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In this paper, we analyze the dynamics of a multi-species fisheries system in the presence of harvesting. We solve the problem of finding the optimal harvesting strategy for a mid-term horizon with a fixed final stock of each species, while maximizing the expected present value of total revenues. The problem is formulated as an optimal control problem. For its solution, we combine techniques derived from Pontryagin's Maximum Principle, cyclic coordinate descent and the shooting method. The algorithm we develop can solve problems both with inter-species competition and with predator-prey behaviors. Several numerical examples are presented to illustrate the different possibilities of the method and a study of the dependence of the behavior on some parameters is performed.

This paper presents an optimal control problem to analyze the efficacy of counter-terrorism tactics. We present an algorithm that efficiently combines the Minimum Principle of Pontryagin, the shooting method and the cyclic descent of coordinates. We also present a result that allows us to know a priori the steady state solutions. Using this technique we are able to choose parameters that reach a specific solution, of which there are two. Numerical examples are presented to illustrate the possibilities of the method. Finally, we study the sufficient conditions for optimality and suggest an improvement on the functional which also guarantees local optimality.

Collisionless cross-field electron transport in an E x B configuration relevant for electric propulsion is studied using data from a (z, {\theta}) full-PIC simulation. Higher-order spectral analysis shows that transport is dominated by the in-phase interaction of the oscillations of the azimuthal electric field and the electron density associated to the first electron cyclotron drift instability (ECDI) mode. A secondary contribution emanates from a lower-frequency mode, not predicted by linear ECDI theory, while higher modes have a minor direct impact on transport. However, a bicoherence analysis reveals that strong phase couplings exist among the ECDI modes, and a sparse symbolic regression spectral model, based on the three-wave coupling equations, suggests an inverse energy cascade as the most likely explanation, thus suggesting that higher modes contribute indirectly to transport by quadratic power transfer to the first mode. This work provides new insights into the dynamics of anomalous plasma transport in E x B sources and the underlying processes governing energy distribution across different scales, and supports the validity of weak turbulence theory to examine their behavior.

Accurate identification, localization, and segregation of teeth from Cone Beam Computed Tomography (CBCT) images are essential for analyzing dental pathologies. Modeling an individual tooth can be challenging and intricate to accomplish, especially when fillings and other restorations introduce artifacts. This paper proposes a method for automatically detecting, identifying, and extracting teeth from CBCT images. Our approach involves dividing the three-dimensional images into axial slices for image detection. Teeth are pinpointed and labeled using a single-stage object detector. Subsequently, bounding boxes are delineated and identified to create three-dimensional representations of each tooth. The proposed solution has been successfully integrated into the dental analysis tool Dentomo., Comment: 7 pages, 6 figures, 4 tables

Robust road surface estimation is required for autonomous ground vehicles to navigate safely. Despite it becoming one of the main targets for autonomous mobility researchers in recent years, it is still an open problem in which cameras and LiDAR sensors have demonstrated to be adequate to predict the position, size and shape of the road a vehicle is driving on in different environments. In this work, a novel Convolutional Neural Network model is proposed for the accurate estimation of the roadway surface. Furthermore, an ablation study has been conducted to investigate how different encoding strategies affect model performance, testing 6 slightly different neural network architectures. Our model is based on the use of a Twin Encoder-Decoder Neural Network (TEDNet) for independent camera and LiDAR feature extraction, and has been trained and evaluated on the Kitti-Road dataset. Bird's Eye View projections of the camera and LiDAR data are used in this model to perform semantic segmentation on whether each pixel belongs to the road surface. The proposed method performs among other state-of-the-art methods and operates at the same frame-rate as the LiDAR and cameras, so it is adequate for its use in real-time applications., Comment: Source code: https://github.com/martin-bayon/TEDNet

An algorithm to obtain data-driven models of oscillatory phenomena in plasma space propulsion systems is presented, based on sparse regression (SINDy) and Pareto front analysis. The algorithm can incorporate physical constraints, use data bootstrapping for additional robustness, and fine-tuning to different metrics. Standard, weak and integral SINDy formulations are discussed and compared. The scheme is benchmarked in the case of breathing-mode oscillations in Hall effect thrusters, using PIC/fluid simulation data. Models of varying complexity are obtained for the average plasma properties, and shown to have a clear physical interpretability and agreement with existing 0D models in the literature. Lastly, the algorithm applied is also shown to enable the identification of physical subdomains with qualitatively different plasma dynamics, providing valuable information for more advanced modeling approaches.

Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis. Methods: We used natural language processing (NLP), ML, and DL models (including decision trees, XGB, KNN, RNN, LSTM, Bi-LSTM, BERT, and BERTweet) to analyze 404,627 tweets, classifying them based on ASD or non-ASD authors. A subset of 90,000 tweets was used for model training and testing. Results: Our AI models showed high accuracy, with an 88% success rate in identifying texts from individuals with ASD. Conclusion: The study demonstrates AI's potential in improving ASD diagnosis, especially in children, highlighting the importance of early detection.

Radio advertising remains an integral part of modern marketing strategies, with its appeal and potential for targeted reach undeniably effective. However, the dynamic nature of radio airtime and the rising trend of multiple radio spots necessitates an efficient system for monitoring advertisement broadcasts. This study investigates a novel automated radio advertisement detection technique incorporating advanced speech recognition and text classification algorithms. RadIA's approach surpasses traditional methods by eliminating the need for prior knowledge of the broadcast content. This contribution allows for detecting impromptu and newly introduced advertisements, providing a comprehensive solution for advertisement detection in radio broadcasting. Experimental results show that the resulting model, trained on carefully segmented and tagged text data, achieves an F1-macro score of 87.76 against a theoretical maximum of 89.33. This paper provides insights into the choice of hyperparameters and their impact on the model's performance. This study demonstrates its potential to ensure compliance with advertising broadcast contracts and offer competitive surveillance. This groundbreaking research could fundamentally change how radio advertising is monitored and open new doors for marketing optimization.

Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

Despite their catastrophic implications in flood events, the mobilization and transport of large, loose objects - termed Urban Flood Drifters (UFDs) - are often overlooked in flood management. This oversight stems from our limited understanding of how flowing water interacts with these heterogeneous objects. To bridge this knowledge gap, we introduce a mechanistic stability model that predicts the onset of UFD mobilization across a diverse array of loose objects, from plastics to heavy vehicles. Built on an inventory of key physical properties of UFDs, this model is also validated against existing mobilization studies. Our model generates stability curves that delineate flow conditions leading to their mobilization. We further enhance the reliability of our model by incorporating a Monte Carlo-based probabilistic framework that accounts for uncertainties and interdependencies among the input parameters. These probabilistic stability curves enable us to estimate the likelihood of mobilization for specific categories of UFDs under certain flow conditions. When integrated with flood maps or two-dimensional (2D) hydrodynamic models, our stability curves can guide urban planning efforts to predict and mitigate the impacts of UFDs during extreme flood events., Comment: This preprint is currently under revision (24/11/2023)

Assistive ankle-foot orthoses (AAFOs) are powerful solutions to assist or rehabilitate gait on humans. Existing AAFO technologies include passive, quasi-passive, and active principles to provide assistance to the users, and their mechanical configuration and control depend on the eventual support they aim for within the gait pattern. In this research we analyze the state-of-the-art of AAFO and classify the different approaches into clusters, describing their basis and working principles. Additionally, we reviewed the purpose and experimental validation of the devices, providing the reader with a better view of the technology readiness level. Finally, the reviewed designs, limitations, and future steps in the field are summarized and discussed., Comment: Figures appear at the end. Article submitted to Frontiers in Bioengineering and Biotechnology (currently under review)

Extreme floods threaten lives, assets and ecosystems, with the largest impacts occurring in urbanised areas. However, flood mitigation schemes generally neglect the fact that urban floods carry a considerable amount of solid load. In this study, we define Urban Flood Drifters (UFDs) as loose objects present in the urban landscape that can become mobile under certain flow conditions, thereafter blocking drainage infrastructure and endangering both downstream and upstream communities. Based on 270 post-flood photographic records from 63 major inundations of the past quarter-century across 46 countries, we provide a comprehensive analysis of UFDs and their flood-hazard implications. We show that a variety of vehicles, furniture and a heterogeneous mixture of drifters are present in post-flooding scenarios. Plastic, construction debris and wood (natural or anthropogenic) dominate the statistics of transported drifters in urban floods (with frequencies of roughly 50-60% each), followed by cars (present in 31.5% of post-flood images). Other heavy vehicles are readily observed in post-flood imagery and furniture such as bins, garden sheds or water tanks also appear occasionally, therefore suggesting that they can play a relevant role in extreme floods., Comment: This is an updated version (v2) of a preprint that is currently under revision (29/01/2024)

We settle the question of how to compute the entry and leaving arcs for turnpikes in autonomous variational problems, in the one-dimensional case using the phase space of the vector field associated to the Euler equation, and the initial/final and/or the transversality condition. The results hinge on the realization that extremals are the contours of a well-known function and that that the transversality condition is (generically) a curve. An approximation algorithm is presented and an example included for completeness., Comment: 15 pages, 8 figures