Modular development of mobile robots with open source hardware and software components

Prototyping and engineering robot hardware and low-level control often require time and efforts thus subtracted to core research activities, such as SLAM or planning algorithms development, which need a working, reliable, platform to be evaluated in a real world scenario. In this paper, we present Rapid Robot Prototyping (R2P), an open source, hardware and software architecture for the rapid prototyping of robotic applications, where off-the-shelf embedded modules (e.g., sensors, actuators, and controllers) are combined together in a plug-and-play fashion, enabling the implementation of a complex system in a simple and modular way. R2P makes people involved in robotics, from researchers and designers to students and hobbyists, dramatically reduce the time and efforts required to build a robot prototype

Complexity Reduction: Local Activity Ranking By Resource Entropy For QoS-aware Cloud Scheduling

The principle of local activity originated from electronic circuits, but can easily translate into other non-electrical homogeneous/heterogeneousmedia.Cloudresourceisanexample of a locally-active device, which is the origin of complexity in cloud scheduling system. However, most of the researchers implicitly assume the cloud resource to be locally passive when constructing new scheduling strategies. As a result, their research solutions perform poorly in the complex cloud environment. In this paper, we ?rst study several complexity factors caused by the locally-active cloud resource. And then we extended the ”Local Activity Principle” concept with a quantitative measurement based on Entropy Theory. Furthermore, we classify the scheduling system into ”Order” or ”Chaos” state with simulating complexity in the cloud. Finally, we propose a new approach to controlling the chaos based on resource’s Local Activity Ranking for QoS-aware cloud scheduling and implement such idea in Spark. Experiments demonstrate that our approach outperforms thenativeSparkFairSchedulerwithservercostreducedby23%, average response time improved by 15% - 20% and standard deviation of response time minimized by 30% - 45%

Making State Explicit for Imperative Big Data Processing

Data scientists often implement machine learning algo- rithms in imperative languages such as Java, Matlab and R. Yet such implementations fail to achieve the per- formance and scalability of specialised data-parallel pro- cessing frameworks. Our goal is to execute impera- tive Java programs in a data-parallel fashion with high throughput and low latency. This raises two challenges: how to support the arbitrary mutable state of Java pro- grams without compromising scalability, and how to re- cover that state after failure with low overhead. Our idea is to infer the dataflow and the types of state accesses from a Java program and use this information to generate a stateful dataflow graph (SDG). By explic- itly separating data from mutable state, SDGs have spe- cific features to enable this translation: to ensure scala- bility, distributed state can be partitioned across nodes if computation can occur entirely in parallel; if this is not possible, partial state gives nodes local instances for in- dependent computation, which are reconciled according to application semantics. For fault tolerance, large in- memory state is checkpointed asynchronously without global coordination. We show that the performance of SDGs for several imperative online applications matches that of existing data-parallel processing frameworks

The Innovative FlexPlan Grid-Planning Methodology: How Storage and Flexible Resources Could Help in De-Bottlenecking the European System

The FlexPlan Horizon2020 project aims at establishing a new grid-planning methodology which considers the opportunity to introduce new storage and flexibility resources in electricity transmission and distribution grids as an alternative to building new grid elements, in accordance with the intentions of the Clean Energy for all Europeans regulatory package of the European Commission. FlexPlan creates a new innovative grid-planning tool whose ambition is to go beyond the state of the art of planning methodologies by including the following innovative features: assessment of the best planning strategy by analysing in one shot a high number of candidate expansion options provided by a pre-processor tool, simultaneous mid- and long-term planning assessment over three grid years (2030, 2040, 2050), incorporation of a full range of cost–benefit analysis criteria into the target function, integrated transmission distribution planning, embedded environmental analysis (air quality, carbon footprint, landscape constraints), probabilistic contingency methodologies in replacement of the traditional N-1 criterion, application of numerical decomposition techniques to reduce calculation efforts and analysis of variability of yearly renewable energy sources (RES) and load time series through a Monte Carlo process. Six regional cases covering nearly the whole European continent are developed in order to cast a view on grid planning in Europe till 2050. FlexPlan will end up formulating guidelines for regulators and planning offices of system operators by indicating to what extent system flexibility can contribute to reducing overall system costs (operational + investment) yet maintaining current system security levels and which regulatory provisions could foster such process. This paper provides a complete description of the modelling features of the planning tool and pre-processor and provides the first results of their application in small-scale scenariosThe research leading to these results/this publication received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 86381

Detection of ST1702 Escherichia coli blaNDM-5 and blaCMY-42 genes positive isolates from a Northern Italian hospital

We describe two multi drug-resistant (MDR) carbapenemase-producing Escherichia coli clinical isolates from an acute hospital in Milan. Both strains, isolated from a surgical wound sample and a surveillance rectal swab respectively, were positive for a blaNDM-type gene by Xpert Carba-R test. The whole-genome sequence characterization disclosed several resistance determinants: blaNDM-5, blaCMY-42, blaTEM-198, rmtB, mphA. The two isolates belonged to phylogenetic group A, sequence type (ST) 1702 and serotype O89:H9. PCR-based replicon typing and conjugation assay demonstrated an IncI1 plasmid localization for both blaNDM-5 and blaCMY-42 genes. This is the first report of a ST1702 NDM-5 and CMY-42- producing E. coli clone in Italy

Dual-regulated lentiviral vector for gene therapy of X-linked chronic granulomatosis

Regulated transgene expression may improve the safety and efficacy of hematopoietic stem cell (HSC) gene therapy. Clinical trials for X-linked chronic granulomatous disease (X-CGD) employing gammaretroviral vectors were limited by insertional oncogenesis or lack of persistent engraftment. Our novel strategy, based on regulated lentiviral vectors (LV), targets gp91(phox) expression to the differentiated myeloid compartment while sparing HSC, to reduce the risk of genotoxicity and potential perturbation of reactive oxygen species levels. Targeting was obtained by a myeloid-specific promoter (MSP) and posttranscriptional, microRNA-mediated regulation. We optimized both components in human bone marrow (BM) HSC and their differentiated progeny in vitro and in a xenotransplantation model, and generated therapeutic gp91(phox) expressing LVs for CGD gene therapy. All vectors restored gp91(phox) expression and function in human X-CGD myeloid cell lines, primary monocytes, and differentiated myeloid cells. While unregulated LVs ectopically expressed gp91(phox) in CD34(+) cells, transcriptionally and posttranscriptionally regulated LVs substantially reduced this off-target expression. X-CGD mice transplanted with transduced HSC restored gp91(phox) expression, and MSP-driven vectors maintained regulation during BM development. Combining transcriptional (SP146.gp91-driven) and posttranscriptional (miR-126-restricted) targeting, we achieved high levels of myeloid-specific transgene expression, entirely sparing the CD34(+) HSC compartment. This dual-targeted LV construct represents a promising candidate for further clinical development