Results 121 to 130 of about 237,695 (301)

Exact inference in diagnosing value-at-risk estimates: A Monte Carlo device [PDF]

open access: yes
In this note a Monte Carlo approach is suggested to determine critical values for diagnostic tests of Value-at-Risk models that rely on binary random variables. Monte Carlo testing offers exact significance levels in finite samples.
Herwartz, Helmut
core  

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

open access: yesAdvanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv   +11 more
wiley   +1 more source

Endogenous Engineering Reprograms Extracellular Vesicles for Enhanced Therapeutic Function

open access: yesAdvanced Science, EarlyView.
This review explains how Extracellular vesicles‐producing cells can be endogenously engineered to load therapeutic proteins and nucleic acids. We summarize physiological and genetic strategies that harness native sorting pathways for selective cargo loading.
Jinghui Wang   +10 more
wiley   +1 more source

Conditional Value at Risk Portfolio With Monte Carlo Control Variates

open access: yesJambura Journal of Mathematics
Stock investment is one of the instruments investors favor due to its potential for high returns, but the risks stemming from stock price volatility cannot be overlooked.
Fahmi Giovani Maga   +2 more
doaj   +1 more source

Filtered Extreme Value Theory for Value-At-Risk Estimation

open access: yes
Extreme returns in stock returns need to be captured for a successful risk management function to estimate unexpected loss in portfolio. Traditional value-at-risk models based on parametric models are not able to capture the extremes in emerging markets ...
Yilmazer, Sait   +2 more
core  

Engineering Microbial Particles for Next‐Generation Biomedical Platforms

open access: yesAdvanced Science, EarlyView.
Microbe‐derived particles (MDPs), which include extracellular vesicles, outer membrane vesicles, inclusion bodies, polysaccharide particles, and virus‐like particles, represent a rapidly expanding category of bioinspired nanomaterials. With their natural origin, intrinsic biocompatibility, and highly programmable functionality, MDPs serve as a ...
Yuting Li   +7 more
wiley   +1 more source

Monte Carlo estimation of value-at-risk, conditional value-at-risk and their sensitivities [PDF]

open access: yesProceedings of the 2011 Winter Simulation Conference (WSC), 2011
L. Jeff Hong, Guangwu Liu
openaire   +1 more source

SETD1A Regulates Glycolysis and Senescence of Nucleus Pulposus Cells via H3K4me3–HELZ2/PPARα‐HIF1α Axis to Drive Intervertebral Disc Degeneration

open access: yesAdvanced Science, EarlyView.
SETD1A is a key epigenetic regulator in NPCs during IDD. In normal NPCs, it sustains H3K4me3–HELZ2/PPARα–HIF1α signaling to maintain glycolytic energy metabolism and proliferation. In degenerated NPCs, reduced SETD1A disrupts this axis, impairing glycolysis and accelerating senescence, highlighting a promising therapeutic target for IDD.
Jiawei Fu   +11 more
wiley   +1 more source

Targeting Endogenous Lipophagy: A Novel Strategy to Enhance MSC Osteogenesis and Mineralization for Senile Osteoporosis Therapy

open access: yesAdvanced Science, EarlyView.
Schematic representation of the role of lipophagy in bone mesenchymal stem cells(MSCs). In healthy MSCs, functional lipophagy efficiently degrades lipid droplets to support oxidative phosphorylation and cellular energy production, thereby facilitating osteogenic differentiation and matrix mineralization.
Chaoqiang Chen   +8 more
wiley   +1 more source

Evaluation of Hedge Fund Returns Value at Risk Using GARCH Models [PDF]

open access: yes
The aim of this research paper is to evaluate hedge fund returns Value-at-Risk by using GARCH models. To perform the empirical analysis, one uses the HFRX daily performance hedge fund strategy subindexes and spans the period March 2003 – March 2008.
Sabrina Khanniche
core  

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