Solar energy is expected to be the fastest developing of the Renewable Energy Sources (RES) in the next years. With the introduction of Distributed Generation into space limited urban areas, the need to find new solutions that allow the installations of Photovoltaic (PV) systems in cities has become crucial for the constant and sustainable energetic development of the world. This study presents new experimental and simulation analyses of the implementation of vertical Building Integrated Photovoltaics (BIPV), one of the most accepted alternatives for the installation of PV technologies in urban areas. Emphasis is made on the viability of BIPV in selected tropical regions from an energy generation standpoint. The results suggest that the implementation of these technologies is viable for urban spaces in the studied regions, as it was found that east and west facing vertical modules require only twice the space of horizontal modules to equal their energy generation, and the vertical area available in buildings can be up to 20 times larger than the horizontal. Additionally, energy generation in this location was found to be relatively constant throughout the year for east and west facing vertical BIPV installations.

This paper presents the design of an autonomous robot guide for a museum-like environment in a motorcycle assembly plant. The system integrates Industry 4.0 technologies such as artificial vision, indoor positioning, generative artificial intelligence, and cloud connectivity to enhance the visitor experience. The development follows the Design Inclusive Research (DIR) methodology and the VDI 2206 standard to ensure a structured scientific and engineering process. A key innovation is the integration of mmWave sensors alongside LiDAR and RGB-D cameras, enabling reliable human detection and improved navigation safety in reflective indoor environments, as well as the deployment of an open-source large language model for natural, on-device interaction with visitors. The current results include the complete mechanical, electronic, and software architecture; simulation validation; and a preliminary implementation in the real museum environment, where the system demonstrated consistent autonomous navigation, stable performance, and effective user interaction.

Cocoa production is a major economic driver across Latin America, yet it is severely threatened by Frosty Pod Rot, caused by the fungal pathogen Moniliophthora roreri, which can cause yield losses of up to 90% in affected plantations. Conventional disease management relies on manual visual inspection, which is subjective and typically detects damage only after visible symptoms appear, while public annotated multispectral datasets to support earlier, image-based detection remain scarce. To address this gap, we present a high-resolution multispectral image dataset of cocoa pods (Theobroma cacao L.) captured under real field conditions in Colombia. The dataset comprises 1032 images (4000 × 3000 px, JPG) across two spectral configurations, Red Edge (RE, 441 images) and Red-Green-Near Infrared (RGN, 591 images), collected during three field campaigns across the CNCH12, CNCH13, and SCI-1 cocoa clone varieties. A total of 1688 pod instances were manually annotated at pixel level in CVAT into four pathological classes reflecting disease progression — Healthy, Latent, Spots, and Sporulation — and released in COCO and YOLO formats. The dataset is partitioned into training, validation, and testing subsets, with a geometric augmentation pipeline applied to the training split to balance underrepresented disease classes, expanding it by an additional 1820 images. The complete 8.2 GB dataset is openly hosted on Zenodo. This resource supports the development and benchmarking of computer vision models for early disease detection and enables direct comparison between multispectral and conventional RGB images in tropical agriculture.