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This study presents a comparative analysis of YOLO detection models for the accurate identification of bean leaf diseases caused by Coleoptera pests in natural environments. By using a manually collected dataset of healthy and infected bean leaves in natural conditions, we labeled at the leaf level and evaluated the performance of the YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 models. Mean average precision (mAP) was used to assess the performance of the models. Among these, YOLOv9e exhibited the best performance, effectively balancing precision and recall for datasets with limited size and variability. In addition, we integrated the Sophia optimizer and PolyLoss function into YOLOv9e and enhanced it, providing even more accurate detection results. This paper highlights the potential of advanced deep learning models, optimized with second-order optimizers and custom loss functions, in improving pest detection, crop management, and overall agricultural yield. [ABSTRACT FROM AUTHOR]

The purpose of this research is to analyze the acquisition of teachers' digital competencies through the use of virtual environments and information and communication technologies to improve the teaching-learning process. A systematic review was conducted through the search and compilation of empirical studies over five years (2019 to 2023), in the specialized databases: Scopus, Redalyc, SciELO, and the academic search engine Google Scholar. Based on the PRISMA statement, and through inclusion and exclusion criteria, a total of fourteen research papers were selected. After examining them, it was possible to identify how digital literacy through the use of virtual learning environments influences the development of teaching work and performance and consequently also contributes to student learning. Among the digital skills most used by teachers that have allowed innovation and improvement of the teaching process are mainly the management of information and mastery of digital tools for the creation and management of educational resources. [ABSTRACT FROM AUTHOR]

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking. [ABSTRACT FROM AUTHOR]

This systematic review explored how 3D printing as an educational tool favors learning and develops creative thinking in higher education students, particularly in the industrial design career. Furthermore, this tool potentiates specific educational and cognitive abilities in students. Therefore, we performed a literature analysis covering the years 2017 to 2022 on how 3D printing has been integrated into the educational area and where the work in the classroom is headed. In this context, we followed the inclusion, exclusion, and relevance criteria to search for articles, resulting in the analysis of 19 documents found in the Google Scholar, Sci-ELO, Redalyc and Scopus databases. We divided the analysis into five steps: planning, selection, extraction, results, and conclusions. The results show that most authors agree that the use of 3D printing helps to develop creativity and generate motivation in students. Therefore, it is necessary to promote an intelligent curriculum with scientific and technological innovations that reinforce the path toward their professional development. [ABSTRACT FROM AUTHOR]

Owing to their large field of view, a catadioptric imaging system is widely used in several research areas, including robot navigation, surveillance, and three‐dimensional (3D) reconstruction. The modelling and calibration of these systems allow the accurate measurement of objects. This paper presents a calibration method for a panoramic 3D reconstruction system based on pattern projection composed of two modules. The first module, which contains a charged coupled device camera and a parabolic mirror allows the acquisition of catadioptric images. The second module is a catadioptric projection system made of a light projector and a parabolic mirror that generates a projected pattern over the target for reconstruction purposes. Both the modules are calibrated independently. The method achieves a reprojection error of 0.6 pixels in the camera module and 1.2 pixels in the catadioptric projection system. To evaluate the performance of the proposed method, the results show 3D reconstructions of multiple objects with tolerances around 1.3 mm. [ABSTRACT FROM AUTHOR]

The automotive industry has recently adopted multicore processors and microcontrollers to meet the requirements of new features, such as autonomous driving, and comply with the latest safety standards. However, inter-core communication poses a challenge in ensuring real-time requirements such as time determinism and low latencies. Concurrent access to shared buffers makes predicting the flow of data difficult, leading to decreased algorithm performance. This study explores the integration of Logical Execution Time (LET) and Time-Division Multiple Access (TDMA) models in multicore embedded systems to address the challenges in inter-core communication by synchronizing read/write operations across different cores, significantly reducing latency variability and improving system predictability and consistency. Experimental results demonstrate that this integrated approach eliminates data loss and maintains fixed operation rates, achieving a consistent latency of 11 ms. The LET-TDMA method reduces latency variability to approximately 1 ms, maintaining a maximum delay of 1.002 ms and a minimum delay of 1.001 ms, compared to the variability in the LET-only method, which ranged from 3.2846 ms to 8.9257 ms for different configurations. [ABSTRACT FROM AUTHOR]

This paper introduces a tool for calibrating multiple Kinect V2 sensors. To achieve the calibration, at least three acquisitions are needed from each camera. The method uses the Kinect's coordinate mapping capabilities between the sensors to register data between camera, depth, and color spaces. The method uses a novel approach where it obtains multiple 3D points matches between adjacent sensors, and use them to estimating the camera parameters. Once the cameras are calibrated, the tool can perform point cloud fusion transforming all the 3D points to a single reference. We tested the system with a network of four Kinect V2 sensors and present calibration results. The tool is implemented in MATLAB using the Kinect for Windows SDK 2.0. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer and has the worst prognosis. In patients with TNBC tumors, the tumor cells have been reported to have mesenchymal features, which help them migrate and invade. Various studies on cancer have revealed the importance of microRNAs (miRNAs) in different biological processes of the cell in that aberrations, in their expression, lead to alterations and deregulations in said processes, giving rise to tumor progression and aggression. In the present work, we determined the miRNAs that are deregulated in the epithelial–mesenchymal transition process in breast cancer. We discovered that 25 miRNAs that regulate mesenchymal genes are overexpressed in patients with TNBC. We found that miRNA targets modulate different processes and pathways, such as apoptosis, FoxO signaling pathways, and Hippo. We also found that the expression level of miR-934 is specific to the molecular subtype of the triple-negative breast cancer and modulates a set of related epithelial–mesenchymal genes. We determined that miR-934 inhibition in TNBC cell lines inhibits the migratory abilities of tumor cells. [ABSTRACT FROM AUTHOR]

In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered two sets of data for lettuce seedlings: one is composed of color images and the other of point clouds. In the second stage, we employed the interactive closest point (ICP) method to align the point clouds and extract 3D morphology features for detecting nitrogen deficiencies using machine learning techniques. Next, we trained and compared multiple detection models to identify potassium deficiencies. Finally, we compared the outcomes with traditional lab tests and expert analysis. Our results show that the decision tree classifier achieved 90.87% accuracy in detecting nitrogen deficiencies, while YOLOv9c attained an mAP of 0.79 for identifying potassium deficiencies. This innovative approach has the potential to transform how we monitor and manage crop nutrition in agriculture. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an algorithm with low computational complexity for classifying and recognizing characters based on a random sampling and high‐dimensional binary spaces for the development of real‐time applications. Character classification is performed using uniform random sampling as the feature selection process, subsequently performing encoding as binary strings. Associative memories are commonly used as general classifiers with linear criteria to discriminate between data points. In most classifiers, the ability to efficiently detect class membership depends entirely on the expressiveness of the attributes used to encode the data. Each binary pattern encodes the distinct characteristics of several glyphs. Character features are represented as elements of a high‐dimensional binary space, where a criterion of the cluster is defined under the L1 metric. The reduction in computational complexity is analyzed. The reduction in the number of character features through random sampling techniques makes it feasible to manage all the character information in physical architectures; therefore, this approach might use resources on a hardware platform with integer operators typically implemented at the hardware‐register level. Finally, this approach is implemented in a parallel architecture Field‐Programmable Gate Array (FPGA) and tested using a Database (DB) of different fonts, including distortions, therein showing that the efficiency is comparable to the other well‐known approaches. [ABSTRACT FROM AUTHOR]

Connective approaches are useful for developing theoretical tools for classifying, filtering, and modeling raw data. The redundancy of inputs and system states allows the development of approximations for nonlinear problems. One of the most useful connective approaches is the use of associative memory ; it creates a relation between data inputs and data outputs through a linear relationship expressed by a matrix. This relation is usually expressed in the binary domain, and the learning process consists of how the linear matrix is built and weighted. Associative memory approaches are founded on the linear independence concept, which defines when a particular mixture of inputs can be associated with a mixture of outputs. The redundancy of states is useful for creating a better approximation when the input and outputs are not strictly linearly related. This work presents a new family of associative memories, expanding the concept of independence to create the relation between inputs and outputs. This approach is founded on two different types of independence: linear independence and probabilistic independence. We present and discuss the foundations and concepts involved. Then, we show the practical results obtained after applying this new framework as a pattern classifier in image analysis tasks. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a panoramic profilometric system to reconstruct inner cylindrical environments. The system projects circular fringes and uses a temporal phase unwrapping technique. The recovered phase map is used to reconstruct objects placed on the inner cylindrical surface. We derived a phase to depth conversion formula for this system. The use of fringe projection allows dense reconstructions. The panoramic system is composed by a digital projector, two parabolic mirrors and a CCD camera. All these components share a common axis with a reference cylinder. This paper presents results for distinct objects. [ABSTRACT FROM AUTHOR]