Abstract
The reuse of treated wastewater, in particular for irrigation, is an increasingly common practice, encouraged by governments and official entities worldwide. Irrigation with wastewater may have implications at two different levels: alter the physicochemical and microbiological properties of the soil and/or introduce and contribute to the accumulation of chemical and biological contaminants in soil. The first may affect soil productivity and fertility; the second may pose serious risks to the human and environmental health. The sustainable wastewater reuse in agriculture should prevent both types of effects, requiring a holistic and integrated risk assessment. In this article we critically review possible effects of irrigation with treated wastewater, with special emphasis on soil microbiota. The maintenance of a rich and diversified autochthonous soil microbiota and the use of treated wastewater with minimal levels of potential soil contaminants are proposed as sine qua non conditions to achieve a sustainable wastewater reuse for irrigation.

Abstract
Companies focus on software development in order to survive in a highly competitive world. They not only need to keep up to date with the changes that are occurring in their environment, but they also need to assure the effectiveness of their processes. One way to do that is pursuing high quality standards by continuously improving the development processes. In this chapter, the authors describe the project of CMMI (Capability Maturity Model Integration) implementation in a software company specialized in information technology services for banking. The project was initiated with the aim of optimizing the software development process. Throughout the chapter, various aspects of the project are covered, such as the actions taken to implement the CMMI maturity level 2, the tools used to support the implementation, and the obtained results. These are useful and could serve as a reference basis for companies that consider the implementation of a maturity model.

Abstract
This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Random Forests and compare it with existing methods both on artificial and real-world data. We show that it is competitive with the existing methods and sometimes gives more intuitive results. We also provide an overview for outlier detection in labelled data. The main contribution are two methods for class-based outlier description and interpretation.

Abstract

Abstract
In many engineering and science areas, models are developed and validated using high-level programing languages and environments as is the case with MATLAB. In order to target the multicore heterogeneous architectures being used on embedded systems to provide high performance computing with acceptable energy/power envelops, developers manually migrate critical code sections to lower-level languages such as C and OpenCL, a time consuming and error prone process. Thus, automatic source-to-source approaches are highly desirable. We present an approach to compile MATLAB and output equivalent C/OpenCL code to target architectures, such as GPU based hardware accelerators. We evaluate our approach on an existing MATLAB compiler framework named MATISSE. The OpenCL generation relies on the manual insertion of directives to guide the compilation and is also capable of generating C wrapper code to interface and synchronize with the OpenCL code. We evaluated the compiler with a number of benchmarks from different domains and the results are very encouraging.

Abstract
Tire inspection is presently done by workers who have as their main problems, besides identifying the defects, the time available for defect identification and the inherent costs. Companies can become more sustainable by adopting automated methods to perform such type of processes, such as artificial vision, with advantages both in the processing time and in the incurred costs. This paper addresses the development of an artificial vision system that aims to be an asset in the field of tyre inspection, having as main characteristics its execution speed and its reliability. The conjugation of these criteria is a prerequisite for this system to be able to be integrated in inspection machines. The paper focusses on the study of three image processing methods to be used in the identification of marks (red dots) on tires. In this work was used the free Open Computer Vision artificial vision library to process the images acquired by a Basler matrix camera. Two different techniques, namely Background Subtraction and Hough Transform, were tested to implement the solution. After developing the artificial vision inspection application, tests were made to measure the performance of both methods and the results were promising: processing time was low and, simultaneous, the achieved accuracy is high.