Variable Geometry Research Articles

The role of volunteer guardians as a key actor in providing social protection for unaccompanied minors in Italy

AbstractThis article analyses the impact of the volunteer guardian role, as stipulated in Italy's Law no. 47/2017, on the reception of unaccompanied minors. The research, spanning 50 months from March 2018 to May 2019, focused on the metropolitan area of Rome. A combination of methodological tools, including participant observation, questionnaires, qualitative interviews, and focus groups, was employed to ensure comprehensive data collection. The research findings delineate the responsibilities of volunteer guardians, encompassing legal representation, educational support, and networking within the established system. Furthermore, the results integrate the perspectives of social workers and minors regarding the guardian's evolving role and elucidate the nature of relationships formed between these stakeholders. The involvement of multiple decision‐makers in the child's life, including voluntary guardians, social workers, foster care professionals, and juvenile judges, results in a diverse allocation of responsibilities and roles. These are continuously negotiated as part of a shared responsibility with a variable geometry.

A scalable framework for learning the geometry-dependent solution operators of partial differential equations.

Solving partial differential equations (PDEs) using numerical methods is a ubiquitous task in engineering and medicine. However, the computational costs can be prohibitively high when many-query evaluations of PDE solutions on multiple geometries are needed. Here we aim to address this challenge by introducing Diffeomorphic Mapping Operator Learning (DIMON), a generic artificial intelligence framework that learns geometry-dependent solution operators of different types of PDE on a variety of geometries. We present several examples to demonstrate the performance, efficiency and scalability of the framework in learning both static and time-dependent PDEs on parameterized and non-parameterized domains; these include solving the Laplace equations, reaction-diffusion equations and a system of multiscale PDEs that characterize the electrical propagation on thousands of personalized heart digital twins. DIMON can reduce the computational costs of solution approximations on multiple geometries from hours to seconds with substantially less computational resources.

Investigating the influence of plate geometry and detonation variations on structural responses under explosion loading: A nonlinear finite-element analysis with sensitivity analysis

Abstract This study presents the results of a numerical analysis of the response of Domex 700 and Dormex 1100 steel plates with varying geometries, combined with several different parameters such as a thickness of up to 6 mm and a trinitrotoluene (TNT) mass. Using ABAQUS software, finite-element analysis was run to examine the structural response ability of the steel plates to explosions. In terms of deformation and energy dissipation, the results showed a large differentiation. A sensitivity analysis was used to examine each simulation result in terms of the structural response performance to explosions and identify the variables that had the greatest impact on variations in the thickness, material, geometry, and TNT mass. The best numerical simulation results were found using the multi-attribute decision-making (MADM) approach. Annotations are utilized to assist in identifying the various modifications made during testing. Annotations LXXI to LVIII achieved the lowest value of 6.176 × 10−09, signifying the best results according to calculations made using the MADM approach. This is evidenced by the structural events, demonstrating that the deformation, von Mises stress, and energy dissipation in the circular plate structure were not significantly impacted by the explosion. The variables that most significantly affect variations in deformation, von Mises stress, and dissipated energy – variables that significantly impact the structural response to explosions – are identified through the sensitivity analysis approach. The results of this research can be used to optimize the structural response performance of circular plates.

Mathematical model of solute transport in channels with variable geometry and permeable walls

This paper investigates solute transport dynamics within a steady, viscous, Newtonian fluid flow through channels of varying geometry (convergent–divergent boundaries) and permeable walls. The significant impact of osmotic pressure on solute concentration is particularly highlighted by modeling osmotic pressure as a cubic function of solute concentration. Approximate solutions to the Navier–Stokes equations and solute transport equations, along with the corresponding boundary conditions, have been derived. The effects of varying geometry and various emerging parameters on hydrostatic and osmotic pressures, solute concentration, and solute clearance are illustrated through graphs. Results indicate a linear decline in hydrostatic pressure and a nonlinear rise in osmotic pressure along the channel length, with divergent channels producing higher solute and wall concentrations than uniform or convergent channels. Increased ultrafiltration and absence of osmotic pressure yield greater wall concentrations. Solute clearance rates increase with the wall slope, transmittance coefficient, permeability, and ultrafiltration parameters, underscoring intricate parameter relationships within filtration. A dataset is used to visually present and analyze the influence of different physiological factors on solute concentration, with graphs corresponding to real physiological conditions. For channels with the uniform width, the findings closely align with previous results, demonstrating its applicability to solute exchange in glomerular capillaries as well as to other blood vessels or capillaries.

Variation in the cross-sectional geometry of hot-rolled I-sections: Measurements, statistics, probabilistic modelling

This paper presents the evaluation of an extensive measurement campaign on the cross-sectional dimensions of hot-rolled I-sections. As a preliminary step, the reliability of the measuring process was quantified as well as checked to what extent the cross-sectional dimensions remain constant along a beam. In the main part of the investigation, a total of 561 statistically independent I-sections with a wide range of different sizes were surveyed and statistically analysed. Overall, the observed variation of the dimensions revealed a good agreement with the characterisation in the new Annex E of EN 1993-1-1. However, a more detailed evaluation showed that there is a certain dependence of these statistical parameters on the nominal size of the dimensions. Furthermore, several other aspects were checked, such as the correlation between the individual dimensions, the compliance with manufacturing tolerances according to EN 10034 and the uniformity of the web and flange thickness at various positions within the cross-section. Subsequently, the measured profiles were also analysed based on the resulting cross-sectional properties. In addition to a statistical evaluation in respect to the corresponding nominal values, the influence of the common shape idealisation to a perfectly double-symmetrical cross-section with constant flange thicknesses was addressed. Finally, a comparison of several options for the probabilistic modelling of the geometric variability was carried out, based on the lower 5%-quantiles of the resulting cross-sectional properties.

Modeling Microbial Inactivation by High-Pressure Homogenization with a Machine Learning Approach

This study leverages machine learning to create advanced predictive models for microbial inactivation during high-pressure homogenization (HPH). Unlike conventional models, which often focus solely on operating conditions, these models integrate additional factors, such as homogenizer-specific hydrodynamics, liquid media properties, and microorganism-specific characteristics. These factors are typically omitted in conventional models due to their wide variability across studies and the challenge of transforming them into a limited set of quantifiable variables. For instance, the influence of variations in homogenization valve geometry or changes in fluid viscosity are rarely incorporated, despite their significant impact on HPH outcomes. Through a comprehensive meta-analysis of literature data and the incorporation of dimensionless number to cluster diverse independent operating variables, various models, including artificial neural network (ANN) and random forest (RF), are trained and tested. While RF models exhibit faster runtimes without sacrificing performance compared to neural networks, a hybrid model was also devised to enhance prediction accuracy. This hybrid approach integrates RFs with the empirical Weibull model, linking microbial inactivation with applied pressure and the number of HPH passes. Notably, the hybrid model outperforms others, aligning well with expected inactivation trends. Challenges persist, such as the need for additional data and the inclusion of more relevant variables, underscoring the study's significance in advancing our comprehension of HPH's impact on microbial inactivation, thereby bolstering food safety and prolonging shelf-life.

Advancing graph neural network architecture for fluid flow and heat transfer surrogate modeling: Variable boundary conditions and geometry

Graph neural networks (GNNs) represent a promising instrument for surrogate modeling, capable of handling unstructured computational meshes naturally. We address a typical issue of the accuracy degradation for larger computational domains due to the limited receptive field of GNN models and long-range global interactions between nodes of the mesh. We propose a modification of the GNN architecture allowing to improve the accuracy by a factor of 3 without significant increase in computational costs. The validation tests of the model concentrate on the two-dimensional stationary fluid flow around a bluff body in a channel and corresponding heat transfer. The problem formulation includes bluff bodies of randomly generated shapes and various boundary conditions. The model shows a robust performance for the out-of-domain data, i.e., the flow over an airfoil for different angles of attack.

Numerical investigation on failure modes and TDA-based mitigation measure of jointed rigid pipes under ground subsidence

Jointed rigid pipes are vulnerable to permanent ground deformation (PGD). Compressible and lightweight materials, such as tire-derived aggregate (TDA), geofoams and straws, can be placed around buried pipes to reduce the loads from PGD and minimize the risk of pipe failures. In this study, a calibrated three-dimensional finite element model incorporating a modified Mohr-Coulomb model for simulating the strain softening behavior of surrounding soils is employed to analyze the failure modes of jointed rigid pipes under ground subsidence with various crossing scenarios, where the fault plane intersects different positions of the pipeline. Then, the efficiency of TDA mitigation in reducing the risk of pipe failure is evaluated, considering variations in geometry, size and density of TDA zone. It is found that the most detrimental fault-pipe crossing position locates at 3/4L of the pipe barrel, where excessive bending moment can lead to longitudinal cracking. TDA mitigation mainly prevents structural failure of pipelines, while its influence on the kinematics of joints is negligible. The most pronounced loading reduction for pipelines is achieved by employing TDA with a lower degree of compaction, combined with the burial configuration of full-surrounded layout pattern of TDA.

Smoke Line Geometry Impact on Airfoil Model Flow Visualization

This study discusses the analysis of airflow visualization on a NACA 4412 airfoil model using a fluid mixture technique with variations in smoke line geometry. The main objectives of this study are to understand the behavior of airflow around the airfoil surface under different test conditions and methodology used involves flow simulation and experimental tests with fluid mixtures that produce flow visualization through various smoke line geometries, such as straight lines, zigzags, and curves. The test results show that variations in the shape of the smoke line produce different flow at the boundaries of the airfoil surface, which significantly affect the pressure distribution and lift coefficient. These findings provide important insights for the development of more efficient airfoil designs and the application of flow visualization techniques in aerodynamic testing.

Analysis of the Molecular Structure of Lipase-Dependent Chaperone from Ralstonia pickettii Strain BK6

Several biotechnology industries are exploring the characteristics of lipase-dependent chaperones due to their distinctive biochemical traits. This study aimed to employ bioinformatics to analyze the molecular structure of Ralstonia picketii BK6's lipase-dependent chaperon (LipRM). The sequence mapping and amino acid distribution were examined using BioEdit (version 7.0.9.1). SignalP 5.0 and Interpro are employed for signal peptide detection, whereas Swiss-Model and VMD 1.9.2 are used for molecular dynamics modelling. The results showed that the Shine-Dalgarno sequence was discovered in the LipRM promoter, seven nucleotides upstream of the initiation codon (AUG) with the 5'-AGGAGA-3', and has a terminator region that facilitates the formation of a secondary structure. The protein's 3D structure prediction results indicate differences in the alpha helix chains (residues 166-174 and 254-271) between LipRM and the reference lipase. LipRM's molecular structure comprises a detachable signal peptide, and with variations in helix alpha chain conformation and ligand geometry.

Rational Molecular Design of Indeno[1,2-b]quinoxaline-Based Passivators with Multifunctional Groups to Effectively Retard Recombination and Enhance Voc in Perovskite Solar Cells.

Three indeno[1,2-b]quinoxaline-based passivators (CQs) with different functionalities including ketone, malononitrile, nitrile, and amine were prepared and used as passivators in perovskite solar cells (PSCs). All of them exhibit good thermal stability, low cost, and ease of preparation. The variation in molecular geometries, in planar and spiro-shaped designs with appropriate functional groups, highlights a comparison between their passivation properities. The CQs can effectively passivate defects in the perovskite layer associated with unsaturated Pb2+ coordination. This leads to enhanced perovskite grain size, improved short-circuit current (Jsc), and reduced recombination, thereby increasing the open-circuit voltage (Voc) values of PSCs. The use of spiro-shaped CQ-Spiro as a passivator results in optimal performance, with Jsc of 24.72 mA∙cm-2, Voc of 1.14 V, fill factor of 0.76, and a corresponding overall conversion efficiency of 21.42% (compared to the control device with Spiro-OMeTAD, which has an efficiency of 18.1%). After 264 hours operation, the device can maintain 89.6% of original efficiency. It is demonstrated that spiro-shaped derivatives of CQ-Spiro containing multifunctional groups with Lewis base properties can provide another promising choice for passivators in PSCs.

Global evaluation of NOAA-20 VIIRS dark target aerosol products over land and ocean

The Dark Target (DT) algorithm has been applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi-NPP (SNPP) and NOAA-20 satellites to extend the aerosol data record from the Moderate Resolution Imaging Spectrometer (MODIS). The newly released VIIRS DT Version 2.0 (V2.0) dataset includes NOAA-20 aerosol products for the first time. This study provides the first evaluation of NOAA-20 VIIRS DT aerosol products, including aerosol optical depth (AOD) and Ångstrom exponent (AE), for the period 2018–2022 using Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) measurements. In addition, the operational DT aerosol products from SNPP VIIRS (V2.0) and MODIS (Collection 6.1) are also used for comparison purposes. Overall, NOAA-20 AODs over land and ocean exhibit good validation metrics globally, showing comparability to MODIS products and superiority over overestimated SNPP products. Nevertheless, it is worth noting that in South Africa specifically, NOAA-20 land AODs tends to display more pronounced negative biases, despite all products being underestimated. Furthermore, all ocean AE products demonstrate high correlation coefficients (>0.83) with the ground-based data, meeting the fraction of expected accuracy (>80%). Encouragingly, NOAA-20 AE product has significantly improved the persistent issue of overestimation at low-value in MODIS product, making it a more preferable choice for usage. Error analysis reveals that the performance of all three land products decreases in sparsely vegetated areas and when the solar zenith angle is small. However, NOAA-20 AOD exhibits relatively stable performance and is less affected by variations in aerosol loading, observation geometry, and surface vegetation cover. In addition, the performance of AOD and AE over ocean is significantly influenced by scattering angle and wind speed. This research is anticipated to serve as a reference for the utilization of operational VIIRS DT aerosol products and possible algorithm optimization.

Physics-Informed Geometry-Aware Neural Operator

Engineering design problems often involve solving parametric Partial Differential Equations (PDEs) under variable PDE parameters and domain geometry. Recently, neural operators have shown promise in learning PDE operators and quickly predicting the PDE solutions. However, training these neural operators typically requires large datasets, the acquisition of which can be prohibitively expensive. To overcome this, physics-informed training offers an alternative way of building neural operators, eliminating the high computational costs associated with Finite Element generation of training data. Nevertheless, current physics-informed neural operators struggle with limitations, either in handling varying domain geometries or varying PDE parameters. In this research, we introduce a novel method, the Physics-Informed Geometry-Aware Neural Operator (PI-GANO), designed to simultaneously generalize across both PDE parameters and domain geometries. We adopt a geometry encoder to capture the domain geometry features, and design a novel pipeline to integrate this component within the existing Deep Compositional Operator Network architecture. Numerical results demonstrate the accuracy and efficiency of the proposed method. All the codes and data related to this work are available on GitHub: https://github.com/uq-group/codes/tree/main/PI-GANO.

Hierarchically Structured and Tunable Hydrogel Patches: Design, Characterization, and Application.

Recent studies show the importance of hydrogel geometry for various applications, such as encoding, micromachines, or tissue engineering. However, fabricating hydrogel structures with micrometer-sized features, advanced geometry, and precise control of porosity remains challenging. This work presents hierarchically structured hydrogels, so-called hydrogel patches, with internally deviating regions on a micron-scale. These regions are defined in a one-step, high-throughput fabrication process via stop-flow lithography. Between the specified projection pattern during fabrication, an interconnecting lower crosslinked and more porous hydrogel network forms, resulting in at least two degrees of crosslinking within the patches. A detailed investigation of patch formation is performed for two material systems and pattern variations, revealing basic principles for reliable patch formation. In addition to the two defined crosslinked regions, further regions are implemented in the patches by adapting the pattern accordingly. The variations in pattern geometry impact the mechanical characteristics of the hydrogel patches, which display pattern-dependent compression behavior due to predefined compression points. Cell culture on patches, as one possible application, reveals that the patch pattern determines the cell area of L929 mouse fibroblasts. These results introduce hierarchically structured hydrogel patches as a promising and versatile platform system with highcustomizability.

Free vibration characteristics of honeycomb sandwich cylindrical shells reinforced with graphene nanoplatelets/polymer coatings

This study conducts a comprehensive analysis of the free vibration characteristics of honeycomb sandwich cylindrical shells reinforced with graphene nanoplatelets (GNPs). The shells are modeled with a hexagonal honeycomb core, which not only provides a lightweight structure but also enhances the stiffness and dynamic stability. This core design is ideal for high-performance applications such as aerospace and light transport equipment. In addition, the incorporation of graphene nanoplatelet/polymer coatings further improves the structural stiffness, making these shells suitable for demanding environments where both strength and lightweight properties are critical. In the theoretical model, the refined trigonometric shear deformation theory (RTSDT) is applied to accurately represent both bending and shear deformations, capturing the complex behavior of honeycomb sandwich cylindrical shells without requiring shear correction factors. Various parameters, including axial and circumferential wave numbers, as well as geometry and material properties, are examined to assess their impact on natural frequencies. Comparisons with existing literature validate the theoretical models, demonstrating strong agreement with previous results. The analysis reveals that the fundamental natural frequency and other natural frequencies are significantly affected by the inclusion of GNPs and variations in sandwich cylindrical shell geometry. The findings underscore the effectiveness of the RTSDT in modeling these complex sandwich structures.

Precise wavelength alignment of second-harmonic generation in thin-film lithium niobate resonators.

Second-harmonic generation (SHG) plays a significant role in modern photonic technology. Integrated photonic resonators fabricated with thin-film lithium niobate can achieve ultrahigh efficiencies by combining small mode volumes with high material nonlinearity. Cavity-enhanced SHG requires accurate phase and frequency matching conditions, where fundamental and second-harmonic wavelengths are both on resonance. However, this double-resonance condition can typically be realized only at a fixed random wavelength due to the high sensitivity of photonic resonances to the device geometry and fabrication variations. Here, we propose a novel method that can achieve the double-resonance condition over a large wavelength range. We combine thermal-optic and electro-optic (EO) effects to realize the separate tuning of fundamental and second-harmonic resonances. We demonstrated that the optimum SHG efficiency can be maintained over a wavelength range that exceeds the limit achievable with only thermal tuning. With this flexible tuning capability, we further show the precise alignment of SHG wavelengths of two separate thin-film lithium niobate resonators without sacrificing efficiencies.

Robust generation of intrinsic C points with magneto-optical bound states in the continuum.

C points, circular polarization in momentum space, play crucial roles in chiral wave manipulations. However, conventional approaches of achieving intrinsic C points using photonic crystals with broken symmetries suffer from a low Q factor and high sensitivity to structural geometry, rendering them fragile and susceptible to perturbations and disorders. We report magneto-optical (MO) bound states in the continuum (BICs) with a symmetry-preserved planar photonic crystal. We achieve intrinsic C points at Γ point that are robust against variation in both structural geometry and external magnetic field. MO coupling between two modes induces Zeeman splitting, leading to MO BICs and quasi-BICs with circular eigenstates for high-Q chiral responses. Furthermore, switchable C point handedness and circular dichroism are enabled by reversing the magnetic field. These findings unveil BICs and quasi-BICs with circular eigenstates and on-demand control of C points, paving the way for advanced chiral wave manipulation with enhanced light-matter interaction.

Effect of cutting fluid supply conditions on tool loads during continuous and interrupted orthogonal cutting

In metalworking, cutting fluids play an important role by reducing heat and friction during machining, extending tool life, and improving surface finish. Although the positive effects of cutting fluid have been confirmed in many studies, the relevant cutting fluid parameters such as nozzle cross-section area and supply pressure, as well as their influence on the thermo-mechanical loads of the cutting tool, have been insufficiently investigated. This study investigates the effects of cutting fluid supply conditions on tool loads during continuous and interrupted orthogonal cutting processes. The research specifically addresses the impact of nozzle geometry and fluid jet orientation on the thermo-mechanical loads on the cutting tool, which have been underexplored in previous studies. A prototype tool holder, designed and additively manufactured for this purpose, allows for variations in nozzle geometry and jet orientation. Experiments were conducted under varying cutting parameters, nozzle geometries, and fluid pressures, with tool temperature being monitored through an embedded thermocouple. The results show that nozzle geometry significantly affects chip shape, which directly affects cooling efficiency and, consequently, tool temperature. The study also uses an inversely calibrated analytical model to analyze the tool temperature distribution, which shows that the highest temperatures occur in the tool-chip contact area, while temperatures outside this area decrease rapidly. In addition, the percentage of heat conducted into the tool decreases with increasing Péclet number, which is consistent in both continuous and interrupted cutting scenarios. These findings provide a deeper understanding of how cutting fluid nozzle design affects tool performance and establish a foundation for model-based temperature analysis in machining processes.

A novel linetype recognition algorithm based on track geometry detection data

Abstract The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm's potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

COMPARISON OF THE EFFECTS OF STANDARD NOZZLES AND EXTENSION TUBES ON THE EROSION OF PMMA USING PULSATING WATER JET TECHNOLOGY

The study of bone cement disintegration is important for advancing orthopedic and trauma surgery outcomes. Bone cement, commonly used in joint replacement procedures, plays a vital role in fixation implants. However, the long-term stability and integrity of bone cement are critical for the success of these procedures. This study focuses on the use of ultrasonic pulsating water jet technology for the selective removal of bone cement, aiming to provide a precise and efficient method for revision surgeries. The disintegration efficiency is measured in terms of depth, width and volume of the disintegrated bone cement as a result of variations in the nozzle geometry, supply pressure and traverse speed. Two different nozzle types, the standard nozzle insert and nozzle with an extension tube of 100 mm having a diameter of 0.3 mm, are used. Two supply pressure levels were taken as 10 and 20 MPa with five levels of traverse speeds as 0.5, 1, 1.5, 2 and 2.5 mm/s. The results showed an increased disintegration efficiency for all experimental conditions using an extension tube nozzle as compared to a standard nozzle (20 – 25% in terms of disintegration volume). Also, the disintegration efficiency increased with higher pressure level values (8.2 mm3 and 4.85 mm3 for p = 20 and 10 MPa, respectively) and lower traverse speed values. The results showed a promising direction in terms of the utilization of an ultrasonic pulsating jet with a modified nozzle type for higher bone cement disintegration efficiency.