Technology is currently a significant benchmark in any application area; science and technology have permitted the invention of tools and devices that simplify daily activities by developing software engineering applications that provide automated solutions. In this sense, this work proposes two architectures that allow communication between the electronic device and the mobile application remotely, using the GSM/GPRS communication services and the Twitter social network. This development aims to control dogs' feeding adequately and healthily, providing the ration of food a dog needs according to the daily energy requirements. A nutritional assessment has also been performed considering different factors such as the size, breed, and weight of the dog to calculate the daily ration of healthy and balanced food according to daily energy requirements. Essentially, the electronic device consists of two parts: on the one hand, the electronic design is formed with an Arduino board, a Sim900 module to send and receive text messages, and the ESP8266 Wi-Fi serial transceiver module, which allows establishing the internet connection to receive the tweet that users post, both modules permit remote communication with the device using the Arduino board. On the other hand, the mobile application developed on Android uses a standard design according to the Google material design guidelines, allowing the owner to feed, schedule the feeding, review the dog's food history, and receive alerts when the food is going to be finished.

In recent years, unmanned aerial vehicles (UAVs) have been used in different areas of applications such as rescue operations, surveillance, agriculture, aerial mapping, engineering applications and research, among others, in order to perform tasks with greater efficiency. This work focuses on the use of UAVs in the fishing sector in order to optimise the detection process of a shoal of fish. In this sense, the main idea is to perform images recognition using the images acquired through videos captured by UAV in the open sea; to achieve the objective the convolutional neural networks were used, a new dataset with different images captured through UAV videos in the open sea were taken into account, these classes correspond to dolphin, dolphin_pod, open_sea, and seabirds. The training tests were by transfer of learning using the following models: Inception V3, MobileNet V2, and NASNet‐A (large) trained on TensorFlow platform. The experimental results show the detection performance with high‐precision values in reasonable processing time. This study ends with a critical discussion of the experimental results.

En la actualidad, ha incrementado considerablemente los avances e innovaciones tecnológicas, lo que ha permitido el desarrollo de nuevas herramientas tecnológicas y entornos virtuales para optimizar el proceso de enseñanza-aprendizaje. Este trabajo se centra en evaluar el desempeño académico de los estudiantes de la Licenciatura en Gastronomía de la Universidad Autónoma de Sinaloa (UAS) en entornos virtuales, específicamente en la asignatura de estadística. La metodología aplicada fue cuasi-experimental con un enfoque longitudinal descriptivo mixto por encuesta. El análisis de contexto se realizó a través de un instrumento para recolectar información relacionada con el uso y manejo de herramientas tecnológicas. Además, se aplicó el modelo de Progrogramación Neurolingística (PNL) para conocer el estilo de aprendizaje predominante en los estudiantes. Finalmente, se analizó el desempeño académico de los estudiantes durante el desarrollo de dos unidades de aprendizaje. Este documento finaliza con una discusión crítica de los resultados obtenidos, las conclusiones y recomendaciones.

This article presents a systematic literature review on Information and Communication Technologies (ICTs) applied to precision agriculture, focusing on their relevance to Colombia. It identifies key technical and administrative needs for digital transformation in the sector and proposes a conceptual roadmap for implementation. Findings highlight the potential of early warning systems (EWSs), the Internet of Things (IoT), and artificial intelligence (AI) to improve productivity, sustainability, and climate resilience. The study outlines current adoption barriers and proposes future empirical validation through field experiments.

In the last two-decade, robotics has attracted a lot of attention from the biomedical sectors, to help physically disabled people in their quotidian lives. Therefore, the research of robotics applied in the control of an anthropomorphic robotic arm to people assistance and rehabilitation has increased considerably. In this context, robotic control is one of the most important problems and is considered the main part of trajectory planning and motion control. The main solution for robotic control is inverse-kinematics, because it provides the angles of robotic arm joints. However, there are disadvantages in the algorithms presented by several authors because the trajectory calculation needs an optimization process which implies more calculations to generate an optimized trajectory. Moreover, the solutions presented by the authors implied devices where the people are dependent or require help from other people to control these devices. This article proposes an algorithm to calculate an accuracy trajectory in any time of interest using an LCD touch screen to calculate the inverse-kinematics and get the end-point of the gripper; the trajectory is calculated using a novel distribution function proposed which makes an easy way to get fast results to the trajectory planning. The obtained results show improvements to generate a safe and fast trajectory of an anthropomorphic robotic arm using an LCD touch screen allowed calculating short trajectories with minimal fingers moves.