You thought it would never happen, but you´re watching it happen. Your world upsidedown at an unexpected speed. Ecologists announced a different world according to their believes, but it turns out that in the end it will be the cold sceptics of the Excel sheet who will do it. Ukraine war has caused an energetic crisis, and we wil se if it won´t also be food, that it doesn´t only brings us high energy prices, but also could cause shortage of gas, petroleum and offshots.
We are seeing that in order to resolve this situation it is being proposed to tap into Europe´s subsoil resources, especially shale gas, and to increase generation capcity based on nuclear fission. All these measures could serve to alleviate the energy crisis, although it does not seem at this stage to be willing to disengage from greenhouse gas and pollutant emissions. So it is likely that we will not see much hydraulic breakup, we will probably see more nuclear reactors and, above all, we may see a strengthening of the energy efficiency and renewable generation policies that the European Union has been promoting for some time. And it will not be for environmental reasons, but simply to maintain an economic system that does not take us back to the 18th century.
The sun and its child, the wind, will increase their weight in the electric system faster than expected if access to the raw materials needed to manufacture generators is not interrupted. The stoarge of energy could be developed with intensity and we end up getting acquainted with hydrogen as we have made in the past with butane. But surely what we have the hardest time getting usd to would be the new figures that will appear in the energy system management.
The energy communities are one of the news that are getting shape in Spain. Although still aren´t frequent, there are several examples of people that joint to generate and manage the energy they consume. The downgrading of the photovoltaic panels favours their installation in domestic roofs, which achieves that generation and consumption are close. Energy management could be done from the cloud thansk to Internet of Things and specialized companies could offer this service to communities. Hydrogen and batteries seems to be called to be the energy storage medium, although it will depend on the cost and availability of raw materials. Internet of Things woul allow to manage demand flexibility inside the community. It seems to start being possible that a group more or less big of citizens constitute their own electricity generation company.
But for these participative companies, this capitalism at a human scale, could be possible, we have to defeat some obstacles. And leaving aside reluctance to change, the mosr important is the cost of setting up such a community. Are being made huge efforts to understand people motivations1 to get involved in an energy community and to design mechanisms to set them in motion2, but perhaps not as much effort is being put into designing the business models that would make them economically viable.
We can think of some business models for energy communities. The most clear is the save in energy purchase. If the community generates their own energy and distributes it betweent their members, they will save at least the trasnport tolls that are payed in a conventional bill. Other possible business would be the sale of energy surplus, but current legislation imposes limitations on the distance at which the buyer can be located. The demand flexibility could also give rise to another businees model based on promote a distribution grid of auxiliary services, but this is not easy. If this were to be attempted through balancing markets, the regulations impose minimum power values that will be difficult for many communities to achieve. Moreover, it should be borne in mind that it is not possible to interact with the network without complying with a whole series of complex technical rules. It becomes necessary the independent aggregator figure, which is already provided for in existing legislation, but which is not fully developed and which would have to intermediate between the community and the electricity grid. These problems could be solve if they existed energy local markets or flexibility markets, but in Spain are in an embryonic state and it will still take some time to see them in operation.
But, despite of these deficiencies, nowadays energetic crisis overview joint with the directives that came from the European Union will boost the development of energy communities. The problem will be finding resources to do so. Administrations and the cold sceptics of Excel spreadsheets who come up with innovative business models may have the last word.
Cultural and Natural Heritage (CNH) are irreplaceable sources of life and inspiration, according to the UNESCO definition. Europe´s rural areas represent outstanding examples of cultural, either tangible or intangible, and antural heritage that need, not only to be safeguarded, but also promoted as an engine for competitiveness, growth and sustainable and inclusive deveopment1. According to the PAHIS 2020 Plan2 , there has been a deepening of the so-called Cultural Heritage Economy in recent years, in accordance with current criteria which establish that cultural heritage assets should no longer be perceived as a burden but as a resource capable of generating development and social cohesion. This post gives a brief summary into the study of computer technologies applied to modelling and monitoring how the CNH can support the sustainable development of rural areas.
The EU communication “A Long-Term Vision for the EU´s Rural Areas”3 mentions the EU Rural Observatory, whose main objective is to further improve data collection and analysis on rural areas, but first results are expected by the end of 2022. This observatory is intended to increase the quantity and quality of available data as this is essential to understand the rural conditions to act on them properly.
Photo author: Santiago Sierra Durán (Salento, Colombia)
Rural areas are facing challenges such as ageing and depopulation. Heritage based regeneration plans can contribute to the sustainable development of these rural areas. This is a complex task, however, where a trade-off among the different regeneration plans and the limited available resources should be found and where computational methods can be useful to predict the best strategy.
One common approach when facing situations like this is through the analysis of some selected best practices or success stories (aka Role Models), and how innovation activities and cross-cutting themes successfully interacted in these Role Models. Then, these lessons learnt are adapted and replicated in other rural areas )aka replicators) for supporting the creation and implementation of heritage-led regeneration strategies.
In order to get quantifiable evidences, compara and appraise the effectiveness, impact and validity of the heritage-led regeneration actions, it is necessary to establish a robust monitoring systems based on a set of selected corss-thematic and multiscale Key Performance Indicators (KPIs) and evaluation procedures that ensure the production of a solid and reliable impact assessment of the strategies. Parameters obtained from role models and replicators baseline have been used to define an initial set of KPIs, which has been used for the first appraisal of the replicators baseline.
The methodology developed here allows to analysing an initial set of indicators as large as needed and, via several objective criteria, reduce the set of KPIs to a number that can be easily handled. But probably, the resulting set of KPIs will be diverse and not so easy to combine or compare, so group decision making techniques are useful to reach a trade-off among the experts´ opinions about how to combine the data from the indicators and get meaningful KPIs.
The impact of the strategies is assessed through KPIs in terms of Cultural and Natural Heritage according to the Communities Capital Framework (CCF). The KPIs intially considered for each replicator are re-tailored and further analysed by means of System Dynamics (SD), a suitable modelling technique for dealing with the nonlinear behaviour of complex systems over time suing sotcks, flows, internal feedback loops and time delays.
The RURITAGE project has identified 6 Systemic Innovation Areas (pilgrimage; sustainable local food production; migration; art & festivals; resilience; and integrated landscape management) which, integrated with cross-cutting themes, show case heritage potential as an engine for economic, social and environmental development of rural areas. CARTIF is in charge of developing the monitoring platfomr for assessing the impact of the action plans to regenerate the rural areas. Several dashboards have been designed focusing on KPIs values and their evolution4. RURITAGE has developed and set up a monitoring scheme to assess the performance pf the deployed regeneration action plans in six replicators. Performance monitoring is still ongoing and will last 2.5 years within project life.
1 RURITAGE, Rural regeneration through systemic heritage-led strategies, 2018. (https://www.ruritage.eu) Horizon 2020, Grant agreement No 776465.
2 Consejería de Cultura y Turismo, Plan PAHIS 2020 del Patrimonio Cultural de Castilla y León, Junta de Castilla y León. Consejería de Cultura y Turismo, 2015.
It is common knowledge that the moon goes through phases depending on its relative position between the earth and the sun. Thanks to that nights can be a showcase for looking to the starry skies or the perfect environment so that lycanthropes can take on vampires.
In science there are also phases, and the phase shifting, relative to the state in which matter is found. However, changes in this case have to do with temperature and heat and not with the states of the moon.
State transitions, have an important advantage and is that they are produced at constant temperature, allowing the matter to acquire and give up heat without changing temperature and thus reducing the impact over the environment that surrounds them. Are changes that, unlike the transformation suffered by David Naughton in “American Men in London” (film that achieve an Oscar in 1981 to the best makeup), aren´ t visible, but they are perceived.
The application of phase change materials, in particular those that have inside the homes usual transition temperatures between 18ºC-25ºC, can be used as recoveries in walls with which can reach a bigger comfort by stabilising the inside radiant temperature. It´ s not rare to found homes that because of a bad insulation are like thermic vampires, they remove us the heat, increasing the energy bill.
Inside the SUDO-SUDOKET project, which objective is the development of Key Enabling Technologies (KET) applied to innovative buildings, phase change materials dissolved in mortars have been studied to check its effect over the inside comfort conditions, as well as the effect over the climatization consume.
The results of the project had led to conclusions such as that a better stabilization of inside temperatures are reached if the radiant temperature is improved and, moreover, a reduction in the consume of climatization equipment, reaching energy saving, working as if it were a ring of garlic tied around the neck of our air-conditioning system.
The same as our favourite satellite goes from new to full, the enclosures of our homes will evolve to a future with a better control in superficial temperature and evenmore with adaptative enclosures that change of phase depending on the outside conditions.
Acknowledgments
The work has been done inside SUDOKET – mapping, consolidation and dissemination of Key Enabling Technologies (KETs) project for the construction sector at the SUDOE space, ref: SOE2/P1/E0677 that is co-financed by the Europeand Found of Regional Development (FEDER) through the INTERREG SUDOE programme.
Computer vision is a discipline that has made it possible to control different production processes in industry and other sectors for many years. Actions as common as the shopping process in a supermarket require vision techniques such as scanning barcodes.
Until a few years ago, many problems could not be solved in a simple way with classical vision techniques. Identifying people or objects located at different positions in images or classifying certain types of inhomogeneous industrial defects were highly complex tasks that often did not provide accurate results.
Advances in Artificial Intelligence (AI) have also accompanied the field of vision. While Alan Turing established the Turing test in 1950, where a person and a machine were placed behind a wall, and another person asked questions trying to discover who was the person and who was the machine, in computer vision through AI, systems capable of reproducing the behaviour of humans are sought.
One of the fields of AI is neural networks. Used for decades, it was not unitl 2012 that they began to play an important role in the field of vision. AlexNet1 , designed by Alex Krizhevsky, was one of the first networks to implement the 8-layer convolution filter design. Years earlier, a worldwide championship had been established where the strongest algorithms tried to correctly classify images from ImageNet2 , a database with 14 million images representing 1,000 different categories. While the best of the classical algorithms, using SIFT and Fisher vectors, achieved 50.9% accuracy in classifying ImageNet images, AlexNet brought the accuracy to 63.3%. This result was a milestone and represented the beginning of the exploration of Deep Learning (DL). Since 2012, the study of deep neural networks has deepened greatly, creating models with more than 200 layers of depth and taking ImageNet´ s classification accuracy to over 90% with the CoAtNet3 model. which integrates convolution layers with attention layers in an intelligent, deep wise way.
Turning to the relationship of modern computer vision models to AI, Dodge et. al (2017)4 found that modern neural networks classifying ImageNet images made fewer errors than humans themselves, showing that computer systems are capable of doing tasks better and much faster than people.
Among the most common problem solved by computer vision using AI are: image classification, object detection and segmentation, skeleton recognition (both human and object), one shot learning, re-identification, etc. Many of the problems are solved in two dimensions as well as in 3D.
Various vision problems solved by AI: Segmentation, classification, object detection
Classification simply tells us what an image corresponds to. So for example, a system could tell whether an image has a cat or a dog in it. Object detection allows us to identify several objects in an image and delimit the rectangle in which they have been found. For example, we could detect several dogs and cats. Segmentation allows us to identify the boundaries of the object, not just a rectangle. There are techniques that allow us to segment without knowing what is being segmented, and techniques that allow us to segment knowing the type of object we are segmenting, for example a cat.
Skeletal recognition allows a multitude of applications, ranging from security issues to the recognition of activities and their subsequent reproduction in a robot. In addition, there are techniques to obtain key points from images, such as points on a person´ s face, or techniques to obtain three-dimensional orientation from 2D images.
Industry segmentation using MaskRCNN5
One Shot Learning allows a model to classify images from a single known sample of the class. This technique, typically implemented with Siamese neural networks, avoids the need to obtain thousands of images of each class to train a model. In the same way, re-identification systems are able to re-identify a person or object from a single image.
The high computational cost of DL models led early on to the search for computational alternatives to CPUs, the main processors in computers. GPUs, or graphics processing units, which were originally developed to perform parallel computations for smoothly generating images for graphics applications or video games, proved to be perfectly suited to parallelising the training of neural networks. In neural network training there are two main stages, forward and back-propagation. During the forward process, images enter the network and pass through successive layers that apply different filters in order to extract salient features and reduce dimensionality. Finally, one or more layers are responsible for the actual classification, detection or segmentation. In backward propagation, the different parameters and weights used by the network are updated, in a process that goes from the output, comparing the obtained and expected output, to the input. The forward process can be parallelised by creating batches of images. Depending on the memory size of the GPUs, copies of the model are created that process all images in a batch in parallel. The larger the batch size we can process, the faster the training will be. This same mechanism is used during the inference process, a process that also allows parallelisation to be used. In recent years, some cloud providers have started to use Tensor Processing Units (TPUs), with certain advantages over GPUs. However, the cost of using these services is often high when performing massive processing.
Skeleton acquisition, activity recognition and reproduction on a Pepper robot6
CARTIF has significant deep neural network training systems, which allows us to solve problems of high computational complexity in a relatively short time. In addition, we have refined several training algorithms using the latest neural networks7 . We have also refined One Shot Learning systems using Siamese networks8. We also use state-of-the-art models in tasks such as object and human recognition, segmentation and detection, image classification, including industrial defects, and human-robot interaction systems using advanced vision algorithms.
1Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
2Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … & Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3), 211-252.
3Dai, Z., Liu, H., Le, Q., & Tan, M. (2021). Coatnet: Marrying convolution and attention for all data sizes. Advances in Neural Information Processing Systems, 34.
4 Dodge, S., & Karam, L. (2017, July). A study and comparison of human and deep learning recognition performance under visual distortions. In 2017 26th international conference on computer communication and networks (ICCCN) (pp. 1-7). IEEE.
5He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
6Domingo, J. D., Gómez-García-Bermejo, J., & Zalama, E. (2021). Visual recognition of gymnastic exercise sequences. Application to supervision and robot learning by demonstration. Robotics and Autonomous Systems, 143, 103830.
7Domingo, J. D., Aparicio, R. M., & Rodrigo, L. M. G. (2022). Cross Validation Voting for Improving CNN Classification in Grocery Products. IEEE Access.
8Duque Domingo, J., Medina Aparicio, R., & González Rodrigo, L. M. (2021). Improvement of One-Shot-Learning by Integrating a Convolutional Neural Network and an Image Descriptor into a Siamese Neural Network. Applied Sciences, 11(17), 7839.
I´ m on my way to work and I hear an ad for a soft drink on the radio; during my break I see that my favourite singer encourages me to try it on social netwroks, he tells me it´ s great!; in the afternoon I go to the supermarket and I find a promotion of that soft drink which I can try it for free and there is also a 3X2 promotion; at night I´ m watching a series with my family and I see how the main character drinks that same soft drink with the brand clearly visible and shows an incredible satisfaction after drinking it… where is the limit between advertising and influence?
I am an adult with critical sense who can make the decision to consume a product or not, but… what about a child? Can we consider that children are free to make healthy choices taking into account all the advertising environment that surrounds us?
In Spain, 40.6% of children between 6 and 9 years of age suffer from overweight and obesity1, alarming figures similar to those of other countries such as United States or Mexico. The prevalence of childhood obesity in Spain is among the highest in Europe according to the WHO.
Today´ s lifestyle has changed drastically in recent decades and is believed to be responsible for the increase in overweight and obesity in all age groups and especially in childhood: children now consume more fast food and sugary drinks, eat away from home more often and spend less time eating as a family than previous generations. In addition, processed foods are more accessible than ever and are available in larger portions. Moreover, television and Internet use have led to a more inactive and sedentary lifestyle, as well as greater exposure to the marketing of products high in fat, sugar and/or salt (known as HFSS).
It is clear that to reverse this high prevalence of overweight and obesity in children, there is no single solution but it must be a set of actions aimed at reducing sedentary lifestyles and increasing energy expenditure in addition to improving consumption decisions towards healthier products, but, I ask again the question from before, can we expect a child to make healthy consumption decisions when in their daily life they have so many impacts of unhealthy products specifically aimed at children? According to a study by the OCU (Consumers and Users Organization), nine out of ten food advertisements aimed at children are for products with an unhealthy nutritional profile2 : cookies, breakfast cereals, industrial pastries, chocolates, enregy drinks. And many of them are advertised by influential characters or cartoons, accompanied by promotional gifts or collectible stickers that encourage repeat purchases and capture the interest of children, or endorsed by certain health associations.
In terms of advertising, there is some consensus3 that until the age of five, children are incapable of perceiving the differences betweent programming and advertisements, or that they do not begin to identify a persuasive interest in advertising until they are about eight years old. Not even after the age of eight it is guaranteed that minors will be able to identify messages as biased, since, as adults know, they tend to emphasize the positive aspects and ignore the negative aspects of the product.
In Spain, the PAOS Code was signed in 2005 with the aim of establishing a set of rules to regulate advertising and promotional activities aimed at children and to guide companies to comply with it. However, reality shows that children continue to be the target of many unhealthy food advertising and the figures for overweight and obesity continue to be alarming.
For this reason, the Ministry of Consumer Affairs intends to approve a Royal Decree regulating the broadcasting of unhealthy food and beverage advertising when it is aimed at children and adolescents up to 16 years of age.
The regulation that will start to be applied in this 2022 year will affect five categories of products that will not be allowed to advertise to children under 16 regardless of the nutrient content: chocolate and sugar confectionery products, energy bars and sweet toppings and desserts; pastry and biscuit products; juices; energy drinks and ice creams. For the rest of the product categories, a limit of nutrient content per 100 grams is established. In this case, they may be advertised as long as total and saturated fats, total and added sugar and salt levels remain below the limits established for each product. These limits correspond to the nutritional profiles established by the World Health Organization.
Advertising on television, radio, cinema and internet, social networks, websites or mobile apps will be regulated and there will be limitations on advertising in print media. There will be reinforced protection schedules in generalist television channels set from Monday to Friday, between 08:00 and 09:00 in the morning and from 17:00 to 20:00 hours in the afternoon, and on Saturdays and Sundays, between 09:00 and 12:00 hours, while the prohibition in children´ s television channels will be permanent.
Source: Consume Ministry
The intention of Royal Decree is in line with the recommendations of the European Commission in its Action Plan against Childhood Obesity and which is already applied in countries such as Norway, Portugal or the United Kingdom. In 2017, the European Commission published a report on the exposure of children to HFSS food advertising and marketing4. Some of the conclusions of this study were:
64% of food and beverage advertisements for children under 18 were for HFSS products.
A child under the age of 12 may be exposed to a total of 732 HFSS ads in a month.
80% of online HFSS ads are advertised on YouTube and 20% on traditional web pages
The most promoted category is sweet snacks.
Children see approximately 10 times more HFSS ads than health food ads in Romania, 6 times more in Sweden and 3.5 times more in Lithuania and Italy.
At the food industry level, there are also initiatives to adapt and improve this situation. This is the case of the EU Pledge initiative that promotes among its members the commitment, by January 1, 2022, in relation to the restrictions on the marketing of HFSS products, either not to advertise any food and beverage products aimed at children under 13 years of age, or only to advertise products that meet the EU Pledge nutritional criteria. The EU Pledge is currently adhered to by 23 companies that account for 80% of advertising expenditure in the EU.
The need for a regulation that regulates the advertising and promotion of unhealthy foods through all media that reach the child population is a reality. It is not only a matter of placing limits on food choices that can lead to health damage, but also of limiting the influence, incitement or suggestion of products in an unfair way, hiding their harmful condition, especially when the consumer cannot reasonably identify it.
The food industry also has a fundamental role in this task, both at the level of advertising regulation, as well as in the reformulation of existing products and in the research of other healthy and attractive options for children. From CARTIF, we continuosly collaborate with the food industry for this purpose,as in the projects PROBIOMIC (Design of new cereal products with probiotics adapted to optimal child nutrition through omic technologies) or TOLERA (Development of more effective and safer ingredients and foods, aimed at people with food allergies and intolerances), among others.
Caves were our first home but, have we stopped to think about how our ancestors felt in the cold mornings of winter? And in hot summer days? We may be surprised…
Humanity had had multiple and different homes. From the tipis of the american indians to the skyscrapers that flood nowadays the city of New York. Currently, buildings represents 40% of the energy consume and 36% of the greenhouse effects. Much of them, moreover, are from the 70s. Definetly, we need a change if we want to mitigate the climate change.
In the Palaeolithic, the first dwellings, in the form of huts made of animal skins and logs, protected our ancestors from the cold and wind. During the Neolithic period, the construction of villages with adobe houses provided our ancient inhabitants with habitable conditions. And all this without consuming a single kilowatt hour and using the resources that nature offered them to obtain certain conditions of comfort.
If we look at the evolution of buildings throughout history, we can see that adobe houses gave way to the dwellings of ancient Egypt, which were made of straw and wood. Ancient Rome introduced concrete and stone, as well as technologies such as the round arch, the arcade, the vault and the doem. Leaping forward to the Renaissance, this era marked an architectural breakthrough, including materials such as marble, stucco and tiles. Until the evolution towards the brick that makes up the majority of existing buildings. But despite the evolution in the use of materials… are we really improving our comfort conditions and the energy efficiency of buildings?
The answer today is that we need more efficient and smarter buildings, but what is stopping us froom changing the way we use buildings? Platón, in his myth of the cave, tells us that it is a lack of knowledge that hides reality from us. Extrapolated to the present day, the lack of useful and valuable information limits us when it comes to making more objective decisions, based on knowledge and reducing subjectivity.
To answer the question of how we improve the knowledge of buildings, the concept of intelligent buildings comes into play. According to the European Commission, an intelligent building is one that is connected, is able to interact with the systems around it, including users, and can be managed remotely. In other words, it has to behave interactively both with the building´ s energy sytems and with other buildings and even the users themselves. Furthermore, it changes its behaviour from reactive to pro-active to make efficient and effective use of its own resources.
The main enablers of smart buildings are new technologies. Firstly, the IoT (Internet of Things) which, in a nutshell, is defined as the connectivity through the Internet of common elements such as household appliances, cars, mobile phones, etc. It is this technology that makes it possible to turn a traditional building into a connected building, capable of providing data thanks to IoT sensors. Secondly, Artificial Intelligence, which uses data to extract knowledge; the same knwoeldge that, following Platon´ s myth, will guide us out of the cave. Artificial Intelligence is a technique capable of learning from data, extracting patterns of behaviour and predicting future situations. In this way, it is able to anticipate events and enable the building to act proactively. In other words, it is bringing human reasoning to buildings, but making decisions based on objective information.
At CARTIF, we have been working for years in the line of research for the transformation of current buildings into samrter, more comfortable and environmentally friendly buildings. Projects such as BRESAER are a clear example of this transformation. In this project, a decision-making system based on Artificial Intelligence has been developed. This solution allows the building to determine one hour in advance the energy needs to meet the comfrot conditions and to choose the available sources to heat or cool the building.
All this without forgetting that buildings are for us and, therefore, users must be the protagonists. Consumers must be better informed about the behaviour of the building, just as the building must adapt to the preferences of the inhabitant. For example, smart thermostats that learn our habits to ensure a comfortable temperature without the need to configure it. Or even detecting when we leave to switch off and stop consuming gas or electricity, which makes even more sense with today´ s prices. The example of this technology is part if the COMFOStat project.
In conclusion, smart buildings represent the perfect solution that combines today´ s better living conditions with the reduced gas emissions of old. Data and Artificial Intelligence generate the necessary knowledge that will have guided us out of the cave. If you still can´ t find your way, our door is always open to help you.
There is only one good: knowledge. There is only one evil: ignorance.
