Abstract
The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.

Abstract
Purpose Service design is a multidisciplinary approach that is key to service innovation, as it brings new service ideas to life. In this context, the development of new service design methods and models for creating new service futures is an important stream of service design research. Such developments can benefit from a systematized research methodology that builds on existing knowledge and robustly evaluates the suitability of research contributions. To address this challenge, the purpose of this paper is to present design science research (DSR), an established methodology from the information systems field, and examine how it can be useful for service design research by supporting the development of new artifacts, such as service design constructs, methods and models. Design/methodology/approach This paper presents DSR and related literature and shows how DSR can support service design research through a step-by-step approach. A methodology to develop prescriptive-driven solutions for classes of problems, DSR can support service design research in developing rigorous and relevant research. One illustrative example of a service design research effort using the DSR approach is presented. Findings Building on DSR's robust methodological background, this paper discusses how DSR can support service design research, namely, through the development of new methods and models, and how DSR can be adapted to leverage service design research participatory, iterative, human-centric and creative approach. Originality/value This paper provides an overview of DSR and proposes it as a methodology to conduct service design research, offering step-by-step guidance on the application of DSR in service design research and discussing how it can be adapted according to the specific characteristics of service design research and drive future research.

Abstract
Autonomous Underwater Vehicles (AUVs) are widely used as a cost-effective mean to carry out underwater missions. During long-term missions, AUVs may collect large amounts of data that usually needs to be sent to shore. An AUV may have to travel several kilometers before reaching an area of interest near the seafloor, thus surfacing is unpractical for most cases. Long-range underwater communications rely mostly on acoustic communications, which are characterized by very low bitrates, thus making the transfer of large amounts of data too slow. GROW is a novel solution for long-range, high bitrate underwater wireless communications between a survey unit (e.g., deep sea lander, AUV) and a central station at surface. GROW combines AUVs as data mules, short-range high bitrate wireless RF or optical communications, and long-range low bitrate acoustic communications for control. In this paper we present the Underwater Data Muling Protocol (UDMP), a communications protocol that enables the control and the scheduling of the Data Mule Units within the GROW framework. Experimental results obtained using an underwater testbed show that the use of UDMP and data mules can outperform acoustic communications, achieving equivalent throughput up to 150 times higher within the typical range of operation of the latter.

Abstract
Disinfection by-products (DBP) such as trihalomethanes (THM) are formed when chlorine and bromine interact with natural organic materials in chlorine-treated swimming pools. Epidemiological evidence demonstrated an association between exposure to swimming pool environment and adverse health effects. Therefore, this study aimed to assess carcinogenic and non-carcinogenic risk of long-term exposure of elite swimmers and their coaches. In an Olympic-size indoor chlorinated swimming pool, THM levels were determined in water (21-69 mu g/L), in the boundary layer above the water surface (59-397 mu g/m(3)), and in the air surrounding the pool (28-390 mu g/m(3)). These values were used to predict multi-pathway chronic daily intake (CDI), cancer risk (CR) and hazard index (HI). Oral and dermal CDI for swimmers were 2.4 x 10(-6) and 2.0 x 10(-8), respectively. The swimmers' inhalation CDI (1.9 x 10(-3) mg/kg/day) was estimated to be sixfold higher than levels obtained for coaches (3.3 x 10(-4) mg/kg/day). According to guidelines, the HI was acceptable, but CR exceeded the recommended limit for both, coaches (CR: 5.5 x 10(-7)-8.5 x 10(-5); HI: 6.5 x 10(-4)-1 x 10(-1)) and swimmers (CR: 1.4 x 10(-5)-3.6 x 10(-4) HI: 1.6 x 10(-2)-4.3 x 10(-1)). Our findings provide further support to the need to develop comprehensive guidelines to safeguard the health of individuals involved in elite swimming.

Abstract
3D reconstruction from anatomical slices allows anatomists to create three dimensional depictions of real structures by tracing organs from sequences of cryosections. However, conventional user interfaces rely on single-user experiences and mouse-based input to create content for education or training purposes. In this work, we present Anatomy Studio, a collaborative Mixed Reality tool for virtual dissection that combines tablets with styli and see-through head-mounted displays to assist anatomists by easing manual tracing and exploring cryosection images. We contribute novel interaction techniques intended to promote spatial understanding and expedite manual segmentation. By using mid-air interactions and interactive surfaces, anatomists can easily access any cryosection and edit contours, while following other user's contributions. A user study including experienced anatomists and medical professionals, conducted in real working sessions, demonstrates that Anatomy Studio is appropriate and useful for 3D reconstruction. Results indicate that Anatomy Studio encourages closely-coupled collaborations and group discussion, to achieve deeper insights.

Abstract
Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.