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.

Abstract
Highlights What are the main findings? The SLAM method, based on the registration of 3D profiling sonar scans using the 3DupIC method, avoids the construction of submaps and thereby overcomes the limitations of other state-of-the-art approaches. Simultaneous optimization of the trajectory and extrinsic parameters, using the proposed SLAM and calibration method, ensures high accuracy in trajectory and map estimation. What is the implication of the main finding? Direct registration of raw scans supports two distinct applications. On the one hand, it enables pose estimation through odometry. On the other hand, it provides loop-closure constraints for the SLAM process. 3D profiling sonars are highly effective sensors for mapping, localization, and SLAM applications. This demonstration is particularly important as newer, smaller, and more affordable sonars in this category become available, contributing to their wider adoption.Highlights What are the main findings? The SLAM method, based on the registration of 3D profiling sonar scans using the 3DupIC method, avoids the construction of submaps and thereby overcomes the limitations of other state-of-the-art approaches. Simultaneous optimization of the trajectory and extrinsic parameters, using the proposed SLAM and calibration method, ensures high accuracy in trajectory and map estimation. What is the implication of the main finding? Direct registration of raw scans supports two distinct applications. On the one hand, it enables pose estimation through odometry. On the other hand, it provides loop-closure constraints for the SLAM process. 3D profiling sonars are highly effective sensors for mapping, localization, and SLAM applications. This demonstration is particularly important as newer, smaller, and more affordable sonars in this category become available, contributing to their wider adoption.Abstract High resolution underwater mapping is fundamental to the sustainable development of the blue economy, supporting offshore energy expansion, marine habitat protection, and the monitoring of both living and non-living resources. This work presents a pose-graph SLAM and calibration framework specifically designed for 3D profiling sonars, such as the Coda Octopus Echoscope 3D. The system integrates a probabilistic scan matching method (3DupIC) for direct registration of 3D sonar scans, enabling accurate trajectory and map estimation even under degraded dead reckoning conditions. Unlike other bathymetric SLAM methods that rely on submaps and assume short-term localization accuracy, the proposed approach performs direct scan-to-scan registration, removing this dependency. The factor graph is extended to represent the sonar extrinsic parameters, allowing the sonar-to-body transformation to be refined jointly with trajectory optimization. Experimental validation on a challenging real world dataset demonstrates outstanding localization and mapping performance. The use of refined extrinsic parameters further improves both accuracy and map consistency, confirming the effectiveness of the proposed joint SLAM and calibration approach for robust and consistent underwater mapping.

Abstract
This systematic review examines the potential of hydrogen fuel cell electric vehicles (HFCEVs) and battery electric vehicles (BEVs) as sustainable alternatives to traditional internal combustion vehicles (ICEs) based on fossil fuels in transitioning to greener mobility. By exploring recent literature and applying PRISMA guidelines, we focus on key aspects such as the technological performance, cost-effectiveness, and environmental impact of HFCs compared to EVs. The review emphasizes the critical role of hydrogen production, mainly green hydrogen, and the challenges associated with refueling infrastructure and market viability. Analyzing selected studies, we present insights into the advantages, obstacles, and prospects of HFCEVs in the context of global efforts toward sustainable transportation.