The accelerated pace of digital transformation has significantly reshaped the cybersecurity domain, fostering an interconnected ecosystem in which cyber threats have expanded in both their complexity and scope. Traditional cybersecurity methods are increasingly inadequate for addressing the rapidly evolving threat landscape, emphasizing the critical need for intelligent, adaptive, and proactive defensive strategies. This study introduces Dynamic Industrial Cyber Risk Modelling Based on Evidence (DICYME), a comprehensive system that integrates diverse analytical techniques to identify patterns and characteristics that reveal emerging threat trends, enabling organizations to proactively defend against potential future attacks. Beyond threat detection, DICYME operates as a pipeline that retrieves data from diverse cyber incident reports, specialized databases, and other relevant sources of cyber-related information, applies specialized techniques for victim identification, indicator computation, threat actor profiling, Common Vulnerability and Exposure (CVE) relationship mapping, and ultimately performs the Cyber Risk Quantification (CRQ). This final stage represents the system's most distinctive contribution, as it translates complex analytical outputs into actionable risk insights, empowering organizations to make informed strategic decisions in the face of evolving cyber threats. Alternatively, the system implements an automatic workflow that constructs new datasets of compromised entities, enabling these datasets to be used by all components of the system. Experiments on real cyber incident datasets demonstrate the system's ability to automatically construct high-quality victim profiles and estimate annualized financial risk, offering a scalable and data-driven approach for proactive cybersecurity management.

The use of management by objectives (MBOs) methodologies, particularly the objectives and key results (OKRs) framework, has gained widespread attention in recent years as a means of improving organizational performance. This article aims to show that OKRs, a key MBO method, can enhance company performance and employee satisfaction. We conducted a comparative and retroactive analysis of MBO methods used in the 20th and 21st centuries, and surveyed 238 people in nine countries and various companies and positions. We analyzed the results of the survey and compared the variables associated with best practices in the OKR methodology. Our study examined a diverse range of companies, positions, qualifications, and ages, which provides an analysis of whether or not OKRs are being used in the organization and a comprehensive understanding of how OKRs can be implemented across in different contexts. By identifying the best variables associated with optimal performance and employee satisfaction, this article provides practical guidance for organizations seeking to adopt OKR methodology. Furthermore, this article can contribute to the development of best practices in the area of management by objectives, particularly in the context of OKRs, leading to more effective and efficient goal-setting practices in organizations.

The objective of this study is to explore how various personality types correlate with enhanced work performance. The Enneagram type of the participants in the experiment was established by using the simplest version of the Riso–Hudson test. A two-way ANOVA was performed under the principles of the Design of Experiments, which allowed the identification of main effects and interactions in the response, i.e., the marks of the university teams. We found that the interactions between certain Enneagram types seem to increase the average performance marks as a primary effect. Conversely, when certain Enneagram types coincided within a team, the marks significantly decreased, posing a risk to project success. According to our results, the Enneagram framework may be used as a preliminary stage for identifying potential team members for future projects.

Data visualization is an essential task during the lifecycle of any Data Science (DS) project, particularly during the Exploratory Data Analysis (EDA) for a correct data preparation and understanding. In classification problems, data visualization is useful for revealing the existence of class separability patterns within the dataset. This information is very valuable and can be later used during the process of building a Machine Learning (ML) model. High-Dimensional Data (HDD) arise as one of the biggest challenges in DS . HDD require special treatment since traditional visualization techniques, such as the scatterplot matrix (SPLOM) , have limitations when dealing with them due to space restrictions. Other visualization methods involve dimensionality reduction techniques, which can lead to losing important information and reducing the interpretability of the data. In this paper, the Class Separability Visualization (CSViz) method is introduced as a new Visual Analytics (VA) approach to address the challenge of visualizing labeled HDD through subspaces. The proposed method enables an overview of the class separability offering a series of 2-Dimensional subspaces visualizations containing exclusive subsets of points of the original variables that encompass the most valuable and significant separable patterns. The proposed method is tested over 50 datasets with different characteristics providing promising results. In all cases, more than 90% of the data observations are shown with three plots or less. Hence, the presented CSViz significantly eases the EDA by reducing the number of plots to be inspected in a SPLOM and thus, the amount of time invested in it. Graphical Abstract CSViz graphical abstract

Counterfactual explanations are a well-known technique in Explainable Machine Learning (XML) to provide simple explanations on complex Machine Learning (ML) models. Through understandable "what if" scenarios, counterfactuals explore how changes in the input data affect the results of a model. This article leverages counterfactual explanations for sustainable tourism, an emerging approach within the tourism industry to mitigate the negative impacts of mass tourism on ecological systems and local communities. The proposed method analyzes the relationships between several Sustainable Tourism Indicators (STIs) defined for a specific tourist destination and its general sustainability assessment. It identifies the key changes needed in the STIs to achieve an improved global sustainability score. As a result, a decision-making system is offered for sustainable tourism management, which domain experts can use to make more informed decisions. The effectiveness of the proposed method is illustrated through its application to Mallorca, a popular Spanish tourist destination.

This study comprehensively analyses the performance of the artificial intelligence (AI)-based language model, ChatGPT 4.0, in solving Spanish university admission tests in applied mathematics in social sciences. Using exams taken at public universities in Madrid, we have analysed ChatGPT’s answers and concluded that its performance varies significantly across different areas of mathematics, excelling in probability and statistics exercises, but performing significantly worse in algebra and calculus. When compared with students, ChatGPT clearly outperforms them in all areas except algebra. Despite the model’s limitations in interpreting complex mathematical ideas, in some cases its responses are positively surprising, indicating its potential as a valuable tool in certain mathematical problem-solving scenarios. Our results suggest significant potential for the introduction of these AI-based systems into the classroom. Despite the progress made, much remains to be explored regarding the efficient integration of chatbots into course development and the subsequent impact on education.

In this paper, we assess the efficacy of ChatGPT (version Feb 2023), a large-scale language model, in solving probability problems typically presented in introductory computer engineering exams. Our study comprised a set of 23 probability exercises administered to students at Rey Juan Carlos University (URJC) in Madrid. The responses produced by ChatGPT were evaluated by a group of five statistics professors, who assessed them qualitatively and assigned grades based on the same criteria used for students. Our results indicate that ChatGPT surpasses the average student in terms of phrasing, organization, and logical reasoning. The model's performance remained consistent for both the Spanish and English versions of the exercises. However, ChatGPT encountered difficulties in executing basic numerical operations. Our experiments demonstrate that requesting ChatGPT to provide the solution in the form of an R script proved to be an effective approach for overcoming these limitations. In summary, our results indicate that ChatGPT surpasses the average student in solving probability problems commonly presented in introductory computer engineering exams. Nonetheless, the model exhibits limitations in reasoning around certain probability concepts. The model's ability to deliver high-quality explanations and illustrate solutions in any programming language, coupled with its performance in solving probability exercises, suggests that large language models have the potential to serve as learning assistants.

BACKGROUND:Statistical Process Control (SPC) is a powerful statistical tool that can be used in animal production to evaluate the evolution of production parameters overtime in response to the implementation of a specific strategy. The aim of this study was to evaluate the effect of supplementing growing-finishing pigs with isoquinoline alkaloids (IQ) on growth performance parameters by using the SPC method. IQ are natural secondary plant metabolites which have been extensively investigated in food animals due to their efficacy in supporting growth performance and the overall health status. Performance parameters and medication usage were collected from 1,283,880 growing-finishing pigs fed the same basal diet, 147,727 of which were supplemented with IQ from day 70 of life until slaughter.RESULTS:Supplementation with IQ improved feed conversion ratio, while feed intake and daily gain were maintained.CONCLUSION:SPC methods are useful statistical tools to evaluate the effect of using a new feed additive in the feed of pigs on growth performance at a commercial level. Additionally, IQ supplementation improved growth performance and it can be considered as a good strategy to reduce feed conversion in growing-finishing pigs.

INTRODUCTION:BNT162b2 (BioNTech and Pfizer) is a nucleoside-modified mRNA vaccine that provides protection against SARS-CoV-2 infection and is generally well tolerated. However, data about its efficacy, immunogenicity and safety in people of old age or with underlying chronic conditions are scarce. PURPOSE:To describe BNT162b2 (BioNTech and Pfizer) COVID-19 vaccine immunogenicity, effectiveness and reactogenicity after complete vaccination (two doses), and immunogenicity and reactogenicity after one booster, in elders residing in nursing homes (NH) and healthy NH workers in real-life conditions. METHODS:Observational, ambispective, multicenter study. Older adults and health workers were recruited from three nursing homes of a private hospital corporation located in three Spanish cities. The primary vaccination was carried out between January and March 2021. The follow-up was 13 months. Humoral immunity, adverse events, SARS-CoV-2 infections, hospitalizations and deaths were evaluated. Cellular immunity was assessed in a participant subset. RESULTS:A total of 181 residents (mean age 84.1 years; 89.9% females, Charlson index ≥2: 45%) and 148 members of staff (mean age 45.2 years; 70.2% females) were surveyed (n:329). After primary vaccination of 327 participants, vaccine response in both groups was similar; ≈70% of participants, regardless of the group, had an antibody titer above the cut-off considered currently protective (260BAU/ml). This proportion increased significantly to ≈ 98% after the booster (p<0.0001 in both groups). Immunogenicity was largely determined by a prior history of COVID-19 infection. Twenty residents and 3 workers were tested for cellular immunity. There was evidence of cellular immunity after primary vaccination and after booster. During the study, one resident was hospitalized for SARS-CoV-2. No SARS-CoV-2-related deaths were reported and most adverse events were mild. CONCLUSIONS:Our results suggest that the BNT162b2 mRNA COVID-19 vaccine is immunogenic, effective and safe in elderly NH residents with underlying chronic conditions.

EMPOWERING ACADEMIC PERFORMANCE: DATA-DRIVEN MENTORING FOR PERSONALIZED EDUCATION THROUGH LEARNING ANALYTICS

E. López Cano M. Cuesta C. Lancho C. Alfaro M.J. Algar A. Alonso-Ayuso A. Fernández-Isabel J. Gomez I. Martin de Diego J. Moguerza F. Ortega A. Udias

Sensors have become a key element for the development of the Information Society [...].

In this paper, a method to classify behavioural patterns of cattle on farms is presented. Animals were equipped with low-cost 3-D accelerometers and GPS sensors, embedded in a commercial device attached to the neck. Accelerometer signals were sampled at 10 Hz, and data from each axis was independently processed to extract 108 features in the time and frequency domains. A total of 238 activity patterns, corresponding to four different classes (grazing, ruminating, laying and steady standing), with duration ranging from few seconds to several minutes, were recorded on video and matched to accelerometer raw data to train a random forest machine learning classifier. GPS location was sampled every 5 min, to reduce battery consumption, and analysed via the k-medoids unsupervised machine learning algorithm to track location and spatial scatter of herds. Results indicate good accuracy for classification from accelerometer records, with best accuracy (0.93) for grazing. The complementary application of both methods to monitor activities of interest, such as sustainable pasture consumption in small and mid-size farms, and to detect anomalous events is also explored. Results encourage replicating the experiment in other farms, to consolidate the proposed strategy.

Context: There are multiple papers in the literature discussing the way in which the technical skills of team members affect the final outcome. However, less work is published about how the soft skills of the team members affect the final outcome. In this paper we investigate that by using the Enneagram model. Objectives: In this paper we investigate the impact of soft skills in achieving results using a personality model. The aim is to examine the results of mix- ing the different personalities of the Enneagram model for team formation on different software development topics. Method: The Enneagram type for each participant in the study was deter- mined using the Riso-Hudson Test in its simplest version. For the statistical analysis, a two-way ANOVA was performed under the Design of Experiments principles, that allowed to identify main effects and interactions in the response, i.e., the marks of the teams. Results: We found that two enneatypes increased the average mark as a main effect. Moreover, the interactions are the more remarkable results. There is one combination that boosts the results of the team, but there were other three dan- gerous team configurations that may spoil a project as, when those enneatypes coincide in a team, the marks significantly lower down. Conclusions: According to our results we could recommend before starting a new project, to classify the team members using the Enneagram and create the team following our results, in order to make a more efficient team. Always trying to avoid configurations that we know, based on the results obtained, are detrimental to the overall performance of the project.

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 6177-6185ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1454Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5800-5806ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1361Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain

Statistical process control (SPC) is a statistical method that can be used to evaluate the production variation in swine operations, thus facilitating decision making. The objective of this study was to evaluate the effect of plant-derived isoquinoline alkaloids (IQs) supplementation on production performance of growing-finishing pigs by using SPC.The experiment was carried out in a commercial swine integration in Spain. Historical control (calibration) data was compiled from 2017 to June 2020. During this period, all animals received a standard commercial diet. During the treatment period, which started in July 2020 and lasted until February 2021, all pigs were fed the standard diet supplemented with 1 kg/t feed of a plant-based IQ product (Phytobiotics Futterzusatzstoffe GmbH, Eltville, Germany), from day 70 of life until slaughter. Data recorded during both, historical control and treatment periods included feed conversion ratio (FCR), average daily gain (ADG, g/d), average daily feed intake (ADFI, g/d), cost of medicines (Euro/pig), runts (%) and mortality rate (%). SPC tools were used to monitor the previously described performance parameters. CUSUM control charts were obtained for all parameters and for each nutritional and health cluster to show the evolution or the changes of each parameter. The results showed that pigs supplemented with IQs had a lower FCR, increased ADG and reduced cost in medications as compared to the control period (p ≤ 0.05; Table 1). SPC methods were successfully implemented to evaluate the effect of IQs supplementation on growth performance of grow-finish pigs. The results of the study indicated that IQs supplementation improved FCR and ADG, whereas the cost of medication was significantly reduced. Therefore, IQs supplementation in pigs from day 70 of life until slaughter could be a good strategy to improve the efficiency and profitability of the production system.

Some of the most overlooked valuation systems in current literature are those based on expert algorithms. Yet these algorithms can form the basis of a good estimation of the value of real estate since they allow simple computational methods that use big data to be integrated with the appraiser' own knowledge of the situation. The main usefulness of the methodology is an ongoing mortgage risk appraisal for banking institutions. The current expert algorithms based on the sales comparison approach use the arithmetic mean of the comparable prices. But this mean gives equal importance to all neighbouring dwellings instead of giving more importance to those dwellings which are more similar and are nearer to the target dwelling. Improving the classical arithmetic mean or the more robust median, this article proposes a computer-assisted expert algorithm which includes a weighted estimator able to consider the differences in characteristics compared to similar properties and their relative locations. It allows to estimate, in a simple and rapid way using objective criteria, the value of any residential property in Spain. The results show good fit for large cities in terms of the usual error margins while improving the results with regards to smaller cities. In all cases, in terms of mean absolute percentage error, the weighted estimator improves the arithmetic mean or median results.

The success on the design of new oral nanocarriers greatly depends on the identification of the best physicochemical properties that would allow their diffusion across the mucus layer that protects the intestinal epithelium. In this context, particle tracking (PT) has arisen in the pharmaceutical field as an excellent tool to evaluate the diffusion of individual particles across the intestinal mucus. In PT, the trajectories of individual particles are characterized by the mean square displacement (MSD), which is used to calculate the coefficient of diffusion (D) and the anomalous diffusion parameter (α) as MSD=4Dτα. Unfortunately, there is no stablished criteria to evaluate the goodness-of-fit of the experimental data to the mathematical model. This work shows that the commonly used R2 parameter may lead to an overestimation of the diffusion capacity of oral nanocarriers. We propose a screening approach based on a combination of R2 with further statistical parameters. We have analyzed the effect of this approach to study the intestinal mucodiffusion of lipid oral nanocarriers, compared to the conventional screening approach. Last, we have developed software able to perform the whole PT analysis in a time-saving, user-friendly, and rational fashion.