Abstract
Lava tubes formed during the 2021 Tajogaite eruption on La Palma (Canary Islands, Spain) provide a rare opportunity to investigate the earliest stages of microbial colonization in a newly created subterranean volcanic environment. Although microbial communities in volcanic terrains have been widely studied, the first colonizers of newly formed lava tubes and their interactions with new mineral surfaces remain poorly understood. Here, we combined microscopy, mineralogy, stable isotope analysis, culture-dependent techniques, and 16S rRNA gene high-throughput sequencing to characterize mineral substrates, microenvironmental parameters and pioneer microbial communities 12-24 months after lava tube formation. Microbial assemblages were dominated by bacteria from the Actinomycetota, Bacillota, and Pseudomonadota phyla, together with archaea from the Methanobacteriota phylum. Organic debris from animals (notably seabirds and rodents) likely contributed to early microbial seeding and community development, as indicated by the detection of host-associated and opportunistic bacteria (e.g., Staphylococcus, Sphingomonas, Pseudomonas). Biofilm-like structures and extracellular polymeric substances were observed in association with sodium-rich sulfate and carbonate minerals, suggesting incipient microbe-mineral interactions during early mineral weathering. Archaeal-enriched communities adapted to highly saline and oligotrophic substrates (e.g., Halostagnicola, Halonotius, and Halorubrum) in some of these nascent tubes further support environmental filtering by microhabitat geochemistry and mineralogical context during early community assembly. Our findings indicate that early microbial colonization is initiated by stochastic seeding processes (via aerosols, animals, or atmospheric inputs), whereas environmental stressors related to volcanic degassing, ventilation, and extreme geochemical conditions act as strong deterministic filters that shape the final community structure in newly formed basaltic lava tubes, as reflected by the significant role of delta 13C-CO2.

Abstract
We consider the problem of selecting bases for firefighting activities (e.g., vigilance, water refill, initial attack) and links between them in the context of wildfire promptness. Bases can be facilities, such as watchtowers and water tanks, or positions from where an initial attack is conducted. It is assumed that it is advantageous to connect bases in such a way that resources (e.g. ground crews) can quickly move between them. The general problem is modelled in a general way as integration of a set covering problem (for selecting the location of the bases) and a travelling salesman problem where the cities are the selected locations and the arcs the links that connect them. We propose a mixed integer programming model where objectives are addressed by lexicographic optimization. The first objective is related to cover potential ignition points with a high estimate of their initial spread rate of the fire at the detection time. Computational experiments are discussed for a scenario, of an actual landscape, with parameters estimated from a fire behaviour model that takes into account slope, fuels, and wind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Shipping container unloading is a physically demanding task often carried out under challenging conditions, which motivates the use of automation. However, automating this process is complex due to the unpredictable sizes and quantities of each shipment. Existing solutions tend to be task-specific, rely on closed software stacks, and offer limited information on performance in non-controlled environments, which restricts their adaptability. We present CARGO, a modular pipeline that enables a mobile manipulator equipped with regular sensors and actuators to unload containers autonomously. The pipeline employs a predefined, layered workflow composed of reconfigurable modules that can be adapted to various robots, ensuring that all boxes in a stack are systematically handled. In simulation, the pipeline successfully unloaded a full container without collisions, thereby validating the complete workflow. Laboratory tests further confirmed these results, with the mobile manipulator successfully unloading boxes across multiple trials, with a success rate of 97%. These results demonstrate that a versatile mobile manipulator can handle mixed box sizes and chaotic layouts using a generic, modular pipeline, highlighting a promising direction for flexible container-unloading automation.

Abstract
Context Downy mildew, caused by Plasmopara viticola, remains one of the most damaging diseases affecting grapevines, especially in humid viticultural regions such as the Vinhos Verdes in northern Portugal. Traditional detection relies on visual inspection and laboratory techniques, which are subjective and reactive, often delaying effective intervention. Aims This study aims to evaluate the potential of field spectroscopy combined with machine learning to detect downy mildew in Vitis vinifera cv. Loureiro field conditions. The focus is on providing an early, non-destructive detection method that can be used in precision viticulture, reducing the need for costly, widespread pesticide applications. Methods and Key Results Leaf spectral reflectance data were collected along the 2023 and 2024 growing seasons using a portable spectroradiometer. Measurements were obtained from both untreated and fungicide-treated grapevines, covering different infection stages. Spectral signatures from 600 grapevine leaves were used to train and validate classification models using Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Random Forest (RF) models. Both RF and PLS-LDA models showed an overall accuracy of 95.1% when trained with all spectral features from the dataset. Red edge (700-750 nm) and visible (400-700 nm) wavelengths demonstrated the highest classification contribution. Moreover, the twenty most informative wavelengths for infection discrimination were identified for each model. Conclusion The results confirm the effectiveness of field spectroscopy, making it possible to detect downy mildew symptoms in different stages of infection. This method offers a rapid, cost-effective, and sustainable tool for early disease detection, which can greatly benefit winegrowers by enabling more timely and specific interventions and reducing the reliance on chemical treatments.

Abstract
The present letter proposes the implementation of Vernier-effect harmonics through the virtualization of different reference cavities. A Fabry-Perot interferometer (FPI), actuated by a piezoelectric transducer (PZT), was employed as the sensing element. Subsequently, the sensitivity of the dynamic range was investigated for both the individual interferometer and the implementation of the Virtual Vernier effect. A sensitivity of (8 +/- 0.05)x10(-3) nm/nm was achieved for the single sensor measurement. Considering the implementation of the Vernier effect, the following sensitivities were obtained: (65.6 +/- 0.08)x10(-3) nm/nm for the fundamental, (132 +/- 1)x10-3 nm/nm for the first harmonic, and (192 +/- 1)x10(-3) nm/nm for the second harmonic. Furthermore, a maximum dynamic range of 11.25 mu m and a maximum resolution of 5 pm were achieved. This study highlights the advantages of simultaneously measuring both a single sensor cavity and a harmonic of the Virtual Vernier effect, in order to achieve large dynamic ranges along with high resolution.

Abstract
PurposeThis study explores the role of ports in achieving the United Nations Sustainable Development Goals (SDGs), focusing on the interconnection between key SDGs. It aims to characterize how port sustainability initiatives address multiple SDGs to increase their impact.Design/methodology/approachIt examines through mixed methods research how ports align their development projects with these SDGs, analyzing the policy implications of integrating economic, environmental, and social objectives. It considers 401 projects from the World Ports Sustainability Program (WPSP).FindingsThe findings indicate the most achieved SDGs and reveal six common themes that ports follow to address multiple SDGs. These characteristics include environmental sustainability, energy transition, social inclusion, digitalization, governance and ethical practices, and innovation and collaboration.Originality/valueThis study brings original contributions on the SDGs addressed by ports, considering not only the individual SDGs addressed by the sustainability initiatives promoted by ports, but also the joint effects of addressing multiple SDGs. This study also emphasizes the need for supporting these initiatives in international collaboration, green technologies, and climate resilience.