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Microgrids are recognized as effective solutions to address the urbanization challenges, promoting the diffusion of DER and the realization of low carbon districts. The optimal design of microgrids, served by complex mixes of energy systems, can be modeled as an optimization problem that, once solved, grants decision making support to urban planners and engineers. This work presents a methodology to promote the economically optimal and sustainable design of microgrids, using the Primary Energy Savings (PES) as a constraining factor. A Mixed Integer Linear Programming (MILP) formulation is implemented and tested on a retrofit scenario, using actual data from an industrial microgrid. The optimal energy mix is analyzed as it changes accordingly to the technical, economical and sustainability constraints enforced. A sensitivity analysis on the PES constraint factor highlights how aiming to an environmentally friendly design substantially affects the optimal energy mix of the microgrid. In particular, forcing a low carbon design, the optimization tool promotes the installation of a polygeneration system, in this case in a heat-lead design.

Ultra-low power operation and extreme energy efficiency are strong requirements for a number of high-growth application areas, such as E-health, Internet of Things, and wearable Human–Computer Interfaces. A promising approach to achieve up to one order of magnitude of improvement in energy efficiency over current generation of integrated circuits is near-threshold computing. However, frequency degradation due to aggressive voltage scaling may not be acceptable across all performance-constrained applications. Thread-level parallelism over multiple cores can be used to overcome the performance degradation at low voltage. Moreover, enabling the processors to operate on-demand and over a wide supply voltage and body bias ranges allows to achieve the best possible energy efficiency while satisfying a large spectrum of computational demands. In this work we present the first ever implementation of a 4-core cluster fabricated using conventional-well 28 nm UTBB FD-SOI technology. The multi-core architecture we present in this work is able to operate on a wide range of supply voltages starting from 0.44 V to 1.2 V. In addition, the architecture allows a wide range of body bias to be applied from −1.8 V to 0.9 V. The peak energy efficiency 60 GOPS/W is achieved at 0.5 V supply voltage and 0.5 V forward body bias. Thanks to the extended body bias range of conventional-well FD-SOI technology, high energy efficiency can be guaranteed for a wide range of process and environmental conditions. We demonstrate the ability to compensate for up to 99.7% of chips for process variation with only ±0.2 V of body biasing, and compensate temperature variation in the range −40 °C to 120 °C exploiting −1.1 V to 0.8 V body biasing. When compared to leading-edge near-threshold RISC processors optimized for extremely low power applications, the multi-core architecture we propose has 144× more performance at comparable energy efficiency levels. Even when compared to other low-power processors with comparable performance, including those implemented in 28 nm technology, our platform provides 1.4× to 3.7× better energy efficiency.

Technology scaling enables today the design of ultra-low power wearable bio-sensors for continuous vital signs monitoring or wellness applications. Such bio-sensing nodes are typically integrated in Wireless Body Sensor Network (WBSN) to acquire and process biomedical signals, e.g., Electrocardiogram (ECG), and transmit them to the WBSN gateway, e.g., smartphone, for online reconstruction or features extraction. Both bio-sensing node and gateway are battery powered devices, although they show very different autonomy requirements (weeks versus days). The rakeness -based Compressed Sensing (CS) proved to outperform standard CS, achieving a higher compression for the same quality level, therefore reducing the transmission costs in the node. However, most of the research focus has been on the efficiency of the node, neglecting the energy cost of the CS decoder. In this work, we evaluate the energy cost and real-time reconstruction feasibility on the gateway, considering different signal reconstruction algorithms running on a heterogeneous mobile SoC based on the ARM big.LITTLETM architecture. The experimental results show that it is not always possible to obtain the theoretical QoS under real-time constraints. Moreover, the standard CS does not satisfy real-time constraints, while the rakeness enables different QoS-energy trade-offs. Finally, we show that in the optimal setup (OMP, $n=128$ ) heterogeneous architectures make the CS decoding task suitable for wearable devices oriented to long-term ECG monitoring.

Supercomputer installed capacity worldwide increased for many years and further growth is expected in the future. The next goal for high performance computing (HPC) systems is reaching Exascale. The increase in computational power threatens to lead to unacceptable power demands, if future machines will be built using current technology. Therefore reducing supercomputer power consumption has been the subject of intense research. A common approach to curtail the excessive power demands of supercomputers is to hard-bound their consumption, power capping. Power capping can be enforced by reactively throttling system performance when the power bound is hit, or by scheduling workload in a proactive fashion to avoid hitting the bound. In this paper we explore the second approach: our scheduler meets power capping constraints and minimizes quality-of-service (QoS) disruption through smart planning of the job execution order. The approach is based on constraint programming in conjunction with a machine learning module predicting the power consumptions of HPC applications. We evaluate our method on the Eurora supercomputer, using both synthetic workloads and historical traces. Our approach outperforms the state-of-the-art power capping techniques in terms of waiting time and QoS, while keeping schedule computation time under control.

Klebsiella pneumoniae is an opportunistic nosocomial pathogen belonging to the Enterobacteriaceae family that is associated with a wide range of infections. In the 1980s a new hypervirulent (hypermucoviscous) variant of Klebsiella pneumoniae (hvKP) emerged in southeast Asia and is now increasingly spreading to Western countries due to an invasive syndrome. hvKP isolates can cause serious, life-threatening community-acquired infections in younger healthy hosts, including liver abscess, pneumonia, meningitis and endophthalmitis. We present a case of an 83-year-old man who was examined in the Medicine Department of San Camillo Hospital in Treviso for dehydration in gastroenteritis. Since he presented fever on admission, microbiological investigations were performed and empiric antibiotic therapy with cefotaxime was started. Blood analysis showed a high level of cholestasis indexes and transaminases. Blood cultures were found positive for K. pneumoniae that showed hypermucoviscosity. The hypermucoviscous phenotype of this K. pneumoniae isolate was easily identified by the "string test". Abdominal computed tomography and ultrasonography did not show presence of liver abscesses. After a few days of antibiotic therapy the patient's clinical condition improved. Correct microbiology identification of this kind of strain was essential for appropriate clinical management.

International audience; As side effects of the end of Dennard’s scaling, power and thermal technological walls stand in front of the evolution of supercomputers towards the exaflops era. Energy and temperature walls are big challenges to face for assuring a constant grow of performance in future. New generation architectures for HPC systems implement HW and SW components to address energy and thermal issues for increasing power and efficient computing in scientific workload. In thermal-bound HPC machines, workload-aware runtimes can leverage hardware knobs to guarantee the best operating point in term of performance and power saving without violating thermal constraints.In this paper, we present an integer-linear programming formulation for job mapping and frequency selection for thermal-bound HPC nodes. We use a fast solver and workload traces extracted from a real supercomputer to test our methodology. Our runtime is integrated into the MPI library, and it is capable of assigning high-performance cores to performance-critical processes. Critical processes are identified at execution time through a mathematical formulation, which relies on the characterization of the application workload and on the global synchronization barriers. We demonstrate that by combining long and short horizon predictions with information on the critical processes retrieved from the programming model, we can drastically improve the performance of the target application w.r.t. state-of-the-art DTM solutions.

The aim of this study was to evaluate the distribution of parasitic intestinal infections in patients attending Padua teaching hospital during a two-year period. Between 1st March 2011 and 28th February 2013, we examined stool specimens from 7341 patients (6127 Italians, 1214 non-Italians) for ova and parasites using microscopy, rapid enzyme immunoassays, culture techniques and molecular methods. Stools of 1080 patients (14.71%) were positive for parasites; a total of 1349 intestinal parasites were counted. Protozoa were detected in 1028/1080 patients (95.19%), while helminths were present in 80/1080 patients (7.41%). The protozoa most commonly detected were Blastocystis spp., Dientamoeba fragilis and Giardia duodenalis. Enterobius vermicularis was the helminth most frequently encountered. Of the 1080 infected patients, 227 (21.02%) had more than one parasite in their stool. To conclude, in Italy intestinal parasitoses must be unquestionably considered in differential diagnoses of gastrointestinal diseases. For this purpose, sound knowledge of epidemiology is essential.

Design and planning of low carbon cities and districts must consider the synergies between all the energy networks available. Energy systems optimal design thus assumes a critical importance in determining both costs and environmental impact of operating such districts. This is particularly true following the concept of Local Energy Community, with a single entity representing both the demand and the manager of the energy generation assets. This paper proposes an innovative model for the optimal design of an energy community aiming at lowering its carbon footprint. The community is modeled as a network of spatially dislocated energy hubs, each with its own demand of electricity, heating and cooling energy. The model aims at defining the optimal mix of energy systems, thermal and electric energy storages and energy network infrastructures needed to satisfy the district’s users energy demands. The model is validated using energy demand data from the Nanyang Technological University campus in Singapore by analyzing three scenarios. In the first one, the optimization goal is purely economic and it aims at minimizing the overall cost of operating the district. The second and third scenarios focus on reducing the carbon footprint of the district by imposing an additional constraint, which limits the overall primary energy consumption. In all the scenarios the algorithm chooses to partially or totally connect the five sites with a district cooling network and take advantage of cold thermal storage, proving their potential in hot climates. In the first scenario, the advantages of the district cooling solution are mainly related to the savings in the capital cost of electric chillers that partially offset the cost of the district cooling network; indeed, district cooling network allows the sites to share cooling power thus achieving a reduction in chillers total installed size of 33%. In the second scenario, in order to meet the target of 10% reduction of the overall primary energy, the optimal solution also requires the installation of a photovoltaic system. In the third scenario, imposing a 20% reduction of the overall primary energy, also a natural gas fed trigeneration plant comes into play.

VIM and KPC are two major families of carbapenemases involved in nosocomial outbreaks of multidrug-resistant Gram-negative bacilli. To rapidly detect bla(VIM)- and bla(KPC)-encoding strains, three multiplex PCR-based methods were designed and validated: (i) a real-time PCR to detect all reported VIM alleles, namely bla(VIM-1-19, 23-37); (ii) a real-time PCR to identify bla(VIM)-type and bla(KPC) carbapenemases in an ultrarapid single reaction; and (iii) a standard PCR to amplify and sequence all VIM alleles. All three methods detected 33 VIM-positive samples among 107 Gram-negative isolates with imipenem and meropenem minimum inhibitory concentrations ≥1 mg/L. The three methods displayed 100% sensitivity, specificity and concordance. Sequencing of the bla(VIM) amplicons revealed that 30 samples encoded bla(VIM-1) and 3 samples encoded bla(VIM-2). The real-time assay, optimised for the simultaneous detection of bla(VIM) and bla(KPC), identified 3 and 12 isolates positive for both bla(VIM)/bla(KPC) and for bla(KPC), respectively. The analytical sensitivity of the real-time assays was linear over 6 log dilutions, with a reproducible detection limit of 1 CFU. No cross-reactivity was detected. The developed assays provide powerful tools for rapid identification of VIM and KPC carbapenemase producers, therefore contributing to the prevention and containment of resistance dissemination.

Background and aims. The aim of this study is to define the epidemiology and the antimicrobial resistance profile of Ureaplasma spp. and Mycoplasma hominis isolated from urogenital specimens of patients attending the Microbiology and Virology Unit of Padua between January 2014 and December 2015. Materials and methods. The analysis was carried out on a total of 10861 samples. Species identification and antimicrobial susceptibility tests were performed using bioMérieux Mycoplasma IST 2. Results. 2668 samples (24.6%) from 2043 patients were positive: 2466 samples positive for Ureaplasma spp. (1897 patients) and 8 samples positive for M. hominis (8 patients), while in 194 samples (138 patients) was detected a coinfection. As for antimicrobial susceptibility of Ureaplasma spp. we found a resistance (R+I) rate of 90.6% for ciprofloxacin, of 74,1% for ofloxacin, of 52.8% for azithromycin and of 47.0% for erythromycin.Conclusions. Our report shows a high prevalence of Ureaplasma spp. in the study population. Surveillance of antibiotic resistance is critical for an appropriate therapeutic approach, which must always be contextualized with patient’s symptomatology.

In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumption of sensing nodes in biomedical signal processing devices. This is due to the fact the CS is capable of reducing the amount of data to be transmitted to ensure correct reconstruction of the acquired waveforms. Rakeness-based CS has been introduced to further reduce the amount of transmitted data by exploiting the uneven distribution to the sensed signal energy. Yet, so far no thorough analysis exists on the impact of its adoption on CS decoder performance. The latter point is of great importance, since body-area sensor network architectures may include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this paper, we fill this gap by showing that rakeness-based design also improves reconstruction performance. We quantify these findings in the case of ECG signals and when a variety of reconstruction algorithms are used either in a low-power microcontroller or a heterogeneous mobile computing platform.

Objectives We described Klebsiella pneumoniae producing carbapenemase (CPKP) spread from 01/01/2015 to 13/09/16 in a tertiary level hospital. Methods The first positive surveillance rectal swab (SRS) or clinical sample (CS) collected in the medical department (MD), surgical department (SD) and intensive care department (ICD) were included in the study. A validated in-house Real-Time PCR method was used to detect carbapenemases; multilocus sequence typing (MLST) was used for further characterization of the strains. Results 21535 patients were included: 213 CPKP strains from surveillance rectal swab (SRS) and 98 from clinical samples (CS) were collected. The percentage of CPKP detected in SRS with respect to CS increased in the medical MD from 2015 to 2016 (p=0.01) and in ICD from 2012 to 2015 (p=0.0001), while it decreased in SD from 2014 to 2016 (p=0.003); 68.5% of the positive SRS had a previous negative SRS; CPKP was more frequently identified in CS than in SRS in MD. Twelve strains harboured more than one carbapenemase gene. Many other species harbouring a carbapenemase gene were collected. Conclusions MDs need more inclusive surveillance criteria. The late detection of positive SRS underlined the risk of colonization during hospitalization.

IEEE Transactions on Biomedical Circuits and Systems, 11 (4), ISSN:1932-4545, ISSN:1940-9990

With CMOS scaling beyond 14 nm, reliability is a major concern for IC manufacturers. Reliability-aware design has a non-negligible overhead and cannot account for user experience in mobile devices. An alternative is dynamic reliability management (DRM), which counteracts degradation by adapting the operating conditions at runtime. In this paper, for the first time we formulate DRM as an optimization problem that accounts for reliability, temperature and performance. We develop an optimal policy for multicores using convex optimization, and show that it is not feasible to implement on real systems. For this reason, we propose workload-aware reliability management (WARM), a fast DRM technique adapting to diverse workload requirements to trade reliability and user experience. WARM is implemented and tested on a real Android device. WARM approximates the solution of the convex solver within 5% on average, while executing more than $400 {\times }$ faster. WARM integrates a thermal controller that allocates tasks to meet thermal constraints. This is required since degradation strongly depends on temperature. We show that WARM meets temperature constraints within 5% in 87.5% more cases than the state-of-the-art. We show that WARM task allocation achieves up to one year lifetime improvement for a multicore platform. It can achieve up to 100% of performance improvement on cluster architectures, such as big.LITTLE, while still guaranteeing the reliability target. Finally, we show that it achieves performance in the 4% of the maximum for a broad range of a applications, while meeting the reliability constraints.

The design, control, and management of large-scale Smart Grids poses a number of challenging problems. Successfully addressing them requires high-level, flexible, efficient and scalable dynamic modelling and simulation frameworks. In this paper, the requirements for such frameworks are first stated. Then, it is shown how the equation-based, object-oriented modelling approach and the Modelica modelling language can fit those requirements.

This work presents the research findings of the Multithermal ERC-Advanced project, in terms of multi-scale thermal control of complex large scale computing platforms such as High Performance Computing Systems, and datacenters. In this respect, the challenges and opportunities concerning thermal control of computing systems are discussed, along with the proposed innovative solutions. Control and management problem is divided at different hierarchical, and scale, levels (compute node and system). Then, given the control knobs and sensors available at each level, advanced control and system identification techniques are proposed, to achieve a holistic thermal management of complex computing platforms, with benefits for thermal stability guarantees and energy consumption optimization. Such features are of utmost importance for advancements in next generation large scale computing solutions.

The design of ultra-low cost wireless body sensor networks for wearable biomedical monitors has been made possible by today technology scaling. In these systems, a typically multi-channel biosignal sensor takes care of the operations of acquisition, data compression and final output transmission or storage. Furthermore, since these sensors are usually battery powered, the achievement of minimal energy operation is a fundamental issue. To this aim, several aspects must be considered, ranging from signal processing to architectural optimization. In this paper we consider the recently proposed rakeness-based compressed sensing (CS) paradigm along with its zeroing companion. With respect to a standard CS base sensor, the first approach allows us to further increase compression rate without sensible signal quality degradation by exploiting localization of input signal energy. The latter paradigm is here formalized and applied to further reduce the energy consumption of the sensing node. The application of both rakeness and zeroing allows for trading off energy from the compression stage to the transmission or storage one. Different cases are taken into account, by considering a realistic model of an ultra-low-power multicore DSP system.

In the last decade, improvements on technology scaling have enabled the design of a novel generation of wearable bio-sensing monitors. These smart Wireless Body Sensor Nodes (WBSNs) are able to acquire and process biological signals, such as electrocardiograms, for periods of time extending from hours to days. The energy required for the on-node digital signal processing (DSP) is a crucial limiting factor in the conception of these devices. To address this design challenge, we introduce a domain-specific ultra-low power (ULP) architecture dedicated to bio-signal processing. The platform features a light-weight strategy to support different operating modes and synchronization among cores. Our approach effectively reduces the power consumption, harnessing the intrinsic parallelism and the workload requirements characterizing the target domain. Operations at low voltage levels are supported by a heterogeneous memory subsystem comprising a standard-cell based ultra-low voltage reliable partition. Experimental results show that, when executing real-world bio-signal DSP applications, a state-of-the-art multi-core architecture can improve its energy efficiency in up to 50 percent by utilizing our proposed approach, outperforming traditional single-core alternatives.

International audience; The ANTAREX project aims at expressing the application self-adaptivity through a Domain Specific Language (DSL) and to run-time manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to Exas-ale. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. We show through a mini-app extracted from one of the project application use cases some initial exploration of application precision tuning by means enabled by the DSL.

The thermal wall for many-core systems on-chip calls for advanced management techniques to maximize performance, while capping temperatures. Distributed and compact thermal models are a cornerstone for such techniques. System identification methodologies allow to extract models directly from the target device thermal response. Unfortunately, standard Auto-Regressive eXogenous models and Least Squares techniques cannot effectively tackle both model approximation and measurement noise typical of real systems. In this work, we propose a novel distributed identification strategy to derive distributed interacting thermal models. The presented method can cope with both process noise and temperature sensor noise affecting inputs and outputs of the adopted models. Online and offline versions are presented, and issues related to model order, sampling time and input stimuli are addressed. The proposed method is applied to the Intel's Single-chip-Cloud-Computer many-core prototype.

This paper presents our recent advancements in the development of the underwater optical wireless modems, designed within the OptoCOMM project. The target performance of the optical modem is to achieve the data rate of 10 Mbit/s at a distance of at least 10 m in presence of sunlight. The transmitter is based on blue Light Emitting Diode (LED) technology. The modem will be developed in three versions to be integrated in Littoral Ocean Observatory Network (LOON) test-bed, and add a novel technology to the infrastructure of the FP7-SUNRISE project. We tested the first version of the modems in a typical shallow harbour water (1 m depth), in the same location of the LOON to validate the adopted technology. The seawater of the test-bed is characterized by a medium-high turbidity (about 1 FTU), which heavily affects the optical communication in terms of attenuation. All tests were run in sunny summer days with very high sunlight illumination level (∼ 100 klux). Despite the challenging conditions, we achieved successful transmission at 10 Mbit/s over 7.5 m distance.

Emerging and future HealthCare policies are fueling up an application-driven shift toward long-term monitoring of biosignals by means of embedded ultra-low power Wireless Body Sensor Networks (WBSNs). In order to break out, these applications needed the emergence of new technologies to allow the development of extremely power-efficient bio-sensing nodes. The PHIDIAS project aims at unlocking the development of ultra-low power bio-sensing WBSNs by tackling multiple and interlocking technological breakthroughs: (i) the development of new signal processing models and methods based on the recently proposed Compressive Sampling paradigm, which allows the design of energy-minimal computational architectures and analog front-ends, (ii) the efficient hardware implementation of components, both analog and digital, building upon an innovative ultra-low-power signal processing front-end, (iii) the evaluation of the global power reduction using a system wide integration of hardware and software components focused on compressed-sensing-based bio-signals analysis. PHIDIAS brought together a mixed consortium of academic and industrial research partners representing pan-European excellence in different fields impacting the energy-aware optimization of WBSNs, including experts in signal processing and digital/analog IC design. In this way, PHIDIAS pioneered a unique holistic approach, ensuring that key breakthroughs worked out in a cooperative way toward the global objective of the project.

Energy-efficiency is of primary interest in future HPC systems as their computational growth is limited by the supercomputer peak power consumption. A significant part of the power consumed by a supercomputer machine is caused by the cooling infrastructure. Todays thermal design is based on coarse grain models which consider the silicon die of the processing elements as an isothermal surface. Similarly feedback control loops uses the same assumption to modulate the cooling effort with the goal of reducing cooling cost and maintaining the silicon temperature in a safe working range. Recent processors development has brought into the market CPUs that integrate a large number of complex cores. Differently from massively parallel CPUs for which the area and power consumption of each core is very limited, the cores of these processors can consume tens of watts and thus, under heterogeneous workloads, creating significant thermal gradients. In this paper we first characterize the power and thermal characteristics of new server-class Intel Xeon computing node based on Haswell v3 architecture considering both the computational and the cooling components. We show that these systems are characterized by significant on-die thermal gradients and that the current O.S. task allocation strategy is not capable of taking advantage of that, leading to max CPU temperature and extra cooling activity. To solve this issue we propose a novel task allocation strategy that reduces the cooling power while matching the HPC performance requirements., 2016 International Conference on High Performance Computing & Simulation (HPCS 2016), ISBN:978-1-5090-2088-1, ISBN:978-1-5090-2089-8

3-D medical ultrasound imaging enables new diagnostic possibilities and modalities. In a computational process called beamforming, a 3-D volume is reconstructed from several thousands of analog signals. Today's systems rely on massive analog preprocessing to reduce the computational burden of the subsequent digital processing system. In this paper, we present a configurable beamformer (BF) architecture, which demonstrates for the first time that it is possible to implement the entire 3-D delay and sum beamforming fully digitally and on one single chip, without requiring the off-chip memories. We present a presilicon implementation of a single-chip BF in an advanced 28-nm silicon-on-insulator technology. The BF targets a fully sampled 10k element 8-MHz bandwidth transducer head and is able to produce 298.1M focal points (FPs) per second - enough to produce a high-resolution volume with 16.3MFP at 15 Hz. All delays are computed online and on-chip to eliminate the power-hungry external memories for delay storage. The final design (register-transfer-level and floorplan) has a complexity of 342M gate equivalents requiring 1.68cm2 of area. The core power is estimated to be 30.3 W, resulting in an unprecedented power efficiency of 98.4G beamforming operations per watt.

Compressed sensing has recently been actively investigated as a mean of lowering the power consumption of sensing nodes in biomedical signal devices due to its capability to reduce the amount of data to be transmitted for the correct reconstruction of the acquired waveforms. The rakeness-based design of compressed sensing stages exploits the uneven distribution of energy in the sensed signal and has proved to be extremely effective in maximizing the energy savings. Yet, many body-area sensor network architectures include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this case, the decoder-side power consumption is also an issue. In this paper, with particular reference to ECG signals, we show that rakeness-based design is also capable to reduce resources required at the decoder side for reconstruction. This happens across a variety of reconstruction algorithms that see their running time substantially reduced. Actual savings are then experimentally quantified by measuring the energy requirements of one of the algorithms on a common mobile computing platform.

2016 International Conference on High Performance Computing & Simulation (HPCS 2016), ISBN:978-1-5090-2088-1, ISBN:978-1-5090-2089-8

Scheduling and dispatching tools for high-performance computing (HPC) machines have the key role of mapping jobs to the available resources, trying to maximize performance and quality-of-service (QoS). Allocation and Scheduling in the general case are well-known NP-hard problems, forcing commercial schedulers to adopt greedy approaches to improve performance and QoS. Search-based approaches featuring the exploration of the solution space have seldom been employed in this setting, but mostly applied in off-line scenarios. In this paper, we present the first search-based approach to job allocation and scheduling for HPC machines, working in a production environment. The scheduler is based on Constraint Programming, an effective programming technique for optimization problems. The resulting scheduler is flexible, as it can be easily customized for dealing with heterogeneous resources, user-defined constraints and different metrics. We evaluate our solution both on virtual machines using synthetic workloads, and on the Eurora HPC with production workloads. Tests on a wide range of operating conditions show significant improvements in waitings and QoS in mid-tier HPC machines w.r.t state-of-the-art commercial rule-based dispatchers. Furthermore, we analyze the conditions under which our approach outperforms commercial approaches, to create a portfolio of scheduling algorithms that ensures robustness, flexibility and scalability.

The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing (NTC) can be used to improve energy efficiency in the post-Dennard scaling era. This paper describes an architecture based on the FD-SOI process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server.

Scheduling and dispatching are critical enabling technologies in supercomputing and grid computing. In these contexts, scalability is an issue: we have to allocate and schedule upi?źto tens of thousands of tasks on tens of thousands of resources. This problem scale is out of reach for complete and centralized scheduling approaches. We propose a distributed allocation and scheduling paradigm called DARDIS that is lightweight, scalable and fully customizable in many domains. In DARDIS each task offloads to the available resources the computation of a probability index associated with each possible start time for the given task on the specific resource. The task then selects the proper resource and start time on the basis of the above probability. The scheduler can be customized with different policies to fit several objective functions like load balancing or makespan. We evaluate our approach in the domain of grids and supercomputers. We compare DARDIS with the most widely used algorithms used in these specific domains to show that this approach can reach better solutions in several cases.

Variability is the real big challenge for integrated circuits. Today, simulators help to estimate the effect of variability, but fail to capture real workload dynamics and user interactions, which are fundamental to mobile devices. This paper presents VarDroid, a low-overhead tool to emulate power and performance variability on real platforms, running on top of the Android operating system. VarDroid enables analyzing the effect of variability in power and performance while capturing the complex interactions characteristic of mobile workloads, thus relating to user's quality of experience. The paper presents use cases to show the utility of VarDroid to test applications, device and OS robustness under the effects of variability. Our results show that a variability-agnostic OS can incur in a performance penalty of up to 60% and a power penalty of up to 20%.

International audience; The main goal of the ANTAREX project is to express by a Domain Specific Language (DSL) the application self-adaptivity and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to the Exascale level. Key innovations of the project include the introduction of a separation of concerns between self-adaptivity strategies and application functionalities. The DSL approach will allow the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management.

Process and environmental temperature variations have a detrimental effect on performance and reliability of modern embedded systems. This sensitivity to operating conditions significantly increases in ultra-low-power (ULP) devices and in all those applications that rely on reduced supply voltage to achieve energy efficiency. We propose a lightweight runtime solution to tolerate process and environmental temperature variations. The novelty of our solution is the ability to tackle both hold time and setup time sensitivity to variations by dynamically adapting latencies of the datapaths without compromising execution correctness. We extensively tested our solution evaluating the trade-offs, demonstrating the cost, performance, reliability gain compared to state-of-the-art static solutions. The proposed solution is able to reach a performance gain up to 30% with a very low (≈ 4%) area overhead.

Background The aim of this study was to characterize the spread of carbapenemase-producing Klebsiella pneumoniae (CPKP) in a tertiary level hospital using ongoing active surveillance with rectal swab cultures. Furthermore, this study analyzed the presence of CPKP in the clinical samples (CS) of a single patient as well as the evolution of Colistin-sensitive strains (CoS) to Colistin-resistant strains (CoR). Methods This study was performed from January 1, 2012 to December 31, 2014. In 2012, a survey was conducted in the Intensive Care Department. In autumn 2013, active monitoring was extended to the Surgery Department, and since mid-2014, the surveillance has included the Medical Department as well. Only the first isolated strain from each patient was included. Antimicrobial susceptibility testing was performed on CPKP isolates: Klebsiella pneumoniae carbapenemase, oxacillinase-48, Verona integron-encoded metallo-β-lactamase and New Delhi metallo-β-lactamase were detected using a validated in-house PCR method, and multilocus sequence typing (MLST) was used to investigate the clonal transmission of strains. Results A total of 15,104 patients were included in the study, and 496 consecutive non-replicated strains of CPKP were collected: 149 strains were collected in 2012 (39 [26.2 %] from surveillance rectal swabs [SRS]), 133 strains were collected in 2013 (70 [52.6 %] from SRS) and 214 strains were collected in 2014 (164 [76.6 %] from SRS). We observed a significant increase in the percentage of positive SRS cases in 2014 relative to 2013 and 2012 (p = 0.0001 and p = 0.0172, respectively) and in the proportion of CPKP first isolated by SRS relative to those identified by CS (p

Hot-water liquid cooling is a key technology in future green supercomputers as it maximizes the cooling efficiency and energy reuse. However the cooling system still is responsible for a significant percentage of modern HPC power consumption. Standard design of liquid-cooling control relies on rules based on worst-case scenarios, or on CFD simulation of portion of the entire system, which cannot account for all the real supercomputer working conditions (workload and ambient temperature). In this work we first introduce an analytical model, based on lumped parameters, which can effectively describe the cooling components and dynamics, and can be used for analysis and control purposes. We then use it to design an energy-optimal control strategy which is capable to minimize the pump and chiller power consumption while, meeting the supercomputer cooling requirements. We validate the method with simulation tests, taking data from a real HPC cooling mechanism, and comparing the results with state-of-the-art commercial cooling system control strategies.

Objectives The aim of the present prospective, randomized, double-blind, and placebo-controlled investigation (approved by the Ethical Committee of Padova University Hospital [Italy]) was to assess the effect of a nasal gel containing a combination of silver sucrose octasulfate and potassium sucrose octasulfate (Silsos gel® [SG]) in wound healing after endoscopic sinus surgery (ESS) for chronic rhinosinusitis in terms of: nasal symptoms (SNOT22), endoscopic appearance of the sinonasal mucosa (Lund–Kennedy score), nasal air flow (anterior active rhinomanometry), evidence of mucosal inflammatory processes (nasal cytology and histology), and microbiological growth. Methods Thirty-four patients with chronic rhinosinusitis were randomized on a 1:1 ratio to receive after ESS either SG or placebo (contained only the excipients [carbopol and propylene glycol] in the same concentrations as in SG). Results/conclusions Judging from the present prospective investigation on patients who underwent ESS for chronic rhinosinusitis, treatment with SG seems to enable a significantly faster improvement in specific symptoms (assessed on the validated SNOT22 scale) than placebo. Patients treated with SG also had a quicker improvement in the endoscopic appearance of their nasal mucosa after ESS than patients treated with placebo. These endoscopic improvements in the SG group were also confirmed at the long-term follow-up, while the same did not apply to the placebo-treated group.

Novel low-voltage, low latency, non-volatile memory (NVM) technologies allow long-term wearable biomedical monitors to benefit from large storage capability, avoiding costly wireless transmissions and enabling, along with proper signal processing and architectural optimization, minimal energy operations and extended battery life. The recently proposed rakeness-based Compressed Sensing (RCS) offers high compression rate with an associated low computational power. This allows an energy trade-off between the compression stage and the storage stage. In this paper we introduce a novel approach, namely zeroing CS, which reduces RCS computational requirements to extremely low levels. The new energy trade-off is analyzed, considering a suitable multi-core DSP and different NVM technologies for local storage. According to our analysis, the proposing zeroing approach is up to 80% more efficient than a standard CS solution and 70% w.r.t. RCS when overall energy requirement is not dominated by storage.

Background: Multidrug resistance and, in particular, carbapenem resistance is spreading worldwide at an alarming rate, comprehending a variety of bacterial species and causing both nosocomial and community acquired outbursts. Early and efficient detection of infected patients or colonized carriers are mandatory steps in infection control and prevention of multidrug resistance diffusion. The latest EUCAST guidelines for detection of carbapenemase-producing Enterobacteriaceae have set low clinical breakpoints to ensure the maximum detection sensitivity of positive samples. Current workflows involve an initial screening step for species and resistance pattern detection, followed by phenotypic and/or genotypic confirmation. The aim of the present study was to assess the efficiency of six widely used and validated phenotypic assays for the detection of carbapenemases/AmpC in Enterobacteriaceae, to estimate the best workflow in the routine characterization of Enterobacteriaceae isolates. Methods: A panel of 108 non-repetitive Enterobacteriaceae isolates with reduced susceptibility to carbapenems was analyzed by means of 1) Modified Hodge Test, 2) Metallo Beta Lactamase Etest, 3) Double disk test with EDTA, 4) Rosco Diagnostica KPC and MBL confirm kit (RDCK™), 5) AmpC Etest and 6) Cloxacillin inhibition test. Confirmation and validation of results was achieved by genotypic analysis. Results: The most accurate identification of resistance determinants was obtained with the combined disc test (Rosco Diagnostica KPC and MBL confirm kit) which had to be coupled with the cloxacillin inhibition test for correct detection of AmpC enzymes. However, in general, phenotypic tests failed to characterize isolates harboring multiple carbapenem resistance determinants, which were successfully assessed only by PCR-based analysis. Conclusions: To detect and control the spread of pathogens with complicated resistance patterns, both optimized phenotypic analysis (i.e. Rosco Diagnostica KPC and MBL confirm kit coupled with the cloxacillin inhibition test) and genotypic assays are recommended in the routine diagnostic of clinical laboratories.

Technology scaling enables today the design of ultra-low cost wireless body sensor networks for wearable biomedical monitors. The typical behaviour of such systems consists of multi-channel input biosignals acquisition data compression and final output transmission or storage. To achieve minimal energy operation and extend battery life several aspects must be considered ranging from signal processing to architectural optimizations. The recently proposed Rakeness-based Compressed Sensing (CS) paradigm deploys the localization of input signal energy to further increase compression without sensible RSNR degradation. Such output size reduction allows for trading off energy from the compression stage to the transmission or storage stage. In this paper we analyze such tradeoffs considering a multi-core DSP for input biosignal computation and different technologies for either transmission or local storage. The experimental results show the effectiveness of the Rakeness approach (on average ≈ 44% more efficient than the baseline) and assess the energy gains in a technological perspective.

Introduction: In the management of nosocomial infections, the emergence of carbapenem-resistance in Enterobacteriaceae strains is a matter of increasing concern. The aim of this study was to detect and actively survey the presence of these strains by using phenotypic and genotypic assays. Methods: The screening of the strains with reduced carbapenem susceptibility was carried out using the automated Vitek®2 System (bioMerieux). Subsequently, we performed both different confirmation phoenotypic assays (Modified Hodge Test, Kirby-Bauer test, Etest® MBL MP/MPI, Etest® AmpC, Mueller-Hinton/cloxacillin test) and molecular identification tests ( PCR assays). Results: All the screened carbapenemase-producing strains (on the bases of the phenotypic tests results) were confirmed to harbour β-lactamase genes (by PCR analysis): 460 strains were positive for the presence of the blaKPC and 6 for the presence of the blaVIM gene. Conclusions: KPC-positive Klebsiella pneumoniae strains caused in our hospital,in the period 2008-2012, an outbreak of infection that was subsequently restricted with effective containment measures. It is mandatory that all carbapenemase-producing species will be closely monitored in hospital setting, to contain the possible rapid spread of strains showing more expansion properties and virulence.

V–Sn–O mixed oxides have been prepared by reaction between tin oxohydrate and vanadium pentoxide at 700 °C. The samples were characterized by means of XPS and UV–VIS diffuse reflectance spectroscopy (DRS). It has been found that different vanadium species formed, the relative amounts of which were a function of the vanadia loading. At a vanadium loading of 4.3 atom%, the SnO2 surface was covered with a dispersed VV species, likely to be in tetrahedral coordination. In addition, VIV dispersed in the cassiterite matrix led to the formation of a VxSn1 –xO2 solid solution. When the V content is 10 atom%, additional formation of bulk V2O5 has been observed. The VIV in solid solution modified the electronic properties of the SnO2. The calcination temperature also affected the distribution of the vanadium species. Temperatures higher than 700 °C favoured the segregation of SnO2 and V2O5. The samples were tested in the oxidation of o-xylene to phthalic anhydride. It has been found that bulk VV oxide is necessary in order to achieve good selectivity, since the VIV-doped SnO2 was not selective to phthalic anhydride. In contrast, the activity was proportional to the overall vanadium content.

This paper presents a visual environment for process modeling and simulation, together with its specialization to thermoelectric power plants. Though attention is focused on a specific class of plants, the environment has been designed under rigorous and more general specifications, concerning both architectural and user interface aspects. Features of power plant processes are specifically considered by implementing process libraries and solution schemes that allow the analyst to optimally structure its applications

The novel real-time PCR assay developed as described here was able to detect bla(KPC1/2-12) (bla(KPC-1/2) to bla(KPC-12)) from easily available clinical specimens in less than 2 h. The genotypic assay was highly sensitive (100%) and specific (98%). In some cases, it was able to detect bla(KPC) 48 h before positive detection by standard phenotypic assay on patients who were monitored daily. The high sensitivity and rapidity of the molecular method make it the method of choice for KPC surveillance and, thus, containment purposes.

The novel real-time PCR assay developed as described here was able to detect bla KPC1/2-12 ( bla KPC-1/2 to bla KPC-12 ) from easily available clinical specimens in less than 2 h. The genotypic assay was highly sensitive (100%) and specific (98%). In some cases, it was able to detect bla KPC 48 h before positive detection by standard phenotypic assay on patients who were monitored daily. The high sensitivity and rapidity of the molecular method make it the method of choice for KPC surveillance and, thus, containment purposes.

Computational sprinting has been recently proposed as an effective solution to mitigate the upcoming dark silicon uti- lization wall problems. As most applications do not re- quire constantly the maximum performance level and par- allelism, computational sprinting uses the intrinsic thermal capacitance of the heat dissipation system, possibly aug- mented with Phase Change Materials, as heat buffer for tolerating bursts of intensive computational resource usage largely exceeding the steady-state thermal design power. It is clear that sprinting poses peculiar challenges on the dy- namic thermal control policy. In this paper we introduce and evaluate an innovative and low-overhead hierarchical model- predictive controller that enables thermally-safe sprinting while guaranteeing a predictable re-sprinting rate.

This manuscript describes a study on primary spinning reserve scheduling in a 420 MW combined cycle power plant, conducted by a convenient use of Object-Oriented Modelling and Simulation (OOMS). This allowed to optimise the control system to comply with the network code requirements in the presence of dynamic constraints posed by the machinery. An accurate simulation model was tailored to represent only the plant physical parts strictly related to the problem. This allowed to test different control solutions, and to find the best one right from the system engineering phase, with quite moderate an effort, thus drastically reducing the commissioning time, and helping also in the definition of the test schedule. Selected simulation and experimental results are presented and discussed, as both a validation of the model, and a proof for the efficacy of the adopted approach.

Many-cores systems on chip provide the highest performance scaling potential due to the massive parallelism, but they suffer from thermal issues due to their high power densities. Thermal sensors and feedback strategies are used to reduce these threats but sensor accuracy directly impact control performance. In this paper we propose a novel technique to calibrate thermal sensors. Our approach can be applied to general multi-core platforms since it combines stress patterns and least-square fitting to perform thermal sensor characterization directly on the target device. We experimentally validate our approach on the Single Chip Cloud (SCC) prototype by Intel.