Dengue fever remains a persistent and growing public health concern in many tropical and subtropical regions, where warm climates and urbanization create ideal conditions for mosquito proliferation, such as in Asunción, Paraguay, our focal city. Epidemic control strategies targeting the control of mosquito populations, particularly those of the Aedes aegypti (AE), are essential for mitigating outbreaks and protecting vulnerable communities. This work aims to tackle this problem by introducing a computer vision system that utilizes the You Only Look Once (YOLO) architecture for counting mosquitoes and classifying their sex in real-time, while also addressing issues related to out-of-focus and incomplete specimens. The distilled YOLOv8-nano model reached an mean Average Precision (mAP)50of 88.7% for female mosquitoes and 94.1% for male mosquitoes, with inference times around 4.3 milliseconds on Graphics Processing Unit (GPU) and 1 second on a Raspberry Pi 5. The results indicate that the model can be integrated into embedded systems for automated vector surveillance.

Dengue fever represents a public health problem and accurate forecasts can help governments take the best preventive actions. As the volume of data provided continuously increases, machine learning and deep learning (DL) models have become an attractive approach. However, it is difficult to perform accurate predictions in areas with fewer cases. In this work, we compare traditional approaches such as LASSO Regression (LR), Random Forest (RF), Support Vector Regression (SVR) vs DL models based on long short-term memory (LSTM), considering weekly dengue incidence and climate, in 217 cities in Paraguay. Several city models may present heterogeneous behaviors and poor accuracy. To mitigate this problem, a clustering analysis between time series is performed based on silhouette scores and measuring how well an observation is clustered. Our results indicate the hierarchical clustering combined with Spearman correlation is the most appropriate approach. Then several LSTM models are compared on subgroups of similar time series. The root mean squared error (RMSE) confirms that the LSTM clustered models improve the accuracy by 31.6% approximately. The main contribution of this work is that LSTM clustered models can perform predictions in cities with low incidence by combining information from similar time-series and weather data.

A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also defined.

In this paper it is considered the image registration (IR) between medical images of computed tomography and magnetic resonance. Our approach formulates the IR as an optimization problem where mutual information cost function is used as a similarity metric (cost function). The Extremal Optimization algorithm is implemented as the optimizer. The numerical results are contrasted against two state of the art optimization algorithms for this kind of problems (being one deterministic and another evolutionary). Our approach is competitive with the deterministic algorithm in accuracy and with the evolutionary algorithms in computational cost. The qualitative results are quite satisfactory with a 83 % of success, whilst the quantitative results present an average error of 0.36mm with registrations of CT with proton density MR. The results show that the proposal is useful for multimodal registrations.

This work presents implementation details of the Parareal method using PETSc in a distributed and multicore architecture, which is used for the resolution of a parabolic optimal control problem. To this end, this optimization problem is discretized yielding a large KKT linear system. In the context of this work, the Parareal method allows not only to reach problem sizes which normally can not be solved using a single computer, but also allows to speed up the computational resolution time. The implementation developed in this work offers a parallelization relative efficiency for the strong scaling of approximately 70% each time the processes count doubles, while for the weak scaling it is 75 % each time the processes count doubles for a constant solution size per process and 96% each time the processes count doubles for a constant data size per process.

In a group of individuals that come together to produce a good or provide a service the cooperators who pay to produce the good, are often exploited by those who receive the benefit without paying the cost. Models were developed over time using incentives (rewards or punishment) and the option of leaving the initiative to promote and stabilize the cooperation. In this paper we analyze several models that use as a framework the evolutionary game theory and public goods games. We compare them and systematized their characteristics in a table to select the most suitable for a specific problem. To apply the models we chose the problem of cooperation in community projects of water supply. The comparative results demonstrate that the level of cooperation obtained depends on the mechanisms used, how they are applied and the initial composition of the population.

The hemoflagellate protozoan parasite Trypanosoma cruzi is the causative agent of the Chagas disease. The two drugs used clinically have a high level of toxicity and are active only during the acute phase of the disease, making it urgent the development of new safe and effective treatment. The first step in the screening of new compounds is to identify their effectiveness by manual microscopic counting of the intracellular parasite form (amastigotes), which is a slow and tedious methodology. This paper presents an approach for the automatic counting of intracellular parasites using watershed transform with internal and external markers as segmentation technique, and connected components labeling for subsequent counting. The contrast against the classic counting, conducted by experts in the field, validated the technique, showing that the proposal is very efficient and with a low error rate.

The analysis of network traffic is a key area for the management of fault-tolerant systems, since anomalies in network traffic can affect the availability and quality of service (QoS). This work proposes an intrusion detection tool based on the two-dimensional wavelet transform to quickly and effectively detect anomalies in computer networks generated by denial of service (DoS). Experiments were performed using two databases: a synthetic (DARPA) and another one from data collected at the Federal University of Santa Maria (UFSM), allowing analysis of the intrusion detection tool under different scenarios. The wavelets considered for the tests were all from the orthonormal family of Daubechies: Haar (Db1), Db2, Db4 and Db8 (with 1, 2, 4 and 8 null vanishing moments respectively). For the DARPA database we obtained a detection rate up to 100% and 95% for the UFSM database.