University of Malaga
  • Málaga, Spain
Recent publications
Residential compounds have become a widespread and popular way of life. Today, we find a great structural and social diversity of compounds. While evidence suggests that living in the most closed settings, such as gated communities, is often linked to the desire of the upper-middle class’s desire to associate with people of similar status, research on what kind of elective belonging can be found among working classes remains limited. This qualitative study analyses how class identity is constructed through interviews with residents and non-residents of upper-middle and working-class compounds in the metropolitan area of Granada. The results indicate that residents of compounds value the development of a sense of belonging more highly, and reproduce it more noticeably in their discourses, than non-residents. Residential compounds appear to function as status drivers, both materially and symbolically, but operate differently depending on the social composition of the compounds themselves.
This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car’s navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car’s driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car’s evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations.
The proliferation of Internet of Things and cyberphysical systems has introduced unprecedented challenges in ensuring the integrity and confidentiality of critical data, making robust security mechanisms essential. There are several mechanisms intended to assure trust with respect to the software loaded into the system and the trustworthiness of the boot process. These mechanisms start from a Root of Trust (RoT), from where all the other trusts, e.g., for components and software are derived. As part of the RoT, a Secure Storage is needed. This Secure Storage can be considered as part of the RoT or considered a separate component. After a RoT is established, a Trusted Boot can be performed. The execution of computational processes can then be supported by using separate execution zones (Zone Isolation). More complex trust functions such as remote attestation can be performed by a Trusted Platform Module(TPM). In this paper, we propose security patterns for these components. The abstraction power of patterns can be used to define the basic aspects that each of these components must have, thus serving as reference for designers and for security evaluation.
This article continues the work of Rojas-de-Gracia et al. (2019) in which they question the appropriateness of relying on a single partner, male or female, to identify the decision-maker in tourism decisions. That study concluded that there is no general consensus among the members of the couple. As a solution, they proposed including the child as an impartial observer. This research tests that suggestion and shows that children’s perceptions differ from those of their parents, considering tourism decisions mostly autonomous and mainly dominated by the mother, contrary to parents’ perceptions of joint decision making. The practical implications of this finding are discussed.
Spot-size converters (SSCs) are key for efficient coupling of light between waveguides of different sizes. While adiabatic tapers are well suited for small size differences, they become impractically long for expansion factors around ×100, which are often required when coupling integrated waveguides and free-space beams. Evanescent couplers and Bragg deflectors can be used in this scenario, but their operation is inherently limited in bandwidth. Here, we propose a solution based on a parabolic dielectric interface that couples light from a 0.5μm0.5\,\mathrm{\mu} \rm {m} wide waveguide to a 285μm285\,\mathrm{\mu} \rm {m} wide waveguide, i.e., an expansion factor of ×570. We experimentally demonstrate an unprecedented bandwidth of more than 380 nm380\ \rm {nm} with insertion losses below 0.35 dB0.35\ \rm {dB} . We furthermore provide analytical expressions for the design of such parabolic spot-size converters for arbitrary expansion factors.
Uncertainty is an inherent property of any complex system, especially those that incorporate physical parts or operate in real environments. In this paper, we focus on the Digital Twins of adaptive systems, which are particularly complex to design, verify, and optimize. One of the problems of having two systems (the physical one and its digital replica) is that their behavior may not always be consistent. In addition, both twins are normally subject to different types of uncertainties, which complicates their comparison. In this paper we propose the explicit representation and treatment of the uncertainty of both twins, and show how this enables a more accurate comparison of their behaviors. Furthermore, this allows us to reduce the overall system uncertainty and improve its behavior by properly averaging the individual uncertainties of the two twins. An exemplary incubator system is used to illustrate and validate our proposal.
We define Mackey functors over posets mimicking the classical notion and introduce a weak version of them. Then we show that they are acyclic by analyzing cofibrant and pseudo-projective objects in the category of functors indexed in a filtered poset. As application, we study homotopy colimits over posets and we give a homology decomposition for the classifying space of the Bianchi group Γ1\Gamma _1 Γ 1 .
The aim of this work is to provide a didactic approximation to memetic algorithms (MAs) and how to apply these techniques to an optimization problem. MAs are based on the synergistic combination of ideas from population-based metaheuristics and trajectory-based search/optimization techniques. Most commonly, MAs feature a population-based algorithm as the underlying search engine, endowing it with problem-specific components for exploring the search space, and in particular with local-search mechanisms. In this work, we describe the design of the different elements of the MA to fit the problem under consideration, and go on to perform a detailed case study on a constrained combinatorial optimization problem related to aircraft landing scheduling. An outline of some advanced topics and research directions is also provided.
Building upon the circumplex approach to (de)motivating styles defined by self-determination theory , this research aimed: (a) to analyse the extent to which physical education (PE) teachers' (de) motivating teaching approaches differ across gender, school level, and years of teaching experience , and (b) to test paths from PE teachers' need-based experiences to their (de)motivating teaching approaches, via motivation quality. A purposive and cross-sectional sample of 667 Spanish PE teachers (63.7% male; 54.7% primary; mean teaching experience = 10.77 years) participated. The overall results found that male, secondary school, and more experienced teachers scored lower on autonomy-supportive approaches, and higher on controlling and chaotic approaches. The findings also showed that, after controlling for gender, school level, and teaching experience, need satisfaction showed a direct significant effect on autonomous motivation and an indirect effect on participative, attuning, guiding, and clarifying approaches via autonomous motivation. Need frustration showed a direct significant effect on controlled motivation and amotivation and an indirect effect on demanding, domineering, abandoning, and awaiting approaches via controlled motivation and amotivation. Another noteworthy result is the positive relationship between need satisfaction and controlled motivation. Our results underscore the role that PE teachers’ personal traits play in the adaptive motivational mechanisms underlying their variety of (de)motivating approaches to PE teaching.
The integration of renewable energy resources and electric vehicle (EV) fleets with community microgrids (CMG) has increased fluctuations in net load. To address this and ensure safe operation, tapping into demand-side flexibility capacities in local electricity markets (LEM) is essential. Hence, this article presents a multilevel methodology for settling energy and flexibility markets among CMGs, utilizing the potential of Internet-of-Things-enabled appliances (IoT-EA), thermostatically-controlled loads (TCLs), and EVs in smart residential buildings (SRB) to enhance system performance. At level 1, SRBs are modeled using the virtual energy storage system (VESS) concept. Level 2 involves CMG scheduling, and at level 3, the distribution system operator settles the energy and flexibility markets using an adaptive alternating direction method of multipliers (ADMM) algorithm. Strong duality theory (SDT) and Karush-Kuhn-Tucker (KKT) conditions form a mathematical program with equilibrium constraints (MPEC) where market prices are variable for all participants. By unlocking the potential of SRBs, the proposed framework reduces flexibility market costs by 49.67%, network losses by 24.1%, and improves the voltage profile. The results confirm that the proposed market clearing mechanism ensures market efficiency and protects CMGs' privacy.
Establishing design principles to engineer molecules that exhibit strong circularly polarized luminescence (CPL)‐brightness across the UV, visible and NIR spectral range remains an unsolved challenge. To achieve a large CPL‐brightness, the main difficulty is to optimize the luminescence dissymmetry factor glum. Herein is described the discovery that a multi‐helicene system based on a series of annulated [6]helicenes and perylenediimides, exhibits larger‐than‐linear amplification of glum with helicene length. Large enhanced fluorescent quantum yields and large extinction coefficients are also observed for this length series. Consequently, this series of oligomers exhibit exceptional CPL brightness (BCPL) above 1 × 10³ mol⁻¹ cm⁻¹. Using computational methods based on density functional theory (DFT), the experimental trend shows that the increase of glum is due to the progressive alignment of magnetic (|m|) and electric transition (|µ|) dipole moment vectors, as the helical backbone grows longer.
Aim To investigate the detection and initial management of first psychotic episodes, as well as established schizophrenia, within the primary care of the Andalusian Health System. Background Delay in detecting and treating psychosis is associated with slower recovery, higher relapse risk, and poorer long-term outcomes. Often, psychotic episodes go unnoticed for years before a diagnosis is established. Primary care physicians are crucial for early recognition of psychosis and schizophrenia, especially in Spain, where primary care is the main entry point to healthcare services. Methods Cross-sectional exploratory study. All active primary care physicians in the Malaga Guadalhorce Health District were invited to participate. Due to the COVID-19 pandemic, the survey was conducted online. The survey, adapted and validated for the local context, included 22 items and 5 sociodemographic questions covering early detection, treatment options, physical health monitoring, and management challenges. Descriptive and bivariate analyses summarized the data and explored correlations between key variables. Findings The study included 142 primary care physicians (response rate 35.5%), with 28.9% men and 71.1% women, primarily from urban areas (83.1%). Most had completed residency training (86.6%), with 79.6% receiving psychiatric training. However, only 5.6% had participated in training sessions in the last 5 years, with all such sessions lasting less than 30 h. Physicians typically managed 0-10 patients with diagnosed schizophrenia and saw these patients 2 to 3 times annually. They often felt capable of managing these patients, especially with mental health consultancy support. Physical health monitoring was common, though some relied on mental health services to do this. Collaboration with mental health services was moderate, with high utility perceived for having a list of patients with severe mental disorders in their care panel. Conclusions Family physicians are generally confident in managing psychosis and schizophrenia but lack recent specialized training. Mental health consultancy services are valued, but collaboration with these services needs improvement. Clear guidelines and enhanced training are essential to ensure comprehensive care, addressing both mental and physical health needs of these patients.
Different well-balanced high-order finite-volume numerical methods for the one-dimensional compressible Euler equations of gas dynamics with gravitational force and for the Ripa model have been proposed in the literature. Most of them preserve either a given family of hydrostatic stationary solutions exactly or all of them approximately. The goal of this paper is to design a general methodology to obtain high-order finite-volume numerical methods for a class of one-dimensional hyperbolic systems of balance laws that preserve approximately all the hydrostatic equilibria and exactly a given family of them. Many fluid models for which the velocity is an eigenvalue of the system belong to this class, the Euler equations and the Ripa model among them. The methods proposed here are based on the design of well-balanced reconstruction operators that require the exact or the approximate computation of local hydrostatic equilibria. To check the efficiency and the well-balancedness of the methods, a number of numerical tests have been performed: the numerical results confirm the theoretical ones.
The dynamical boundary value problem for viscoelastic half-space with cut in the form of a strip is considered. The problem is reduced to the singular integral equation of first kind. Using the method of orthogonal polynomials, the integral equation is reduced to an infinite system of linear algebraic equations. The quasi-completely regularity of the obtained system is proved and the reduction method for approximate solution is developed.
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8,678 members
Jose Miguel Morales Asencio
  • Department of Nursing
Adolfo Romero
  • Department of Nursing and Podiatry
Rubén Saborido Infantes
  • Department of Computer Sciences and Languages
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Málaga, Spain
Head of institution
José Ángel Narváez Bueno
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