In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.

In dairy sheep, optimum nutrition is required to maintain a positive energy and nitrogen balance, and therefore, achieve adequate performance levels. In this sense, supplemental rumen-protected methionine (RPMet) has been a nutritional strategy used to improve the availability of amino acids in sheep for milk production. The aim of the current study is to assess the effect of RPMet supplementation on the milk yield and milk composition of crossbred dairy sheep. Twenty F1 sheep (50% Pelibuey × 50% East Friesian) were randomly assigned to one of the following three treatments: C (control: 0 g/d), 3 g (3.0 g/d), or 6 g (6 g/d) of RPMet added to the basal diet from day 2 to 120 after lambing. Milk yield was significantly higher in sheep supplemented with 6 g of RPMet (p = 0.04) with a linear effect (p < 0.001). In relation to protein yield, the 6 g treatment showed the highest values (55.5 g/day) in comparison with the control (35.2 g/day) and 3 g (42.3 g/day) treatments. Also, lactose (p = 0.02) and non-fat solid yield (p = 0.03) were positively affected by the methionine supplementation. Our results suggest that methionine supplementation is an option to increase milk production, likely associated with the better availability of nutrients of dairy sheep farms using crossbred sheep in regions with an incipient dairy sheep industry.

La existencia de app nativas dirigidas al comercio del sector agropecuario en México es mínima, a pesar de que se ha detonado la comercialización de productos en línea (e-commerce); además, el pequeño agricultor posee casi nula relación con las aplicaciones para teléfonos móviles, lo que provoca una desventaja al momento de vender sus productos en forma electrónica. Ante esta circunstancia, se requiere una herramienta tecnológica acorde a las necesidades de los agricultores, para que puedan comercializar sus productos, sin necesidad de intermediarios. Por las razones anteriores, en este artículo de investigación se propone un sistema de compra venta basado en el modelo C2C (Consumer to Consume) desarrollado en Android Studio, con una metodología ágil de desarrollo impulsado (DIC), que cubre los requerimientos y diseño de la arquitectura de una app de simple manejo para el sector de pequeños y medianos agricultores.