Coastal marine ecosystems face mounting pressures from anthropogenic activities and climate change, necessitating advanced monitoring and modeling approaches for effective management. This paper pioneers the development of a Marine Digital Twin Platform aimed at modeling the Mar Menor Coastal Lagoon Ecosystem in the Region of Murcia. The platform leverages Artificial Intelligence to emulate complex hydrological and ecological models, facilitating the simulation of what-if scenarios to predict ecosystem responses to various stressors. We integrate diverse datasets from public sources to construct a comprehensive digital representation of the lagoon’s dynamics. The platform’s modular design enables real-time stakeholder engagement and informed decision-making in marine management. Our work contributes to the ongoing discourse on advancing marine science through innovative digital twin technologies.

Efficient water management in agriculture is a critical challenge to ensure sustainability, especially in water-constrained regions. Advanced technologies, such as FloraPulse microtensiometers, offer an innovative solution by enabling accurate, real-time monitoring of plant water status by measuring Trunk Water Potential (TWP). This information is essential for assessing water stress and optimising irrigation decisions. This work is the first stage of an overall objective to predict the optimal timing and amount of irrigation. It is important to note that the current models were trained and tested using data from the 2023 season, when all trees were irrigated to 100% of E T c . In this first phase, predictive models are developed that uses advanced Machine Learning (ML) and Deep Learning (DL) techniques to estimate TWP, providing a key tool to anticipate plant water stress as a function of climatic and edaphic variables. According to the initial comparison where ML models (RF, SVR and KNN) and DL model (LSTM) were implemented without using the previous TWP values as input to make the next hour prediction, the LSTM model outperformed the ML models, achieving average R 2 =0.75, MAE=0.19 MPa and CVRMSE=19.22%. Using the lagged TWP values, the LSTM model showed a significant improvement, with average R 2 =0.98, MAE=0.036MPa, and CVRMSE=4%. In addition, LSTM models with 6, 12 and 24 h prediction horizons were developed, all with R 2 greater than 0.89, MAE less than 0.12 MPa and CVRMSE less than 17%. These results allow the precise prediction of future TWP values and thus offer the design of more accurate and efficient irrigation systems. • Trunk Water Potential (TWP) data were collected using the FloraPulse microtensiometer sensor. • Machine Learning (ML) and Deep Learning (DL) models were used to predict the TWP values. • Comparisons were made between ML (RF, SVR, and KNN) and DL (LSTM) models. • LSTM models of 1, 6, 12, and 24 h prediction horizons were implemented. • Results reveal great potential for TWP prediction and irrigation scheduling.

The recent technological advances and their applications to agriculture provide leverage for the new paradigm of smart agriculture. Remote sensing applications can help optimize resources, making agriculture more ecological, increasing productivity and helping farmers to anticipate events that could not otherwise be avoided. Considering that losses caused by anomalies such as diseases, weeds and pests account for 20-40 % of overall agricultural productivity, a successful research effort in this area would be a breakthrough for agriculture. In this paper, we propose a methodology with which to discover and classify anomalies in images of crops, taken from a wide range of distances, using different Convolutional Neural Network architectures. This methodology also deals with several difficulties that usually appear in this kind of problems, such as class imbalance, the insufficient and small variety of images, overtraining or lack of models generalisation. We have implemented four convolutional neural network architectures in a high-performance computing environment, and propose a methodology based on data augmentation with the addition of Gaussian noise to the images to solve the above problems. Our approach was tested using two well-established open datasets that are unalike: DeepWeeds, which provides a classification of 8 weed species native to Australia using images that were taken at a distance of 1 m, and Agriculture-Vision, which classifies 6 types of crop anomalies using multispectral satellite imagery. Our methodology attained accuracies of 98 % and 95.3% respectively, improving the state-of-the-art by several points. In order to ease reproducibility and model selection, we have provided a comparison in terms of computational time and other metrics, thus enabling the choice between architectures to be made according to the resources available. The complete code is available in an open repository in order to encourage reproducibility and promote scientific advances in sustainable agriculture.

Federated learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it has been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In the context of the Internet of Things (IoT), this can be exacerbated due to intrusion detection systems (IDSs), which are intended to detect security attacks by analyzing the devices’ network traffic. Our work provides a comprehensive evaluation of differential privacy techniques, which are applied during the training of an FL-enabled IDS for industrial IoT. Unlike previous approaches, we deal with nonindependent and identically distributed data over the recent ToN_IoT dataset, and compare the accuracy obtained considering different privacy requirements and aggregation functions, namely FedAvg and the recently proposed Fed+. According to our evaluation, the use of Fed+ in our setting provides similar results even when noise is included in the federated training process.

BACKGROUND: Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. RESULTS: PhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. CONCLUSIONS: We developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G' and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam . Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/ .

The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through an effective and efficient detection process. In the context of the Internet of Things (IoT), most ML-enabled IDS approaches use centralized approaches where IoT devices share their data with data centers for further analysis. To mitigate privacy concerns associated with centralized approaches, in recent years the use of Federated Learning (FL) has attracted a significant interest in different sectors, including healthcare and transport systems. However, the development of FL-enabled IDS for IoT is in its infancy, and still requires research efforts from various areas, in order to identify the main challenges for the deployment in real-world scenarios. In this direction, our work evaluates a FL-enabled IDS approach based on a multiclass classifier considering different data distributions for the detection of different attacks in an IoT scenario. In particular, we use three different settings that are obtained by partitioning the recent ToN_IoT dataset according to IoT devices’ IP address and types of attack. Furthermore, we evaluate the impact of different aggregation functions according to such setting by using the recent IBMFL framework as FL implementation. Additionally, we identify a set of challenges and future directions based on the existing literature and the analysis of our evaluation results.

The technological development of the food industry is bringing new opportunities for the optimization of cold chain logistics. Many businesses of all sizes have been capturing their trips’ data and they are ready to apply Big Data analytics and use Big Data tools in order to extract knowledge and patterns that can be useful for decision-making and therefore provide added value to their business. In this line, we developed a shiny dynamic dashboard that provides a friendly user interface to the business in order to visualize and understand their data in the form of rankings, trips duration, demand seasonality, and geographic representation of the trips. The dashboard connects seamlessly with two Big Data tools, elasticSearch and the DEEP training facility from the European Open Science Hub (EOSC). More specifically, those tools were used for data indexing and demand estimation of products using cloud computing respectively. Thanks to the interconnection of these tools, we are able to enhance the dynamic dashboard with Big Data Analytics in the form of multivariate demand prediction of the products using a Long Short Term Memory Network and ease query time and computation, creating real value for the cold chain logistics industry. The development of this solution was done with the support of the EOSC-Digital Innovation Hub initiative through a business pilot.The dashboard functionalities were implemented using a real-case dataset which cannot be shown for privacy reasons. In its place, we have simulated a meat-based products database for the purpose of this paper.

Internet of Things (IoT) enables the seamless integration of sensors, actuators, and communication devices for real-time applications. IoT systems require good quality sensor data in order to make real-time decisions. However, values are often missing from the sensor data collected owing to faulty sensors, a loss of data during communication, interference, and measurement errors. Considering the spatiotemporal nature of IoT data and the uncertainty of the data collected by sensors, we propose a new framework with which to impute missing values utilizing Bayesian maximum entropy (BME) as a convenient means to estimate the missing data from IoT applications. Missing sensor measurements adversely affect the quality of data, and consequently the performance and outcomes of IoT systems. Our proposed framework incorporates BME in order to impute missing values in diverse IoT scenarios by making use of the combination of low- and high-precision sensors. Our approach can incorporate the measurement errors of low-precision sensors as interval quantities along with the high-precision sensor measurements, making it highly suitable for real-time IoT systems. Our framework is robust to variations in data, requires less execution time, and requires only a single input parameter, thus outperforming existing IoT data imputation methods. The experimental results obtained for three IoT data sets demonstrate the superiority of the BME framework as regards accuracy, running time, and robustness. The framework can additionally be extended to distributed IoT nodes for the online imputation of missing values.

The research showed in this document consisted on monitoring 546 air conditioners of individual offices located in two large buildings for the later evaluation of the behaviors of users with respect to their controllers. Data was collected over 14 months and provided important insights about the phenomenon. It was seen that users can be separated in two groups, one that likes to interact with the controllers often and change the temperature at least once a week and another that interact less. It was seen that the variability of users with respect to the thermostat values they prefer is high, and that this should be taken into account when creating a "one fits all" solution. Also, it appears that adaptive thermal comfort theories that suggest users want lower temperatures in cold months are not reflected on the set-points chosen. In addition, we have seen that people interacting more with the controllers tend to waste less energy, this would be interesting if an app to interact with the user for this purpose is design. More communication with the user may imply less energy wasted.

The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e., data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes the Discrete Cosine Transform (DCT), and quantizes them. The key information is contained in the upper-left part of the resulting matrix. We extract this sub-matrix, compute the modulus of its eigenvalues, and remove duplicates. The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation (SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.

Energy efficiency is in the interest of everyone, from individuals to governments, since it yields economical savings, reduces greenhouse gas emissions and alleviates energy poverty. Buildings are one of the largest consumers of primary energy and attaining their efficiency is, therefore, an important goal. The Internet of Things currently provides vast amounts of data that can be used to extract knowledge of all kinds, including that regarding energy prediction. This has motivated us to test wether the prior information on the physics of building heat transfer, that is currently available is now redundant owing to the completeness of the data from the system. We propose a machine learning approach and a grey-box model approach with which to test this hypothesis. The former is blind to the physiscs of the problem, while the latter is greatly influenced by it. The energy consumption prediction models were created with both approaches and then used to estimate energy consumption in a normal operation state and compare it with energy consumption when an energy efficiency campaign is run. Our black-box method, which is based on a combination of statistical and machine learning models and on a time series structurization of the data, shows better prediction accuracy than the so-called grey-box methods that include basic physical equations. This shows that also a data driven approach outperforms more informed methods in this, like other fields.

This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.