Abstract
The current era where living demands an accelerated digital transition mainly focused on encouraging a smarter, healthier, and more sustainable mobility, in all its dimensions – a must concern for the young generations. The convergence through several digital services and APP can be an attitudes and perception changer within the group of academic mobility users’, promoting a more sustainable and better mobility choices that impact on the academic user’s mobility routines. Thus, encouraging a global shift to shared and active mobility services and systems bringing significant contributions to environmental sustainability and, also, to users’ health. The Academic Mobility as a Service (AMaaS) provide a digital service with mobility alternatives to support the academic population geographically located in different faculty campuses and Higher Education Institutions (HEI). The AMaaS applied to a restrict group is helpful to test innovative transport solutions and its high cybersecurity vulnerabilities. Despite the shortage of AMaaS case studies and the lack of security reference, it is imperative that a cybersecurity by design is planned and included in AMaaS design. In this paper AMaaS critical cybersecurity challenges, and potential risks are discussed and AMaaS Security by Design framework is described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The lower refractive index sensitivity (RIS) of plasmonic nanoparticles (NP) in comparison to their plasmonic thin films counterparts hindered their wide adoption for wavelength-based sensor designs, wasting the NP characteristic field locality. In this context, high aspect-ratio colloidal core-shell Ag@Au nanorods (NRs) are demonstrated to operate effectively at telecommunication wavelengths, showing RIS of 1720 nm/RIU at 1350 nm (O-band) and 2325 nm/RIU at 1550 nm (L-band), representing a five-fold improvement compared to similar Au NRs operating at equivalent wavelengths. Also, these NRs combine the superior optical performance of Ag with the Au chemical stability and biocompatibility. Next, using a side-polished optical fiber, we detected glyphosate, achieving a detection limit improvement from 724 to 85 mg/L by shifting from the O to the C/L optical bands. This work combines the significant scalability and cost-effective advantages of colloidal NPs with enhanced RIS, showing a promising approach suitable for both point-of-care and long-range sensing applications at superior performance than comparable thin film-based sensors in either environmental monitoring and other fields.

Abstract
This project focuses on the development and implementation of a Mini Learning Factory (Mini LF) 5.0, aligned with the principles of Industry 5.0, Cyber-Physical Systems (CPS), and Open Design. Industry 5.0 emphasizes human-centric innovation, fostering collaboration between humans and machines while promoting sustainability. CPS facilitates the integration of the physical and digital realms, enabling more agile and flexible production processes. Open Design plays a pivotal role by encouraging collaborative participation, transparency, and the democratization of knowledge, which leads to more personalized and sustainable solutions in product and service design. The research adopts the Design Science Research (DSR) methodology, involving problem identification, artifact development, evaluation, and iterative improvement. The goal is to create a replicable, low-cost training environment that equips students with practical skills in line with Industry 5.0's requirements. The Mini LF 5.0 also aims to explore new methods for human-machine interaction, collaborative communication, and sustainable production, while ensuring the technical and financial viability of the project for wider adoption. © 2025 The Authors.

Abstract

Abstract
Automated sound-event detection is crucial for large-scale passive acoustic monitoring of wildlife, but the availability of ready-to-use tools is narrow across taxa. Machine learning is currently the state-of-the-art framework for developing sound-event detection tools tailored to specific wildlife calls. Gray wolves (Canis lupus), a species with intricate management necessities, howl spontaneously for long-distance intra- and inter-pack communication, which makes them a prime target for passive acoustic monitoring. Yet, there is currently no pre-trained, open-access tool that allows reliable automated detection of wolf howls in recorded soundscapes. We collected 50 137 h of soundscape data, where we manually labeled 841 unique howling events. We used this dataset to fine-tune VGGish-a convolutional neural network trained for audio classification-effectively retraining it for wolf howl detection. HOWLish correctly classified 77% of the wolf howling examples present on our test set, with a false positive rate of 1.74%; still, precision was low (0.006) granted extreme class imbalance (7124:1). During field tests, HOWLish retrieved 81.3% of the observed howling events while offering a 15-fold reduction in operator time when compared to fully manual detection. This work establishes the baseline for open-access automated wolf howl detection. HOWLish facilitates remote sensing of wild wolf populations, offering new opportunities in non-invasive large-scale monitoring and communication research of wolves. The knowledge gap we addressed here spans across many soniferous taxa, to which our approach also tallies.

Abstract