shots are not necessarily the same. Finally, we
optimize precursor labeling using the onset times inferred by the anomaly
detection predictor and test the optimized labels on supervised learning
disruption predictors. The results on J-TEXT and EAST show that the models
trained on the optimized labels outperform those trained on fixed onset time
labels.Comment: 21 pages, 11 figure
Validation of the plasma-wall self-organization model for density limit in ECRH-assisted start-up of Ohmic discharges on J-TEXT
A recently developed plasma-wall self-organization (PWSO) model predicts a
significantly enhanced density limit, which may be attainable in tokamaks with
ECRH-assisted ohmic startup and sufficiently high initial neutral density.
Experiments have been conducted on J-TEXT to validate such a density limit
scenario based on this model. Experimental results demonstrate that increasing
the pre-filled gas pressure or ECRH power during the startup phase can
effectively enhance plasma purity and raise the density limit at the flat-top.
Despite the dominant carbon fraction in the wall material, some discharges
approach the edge of the density-free regime of the 1D model of PWSO.Comment: 17 pages, 8 figure
Critical roles of edge turbulent transport in the formation of high-field-side high-density front and density limit disruption in J-TEXT tokamak
This article presents an in-depth study of the sequence of events leading to
density limit disruption in J-TEXT tokamak plasmas, with an emphasis on boudary
turbulent transport and the high-field-side high-density (HFSHD) front. These
phenomena were extensively investigated by using Langmuir probe and
Polarimeter-interferometer diagnostics
The mediating role of negative symptoms in “secondary factors” determining social functioning in chronic schizophrenia
BackgroundChronic schizophrenia is significantly influenced by negative symptoms, with several known contributors to secondary negative symptoms. However, the impact of these factors and negative symptoms on social functioning warrants further exploration.MethodsWe assessed the clinical symptoms, antipsychotic adverse reactions, and social functioning of 283 hospitalized patients with chronic schizophrenia using various standardized interviews and scales. We conducted multiple regression and mediation analyses to elucidate the impact of secondary factors on negative symptoms, and the relationship among these “secondary factors,” negative symptoms, and social functioning.ResultsOur findings identified depressive symptoms, extrapyramidal symptoms, and positive symptoms as significant contributors to secondary negative symptoms. We found that negative symptoms play a notable mediating role in the effect of depressive and positive symptoms on social functioning. However, the relationship between positive symptoms, negative symptoms, and social functioning proved to be intricate.ConclusionOur findings propose that negative symptoms act as pivotal mediators in the correlation between “secondary factors” (including the depressive symptoms and positive symptoms) and social functioning. The treatment of chronic schizophrenia necessitates focusing on key factors such as depressive and positive symptoms, which might significantly contribute to the development of secondary negative symptoms. Further research is essential to clarify the complex relationship among positive symptoms, negative symptoms, and social functioning in schizophrenia
Chronic Antagonism of the Mineralocorticoid Receptor Ameliorates Hypertension and End Organ Damage in a Rodent Model of Salt-Sensitive Hypertension
We investigated the effects of chronic mineralocorticoid receptor blockade with eplerenone on the development and progression of hypertension and end organ damage in Dahl salt-sensitive rats. Eplerenone significantly attenuated the progressive rise in systolic blood pressure (SBP) (204 ± 3 vs. 179±3 mmHg, p < 0.05), reduced proteinuria (605.5 ± 29.6 vs. 479.7 ± 26.1 mg/24h, p < 0.05), improved injury scores of glomeruli, tubules, renal interstitium, and vasculature in Dahl salt-sensitive rats fed a high-salt diet. These results demonstrate that mineralocorticoid receptor antagonism provides target organ protection and attenuates the development of elevated blood pressure (BP) in a model of salt-sensitive hypertension
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