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Validation of the Problematic Pornography Consumption Scale: Short Form (PPCS-6) in a Spanish Clinical Population with Gambling Disorder(Archives of Sexual Behavior, 2025) Jiménez-Murcia, Susana; Granero, Roser; Testa, Giulia; Tarragón, Ernesto; Potenza, Marc N.; Bőthe, Beáta; Demetrovic, Zsolt; Uríszar, Juan Carlos; Chiclana‑Actis, Carlos; Fernández‑Aranda, Fernando; Mestre‑Bach, GemmaThe prevalence of problematic pornography use (PPU) and its potential negative effects have raised concerns, necessitating the availability of accurate assessment tools. This study aimed to validate the Problematic Pornography Consumption Scale (PPCS-6) in a Spanish sample with gambling disorder. The sample consisted of 359 adults (92.2% men, M = 39.5 years, SD = 13.6) seeking treatment for gambling disorder. Other than the PPCS-6, various psychometrically sound instruments were used to assess variables related to PPU, gambling behavior, psychopathology, emotional dysregulation, impulsivity, and personality features. Confirmatory factor analysis and correlation coefficients were used for data analysis to examine the factor structure and assess convergent-discriminative validity of the PPCS-6. The psychometric properties of the PPCS-6 were supported in the present treatment-seeking population, showing a one-dimensional solution with good fit and internal consistency. Higher PPCS-6 scores were associated with more severe psychopathology, higher impulsivity, more emotion regulation difficulties, and lower self-directedness. Additionally, positive correlations were observed between PPCS-6 scores and specific motivations for using pornography. This study validates the Spanish version of the PPCS-6 as a reliable screening tool for assessing PPU in clinical populations, specifically in individuals with gambling disorder.
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Quantitative Solids Mixing Analysis Using Interpretable Image Processing Methods(Explainable Intelligence in Digital Twins, 2026) Medina Baca, Ansony Rolando; Moujahid, Abdelmalik; Sánchez-Prieto, Javier; Tran-Trung, KietThis study presents a quantitative framework for evaluating lateral mixing in a bubbling fluidized bed based on high-speed image analysis and Lacey mixing index estimation. Experiments were conducted in a pseudo-two-dimensional fluidized bed using optically distinguishable glass beads, enabling detailed tracking of mixing dynamics over time. Recorded images were processed through a pipeline comprising contrast enhancement, noise reduction, and segmentation via adaptive thresholding to isolate tracer particles. Spatial concentration fields were then extracted from the segmented images and used to estimate local variances necessary for computing the Lacey index as a time-resolved measure of mixing progression from a fully segregated to a randomly mixed state. By providing clear, quantitative indicators of mixing evolution, the proposed methodology offers a potential basis for integrating interpretable measurements into digital models of fluidized bed systems. These image-based insights could contribute to improving the transparency of digital representations of fluidized beds and support future efforts toward explainable digital twins for process monitoring and control. The results suggest that combining classical image processing with statistical metrics provides a reliable means to analyze and understand complex mixing dynamics in a way that may be incorporated into interpretable digital frameworks.- Restringido
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Graph attention-guided dynamics for adaptive community evolution in complex networks(Journal of Computational Science, 2026) Moujahid, Abdelmalik; Rabazo Márquez, CarlosWe propose an adaptive framework for community detection in complex networks by integrating coupled Rössler oscillators with graph neural network embeddings. Oscillator coupling strengths are modulated by node similarities learned through a graph convolutional network (GCN) with orthogonalized embeddings, refined via a graph attention network (GAT) mechanism. At periodic intervals, a feature matrix combining oscillator states, frequencies, phases, and graph metrics is processed through a three-layer GCN with a composite loss balancing kernel -means clustering, spectral alignment via an adaptively fused Laplacian, and adjacency reconstruction. The resulting embeddings are clustered to reveal mesoscale community structure. The reconstructed adjacency modulates coupling via elementwise multiplication with the original graph, row-normalization, and a hyperbolic tangent activation, while oscillator frequencies update to the median of neighbors, promoting intra-community synchronization. The framework is validated on four benchmark networks, Karate, Dolphins, Jazz, and Football, spanning different sizes and structural complexities. Results are compared against established methods, including Joint Spectral Clustering, Spectral Clustering, and Walktrap. Modularity (Q) and Normalized Mutual Information (NMI) are evaluated across multiple community resolutions, revealing hierarchical structures consistent with prior literature. The proposed method achieves competitive modularity across all networks, with perfect agreement with ground truth on the Karate network (NMI=1.0) and a near perfect NMI of 0.9025 on the Football network, surpassing state of the art results. By capturing meaningful community structures at different granularities, the framework offers a principled, data driven pathway to study community formation in physical, biological, and engineered networks.
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