The University of Málaga (UMA, Universidad de Málaga) is a public university ranked 23 among all Spanish universities and 683 in the world. It was established in 1972 and has, as of 2016, 30,203 Bachelor students and 2576 on a Master's program, 1255 tenured and 1056 temporary teachers. The UMA offers 65 degree courses and 6 double degrees, over 21 doctoral programmes, 64 master's Degrees, and 100 courses throughout the academic year. Education takes place in 18 centres by appointed teachers from 81 departments. The great majority of the teaching is organised within the two campuses, although classes also take place in locations spread around the city centre, as well as in Ronda and Antequera.
The integration of stochastic renewable energy sources has made flecan be reformuxibility aggregators crucial for power system balancing, yet the uncertainty they introduce challenges system security and market efficiency. Current trends show a move towards probabilistic reliability rules for bid qualification, but a key research gap remains: how can a Transmission System Operator (TSO) optimally set these reliability thresholds while considering competitive aggregators in the market? This paper proposes a novel methodology using a Strengthened Faster Linear Approximation (SFLA) to reformulate the game-theoretic problem, which includes lower-level distributionally robust joint chance constraints, into a tractable single-level optimization problem. A case study with real-world data from Spanish Reserve Markets demonstrates the method’s ability to balance system costs and security, quantifying the trade-off between reserve procurement and delivery risk. This research provides TSOs with a computationally tractable decision-making tool to set reliability thresholds for aggregators while ensuring security.
This study compared the effects of two pitch sizes (40 × 20 m and 30 × 15 m) on mental workload (MWL), perceived physical effort (RPE), and physical performance during 8-vs-8 small-sided football games (SSGs). Sixteen amateur players (an average age of 13.95 years) participated in a cross-over design over three weeks, playing on both pitch sizes. Pre- and post-tests measured neuromuscular fatigue via countermovement jumps (CMJ), the RPE scale, and MWL with the NASA-TLX. VO2Max was assessed once to explore its relationship with MWL changes. Results showed a significant effect of time on neuromuscular fatigue and a significant pitch size effect on RPE, with a 9.39
We develop nonlocal structural models for straight and curved MEMS/NEMS beam actuators, based on higher-order, Timoshenko, and Euler–Bernoulli kinematics. The governing equations are derived in two equivalent forms: a nonlinear differential-equation formulation and a Green’s-function integro-differential formulation. Numerical algorithms for both approaches are implemented in Mathematica, enabling consistent, side-by-side comparisons of accuracy and computational cost. The models are applied to study pull-in instability of nanobeams over a broad range of geometric (aspect ratio, thickness) and microstructural (nonlocal) parameters and for several boundary conditions. Results show that, for slender beams, all kinematic models provide similar predictions, whereas for relatively thick beams and/or strong nonlocal effects the higher-order and Timoshenko formulations are required for reliable estimates. Nonlocality produces a systematic softening of the response and can reduce the pull-in voltage by more than a factor of three relative to the classical (local) limit. The Green’s-function formulation offers improved numerical robustness and efficiency while remaining consistent with the differential-equation approach.
Neltuma juliflora (Sw.) Raf. (syn. = Prosopis juliflora (Sw.) DC.) is among the world's most aggressive woody invaders, yet its ecological impacts remain poorly quantified in hyper-arid environments, where soils are calcareous and ecosystems recover slowly from disturbance. In this study, we tested two hypotheses: (1) the presence of N. juliflora changes native plant diversity, as well as soil and key physicochemical properties in hyper-arid Qatar, and (2) agricultural farms act as primary sources of N. juliflora invasion. Using a comparative observational design across 62 sites (45 invaded and 17 non-invaded), we applied a generalised additive model (GAM) and a generalised linear mixed model (GLMM) to quantify invasion drivers and the impact of invasion on perennial species diversity, respectively. Additionally, we used the Wilcoxon rank-sum test to compare the soil properties in the invaded and non-invaded sites. Our results indicate that N. juliflora is positively associated with farms, with the probability of occurrence declining by ca. 20% for each kilometre farther away from agricultural farms. This pattern suggests substantial propagule pressure from agricultural farms. Perennial species richness declined from 7.5 species at 0% N. juliflora cover to 4.8 species at full cover (36% reduction). Invaded sites were characterised by higher amounts of coarse sand (16%); reduced silt-clay fractions (5%); and elevated salinity indicators, including electrical conductivity (0.744 dS m-1) and total dissolved solids (476 mg L-1), while major N-P-K pools remained unchanged. These findings demonstrate measurable invasion-related changes in soil conditions and native perennial diversity in hyper-arid ecosystems and highlight the role of agricultural land use as a key driver of biological invasion. From a sustainability perspective, early detection, targeted control near agricultural and grazing zones, and integration of invasive species monitoring into land-use planning frameworks are essential to prevent further ecosystem degradation, protect biodiversity, and enhance the resilience of desert landscapes under increasing climate and land-use pressures.
Several techniques have been proposed to prioritize risks identified in projects. In academia and business, the most standard use is the probability-impact matrix, also known as the risk matrix. Although widespread, this tool has specific limitations, as demonstrated in numerous studies. This article introduces a quantitative method based on Monte Carlo simulation to enhance project risk prioritization.Query Our proposed method quantifies the impact of each risk on both duration and total cost objectives, enabling us to determine each risk’s relative importance based on these values. In contrast to previous work, this paper proposes a methodology that integrates the effects of risk interactions and the structure of the project-defining network. We conducted two simulation studies, which are detailed in this article. The first study uses fictitious projects to examine the influence of project network structure on the effectiveness of the risk matrix, compared with our proposed methodology. In the second study, we apply our method to two distinct real-world projects and compare these findings with results from simulations of fictitious projects. The conclusions indicate that the traditional risk matrix method produces results that diverge from those generated by our proposed approach, which quantifies impacts. Moreover, it was observed that the risk matrix provides more reliable results when a project’s structure approximates a serial configuration. However, its accuracy in risk prioritization declines for projects with a more parallel structure.
