Results 141 to 150 of about 24,987 (266)
Discussion of Paper by Chin, Cheng, Lepage, and Lequesne
Earthquake Engineering &Structural Dynamics, EarlyView.
Prateek Shah +3 more
wiley +1 more source
Visitor‐I and dual worldmaking: Queer museology between Tuntenhaus and the Schwules Museum
Abstract This article develops the visitor‐I as an embodied protocol for analyzing how queer archival exhibitions choreograph perception, affect, and learning, and it uses dual worldmaking as a bounded heuristic to name the relation between lived worldmaking in the Tuntenhaus squat and curatorial worldmaking in the museum, and I argue that the visitor ...
Melike Atmanoğlu
wiley +1 more source
Forecasting compressive strength of concrete containing rice husk ash using various machine learning algorithms. [PDF]
Al-Shamasneh AR +6 more
europepmc +1 more source
Abstract Soft robots, engineered from highly compliant materials, offer superior adaptability and safety in unstructured environments compared to their rigid counterparts. Recent advancements, fueled by bio‐inspiration and material programmability, have led to the rapid co‐evolution of their core modules: actuation, sensing, protection, energy, and ...
Qiulei Liu +3 more
wiley +1 more source
Effect of Rubber Granulate Content on the Compressive Strength of Concrete for Industrial Vibration-Isolating Floors. [PDF]
Gruszczyński M +2 more
europepmc +1 more source
The Impact of Uncertainty on Forecasting the US Economy
ABSTRACT This paper examines the predictive value of uncertainty measures for key macroeconomic indicators across multiple forecast horizons. We evaluate how different uncertainty proxies—economic policy uncertainty (EPU), VIX, geopolitical risk, and measures of macroeconomic and financial uncertainty—enhance forecast accuracy for industrial production,
Angelica Ghiselli
wiley +1 more source
Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning. [PDF]
Fathy IN +4 more
europepmc +1 more source
The Compressive Strength of the Lean-Concrete
Ote, Keiji, Takahashi, Tetsuo
openaire +1 more source
Prediction of Compressive Strength of Concrete Specimens Based on Interpretable Machine Learning. [PDF]
Wang W +5 more
europepmc +1 more source

