Alternative Concepts for Extruded Power Cable Insulation: from Thermosets to Thermoplastics

The most common type of insulation of extruded high-voltage power cables is composed of low-density polyethylene (LDPE), which must be crosslinked to adjust its thermomechanical properties. A major drawback is the need for hazardous curing agents and the release of harmful curing byproducts during cable production, while the thermoset nature complicates reprocessing of the insulation material. This perspective explores recent progress in the development of alternative concepts that allow to avoid byproducts through either click chemistry type curing of polyethylene-based copolymers or the use of polyolefin blends or copolymers, which entirely removes the need for crosslinking. Moreover, polypropylene-based thermoplastic formulations enable the design of insulation materials that can withstand higher cable operating temperatures and facilitate reprocessing by remelting once the cable reaches the end of its lifetime. Finally, polyethylene-based covalent and non-covalent adaptable networks are explored, which may allow to combine the advantages of thermoset and thermoplastic insulation materials in terms of thermomechanical properties and reprocessability

Acoustic features as a tool to visualize and explore marine soundscapes: applications illustrated using marine mammal Passive Acoustic Monitoring datasets

Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of two PAM datasets. We combine pre-trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low- and high-frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large-scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species. Our study explores the use of VGGish acoustic features and UMAP dimensionality reduction for the analysis of marine soundscapes. We combine pre-trained acoustic classification models, dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.imag

Life cycle assessment of plastic and paper-based ultra high frequency RFID tags

The aim of the work is to assess the environmental impacts of Ultra High Frequency RFID tags. Through a Life Cycle Assessment approach, two case studies have been investigated, namely a standard plastic and a paper-based tags. Primary data on tags' components, manufacturing and transportation were collected, while secondary data for the raw materials processing and tags' end of life were retrieved. The Recipe Midpoint method was used to evaluate the impacts. Results show that, for both tags, the greatest contributions to global warming, terrestrial acidification, mineral and fossil resource scarcity are due to raw material extraction (more than 50%) and manufacturing phase (30-50%), which resulted impactful also on the ionizing radiation (70%). Interestingly, the paper tag allows to save up to 23% of the greenhouse gas emissions and decreases the impact on the above-mentioned categories, resulting the eco-friendly option. The conclusion of the work contributes to update the scientific literature, still poor in RFID environmental evaluations, and are useful for researchers interested in comparing the traditional handling systems' impacts to the RFID scenario. Furthermore, the outcomes will be used as input for subsequent research, aimed at developing a tool to measure the return on the environment of RFID deployments

Long-acting exenatide does not prevent cognitive decline in mild cognitive impairment: a proof-of-concept clinical trial

Purpose: According to preclinical evidence, GLP-1 receptor may be an actionable target in neurodegenerative disorders, including Alzheimer's disease (AD). Previous clinical trials of GLP-1 receptor agonists were conducted in patients with early AD, yielding mixed results. The aim was to assess in a proof-of-concept study whether slow-release exenatide, a long-acting GLP-1 agonist, can benefit the cognitive performance of people with mild cognitive impairment (MCI). Methods: Thirty-two (16 females) patients were randomized to either slow-release exenatide (n = 17; 2 mg s.c. once a week) or no treatment (n = 15) for 32 weeks. The primary endpoint was the change in ADAS-Cog11 cognitive test score at 32 weeks vs baseline. Secondary endpoints herein reported included additional cognitive tests and plasma readouts of GLP-1 receptor engagement. Statistical analysis was conducted by intention to treat. Results: No significant between-group effects of exenatide on ADAS-Cog11 score (p = 0.17) were detected. A gender interaction with treatment was observed (p = 0.04), due to worsening of the ADAS-Cog11 score in women randomized to exenatide (p = 0.018), after correction for age, scholar level, dysglycemia, and ADAS-Cog score baseline value. Fasting plasma glucose (p = 0.02) and body weight (p = 0.03) decreased in patients randomized to exenatide. Conclusion: In patients with MCI, a 32-week trial with slow-release exenatide had no beneficial effect on cognitive performance. Trial registration number: NCT03881371, registered on 21 July, 2016

Multi-Class Quantum Convolutional Neural Networks

Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The model is implemented using PennyLane. The optimization process is conducted by minimizing the cross-entropy loss through parameterized quantum circuit optimization. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes. The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network

Boundary dynamics for holomorphic sequences, non-autonomous dynamical systems and wandering domains

There are many classical results, related to the Denjoy-Wolff theorem, concerning the relationship between orbits of interior points and orbits of boundary points under iterates of holomorphic self-maps of the unit disc. Here, we address such questions in the very general setting of sequences ( F n ) of holomorphic maps between simply connected domains. We show that, while some classical results can be generalised, with an interesting dependence on the geometry of the domains, a much richer variety of behaviours is possible. One of our main results is new even in the classical setting. Our methods apply in particular to non -autonomous dynam- ical systems, when ( F n ) are forward compositions of holo- morphic maps, and to the study of wandering domains in holomorphic dynamics. The proofs use techniques from geometric function theory, measure theory and ergodic theory, and the construction of examples involves a 'weak independence' version of the second Borel-Cantelli lemma and the concept from ergodic theory of 'shrinking targets'. (c) 2024 Published by Elsevier Inc

Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet

Over the last decades, the Mediterranean diet gained enormous scientific, social, and commercial attention due to proven positive effects on health and undeniable taste that facilitated a widespread popularity. Researchers have investigated the role of Mediterranean-type dietary patterns on human health all around the world, reporting consistent findings concerning its benefits. However, what does truly define the Mediterranean diet? The myriad of dietary scores synthesizes the nutritional content of a Mediterranean-type diet, but a variety of aspects are generally unexplored when studying the adherence to this dietary pattern. Among dietary factors, the main characteristics of the Mediterranean diet, such as consumption of fruit and vegetables, olive oil, and cereals should be accompanied by other underrated features, such as the following: (i) specific reference to whole-grain consumption; (ii) considering the consumption of legumes, nuts, seeds, herbs and spices often untested when exploring the adherence to the Mediterranean diet; (iii) consumption of eggs and dairy products as common foods consumed in the Mediterranean region (irrespectively of the modern demonization of dietary fat intake). Another main feature of the Mediterranean diet includes (red) wine consumption, but more general patterns of alcohol intake are generally unmeasured, lacking specificity concerning the drinking occasion and intensity (i.e., alcohol drinking during meals). Among other underrated aspects, cooking methods are rather simple and yet extremely varied. Several underrated aspects are related to the quality of food consumed when the Mediterranean diet was first investigated: foods are locally produced, minimally processed, and preserved with more natural methods (i.e., fermentation), strongly connected with the territory with limited and controlled impact on the environment. Dietary habits are also associated with lifestyle behaviors, such as sleeping patterns, and social and cultural values, favoring commensality and frugality. In conclusion, it is rather reductive to consider the Mediterranean diet as just a pattern of food groups to be consumed decontextualized from the social and geographical background of Mediterranean culture. While the methodologies to study the Mediterranean diet have demonstrated to be useful up to date, a more holistic approach should be considered in future studies by considering the aforementioned underrated features and values to be potentially applied globally through the concept of a “Planeterranean” diet