Next generation wireless and mobile networks will utilize millimeter-wave (mmWave) communication to achieve significantly increased data rates. However, since mmWave radio signals experience high path loss, the operation of mmWave networks will require accurate channel models designed for specific deployment sites. In this paper, we focus on the deployment area of the PAWR COSMOS testbed [1, 2] in New York City and report extensive 28 GHz channel measurements. These include over 24 million power measurements collected from over 1,500 links on 13 sidewalks in 3 different sites and in different settings during March–June, 2019. Using these measurements, we study the effects of the setup and environments (e.g., transmitter height and seasonal effects). We then discuss the obtained path gain values and their fitted lines, and the resulting effective azimuth beamforming gain. Based on these results, we also study the link SNR values that can be supported on individual sidewalks and the corresponding theoretically achievable data rates. We believe that the results can inform the COSMOS testbed deployment process and provide a benchmark for other deployment efforts in dense urban areas.

In this work, we explore an extension of the Standard Model designed to elucidate the fermion mass hierarchy, account for the dark matter relic abundance, and explain the observed matter-antimatter asymmetry in the universe. Beyond the Standard Model particle content, our model introduces additional scalars and fermions. Notably, the light active neutrinos and the first two generations of charged fermions acquire masses at the one-loop level. The model accommodates successful low-scale leptogenesis, permitting the mass of the decaying heavy right-handed neutrino to be as low as 10 TeV. We conduct a detailed analysis of the dark matter phenomenology and explore various interesting phenomenological implications. These include charged lepton flavor violation, muon and electron anomalous magnetic moments, constraints arising from electroweak precision observables, and implications for collider experiments.

In this work, we explore an extension of the Standard Model designed to elucidate the fermion mass hierarchy, account for the dark matter relic abundance, and explain the observed matter-antimatter asymmetry in the universe. Beyond the Standard Model particle content, our model introduces additional scalars and fermions. Notably, the light active neutrinos and the first two generations of charged fermions acquire masses at the one-loop level. The model accommodates successful low-scale leptogenesis, permitting the mass of the decaying heavy right-handed neutrino to be as low as 10 TeV. We conduct a detailed analysis of the dark matter phenomenology and explore various interesting phenomenological implications. These include charged lepton flavor violation, muon and electron anomalous magnetic moments, constraints arising from electroweak precision observables, and implications for collider experiments.

This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports.

The most commonly used probabilistic model in reliability studies is the Perfect Renewal Process (PRP), which is characterized by the condition or type of maintenance represented: once the maintenance activities are executed, the equipment is restored to its original condition, leaving it “as good as new.” It is widely used since it represents an optimistic state when an item is replaced, assuming a perfect operational condition of the item after the maintenance. Some models have been developed for determining optimum preventive maintenance (PM) based on different criteria, and almost all aimed at PRP reliability modeling. The contribution of this paper is to analyze a model for determining the optimal preventive maintenance policy for a long time run under PRP and developing a general and chart-based tool for the problem, making it easier to solve the day-to-day practice and operation of equipment. As a result, a generalized chart was developed to support maintenance decisions through the elaboration of an original isometric table and complemented with a step-by-step methodology to determine the optimum time in which the preventive maintenance activities must be implemented. In most cases, these types of maintenance activities will consider a replacement activity.

This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards.

This article studies robust output tracking and disturbance rejection for boundary-controlled infinite-dimensional Port– Hamiltonian systems including second-order models such as the Euler–Bernoulli beam equation. The control design is achieved using the internal model principle and the stability analysis using a Lyapunov approach. Contrary to existing works on the same topic, no assumption is made on the external well-posedness of the considered class of PDEs. The results are applied to robust tracking of a piezo actuated tube used in atomic force imaging.

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 behavior of a fluid in pipes with irregular geometries is studied. Departing from the partial differential equations that describe mass and momentum balances a scalable lumped-parameter model is proposed. To this end the framework of port-Hamiltonian systems is instrumental to derive a modular system which upon interconnection describes segments with different cross sections and dissipation effects. In order to perform the interconnection between different segments the incompressibility hypothesis is relaxed in some infinitesimal section to admit density variations and energy transference between segments. Numerical simulations are performed in order to illustrate the model.

This search, a type not previously performed at ATLAS, uses a comparison of the production cross sections for e+μ− and e−μ+ pairs to constrain physics processes beyond the Standard Model. It uses 139 fb−1 of proton–proton collision data recorded at √s = 13 TeV at the LHC. Targeting sources of new physics which prefer final states containing e+μ− to e−μ+, the search contains two broad signal regions which are used to provide model-independent constraints on the ratio of cross sections at the 2% level. The search also has two special selections targeting supersymmetric models and leptoquark signatures. Observations using one of these selections are able to exclude, at 95% confidence level, singly produced smuons with masses up to 640 GeV in a model in which the only other light sparticle is a neutralino when the Rparity-violating coupling λ 231 is close to unity. Observations using the other selection exclude scalar leptoquarks with masses below 1880 GeV when geu 1R = g μc 1R = 1, at 95% confidence level. The limit on the coupling reduces to geu 1R = g μc 1R = 0.46 for a mass of 1420 GeV.

Neutral-point-clamped multilevel converters are currently a suitable solution for a wide range of applications. It is well known that the capacitor voltage balance is a major issue for this topology. In this paper, a brief summary of the basic topologies, modulations, and features of neutral-point-clamped multilevel converters is presented, prior to a detailed description and analysis of the capacitor voltage balance behavior. Then, the most relevant methods to manage the capacitor voltage balance are presented and discussed, including operation in the overmodulation region, at low frequency-modulation indexes, with different numbers of AC phases, and with different numbers of levels. Both open- and closed-loop methods are discussed. Some methods based on adding external circuitry are also presented and analyzed. Although the focus of the paper is mainly DC–AC conversion, the techniques for capacitor voltage balance in DC–DC conversion are discussed as well. Finally, the paper concludes with some application examples benefiting from the presented techniques.

The tasks execution scheduling is a common problem in computer science. The typical problem, as in industrial or computer processing applications, has some restrictions that are inapplicable for certain cases. For example, all available tasks have to be executed at some point, and ambient factors do not affect the execution order. In the astronomical observations field, projects are scheduled as observation blocks, and their execution depends on parameters like science goals priority and target visibility, but is also restricted by external factors: atmospheric conditions, equipment failure, etc. A telescope scheduler is mainly in charge of handling projects, commanding the telescope’s high level movement to targets, and starting data acquisition. With the growth of observatories’ capacities and maintenance costs, it is now mandatory to optimize the observation time allocation. Currently, at professional observatories there is still strong human intervention dependency, with no fully automatic solution so far. This paper aims to describe the dynamic scheduling problem in astronomical observations, and to provide a survey on existing solutions, opening some new application opportunities for computer science.

<p style='text-indent:20px;'>In this paper the Single Particle Model is used to describe the behavior of a Li-ion battery. The main goal is to design a feedback input current in order to regulate the State of Charge (SOC) to a prescribed reference trajectory. In order to do that, we use the boundary ion concentration as output. First, we measure it directly and then we assume the existence of an appropriate estimator, which has been established in the literature using voltage measurements. By applying backstepping and Lyapunov tools, we are able to build observers and to design output feedback controllers giving a positive answer to the SOC tracking problem. We provide convergence proofs and perform some numerical simulations to illustrate our theoretical results.</p>

In this paper we consider the physical-based modeling of 3D and 2D Newtonian fluids including thermal effects in order to cope with the first and second principles of thermodynamics. To describe the energy fluxes of non-isentropic fluids we propose a pseudo port-Hamiltonian formulation, which includes the rate of irreversible entropy creation by heat flux. For isentropic fluids, the conversion of kinetic energy into heat by viscous friction is considered as an energy dissipation associated with the rotation and compression of the fluid. Then, a dissipative port-Hamiltonian formulation is derived for this class of fluids. In the 2D case we modify the vorticity operators in order to preserve the structure of the proposed models. Moreover, we show that a description for inviscid or irrotational fluids can be derived from the proposed models under the corresponding assumptions leading to a pseudo or dissipative port-Hamiltonian structures.

The Cherenkov Telescope Array (CTA) observatory is the next generation of ground-based imaging atmospheric Cherenkov telescopes (IACTs). Building on the strengths of current IACTs, CTA is designed to achieve an order of magnitude improvement in sensitivity, with unprecedented angular and energy resolution. CTA will also increase the energy reach of IACTs, observing photons in the energy range from 20 GeV to beyond 100 TeV. These advances in performance will see CTA heralding in a new era for high-energy astrophysics, with the emphasis shifting from source discovery, to population studies and precision measurements. In this talk we discuss CTA’s ability to conduct source population studies of W-ray bright active galactic nuclei and how this ability will enhance our understanding on the redshift evolution of this dominant W-ray source class.

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.

In Chile, there are several abandoned mine tailing impoundments near population centers that need to be remediated. In this study, the ability of Oxalis gigantea, Cistanthe grandiflora, and Puya berteroniana to remove Zn, Ni, and Cr from mine tailings was evaluated. The plants’ removal efficiency, bioconcentration, and translocation factors regarding these metals were determined to assess the ability of certain endemic species from Northern and Central Chile to extract or stabilize metals. After a period of seven months, the chemical analysis of plants and tailings, together with the statistical treatment of data, indicated the inability of all the species to translocate Ni, Cr, or Zn with a translocation factor lower than one. The results showed the stabilizing character of Oxalis gigantea, Puya berteroniana, and Cistanthe grandiflora for Zn, with a bioconcentration factor close to 1.2 in all cases, and the same ability of the latter two species for Cr, with a bioconcentration factor of 1.5 in the case of Cistanthe grandiflora and 1.7 for Puya berteroniana. Finally, a removal efficiency of 9.3% was obtained with Cistanthe grandiflora for Cr and 15% for Ni; values lower than 6.4% were obtained for Zn in all cases. Improvements in the process should be sought to enhance the performance of these species for the accumulation of the target metals.

White dwarf studies carry significant implications across multiple fields of astrophysics, including exoplanets, supernova explosions, and cosmological investigations. Thus, accurate determinations of their fundamental parameters (Teff and log g) are of utmost importance. While optical surveys have provided measurements for many white dwarfs, there is a lack of studies utilizing ultraviolet (UV) data, particularly focusing on the warmer ones that predominantly emit in the UV range. Here, we present the medium-resolution far-UV spectroscopic survey of 311 DA white dwarfs obtained with Cosmic Origins Spectrograph (COS) onboard Hubble Space Telescope confirming 49 photometric Gaia candidates. We used 3D extinction maps, parallaxes, and hydrogen atmosphere models to fit the spectra of the stars that lie in the range $12\, 000 \lt \mbox{$T_{\mathrm{eff}}$}\lt 33\, 000$ K, and $7 \le \mbox{$\log g$}\lt 9.2$. To assess the impact of input physics, we employed two mass–radius relations in the fitting and compared the results with previous studies. The comparisons suggest the COS Teff are systematically lower by 3 per cent, on average, than Balmer line fits while they differ by only 1.5 per cent from optical photometric studies. The mass distributions indicate that the COS masses are smaller by ≈0.05  and 0.02 M⊙ than Balmer lines and photometric masses, respectively. Performing several tests, we find that the discrepancies are either arising due to issues with the COS calibration, broadening theories for hydrogen lines, or interstellar reddening which needs further examination. Based on comparative analysis, we identify 30 binary candidates drawing attention for follow-up studies to confirm their nature.

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.

Industrial combustion processes are an important source of particulate matter, causing significant pollution problems that affect human health, and are a major contributor to global warming. The most common method for analyzing the soot emission propensity in flames is the Smoke Point Height (SPH) analysis, which relates the fuel flow rate to a critical flame height at which soot particles begin to leave the reactive zone through the tip of the flame. The SPH and is marked by morphological changes on the flame tip. SPH analysis is normally done through flame observations with the naked eye, leading to high bias. Other techniques are more accurate, but are not practical to implement in industrial settings, such as the Line Of Sight Attenuation (LOSA), which obtains soot volume fractions within the flame from the attenuation of a laser beam. We propose the use of Video Magnification techniques to detect the flame morphological changes and thus determine the SPH minimizing observation bias. We have applied for the first time Eulerian Video Magnification (EVM) and Phase-based Video Magnification (PVM) on an ethylene laminar diffusion flame. The results were compared with LOSA measurements, and indicate that EVM is the most accurate method for SPH determination.