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The primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns that conventional linear regression models cannot. Furthermore, these methods are also powerful for leveraging environmental covariates, such as genotype × environment (G×E) prediction, among others. In this study we provide the building process of seven kernel methods: linear, polynomial, sigmoid, Gaussian, Exponential, Arc-cosine 1 and Arc-cosine L. Additionally, we highlight illustrative examples for implementing exact kernel methods for genomic prediction under a single-environment, a multi-environment and multi-trait framework, as well as for the implementation of sparse kernel methods under a multi-environment framework. These examples are followed by a discussion on the strengths and limitations of kernel methods and, subsequently by conclusions about the main contributions of this paper.

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Discovering the appropriate services in ad-hoc computing environments where a great number of devices and software components collaborate discreetly and provide numerous services is an important challenge. Service discovery protocols make it possible for participating nodes in a network to locate and advertise services with minimum user intervention. However, because it is not possible to predict at design time which protocols will be used to advertise services in a given context/ environment, it is now becoming clear that dynamic discovery mechanisms are required by mobile nodes to cope with the heterogeneity of discovery platforms. Existing adaptive mobile middleware solutions such as ReMMoC and INDISS have investigated this style of dynamic discovery. However, these have yet to consider the emerging suite of protocols for discovery in ad-hoc networks. In this paper we present a component-based service discovery framework for the development of an adaptive multi-personality service discovery middleware, which will operate in diverse environments e.g. fixed and ad-hoc networks. This supports a common architecture for individual discovery protocols to enhance configurability and re-configurability of the framework, and minimize resource usage through component reuse. Finally, to evaluate this framework we investigate the development of four existing ad-hoc service discovery protocols using our approach.

Nowadays, modern society faces serious problems with transportation systems. There are more traffic jams, accidents, and fatalities, and CO2 emissions are increasing fast. Thus, improving the safety and efficiency of transportation systems is imperative. Developing a sustainable transportation system requires a better usage of the existing infrastructure, the adoption of emerging technologies (e.g. embedded devices, sensors, and short range radio transmitters), and the development of applications capable of operating in wireless and spontaneous networks. This chapter gives readers a global vision of the issues related to the development of applications for vehicular ad-hoc networks(VANET). It also presents a classification and an overview of the top-level application domain. In addition, it investigates the importance of information in vehicular networks and analyses the requirements for different types of vehicular applications. Finally, the communication schemes that underpin the operation of VANET applications, as well as the security threats they are exposed to, are studied.