Modeling Approach Research Articles

Joint Modelling of Longitudinal Measurements and Time-to-Event Outcomes With a Cure Fraction Using Functional Principal Component Analysis.

In studying the association between clinical measurements and time-to-event outcomes within a cure model, utilizing repeated observations rather than solely baseline values may lead to more accurate estimation. However, there are two main challenges in this context. First, longitudinal measurements are usually observed at discrete time points and second, for diseases that respond well to treatment, a high censoring proportion may occur by the end of the trial. In this article, we propose a joint modelling approach to simultaneously study the longitudinal observations and time-to-event outcome with an assumed cure fraction. We employ the functional principal components analysis (FPCA) to model the longitudinal data, offering flexibility by not assuming a specific form for the longitudinal curve. We used a Cox's proportional hazards mixture cure model to study the survival outcome. To investigate the longitudinal binary observations, we adopt a quasi-likelihood method which builds pseudo normal distribution for the binary data and use the E-M algorithm to estimate the parameters. The tuning parameters are selected using the Akaike information criterion. Our proposed method is evaluated through extensive simulation studies and applied to a clinical trial data to study the relationship between the longitudinal prostate specific antigen (PSA) measurements and overall survival in men with metastatic prostate cancer.

Optimizing electrical properties and efficiency of copper-doped CdS and CdTe solar cells through advanced ETL and HTL integration: a comprehensive experimental and numerical study

Abstract Copper is a commonly preferred dopant for cadmium telluride based solar cells. Even though it is widely used as it enhances the electrical properties, it has the tendency to diffuse into the CdTe layer as well as the CdS/CdTe junction interface which adversely affects the performance of CdTe solar cells. In this experimental study copper doping of buffer cadmium sulfide layer has been performed to analyse its effect on structural, electrical, and optical properties of CdS and CdTe layers. While from the X-ray diffraction analysis it was observed that there was reduction in peak intensities and crystallite sizes of both the CdS and CdTe layers with the increase in amount of copper dopant, from the electrical properties it was found that there were improvements in carrier concentration, mobility, and conductivity of both the layers. To mitigate the losses due to Cu doping, enhance the efficiency and stability of CdTe solar cells an extensive numerical modelling approach was undertaken to employ electron transport layers (ETL) and hole transport layers (HTL) to the copper-doped CdS/CdTe solar cells. We obtained optimum results with titanium dioxide and copper barium thiostannate as ETL and HTL respectively. Finally, CdTe-based solar cells were modelled integrating copper-doped CdS as the buffer layers, TiO2 as ETL and CBTS as HTL respectively. The obtained experimental values of Cu-doped CdS and CdTe layers were implemented into this model. This superstrate configuration yielded impressive output parameters: open circuit voltage of 1.07 V, short-circuit current density of 29.32 mA cm−2, fill factor of 85.08 %, and efficiency of 26.67 %.

S5: New insights from deep spectroscopic observations of the tidal tails of the globular clusters NGC 1261 and NGC 1904

As globular clusters (GCs) orbit the Milky Way, their stars are tidally stripped and form tidal tails that follow the orbit of the cluster around the Galaxy. The morphology of these tails is complex and shows correlations with the phase of orbit and the orbital angular velocity, especially for GCs on eccentric orbits. Here we focus on two GCs, NGC 1261 and NGC 1904, that were potentially accreted alongside Gaia-Enceladus and that have shown signatures of having, in addition to tidal tails, structures formed by distributions of extra-tidal stars that are misaligned with the general direction of the clusters' respective orbits. To provide an explanation for the formation of these structures, we made use of spectroscopic measurements from the Southern Stellar Stream Spectroscopic Survey ($S^5$) as well as proper motion measurements from $Gaia$'s third data release (DR3), and applied a Bayesian mixture modelling approach to isolate high-probability member stars. We recovered extra-tidal features surrounding each cluster matching findings from previous work. We then conducted N-body simulations and compared the expected spatial distribution and variation in the dynamical parameters along the orbit with those of our potential member sample. Furthermore, we used Dark Energy Camera (DECam) photometry to inspect the distribution of the member stars in the colour-magnitude diagram (CMD). We find that potential members agree reasonably with the N-body simulations, and that the majority follow a simple stellar population distribution in the CMD, which is characteristic of GCs. We link the extra-tidal features with their orbital properties and find that the presence of the tails agrees well with the theory of stellar stream formation through tidal disruption. In the case of NGC 1904, we clearly detect the tidal debris escaping the inner and outer Lagrange points, which are expected to be prominent when at or close to the apocentre of its orbit. Our analysis allows for further exploration of other GCs in the Milky Way that exhibit similar extra-tidal features.

Spatiotemporal modeling quantifies cellular contributions to uptake of Aspergillus fumigatus in the human lung

The human lung is confronted daily with thousands of microbial invaders reaching the lower respiratory tract. An efficient response by the resident type 1 and type 2 alveolar epithelial cells (AECs) and alveolar macrophages (AMs) cells during the early hours of innate immunity is a prerequisite to maintain a non-inflammatory state, but foremost to rapidly remove harmful substances. One such human-pathogenic invader is the opportunistic fungus Aspergillus fumigatus. If the spherical conidia are not cleared in time, they swell reaching approximately twice of their initial size and germinate to develop hyphae around six hours post-infection. This process of morphological change is crucial as it enables the pathogen to invade the alveolar epithelium and to reach the bloodstream, but also makes it conspicuous for the immune system. During this process, conidia are first in contact with AECs then with migrating AMs, both attempting to internalize and clear the fungus. However, the relative contribution of AMs and AECs to uptake of A. fumigatus remains an open question, especially the capabilities of the barely investigated type 1 AECs. In this study, we present a bottom-up modeling approach to incorporate experimental data on the dynamic increase of the conidial diameter and A. fumigatus uptake by AECs and AMs in a hybrid agent-based model (hABM) for the to-scale simulation of virtual infection scenarios in the human alveolus. By screening a wide range of parameters, we found that type 1 AECs, which cover approximately 95% of the alveolar surface, are likely to have a greater impact on uptake than type 2 AECs. Moreover, the majority of infection scenarios across the regime of tested parameters were cleared through uptake by AMs, whereas the contribution to conidial uptake by AECs was observed to be limited, indicating that their crucial support might mostly consist in mediating chemokine secretion for AM recruitment. Regardless, as the first host cell being confronted with A. fumigatus conidia, our results evidence the large potential impact of type 1 AECs antimicrobial activities, underlining the requirement of increasing experimental efforts on this alveolar constituent.

Chemical Inhibitors in Gas Hydrate Formation: A Review of Modelling Approaches

Gas hydrate inhibition using chemicals has been under continuous investigation, and several modelling studies have been published since its inception. Since it is not always feasible to conduct experimental research, it is especially crucial to forecast the conditions under which gas hydrates may form and dissociate in the presence of chemical inhibitors. As a result, a reliable forecasting tool is vital. This article provides an exhaustive review of various modelling methodologies in the context of gas hydrate chemical inhibition. The key aspects of empirical models, thermodynamic models, kinetic models, artificial intelligence-based models and quantum chemistry-based models are presented. Critical analysis of each modelling approach has been performed, highlighting strengths, limitations, and areas where further investigations are still crucial. Rapid progress has been made with respect to gas hydrate modelling approaches in the context of chemical inhibition; however, further research is still vital to bridge the gaps that have been identified in this review. Potential improvements to existing models have been proposed, particularly in terms of integrating experimental data and utilizing hybrid approaches, which could serve as valuable future directions for the field.

The Impact of Urban Green Space on Housing Value: A Combined Hedonic Price Analysis and Land Use Modeling Approach

Urban green space (UGS) is increasingly considered as a nature-based solution to mitigate negative climate change effects and improve the quality of living environments. Although hedonic price (HP) modeling studies have yielded valuable insights about the UGS influence on housing value, from an area-development perspective the effects of UGS depend critically on the spatial allocation of UGS, which has not received much attention in the literature. In this paper, we combine HP analysis and land-use modeling to optimize the spatial allocation and evaluate the area-wide impact of UGS on housing value. Using transaction data from two middle-sized cities in the Netherlands, we estimate HP models considering different types of UGS and different types of housing. We show how the results can be incorporated into a housing land-use model and how the land-use model can be used to identify the spatial allocation of UGS that maximizes an area-wide (net) housing value. The results suggest that spreading green areas in neighborhoods results in higher housing value than centralizing green in a large urban park. Among the three housing types, detached houses benefit most from the UGS spreading strategy. Moreover, allocating UGS as green buffers is an effective way to increase housing value.

(Не)стереотипи любові: «Світова річ» Олени Пчілки у XXI столітті

The article presents the peculiarities of constructing the image of love in Olena Pchilka's comedy “The World Thing” given the modernization of Ukrainian society at the end of the 19th century. Poetological, gender, and feminist-critical methods and a modelling approach were used to implement the research. Intelligence about a woman's place in society and marriage at the end of the 19th and the beginning of the 20th century proved to be helpful for the identification of artistic models of the image of love since at that time the vector of a girl's upbringing was strictly limited to her future social roles inherent in her gender. It was established that three models of the image of love are implemented in the play – traditional mesalliance, romanticized adultery, and modern marriage for love. The system of characters is quite heterogeneous since the author depicts protagonists with opposite views on relationships and marriage. The views traditional at that time are embodied in the more mature characters of Krasovska, Marya, Zaychevska, Pavluschenko, and Bogush. Marriage as a business contract and a woman as a commodity are common characteristics of “love” relationships rooted in the thinking of the older generation. While young people prefer marriage over love, this is exactly the position advocated by Holub and Sasha, in whom Olena Pchilka embodies the features of the modernized future. The basis of the first model of the image of love is the unrealized marriage of Pavlushchenko with Sasha, the motives of which lie in the preservation of the Krasovska family estate. Yakiv Stepanovych is a typical representative of a patriarchal society that considers his wife as property. Using a comic effect, the playwright portrays Pavluschenko's true character – an “educated” character with conservative views on marriage, from time to time diversifying the husband's speech with expressive mistakes and rude addresses to his chosen one. Romanticized adultery is the second artistic model of the image of love, which is realized through the relationship of Alexandra and Victor, in the image of which the features of a typical hero-lover, a seducer, with distinct features of a trickster, can be traced. The “innovative” views of Tamalii debunk the permanence of the institution of marriage, and provoke non-acceptance on the part of Alexandra, due to which their relationship is short-lived. The cornerstone of the third is the background and at the same time cross-cutting relations between Sasha and Golub, which represent a shift in emphasis in the value system – the leveling of the material and the elevation of the spiritual. The feeling of needing each other occurs only after a long separation and is accompanied by certain deviations in usual behavior. A particularly expressive artistic technique in the play is the identification of a wedding with death – the parallelism and representation of the ritual as a transition reinforce the absurdity of the outdated traditional arranged marriage, thus emphasizing the need to rethink the value system. We see the prospects of continuing studies of Olena Pchilka's work, in particular dramaturgy, in the possibility of including it in the context of early Ukrainian feminism.

Exploring Factors Influencing Continuance Intention of Pre-Service Teachers in Using Generative Artificial Intelligence

Generative Artificial Intelligence (GAI) holds significant potential to enhance pre-service teacher professional development. However, research has primarily focused on initial acceptance, neglecting post-acceptance behaviours, particularly the factors influencing continued GAI use among pre-service teachers. To address this research gap, this study extends an Expectation-Confirmation Model (ECM) to include information quality and AI self-efficacy as additional determinants. Using the partial least squares structural equation modelling (PLS-SEM) approach, we analysed data from 506 Chinese pre-service teachers. Findings reveal that information quality positively impacts perceived usefulness and expectation confirmation, both of which enhance use satisfaction. Together with AI self-efficacy, these elements emerged as key predictors of intention to continue using GAI, with perceived usefulness as the most direct factor. Contrary to the hypothesis, personal major did not moderate these relationships. This study contributes to a deeper understanding of the behaviours and motivations of pre-service teachers post-GAI adoption, offering new insights into the sustained development and integration of GAI in teacher education.

Integration of Neural Networks and First-Principles Model for Optimizing l-Lactide Branched Polymerization.

Addressing the growing demand for sustainable materials, this research paves the way for the efficient consumption and sustainable production of branched polylactide (PLA). A novel hybrid modeling approach combines first-principles (FP) model with artificial neural network (ANN) for ring-opening polymerization (ROP). The hybrid ANN, trained with FP model data, demonstrated optimal performance with a hidden layer of 20 neurons, achieving a root mean square error (RMSE) of 0.004 and a regression coefficient (R2) of 0.99. The hybrid model accurately predicted key polymer properties, including average molecular weights (Mn and Mw), polydispersity index (PDI), degree of branching (DB), monomer conversion, and polymerization time. Validation was performed on various branched PLA compositions (PLLH80, PLLH94, and PLLH97). Multiobjective optimization (MOO) using NSGA-II showed strong agreement between FP model and hybrid ANN across six case studies, highlighting their effectiveness in predicting polymerization outcomes.

Shifting phenology as a key driver of shelf zooplankton population variability

AbstractThe timing of biological events, known as phenology, plays a key role in shaping ecosystem dynamics, and climate change can significantly alter these timings. The Gulf of Maine on the Northeast U.S. Shelf is vulnerable to warming temperatures and other climate impacts, which could affect the distribution and production of plankton species sensitive to phenological shifts. In this study, we apply a novel data‐driven modeling approach to long‐term datasets to understand the population variability of Calanus finmarchicus, a lipid‐rich copepod that is fundamental to the Gulf of Maine food web. Our results reveal how phenology impacts the complex intermingling of top‐down and bottom‐up controls. We find that early initiation of the annual phytoplankton bloom prompts an early start to the reproductive season for populations of C. finmarchicus in the inner Gulf of Maine, resulting in high spring abundance. This spring condition appears to be conducive to enhanced predation pressure later in the season, consequently resulting in overall low C. finmarchicus abundance in the fall. These biologically controlled dynamics are less pronounced in the outer Gulf of Maine, where water exchanges near the boundary have a greater influence. Our analysis augments existing hypotheses in fisheries oceanography and classical ecological theory by considering unique plankton life‐history characteristics and shelf sea dynamics, offering new insights into the biological factors driving C. finmarchicus variability.

Temperature and plastic strain dependent Chaboche model for 316 L used in simulation of cold pilgering

Cold pilgering is a complex forming process used to produce seamless tubes, posing significant challenges in material modeling due to its non-proportional loading history and extensive accumulated plastic strain. In this study, a temperature- and plastic strain-dependent Chaboche model for 316 L stainless steel was developed and calibrated. To simulate the complex loading conditions, unique alternating compression-compression tests were conducted, and the model parameters were optimized accordingly. The calibrated model was integrated into a thermo-mechanical finite element simulation of the cold pilgering process, resulting in improved accuracy in predicting stress-strain responses and yield stress evolution. Close agreement with experimental tensile tests of the final tube was demonstrated, illustrating the model’s capability to predict hardening behavior during cold pilgering. Valuable insights and a practical modeling approach for enhancing the simulation and optimization of cold pilgering processes are provided by this work.

Numerical Modelling of the Punching Shear Response of Shearhead Connections between a CFT Column and a Flat Reinforced Concrete Slab

In this article, the punching shear response of shearhead connections between a Reinforced Concrete (RC) flat slab and interior Concrete-Filled steel Tubular (CFT) columns under static load is investigated by using a 3D nonlinear finite element modelling approach. The accuracy of the numerical model is verified against recent experimental results by comparing the results of the load–displacement relationship, failure models and cracking patterns. Based on validated numerical models, parametric investigations are conducted to examine the effects of slab thickness, concrete compressive strength, length of shearhead arm, size of shearhead cross section and flexural reinforcement ratio on the punching shear response of the connections. Then, some recommendations are given for the design of the effective parameters of shearhead connections, particularly focusing on the effective length and cross-section size of shearhear arms. Finally, a design equation considering the effect of the effective length of shearhead arms and flexural reinforcement ratio is proposed to predict the punching shear strength of shearhead connections. The comparison of punching shear strengths predicted by the proposed equation and some design codes shows that the proposed equation can predict the punching shear strength of shearhead connections more accurately than those from ACI 318-14 and Eurocode 2.

Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks

In the past two decades, advancements in thermochemical technologies have improved biomass gasification for distributed power generation, enhancing efficiency, scalability, and emission control. This study aims to optimize syngas production from biomass gasification by comparing two computational models: a quasi-equilibrium thermodynamic model implemented in Aspen Plus and an artificial neural network (ANN) model. Operating at 850 °C with varying steam-to-biomass (S/B) ratios, both models were validated against experimental data. Results show that hydrogen concentration in syngas increased from 19.96% to 43.28% as the S/B ratio rose from 0.25 to 0.5, while carbon monoxide concentration decreased from 24.6% to 19.1%, consistent with the water–gas shift reaction. The ANN model provided rapid predictions, showing a mean absolute error of 3% for hydrogen and 2% for carbon monoxide compared to experimental data, though it lacks thermodynamic constraints. Conversely, the Aspen Plus model ensures mass and energy balance compliance, achieving a cold gas efficiency of 95% at an S/B ratio of 0.5. A Multivariate Statistical Analysis (MVA) further clarified correlations between input and output variables, validating model reliability. This combined modelling approach reduces experimental costs, enhances gasification process control and offers practical insights for improving syngas yield and composition.

On detecting and characterizing planetary oceans in the solar system using a distance-based ensemble modelling approach: application to the Uranus system.

The discovery of Europa's subsurface ocean has spawned a strong desire by the planetary community to return and assess the ocean's habitability using the magnetic induction signal that Europa generates. NASA has since formulated and developed the Europa Clipper mission with that same goal, anticipating its arrival in the Jovian system in the early 2030s. In parallel, ESA has developed the JUpiter Icy moons Explorer mission to further investigate the interior of Ganymede and other Jovian moons, scheduled to arrive approximately one year later. As a result, extensive work has now been devoted to developing and refining methods to analyse magnetic induction measurements with the goal of characterizing oceans within icy moons, including those in the Neptune and Uranus systems, which are ideal laboratories for such investigations. We present one such method, involving a distance-based inverse and forward modelling approach that leverages self-consistent interior models used to infer ocean and ice-shell properties of various moons that respond inductively to the dynamic magnetic environments in which they reside. We demonstrate the method on a hypothetical ocean within Umbriel, showing the ocean thickness and conductivity constraints that can be inferred from a Monte Carlo error analysis using a three-flyby mission concept.This article is part of the theme issue 'Magnetometric remote sensing of Earth and planetary oceans'.

Social Media Marketing pada Kopi Klotok Yogyakarta: Studi Empiris pada Instagram Kopi Klotok

The objective of this research is to test and analyze the influence of social media marketing activities, brand awareness, brand image, electronic word of mouth, and commitment. Testing of the model and hypothesis was done with a sample of 225 respondents who were Yogyakarta klotok coffee consumers. Purposive sampling was the method of sampling that was applied. Partial Least Square (PLS) versi 3.0 was used to process the study hypothesis before the Structural Equation Modeling (SEM) approach was created to test it. Social media marketing initiatives, brand awareness, brand image, electronic word- of-mouth, and commitment are among the data variables employed in this instance. Six hypotheses are based on these variables. It is established from the study's findings that social media marketing initiatives improve brand recognition and perception. Positive brand awareness and image impact electronic word-of-mouth. Brand recognition and image positively impact commitment

Factors controlling basalt weathering in the Deccan traps: A small catchment perspective

Chemical weathering fluxes from small basalt catchments, the Ambika and the Girna in the Deccan Traps, have been compared. The two catchments are characterized by distinct climates: humid in the Ambika and semi-arid in the Girna River. We collected water samples at the sub- catchment scale of these basins and analysed concentrations of major ions and dissolved Sr in them to estimate the weathering rates and to assess their controlling factors. The catchment area, slope and relief of the catchments were derived using Digital Elevation Model (DEM) on ArcGIS platform whereas the Soil and Water Assessment Tool (SWAT) was used to derive the sub-catchment scale runoffs. The SWAT simulated annual discharge shows a nearly three times larger discharge in the Ambika River compared to the Girna River. The various sources, viz., basalt, carbonates, sea-salts and saline alkaline soils (SAS) contributing to major ions in dissolved load, were quantified using inverse modelling approach. The total chemical denudation rates (TCDR*) during monsoon, 1033 ± 512 tons km−2 y-1, is ∼ 12 times higher compared to non-monsoon (82 ± 42 tons km−2 y-1) in the Ambika River, and ∼ 3 times higher in the Girna River (monsoon: 185 ± 155 tons km−2 y-1 vs. non-monsoon: 51 ± 25 tons km−2 y-1). Annually, TCDR* is around 6-fold higher in the Ambika River (∼683 tons km−2 y-1) than in the Girna River (∼113 tons km−2 y-1) at their outlets. Basalt (silicate) weathering rates (BasWR) in the Ambika and the Girna rivers are 88 ± 19 and 12 ± 3 tons km−2 y-1, respectively. The chemical weathering rates in the two adjacent catchments is primarily controlled by rainfall/runoff, while relief plays secondary control. CO2 consumption rates (CCR) of the Ambika with higher rainfall are much higher ((13 ± 4) × 105 mol km−2 y-1) compared to most of the inactive basalt cathchments globally.

The theoretical approach for description of magnetic properties and magnetocaloric effect in all-d-metal Heusler alloys Ni2−xCoxMn1.25Ti0.75

This paper investigates the influence of Co addition and atomic ordering on the magnetic and magnetocaloric properties of all-d-metal Heusler alloys Ni2−xCoxMn1.25Ti0.75, which exhibit a second-order magnetic phase transition. The modeling approach employed is based on the density functional theory and Monte Carlo method. The atomic ordering is considered with reference to the fully ordered structure, L21, and the partially ordered structure, B2. It is demonstrated that in both structures, the predominant magnetic state within the cubic austenitic phase is characterized by ferromagnetic ordering. An increase in the Co content results in the strengthening of the ferromagnetic exchange interactions between Mn, Ni, and Co, as well as an increase in the Curie temperature. For the ground state L21 structure, the largest Curie temperature values are observed, exceeding those of the B2 structure by almost 100 K. The temperature dependencies of the magnetization and magnetocaloric effect (ΔSmag) in magnetic fields up to 2 T are calculated using the Heisenberg Hamiltonian by the Monte Carlo method. The largest effect (ΔSmag≈1.4 J/kg K) is observed for the compound with x = 0.375 and B2 structure at temperature ≈150 K, whereas for L21-Ni1.5Co0.5Mn1.25Ti0.75 and B2-Ni1.25Co0.75Mn1.25Ti0.75, ΔSmag of 1.05 J/kgK appears in the vicinity of room temperature.

Design and Analytical Modeling of a Dumbbell-Shaped Balloon Anchoring Actuator for Safe and Efficient Locomotion Inside Gastrointestinal Tract.

Colorectal cancer stands as one of the most prevalent cancers globally, representing 9.8% of total cases and contributing to 9.2% of mortalities annually. Robotic "front-wheel" navigating colonoscopes mitigate aggressive stretching against the long and tortuous colonic wall, alleviating associated discomfort and pain typically experienced by patients inspected by conventional "back-wheel" navigating colonoscopes. The anchoring unit of most "front-wheel" navigating colonoscopes plays a crucial role in ensuring effective locomotion by preventing slipping during elongation/contraction of the central actuation part. The soft balloon anchoring actuator emerges as a promising solution due to its high compliance. This study introduces a dumbbell-shaped balloon anchoring actuator (DBAA) integrating an "inflation and suction" mechanism to address the inherent conflict between achieving sufficient anchoring force and minimizing expansion and potential trauma of the colonic wall, commonly encountered in current balloon anchoring actuators. Analytical modeling of DBAA and soft external lumen, encompassing geometric deformation and anchoring force, were proposed to characterize the actuator and provide guidelines for designing and controlling DBAA in further applications, enabling autonomous anchoring within different diameter lumens and achieving the expected anchoring force. A comprehensive set of validation experiments was conducted, and the outcomes revealed high consistency with analytical predictions, confirming the effectiveness of the proposed analytical modeling approach. Furthermore, the results demonstrated a significant enhancement in anchoring force with the proposed actuator and corresponding mechanism while concurrently maintaining low-lumen expansion. For instance, in a lumen sample with the anchoring force reaches 14.5 N with 50 kPa negative pressure, which is 12.4 times of the force (1.17 N) observed without applying negative pressure.

Three-Dimensional Model of Ionic Species Concentration and Flux During Localised Corrosion of Steel in Marine Environment

Localized corrosion, exemplified by pitting and crevice corrosion, presents a significant challenge in industrial and marine settings due to the presence of chloride ions in seawater, brines and industrial fluids. It initiates and spreads at specific points on metal surfaces and its hidden rapid metal degradation beneath intact surface layers complicates detection and monitoring, increasing the risk of unforeseen structural failures and jeopardizing critical infrastructure integrity. Factors influencing its severity include chloride concentration, pH levels, temperature variations and aggressive ion presence, which degrade protective oxide layers and expose metal to accelerated corrosion. This study aims to enhance predictive capabilities to effectively manage corrosion in chloride-rich environments. Using COMSOL Multiphysics software, this study models a microscale geometry representing a micropit and simulates chloride-induced localized corrosion in steel, by integrating Nernst-Planck equations to incorporate electrochemical reactions, ion transport dynamics and three-dimensional geometrical features. Key parameters such as chloride concentration, pH and material properties are crucial in the modelling approach to simulate the formation and growth of corrosion sites. For 0 to 800 seconds simulation, the model displays the flux of ionic species into and out of the pit. At 800 second, the results demonstrate a peak of Fe2+ concentration of approximately 2.53×103 mol/m3 at the deepest section, the Cl- of 7×103 mol/m3 and an increase in H+ inside the pit, reducing the pH from 8 to about 4.9. These concentration changes indicate substantial metal dissolution is actively occurring inside the pit, making COMSOL Multiphysics software a versatile platform for coupling multiphysical phenomena, enabling comprehensive analysis and 3D visualization of corrosion processes.

Endemic-epidemic modelling of school closure to prevent spread of COVID-19 in Switzerland

The goal of this work was to quantify the effect of school closure during the first year of coronavirus disease 2019 (COVID-19) pandemic in Switzerland. This allowed us to determine the usefulness of school closures as a pandemic countermeasure for emerging coronaviruses in the absence of pharmaceutical interventions. The use of multivariate endemic-epidemic modelling enabled us to analyse disease spread between age groups which we believe is a necessary inclusion in any model seeking to achieve our goal. Sophisticated time-varying contact matrices encapsulating four different contact settings were included in our complex statistical modelling approach to reflect the amount of school closure in place on a given day. Using the model, we projected case counts under various transmission scenarios (driven by implemented social distancing policies). We compared these counterfactual scenarios against the true levels of social distancing policies implemented, where schools closed in the spring and reopened in the autumn. We found that if schools had been kept open, the vast majority of additional cases would be expected among primary school-aged children with a small fraction of cases filtering into other age groups following the contact matrix structure. Under this scenario where schools were kept open, the cases were highly concentrated among the youngest age group. In the scenario where schools had remained closed, most reduction would also be expected in the lowest age group with less effects seen in other groups.