AIMC Topic: Electronics

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Neuromorphic Liquids, Colloids, and Gels: A Review.

Chemphyschem : a European journal of chemical physics and physical chemistry
Advances in flexible electronic devices and robotic software require that sensors and controllers be virtually devoid of traditional electronic components, be deformable and stretch-resistant. Liquid electronic devices that mimic biological synapses ...

Recent advances in biomimetic soft robotics: fabrication approaches, driven strategies and applications.

Soft matter
Compared to traditional rigid-bodied robots, soft robots are constructed using physically flexible/elastic bodies and electronics to mimic nature and enable novel applications in industry, healthcare, aviation, military, Recently, the fabrication of...

Nonlinear decision-making with enzymatic neural networks.

Nature
Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks. Non-enzymatic networks could in...

Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm version 2 for quantifying alcohol exposure in electronic images.

Alcoholism, clinical and experimental research
BACKGROUND: Seeing alcohol in media has been demonstrated to increase alcohol craving, impulsive decision-making, and hazardous drinking. Due to the exponential growth of (social) media use it is important to develop algorithms to quantify alcohol ex...

Multicomponent and multifunctional integrated miniature soft robots.

Soft matter
Miniature soft robots with elaborate structures and programmable physical properties could conduct micromanipulation with high precision as well as access confined and tortuous spaces, which promise benefits in medical tasks and environmental monitor...

Electronic Configurations of 3d Transition-Metal Compounds Using Local Structure and Neural Networks.

The journal of physical chemistry. A
Machine learning (ML) methods extract statistical relationships between inputs and results. When the inputs are solid-state crystal structures, structure-property relationships can be obtained. In this work, we investigate whether a simple neural net...

Prediction of e-waste generation: Application of modified adaptive neuro-fuzzy inference system (MANFIS).

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
An accurate estimation of generated electronic waste (e-waste) plays a pivotal role in the development of any appropriate e-waste management plan. The present study aimed to exploit modified adaptive neuro-fuzzy inference system (MANFIS) for the esti...

Realization of optical logic gates using on-chip diffractive optical neural networks.

Scientific reports
Optical computing is highly desired as a potential strategy for circumventing the performance limitations of semiconductor-based electronic devices and circuits. Optical logic gates are considered as fundamental building blocks for optical computatio...

Automated Identification of Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow.

Methods of information in medicine
BACKGROUND: Clinical procedures are often performed in outpatient clinics without prior scheduling at the administrative level, and documentation of the procedure often occurs solely in free-text clinical electronic notes. Natural language processing...

Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property.

The journal of physical chemistry letters
The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossib...