Abstract
Management and reuse of wood waste can be a challenging process due to the frequent presence of hazardous contaminants. Conventional detection methods are often limited by the need for excessive sample preparation and lengthy and expensive analysis. Laser-induced Breakdown Spectroscopy (LIBS) is a rapid and micro- destructive technique that can be a promising alternative, providing in-situ and real-time analysis, with minimal to no sample preparation required. In this study, LIBS imaging was used to analyze wood waste samples to determine the presence of contaminants such as As, Ba, Cd, Cr, Cu, Hg, Pb, Sb, and Ti. For this analysis, a methodology based on detecting three lines per element was developed, offering a screening method that can be easily adapted to perform qualitative analysis in industrial contexts with high throughput operations. For the LIBS experimental lines selection, control and reference samples, and a pilot set of 10 wood wastes were analysed. Results were validated by two different X-ray Fluorescence (XRF) systems, an imaging XRF and a handheld XRF, that provided spatial elemental information and spectral information, respectively. The results obtained highlighted LIBS ability to detect highly contaminated samples and the importance of using a 3-line criteria to mitigate spectral interferences and discard outliers. To increase the dataset, a LIBS large-scale study was performed using 100 samples. These results were only corroborated by the XRF-handheld system, as it provides a faster alternative. In particular cases, ICP-MS analysis was also performed. The success rates achieved, mostly above 88 %, confirm the capability of LIBS to perform this analysis, contributing to more sustainable waste management practices and facilitating the quick identifi- cation and remediation of contaminated materials.

Abstract
In recent advancements within sensing technology, driven by the Internet of Things (IoT), significant impacts are observed on health sector applications, notably through wearable electronics like electronic tattoos (e-tattoos). These e-tattoos, designed for direct contact with the skin, facilitate precise monitoring of vital physiological parameters, including body heat, a critical indicator for conditions such as inflammation and infection. Monitoring these indicators can be crucial for early detection of chronic conditions, steering toward proactive healthcare management. This study delves into a thermoelectric sensor e-tattoo designed for detailed skin temperature mapping. Utilizing a novel design, this sensor detects temperature variations across thermoelectric stripes, leveraging screen-printed films of p-type Bi0.35Sb1.65Te3, n-type Bi2Te2.8Se0.2, and poly(vinyl alcohol) (PVA) for enhanced thermoelectric and flexible properties. The application of a prototype printed thermoelectric device on temporary tattoo paper, a pioneering development in wearable health technology is demonstrated. This device, validated through numerical simulations, exhibits significant potential as a non-invasive tool for temperature monitoring, highlighting its value in health diagnostics and management.

Abstract
This work addresses the historical development of techniques and methodologies oriented to the measurement of the internal diameter of transparent tubes since the original contributions of Anderson and Barr published in 1923 in the first issue of Measurement Science and Technology. The progresses on this field are summarized and highlighted the emergence and significance of the measurement approaches supported by the optical fiber.

Abstract
This paper presents the examination of a planar waveguide sensor featuring a bimetallic layer, revealing its potential applicability across both the visible and infrared spectrums. The bimetallic layer consists of adjacent gold and silver slabs positioned atop the waveguide's core. This arrangement demonstrates the activation of two distinct plasmon resonances, indicating promising prospects for multiparameter sensing applications.

Abstract
Purpose: Inherited retinal diseases (IRDs) are a group of degenerative disorders of the retina, that can be potentially associated with changes in the anterior segment, but their prevalence and impact are not known. Exploring these concomitant ophthalmologic changes with biomechanical assessment may help identify other non-retina causes of vision loss in these patients, such as corneal ectasia or susceptibility to glaucoma. This study aimed to measure and compare corneal biomechanics in patients with and without IRDs. Methods: A total of 77 patients (154 eyes) with IRD were recruited as the study group. The control group consisted of 77 healthy adults (154 eyes) with matched age and sphere equivalents. All participants underwent a comprehensive assessment including corneal tomography (Pentacam (R)) and biomechanical assessment (Corvis ST (R)). A total of 4 second-generation biomechanical parameters and 3 indexes were collected: Ambrosio Relational Thickness (ARTh), Deflection Amplitude Ratio Max (DARM), Integrated Radius (IR) and Stiffness Parameter at Applanation (SP-A1), the final deviation value D of the Belin/Ambrosio Enhanced Ectasia Display (BADResults: For IRD patients, there was a higher DARM (p < 0.001), lower ARTh (p < 0.001), higher CBI (p < 0.001), higher TBI (p<0.001), and higher BAD-D (p < 0.001) compared to the control group. Regarding discrimination of healthy subjects and IRD patients, ARTh was the most sensitive parameter. Conclusion: The results showed that IRD patients tend to have softer corneal behaviour, compared to eyes without pathology, which may predispose patients to corneal ectasia or glaucoma development. ARTh could be used to screen IRD patients if a non-retina cause of vision loss is suspected.

Abstract
Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.