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Background: Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices. Methods: First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology. Results: The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 − 96.26%]), whilst the specificity was 85.45% [77.45 − 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 − 95.13%) and 87.85% (80.12 − 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 − 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy. Conclusions: Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics. [ABSTRACT FROM AUTHOR]

The impact of artificial intelligence on people’s lives is demonstrated today. Previous literature has shown that the use of a specific technology is directly linked to the individuals’ intention to use it. The aim of this paper is to study the factors that determine the adoption and use of artificial intelligence and big data in Spain, using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. (2003). This work addresses the specific gap in the validation of the original theoretical model of UTAUT in two dimensions, with respect to the adoption of artificial intelligence by citizens and with respect to the factors that influence this adoption, evaluating the previous ones and proposing some new ones considering the current context. The methodology used is based on a national survey, and it analyzes the research model using the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), which details the mediating and moderating relationships between constructs. The results show that Intention to Use has a direct positive influence on the Use of artificial Intelligence and big data, confirming previous literature. Performance Expectancy is the strongest predictor of Intention to Use, and indirectly of the adoption of artificial intelligence and big data applications. Effort Expectancy, in its application to the adoption of AI and big data by citizens, is an indirect determinant mediated by the Intention to Use, but its total effect (direct + indirect) is not significant. [ABSTRACT FROM AUTHOR]

The role of artificial intelligence and its place in the new disinformation strategies is perhaps one of the most difficult issues to focus on nowadays, since we are at the beginning of a process of definition and ways of exploration. In this paper, first of all, we analyze the different approaches that are being applied to the regulation of artificial intelligence and that may affect the different disinformation strategies that are being identified. Secondly, we study how artificial intelligence is being used to identify disinformation content. In this regard, from the point of view of verification processes, one of the main challenges is when identifying deepfakes (images and video, mainly) linked to news cycles. From this perspective, a typology of deepfakes is proposed and its main characteristics will be described according to the verifications carried out by the Spanish fact-checking organizations. Finally, a set of recommendations will be presented to work from a media literacy point of view with the identification of deepfakes. [ABSTRACT FROM AUTHOR]

This study aims to understand the development trends and research structure of articles on artificial intelligence (AI) and information processing in the past 10 years. In particular, this study analyzed 13,294 papers published from 2012 to 2021 in the Web of Science, used the bibliometric analysis method to visualize the data of the papers, and drew a scientific knowledge map. By exploring the development of mainstream journals, author and country rankings, keyword evolution, and research field rankings in the past 10 years, this study uncovered key trends affecting AI progress and information processing that provide insights and serve as an important reference for future AI research and information processing. The results revealed a gradual increase in publications over the past decade, with explosive growth after 2020. The most prolific researchers in this field were Xu, Z.S.; Pedrycz, W.; Herrera-Viedma, E.; the major contributing countries were China, the USA, and Spain. In the AI and information processing research, keywords including "Deep learning", "Machine learning", and "Feature extraction" are components that play a crucial role. Additionally, the most representative research areas were "Engineering", "Operations Research and Management Science", and "Automation Control Systems". Overall, this study used bibliometric analysis to provide an overview of the latest trends in artificial intelligence and information processing. Although AI and information processing have been applied to various research areas, many other sub-topics can be further applied. Based on the findings, this study presented research insights and proposed suggestions for future research directions on AI and information processing. [ABSTRACT FROM AUTHOR]

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed. [ABSTRACT FROM AUTHOR]

High technology has been one of the main assets used by the media around the world to stand out from the masses and reach an increasingly fragmented audience, including drone journalism, artificial intelligence, and virtual reality. In this regard, the mid-2010s saw a whole phase of experimentation that began with so-called immersive journalism, a new way of producing news content using virtual reality and 360° video, which introduced various novelties in the way stories are both told and consumed/watched. Among these, 360° real-image videos are the most common form, which can be partly explained by factors such as time and cost, although it is also possible to find completely computer-generated (CG) nonfiction experiences. Legacy media such as The New York Times, El País, or the BBC have been experimenting with the possibilities of this immersive storytelling, but what about digital native media? The aim of this paper is to explore the use of 360° video by Spanish digital native media in order to observe the degree of development of immersive storytelling, as well as the quality of the pieces available more than 5 years after the start of this phenomenon known as immersive journalism. For this purpose, a content analysis of 360° videos published during the period 2015-2019 by El español, eldiario.es, El independiente, and El HuffPost has been carried out. The study of these particular cases reveals the absence of a narrative strategy aligned with the immersive format; in general, these are simple videos that provide little added value compared with the conventional format. Furthermore, the results show that the history of immersive journalism by these cybermedia has been limited in terms of both volume and time, with little guarantee that it will continue into the future. [ABSTRACT FROM AUTHOR]

The active cervical range of motion (aROM) is assessed by clinicians to inform their decision-making. Even with the ability of neck motion to discriminate injured from non-injured subjects, the mechanisms to explain recovery or persistence of WAD remain unclear. There are few studies of ROM examinations with precision tools using kinematics as predictive factors of recovery rate. The present paper will evaluate the performance of an artificial neural network (ANN) using kinematic variables to predict the overall change of aROM after a period of rehabilitation in WAD patients. To achieve this goal the neck kinematics of a cohort of 1082 WAD patients (55.1% females), with mean age 37.68 (SD 12.88) years old, from across Spain were used. Prediction variables were the kinematics recorded by the EBI® 5 in routine biomechanical assessments of these patients. These include normalized ROM, speed to peak and ROM coefficient of variation. The improvement of aROM was represented by the Neck Functional Holistic Analysis Score (NFHAS). A supervised multi-layer feed-forward ANN was created to predict the change in NFHAS. The selected architecture of the ANN showed a mean squared error of 308.07–272.75 confidence interval for a 95% in the Monte Carlo cross validation. The performance of the ANN was tested with a subsample of patients not used in the training. This comparison resulted in a medium correlation with R = 0.5. The trained neural network to predict the expected difference in NFHAS between baseline and follow up showed modest results. While the overall performance is moderately correlated, the error of this prediction is still too large to use the method in clinical practice. The addition of other clinically relevant factors could further improve prediction performance. [ABSTRACT FROM AUTHOR]

As is widely acknowledged, the transportation of goods by road can, in one way or another, be linked to a range of macroeconomic indicators. A hybrid artificial intelligence system is proposed in this paper to analyse the interaction between transportation patterns and the economy. The temporal patterns of road transportation and macroeconomic trends are studied, by combining the use of both (supervised and unsupervised) neural networks and clustering techniques. The proposed system is validated, by establishing links between road transportation data from Spain and macroeconomic trends over 6 years (2011–2017). The results reveal an interesting inner structure of the data, through data visualizations of intermeshed relations between road transportation patterns and macroeconomic indicators. The same data structure was also visible in the output of the clustering techniques. Furthermore, a number of high-quality predictions were advanced by processing the road transportation data as time series, and forecasting the future values of the main series. These results demonstrated the validity of the proposed linkage between road transportation data and macroeconomic indicators. [ABSTRACT FROM AUTHOR]

• A new hybrid prediction system is proposed to predict wind speed. • Simplex and chaotic mapping optimization cuckoo algorithm are proposed. • An uncertainty prediction model based on Monte Carlo method is proposed. • The energy theory is proposed to optimize the decomposition mode number. • Theories proofs and experiments verify the effectiveness of the model. Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system. [ABSTRACT FROM AUTHOR]

This paper studies the economically significant problem of the optimization of customs fraud control, which is a critical issue for many countries. The European Union (EU) alone handles 4693 tons of goods every minute (2018 figures). Even though 70% of goods are imported at zero tariff, the EU raised EUR 25.4 billions in 2018, and customs-related income transferred by member states to the EU accounts for nearly 13% of its overall budget. In this field, (a) the conflicting objectives are qualitative and cannot be reduced to a common measure (security and terrorism, health, drug market access control, taxes, etc.); (b) each submitted item has dozens of characteristics; (c) there are constraints; and (d) risk analysis systems have to make decisions in real time. Although the World Customs Organization has promoted the use of artificial intelligence to increase the precision of controls, the problem is very complex due to the data characteristics and interpretability, which is a requirement established by customs officers. In this paper, we propose a new Bayesian-based hybrid approach combining machine learning and multi-objective linear programming (MOLP), called multi-objective Bayesian with dynamic optimization (MOBADO). We demonstrate that it is possible to more than double (with a 237% increase) the precision of current inspection systems, freeing up almost 50% of human resources, and outperform past results with respect to each of the above objectives. MOBADO is an optimization technique that could be combined with any artificial intelligence approach capable of optimizing the quality of multi-objective risk analysis in real time. [ABSTRACT FROM AUTHOR]

In studies concerned with sustainability the underlying models are, in most cases, not strictly numerical since they depend on many conditions that can be regarded as qualitative. In this paper, a model to evaluate citizens' satisfaction learnt from data collected from a survey is presented. The model, which involves the use of RBF neural networks, will provide local councillors with useful information, enabling them to evaluate trends and improve strategies focused on enhancing sustainability. In this paper a contribution describing a practical experience with a model-based system applied to a study commissioned by the town council of Vilanova i la Geltrú (Catalonia, Spain) is presented. [ABSTRACT FROM AUTHOR]

End-user development is a new trend to provide tailored services to dynamic environments such as hospitals. These services not only facilitate daily work for pharmacy personnel but also improve self-care in elder people that are still related to hospital, such as discharged patients. This paper presents an ambient intelligence (AmI) environment for End-user service provisioning in the pharmacy department of Gregorio Marañón Hospital in Madrid, composed of a drug traceability infrastructure (DP-TraIN) and a ubiquitous application for enabling the pharmacy staff to create and execute their own services for facilitating drug management and dispensing. The authors carried out a case study with various experiments where different roles from the pharmacy department of Gregorio Marañón Hospital were involved in activities such as drug identification, dispensing and medication administering. The authors analyzed the effort required to create services by pharmacy staff, the discharged patients' perception of the AmI environment and the quantifiable benefits in reducing patient waiting time for drug dispensing. [ABSTRACT FROM AUTHOR]

The paper analyses and compares the merit order effects of photovoltaic and wind power on final electricity prices in the Spanish spot market and the cost of electricity tariffs. Artificial intelligence techniques are used to model the electricity auction clearing process. Several algorithms are studied before the M5P learning algorithm is finally applied to create a tree model of the spot market. Electricity tariffs for domestic consumers are also recalculated for fictional scenarios with no photovoltaic or wind power production. The conclusion is that the influence of photovoltaic and wind power is uneven. Wind power reduces final electricity prices by €9.10/MW h, generating an overall saving for the system of €364.0 million and for the average domestic consumer of €1.95 using 2012 figures; photovoltaic power reduces electricity prices by €2.18/MW h, generating an overall cost overrun for the system of €2034.1 million and for the average domestic consumer of €38.82. [ABSTRACT FROM AUTHOR]

Limitations of formal learning (e.g., one-way communication, rigid methodology, results-oriented approach) can significantly influence the motivation and expectation of students, thus resulting in an academic progress reduction. In order to make learning processes more playful and motivating, this paper presents a new educational experience developed by two groups of Computer Science students at the University of Huelva (Spain). As a result, an authentic real experience was incorporated into the classical teaching of Artificial Intelligence courses where classroom sessions were changed during some days for an international online competition. A comprehensive study considering the competition ranking, the students' opinion and their academic progress was analysed to assess the followed methodology. We found out that the educational experience improved the students' motivation, thereby enhancing their academic performance and personal skills as a result of learning through play. Moreover, additional teaching goals (e.g., learning new programming languages or increasing exam attendance) were obtained because of the positive motivation experienced by the competition. As a conclusion, this paradigm of real-life experience - not otherwise provided by traditional practical lessons - allowed us to ascertain that the process is more important than the outcome, which could be adapted to different teaching scenarios within an institution. [ABSTRACT FROM AUTHOR]

Disinformation does not always take the form of a fake news item, it also appears in much less evident formats which are subtly filtered into public opinion, thus making its detection more difficult. A method is proposed in this paper to address the study of "widespread" disinformation by combining social science methods with artificial intelligence and text mining. The case study chosen was the expression "right of self-determination" as a generator of disinformation within the context of the Catalan independence process. The main work hypothesis was that the (intentional or unintentional) confusion around the meaning and scope of this right has become widely extended within the population, generating negative emotions which favour social polarisation. The method utilised had three stages: (1) Description of the disinformation elements surrounding the term with the help of experts; (2) Detection of these elements within a corpus of tweets; (3) Identification of the emotions expressed in the corpus. The results show that the disinformation described by experts clearly dominates the conversation about "self-determination" on Twitter and is associated with a highly negative emotional load in which contempt, hatred, and frustration prevail. [ABSTRACT FROM AUTHOR]

Abstract: Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain. [Copyright &y& Elsevier]

On-Line monitoring evaluates instrument channel performance by assessing its consistency with other plant indications. Industry and EPRI experience at several plants has shown this overall approach to be very effective in identifying instrument channels that are exhibiting degrading or inconsistent performance characteristics “On-Line Monitoring of Instrument Channel Performance by EPRI (2000)”.On-Line monitoring of instrument channels provides information about the condition of the monitored channels through accurate, more frequent monitoring of each channel''s performance over time. This type of performance monitoring is a methodology that offers an alternate approach to traditional time-directed calibration. On-line monitoring of these channels can provide an assessment of instrument performance and provide a basis for determining when adjustments are necessary. Elimination or reduction of unnecessary field calibrations can reduce associated labor costs, reduce personnel radiation exposure and reduce the potential for miss-calibration.PEANO “A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data by Fantoni (2000)” is a system for on-line calibration monitoring developed in the years 1995–2000 at the Institutt for energiteknikk (IFE), Norway, which makes use of Artificial Intelligence techniques for its purpose. The system has been tested successfully in Europe in off-line tests with EDF (France), Tecnatom (Spain) and ENEA (Italy). PEANO is currently installed and used for on-line monitoring at the HBWR reactor in Halden. This paper describes the results of performance tests on PEANO with real data from a US PWR plant, in the framework of a co-operation among IFE, EPRI and Edan Engineering, to evaluate the potentials of PEANO for future installations in US nuclear plants. [Copyright &y& Elsevier]

The purpose of the Symposium on Combinatorial Search (SoCS) is to promote the study and understanding of combinatorial search algorithms through the organization of scientific meetings, publications, tutorials, and other public scientific and educational activities. The most prominent among its activities is the annual Symposium on Combinatorial Search that has been organized regularly since 2008 as a result of the success of other scientific meetings, typically workshops co-located with high-level Artificial Intelligence conferences (AAAI and IJCAI). This paper reviews the history of the Symposium making emphasis on the last edition that was held in July 2011 in Barcelona (Spain). [ABSTRACT FROM AUTHOR]