Abstract
In the past few years, cable-driven robots have received some attention by the scientific community and the industry. They have special characteristics that made them very reliable to operate with the level of safeness that is required by different environments, such as, handling of hazardous materials in construction sites. This paper presents a cable-driven robot called SPIDERobot, that was developed for automated construction of architectural projects. This robot has a rotating claw and it is controlled by a set of 4 cables that allow 4 degrees of freedom. In addition to the robot, this paper introduces a Dynamic Control System (DCS) that controls the positioning of the robot and assures that the length of cables is always within a safe value. Results show that traditional force-feasible approaches are more influenced by the pulling forces or the geometric arrangement of all cables and their positioning is significantly less accurate than the DCS. Therefore, the architecture of the SPIDERobot is designed to enable an easily scaling up of the solution to higher dimensions for operating in realistic environments.

Abstract
Syndromic surveillance systems continuously monitor multiple pre-diagnostic daily streams of indicators from different regions with the aim of early detection of disease outbreaks. The main objective of these systems is to detect outbreaks hours or days before the clinical and laboratory confirmation. The type of data that is being generated via these systems is usually multivariate and seasonal with spatial and temporal dimensions. The algorithm What's Strange About Recent Events (WSARE) is the state-of-the-art method for such problems. It exhaustively searches for contrast sets in the multivariate data and signals an alarm when find statistically significant rules. This bottom-up approach presents a much lower detection delay comparing the existing top-down approaches. However, WSARE is very sensitive to the small-scale changes and subsequently comes with a relatively high rate of false alarms. We propose a new approach called EigenEvent that is neither fully top-down nor bottom-up. In this method, we instead of top-down or bottom-up search, track changes in data correlation structure via eigenspace techniques. This new methodology enables us to detect both overall changes (via eigenvalue) and dimension-level changes (via eigenvectors). Experimental results on hundred sets of benchmark data reveals that EigenEvent presents a better overall performance comparing state-of-the-art, in particular in terms of the false alarm rate.

Abstract
AbstractMutations in Parkin and PINK1 cause an inherited early-onset form of Parkinson's disease. The two proteins function together in a mitochondrial quality control pathway whereby PINK1 accumulates on damaged mitochondria and activates Parkin to induce mitophagy. How PINK1 kinase activity releases the auto-inhibited ubiquitin ligase activity of Parkin remains unclear. Here, we identify a binding switch between phospho-ubiquitin (pUb) and the ubiquitin-like domain (Ubl) of Parkin as a key element. By mutagenesis and SAXS, we show that pUb binds to RING1 of Parkin at a site formed by His302 and Arg305. pUb binding promotes disengagement of the Ubl from RING1 and subsequent Parkin phosphorylation. A crystal structure of Parkin ?86–130 at 2.54 Å resolution allowed the design of mutations that specifically release the Ubl domain from RING1. These mutations mimic pUb binding and promote Parkin phosphorylation. Measurements of the E2 ubiquitin-conjugating enzyme UbcH7 binding to Parkin and Parkin E3 ligase activity suggest that Parkin phosphorylation regulates E3 ligase activity downstream of pUb binding.

Abstract
Developments in advanced autonomous production resources have increased the interest in the Single-Machine Scheduling Problem (SMSP). Until now, researchers used SMSP with little to no practical application in industry, but with the introduction of multi-purpose machines, able of executing an entire task, such as 3D Printers, replacing extensive production chains, single-machine problems are becoming a central point of interest in real-world scheduling. In this paper we study how simple, easy to implement, Just-in-Time (JIT) based, constructive heuristics, can be used to optimize customer and enterprise oriented performance measures. Customer oriented performance measures are mainly related to the accomplishment of due dates while enterprise-oriented ones typically consider other time-oriented measures.

Abstract
Hotspot detection aims at identifying sub-groups in the observations that are unexpected, with respect to some baseline information. For instance, in disease surveillance, the purpose is to detect sub-regions in spatiotemporal space, where the count of reported diseases (e.g. cancer) is higher than expected, with respect to the population. The state-of-the-art method for this kind of problem is the space-time scan statistics, which exhaustively search the whole space through a sliding window looking for significant spatiotemporal clusters. Space-time scan statistics makes some restrictive assumptions about the distribution of data, the shape of the hotspots and the quality of data, which can be unrealistic for some non-traditional data sources. A novel methodology called EigenSpot is proposed where instead of an exhaustive search over the space, it tracks the changes in a space-time occurrences structure. The new approach does not only present much more computational efficiency but also makes no assumption about the data distribution, hotspot shape or the data quality. The principal idea is that with the joint combination of abnormal elements in the principal spatial and the temporal singular vectors, the location of hotspots in the spatiotemporal space can be approximated. The experimental evaluation, both on simulated and real data sets, reveals the effectiveness of the proposed method.

Abstract
ENDURE and BLUECOM+ are two EEA Grants projects that aim at enabling the monitoring of large and remote ocean areas by providing wireless communications and energy to humans and systems, namely unmanned vehicles, which are crucial for making large scale ocean monitoring cost-effective. ENDURE targets enabling autonomous underwater vehicles (AUVs) to remain in operation for longer periods of time than what is practical today, thus increasing the possibility of covering larger areas at lower costs. BLUECOM+ aims at connecting systems and humans in remote ocean areas by providing cost-effective, broadband, and reliable communications in alternative to satellite communications. Together these projects will bring up the enablers for data collection under the environmental monitoring programme, as well as to the achievement and sustainability of a GES in marine waters. © 2016 Taylor & Francis Group, London.