Time measurements by means of digital sampling techniques: a study case of 100 ps FWHM time resolution with a 100 MSample/s, 12 bit digitizer

Abstract An application of digital sampling techniques is presented which can simplify experiments involving sub-nanosecond time-mark determinations and energy measurements with nuclear detectors, used for Pulse Shape Analysis and Time of Flight measurements in heavy ion experiments. The basic principles of the method are discussed as well as the main parameters that influence the accuracy of the measurements. The method allows to obtain both time and amplitude information with an electronic chain simply consisting of a charge preamplifier and a fast high resolution ADC (in the present application: 100 MSample/s , 12 bit ) coupled to an efficient on-line software. In particular an accurate Time of Flight information can be obtained by mixing a beam related time signal with the output of the preamplifier. Examples of this technique applied to Silicon detectors in heavy-ions experiments involving particle identification via Pulse Shape analysis and Time of Flight measurements are presented. The system is suited for applications to large detector arrays and to different kinds of detectors

QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuum

Function-as-a-Service (FaaS) paradigm is increasingly attractive to bring the benefits of serverless computing to the edge of the network, besides traditional Cloud data centers. However, FaaS adoption in the emerging Cloud-to-Edge Continuum is challenging, mostly due to geographical distribution and heterogeneous resource availability. This emerging landscape calls for effective strategies to trade off low latency at the edge of the network with Cloud resource richness, taking into account the needs of different functions and users. In this paper, we present QoS-aware offloading policies for serverless functions running in the Cloud-to-Edge continuum. We consider heterogeneous functions and service classes, and aim to maximize utility given a monetary budget for resource usage. Specifically, we introduce a two-level approach, where (i) FaaS nodes rely on a randomized policy to schedule every incoming request according to a set of probability values, and (ii) periodically, a linear programming model is solved to determine the probabilities to use for scheduling. We show by extensive simulation that our approach outperforms alternative approaches in terms of generated utility across multiple scenarios. Moreover, we demonstrate that our solution is computationally efficient and can be adopted in large-scale systems. We also demonstrate the functionality of our approach through a proof-of-concept experiment on an open-source FaaS framework

A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial Images

Solar energy production has significantly increased in recent years in the European Union (EU), accounting for 12% of the total in 2022. The growth in solar energy production can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources. As a result, there is a growing need for efficient methods for detecting and mapping the locations of PV panels. Automated detection can in fact save time and resources compared to manual inspection. Moreover, the resulting information can also be used by governments, environmental agencies and other companies to track the adoption of renewable sources or to optimize energy distribution across the grid. However, building effective models to support the automated detection and mapping of solar photovoltaic (PV) panels presents several challenges, including the availability of high-resolution aerial imagery and high-quality, manually-verified labels and annotations. In this study, we address these challenges by first constructing a dataset of PV panels using very-high-resolution (VHR) aerial imagery, specifically focusing on the region of Piedmont in Italy. The dataset comprises 105 large-scale images, providing more than 9,000 accurate and detailed manual annotations, including additional attributes such as the PV panel category. We first conduct a comprehensive evaluation benchmark on the newly constructed dataset, adopting various well-established deep-learning techniques. Specifically, we experiment with instance and semantic segmentation approaches, such as Rotated Faster RCNN and Unet, comparing strengths and weaknesses on the task at hand. Second, we apply ad-hoc modifications to address the specific issues of this task, such as the wide range of scales of the installations and the sparsity of the annotations, considerably improving upon the baseline results. Last, we introduce a robust and efficient post-processing polygonization algorithm that is tailored to PV panels. This algorithm converts the rough raster predictions into cleaner and more precise polygons for practical use. Our benchmark evaluation shows that both semantic and instance segmentation techniques can be effective for detecting and mapping PV panels. Instance segmentation techniques are well-suited for estimating the localization of panels, while semantic solutions excel at surface delineation. We also demonstrate the effectiveness of our ad-hoc solutions and post-processing algorithm, which can provide an improvement up to +10% on the final scores, and can accurately convert coarse raster predictions into usable polygons

A DSP equipped digitizer for online analysis of nuclear detector signals

In the framework of the NUCL-EX collaboration, a DSP equipped fast digitizer has been implemented and it has now reached the production stage. Each sampling channel is implemented on a separate daughter-board to be plugged on a VME mother-board. Each channel features a 12-bit, 125 MSamples/s ADC and a Digital Signal Processor (DSP) for online analysis of detector signals. A few algorithms have been written and successfully tested on detectors of different types (scintillators, solid-state, gas-filled), implementing pulse shape discrimination, constant fraction timing, semi-Gaussian shaping, gated integration

Extensão em educação financeira: resultados dos cinco anos de experiência em parceria com a Equilíbrio Assessoria Econômica

O alto grau de inadimplência das famílias no Brasil, decorrente do endividamento, é um problema preocupante, pois afeta tanto a economia quanto a saúde dos brasileiros. Nesse contexto cabe destacar que segundo o estudo da Confederação Nacional do Comércio de Bens, Serviços e Turismo (CNC, 2016), o conceito de endividado diz respeito ao indivíduo que possui dívidas contraídas com cheques pré-datados, cartões de crédito, carnês de loja, empréstimo pessoal, compra de imóvel ou prestações de carro e de seguros, entre outros, sem considerar se as parcelas estão sendo pagas em dias ou não. No entanto, quando o indivíduo não consegue mais quitar as dívidas contraídas, ele passa a se enquadrar como inadimplente.A partir da conjuntura apresentada, surgiu a preocupação da Equilíbrio Assessoria Econômica (EAE), empresa júnior da Faculdade de Ciências Econômicas da UFRGS, de contribuir com a sociedade através de ações envolvendo a educação financeira

Gly482Ser PGC-1α gene polymorphism and exercise-related oxidative stress in amyotrophic lateral sclerosis patients

The role of exercise in Amyotrophic lateral sclerosis (ALS) pathogenesis is controversial and unclear. Exercise induces a pleiotropic adaptive response in skeletal muscle, largely through the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a transcriptional coactivator that regulates mitochondrial biogenesis and antioxidant defense mechanisms. It has been suggested that a Gly482Ser substitution in PGC-1α has functional relevance in human disorders and in athletic performance. To test this hypothesis, we examined the genotype distribution of PGC-1α Gly482Ser (1444 G > A) in ALS patients to evaluate whether or not the minor serine-encoding allele 482Ser is involved in oxidative stress responses during physical exercise. We genotyped 197 sporadic ALS patients and 197 healthy controls in order to detect differences in allelic frequencies and genotype distribution between the two groups. A total of 74 ALS patients and 65 controls were then comparatively assessed for plasmatic levels of the oxidative stress biomarkers, advanced oxidation protein products, ferric reducing ability and thiol groups. In addition a subgroup of 35 ALS patients were also assessed for total SOD and catalase plasmatic activity. Finally in 28 ALS patients we evaluated the plasmatic curve of the oxidative stress biomarkers and lactate during an incremental exercise test. No significant differences were observed in the genotype distribution and allelic frequency in ALS patients compared to the controls. We found significant increased advanced oxidation protein products (p A SNP, ALS patients with Gly482Ser allelic variant show increased exercise-related oxidative stress. This thus highlights the possible role of this antioxidant defense transcriptional coactivator in ALS

Correlation between crystal purity and the charge density wave in 1T-VSe2

We examine the charge density wave (CDW) properties of 1T-VSe2 crystals grown by chemical vapor transport (CVT) under varying conditions. Specifically, we find that upon lowering the growth temperature (Tg < 630\u25e6C), there is a significant increase in both the CDW transition temperature and the residual resistance ratio (RRR) obtained from electrical transport measurements. Using x-ray photoelectron spectroscopy, we correlate the observed CDW properties with stoichiometry and the nature of defects. In addition, we have optimized a method to grow ultrahigh-purity 1T-VSe2 crystals with a CDW transition temperature TCDW = (112.7 \ub1 0.8) K and maximum residual resistance ratio RRR 48 49, which is the highest reported thus far. This work highlights the sensitivity of the CDW in 1T-VSe2 to defects and overall stoichiometry and the importance of controlling the crystal growth conditions of strongly correlated transition metal dichalcogenides

Prompt atmospheric neutrinos and muons: dependence on the gluon distribution function

We compute the next-to-leading order QCD predictions for the vertical flux of atmospheric muons and neutrinos from decays of charmed particles, for different PDF's (MRS-R1, MRS-R2, CTEQ-4M and MRST) and different extrapolations of these at small partonic momentum fraction x. We find that the predicted fluxes vary up to almost two orders of magnitude at the largest energies studied, depending on the chosen extrapolation of the PDF's. We show that the spectral index of the atmospheric leptonic fluxes depends linearly on the slope of the gluon distribution function at very small x. This suggests the possibility of obtaining some bounds on this slope in ``neutrino telescopes'', at values of x not reachable at colliders, provided the spectral index of atmospheric leptonic fluxes could be determined.Comment: 20 pages including 8 figure

Measuring the prompt atmospheric neutrino flux with down-going muons in neutrino telescopes

In the TeV energy region and above, the uncertainty in the level of prompt atmospheric neutrinos would limit the search for diffuse astrophysical neutrinos. We suggest that neutrino telescopes may provide an empirical determination of the flux of prompt atmospheric electron and muon neutrinos by measuring the flux of prompt down-going muons. Our suggestion is based on the consideration that prompt neutrino and prompt muon fluxes at sea level are almost identical.Comment: 4 pages, 3 figure

Linear electronics for Si-detectors and its energy calibration for use in heavy ion experiments

Abstract The design and implementation of linear electronics based on small-size, low-power charge preamplifiers and shaping amplifiers, used in connection with Si-detector telescopes employed in heavy ion experiments, are presented. Bench tests and "under beam" performances are discussed. In particular, the energy calibration and the linearity test of the overall system (Si-detector and linear and digital conversion electronics) has been performed with a procedure which avoids the pulse height defect problems connected with the detection of heavy ions. The procedure, basically, consists of using bursts of MeV protons, releasing up to GeV energies inside the detector, with low ionization density