Fundación Prodintec

This paper presents the learnings from a study that used qualitative research methods to explore how to promote agroecological transitions (AETs), conducted over four years with Bolivian farmers and consumers in Cochabamba. The study examines how different motivations and production practices shape farmers’ engagement with agroecology. The research applied thematic analysis to construct farmer typologies and identify factors influencing perceptual and behavioural transformation towards AE practices. The results show three farmer typologies based on motivations and production practices: i) conventional production and offer; ii) mixed production and offer; and iii) AE production and offer. Thematic analysis identified a set of primary and secondary factors common across typologies that affect transformations towards AE, with the main factors including risk-aversion, plot size and family tradition, and secondary ones such as health and new AE knowledge versus family tradition. A gap emerged in how these factors affect changes in perspectives and behaviours across farmer types. Understanding the role of context-specific factors in AE transformations is essential to improve AE training and implementation approaches, and the discussion interprets farmers’ risk perceptions through behavioural economics, particularly Prospect Theory, which highlights loss aversion and reference-dependent decision-making.

The development of photocurable hydrogel biomaterial inks with suitable rheology, low cytotoxicity, and tuneable mechanical properties is essential for reliable biofabrication. This study aimed to formulate PEGDA-gelatine-collagen inks using lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) as photoinitiator. Rheological characterisation and flow-model fitting were performed, mechanical stiffness modulation under different light intensities was evaluated, complex structures were printed using direct extrusion and FRESH methodologies, and PEGDA/LAP extractables were quantified by NMR after controlled washing procedures. In vitro assays assessed cell viability and proliferation on the resulting scaffolds. The Herschel-Bulkley model best described the flow behaviour across formulations; while viscoelastic measurements showed that increasing light intensity progressively enhanced hydrogel stiffness, enabling fine control over final mechanical properties. NMR analysis showed that washing removed a substantial fraction of residual LAP, in agreement with the biological findings: fibroblasts failed to survive on unwashed scaffolds but exhibited robust proliferation and recovered their characteristic elongated morphology on washed constructs. Among all inks, PeGeCol_10_2 provided the best combination of shear-thinning behaviour, structural integrity, low residual photoinitiator, and tuneable mechanics. Using this formulation, we successfully printed large anatomical models with high fidelity and excellent handling properties, underscoring its potential for soft-tissue prosthetics and broader tissue-engineering applications.

In this paper we evaluate the implementation of renewable energy to process sugar and alcohol production. This has been applied to dynamic simulation techniques that provide quantitative information production in a Caribbean climate

The current report contains information about the following 2 Cognitive Solutions: CAS1 - Sensor for bitumen content [Asphalt] CAS2 - Sensor for amount of filler [Asphalt] Each Cognitive Solution listed above is equipped with: A number of attachments of different nature (video, data, metadata, …), containing additional information that helps to better understand the final output of the CS; A textual part, available in Chapter 4, which complements the “physical” content in the attachment, explaining what it is and how to use it. All attachments are available under CAPRI Zenodo folder and, in case of videos, under the CAPRI YouTube channel. The table below lists all the links (Zenodo and YouTube) of the different files described in the present report.

The current report contains information about the following 1 Cognitive Solution: CPP1 – Cognitive Planning Concept [Pharma] Each Cognitive Solution listed above is equipped with: A number of attachments of different nature (video, data, metadata, …), containing additional information that helps to better understand the final output of the CS; A textual part, available in Chapter 4, which complements the “physical” content in the attachment, explaining what it is and how to use it. All attachments are available under CAPRI Zenodo folder and, in case of videos, under the CAPRI YouTube channel. The table below lists all the links (Zenodo and YouTube) of the different files described in the present report.

The current report contains information about the following 3 Cognitive Solutions: CSS2 – Sensor for Solidification [Steel] CSS3 – Sensor for Product temperature [Steel] CSS4 – Scale sensor for scale build-up [Steel] Each Cognitive Solution listed above is equipped with: A number of attachments of different nature (video, data, metadata, application, code, …), containing additional information that helps to better understand the final output of the CS. A textual part, available in Chapter 4 that complements the “physical” content in attachment, explaining what it is and how to exploit it. All attachments are available under CAPRI Zenodo folder and, in case of videos, under the CAPRI YouTube channel. The table below lists all the links (Zenodo and YouTube) of the different files described in the present report.

The current report contains information about the following 1 Cognitive Solution: CAP1 – Planning and control of asphalt production [Asphalt] Each Cognitive Solution listed above is equipped with: A number of attachments of different nature (video, data, metadata, …), containing additional information that helps to better understand the final output of the CS; A textual part, available in Chapter 4, which complements the “physical” content in the attachment, explaining what it is and how to use it. All attachments are available under CAPRI Zenodo folder and, in case of videos, under the CAPRI YouTube channel. The table below lists all the links (Zenodo and YouTube) of the different files described in the present report.

A combined thermochemical-biochemical Biomass-to-Liquid (BtL) pathway for the production of aviation and maritime liquid fuels is presented. The presence of a semi-commercially proven technology like Dual Fluidized Bed Gasification (DFBG) ensures extended fuel flexibility, syngas of high quality, complete fuel conversion and optimal heat integration while avoiding CAPEX intensive equipment like Air Separation Unit. Then, a two-stage biochemical route is proposed: initially syngas fermentation (anaerobic) into acetate and subsequently acetate fermentation (aerobic) into targeted triglycerides (TAGs) that will be finally purified and hydrotreated to form the desired drop-in biofuels. The tolerance of the bacteria to syngas contaminants minimizes the gas cleaning requirements. Moreover, the low-pressure requirements (1-10 bar) along with the mild operating temperatures (30-60°C) reduce drastically the capital and operational cost of the process. In terms of efficiency, the biological process of syngas fermentation inherently has limited side products, a fact that reduces the risk of deactivation of hydrotreatment catalysts. The aim of this study is to develop the process model of this novel biorefinery in Aspen Plus TM and to perform the energy and mass balance calculations of the whole value chain, to determine the appropriate key process specifications and to estimate the production cost of the targeted drop-in biofuels.

Over the last few years, several techniques have been developed with the aim of implementing one-shot learning, a concept that allows classifying images with only a single image per training category. Conceptually, these methods seek to reproduce certain behavior that humans have. People are able to recognize a person they have only seen once, but they are probably not able to do the same with certain animals, such as a monkey. This is because our brains have been trained for years with images of people but not so much of animals. Among the one-shot learning techniques, some of them have used data generation, such as Generative Adversarial Networks (GAN). Other techniques have been based on the matching of descriptors traditionally used for object detection. Finally, one of the most prominent techniques involves using Siamese neural networks. Siamese networks are usually implemented with two convolutional nets that share their weights. They receive two images as input and can detect whether they belong to the same category or not. In the field of grocery products, there has been a lot of research on the one-shot learning problem but not so much on the use of Siamese networks. In this paper, several classifiers are firstly evaluated to decide on a convolutional model to be used with the Siamese and to improve the baseline results obtained in the dataset used. Then, two existing techniques are integrated within the Siamese model: a convolutional net and a Local Maximal Occurrence (LOMO) descriptor. The latter was initially used for the re-identification of people although it has shown its effectiveness to improve the values of a traditional Siamese with only convolutional sisters. The whole network is trained on categories and responds to different categories, showing its strong capacity to deal with the problem of having only one image per category.

Gaze control represents an important issue in the interaction between a robot and humans. Specifically, deciding who to pay attention to in a multi-party conversation is one way to improve the naturalness of a robot in human-robot interaction. This control can be carried out by means of two different models that receive the stimuli produced by the participants in an interaction, either an on-center off-surround competitive network or a recurrent neural network. A system based on a competitive neural network is able to decide who to look at with a smooth transition in the focus of attention when significant changes in stimuli occur. An important aspect in this process is the configuration of the different parameters of such neural network. The weights of the different stimuli have to be computed to achieve human-like behavior. This article explains how these weights can be obtained by solving an optimization problem. In addition, a new model using a recurrent neural network with LSTM layers is presented. This model uses the same set of stimuli but does not require its weighting. This new model is easier to train, avoiding manual configurations, and offers promising results in robot gaze control. The experiments carried out and some results are also presented.