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Abstract Carbon nanotube (CNT) networks enable CNTs to be used as building blocks for synthesizing novel advanced materials, thus taking full advantage of the superior properties of individual CNTs. Multiscale analyses h...

We show that coherent laser networks (CLNs) exhibit emergent neural computing capabilities. The proposed scheme is built on harnessing the collective behavior of laser networks for storing a number of phase patterns as s...

Abstract Active power distribution networks are evolving with the integration of distributed energy resources (DERs) and advanced optimization techniques to enhance grid flexibility and efficiency. However, radial distri...

This paper addresses the challenge of packet-based information routing in large-scale communication networks. The problem is framed as a constrained statistical learning task, where each network node operates using only ...

Photonic neural networks are brain-inspired information processing technology using photons instead of electrons to perform artificial intelligence (AI) tasks. However, existing architectures are designed for a single ta...

Functionalizacion of epoxy-based networks by the preferential surface enrichment of perfluorinated tails to achieve hydrophobic surface is described. The selected fluorinated epoxies (FE) were: 2,2,3,3,4,4,5,5,6,6,7,7,8,...

The rapid development of artificial intelligence has stimulated the interest in the novel designs of photonic neural networks. As three-dimensional (3D) neural networks, the diffractive neural networks (DNNs) relying on ...

Optical neural networks have significant advantages in terms of power consumption, parallelism, and high computing speed, which has intrigued extensive attention in both academic and engineering communities. It has been ...

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; howev...

The article examines current approaches to optimizing the operating modes of distributed technical systems based on automation and intelligent control technologies, using the example of 10 kV electric networks. The analy...

We study the structural properties of networks formed by random sets of bit strings—namely the ordered arrays of binary variables representing, for instance, genetic information or cultural profiles. Two bit strings ar...

How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determin...

Although the study of brain development in non-human animals is an old one, recent imaging methods have allowed non-invasive studies of the grey and white matter of the human brain over the lifespan. Classic animal studi...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater dema...

This study addresses the concepts of territorial systems of agricultural innovation and social network analysis. The general purpose of this research was analyzing a relationship between trust networks (technical, strate...

Observing an action performed by another individual activates, in the observer, similar circuits as those involved in the actual execution of that action. This activation is modulated by prior experience; indeed, sustain...

Who decides the formulation of social policy? What resources do actors bring to decision-making processes? How do those resources position them within decision making networks? This book addresses these questions by comb...

Recent results revived the interest in the implementation of analog devices able to perform brainlike operations. Here we introduce a training algorithm for a memristor network which is inspired by previous work on biolo...

"Conventional network generation models exhibit limitations in capturing complex structural patterns influenced by nodal and link attributes. While recent models have partially addressed this issue, significant gaps pers...

Low-power and Lossy IoT Networks (LLNs) comprise physical sensors, processing capability, power, and other technologies to exchange information between systems and devices over the internet. However, these networks are s...

To address the challenges of voltage deviation and increased network losses arising from the high integration of photovoltaic (PV) systems in distribution networks, this paper proposes a bi-level optimisation model for c...

Abstract Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis, while the noise mixed in measured signals harms the prediction accuracy of networks. Existing denoising ...

Research background: innovation is a key factor in determining the growth of the creative industries sector. However, it also highlights the importance of the creative industries to the broader innovation process within ...

Although transparent conductive oxides such as indium tin oxide (ITO) are widely employed as transparent conducting electrodes (TCEs) for applications such as touch screens and displays, new nanostructured TCEs are of in...