Results 131 to 140 of about 7,097,824 (285)

An Integrated Artificial Intelligence Framework for Nanomedicine Engineering

Advanced Science, EarlyView.
This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Meichen Dong, Qin Luo, Xinyi Ding, Chaoqi Lu, Ru Zhang, Qingqing Tan, Zhiqing Pang, Defang Ouyang, Dongsheng Li, Jiaxuan Xia, Jianxin Wang   +10 more
wiley   +1 more source

From Safety Climate to Proactive Safety Behavior: The Cross-Level Mediating Role of Leader–Member Exchange in Construction Workers’ Safety Citizenship Behavior

Buildings
Construction projects are high-risk environments characterized by workforce mobility, fragmented operations, schedule pressure, and multilayer subcontracting.
Jie Yang, Shichao Tian, Lingchuan Song
doaj   +1 more source

Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning

Advanced Science, EarlyView.
A structure‐centric generative framework searches voxelated design space directly, coupling a variational autoencoder with multi‐objective optimization and active learning to progressively expand the validated design domain. The discovered nano‐architectures, realized as pyrolytic carbon by two‐photon lithography, deliver 18–23% higher stiffness and 26–
Jinwook Yeo, Peter Serles, Donggeun Park, Julia R. Greer, Seunghwa Ryu   +4 more
wiley   +1 more source

LLM‐Assisted Design of Ultralow‐κ$\kappa$ Chalcogenides

Advanced Science, EarlyView.
An AI assisted, physics guided workflow accelerates the discovery of ultralow thermal conductivity materials by combining Bayesian optimization, evidence conditioned LLM reasoning, and automated physics‐based validation. The framework uncovers dynamically stable Ag based chalcogenides with ultralow thermal conductivity and promising thermoelectric ...
Yujie Liu, Congran Jia, Yuzhou Hao, Xuejie Li, Yang Wang, Artem Oganov, Jun Sun, Xiangdong Ding, Turab Lookman, Zhibin Gao   +9 more
wiley   +1 more source

Temporally Correlated Stochastic Spiking Neural Networks Enabled by 2D Ferroelectric Synapse‐Neuron Integration

Advanced Science, EarlyView.
We demonstrate a stochastic spiking neural network by integrating an α‐ln2Se3/h‐BN/CIPS FeFET synapse with an Au/CIPS/Au neuron. Unlike conventional memoryless stochastic neurons, the proposed neuron generates stochastic spikes through the evolution of internal ferroelectric polarization states, giving rise to intrinsic temporal correlations and ...
Hanseo Jeon, Ui Do Ji, Sangmin Lee, Gihyeon Bhin, Chang Yong Park, Ji‐Sang Park, Byungchul Jang, Sungjoo Lee   +7 more
wiley   +1 more source

Precision Nanomedicine for Traumatic Neural Injury: Functional Paradigms and Translational Frameworks

Advanced Science, EarlyView.
This review presents a pathology‐driven framework unifying nanomedicine across TBI, SCI, and PNI. Epidemiological evidence and comparative single‐cell transcriptomic reanalysis define shared pathological modules, injury‐specific effectors, and therapeutic bottlenecks. Six functional paradigms connect nanomaterial structure and biological fate to injury‐
Mouyuan Sun, Shuangyang Li, Zhixu He, Jingyi Xie, Yaxian Luo, Xuankai Fan, Jingyu Zhang, Luying Qin, Lianjie Peng, Tao Qiu, Tian Zhang, Huiming Wang, Mengfei Yu   +12 more
wiley   +1 more source

Bayesian Structural Equation Models of Correlation Matrices

Structural Equation Modeling: A Multidisciplinary Journal
We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean based on
openaire   +1 more source

Data‐Driven and Physics‐Informed Modeling Perspectives for Soft Manipulators: From SINDy to Hybrid Intelligence

Advanced Science, EarlyView.
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui, Yunyi Wang, Teng Zhang, Yang Chen, Min Pan   +4 more
wiley   +1 more source

A structural equation modeling analysis on solvency, operation and profitability of life insurers [PDF]

, 2013
textThe abilities of life insurers can be divided and measured from various aspects. Through the use of structural equation modeling, we investigate the relations among solvency, operation ability and profitability in year 1994, 1995 and 1996.
Zhu, Shuangshuang
core  

Multiscale Regulation of Ru‐Based Catalysts for Upgrading Biomass‐Derived Levulinic Acid and Levulinates to γ‐Valerolactone: Mechanisms, Advances, and Challenges

Advanced Science, EarlyView.
This review summarizes recent advances in the mechanistic, thermodynamic, and kinetic studies of biomass‐derived levulinic acid/levulinates conversion to γ‐valerolactone, along with the corresponding multiscale regulation strategies of Ru‐based catalysts.
Xin Ni, Cailu Xiao, Gongbing Zhou, Jingzi Zhang   +3 more
wiley   +1 more source