Abstract
The design of retail backroom storage has a great impact on in-store operations, customer service levels and store life-cycle costs. Moreover, backroom storage in modern retail stores is crucial to several functions, such as acting as a buffer against strong demand lifts yielded by an increasing promotional activity, seasonal peak demand and e-commerce activities. Despite having similar functions to a distribution center, backroom storage facilities have particularities that deserve a distinct analysis. In this paper we aim to draw attention to the lack of research about this topic.

Abstract
Purpose: This paper presents the Rehab@home system, a tool specifically developed for helping neurological patients performing rehabilitation exercises at home, without the presence of a physiotherapist. It is centred on the rehabilitation of balance and on the sit-to-stand (STS) movement. Method: Rehab@home is composed of two Wii balance boards, a webcam and a computer, and it has two main software applications: one for patients to perform rehabilitation exercises and another one for therapists to visualize the data of the exercises. During the exercises, data from the boards and the webcam are processed in order to automatically assess the correctness of movements. Results: Rehab@home provides exercises for the rehabilitation of balance (in sitting and in standing positions), and for the execution of the STS movement. It gives automatic feedback to the patient and data are saved for future analysis. The therapist is able to adapt the difficulty of the exercises to match with each patient's needs. A preliminary study with seven patients was conducted for evaluating their feedback. They appreciated using the system and felt the exercises more engaging than conventional therapy. Conclusions: Feedback from patients gives the hope that Rehab@home can become a great tool for complementing their rehabilitation process.

Abstract
The organizations' demand to integrate several disparate data sources and an ever-increasing amount of data is intensifying the occurrence of data quality problems. Currently, data cleaning approaches are tailored for data sources having different schemas but sharing the same data model (e.g. relational model), and are highly dependent on a domain expert to specify data cleaning operations. This paper presents a novel and generic data cleaning methodology aiming to assist the domain expert during the specification of data cleaning operations through reusing knowledge previously expressed for other data sources even if those sources have different data models and/or schemas. This is achieved by abstracting data source models and schemas to a closer human level and by the use of vocabulary to describe the structure and the semantics of data cleaning operations.

Abstract
Many Wireless Sensor Network (WSN) applications operate autonomously in unreliable or inaccessible environments, precluding maintenance or human intervention. Redundant deployment schemes are usually considered in this scenario, making the network resilient to failure and environmental changes. Furthermore, sleep-scheduling techniques can also be applied, enabling redundant nodes to turn off their radios, while active nodes perform monitoring services. This paper investigates the behavior of the (m,k)-Gur Game approach. The main goal of the (m,k)-Gur Game is to provide an uniform network coverage for monitoring applications, with autonomic nodes performing a self-regulated choice between sending message to a base station or sleep until the next period. The proposal was evaluated using the OMNeT++ simulator tool under the MiXiM framework. Preliminary results shows that the (m,k)-Gur Game outperforms the traditional GurGame approach in terms of QoS provision and network coverage.

Abstract
The microbial communities of bulk soil of rice paddy fields under an ancient organic agriculture regimen, consisting on an alfalfa-rice rotation system, were characterized. The drained soil of two adjacent paddies at different stages of the rotation was compared before rice seeding and after harvesting. The relationships among the soil microbial, physicochemical, and biochemical parameters were investigated using multivariate analyses. In the first year of rice cropping, aerobic cultivable heterotrophic populations correlated with lineages of presumably aerobic bacteria (e.g., Sphingobacteriales, Sphingomonadales). In the second year of rice cropping, the total C content correlated with presumable anaerobic bacteria (e.g., Anaerolineae). Independently of the year of rice cropping, before rice seeding, proteolytic activity correlated positively with the cultivable aerobic heterotrophic and ammonifier populations, the soil catabolic profile and with presumable aerobes (e.g., Sphingobacteriales, Rhizobiales) and anaerobes (e.g., Bacteroidales, Anaerolineae). After harvesting, strongest correlations were observed between cultivable diazotrophic populations and bacterial groups described as comprising N-2 fixing members (e.g., Chloroflexi-Ellin6529, Betaproteobacteria, Alphaproteobacteria). It was demonstrated that chemical parameters and microbial functions were correlated with variations on the total bacterial community composition and structure occurring during rice cropping. A better understanding of these correlations and of their implications on soil productivity may be valid contributors for sustainable agriculture practices, based on ancient processes.

Abstract