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In the context of energy storage systems, optimizing the life cycle of power converters is crucial for reducing costs, making informed decisions, and ensuring sustainability. This study presents a comprehensive methodology for calculating the life cycle cost (LCC) of power converters, employing a nine-step process that integrates digitalization, Internet of Things (IoT) technologies, and the Asset Health Index (AHI). The methodology adapts the Woodward model to provide a detailed cost analysis, encompassing the acquisition, operation, maintenance, and end-of-life phases. Our findings reveal significant insights into asset management, highlighting the importance of preventive and major maintenance in controlling failure rates and extending asset life. This study concludes that adopting sustainable business models and leveraging advanced technologies can enhance the reliability and maintainability of power converters, ultimately leading to more competitive and environmentally friendly energy storage solutions.

This study proposes a comprehensive framework for digitalizing and managing assets with low initial digital maturity, focusing on their operation and maintenance (O&M) lifecycle. The framework integrates Internet of Things (IoT) networks with Asset Health Index (AHI) models through four interconnected components. The Asset Definition Model ensures standardized data representation based on IEC 81346-1:2022 and ISO 14224:2016, while the Asset Criticality Model prioritizes maintenance actions using risk-informed analysis. The Asset Monitoring Model enables real-time data acquisition through IoT sensors, facilitating condition-based monitoring and dynamic decision-making. Finally, the Intelligent Asset Management Models support long-term planning by simplifying data complexity and aligning with advanced maintenance strategies. A case study on bridge maintenance demonstrates the practical value of the framework, showcasing its ability to integrate real-time monitoring with predictive decision-making tools. By bridging asset monitoring and lifecycle planning, the framework enhances operational efficiency, reduces maintenance costs, and addresses the challenges posed by limited digital maturity in critical infrastructure. This approach represents a significant advancement in the digital transformation of maintenance management.

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.

The image that people have ofrobots/AI often does not correspond to reality. This can have effects on the efective implementation of these technologies in a country, generating a negative impact on its competitiveness. The objective of this article is to analyze whether the idea one has of a robot influences the perception of the impact of robotization on employment. To do this, partially based on the research of Shoss and Ciarlante (2022), it is employed a multilevel model with variables at the individual and country levels, incorporating to study, as a contribution, the density of robots per country as an independent variable into the study. The results confirm that: (i) the more distorted the image an individual has of vehat a robot is (Wrong image), the greater their perception that robots/AI pose a threat to jobs; and (ii) that in those countries vehere the density of robots is higher (operating robots per 10.000 workers), this perceived threat level is lower.

The image that people have of robots/AI often does not correspond to reality. This can have effects on the effective implementation of these technologies in a country, generating a negative impact on its competitiveness. The objective of this article is to analyze whether the idea one has of a robot influences the perception of the impact of robotization on employment. To do this, partially based on the research of Shoss and Ciarlante (2022), it is employed a multilevel model with variables at the individual and country levels, incorporating to study, as contribution, the density of robots per country as an independent variable into the study.  The results confirm that: (i) the more distorted the image an individual has of what a robot is (erroneous image), the greater their perception that robots/AI pose a threat to jobs; and (ii) that in those countries where the density of robots is higher (operating robots per 10.000 workers), this perceived threat level is lower. Keywords: Robotization; Artificial Intelligence; threat perception; Image of the robots; Eurobarometer La imagen que las personas tienen de los robots/IA en muchas ocasiones no se corresponde con la realidad. Esto puede afectar a la implantación efectiva de estas tecnologías en un país, generando un impacto negativo sobre su competitividad. El objetivo del presente artículo es analizar si la idea que se tiene de un robot influye en la percepción del impacto de la robotización en el empleo. Para ello, partiendo parcialmente en la investigación de Shoss y Ciarlante (2022), se utiliza un modelo multinivel con variables a nivel individual y de país, incorporando al estudio, como aportación, la densidad de robots por país como variable independiente. Los resultados confirman que: (i) cuanto más distorsionada es la imagen que un individuo tiene sobre lo que es un robot (Imagen errónea) mayor es su percepción de los robots/IA como amenaza para los empleos; y (ii) que en aquellos países donde la densidad de robots es mayor (robots operativos por cada 10.000 trabajadores), este nivel de amenaza percibida es menor. Palabras clave: Robotización; Inteligencia artificial; percepción de amenaza; Imagen de los robots; Eurobarómetro A imagem que as pessoas têm dos robôs/IA muitas vezes não corresponde à realidade. Isso pode ter efeitos na implementação eficaz dessas tecnologias em um país, gerando um impacto negativo em sua competitividade. O objetivo deste artigo é analisar se a ideia que se tem de um robô influencia na percepção do impacto da robotização no emprego. Para isso, baseando-se parcialmente na pesquisa de Shoss e Ciarlante (2022), utiliza-se um modelo multinível com variáveis ​​individuais e de país, incorporando ao estudo, como contribuição, a densidade de robôs por país como variável independente. Os resultados confirmam que: (i) quanto mais distorcida é a imagem que um indivíduo tem do que é um robô (Imagem errônea), maior é sua percepção dos robôs/IA como uma ameaça aos empregos; e (ii) que nos países onde a densidade de robôs é maior (Robôs operacionais por cada 10000 trabalhadores), este nível de ameaça percebida é menor. Palavras-chave: Robotização; Inteligência artificial; percepção de ameaça; Imagem de robôs; Eurobarómetro

The use of alternative types of wood has arisen for the aging of the Brandy de Jerez, on a pilot plant level. In particular, besides the use of American oak, two more types of oak have been studied, French oak and Spanish oak, allowed by the Technical File for the ID Brandy de Jerez, and chestnut, which, though it is not officially allowed, is a type of wood which had been traditionally used in the area for the aging of wines and distillates. All of them have been studied with different toasting levels: Intense toasting and medium toasting. The study of the total phenolic composition (TPI), chromatic characteristics, organic acids, and sensory analysis have proven that chestnut leads to distillates with a higher amount of phenolic compounds and coloring intensity than oak. This behavior is the opposite as regards the toasting of the wood. Among the different types of oak, Spanish oak produces aged distillates with a higher phenolic composition and a higher color intensity. Regarding tasting, the best-assessed samples were those aged with chestnut, French oak, and American oak, and the assessors preferred those who had used a medium toasting level to those with an intense level

Fino and Amontillado are Sherry wines, produced in Marco de Jerez area (southern Spain), and aged in Criaderas y Solera system. Fino Sherry wine follows a biological aging process, under a veil of flor yeasts, while Amontillado Sherry wine shares the same biological aging firstly, followed by oxidative aging, which gives them special features. Organic acids, esters, higher alcohols, phenolic compounds and total dry extract of Sherries evolve during aging due to evaporation processes, physical-chemical reactions, wood contributions and microbiological activity. During aging, Sherry wines improve their organoleptic profile, as could be proved in the tasting sessions. Hierarchical Cluster Analysis and Factor Analysis with factor extraction using Principal Components of Sherry wines studied were carried out and natural groupings of the wines according to the type of aging and their age were observed. A strong correlation between the parameters analyzed and the aging of each wine has been seen in the Multiple Linear Regression studies, establishing two different models, one for each type of Sherry wine, that, with only four of all the variables studied estimated the wine age with more than 99% of confidence. This constitutes a useful tool to control the age of these Sherry wines in the winery.

Assessing the sustainability of plastic chemical recycling requires realistic feedstocks and catalysts designed within sustainability-led frameworks (Plastic-to-X). We link catalyst design and systems analysis to study hydrogenolysis of high-density polyethylene (virgin and bottle caps; M w  = 100–200 kDa). We report Ru–Ni alloy nanoparticles (3–4 nm) supported on titania that yield up to 55% liquid C 6 –C 45 products under optimized conditions, whereas monometallic Ru produces virtually no liquids  Operando spectroscopy and simulations reveal structure sensitivity: backbone scission follows dehydrogenation and hydrogenation cycles at defective alloy sites formed in situ. Integrating these mechanistic insights with life cycle and techno-economic analyses indicates profitable processing of plastic caps over the optimal catalyst (2.5 wt% Ru, 5 wt% Ni) with substantially lower CO 2 emissions even when using green H 2 . Furthermore, within the Plastic-to-X framework, we identify a minimum average chain length threshold of C 11 for product distributions as a  critical design  metric to reconcile environmental and economic objectives. Proving sustainable chemical plastic recycling must rely on realistic feedstocks and sustainability-driven catalyst design. Here, the authors report titania-supported Ru–Ni alloy nanoparticles achieving up to 55% liquid (C6 to C45) products for low-carbon and profitable polyethylene hydrogenolysis and determine a metric for sustainable product distributions.

This work investigates the influence of oak-cask ageing on the chemical composition and sensory profile of a variety of grape distillates. Wine spirit, wine distillate, neutral alcohol, and grape marc distillate were investigated. It is known that the characteristics of the ageing casks may have a considerable impact on the ageing process, so casks that had previously contained some type of sherry wine, sherry cask®, and casks that had previously contained brandy were studied. The results showed that ageing in either type of cask resulted in significant changes regarding volatile compound composition and a noticeable increase in phenolic and furfural compound content. Furthermore, sherry casks® contributed with sherry wine characteristic compounds that enriched the aromatic profile of the distillates, such as a greater increase in ethyl esters of organic acids. A less noticeable evolution was exhibited by the distillates with higher levels of congeners (wine spirit and grape marc distillate) when compared to wine distillate or neutral alcohol, where changes due to ageing were more evident. The sensory analysis confirmed that ageing significantly modified the organoleptic characteristics of all the distillates, with an increasing perception of certain notes such as oak, vanilla, spicy, and vinous when aged in sherry cask®.

Brandy de Jerez is a grape-derived spirit produced in Southern Spain with specific characteristics that come from the casks where it is produced, which must have previously contained some type of Sherry wine for at least 12 months. These casks are known as Sherry Cask®. In this work, Brandies de Jerez aged for different aging times (0, 3, 6 and 12 months) in casks seasoned with three different types of Sherry wines (Fino, Oloroso and Amontillado) have been studied. The samples have been analyzed using FT-Raman spectroscopy, and their chemical characterization has also been realized by studying their total content of organic acid, volatile compounds, and phenolic and furanic compounds. Their chemical study showed that the main differences between the studied samples were due to the duration and the type of seasoning performed. However, the spectra obtained through FT-Raman presented noticeable differences according to cask seasoning time and the Sherry wine used for the process. A PCA (Principal Component Analysis) confirmed that the Brandies de Jerez presented significant differences depending on the seasoning time and type that the casks were subjected to. A PLS-R (Partial Least Squares Regression) study enabled establishing a close correlation between specific regions of the FT-Raman spectra and cask seasoning time.