Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna
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An online algorithm for power consumption prediction of HPC workload
Full text linkAs modern High-Performance Computing (HPC) systems push the boundaries of computational capabilities, their power consumption becomes a serious threat to environmental and energy sustainability. In such a context, accurate prediction of the jobs’ power consumption is instrumental to develop efficient power management strategies acting at the system level. To this end, in this paper, we present an online prediction algorithm to predict job power consumption in a production HPC system, prior to job execution. Our solution employs machine learning tools, and it is able to predict the minimum, average and maximum power consumption of a job, aggregated per node throughout its execution. Our approach leverages only information which is available at the time of job submission, and it is validated on two datasets extracted from production supercomputers, namely F-DATA from Supercomputer Fugaku and PM100 from Marconi100. Our experimental results show that our prediction algorithm outperforms state-of-the-art techniques, and it can accurately predict job power consumption, by obtaining an error of less than 12 % on F-DATA and less than 22 % on PM100
The online data filter for the KM3NeT neutrino telescopes
No full textThe KM3NeT research infrastructure comprises two neutrino telescopes located in the deep waters of the Mediterranean Sea, namely ORCA and ARCA. KM3NeT/ORCA is designed for the measurement of neutrino properties and KM3NeT/ARCA for the detection of high-energy neutrinos from the cosmos. Neutrinos are indirectly detected using three-dimensional arrays of photo-sensors which detect the Cherenkov light that is produced when relativistic charged particles emerge from a neutrino interaction. The analogue pulses from the photo-sensors are digitised offshore and all digital data are sent to a station on shore where they are processed in real time using a farm of commodity servers and custom software. In this paper, the design and performance of the software that is used to filter the data are presented. The performance of the data filter is evaluated in terms of its efficiency, purity and capacity. The efficiency is measured by the effective volumes of the sensor arrays as a function of the energy of the neutrino. The purity is measured by a comparison of the event rate caused by muons produced by cosmic ray interactions in the Earth's atmosphere with the event rate caused by the background from decays of radioactive elements in the sea water and bioluminescence. The capacity is measured by the minimal number of servers that is needed to sustain the rate of incoming data. The results of these evaluations comply with all specifications. The count rates of all photo-sensors are measured with a sampling frequency of 10 Hz. These data are input to the simulations of the detector response and will also be made available for interdisciplinary research
Navigating Climatic Risks: Insights from the Wheat Market and Strategies for Financial Hedging
No full textThe overarching goal of this chapter is to conduct a comprehensive analysis of the intricate interplay between climatic risks and agricultural commodity prices. Specifically, we focus our attention on understanding the dynamic relationship between agriculture and weather patterns, with a keen eye on one of the world's most vital agricultural commodities: wheat. As a staple cereal crop cultivated and traded across the globe, wheat serves as an exemplary subject for our investigation.
To achieve this, we embark on a meticulous exploration of the wheat market and its cultivation cycle. Through this exploration, our aim is to identify and elucidate the key weather variables that wield the most substantial influence on wheat production. Additionally, we undertake a detailed examination of the daily correlations between these identified weather variables and fluctuations in wheat prices.
Drawing upon both our empirical findings and the wealth of insights gleaned from the extensive literature on the inseparable relationship between agriculture and climate change, we endeavor to shed light on potential strategies for mitigating these risks. In particular, we turn our attention to the realm of financial engineering, exploring how innovative financial instruments and hedging techniques can offer avenues for managing the inherent volatility and uncertainty arising from climatic risks in agricultural markets. Through this multifaceted analysis, we aim to provide valuable insights and practical recommendations for stakeholders navigating the complex intersection of agriculture, climate, and finance
Towards Implementing Distributed Custom Serverless Function Scheduling in FunLess
No full textAPP is a declarative language for the definition of custom function scheduling on the worker nodes available in serverless Function as a Service (FaaS) platforms. Current APP implementations assume a central control point that users can access to issue the execution of functions. We propose an extension of APP’s implementation to allow for multiple control points, tackling both scaling and resilience issues of existing implementations. To substantiate our proposal, we present an implementation of our extension using the FunLess FaaS platform, tailored for private edge-cloud and multi-cloud environments. We show initial experiments that indicate performance improvements in setups where both the platform and function invocations are spread across multiple locations
High alpine preglacial caves modified by glacial processes and late condensation-corrosion in the Scerscen Valley (Valmalenco, Western Alps, Italy)
No full textThe Scerscen Valley (western Italian Alps) is home to caves at an altitude of around 2600 m, opening close to the edge of a glacier. The aim of the research as part of a multi-disciplinary project was to reconstruct the evolution of the caves related to the geological and paleo-environmental evolution of the area and to evaluate the role of some of the most recent processes, such as condensation-corrosion and sediment deposition. We performed cosmonucleide burial dating, recorded morphology and micrometeorology, carried out mineralogical identifi- cation by XRD, and hydrogeology using dye tracing and physical and chemical analyses. The cosmonucleide dating of quartz pebbles showed that the Veronica Cave is the oldest, with deposits dated at 1.3 ± 0.4 Ma, and possibly even older. It certainly formed at a much lower altitude (approx. 1300 m a.s.l. or lower) during the Alpine uplift. The Morgana and Marsooi caves, given the smaller volume of their phreatic conduits (1/3 of Veronica), are possibly more recent, formed during interglacials and evolved close to a glacial body. The caves initiated in dolomitic marble under the influence of sulfuric acid speleogenesis (SAS) due to pyrite oxidation. The conduits were then enlarged dramatically under phreatic conditions. The caves have evolved since their pre- glacial formation, with phases of filling by fluvio-glacial sediments and unclogging. Water tracing and physico- chemical analysis attest to a well-karstified aquifer, with rapid water circulation (>20 m/h) and low tempera- tures (~2 ◦C), draining towards the main spring, “La Prediletta”, located at the foot of the dolomitic marbles. Microclimatic records (cave temperature and humidity) show seasonal cycles of condensation and evaporation, influenced by air exchanges with the outside atmosphere. These processes contributed to the formation of sec- ondary minerals by evaporation (gypsum, hydromagnesite...) and, above all, to the significant enlargement of passages by the retreat of walls with characteristic morphologies (facets and grooved walls). The Scerscen caves bear witness to a long geological and climatic history, from their formation before the Mid-Pleistocene ice ages to their present-day evolution. They offer valuable insights into karst processes in the high mountains, and interactions between glaciers and aquifers
Reservoir computing for enhanced fidelity in hierarchical digital twin ecosystems
No full textThe growing complexity of Cyber-Physical Systems (CPS) in industrial and manufacturing environments calls for more sophisticated methods to represent heterogeneous assets and processes. In response, hierarchical Digital Twins (DTs)-virtual representations of physical, taxonomy-based processes-offer transparent, layered modeling of diverse data sources. This layered structure fuels renewed interest in intelligent engines capable of extracting meaningful insights and mapping them within the stratified DT ecosystem. While current Intelligent Digital Twin (I-DT) engines based on Deep Learning are computationally demanding, lightweight alternatives like Reservoir Computing (RC) offer efficient solutions with low training costs and fast inference for modeling causal dynamics. This inherent trade-off between performance and practicality underscores the limitations of evaluating I-DTs on accuracy alone. To address this gap, this work introduces a novel metric, Fidelity, designed to provide a comprehensive evaluation. Unlike traditional approaches, Fidelity also accounts for maintainability and deployability, especially in contexts involving time-varying and hierarchical data dynamics. Extensive experiments on two multimodal datasets demonstrate the competitiveness of our RC-based engine and highlight the value of introducing Fidelity for effectively profiling I-DTs. Specifically, our RC-based engine, identified as optimal through a higher Fidelity score, consumes an order of magnitude less energy and achieves up to 39 % higher accuracy (about 10% increase on average) compared to both canonical and other RC-based alternatives
Determination of hair cortisol by liquid chromatography coupled to mass spectrometry (LC-MS/MS) as biomarker of chronic stress and application to academic students
No full textHair cortisol concentration (HCC), a non-invasive biomarker reflecting long-term cortisol exposure, offers valuable insight into chronic stress, complementing established acute stress measures such as salivary or blood cortisol. At the moment, the most valuable use of hair cortisol bio-analysis is to study trends and associations within specific populations rather than establishing a single cut-off value for stress level assessment. In this frame, a rapid LC-MS/MS method for HCC determination has been developed and applied to measure chronic stress in university students with the aim to correlate the analytical results with the perceived stress during the preparation of the exams. A total of 100 students from different academic programs were recruited providing hair samples and data on academic and lifestyle stressors. The method has been validated in accordance with forensic toxicological guidelines ensuring high sensitivity (LOD 2 pg/mg; LOQ 5 pg/mg) and robust performance across selectivity, linearity, accuracy, and stability parameters. Method linearity was assessed in the range 5-50 pg/mg; accuracy and precision calculated on QC were always below 7 %; prepared samples were stable for 4 days at refrigerated temperature. HCC was detectable in 94 % of samples in the range 5--47.7 pg/mg. Students attending Law and Biology courses exhibited the highest mean HCC values. Dietary changes and smoking were associated with higher stress perception. Among academic stressors, balancing work and study, as well as difficulties in study organization, were linked to greater perceived stress. No statistically significant correlation was found between perceived stress and HCC, underscoring the complexity of chronic stress assessment and the value of combining subjective and physiological indicators
Identification of biomarkers for feed efficiency and growth rate by exploring the plasma metabolome of divergent heavy pigs
No full textFeed represents the largest expense in pig farming and significantly affects the sustainability of the production system. Therefore, enhancing feed efficiency is a key strategy to mitigate these costs and environmental impacts. This is particularly relevant in the context of the heavy pig system in which animals are slaughtered at a heavier live weight than in many other production systems to follow the rules of Protected Designation of Origin (PDO) value chains. Since growth rate is correlated with feed efficiency, and under PDO rules, pigs cannot reach the slaughter weight earlier than a set age limit, the daily gain of the pigs needs to be controlled. In this study, we used untargeted metabolomics to identify plasma metabolites in Italian Large White heavy pigs that may differentiate between animals with divergent feed efficiency and growth rate, and that may constitute biomarkers for one or the other trait. From a starting cohort of 672 performance-tested pigs, two partially overlapping datasets of 200 pigs each, extreme and divergent for feed conversion ratio (FCR) and average daily gain (ADG), were selected. Approximately 700 metabolites were analysed in the plasma of these pigs. Metabolomic data were analysed with the Boruta machine learning algorithm. Discriminant metabolites were further evaluated through univariate and multivariate analyses. Boruta identified 10 and 7 metabolites that differentiate between FCR and ADG extreme pigs, respectively, with an additional metabolite shared by the two datasets. Most metabolites selected in the FCR dataset still show significant abilities to discriminate among high and low ADG pigs, even if they have not been selected in the Boruta analysis, showing medium to high values of Area Under the Curve, and highly significant Mann–Whitney test U P-values, while the opposite was not true. Among the metabolites detected, L-carnitine and O-adipoylcarnitine, both involved in fatty acid metabolism, were significantly higher in pigs with high FCR. Isoleucylhydroxyproline and prolylhydroxyproline, linked to collagen turnover, were higher in low FCR pigs, potentially reflecting more efficient protein metabolism. Other metabolites linked to gut microbiome activity significantly differentiate between high and low FCR and ADG pigs, suggesting a potential role of the microbiota in nutrient utilisation. The identified metabolomic profiles confirm that feed efficiency and growth rate are related yet distinct traits, whose independent consideration will enhance the accuracy of biomarker discovery and genetic selection in Italian heavy pigs
WokeIt. Investigating representation, inclusivity and social responsibility in RAI’s fictional audiovisual productions (2015-2022)
No full textWokeIt. Investigating Representation, Inclusivity and Social Responsibility in Rai’s Fictional Audiovisual Productions is a 24-month project funded by the Italian Ministry of University and Research and the Next Generation EU program (PRIN 2022 PNRR).
The project aims to critically investigate the strategies of inclusiveness and representation of minorities in fictional audiovisual content produced by Rai Fiction and Rai Cinema, the production companies owned by the Italian public service broadcaster Rai. Through the interdisciplinary analysis of policies, industrial practices, on-screen representations, and the modes of circulation and reception of Rai-produced (or co-produced) TV series and movies since 2015, the project is designed to develop a systemic framework that can critically read and analyze the level of social responsibility and actual relevance of the public service broadcaster’s industrial and narrative strategies of representation and inclusion
Reliability and minimal detectable change for inertial measurement units − Derived stability, symmetry, and smoothness indexes of gait in people with multiple sclerosis
Full text linkBackground: people with multiple sclerosis (PwMS) experience loss of gait stability, smoothness, and symmetry, which affects quality of life and requires accurate evaluation for effective rehabilitation. Inertial measurement units (IMUs) offer a promising approach to monitor gait quality by quantifying indices reflecting stability, symmetry, and smoothness whose minimal detectable changes (MDCs) are not defined in people with MS (PwMS). Research question: to assess the within-day test-retest reliability and MDCs of IMUs – derived gait stability, symmetry, and smoothness metrics in PwMS during the 10 m walking test (10MWT). Methods: 58 PwMS wore five IMUs and performed the10MWT twice with a 10 min rest between each trial. Log dimensionless jerk (LDLJ) and improved Harmonic Ratio (iHR) were calculated for each gait trial based on the signals from the pelvis – mounted IMU, normalized Root Mean Square (nRMS) were calculated also from the head and sternum-mounted IMUs. Intraclass correlation coefficient (ICC) were calculated between the results of the two 10MWT to assess test -retest reliability, and minimal detectable change scores were calculated. Results: Reliability of the investigated parameters ranged from moderate to excellent values, with ICC ranging from 0.64 to 0.98. MDC values ranged from 0.09 to 0.53 for the nRMS, from 7.54 to 11.36 for the iHR and from 0.15 to 0.20 for the LDLJ. Significance: This study showed moderate to excellent reliability for the investigated indexes when calculated based on 10MWT, with the LDLJ showing the highest reliability, thus providing a reliable smoothness metric in pwMS. Also, nRMS showed good reliability, but caution is warranted with iHR due to its lower reliability and higher MDCs