Technological Corporation of Andalusia

Environmental degradation remains an increasingly urgent challenge, leading to focused debates at the Rio+20 conference (2012) on how to operationalise sustainability. This conference’s central theme was the green economy and the role of institutions in driving the transition to a more sustainable model. Today, concepts such as the green economy, circular economy, bioeconomy, and circular bioeconomy (CBE) are integral to institutional efforts towards sustainable development. The CBE has significant potential as a driver of sustainability. This article examines the challenges, opportunities, and governance structures that the Andalusian (Southern Spain) public administration is implementing in the context of the CBE. The findings are based on qualitative methods, with a comprehensive literature review, semi-structured interviews, and workshops with different stakeholders from the quadruple helix model, conducted as part of the ROBIN project and other related projects. The results systematises the main weaknesses and strengths collected during the fieldwork in terms of the tools of governance. The conclusions highlight the need to develop this model and outline the actions needed to develop the CBE further.

Iris recognition consists of measuring the unique patterns of the coloured and textured part of the eye (the iris), and comparing the obtained "signature" with those of a dataset to verify and authenticate identity. The uniqueness of the iris pattern makes the technique more robust and accurate for verification compared to other biometric systems. One of the main challenges of iris recognition is the ability to work with people on the move and at an increasing distance (over 1 metre). While the first implementations of iris recognition require the eyes to be placed at a close distance to the sensor and to maintain a static position during eye detection and capture, nowadays the iris recognition technique is progressing to be less intrusive. In this scenario, the problem of detecting the iris with an adequate quality (contrast, resolution) to be useful to identify the person, can force the framework to be able to process a large amount of frames per second (fps). This article describes the implementation in an MPSoC (Multiprocessor System-on-Chip) of a core that allows an eye detection framework to quickly evaluate the quality of the captured iris images. Thus, it is possible to remove the poor quality images from the subsequent processing steps. This implementation is intended to discard invalid images for person recognition therefore reducing the load on other parts of the system. It has been successfully integrated into an eye detection framework capable of processing over 50 fps working with a 16 Mpixel sensor.

Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.

Optical spectroscopic techniques have been commonly used to detect the presence of biofilm-forming pathogens (bacteria and fungi) in the agro-food industry. Recently, near-infrared (NIR) spectroscopy revealed that it is also possible to detect the presence of viruses in animal and vegetal tissues. Here we report a platform based on visible and NIR (VNIR) hyperspectral imaging for non-contact, reagent free detection and quantification of laboratory-engineered viral particles in fluid samples (liquid droplets and dry residue) using both partial least square-discriminant analysis and artificial feed-forward neural networks. The detection was successfully achieved in preparations of phosphate buffered solution and artificial saliva, with an equivalent pixel volume of 4 nL and lowest concentration of 800 TU·upmuL−1. This method constitutes an innovative approach that could be potentially used at point of care for rapid mass screening of viral infectious diseases and monitoring of the SARS-CoV-2 pandemic.

A 1,1,2,2-tetrakis(4-aminophenyl)ethene with three paths of π-conjugation, linear-cis, linear-trans and a cross-conjugation, has been prepared. The molecule is able to bind to gold electrodes forming molecular junctions for single-molecule conductance measurements. Only two regimes of conduction are found experimentally. The modelling of the conductance allows to assign them to through-bond transmission in the linear case, while the cross-conjugated channel is further assisted by through-space transmission, partially alleviating the destructive quantum interference.

Fluoreno[2,1-a]fluorene, a molecule comprising fused ortho-quinodimethane units in a 1,5-napthoquinodimethane core, has been prepared and investigated with spectroscopy (UV-Vis-NIR, 1H-NMR and Raman), SQUID magnetometry, spectroelectrochemistry and quantum chemistry. While para-quinodimethanes with a 2,6-substitution pattern exist as closed-shell species and meta-quinodimethanes with 2,7-substitution favour a ground electronic state with very large diradical character, our 1,5-substituted ortho-naphthoquinodimethane-based system exhibits an intermediate degree of diradical character.

This paper describes the mechanical design and a cognition system for a novel concept-of-camera robotic assistant. The system combines the advantages of intra-abdominal devices and autonomous camera navigation. The robotic assistant is composed of a magnetic intra-abdominal camera robot with two internal cable-driven degrees of freedom and an external robot that handles an external magnet. The intelligence of the robot is implemented in a cognitive architecture based on a long-term memory that stores the robot's knowledge and learning capabilities to improve the robot's behavior. The navigation strategy combines a reactive control based on instrument tracking and a proactive control based on predefined behaviors, depending on the actual state of the task. The robot's learning capabilities include a semantic customization, to adapt the camera's behavior to the surgeon's preferences, and reinforcement learning, to improve the camera navigation strategy. This paper details both the hardware implementation of the system and the software implementation in a robotic operating system architecture. The cognition system and the performance of the cable-driven mechanism have been validated with a set of in vitro experiments. Moreover, the camera robot has been evaluated through an in-vivo experiment in a pig.

Copper (Cu) and zinc (Zn) with their unique properties are central for economic growth, quality of life, and the creation of new jobs. The base-metal producing sector is, however, under growing public pressure in respect to energy and water requirements and needs to meet several challenges, including increased demand and lower ore grades, which are generally associated with larger resource use. The development of technologies for metal production from secondary sources is often motivated by increased sustainability, and this paper aims to provide further insights about one specific aspect of sustainability—namely, climate change. The paper presents a review of carbon footprints (CF) for Cu and Zn produced from primary and secondary raw materials by analyzing data taken from scientific literature and the Ecoinvent database. Comparisons are carried out based on the source of data selected as a reference case. The data available in the literature indicate that secondary production of Cu and Zn has the potential to be more beneficial compared to primary production regarding the impact on climate change. However, the technologies used today for the production of both metals from secondary sources are still immature, and more research on this topic is needed. The general variation of data suggests that the standardization of a comparison is needed when assessing the environmental benefits of production in line with the principles of waste valorization, the zero waste approach, and circular economy.

The differences between alternative approaches for quantifying the complex effect of the electronic structure of charged metal-molecule hybrids in SERS (the so-called chemical enhancement mechanism) are highlighted. The discussion is focussed on the predictions obtained by using different methodological tools for modelling the role of the electrode potential in the SERS relative intensities. Finite electric fields are used in the commented paper for this purpose, but we have found some inconsistences in the results and the method for calculating the Raman intensities is not made sufficiently clear, which prevents the assessment of the results.