Abstract
Midblock pedestrian crossing areas between closely spaced roundabouts can affect traffic operations and may result in a trade-off between capacity, environment, and safety benefits. Even though research has been conducted on the impacts of traffic performance on pedestrian crosswalks located at isolated roundabouts, few studies have focused on how pedestrian crosswalks between closely adjacent roundabouts affect traffic operations. A microsimulation approach was used to examine the integrated effect of a pedestrian crosswalk on traffic delay, carbon dioxide emissions, and relative speed between vehicles and pedestrians at different locations between closely spaced two-lane roundabouts. The main purpose of the study was to develop a simulation platform of traffic (VISSIM), emissions (vehicle-specific power), and safety (surrogate safety assessment model) to optimize such variables. The fast nondominated sorting genetic algorithm NSGA-II was mobilized to identify an optimized set of pedestrian crosswalk locations for the roundabout exit section along the midblock segment. One acceptable solution that provided a good balance between traffic performance, emissions, and pedestrian safety benefits was locating the crosswalks at 15, 20, and 30 m from the exit section. Even at low pedestrian demand, crosswalk effectiveness (as determined by capacity and environment) gradually decreased near the circulatory ring delimitation (<10 m). Findings suggest that crosswalks in the midblock segment (55 to 60 m from the exit section) also must be considered, especially under high traffic demand.

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 analysis and interpretation of high-resolution computed tomography (HRCT) images of the chest in the presence of interstitial lung disease (ILD) is a time-consuming task which requires experience. In this paper, a computer-aided diagnosis (CAD) scheme is proposed to assist radiologists in the differentiation of lung patterns associated with ILD and healthy lung parenchyma. Regions of interest were described by a set of texture attributes extracted using differential lacunarity (DLac) and classical methods of statistical texture analysis. The proposed strategy to compute DLac allowed a multiscale texture analysis, while maintaining sensitivity to small details. Support Vector Machines were employed to distinguish between lung patterns. Training and model selection were performed over a stratified 10-fold cross-validation (CV). Dimensional reduction was made based on stepwise regression (F-test, p value < 0.01) during CV. An accuracy of 95.8 +/- 2.2% in the differentiation of normal lung pattern from ILD patterns and an overall accuracy of 94.5 +/- 2.1% in a multiclass scenario revealed the potential of the proposed CAD in clinical practice. Experimental results showed that the performance of the CAD was improved by combining multiscale DLac with classical statistical texture analysis.