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Esta tesis examina la generación procedural de contenido en videojuegos, enfocándose en la creación de niveles y mazmorras. Primero, se analizan las técnicas generales de generación procedural, seguidas de un estudio más detallado sobre la generación de niveles. El trabajo principal se centra en un Algoritmo Evolutivo de 2 Pasos (2-Step EA) para generar mazmorras con misiones de llaves y barreras. La primera fase del algoritmo configura el diseño básico de la mazmorra, y la segunda fase asigna llaves y barreras estratégicamente. Los resultados muestran que el algoritmo propuesto es efectivo en producir mazmorras de alta calidad y eficientes computacionalmente. Este enfoque ofrece una metodología sólida para la generación procedural de contenido dinámico.

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.

Malaria is an infectious disease caused by Plasmodium spp. parasites, with widespread drug resistance to most antimalarial drugs. We report the development of two 3D-QSAR models based on comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and a 2D-QSAR model, using a database of 349 compounds with activity against the P. falciparum 3D7 strain. The models were validated internally and externally, complying with all metrics (q2 > 0.5, r2test > 0.6, r2m > 0.5, etc.). The final models have shown the following statistical values: r2test CoMFA = 0.878, r2test CoMSIA = 0.876, and r2test 2D-QSAR = 0.845. The models were experimentally tested through the synthesis and biological evaluation of ten quinoline derivatives against P. falciparum 3D7. The CoMSIA and 2D-QSAR models outperformed CoMFA in terms of better predictive capacity (MAE = 0.7006, 0.4849, and 1.2803, respectively). The physicochemical and pharmacokinetic properties of three selected quinoline derivatives were similar to chloroquine. Finally, the compounds showed low cytotoxicity (IC50 > 100 µM) on human HepG2 cells. These results suggest that the QSAR models accurately predict the toxicological profile, correlating well with experimental in vivo data.

Electrical trees are one of the main mechanisms of degradation in solid polymeric insulation leading to the failure of high voltage equipment. They are interconnected networks of hollow tubules typically characterized from two-dimensional (2D) projections of their physical manifestation. It is shown that complete characterization requires a three-dimensional (3D) imaging technique such as X-ray computed tomography (XCT). We present a comprehensive set of parameters, quantitatively characterizing two types of tree topology, conventionally known as bush- and branchtype. Fractal dimensions are determined from 3D models and from 2D projections, and a simple quantitative relationship is established between the two for all but dense bush trees. Parameters such as number of nodes, segment length, tortuosity and branch angle are determined from tree skeletons. The parameters most strongly indicative of the differences in the structure are the number of branches, individual channel size, channel tortuosity, nodes per unit length and fractal dimension. Studying two stages of a bush tree's development showed that channels grew in width, while macroscopic parameters such as the fractal dimension and tortuosity were unchanged. These parameters provide a basis for tree growth models, and can shed light on growth mechanisms.

Abstract During a visual search through the Kepler main-field light curves, we have discovered a cataclysmic variable (CV) that experienced only a single 4-d long outburst over four years, rising to three times the quiescent flux. During the four years of non-outburst data the Kepler photometry of KIC 5608384 exhibits ellipsoidal light variations (‘ELVs’) with a ∼12 per cent amplitude and period of 8.7 h. Follow-up ground-based spectral observations have yielded a high-quality radial velocity curve and the associated mass function. Additionally, H α emission lines were present in the spectra even though these were taken while the source was presumably in quiescence. These emission lines are at least partially eclipsed by the companion K star. We utilize the available constraints of the mass function, the ELV amplitude, Roche lobe filling condition, and inferred radius of the K star to derive the system masses and orbital inclination angle: $M_{\rm wd} \simeq 0.46 \pm 0.02 \, \mathrm{M}_\odot$, $M_{\rm K} \simeq 0.41 \pm 0.03 \, \mathrm{M}_\odot$, and i ≳ 70°. The value of Mwd is the lowest reported for any accreting WD in a CV. We have also run binary evolution models using mesa to infer the most likely parameters of the pre-cataclysmic binary. Using the mass-transfer rates from the model evolution tracks we conclude that although the rates are close to the critical value for accretion disc stability, we expect KIC 5608384 to exhibit dwarf nova outbursts. We also conclude that the accreting white dwarf most likely descended from a hot subdwarf and, most notably, that this binary is one of the first bona fide examples of a progenitor of AM CVn binaries to have evolved through the CV channel.

In this paper, we develop a filtering algorithm for Wiener systems written in state-space form which considers correlated noise sources. The output non-linearity is approximated by using a piece-wise linear function. The probability function of the output signal conditioned to the system state is written as a Gaussian mixture distribution. A Gaussian sum filter algorithm to obtain the a posteriori probability density function of the state given the current and past output is developed. The associated statistics of the system state are obtained. The benefits of our proposal are illustrated via numerical simulations.

It is well known that beef produces volatile molecules. In this work, the detection of volatiles released by post-mortem bovine fast-twitch muscles (Musculus longissimus dorsi and Musculus cutaneus trunci) was done using GC/MS–SPME (gas chromatography/mass spectrum–solid-phase microextraction). The releases of volatile molecules were modeled against three factors (rigor-mortis, animal age and oxidative capacity) using a chemometrics approach (experimental design and partial least squares regression). The GC/MS–SPME technique produced more than 30 reproducible chromatographic peaks, but only 13 were associated significantly with two factors (rigor-mortis and animal age). The volatile profile was composed mainly of alcohols, aldehydes and alkanes. The factor “animal age” was the main variable related to the release of volatile molecules. The results strongly suggest that the release of volatile molecules change according to post-mortem metabolism and the animal age.

Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the -dominance has shown a proper balance between convergence and diversity. When using -dominance, diversity is ensured by partitioning the objective space into boxes of size and, typically, a single solution is allowed at each of these boxes. However, there is no easy way to determine the precise value of . In this paper, we investigate how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of along the search without any need of a previous user’s knowledge. We include the proposed co-evolutionary scheme into an MOEA based on -dominance giving rise to a new MOEA. We evaluate the proposed MOEA solving standard benchmark test problems. According to our results, it is a promising alternative for solving multi-objective optimization problems because three main reasons: 1) it is competitive concerning stateof-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally efficient

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.

Partial discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. PD detection is a standardized technique to qualify the state of the insulation in electric assets such as machines and power cables. Generally, the classical phase-resolved partial discharge (PRPD) patterns are used to perform the identification of the type of PD source when they are related to a specific degradation process and when the electrical noise level is low compared to the magnitudes of the PD signals. However, in practical applications such as measurements carried out in the field or in industrial environments, several PD sources and large noise signals are usually present simultaneously. In this study, three different inductive sensors have been used to evaluate and compare their performance in the detection and separation of multiple PD sources by applying the chromatic technique to each of the measured signals.

In medium- and low-voltage three-phase distribution networks, the load imbalance among the phases may compromise the network voltage symmetry. Inverter-interfaced distributed energy resources (DERs) can contribute to compensating for such imbalances by sharing the required negative sequence current while providing active power synchronized with the positive sequence voltage. However, positive and negative sequences are conventionally defined in a steady state and are not directly observed from the instantaneous voltage and current measurements at the DER unit’s point of connection. In this article, an estimation algorithm for sequence separation based on the Kalman filter is proposed. Furthermore, the proposed filter uses a complex vector representation of the asymmetric three-phase signals in synchronous coordinates to allow for the implementation of the Kalman filter in its stationary form, resulting in a simple dynamic filter able to estimate positive and negative sequences even during transient operation. The proposed stationary complex Kalman filter performs better than state-of-the-art techniques like DSOGI and very similarly to other Kalman filter implementations found in the literature but at a fraction of its computational cost (23.5%).

Abstract The combination of maintenance planning and key performance indicators are relevant to create a more holistic scenario of the mining activities. On the one hand, reliability and maintainability are system characteristics suitable for planning maintenance strategies. On the other hand, key performance indicators are suitable to analyze cost and resource consumption information about mining equipment. Nevertheless in practice, both approaches are modeled separately and frequently by different team-works of a mining company. With this in mind, a computer-aided application was conceived to drive with better efficacy the operational and maintenance strategy in a complex process where the equipment is in continuous movement such as the transportation process in an open-mine pit.

In this work, a constraint programming (CP) formulation of the multi-mode resource-constrained project scheduling problem (MMRCPSP) is proposed for solving the flexible job shop scheduling problem (FJSSP) under the makespan minimization criterion. The resulting CP model allows us to tackle the classical instances of the FJSSP (such as where the operations of a given job follow a linear order). It can also handle FJSSP instances where the precedence relationships between operations are defined by an arbitrary directed acyclic graph (sequencing flexibility). The performance of our approach was tested using 271 classical FJSSP instances and 50 FJSSP instances with sequencing flexibility. We establish the validity of our approach by achieving an average relative percentage deviation of 3.04% and 0.18% when compared to the best-known lower and upper bounds, respectively. Additionally, we were able to contribute to the literature with ten new lower bounds and two new upper bounds. Our CP approach is relatively simple yet competitive and can be quickly applied and adapted by new practitioners in the area.

Vertical cities’ growth is argument of discussion worldwide. Population increases and a better soil use are needed, in terms of efficiency and density, in many cities of the world. However, an excessive vertical growth seems to be harmful, especially near the green areas of midtowns. In this paper, the case of Antofagasta is studied. The paper studies different possible future evolutions searching for a bearable development, respecting the society needs and the environment. Parameters analyzed are: temperature, humidity, solar radiation, wind speed and direction in the studied area. Results show the impact of building growth in terms of overheating and wind reduction on the ground area studied. Additionally, the social impact of living in towers is also discussed in the paper, searching for better design in order to guarantee user’s comfort, satisfaction and stimulation in their residences. Thermal, visual and acoustical effects produced by towers are considered in the critical evaluation of the Antofagasta city evolution. Part of this work relates to architectural workshop “energy and architecture” conducted by the authors at the School of Architecture of the Catholic University of the North (UCN) in 2012.

We present an algorithm for a class of statistical inference problems. The main idea is to reformulate the inference problem as an optimization procedure, based on the generation of surrogate (auxiliary) functions. This approach is motivated by the MM algorithm, combined with the systematic and iterative structure of the Expectation-Maximization algorithm. The resulting algorithm can deal with hidden variables in Maximum Likelihood and Maximum a Posteriori estimation problems, Instrumental Variables, Regularized Optimization and Constrained Optimization problems. The advantage of the proposed algorithm is to provide a systematic procedure to build surrogate functions for a class of problems where hidden variables are usually involved. Numerical examples show the benefits of the proposed approach.

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.

This paper evaluates the capacity of an automated algorithm to detect bubbles and estimate bubble size (Sauter mean diameter, D32) from images recorded in industrial flotation machines. The algorithm is previously calibrated from laboratory images. The D32 results are compared with semi-automated estimations, which are used as "ground truth". Although the automated algorithm is reliable to estimate bubble size at laboratory scale, a significant bias is observed from industrial images for D32 > 3.0-4.0 mm. This uncertainty is caused by the presence of small and large bubbles in the same population, with large bubbles forming complex clusters and being observed incomplete, limited by the region of interest. Flotation columns are more prone to this condition, which hinders the estimation of Sauter diameters. The results show the need for bubble size databases that include industrial images. As several image processing tools are currently available, software calibration from ideal bubble images (synthetic or from laboratory rigs) will mostly lead to biased D32 estimations in industrial flotation machines.

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.