Results 131 to 140 of about 14,936,263 (249)
ABSTRACT The US hemp market is a new and nascent industry that has been devoid of research for about half a century. This study examined the effects of exogenous shock on price at each phase of the value chain—Farm (hemp biomass), and its impact on prices at other phases of the value chain—Intermediary Processor (crude cannabidiol hemp) and Final ...
Solomon Odiase +2 more
wiley +1 more source
Cost Pass‐Through in Crisis: Evidence From the German Malt‐Beer Supply Chain
Abstract Global agri‐food supply chains are increasingly exposed to geopolitical shocks, climate volatility, and market consolidation, factors that disrupt traditional price relationships and reshape market power dynamics. Nowhere is this more visible than in the brewing sector, where agricultural raw materials meet complex industrial processing and ...
Nikolas Bublik, Lukáš Čechura
wiley +1 more source
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
A Two‐Stage Characterization Pipeline and Open‐Source Framework for Reproducible Tactile Sensing
The same soft tactile sensor returns different numbers when embodied in different robots. This is an Embodiment Gap that no shared framework currently captures transparently. A two‐stage characterization pipeline, paired with a FAIR open‐source digital datasheet, decouples intrinsic sensor behavior from embodiment effects and condenses cross‐laboratory
Matteo Lo Preti +6 more
wiley +1 more source
A quadrupedal integrated leg‐arm robot with an underactuated reconfigurable body is developed. By using a Sarrus mechanism as the body, the robot enables reconfiguration through its supporting limbs, achieving mode switching without additional actuators.
Xinghan Zhuang +8 more
wiley +1 more source
The method presented in this paper provides a practical sensorless solution for estimating both contact force and contact location using only standard joint encoders and an available robot dynamic model. By reformulating the contact estimation problem using a single scalar equivalent contact parameter, the approach enables fast and robust computation ...
Thanh‐Quan Ta, Shyh‐Leh Chen
wiley +1 more source

