UPCommons (Universitat Politècnica de Catalunya)
Not a member yet
281550 research outputs found
Sort by
Intra-rater reliability and agreement of a portable bioelectrical impedance analysis device for body composition assessment
No full textThis study aimed to analyze the intra-rater reliability of a new bioimpedance device, the Biody XpertZM III (BX3), and to assess its level of agreement when compared with some of the widely used body composition analyzers in clinical and healthcare settings. Its main novelty is that, in addition to the classic hand-foot protocol from a lying position (BX3-S), it proposes a new self-measurement system from a sitting position by placing its tactile electrodes in the Achilles tendon area (BX3-T). Thirty-three young active men participated in the study (22.5 ± 2.9 years, 74.9 ± 8.1 kg, 178.0 ± 6.4 cm, and 23.8 ± 2.5 kg/m2 BMI). An intra-rater reliability analysis (ICC, CV, SEM, MD, SRD) was performed for both BX3-S and BX3-T. Agreement was assessed against BIA Akern 101ASE for raw bioimpedance variables (R, Xc, Z, PhA), Inbody720 for body water content (TBW, ICW, ECW) and the GE Lunar DXA for body composition (BF, LBM, FFM). Results report ICC values ranging between 0.81 and 1.0 for BX3-S and BX3-T. CV values are below or around 10 %, except for body fat (BX3-S: 31.7 % and BX3-T: 28.0 %). There are no significant differences between BX3 (both methods) and the variables measured by DXA (p > 0.05). On the contrary, the largest differences (p < 0.001) are in Xc (BX3-T), PhA, TBW, ICW (BX3-S and BX3-T). All correlations are highly significant in both methods when compared with all the variables analysed (p < 0.001). These findings suggest that the Body XpertZM III is reliable for measuring bioimpedance and demonstrates a strong level of agreement with widely used bioimpedance devices in clinical settings for physically active young men, with advantages in portability and self-measurement capabilities. However, Xc and PhA measurements are not entirely consistent, so further research is needed in this area.All costs associated with the laboratory and its technical material were covered by the National Institute of Physical Education of Catalonia (INEFC), a public center owned by the Catalan government and attached to the University of Barcelona. Regarding the authors, A.C-P. was a pre-doctoral researcher supported by a FPU pre-doctoral contract from the Spanish Ministry of Education, Culture and Sport [FPU20/00998]. J.E-L. was a predoctoral researcher supported by a grant within the field of physical education, physical activity, and sports and its applied sciences given to the INEFC [PINEFC 00012].Postprint (author's final draft
Inverse nonlinear elastostatic analysis of heterogeneous pre-stressed arterial cross-sections with elastic bed supports: an unfitted approach
No full textThis manuscript presents a novel formulation for inverse finite elastostatics in problems with elastic bed support on the external boundary (elastic bed boundary condition), replacing the classical Dirichlet boundary condition. An unfitted strategy to compute the solution of the mechanical problem is devised, inspired in the Immersed Boundary (IB) framework, and using level sets to describe the geometry. The method is applied to study realistic atherosclerotic arterial sections, which are known to exhibit a pre-stressed configuration. Furthermore, we demonstrate that our method effectively identifies the unloaded configuration. Taking as reference a classical finite elements solver in a commercial code, the method presented results in error lower than 3% in displacements, for sufficiently fine meshes.The authors acknowledge financial support provided by the Ministerio de Ciencia, Innovación y Universidades (MCIN/AEI/10.13039/501100011033, MICIU/AEI/10.13039/501100011033/ FEDER, UE) through grants PID2020-113463RB-C33, PID2022-141957OB-C21 and CEX2018-000797-S; and the Fondazione Regionale per la Ricerca Biomedica (Regione Lombardia), via project ID 3432721. The authors acknowledge Zhongzhao Teng of the University of Cambridge for providing the realistic 2D geome-tries used in this work.Peer ReviewedPostprint (published version
Geometric thickness of multigraphs is ¿R-Complete
No full textWe say that a (multi)graph has geometric thickness t if there exists a straight-line drawing and a t-coloring of its edges where no two edges sharing a point in their relative interior have the same color. The Geometric Thickness problem asks whether a given multigraph has geometric thickness at most t. This problem was shown to be NP-hard for (Durocher et al. Comput Geom 56:1–18, 2016. https://doi.org/10.1016/j.comgeo.2016.03.003). In this paper, we settle the computational complexity of Geometric Thickness by showing that it is -complete already for thickness 30. Moreover, our reduction shows that the problem is -complete for 4392-planar graphs, where a graph is k-planar if it admits a topological drawing with at most k crossings per edge. In the course of our paper we answer previous questions on geometric thickness and on other related problems, in particular that simultaneous graph embeddings of 31 edge-disjoint graphs and pseudo-segment stretchability with chromatic number 30 are -complete.Peer ReviewedPostprint (published version
Blind learning of the optimal fusion rule in wireless sensor networks
No full textThis work presents a general framework for blindly estimating the sensor parameters of decision-fusion systems over wireless sensor networks (WSNs). The sensors report their binary decisions to a fusion center (FC) through parallel binary symmetric channels. Then, the FC makes the final decision by combining the noisy sensor decisions according to a certain fusion rule. We present an algorithm for the FC to blindly estimate the sensor parameters from the noisy sensor decisions received after a number of sensing periods. The algorithm covers a wide variety of situations that may arise in WSNs. For example, the algorithm is applicable when the FC knows in advance some of the parameters of some sensors, when it knows the true hypothesis for a subset of sensing periods, or when only a subset of sensors communicates their decisions in each sensing period. Based on the estimates of the system parameters, optimal channel-aware fusion rules are derived considering the minimum Bayes risk criterion. Simulation results show that, after sufficient sensing periods, the estimates of the WSN parameters are accurate enough for the fusion rule to exhibit near-optimal detection performance.This work has been funded by MCIN/AEI/10.13039/501100011033 under grants PID2022-137099NB-C41 and PID2022-137099NB-C43.Peer ReviewedPostprint (published version
The loss of urban form as a communicative system: A morphogenetic critique of the recent reconstruction of Beirut
No full textThe continuing devastation and destruction of cities in the Middle East, accompanied by hasty debates about future reconstruction, is not a new phenomenon. The city of Beirut, severely affected by the Lebanese civil war (1975–1989), has witnessed the transformation of its city centre since the end of the conflict. The reconstruction plan led by the real estate company Solidere has resulted in a significant change in the form, function and identity of the city centre. The article uses the concept of urban morphogenesis to analyse how, over the course of more than three decades since the plan was approved, the urban system has been simplified through the eradication of plot subdivision, topological isolation, hyper-regulation and the use of heritage to select specific historical memories. This analysis demonstrates the tendency towards urban simplification through a sequence of destruction, securitisation and reconstruction, and argues that alternative processes of reconstruction are possible.Peer ReviewedPostprint (published version
Light-activated self-healing poly (lactic acid) for durable FDM 3D printing applications
No full textFused Deposition Modelling (FDM) is an additive manufacturing technique that allows the fabrication of complex and customized parts. However, FDM-based pieces exhibit low mechanical performance due to the inherent limitations of this technology: poor interlayer adhesion and internal voids. As a result, the mechanical durability is reduced contributing to the problem of plastic waste generation. In this sense, the introduction of dynamic crosslinkers into the thermoplastic can be particularly beneficial, since they not only reinforce the polymer matrix but also impart self-healing functionality. As a consequence, the service life of FDM components is further extended. Here, we develop a coumarin-modified poly (lactic acid) formulation capable of intrinsic, light-activated self-healing. Upon ultraviolet irradiation under optimized solid-state conditions of irradiance and temperature, coumarin moieties grafted onto the PLA backbone undergo [2p + 2p] cycloaddition, generating photocrosslinked networks. Evidence of photocrosslinking was confirmed by the formation of a gel fraction (9.6 %) and a 72 % increase in storage modulus. Mechanical scratches on FDM specimens were subsequently irradiated, showing accelerated scratch closure and a smaller reduction in storage modulus (9.5 %) compared to neat PLA (32 %). These results suggest that dimerized coumarin units partially undergo reversible cleavage and re-dimerization during the healing process, enabling repeated recovery of mechanical stability. This represents the first demonstration of light-activated self-healing in FDM-printed PLA. Unlike previously reported extrinsic self-healing approaches in PLA, which rely on single-use microcapsule systems, the present work achieves intrinsic, repeatable self-healing within the PLA matrix. By combining biobased composition with extended functionality and durability, this research advances a sustainable strategy for additive manufacturing, addressing both the mechanical limitations of FDM and the broader challenge of reducing plastic wasteThis research was funded by Ministerio de Ciencia, Innovación y Universidades (MCIU) of the Spanish Government, Spain [grant number PID2020-117802RB-I00]. In addition, Dr. Ardanuy and Dr. Oliver-Ortega acknowledge the financial support of the research group TECTEX (2021 SGR 01056) from the Department de Recerca i Universitats de la Generalitat de CatalunyaPostprint (published version
Machine learning surrogates for unreinforced masonry tensile-strength prediction
No full textWith advances in computational modeling, finite- or discrete-element method-based computational models are often used to conduct nonlinear structural analysis of masonry construction. However, such detailed models are computationally intensive, limiting their usefulness in preliminary analysis or applications requiring real-time simulations (e.g., developing digital twins). A possible solution, increasingly used in other building science areas (e.g., energy efficiency), is developing machine-learning-based surrogate models that mimic the performance of complex physics-based simulation models at significantly reduced computational costs. However, little is known about the premise of this approach in the context of masonry tensile strength prediction due to the scarcity of such applications in the literature. This research proposes a framework to develop and evaluate the performance of machine learning surrogate models in emulating the performance of masonry tensile-strength prediction models. A five-step methodology is proposed: (1) develop computational physics-based models based on the discrete element method (DEM), (2) validate the proposed computational models, (3) generate a data set through parametric variations to support surrogate modeling, (4) train and test data-driven surrogate models to emulate the capabilities of the computational models, and (5) conduct a sensitivity analysis to determine the most influential input parameters. The presented generic approach is demonstrated using a validated discontinuum-based computational modeling strategy based on the DEM. The model predicts the tensile strength and corresponding strain value of unreinforced masonry walls subjected to diagonal compression forces. Results show that the gradient boosting (GB) surrogate models consistently achieved high accuracy levels (¿2>0.9) even when using small data sets (e.g., 100 samples). However, compared to linear regression, GB exhibited increases in training and tuning time for large sample sizes. The proposed framework offers unique insights into the premise of data-driven surrogate models to complement and support computational-based techniques, balancing predictive accuracy and model complexity. Additional case studies are required to generalize results to other masonry configurations and contexts.The second author acknowledges the funding received from the Canada Research Chairs (CRC) program. The third author acknowledges the grant RYC2022-037263-I funded by MICIU/AEI/10.13039/501100011033 and the European Social Fund Plus(ESF+). Also, the corresponding author gratefully acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) grant RGPIN- 2022-04938Peer ReviewedPreprin
Evaluating AI-assisted creative ideation: A crossover study in higher education
No full textAs artificial intelligence (AI) becomes increasingly integrated into educational contexts, its impact on students’ creative thinking remains unclear. Given the critical role of creativity in engineering and design education, understanding how AI tools shape students’ ideation processes is essential for developing effective pedagogical practices. This study examines the impact of generative AI, specifically ChatGPT, on creative ideation among undergraduate design engineering students. The research was conducted through a randomised crossover experiment, with students alternating between AI-assisted and unaided ideation tasks. A mixed-methods approach combined quantitative analyses of the 728 ideas generated with a qualitative evaluation of students’ interactions with AI. Results show that the use of AI did not reduce fluency, flexibility, or originality, nor did it lead to thematic homogenisation. However, semantic divergence was significantly lower in the AI-assisted condition, suggesting convergence in the way ideas were formulated. Additionally, AI-assisted ideas more frequently resembled existing products and exhibited reduced textual elaboration. A mixed between-within subjects ANOVA revealed that students who began with AI support produced more original and diverse ideas across both tasks, pointing to a lasting effect of early AI use. Qualitative analysis of student-AI interactions revealed important patterns, including predominantly passive and directive use, with limited exploratory or collaborative engagement. These findings provide new insights into human-AI co-creation and highlight the importance of promoting intentional, critical, and pedagogically guided use of generative AI tools in education.4 - Educació de QualitatPostprint (published version
Lightning flashes at electric power system towers identified as Recurrent Lightning Spots observed by ground and space-based systems
No full textThis study integrates space-based optical detections with ground-based measurements of lightning flashes on electric power system towers forming Recurrent Lightning Spots (RLS). We found that the lightning-tall tower interaction was related to downward leaders from the thunderstorm charge regions, with no upward flashes recorded. Likewise, cloud-to-ground (CG) strokes in RLSs associated with fast downward leaders exhibit the highest magnitude currents. Our findings suggest that thunderstorms' lower positive charge regions may contribute to increased lightning activity at RLS and revealed a correlation between lightning strokes in RLS with high peak currents and elevated values of integrated optical energy. We identified that the leader's propagation altitude and type and the VHF power emitted by lightning leaders are influential factors in detecting CG strokes from spacePeer ReviewedPostprint (published version
Adaptive multi-objective real-time hierarchical control for isolated microgrid clusters utilizing an enhanced particle swarm optimization strategy to optimize costs and emissions
No full textThis paper introduces an adaptive hierarchical control for an isolated microgrid cluster (IMGC) leveraging a real-time multi-objective particle swarm optimization (MOPSO) algorithm. It simultaneously considers CO2 emissions minimization as a tertiary control objective and total losses minimization as a primary control objective, integrating grid-supporting and grid-feeding inverters for MG interconnection. The effectiveness of the MOPSO-based hierarchical control is demonstrated across multiple scenarios. Compared to a hierarchical control based on proportional power distribution relative to the rated inverter capacities of the MGs, the proposed method shows a 27.21% reduction in total losses and a 7.66% reduction in CO2 emissions. When compared with an optimization based on the fmincon solver, the proposed approach achieves a 22.92% reduction in losses and a 3.5% decrease in emissions. Additionally, centralized secondary control improves MRE indices by 100.09%, ITAE by 28.5%, ITSE by 43.78%, IAE by 30.61%, and ITSE by 47.72%, compared to the primary control strategy based on proportional approach. The MOPSO approach demonstrates robustness and flexibility, maintaining stable frequency and voltage within set thresholds during MG failures and sudden demand changes. Finally, the practical feasibility of the proposed approach is verified in a hardware-in-the-loop experimental setup using an OPAL-RT4512 unit and a dSPACE MicroLabBox. The experimental results, utilizing a time step of 50 µs, are consistent with the simulation outcomes, ensuring voltage and frequency control as its rated references.This work was partially supported by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, FEDER, UE (Grants PID2021-123633OB-C31, PID2021-123633OB-C32 and PID2021- 123633OB-C33 supported by MCIN/AEI/10.13039/501100011033/ FEDER, UE).Peer ReviewedPostprint (published version