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Objective Some conventional laboratory tests are routinely used for the prediction of systemic autoimmune disease activity, such as the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP); however, they can give false-negative results, pointing out the need to identify more specific markers. Methods We evaluated biomarkers in 21 Italian patients naïve to treatment with a diagnosis of autoimmune rheumatic disease according to the 2010 American College of Rheumatology/European League Against Rheumatism Classification Criteria for Rheumatoid Arthritis during 6 months of therapeutic treatments. Results We found a significant difference in interleukin-6 (IL-6), CRP, ESR, platelet count, and fecal calprotectin in diagnosed patients compared with healthy participants and a significant decrease in these values during follow-up, except for IL-6 and platelet count. Conclusion We found that CRP, ESR, and fecal calprotectin seemed to be related to autoimmune rheumatic disorders and to be associated with therapy, whereas serum calprotectin and IL-6 did not seem to be associated with disease improvement after the start of treatment, along with leukocyte count and platelet count. [ABSTRACT FROM AUTHOR]

Data centers are increasing in size and complexity, and we need scalable approaches to support their automated analysis and control. Performance counters and power consumption are their key "vital signs". State-of-the-Art (SoA) monitoring systems provide built-in tools to collect performance measurements, and custom solutions to get insight on their power consumption. However, with the increase in measurement resolution (in time and space) and the ensuing huge amount of measurement data to handle, new challenges arise, such as bottlenecks on the network bandwidth, storage and software overhead on the monitoring units. To face these challenges we propose a novel monitoring platform for data centers, which enables real-time high-resolution profiling (i.e., all available performance counters and the entire signal bandwidth of the power consumption at the plug—sampling up to 20 μ s —with an error below 1%) and analytics, both at the edge (node-level analysis) and on a centralized unit (cluster-level analysis). The monitoring infrastructure is completely out-of-band, scalable, technology agnostic and low cost, and it is already installed in a SoA high-performance compute cluster (i.e., D.A.V.I.D.E. —18th in Green500 November 2017). [ABSTRACT FROM AUTHOR]

Power and energy consumption are becoming key challenges for the supercomputers’ exascale race. HPC systems’ processors waist active power during communication and synchronization among the MPI processes in large-scale HPC applications. However, due to the time scale at which communication happens, transitioning into low-power states while waiting for the completion of each communication may introduce unacceptable overhead. In this article, we present COUNTDOWN, a run-time library for identifying and automatically reducing the power consumption of the CPUs during communication and synchronization. COUNTDOWN saves energy without penalizing the time-to-completion by lowering CPUs power consumption only during idle times for which power state transition overhead is negligible. This is done transparently to the user, without requiring labor-intensive and error-prone application code modifications, nor requiring recompilation of the application. We test our methodology on a production Tier-1 system. For the NAS benchmarks, COUNTDOWN saves between 6 and 50 percent energy, with a time-to-solution penalty lower than 5 percent. In a complete production—Quantum ESPRESSO—for a 3.5K cores run, COUNTDOWN saves 22.36 percent energy, with a performance penalty below 3 percent. Energy saving increases to 37 percent with a performance penalty of 6.38 percent, if the application is executed without communication tuning. [ABSTRACT FROM AUTHOR]

This study investigates how a district with a high capacity of non-controllable renewable electricity generation can entirely self-consume its production at a community level either directly or for heating and cooling, thus potentially fulfilling the concept of "Renewable Energy Community". It investigates the potential role of storage systems and polygeneration in renewables self-consumption, by also exploiting the synergies among different energy networks in a real residential district with high PV penetration. Two scenarios were modeled other than the "baseline": the first one evaluating the optimal portfolio of energy conversion and storage technologies, and a second one achieving the same goal only using batteries. Both scenarios proved to be a viable solution to exploit the excess of electricity production from the PV plants in the district only through local self-consumption. The results show that a multi-energy system is the most cost-effective solution in doing so, exploiting polygeneration technologies (CHP) and the storage of energy as thermal, electrical, and chemical through power-to-gas. In particular, the least cost solution entails a 42 kWe CHP micro gas engine fueled by a natural gas-hydrogen blend, a 135 kWh battery system, and a 2830 kWh hydrogen storage. • P2G coupled to polygeneration is a viable solution to store renewable energy. • Cost-effective optimal solution consists of cogeneration, electric and hydrogen storages. • The use of only batteries is too expensive to store the excess of renewables in a district. • Batteries are more effective than P2G with hydrogen due to the higher roundtrip efficiency: 90% vs. 23%. • Synergies among energy networks and distributed generation can reduce the power ramp. [ABSTRACT FROM AUTHOR]

Thermal-aware design and online optimization of the cooling effort are becoming increasingly important in current and future high-performance computing (HPC) systems. A fundamental requirement to effectively develop such techniques is the availability of distributed and compact models representing the system thermal behavior. System identification algorithms allow to extract models directly from the thermal response of the target device. This article proposes a novel thermal identification approach for real, in-production HPC systems, which is capable of extracting thermal models from a computing node affected by quantization noise on the temperature measurements as well as operating in the free-cooling mode, with variable ambient temperature. The approach allows also to identify the physical floorplan of the CPU dies in supercomputing nodes. The effectiveness of the proposed methodology has been tested on a node of the CINECA Galileo Tier-1 supercomputer system. [ABSTRACT FROM AUTHOR]

Energy efficiency is of paramount importance for the sustainability of high performance computing (HPC) systems. Energy consumption limits the peak performance of supercomputers and accounts for a large share of Total Cost of Ownership (TCO). Consequently, system owners and final users have started exploring mechanisms to trade off performance for power consumption, for example, through frequency and voltage scaling. However, only a limited number of studies have been devoted to explore the economic viability of performance scaling solutions and to devise pricing mechanisms fostering a more energy-conscious usage of resources, without adversely impacting return-of-investment on the HPC facility. How to foster green-computing to take advantage of power management knobs? Would today's accounting systems be sufficient or are novel pricing schemes needed? How to model the link between TCO and power management? To answer these questions, we present a parametrized model to analyze the impact of frequency scaling on energy consumption and to assess the potential cost benefits for the HPC facility and users. We evaluate four pricing schemes, considering both facility manager and the user perspectives. We then perform a design space exploration considering current and near-future HPC systems and technologies. [ABSTRACT FROM AUTHOR]

Supercomputers, nowadays, aggregate a large number of nodes featuring the same nominal HW components (e.g., processors and GPGPUS). In real-life machines, the chips populating each node are subject to a wide range of variability sources, related to performance and temperature operating points (i.e., ACPI p-states) as well as process variations and die binning. Eurora is a fully operational supercomputer prototype that topped July 2013 Green500 and it represents a unique ’living lab’ for next-generation ultra-green supercomputers. In this paper we evaluate and quantify the impact of variability on Eurora's energy-performance tradeoffs under a wide range of workloads intensity. Our experiments demonstrate that variability comes from hardware component mismatches as well as from the interplay between run-time energy management and workload variations. Thus, variability has a significant impact on energy efficiency even at the moderate scale of the Eurora machine, thereby substantiating the critical importance of variability management in future green supercomputers. [ABSTRACT FROM AUTHOR]

Introduction Yeast pathogens are emerging agents of nosocomial as well as community-acquired infections and their rapid and accurate identification is crucial for a better management of high-risk patients and for an adequate treatment. Methods We performed a retrospective review of 156 yeast isolates collected during a 17 months' period of regular clinical practice at the Microbiology Department of San Camillo Hospital in Treviso, Italy and analyzed by the traditional culture-based method combined with matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). Results Out of all the samples collected MALDI-TOF MS was able to characterize with a MT score ≥1.7 (accurate result at species level) 12 different yeast and yeast-like species from 140 samples: Candida albicans (63.7%), Candida glabrata (13.6%), Saccharomyces cerevisiae (6.5%), Candida parapsilosis (5.7%), Candida tropicalis (2.1%), Candida pararugosa (2.1%), Candida guilliermondii (2.1%), Candida kefyr (1.4%), Candida lusitaniae (0.7%), Candida palmioleophila (0.7%), Geotrichum silvicola (0.7%), Rhodotorula mucilaginosa (0.7%). Susceptibility testing toward seven common antifungal agents showed a characteristic MIC distribution of C. albicans isolates for echinocandins: particularly we noticed that 72% and 46% of C. albicans showed an MIC value close to clinical breakpoint as defined by EUCAST, respectively for anidulafungin and micafungin. Conclusion Accurate identification of microorganisms and the study of their antifungal susceptibility allow to understand the epidemiology of a particular area, permitting the choice of the most appropriate early antifungal treatment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Self-heating and high-operating temperature are major concerns in 3-D-chip integration. In this paper, we leverage a 3-D test chip (WideIO dynamic random access memory on top of a logic die) equipped with temperature sensors and heaters to explore thermal effects and to develop advanced thermal modeling strategies suitable for complex 3-D-stacked circuits. We correlate temperature measurements with the power dissipated by the heaters using model learning techniques. Moreover, we defined a thermal basis function obtained using power and thermal data available from the on-chip sensors. This function can be used to predict temperatures at chip locations far from the temperature sensors and to infer the power dissipation at any location of the chip. In addition, the same thermal basic function can be used jointly with formal interpolation frameworks like radial basis function methods to effectively estimate the full-chip thermal map. Results show that this methodology outperforms existing interpolation approaches for sparse integrated sensors. [ABSTRACT FROM AUTHOR]

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 < 0.0001). Among all available samples, the number of CoR isolated from SRS was higher in 2013 and 2014 compared with 2012 (p = 0.0019 and p = 0.008, respectively). ST-258 and ST-512 were more prevalent in the tested specimens, and a new single locus variant (SLV) of ST-512 (ST-745) was isolated. Conclusions: The results of this 3-year study of 15,104 patients highlight the clinical relevance of antimicrobial resistance as well as the drug-selection pressure of colistin therapy. The active surveillance in the three different departments increased the level of CPKP cases isolated by SRS. [ABSTRACT FROM AUTHOR]

Thermal effects are rapidly gaining importance in nanometer CMOS technologies. Increased power density, coupled with spatio-temporal variability of chip workloads, causes on-die temperature non-uniformities. The assumption of a uniform temperature for the delay and power analysis of a large CMOS circuit produces inaccurate results. For this reason, significant design margins are taken to ensure safe operation. To improve design quality, we need precise localization of hotspots at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis needs to be done at multiple levels of granularity using a versatile thermal floorplan. We propose MiMAPT, an approach for analyzing delay, power and temperature in digital circuits. MiMAPT integrates seamlessly into major industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Thermal analysis is done at register-transfer (RT) and then gate-level considering non-regular shapes of on-die units with multiple scales of resolution and speed. To demonstrate the capability of MiMAPT in temperature variation aware delay/power estimation, a widely used IP block is chosen and four different chips are implemented using 65 nm and 40 nm (LVT, HVT) technology nodes. Different temperature patterns are then applied to the design. Accuracy improvements of up to 28% for static power and 16% for minimum clock period are reported in comparison with uniform averaged temperature assumption. Evaluating the ability of MiMAPT in multi-scale thermal analysis, a speed-up of 98 × is reported compared to fine-grained method, while keeping false negatives at zero and the error of temperature estimation below 0.05 °C. [ABSTRACT FROM AUTHOR]

Near-threshold operation is today a key research area in Ultra-Low Power (ULP) computing, as it promises a major boost in energy efficiency compared to super-threshold computing and it mitigates thermal bottlenecks. Unfortunately near-threshold operation is plagued by greatly increased sensitivity to threshold voltage variations, such as those caused by ambient temperature fluctuation. In this paper we focus on a tightly-coupled ULP processor cluster architecture where a low latency, high-bandwidth processor-to-L1-memory interconnection network plays a key role. We propose an architectural scheme to tolerate ambient temperature-induced variations capable of statically (off-line) and dynamically (on-line) adapting the processor-to-L1-memory latency without compromising execution correctness. We extensively tested our solution in different scenarios and we evaluated the different design trade-offs, showing the cost, performance and reliability gain compared to state-of-the-art static solutions. The dynamic solution, thanks to its lightweight runtime overhead, outperforms the static solution and is able to reach a performance gain up to 25% in a typical use case scenario with a very low (<4%) area overhead. [ABSTRACT FROM AUTHOR]

• During variant screening of positive SARS-CoV-2 RNA samples we noticed an "atypical" melting profile. • The subsequent WGS denoted as Kappa the variant with the "atypical" melting profile. • RT-Real-Time PCR associated to melting curve analysis may give the possibility of "suspecting" a new SARS-CoV-2 variants. Although more than a year has passed since the start of the pandemic, SARS-CoV-2 infection still represents a major challenge for public health all over the world due to viral genome capability of gaining rapid mutations. Whole-genome sequencing (WGS) is the gold standard for variant identification, but it is time consuming and relatively expensive. For this reason, assays targeting multiple regions of the SARS-CoV-2 genome may be useful for a rapid traceability of either known or new variants, anyway, not all the manufacturers are able to sustain the rapid development of variants. We tested forty nasopharyngeal swabs, resulted positive for the presence of SARS-CoV-2 RNA at low cycle threshold (CT < 25), with SARS-CoV-2 Variants ELITe MGB® Kit, which was designed to identify Nigerian variant, possible UK variant and South African or Brazilian variant. During the analysis, we noted an atypical melting curve, different from the other variants recognizable by the kit. The subsequent WGS reported this variant as Kappa, so we assess the possibility of "suspecting" the presence of a Kappa variant using SARS-CoV-2 Variants ELITe MGB® Kit. Rapid variant screening followed by WGS offers the opportunity to study mutation dynamics and quickly identify possible variants of interest (VOI) and/or variants of concern (VOC), which is crucial in virus spreading control. Furthermore, an accurate analysis of the melting peak could be useful to suspect the presence of new variants. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aggressive thermal management is a critical feature for high-end computing platforms, as worst-case thermal budgeting is becoming unaffordable. Reactive thermal management, which sets temperature thresholds to trigger thermal capping actions, is too “near-sighted,” and it may lead to severe performance degradation and thermal overshoots. More aggressive proactive thermal managements minimize performance penalty with smooth optimal control. These techniques require knowledge of thermal models, which have to be accurate and simple to make the controls effective, while keeping their complexity limited. In practice, these models are not provided by manufacturers, and in most cases, they strongly depend on the deployment environment. Hence, procedures to automatically derive thermal models in the field are needed. In this paper, we propose a gray-box procedure to learn a compact and physically consistent model for multicore chips. We leverage the physical consistency of the proposed model to tame the model complexity and to face large quantization noise in measurements. We exploit Output Error structures along with Levenberg–Marquardt and Least Squares optimization algorithms. We tackle the problem in a real-life contest: we developed a complete infrastructure for model building and thermal data collection in the Linux environment, and we tested it on an Intel Nehalem-based server CPU. [ABSTRACT FROM AUTHOR]

• A MILP model to compute an energy hub's energy systems design is used. • The impacts of carbon pricing and natural gas prices fluctuations are analyzed. • High carbon pricing and TES can foster the deployment of distributed CCHP systems. • Drops in natural gas supply prices can compromise the effectiveness of carbon pricing. • CO 2 emissions are found to be related to the convenience of CCHP systems usage. Carbon pricing is being implemented by several governments to curb CO 2 emissions. This work studies its impact on distributed energy systems design which are powered by both renewable and fossil fuels. In particular, the analyses investigate a real case study situated in Singapore, characterized by cooling and electricity demands. The goal of the analyses is to determine whether carbon pricing does impact design choices in meeting the energy demands of a user located in a cooling dominated region, and secondly in assessing the effectiveness of carbon pricing as a CO 2 emissions mitigation policy. This is achieved by investigating the optimal design of the energy systems meeting the demands of the test case under different carbon pricing and primary energy supply costs assumptions. The results indicate that the optimal design under current conditions heavily relies on PV with more than 10 MWp worth of capacity and that an increasing carbon pricing would lead to a lower environmental footprint. But if the supply costs for natural gas were to lower, the optimal design would switch to relying on a combined cooling heat and power-based electricity generation system up to 3 MWe, increasing the primary energy consumptions regardless of the carbon pricing scheme in place. This would also happen even at significantly higher prices than the scheme under evaluation. [ABSTRACT FROM AUTHOR]

As result of technology scaling, single-chip multicore power density increases and its spatial and temporal workload variation leads to temperature hot-spots, which may cause nonuniform ageing and accelerated chip failure. These critical issues can be tackled by closed-loop thermal and reliability management policies. Model predictive controllers (MPC) outperform classic feedback controllers since they are capable of minimizing performance loss while enforcing safe working temperature. Unfortunately, MPC controllers rely on a priori knowledge of thermal models and their complexity exponentially grows with the number of controlled cores. In this paper, we present a scalable, fully distributed, energy-aware thermal management solution for single-chip multicore platforms. The model-predictive controller complexity is drastically reduced by splitting it in a set of simpler interacting controllers, each one allocated to a core in the system. Locally, each node selects the optimal frequency to meet temperature constraints while minimizing the performance penalty and system energy. Comparable performance with state-of-the-art MPC controllers is achieved by letting controllers exchange a limited amount of information at runtime on a neighborhood basis. In addition, we address model uncertainty by supporting learning of the thermal model with a novel distributed self-calibration approach that matches well the controller architecture. [ABSTRACT FROM AUTHOR]

Despite its relatively recent history, deep learning (DL)-based source code analysis is already a cornerstone in machine learning for compiler optimization. When applied to the classification of pieces of code to identify the best computational unit in a heterogeneous Systems-on-Chip, it can be effective in supporting decisions that a programmer has otherwise to take manually. Several techniques have been proposed exploiting different networks and input information, prominently sequence-based and graph-based representations, complemented by auxiliary information typically related to payload and device configuration. While the accuracy of DL methods strongly depends on the training and test datasets, so far no exhaustive and statistically meaningful analysis has been done on its impact on the results and on how to effectively extract the available information. This is relevant also considering the scarce availability of source code datasets that can be labeled by profiling on heterogeneous compute units. In this article, we first present such a study, which leads us to devise the contribution of code sequences and auxiliary inputs separately. Starting from this analysis, we then demonstrate that by using the normalization of auxiliary information, it is possible to improve state-of-the-art results in terms of accuracy. Finally, we propose a novel approach exploiting Siamese networks that further improve mapping accuracy by increasing the cardinality of the dataset, thus compensating for its relatively small size. [ABSTRACT FROM AUTHOR]

The need to act on the challenges brought by climate change calls for an increasing penetration of renewable energy sources (RES) in our society's energy supply, but such integration can be challenging. This study analyzes the impact of large numbers of smart electric vehicles (EVs) in a real urban district, using the Italian town Osimo as a case study, to determine the achievable degree of RES self-consumption and CO 2 emission reductions. Osimo features a multi-energy system with electricity, natural gas, district heating, and a 23% share of non-controllable RES capacity, mostly photovoltaics. The presence of EVs is evaluated in the present conditions and in scenarios with an increasing capacity of non-controllable RES. The case study is modeled in the deterministic hourly energy systems simulation model EnergyPLAN, which for these analyses is embedded within a framework aimed at enhancing its capabilities to consider the impact of uncertainties and obtain more robust results. The results show that a 10% EV penetration with vehicle-to-grid (V2G) capability can eliminate the need to export electricity surplus at the current PV capacity, lowering Osimo's CO 2 emissions by 3.5%. A 30% penetration achieves the same with twice the PV capacity, reducing the emissions of 17.6%. • EVs can significantly help in locally welcoming high shares of non-controllable RES. • A V2G infrastructure is mandatory to harness the most benefits from EVs presence. • V2G introduces losses but avoids the need to export electricity. • Deep decarbonization necessarily needs to involve the heating sector. [ABSTRACT FROM AUTHOR]

• Local energy community modeled as a network of multi energy systems. • Primary Energy Saving as a metric for designing low carbon energy communities. • Investigation of low carbon scenarios with 10% and 20% of primary energy savings. • Thermal storage & district cooling allow to shave the cooling peak power installed. • Trigeneration and district cooling are key technologies to achieve low carbon goals. 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. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) processors are nowadays integrated cyber-physical systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor. We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3% the plant's thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads. [ABSTRACT FROM AUTHOR]

Background Cefiderocol is a novel siderophore cephalosporin with promising activity against most carbapenem-resistant Gram-negative bacteria (CRGNB). However, extensive postmarketing experiences are lacking. This study aimed to analyse the early experience on cefiderocol postmarketing use at three tertiary care hospitals in Italy. Methods We retrospectively included patients with infections caused by CRGNB treated with cefiderocol at three Italian tertiary care hospitals from 1 March 2021 to 30 June 2022. A multivariate Cox model was used to identify predictors of 30 day mortality. A propensity score (PS) analysis with inverse probability weighting (IPW) was also performed to compare the treatment effect of cefiderocol monotherapy (CM) versus combination regimens (CCRs). Results The cohort included 142 patients (72% male, median age 67 years, with 89 cases of Acinetobacter baumannii infection, 22 cases of Klebsiella pneumoniae , 27 cases of Pseudomonas aeruginosa and 4 of other pathogens). The 30 day all-cause mortality was 37% (52/142). We found no association between bacterial species and mortality. In multivariate analysis, a Charlson Comorbidity Index >3 was an independent predictor of mortality (HR 5.02, 95% CI 2.37–10.66, P  < 0.001). In contrast, polymicrobial infection (HR 0.41, 95% CI 0.21–0.82, P  < 0.05) was associated with lower mortality. There was no significant difference in mortality between patients receiving CM (n  = 70) and those receiving a CCR (n  = 72) (33% versus 40%, respectively), even when adjusted for IPW-PS (HR 1.11, 95% CI 0.63–1.96, P  = 0.71). Conclusions Real-life data confirm that cefiderocol is a promising option against carbapenem-resistant Gram-negative infections, even as monotherapy. [ABSTRACT FROM AUTHOR]

The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. 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. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases. [ABSTRACT FROM AUTHOR]

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 < 0.0001). Among all available samples, the number of CoR isolated from SRS was higher in 2013 and 2014 compared with 2012 (p = 0.0019 and p = 0.008, respectively). ST-258 and ST-512 were more prevalent in the tested specimens, and a new single locus variant (SLV) of ST-512 (ST-745) was isolated.Conclusions: The results of this 3-year study of 15,104 patients highlight the clinical relevance of antimicrobial resistance as well as the drug-selection pressure of colistin therapy. The active surveillance in the three different departments increased the level of CPKP cases isolated by SRS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

One of the biggest challenges in the design of real-world decision support systems is coming up with a good combinatorial optimization model. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization. In this paper, we propose a methodology called Empirical Model Learning (EML) that relies on Machine Learning for obtaining components of a prescriptive model, using data either extracted from a predictive model or harvested from a real system. In a way, EML can be considered as a technique to merge predictive and prescriptive analytics . All models introduce some form of approximation. Citing G.E.P. Box [1] “Essentially, all models are wrong, but some of them are useful”. In EML, models are useful if they provide adequate accuracy, and if they can be effectively exploited by solvers for finding high-quality solutions . We show how to ground EML on a case study of thermal-aware workload dispatching. We use two learning methods, namely Artificial Neural Networks and Decision Trees and we show how to encapsulate the learned model in a number of optimization techniques, namely Local Search, Constraint Programming, Mixed Integer Non-Linear Programming and SAT Modulo Theories. We demonstrate the effectiveness of the EML approach by comparing our results with those obtained using expert-designed models. [ABSTRACT FROM AUTHOR]

Introduction: Despite the important diagnostic role of peripheral blood morphology, cell classification is subjective. Automated image-processing systems (AIS) provide more accurate and objective morphological evaluation. The aims of this multicenter study were the evaluation of the intra and inter-laboratory variation between different AIS in cell pre-classification and after reclassification, compared with manual optical microscopy, the reference method. Methods: Six peripheral blood samples were included in this study, for each sample, 70 May-Grunwald and Giemsa stained PB smears were prepared from each specimen and 10 slides were delivered to the seven laboratories involved. Smears were processed by both optical microscopy (OM) and AIS. In addition, the assessment times of both methods were recorded. Results: Within-laboratory Reproducibility ranged between 4.76% and 153.78%; between-laboratory Precision ranged between 2.10% and 82.2%, while Total Imprecision ranged between 5.21% and 20.60%. The relative Bland Altman bias ranged between -0.01% and 20.60%. The mean of assessment times were 326 ± 110 s and 191 ± 68 s for AIS post reclassification and OM, respectively. Conclusions: AIS can be helpful when the number of cell counted are low and can give advantages in terms of efficiency, objectivity and time saving in the morphological analysis of blood cells. They can also help in the interpretation of some morphological features and can serve as learning and investigation tools. [ABSTRACT FROM AUTHOR]

In their quest toward Exascale, High Performance Computing (HPC) systems are rapidly becoming larger and more complex, together with the issues concerning their maintenance. Luckily, many current HPC systems are endowed with data monitoring infrastructures that characterize the system state, and whose data can be used to train Deep Learning (DL) anomaly detection models, a very popular research area. However, the lack of labels describing the state of the system is a wide-spread issue, as annotating data is a costly task, generally falling on human system administrators and thus does not scale toward exascale. In this article we investigate the possibility to extract labels from a service monitoring tool (Nagios) currently used by HPC system administrators to flag the nodes which undergo maintenance operations. This allows to automatically annotate data collected by a fine-grained monitoring infrastructure; this labelled data is then used to train and validate a DL model for anomaly detection. We conduct the experimental evaluation on a tier-0 production supercomputer hosted at CINECA, Bologna, Italy. The results reveal that the DL model can accurately detect the real failures, and, moreover, it can predict the insurgency of anomalies, by systematically anticipating the actual labels (i.e., the moment when system administrators realize when an anomalous event happened); the average advance time computed on historical traces is around 45 minutes. The proposed technology can be easily scaled toward exascale systems to easy their maintenance. [ABSTRACT FROM AUTHOR]

Abstract: 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 ≥1mg/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. [Copyright &y& Elsevier]

Spiking Neural Networks (SNN) promise extremely low-power and low-latency inference on neuromorphic hardware. Recent studies demonstrate the competitive performance of SNNs compared with Artificial Neural Networks (ANN) in conventional classification tasks. In this work, we present an energy-efficient implementation of a Reinforcement Learning (RL) algorithm using SNNs to solve an obstacle avoidance task performed by an Unmanned Aerial Vehicle (UAV), taking a Dynamic Vision Sensor (DVS) as event-based input. We train the SNN directly, improving upon state-of-art implementations based on hybrid (not directly trained) SNNs. For this purpose, we devise an adaptation of the Spatio-Temporal Backpropagation algorithm (STBP) for RL. We then compare the SNN with a state-of-art Convolutional Neural Network (CNN) designed to solve the same task. To this aim, we train both networks by exploiting a photorealistic training pipeline based on AirSim. To achieve a realistic latency and throughput assessment for embedded deployment, we designed and trained three different embedded SNN versions to be executed on state-of-art neuromorphic hardware, targeting state-of-the-art. We compared SNN and CNN in terms of obstacle avoidance performance showing that the SNN algorithm achieves better results than the CNN with a factor of 6 × less energy. We also characterize the different SNN hardware implementations in terms of energy and spiking activity. [ABSTRACT FROM AUTHOR]

Power and thermal management are critical components of high-performance-computing (HPC) systems, due to their high-power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise thermal control strategies as well as automated diagnosis. However, when dealing with large-scale systems “in production,” the accuracy of learned thermal models depends on the dynamics of the power excitation, which depends also on the executed workload, and measurement nonidealities such as quantization. In this article we show that, using an advanced system identification algorithm, we are able to generate very accurate thermal models (average error lower than our sensors quantization step of 1 °C) for a large-scale HPC system on real workloads for very long time periods. However, we also show that: 1) not all real workloads allow for the identification of a good model and 2) starting from the theory of system identification it is very difficult to evaluate if a trace of data leads to a good estimated model. We then propose and validate a set of techniques based on machine learning and deep learning algorithms for the choice of data traces to be used for model identification. We also show that deep learning techniques are absolutely necessary to correctly choose such traces up to 96% of the times. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Next-generation Multiprocessor, or Multicore, Systems-on-Chip offer very high computing performance at the expense of a very high power density unevenly distributed on the chip. The hot spots thus generated represent a significant source of performance and reliability degradation, as well as power consumption increase. In recent years, run-time thermal control strategies have been developed to deal with this issue by acting on some "computational knobs" (e.g., clock frequencies and supply voltages). In this context, schemes based on Model Predictive Control (MPC) are particularly suitable due to their capability to deal with constraints explicitly. In this paper, we first discuss relevant properties for the design of predictive controllers for thermal systems. Starting from the Partial Differential Equation (PDE) describing heat diffusion in a solid, we prove meaningful feasibility properties that can be leveraged for constraint reduction. We then present a procedure to derive approximated but effective modular thermal models intended to build an efficient distributed MPC. Finally, a two-layer control solution is proposed to maximize performance while preserving feasibility despite model approximations. The effectiveness of this approach is validated through extensive and realistic numerical simulations. • A distributed controller is proposed for the optimal thermal capping of MPSoCs. • Control feasibility conditions are studied exploiting a PDE thermal model. • A hysteresis controller is designed for decentralized ultimate thermal capping. • A distributed MPC strategy is employed for performance optimization. • The resulting two-layer solution is validated for relevant simulation benchmarks. [ABSTRACT FROM AUTHOR]