An integrated three-stage methodology is proposed for the predictive assessment and proactive maintenance of stacker bridges.A three-dimensional finite element analysis (FEA) is performed to characterize the displacement and von Mises stress fields, identifying critical nodes using a kinematic deformation scale factor of 908.2x. A fractal mapping of cracks in the critical planeis developed, estimating the fractal dimension Dfof the crack trajectories using the box-counting method, which allows for the quantification of nonlinear propagation potential beyond linear elastic fracture mechanics (LEFM). Finally, a Graph Neural Network (GNN) is designed and trained that models the structural mesh as a graph G = (V, E, Φ), where the nodes represent the physical connections and the edges represent the structural beams, enabling the iterative prediction of nodal failure probabilities and the projection of remaining useful life (RUL). Results reveal a Structural Health Index (SHI) of 52.9%, identify node 253 as having the highest failure probability (57.7%), and project imminent systemic failure risk within a 3–6 month horizon. A 60-day intervention plan is formulated, including brace replacement, controlled 30-minute post-weld heat treatment (PWHT), and 100% liquid penetrant non-destructive examination (NDE), achieving a fatigue life improvement factor of 4x relative to the as-welded condition.
The devil fish (Loricariidaefamily) is an invasive species that has spread across water bodies in several countries, where, due to its morphological, physiological, and behavioral characteristics, it has caused significant economic, social, and environmental impacts. In response to this problem, it is necessary to prevent its dispersion and to establish management, control, and eradication strategies through alternative uses of this species. In this study, the effectiveness of a bone char synthesized from devil fish bones and chemically modified with NaNO3was evaluated for Cd(II) removal from aqueous solution at pH 7 and 25 °C using batch experiments. The modified bone char exhibited a maximum adsorption capacity of 46.9 mg g-1. These results demonstrate the potential use of devil fish bones as an alternative strategy to mitigate the negative effects associated with this invasive species.
Nanoparticlesbased on Fe, Ni and Pd (Fe_100, Ni_100, Pd_100, PdFe_5050 y PdNi_5050) were synthesized via chemical reduction with NaBH4 and their catalytic activity was evaluated in the hydrodehalogenation (HDH)of diclofenac in aqueous phase in batch experiments. The results demonstrated the higher catalytic activity of Pd-based systems. Monometallic Ni_100 and Fe_100 showed low catalytic activity. In Fe_100 system, the degradation followed an oxidative pathway dependent on dissolved oxygen, in addition to significant metal leaching due to Fe0oxidation.Conversely, although PdFe_5050 proves to be highly active, the high Fe leaching limited its long-term viability. In contrast, both Pd_100 and PdNi_5050 nanoparticlesachieved complete diclofenac (DFC)dechlorination within 60 min, effectively suppressing the accumulation of chlorinated intermediates. The high efficiency of PdNi_5050 was attributed to the surface enrichment of Pd within the bimetallic system. This synergy allowed PdNi_5050 to match the activity of Pd_100, despite the lower content of the precious metal.Finally, PdNi_5050 proved to be a promising alternative, maintaining high catalytic activity and stability over 10 consecutive cycles. This, coupledwith the significant reduction in the associated material costs, positions PdNi_5050 as a promising option for HDH process.
In this study, the synthesis of iron nanoparticles was optimized using an aqueous extract of Eucalyptus grandis foliage, which was used together with iron (II) chloride tetrahydrate salt and iron (III) chloride hexahydrate salt using water as a solvent, inaddition to a basic sodium hydroxide solution. The nanoparticles were precipitated, filtered, and dried, achieving a yield of 98.99%. The synthesized nanoparticles exhibited a specific surface area of 131.90 m²/g. Their functional groups were analyzed using Fourier transform infrared spectroscopy (FTIR), and particle size was determined by transmission electron microscopy (TEM). The behavior of the synthesized nanoparticles during Hg(II) retention was evaluated and assessed. The adsorption isotherm fit the Freundlich model, typical of a heterogeneous adsorption model, with a maximum adsorption capacity for Hg(II) of 274.92 mg Hg/g of nanoparticle. The particle exhibited a good retention percentage for mercury (79.26%), and this synthesized particle reduces the need for reagents in its preparation, generates no polluting waste, and requires low energy input.
Deoxycholic acid (DCA) modified with chitosan in ionic aqueous solution created amphiphilic polymers that self-assembled. FTIR-ATR spectroscopy allowed the study of its spatial and chemical specificity by identifying C-H, O-H, and N-H vibrations in the spectral range from (2700 cm⁻¹ to 3700 cm⁻¹). The OH stretching band of water was resolved by deconvolution and Gaussian curve fitting, classifying the type of strong hydrogen bonds (~3200-3450 cm⁻¹), weak bonds, or free OH (~3550-3650 cm⁻¹). their distribution allowed us to propose 3D aggregate structures with AI-assisted modeling (MAIA), which coexist with each other through hydrophobic and van der Waals forces, governed by predictable thermodynamic principles and the influence of the synthesis method. Theirthermal stability was determined in a range of 20°C-60°C, observing structural rearrangements that formed elastic hydrogels, which changed to a rigid arrangement generating electrostatic cooperativity when the temperature increased. These studies providedinput for their use as host systems for hydrophobic molecules and other medical applications.
Granulometry is essential for understanding geological granular flows, sediment transport, and deposition processes. Traditionally, sieving has been the standard particle-size analysis method; however, it presents limitations in accuracy and field applicability, particularly in inaccessible environments. This perspective article reviews emerging technologies for granulometric analysis in geosciences, covering studies published between 1967 and 2025 from Web of Science and Scopus. Over the last three decades, digital image analysis, Structure-from-Motion (SfM) photogrammetry, and Light Detection and Ranging (LiDAR) have enabled remote, high-resolution particle characterization. More recently, artificial intelligence and deep learning have accelerated this progress. Applications based on Convolutional Neural Networks (CNNs), transformer architectures, Graph Neural Networks (GNNs), and LiDAR-derived 3D point clouds are discussed. A comparative analysis of methods is presented considering spatial resolution, accuracy, particle-size range, processing time, cost, and Technology Readiness Level (TRL). Results indicate that hybrid approaches generally outperform single-method solutions. Future developments point toward hybrid systems, Bayesian uncertainty quantification, and autonomous field operations supported by edge computing.
A one-dimensional model is presented to represent nocturnal vertical thermal diffusion in the marine atmospheric boundary layer and diagnose operationally relevant temperature inversions. The objective was to evaluate the system response under four synthetic air-sea thermal contrastsand wind scenarios using a parabolic diffusion equation with stability-dependent turbulent diffusivity, a sensible-heat-flux lower boundary condition, and constant radiative cooling. The problem was solved with a Crank-Nicolson scheme in a 600 m column over 6 h. Results show that colder-sea and weaker-wind cases develop stronger inversions, whereas the control case with a warmer sea surface does not exceed the diagnostic threshold. The maximum integrated intensity was obtained in E3. It is concluded that the scheme consistently reproduces the transition between regimes with and without inversion and provides a traceable basis for future validation.
This study analyzes the implementation of Virtual Learning Objects (VLO) in the Materials Chemistry course for first-year students in the Construction Engineering program. The objective was to evaluate their impact on academic performance and student perception. A quasi-experimental design was used, comparing cohorts from the 2023 and 2024 academic years (traditional methodology) with the 2025 cohort (use of VLOs). The data were analyzed using normality tests (Shapiro-Wilk) and nonparametric tests for groupcomparison. The results show that there are no statistically significant differences in academic performance among the cohorts analyzed (p > 0.05). However, moderate acceptance of the tool by students was observed, associated with the level of use of the platform. It is concluded that the implementation of VLO, under the conditions studied, does not significantly impact academic performance, although it shows potential as a complementary tool in the teaching-learning process.
The design, construction, and performance evaluation of a laboratory-scale batch fluidized bed dryer for polypropylene particles is presented. Wet polypropylene particles obtained by water-assisted milling require controlled drying to avoid defects during subsequent processing. The proposed system is a simple and cost-effective configuration capable of operating under conditions that prevent polymer melting or thermal degradation. A heat transfer-based model was developed to describe the drying process, incorporating operating variables and dryer characteristics. A correlation for drying time per unit mass of dry polymer was obtained as a function of air properties, system geometry, and thermal driving force. Experimental tests under different operating conditions allowed identification of optimal temperature and air velocity to ensure efficient moisture removal while preserving polymer properties. Results demonstrate that the developed dryer achieves uniform drying and target moisture content within operational constraints, providing a practical framework for the analysis of fluidized bed drying systems at laboratory scale.
In this work, a magnetic composite of strontium hexaferrite(SrFe12O19) and cobalt ferrite (CoFe2O4)was synthesized using the polymer complex (Pechini) method to remove chromium VI from contaminated water. The structure, morphology, and chemical characterization were determined by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Active sites were measured using the Boehm titration method, and the isoelectric point (IEP) was determined using zeta potential. Adsorption experiments were carriedout at pH 2 and 25 °C, with Cr (VI) concentrations ranging from 10 to 100 mg/L. The results show that the magnetic composite has a particle size of 251 nmand a mixing ratio of 1:1 betweenstrontium hexaferrite and cobalt ferrite. The maximum adsorption capacity for chromium (VI) was 19.72 mg/g, while the Langmuir model predicts a theoretical adsorption capacity of 69.29 mg/g.Due to its high adsorption capacity, excellent magnetic properties that ease material recovery, and low production costs, this material presents a promising option for removing chromium (VI) from contaminated water.
The devil fish (Loricariidae family) is an invasive species that has spread across water bodies in several countries, where, due to its morphological, physiological, and behavioral characteristics, it has caused significant economic, social, and environmental impacts. In response to this problem, it is necessary to prevent its dispersion and to establish management, control, and eradication strategies through alternative uses of this species. In this study, the effectiveness of a bone char synthesized from devil fish bones and chemically modified with NaNO3 was evaluated for Cd(II) removal from aqueous solution at pH 7 and 25 degrees C using batch experiments. The modified bone char exhibited a maximum adsorption capacity of 46.9 mg g(-1). These results demonstrate the potential use of devil fish bones as an alternative strategy to mitigate the negative effects associated with this invasive species.
In this study, the synthesis of iron nanoparticles was optimized using an aqueous extract of Eucalyptus grandis foliage, which was used together with iron (II) chloride tetrahydrate salt and iron (III) chloride hexahydrate salt using water as a solvent, in addition to a basic sodium hydroxide solution. The nanoparticles were precipitated, filtered, and dried, achieving a yield of 98.99%. The synthesized nanoparticles exhibited a specific surface area of 131.90 m2/g. Their functional groups were analyzed using Fourier transform infrared spectroscopy (FTIR), and particle size was determined by transmission electron microscopy (TEM). The behavior of the synthesized nanoparticles during Hg(II) retention was evaluated and assessed. The adsorption isotherm fit the Freundlich model, typical of a heterogeneous adsorption model, with a maximum adsorption capacity for Hg(II) of 274.92 mg Hg/g of nanoparticle. The particle exhibited a good retention percentage for mercury (79.26%), and this synthesized particle reduces the need for reagents in its preparation, generates no polluting waste, and requires low energy input.
An integrated three-stage methodology is proposed for the predictive assessment and proactive maintenance of stacker bridges. A three-dimensional finite element analysis (FEA) is performed to characterize the displacement and von Mises stress fields, identifying critical nodes using a kinematic deformation scale factor of 908.2x. Afractal mapping of cracks in the critical plane is developed, estimating the fractal dimension D(f )of the crack trajectories using the box-counting method, which allows for the quantification of nonlinear propagation potential beyond linear elastic fracture mechanics (LEFM). Finally, a Graph Neural Network (GNN) is designed and trained that models the structural mesh as a graph G = (V, E, Phi), where the nodes represent the physical connections and the edges represent the structural beams, enabling the iterative prediction of nodal failure probabilities and the projection of remaining useful life (RUL). Results reveal a Structural Health Index (SHI) of 52.9%, identify node 253 as having the highest failure probability (57.7%), and project imminent systemic failure risk within a 3-6 month horizon. A 60-day intervention plan is formulated, including brace replacement, controlled 30-minute post-weld heat treatment (PWHT), and 100% liquid penetrant non-destructive examination (NDE), achieving a fatigue life improvement factor of 4x relative to the as-welded condition.
In this work we analyze the lime saturation module for different clay settings of raw material after raw grinding process by means of a multiwavelength Raman spectroscopy study, at excitation wavelengths of 450, 532, 633 and 780 nm. The data reveal that independently of the wavelength used, the results of the Raman shifts can be used to calculate the setpoint for controlling the alumina in the raw mix that needs to be adjusted to have the lime saturation within quality parameters, with a minimum variation compared with other techniques such as XRF and XRD, thus ensuring that the Clinker phases are formed under quality standards. The intensity ratios of Raman spectra were used to calculate LS values which were close to those obtained with the XRF technique. Therefore, this measurement approach can be used successfully as an effective and alternative non-destructive method to determine the LS of cement raw mix.
Nanoparticles based on Fe, Ni and Pd (Fe_100, Ni_100, Pd_100, PdFe_5050 y PdNi_5050) were synthesized via chemical reduction with NaBH4 and their catalytic activity was evaluated in the hydrodehalogenation (HDH) of diclofenac in aqueous phase in batch experiments. The results demonstrated the higher catalytic activity of Pd-based systems. Monometallic Ni_100 and Fe_100 showed low catalytic activity. In Fe_100 system, the degradation followed an oxidative pathway dependent on dissolved oxygen, in addition to significant metal leaching due to Fe-0 oxidation. Conversely, although PdFe_5050 proves to be highly active, the high Fe leaching limited its long-term viability. In contrast, both Pd_100 and PdNi_5050 nanoparticles achieved complete diclofenac (DFC) dechlorination within 60 min, effectively suppressing the accumulation of chlorinated intermediates. The high efficiency of PdNi_5050 was attributed to the surface enrichment of Pd within the bimetallic system. This synergy allowed PdNi_5050 to match the activity of Pd_100, despite the lower content of the precious metal. Finally, PdNi_5050 proved to be a promising alternative, maintaining high catalytic activity and stability over 10 consecutive cycles. This, coupled with the significant reduction in the associated material costs, positions PdNi_5050 as a promising option for HDH process.
This thesis addresses the conceptual design of a hybrid methodology for the early detection of mechanical failures in the transmission system of a mining conveyor belt. The main objective is to mitigate unscheduled shutdowns and the high operating costs associated. The methodology integrates the analysis of two data sources: vibration and temperature. Through signal preprocessing, the Fast Fourier Transform (FFT) is applied for spectral analysis, and data fusion is performed by extracting the Root Mean Square (RMS) value per frequency band and aligning it with the thermal data. Subsequently, a segmented analysis of the progression of these features is implemented to establish rigorous and quantitative alarm thresholds. These criteria allow for monitoring instability and vibratory energy over time, identifying the incipient state of critical failures (bearings, misalignment). This approach lays the groundwork for a robust predictive system, contributing to maintenance optimization within the context of Mining 4.0.
This work investigates the capacity of a regional commercial natural dolomite to remove Cd²⁺from aqueous solutions. The material was characterized by chemical analysis, particle size distribution, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and thermal analysis (DTA–TG). Batch adsorption experiments were carried out to evaluate the effect of the initial cadmium concentration(initial concentration of cadmium 2-100 mg/l, contact time 180 min, 2 g/l of adsorbent, room temperature, solution volume 100 ml), and the experimental data were fitted using Langmuir and Freundlich isotherm models. The results show high removal efficiency(90-99%), with adsorption capacity increasing as the initial metal concentration rises. Both isotherm models adequately describe experimentalbehavior(Qmof 241.70 mg/g). Post-adsorption characterization reveals changes in FTIR bands and thermal behavior. These results indicate that Cd²⁺removal is mainly associated with surface processes. The study highlights the potential of this regional dolomite as a low-cost adsorbent for environmental remediation applications.
This studyproposesan advancedpredictive frameworkbased onhybrid machinelearningarchitectures tomodelthe non-linearrelationshipbetweenthermomechanicalparametersandfailureprobability.Throughrigorousanalysisof heterogeneousexperimentaldata,state-of-the-artensemblemodelswereevaluatedandoptimized,includingStacking,WeightedVoting,andaSuper-Ensemble.Resultsvalidatethesuperiorityofhybridarchitectures,achievinga classificationaccuracyof86.4%andanAreaUndertheROCCurve(AUC)of0.93,outperformingconventionalbase estimators.FeatureimportanceanalysisviaMeanDecreaseinImpuritycorroboratedthephysicalphenomenology oftheprocess,identifyingrotationalandweldingspeedsasthegoverningfactorsofplasticflow(71%ofexplained variance).This work not only demonstrates the feasibility of in-silico defect detection but also establishes the algorithmicfoundationsforthedevelopmentofDigitalTwinsandadaptivecontrolsystemswithintheIndustry4.0 context
Marginal microleakage in dental restorations compromises their durability. Some of the factors contributing to its occurrence include: the polymerization shrinkage of the restorative resin, its mechanical properties (modulus of elasticity), the cavity design (varying restoration depths), and occlusal loads (their magnitude and application method). The aim of this work is to analyze, through the Finite Element Method, the biomechanical behavior of a molar with a Class I cavity restoration in order to evaluate the influence of these factors on the stresses developed within it. A sequential methodology is employed, allowing for the analysis of each variable's influence and aiding the interpretation of the results. The maximum principal stresses are obtained foreach case, and a failure criterion is applied. The results indicate that polymerization shrinkage is the primary factor leading to material failure.
Natural and purified samples of an Argentine bentonite (BG) were investigated as adsorbent materials for atrazine. The adsorption processes were analyzed through the application of kinetic models, with the purpose of predicting characteristic parameters and determining adsorption capacities. The quality of the model fitting was evaluated using the linear correlation coefficient (R²). The kinetic parameters obtained revealed that bentonite exhibits a high adsorption capacity for atrazine, which increases both with decreasing temperature and with the higher content of the clay fraction. This behavior confirms that purification of the material enhances its performance by increasing the availability of active sites and reducing the interference of impurities. Overall, these results highlight the potential of BG bentonite and its purified fraction as effective adsorbents in remediation processes, offering a sustainable alternative to address the environmental issues associated with pesticide use.
