Search Results (5,218)

This study conceptualizes the youth observatory as an analytical and governance-oriented framework for examining social change, inequality, and youth development in urban contexts characterized by persistent structural disparities, using Cartagena, Colombia, as an empirical case study. Rather than conceptualizing the observatory solely as a technical mechanism for data collection and monitoring, the article argues that it should be understood as a platform for knowledge production capable of identifying emerging patterns in youth trajectories, institutional fragmentation, and evolving forms of civic engagement. The research employed a qualitative, descriptive, and interpretive design based on documentary analysis and a focus group comprising institutional stakeholders involved in youth-related initiatives. Findings indicate that the principal challenge in Cartagena is not the absence of youth-related information but the fragmentation, discontinuity, and weak articulation of existing institutional and knowledge-production systems. As a result, valuable evidence remains underutilized in policy formulation and institutional decision-making processes. The findings further demonstrate that education, labour-market integration, political participation, cultural access, mental health, and digital vulnerability should not be treated as discrete dimensions but rather as interconnected and mutually reinforcing components of contemporary youth experience. Ultimately, this article contributes to current debates by proposing the youth observatory as an analytical, interpretive, and governance mechanism that links evidence generation, public deliberation, and policy development. The study provides both empirical and conceptual foundations for the establishment of youth observatories in contexts marked by urban inequality, institutional fragmentation, and accelerated social transformation. Full article

The posterior communicating artery (PComA) aneurysm is a challenging microsurgical problem due to multiple factors. These include the technical complexity of the surgery itself, potential injury to blood vessels during surgery, recovery of cranial nerve function, and overall neurological outcome after the operation. These are all related to the area where the PComA aneurysm is located, but they have fundamentally different biological determinants. Prior methods of describing aneurysms do not adequately describe how the relationships of the internal carotid artery (ICA) and PComA/P1 configuration influence the proximity of the aneurysm to other important structures such as the perforating arteries, the anterior choroidal artery (AChA), cranial nerve III (CN III), and the surgical corridor. We created PComA-CORE, an artificial intelligence-based framework designed to evaluate whether the elements of experienced microsurgeons’ thought processes can be measured individually while still maintaining temporally valid predictions, human interpretability, and explicit estimates of predictive uncertainty. Methods: Using a highly detailed database of clinical, radiographic, anatomical, intraoperative, and longitudinal data from 687 adult patients who underwent microsurgical clipping of PComA aneurysms over the period 1997–2026, we applied PComA-CORE to predict separately: Microsurgical Complexity (C); Oculomotor Recovery (O); Neurovascular Preservation Risk (R); and Expected 90-Day Functional Outcome (E). The models used cases from 1997–2020 (n = 564) for development and cases from 2021–2026 (n = 123) for temporal evaluation. Several architectures, including penalized regression, machine-learning techniques, interpretable machine learning, and ensembles, were compared using nested cross-validation, discrimination metrics, calibration metrics, decision-curve analysis, explainability measures, uncertainty-aware prediction, inter-observer reproducibility, and model-to-score distillation. Results: Four discrete predictive architectures were identified by PComA-CORE. PComA-C was found to be highly dependent upon anatomy because the specific geometric characteristics of individual vascular segments and the presence or incorporation of branches around the aneurysm strongly influenced temporal predictions. PComA-R was found to behave as a distributed susceptibility phenotype based on neurovascular attributes rather than a deterministic injury model and achieved a temporal AUC of 0.703. Among patients with preoperative CN III palsy, PComA-O identified that recovery primarily depended upon the time course of neurological dysfunction and structural deformation of the affected nerve. Temporal validation was not feasible given the small number of recent non-recovery events. Conversely, PComA-E showed that global functional outcome continued to depend predominantly upon clinical neurological severity, with a temporally evaluated penalized model achieving an AUC of 0.878. Uncertainty-aware predictions indicated that some cases would benefit from greater caution in interpretation. High-resolution anatomical phenotypes demonstrated good inter-observer reproducibility. Score distillation demonstrated that simplification preserved predictive information, but did so differently depending on the endpoint. Conclusions: The problem of predicting the consequences of clipping a PComA aneurysm is multidimensional and does not exist as a single “risk” prediction problem. Technical complexity, neurovascular vulnerability, neural recovery, and global disability each exist within distinct predictive spaces and require different levels of anatomical detail and/or computational complexity. PComA-CORE establishes a human-supervised framework to transform expert microsurgical thought processes into explicit, reproducible, uncertainty-aware, and clinically interpretable representations. While prospective multicenter validation will be needed prior to clinical use, it has the potential to establish a basis for explainable AI, precision cerebrovascular neurosurgery, anatomy-informed risk stratification, and clinically interpretable decision-support systems in complex aneurysm surgery. Full article

Biclustering is a two-dimensional data analysis paradigm that aims to identify subsets of rows and columns in a data matrix whose intersection forms a submatrix satisfying predefined properties. In the case of binary data, a bicluster is typically defined as a submatrix containing exclusively ones or exclusively zeros. Numerous approaches to binary biclustering have been proposed over the last several decades. Among them, a distinctive line of research relies on Boolean reasoning, transforming the biclustering task into the problem of finding prime implicants of a data-dependent Boolean function. From a theoretical perspective, it has been shown that every prime implicant of the data-dependent Boolean function corresponds to an inclusion–maximal bicluster in the original dataset, and vice versa. Here, inclusion maximality means that no additional row or column can be added without violating the bicluster property. For discrete datasets, the corresponding Boolean function can be represented in Conjunctive Normal Form (CNF), where clauses contain up to three variables, reducing the general biclustering problem to an instance of the 3-SAT problem. Furthermore, the computational complexity of discrete-data biclustering can be reduced to the analysis of binary matrices. In this case, the associated Boolean functions consist of clauses containing at most two literals, yielding monotone 2-CNF formulas. This paper focuses on improving the efficiency of computations required to derive a disjunctive normal form (DNF) representation composed exclusively of prime implicants. Since the extraction of prime implicants constitutes the computational core of the Boolean biclustering framework, accelerating this process directly enhances the scalability and practical applicability of biclustering methods based on Boolean reasoning. Full article

Accurate hygrothermal characterization and robust numerical modeling are critical for optimizing timber drying processes and evaluating building envelope performance. This work presents a 1D transient numerical model based on the finite volume method (FVM) and a representative elementary volume (REV) formulation to simulate moisture transport during solid wood drying. The governing nonlinear partial differential equations—incorporating moisture content-dependent diffusivity, liquid suction pressure, and vapor diffusion—were discretized using a fully implicit scheme. The solver was validated against consensus numerical solutions from six international institutions in the HAMSTAD Benchmark No. 2 protocol and further verified against published experimental kinetics. Quantitative error analysis yielded low mean absolute errors (MAE $<0.215 {\mathrm{kg}/\mathrm{m}}^3$) and root mean square errors (RMSE $<0.322 {\mathrm{kg}/\mathrm{m}}^3$) across critical drying stages ($100 \mathrm{h}$, $300 \mathrm{h}$, and $1000 \mathrm{h}$). The proposed framework demonstrates high computational stability and precision, offering a transparent numerical tool for hygrothermal building simulations and timber processing applications. Full article

In this work, a bandpass filter (BPF) based on a half-mode substrate integrated waveguide loaded with spoof surface plasmon polaritons (HMSIW-SSPP) is proposed. To explore the high-dimensional metallic pattern space of the SSPP loading region, an artificial intelligence (AI)-assisted surrogate optimization framework is developed. The editable SSPP loading region is discretized into 351 binary cells, and a frequency-weighted principal component analysis (PCA)-based convolutional neural network (CNN) surrogate is constructed to predict full-band electromagnetic responses while preserving key spectral features. Based on the trained surrogate, proximal policy optimization (PPO) is employed to optimize the binary pattern through a sequential keep-or-flip strategy. Full-wave simulations show that the optimized filter achieves a passband of 2.97–7.55 GHz, with return loss better than 12 dB and stopband suppression better than 20 dB. A prototype is fabricated and measured for validation, showing a passband of 2.95–7.52 GHz, a fractional bandwidth of 87.3%, a minimum insertion loss of 0.95 dB, and return loss better than 11.7 dB. These results verify the proposed HMSIW-SSPP topology and demonstrate the effectiveness of the frequency-weighted PCA-CNN-assisted PPO framework. Full article

Cement-stabilized crushed stone is widely used in semi-rigid pavement bases, but fatigue cracking can reduce pavement durability. Supersulfated cement-stabilized crushed stone is a promising low-carbon alternative, yet its fatigue behavior under the micro-expansion induced by ettringite (AFt)-seeded hydration has rarely been clarified. This study investigated the fatigue performance of AFt-seeded SSC-stabilized crushed stone with explicit consideration of the resulting self-stress, through paste expansion tests, splitting fatigue experiments, and a coupled finite element–discrete element analysis. The seeded paste developed a stable free expansion of approximately 400 με, which was converted to an equivalent expansion of 18.5 με and an estimated compressive self-stress of 0.283 MPa at the crushed-stone scale. The material reached a 90-day indirect tensile strength of 1.45 MPa, approximately 57.6% higher than that of the OPC-stabilized reference. Under comparable normalized stress levels of 0.45 and 0.49, the SSC-stabilized crushed stone achieved 17,441 fatigue cycles, exceeding the reference by 28%, with slower stiffness degradation and lower permanent deformation accumulation. Numerical simulation further showed that the self-stress delayed bond breakage and crack penetration, with simulated base fatigue failure at approximately 5.3 × 10⁵ cycles. These findings clarify the role of micro-expansion-induced self-stress in fatigue resistance. They also indicate its potential for durable, low-carbon pavement bases. Full article

Respiratory diseases in chickens are a major concern in poultry production, as they adversely affect animal health and production efficiency. Chicken vocalizations often contain important acoustic cues associated with respiratory conditions. However, conventional sound classification methods typically rely on predefined categories, limiting their ability to capture complex acoustic patterns. To address this limitation, this study introduces an audio captioning framework for the analysis of chicken respiratory sounds and proposes a model termed FeeCLAP for generating descriptive text from chicken vocalizations. The proposed model maps acoustic signals into natural language descriptions of sound quality, rhythm, and related attributes, thereby enabling semantic-level representation of vocal patterns. Built upon the baseline EnCLAP framework, a dataset of chicken respiratory sound descriptions with medically relevant semantic styles was constructed. An Acoustic Token Adapter (ATA) module was then introduced between discrete acoustic tokens and the text encoder to enhance the temporal modeling of acoustic features. In addition, a CLAP (Contrastive Language-Audio Pretraining)-aware semantic alignment mechanism was incorporated to improve consistency between acoustic representations and the semantic space. During inference, a CLAP-based similarity re-ranking strategy was further applied to improve the quality of generated descriptions. Experiments showed that FeeCLAP outperformed existing methods, achieving Consensus-based Image Description Evaluation (CIDEr), Semantic Propositional Image Caption Evaluation (SPICE), and SPIDEr scores of 0.423, 0.202, and 0.313, respectively, with generated descriptions approaching human-annotated references. Overall, the proposed approach demonstrated its effectiveness in generating semantic descriptions of chicken respiratory sounds under the experimental conditions used in this study. These findings suggest its potential to support respiratory health assessment in poultry; however, further validation under commercial poultry production conditions is required before the method can be considered a reliable tool for practical intelligent monitoring or early detection of respiratory abnormalities. Full article

As offshore wind turbines continue to grow in capacity, reducing the structural weight of substructures has become a key factor in securing project economics, and the package installation method has attracted attention as a promising technology for reducing installation costs. This study presents an integrated design procedure for the package installation substructure of a 10 MW fixed offshore wind turbine, ranging from the establishment of classification rule-based design load conditions to surrogate model-based approximate optimization and verification of the optimum designs. Design load conditions for the installation, operation, and survival phases were defined in accordance with DNV classification rules and IEC 61400-3-1, and the structural safety of the initial design was evaluated via finite element analysis. Response data for 243 design matrices were generated using an orthogonal array design in which the thicknesses of nine primary structural members were defined as three-level design variables. Kriging, response surface methodology (RSM), and radial basis function neural network (RBFN) surrogate models were compared using multiple statistical metrics, and the RBFN, exhibiting the highest average coefficient of determination of 0.942 together with the lowest error levels for the stress responses, was selected for the approximate optimization. Discrete and continuous design variable optimizations were carried out in parallel by coupling the RBFN surrogate model with the adaptive simulated annealing (ASA) algorithm. The discrete optimum design reduced the structural weight by 2.5% while satisfying the allowable stress criteria for all load conditions, converged with approximately 94% fewer design evaluations than the continuous approach, and is defined directly in manufacturable plate thicknesses; it was therefore adopted as the final design. Full article

This study proposes a hybrid method for pre-design verification of the geometric and biomechanical compatibility of experimentally recorded gait trajectories with a lower-limb exoskeleton. The method integrates joint-space kinematic analysis, CAD-based self-collision detection in a Unity digital twin, anatomical hip and knee constraints, and experimentally recorded gait trajectories. Gait data were obtained from 30 healthy participants at 12 treadmill speeds ranging from 0.5 to 6.0 km/h using multicamera markerless motion capture. Of 54,481 evaluated configurations, 21,450 remained after self-collision detection and 4080 after anatomical filtering, corresponding to 7.49% of the initial discretized domain. The mean proportion of experimental trajectory points excluded because of mechanical self-collisions increased from 2.70% at 0.5 km/h to 30.49% at 6.0 km/h. Walking speed had a significant and large overall effect (${\chi }_F^2\left(11\right)=242.38$, $p<0.001$, Kendall’s $W=0.734$), and the median participant-specific Spearman coefficient was $\rho =0.920$. The proposed framework provides a quantitative pre-screening tool for identifying critical gait configurations before powered prototype testing and can support mechanical redesign and definition of controller constraints. Full article

In this work, we develop a projection neural network based on a second-order dynamical model (SO-PDM) for solving inverse variational inequality problems (IVIPs) in Hilbert spaces. The proposed framework incorporates inertial and damping components, resulting in improved convergence behavior while ensuring feasibility through a projection operator. Under the Lipschitz continuity assumption on the operator, the proposed SO-PDM admits a unique global trajectory. Under the additional strong monotonicity assumption and suitable parameter conditions, convergence to the unique solution of the IVIP is established. A discrete-time formulation is derived via a finite-difference scheme, leading to a projection-based inertial algorithm with relaxation. Under suitable parameter conditions, the algorithm is shown to converge linearly to the unique solution of the IVIP, and under an additional parameter condition, the global asymptotic stability of the continuous-time SO-PDM is established via Lyapunov analysis. Furthermore, a numerical comparison in a higher-dimensional setting shows that the proposed algorithm converges faster and attains higher accuracy than the existing first-order projection method. Numerical experiments further confirm the effectiveness and stability of the proposed SO-PDM, including its application to sparse signal recovery in compressed sensing. Full article

Non-additive measures and integrals have become important mathematical tools in modern research. They offer powerful frameworks for modelling interaction, uncertainty, and handling incomplete data where traditional additive techniques fail. This paper focuses on the Choquet integral and the corresponding non-additive measure derivatives, which can be considered mutually inverse problems. A comprehensive review of existing direct and indirect approaches is provided for analysis of the derivatives, aiming to refine and generalise current results while highlighting their inconsistencies and limitations. In the discrete case, Choquet integration reduces to a finite sum, and the necessary and sufficient conditions for the existence of measure derivatives form a linear system. In the continuous case, three existing frameworks are evaluated: studying the invariance under rearrangement of integrating functions for the integral; using generalised integral equations for Choquet computations to derive formulas for the result of integration as well as for measure derivatives; and the resulting measure approach, which uses the inverse nature of integration and measure derivatives and pairs integrating and resulting measures to derive explicit derivative formulas. Ultimately, this work maps out current perspectives on non-additive measure derivatives with respect to the Choquet integral, and outlines possible directions for their future theoretical and applied advancements. Full article

This paper studies global asymptotic stability of Cohen–Grossberg neural networks involving discrete time delays in the neuron states and neutral delays in the time derivatives of the neuron states. An appropriate Lyapunov functional, which is defined by the linear combination of three Lyapunov functionals of the quadratic forms, is constructed to determine new criteria for global asymptotic stability of neutral-type neural networks with discrete delay parameters. The proposed stability conditions are established through a set of algebraic inequalities that utilize key matrix properties and parameters of system functions. These criteria are proved to be independent of delay components, and they can be tested by checking some algebraic inequalities. A numerical example is studied to illustrate the efficiency aspects of the derived stability conditions. Full article

This study reports a combined morphological, surface chemical, and mineralogical characterization of fine particulate matter (PM_2.5) collected at three coastal sites—Northern (Abhour), Middle (Alhamraa), and Southern (Alkhomra)—in Jeddah, Saudi Arabia, during the summer (15 June–15 September 2017). The work complements a companion trace-element study of the same campaign by adding scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS/EDX mapping), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD), thereby linking bulk concentrations to particle morphology, surface oxidation state, and crystalline phase. Mean PM_2.5 concentrations were 22.2, 18.9, and 14.2 µg m⁻³ at the North, Middle, and South sites, respectively. Because samples were collected on borosilicate glass-fibre filters, the SEM images are dominated by the intrinsic fibrous matrix of the substrate; the collected aerosol is resolved as discrete sub-micrometre particles and agglomerates decorating the fibres, and the morphological interpretation is framed accordingly. XPS confirmed that surface metals (Fe, Al, Ca, and traces of Pb, Cu, Zn) occur predominantly in oxidized states, with the Middle urban site showing the strongest Fe and Pb signals. XRD identified quartz, calcite, gypsum, hematite/magnetite, and aluminum oxides, with additional Pb and Cu phases at the Middle and South sites. Principal component analysis (PCA) resolved four sources—mixed marine–crustal, terrigenous/industrial (Fe–Ti–Mn), oil combustion and shipping (V–Ni–Cu), and combustion/legacy-traffic (Pb–Zn)—consistent with prior Jeddah and Red Sea studies. The integrated approach provides surface-speciation and mineralogical details not available from bulk elemental analysis alone and establishes baseline information relevant to source management and health-risk assessment in arid coastal cities. Full article

This article presents a critical review of the development of window systems used in civil engineering, interpreting them not as discrete construction products, but as complex technical and material systems whose actual value emerges from the interdependence of structural configuration, material composition, thermal performance, durability, and manufacturing and implementation constraints. The review discusses the evolution of windows from simple envelope elements providing daylight, ventilation, and weather protection into advanced building-envelope systems associated with energy efficiency, occupant comfort, in-service durability, and environmental responsibility. Particular attention is given to the principal families of window systems, including PVC-U, aluminium, timber, steel, façade, hybrid, and composite-based solutions. The analysis shows that improving the thermal insulation of a single component is not, in itself, a sufficient criterion for evaluating system quality. Declared performance may be constrained by thermal bridges at the installation interface, ageing of sealing systems, imperfections in joining processes, material deformation, and difficulties related to repair, disassembly, and recycling. From the perspective of sustainable construction, window systems should therefore be assessed across their whole life cycle, taking into account energy effectiveness, in-service stability, technological feasibility, renovation potential, and the possibility of closing material loops. The review also identifies the need for further research into integrated assessment methods, the long-term durability of advanced frame systems, the role of the window-to-wall interface, and verifiable strategies for circularity. Full article

Today, the use of increasingly ubiquitous synthetic media, or ‘deepfakes’, has become a risk to online trust, information integrity and individual security and is being created by artificial intelligence (AI). The current approaches are mainly based on either spatial features of CNNs or high-level semantic representations of Vision Transformer; both have major drawbacks in effectively leveraging multi-domain forensic cues. This paper presents FAViT (Frequency-Aware Vision Transformer), a hybrid architecture capable of jointly utilizing spatial- and frequency-domain forensic information by the means of a bidirectional cross-attention fusion scheme. We use an 11-channel forensic tensor in each face image (including per-channel Fast Fourier Transform (FFT) magnitude maps, Discrete Wavelet Transform (DWT) sub-bands, channel noise residual maps, Sobel gradient magnitude and channels of Error Level Analysis (ELA)). A Frequency Branch CNN processes this multi-domain tensor and the original RGB image is encoded with a pretrained ViT-B/16 spatial branch. The two streams are combined through the bidirectional cross-attention which allows the model to localize both spatial and spectral manipulation artifacts. We also present an adversarial cleaning simulation pipeline which partitions the training process with five post-processing attack methods, namely GFPGAN neural face restoration, learned autoencoder cleaning, etc., to increase resistance to real-world forensic defenses. Tests of FaceForensics++ C23 (7926 images, consisting of four manipulation types) show that FAViT attains F1-score of 86.22, AUC-ROC of 94.26 and accuracy of 85.55 on the held-out test set. The strength analysis of 21 attack conditions shows that the max degradation in AUC is 30.3, with specific strengths in GFPGAN restoration (AUC = 98.51). Robustness is evaluated based on 21 post-processing attack cases that include JPEG compression, Gaussian blurring, down-sampling, and GFDGAN neural-based restoration; it should be noted that robustness against gradient-based adaptive attacks requires additional attention. Testing on the CIFAKE and Celeb-DF v2 datasets reveals some limitations of domain generalization. Full article