Growth response of broilers to lysine levels and hydrolyzed porcine digestive mucosa (Palbio) inclusion in diet from 1 to 21 d of age

Palbio (PAL, Palbio 50 RD, Bioibérica, Spain) is a protein concentrate based on hydrolyzed porcine digestive mucosa dried under a fluid bed system over a soybean carrier, currently used in piglet feeds. The digestibility of PAL is very high and the product may be an excellent source of protein for young chicks. An experiment was conducted with 1,280 straight-run one-d-old Ross 308 chicks to evaluate the growth response of broilers to dietary inclusion of PAL.

Biokerosene from Babassu and Camelina Oils: production and properties of their blends with fossil kerosene

Babassu and camelina oils have been transesterified with methanol by the classical homogeneous basic catalysis method with good yields. The babassu fatty acid methyl ester (FAME) has been subjected to fractional distillation at vacuum, and the low boiling point fraction has been blended with two types of fossil kerosene, a straight-run atmospheric distillation cut (hydrotreated) and a commercial Jet-A1. The camelina FAME has been blended with the fossil kerosene without previous distillation. The blends of babassu biokerosene and Jet-A1 have met some of the specifications selected for study of the ASTM D1655 standard: smoke point, density, flash point, cloud point, kinematic viscosity, oxidative stability and lower heating value. On the other hand, the blends of babassu biokerosene and atmospheric distillation cut only have met the density parameter and the oxidative stability. The blends of camelina FAME and atmospheric distillation cut have met the following specifications: density, kinematic viscosity at −20 °C, and lower heating value. With these preliminary results, it can be concluded that it would be feasible to blend babassu and camelina biokerosenes prepared in this way with commercial Jet-A1 up to 10 vol % of the former, if these blends prove to accomplish all the ASTM D1655-09 standards.

Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.d

Design and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications?it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ?Activity Monitor? has been designed and implemented: a personal health-persuasive application that provides feedback on the user?s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user?s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.