Abstract
Optical fibers have been extensively applied in optical sensing platforms for their large bandwidth, stability, light weight and accessibility. This work presents a theoretical analysis of an optical fiber surface plasmon resonance system for refractometric sensing applications. The device consists of a multilayer hyperbolic metamaterial (HMM) composed of concentric Au/TiO2 alternate layers in optical fiber matrix. HMMs exhibit hyperbolic dispersion (HD) and the interaction of different plasmonic modes at each interface of the HMM is reported to enhance light-matter coupling, leading to an increased refractometric sensitivity. The HD and its effects on sensor performance are numerically investigated by effective medium theory (EMT) and backed by the exact transfer matrix method (TMM). The maximum sensor performance was attained for a configuration with 2 bilayers with 30 nm thickness for a metal fill fraction (rho) of 0.7, achieving a figure of merit (FOM) of 18.45. A direct comparison with a plasmonic Au optical fiber sensor returned an optimized FOM of 5.74, therefore achieving over a three-fold increase in sensor performance, assessing the potential of HMM as highly refractometric sensitive platforms.

Abstract

Abstract
Digital Twins are becoming an architectural and functional element in a variety of systems managing physical assets. From complex products to the interconnection of assets in complex processes, digital twins are becoming the overarching cognitive concept that operators interact with to design, manage, control and maintain those products and processes. Despite this evolution, there is still limited knowledge on how to design the human interaction and user experience with digital twin-based systems. In this paper, we review the scarce literature on this subject and identify the high-level requirements for designing user experience for both product and process digital twin-based systems. Finally, we instantiate the requirements for a product-process digital twin-based system, with the focus on circularity and sustainability. © 2025 IEEE.

Abstract
This paper presents BiSAID, a dataset for exploring bipolar semantic adjectives in non-speech auditory cues, including earcons and auditory icons, i.e., sounds used to signify specific events or relay information in auditory interfaces from recorded or synthetic sources, respectively. In total, our dataset includes 599 non-speech auditory cues with different semantic labels, covering temperature (cold vs. warm), brightness (bright vs. dark), sharpness (sharp vs. dull), shape (curved vs. flat), and accuracy (correct vs. incorrect). Furthermore, we advance a preliminary analysis of brightness and accuracy earcon pairs from the BiSAID dataset to infer idiosyncratic sonic structures of each semantic earcon label from 66 instantaneous low- and mid-level descriptors, covering temporal, spectral, rhythmic, and tonal descriptors. Ultimately, we aim to unveil the relationship between sonic parameters behind earcon design, thus systematizing their structural foundations and shedding light on the metaphorical semantic nature of their description. This exploration revealed that spectral characteristics (e.g. spectral flux and spectral complexity) serve as the most relevant acoustic correlates in differentiating earcons on the dimensions of brightness and accuracy, respectively. The methodology holds great promise for systematizing earcon design and generating hypotheses for in-depth perceptual studies. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

Abstract
Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.

Abstract
In the original publication [1], there was a minor error in Figure 1 and Table 6. Unfortunately, Figure 1 presented a smaller text size than appropriate, making it difficult for the reader, in addition to the abbreviation “FiO2” instead of “FiO2”. Then, in Table 6, the basal lactate values between the groups were corrected and the lactate peak values were included. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated. © 2024 by the authors.