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Renewable energies have a fundamental role in sustainability, with wind power being one of the most important due to its low production costs. Modern wind turbines are becoming bigger and more complex, and their operation and maintenance must be as optimized as possible. In this context, supervisory control and data acquisition systems provide valuable information, but there is no precise methodology for their analysis. To overcome this need, a generalized methodology is proposed to determine the recognition of critical subsystems through alarm analysis and management. The proposed methodology defines each subsystem in a precise way, shows the indicators for the alarms, and presents a theoretical framework for its application using the quantity and activation times of alarms, along with the real downtime. It also considers the transition of states when the wind turbine is operationally inactive. To highlight the proposal’s novelty, the methodology is exemplified with a case study from the Southern Cone, applying the method through a data management and analysis tool. Four critical subsystems were found, with the alarms of wind vanes, anemometers, and emergency speeds being of relevance. The indicators and the graphical tools recommended helped guide the applied analysis.

The proportional hazards model (PHM) is a vital statistical procedure for condition-based maintenance that integrates age and covariates monitoring to estimate asset health and predict failure risks. However, when dealing with multi-covariate scenarios, the PHM faces interpretability challenges when it lacks coherent criteria for defining each covariate’s influence degree on the hazard rate. Hence, we proposed a comprehensive machine learning (ML) formulation with Interior Point Optimizer and gradient boosting to maximize and converge the logarithmic likelihood for estimating covariate weights, and a K-means and Gaussian mixture model (GMM) for condition state bands. Using real industrial data, this paper evaluates both clustering techniques to determine their suitability regarding reliability, remaining useful life, and asset intervention decision rules. By developing models differing in the selected covariates, the results show that although K-means and GMM produce comparable policies, GMM stands out for its robustness in cluster definition and intuitive interpretation in generating the state bands. Ultimately, as the evaluated models suggest similar policies, the novel PHM-ML demonstrates the robustness of its covariate weight estimation process, thereby strengthening the guidance for predictive maintenance decisions.

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.

Background: Wildfires have caused significant damage in Chile, with critical infrastructure being vulnerable to extreme wildfires. Aim This work describes a methodology for estimating wildfire risk that was applied to an electrical substation in the wildland–urban interface (WUI) of Valparaíso, Chile. Methods Wildfire risk is defined as the product between the probability of a wildfire reaching infrastructure at the WUI and its consequences or impacts. The former is determined with event trees combined with modelled burn probability. Wildfire consequence is considered as the ignition probability of a proxy fuel within the substation, as a function of the incident heat flux using a probit expression derived from experimental data. The heat flux is estimated using modelled fire intensity and geometry and a corresponding view factor from an assumed solid flame. Key results The probability of normal and extreme fires reaching the WUI is of the order of 10−4 and 10−6 events/year, respectively. Total wildfire risk is of the order of 10−5 to 10−4 events/year Conclusions This methodology offers a comprehensive interpretation of wildfire risk that considers both wildfire likelihood and consequences. Implications The methodology is an interesting tool for quantitatively assessing wildfire risk of critical infrastructure and risk mitigation measures.

This lecture presents a short review of the main features of diffractive processes and QCD inspired models. It includes the following topics: (1) Quantum mechanics of diffraction: general properties; (2) Color dipole description of diffraction; (3) Color transparency; (4) Soft diffraction in hard reactions: DIS, Drell-Yan, Higgs production; (5) Why Pomerons interact weakly; (6) Small gluonic spots in the proton; (7) Diffraction near the unitarity bound: the Goulianos-Schlein "puzzle"; (8) Diffraction on nuclei: diffractive Color Glass; (9) CGC and gluon shadowing.

We analyze the recently discovered phenomena in elastic proton–proton scattering at the LHC, challenging the standard Regge-pole theory: the low-|t| “break” (departure from the exponential behavior of the diffraction cone), the accelerating rise with energy of the forward slope B(s, t = 0), absence of secondary dips and bumps on the cone, and the role of the odderon in the forward phase of the amplitude, ρ(13 TeV) = 0.1 ± 0.01 and, especially, its contribution at the dip region, measured recently by TOTEM. Relative contributions from different components to the scattering amplitude are evaluated from the fitted model.

The differential cross section dσ/dq2 of diffractive electroproduction of heavy quarkonia on protons is a sensitive study tool for the interaction dynamics within the dipole representation. Knowledge of the transverse momentum transfer q⃗ provides a unique opportunity to identify the reaction plane, due to a strong correlation between the directions of q⃗ and impact parameter b⃗ . On top of that, the elastic dipole-proton amplitude is subject to a strong correlation between b⃗ and dipole orientation r⃗ . Most of models for b-dependent dipole cross section either completely miss this information, or make unjustified assumptions. We perform calculations basing on a realistic model for r⃗ -b⃗ correlation, which significantly affect the q-dependence of the cross section, in particular the ratio of ψ′(2S) to J/ψ yields. We rely on realistic potential models for the heavy quarkonium wave function, and the Lorentz-boosted Schrödinger equation. Good agreement with data on q-dependent diffractive electroproduction of heavy quarkonia is achieved.

We consider the production of hadrons containing two charmed quarks in pp and ee collisions, and we perform a numerical comparison of the fragmentation approach with the full calculation at 𝒪(αs⁴). We conclude that the non-fragmentation contributions remain important up to transverse momenta as large as about 40 GeV, thus making questionable the applicability of the fragmentation approximation at smaller transverse momenta.

High-multiplicity pp collisions exhibit features, traditionally associated with nuclear effects. Coherence motivates to treat high-multiplicity pp, pA, and AA collisions on an equal footing. We rely on the phenomenological parametrization for mean multiplicities of light hadrons and J=ψ, assuming their linear dependence on Ncoll in pA collisions. The results of this approach underestimate the recently measured production rate of J=ψ at very high hadronic multiplicities. The linear dependence of J=ψ multiplicity on Ncoll is subject to predicted nonlinear corrections, related to mutual boosting of the saturation scales in colliding dense parton clouds. A parameter-free calculation of the nonlinear corrections allows us to explain data for pT-integrated yield of J=ψ at high hadronic multiplicities. Calculations are in a good accord with data binned in several pT intervals as well. As was predicted, ϒ and J=ψ are equally suppressed at forward rapidities in pA collisions. Consequently, their fractional multiplicities at forward rapidities in pp collisions are equal as well, and their magnitude agrees with data.

We propose a solution for the long standing puzzle of a too steeply falling fragmentation function for a quark fragmenting into a pion, calculated by Berger [E.L. Berger, Phys. Lett. B 89 (1980) 241] in the Born approximation. Contrary to the simple anticipation that gluon resummation worsens the problem, we find good agreement with data. Higher quark Fock states slow down the quark, an effect which we call jet lag. It can be also expressed in terms of vacuum energy loss. As a result, the space–time development of the jet shrinks and the z-dependence becomes flatter than in the Born approximation. The space–time pattern is also of great importance for in-medium hadronization.

The violation of baryon number, B, is an essential ingredient for the preferential creation of matter over antimatter needed to account for the observed baryon asymmetry in the Universe. However, such a process has yet to be experimentally observed. The HIBEAM/NNBAR program is a proposed two-stage experiment at the European Spallation Source to search for baryon number violation. The program will include high-sensitivity searches for processes that violate baryon number by one or two units: free neutron–antineutron oscillation (n → ̄n) via mixing, neutron–antineutron oscillation via regeneration from a sterile neutron state (n → [n′, ̄n′] → ̄n), and neutron disappearance (n → n′); the effective ΔB = 0 process of neutron regeneration (n → [n′, ̄n′] → n) is also possible. The program can be used to discover and characterize mixing in the neutron, antineutron and sterile neutron sectors. The experiment addresses topical open questions such as the origins of baryogenesis and the nature of dark matter, and is sensitive to scales of new physics substantially in excess of those available at colliders. A goal of the program is to open a discovery window to neutron conversion probabilities (sensitivities) by up to three orders of magnitude compared with previous searches. The opportunity to make such a leap in sensitivity tests should not be squandered. The experiment pulls together a diverse international team of physicists from the particle (collider and low energy) and nuclear physics communities, while also including specialists in neutronics and magnetics.

Production of heavy flavored hadrons from fragmentation of heavy quarks represents an alternative probe for a medium created after heavy ion collisions. We demonstrate that observed strong suppression of heavy flavored D and B mesons, produced with high transverse momenta pT, is caused by final state interactions with such a medium. The space-time pattern of hadronization of a highly virtual heavy quark is controlled predominantly by intensive gluon radiation, which is ceased at a short time scale in accordance with perturbative QCD calculations and LEP measurements of the fragmentation functions. However, production of heavy flavored hadrons lasts a long time due to prompt multiple breakups of produced colorless (pre)hadrons in the medium. This fact together with the specific shape of heavy quark fragmentation function, peaked at large z, allows to explain the observed strong suppression of D and B mesons in a good accord with data.

Polarized pp elastic scattering at small angles in the Coulomb-nuclear interference (CNI) region offers a unique opportunity to study the spin structure of the Pomeron, since electromagnetic effects in elastic amplitude can be equivalently treated either as Coulomb corrections to the hadronic amplitude (Coulomb phase) or as absorption corrections to the Coulomb scattering amplitude. We perform the first calculation of the Coulomb phase for the spin-flip amplitude and found it significantly exceeding the widely used non-flip Coulomb phase, while the alternative description in terms of absorption corrections, though equivalent, turned out to be a more adequate approach for the Coulomb corrected spin-flip amplitude. Inspired by the recent high statistics measurements of single-spin asymmetry with the HJET polarimeter at the BNL, we also performed a Regge analysis of data aiming at disentangling the Pomeron contribution; however, in spite of an exceptional accuracy of the data, they do not allow to single out the Pomeron term, which strongly correlates with the major sub-leading Reggeons, so that a stable solution can be accessed only by making additional ad hoc assumptions, for example assuming the Pomeron to be a simple Regge pole or fixing some unknown parameters, otherwise in addition to the STAR data at √s = 200 GeV new measurements, say at 100 GeV or 500 GeV, could become decisive.

We study string fragmentation in high multiplicity proton-proton collisions in a model where the string tension fluctuates, and these fluctuations produce exponential pion spectra which are fitted to the transverse momentum distributions of charged particles for different multiplicities. For each multiplicity, the obtained hadronic slope parameter defines the magnitude of the string fluctuations, which in turn determines the produced ratio of strange to light quarks, while PYTHIA string decay simulations are used to convert each ratio of strange to light quarks to the appropriate ratio of strange hadrons to pions.

Heavy quarkonium production in ultraperipheral nuclear collisions (UPC) is described within the QCD dipole formalism. Realistic quarkonium wave functions in the QQ¯ rest frame are calculated by solving the Schrödinger equation with a subsequent Lorentz boost to high energy. We rely on several realistic QQ¯ potentials, which allow us to describe well the quarkonium masses and decay widths, as well as data on diffractive electroproduction of quarkonia on protons. Nuclear effects are calculated with the phenomenological dipole cross sections fitted to deep-inelastic scattering (DIS) data. The higher twist quark shadowing related to the lowest QQ¯ Fock component of the photon, as well as the leading twist gluon shadowing, related to higher components containing gluons, are included. The results for coherent and incoherent photoproduction of charmonia and bottomonia in UPC of heavy nuclei are in good accord with available data from the LHC. They can also be verified in future experiments at electron-ion colliders.