Accurate Representation Research Articles

An advanced high dimensional model representation approach for internal combustion engine modeling and optimization

This work introduces an efficient data-driven approach for engine performance modeling and optimization. A highly accurate and robust high-dimensional model representation (HDMR) surrogate model is developed based on the dataset generated from a coupled KIVA4-CHEMKIN Ⅱ software. The HDMR model is integrated with a multi-objective optimization algorithm, enabling the automatic identification of optimal combustion strategies. Results suggest that the constructed HDMR model is able to accurately predict the engine performance and exhaust gas emissions with negligible CPU costs. Moreover, the model effectively identifies optimal engine operating conditions, leading to enhanced fuel efficiency.

Interfacial behaviour of human bile and its substitution for in vitro lipolysis studies

This study examined the interfacial evolution of individual bile salts (BSs) and their blends with phosphatidylcholine (BS/PC) to simulate the complex behaviour of human bile (HB) during lipolysis at the triglyceride/water interface. Using adsorption and desorption cycles, mimicking exposure to small intestinal fluids, we demonstrate that the interfacial behaviour of HB can be replicated using simple mixtures of BSs and PC. Interfacial tension (IFT) measurements after lipolysis and desorption showed no significant differences (P > 0.05) between HB samples and BS/PC mixtures across the total BS concentrations analysed (2.23–7.81 mM). However, individual BSs without PC yielded significantly different IFT results (P < 0.01) compared to HB, highlighting the importance of phospholipids. Dilatation rheology further emphasised the need for accurate phospholipid representation in bile models. Our results suggest that phospholipids in HB and BS/PC systems enhance resistance to desorption, potentially affecting lipolysis. This is important, as current in vitro digestion models often replicate only intestinal BS concentrations to mimic the behaviour HB in the intestinal lumen. Furthermore, the specific composition of BSs in HB appears less critical than the overall BS and phospholipid content, suggesting that the kinetics of triglyceride digestion is influenced by the combined luminal concentrations of these components. These findings have significant implications for understanding the role of bile in digestion and offer insights for designing more accurate in vitro models to study food emulsions and lipid-based delivery systems.

Fostering a Synergistic Approach to Curriculum Development and TVET-Engineering Qualifications

The involvement of various stakeholders, aligning diverse interests, and evolving industry requirements are some areas that need careful consideration. The dynamics of power and authority between collaborating partners can affect the efficacy of the collaborative relationship; hence, further study is required to identify obstacles in such partnerships and propose solutions for overcoming them. This study aims to build a collaboration paradigm between the TVET engineering sector and the industry, emphasizingcurriculum development and TVET engineering credentials. This quantitative study employs a survey to collect information from 115 Malaysian technologists in three fields. Simple random selection ensures accurate demographic representation and an even distribution of bias. Four experts developed and validated a questionnaire. Data was evaluated using PLS-SEM 3.3.9 software, algorithms, bootstrapping, and blindfolding techniques. The measurement model evaluates the items' precision under validity and reliability requirements. The results indicate a significant relationship between the certification system and curriculum development. The model has a high predictive power value with a significant contribution from the free extract. The influence of size is likewise considerable, but the effect of curriculum TVET is minor. The findings provide valuable insights into the predictive validity of the model and its possible curriculum development applications. Adeliberate effort is required to overcome obstacles such as lack of coordination, scarce resources, and low public awareness.

Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity

With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT domain. To address this challenge, this paper applies quantum computing (QC) technology to KT for the first time. It proposes two personalized KT models incorporating quantum mechanics (QM): quantum convolutional enhanced knowledge tracing (QCE-KT) and quantum variational enhanced knowledge tracing (QVE-KT). Through quantum superposition and entanglement properties, QCE-KT and QVE-KT effectively alleviate the data sparsity problem in the KT domain through quantum convolutional layers and variational quantum circuits, respectively, and significantly improve the quality of the representation and prediction accuracy of students’ knowledge states. Experiments on three datasets show that our models outperform ten benchmark models. On the most sparse dataset, QCE-KT and QVE-KT improve their performance by 16.44% and 14.78%, respectively, compared to DKT. Although QC is still in the developmental stage, this study reveals the great potential of QM in personalized KT, which provides new perspectives for solving personalized instruction problems and opens up new directions for applying QC in education.

Leveraging Open-Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models.

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large-scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community-based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large-scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri-urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM-enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%-100%). The final output, the OSM-enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM-derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open-source geographic databases and apply it to spatial modeling by generating research-specific landscape variables. As our occupancy results show, using context-specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations.

Characterization of spent nuclear fuel canister surface roughness using surface replicating molds

In this study we present a replication method to determine surface roughness and to identify surface features when a sample cannot be directly analyzed by conventional techniques. As a demonstration, this method was applied to an unused spent nuclear fuel dry storage canister to determine variation across different surface features. In this study, an initial material down-selection was performed to determine the best molding agent and determined that non-modified Polytek PlatSil23-75 provided the most accurate representation of the surface while providing good usability. Other materials that were considered include Polygel Brush-On 35 polyurethane rubber (with and without Pol-ease 2300 release agent), Polytek PlatSil73-25 silicone rubber (with and without PlatThix thickening agent and Pol-ease 2300 release agent), and Express STD vinylpolysiloxane impression putty. The ability of PlatSil73-25 to create an accurate surface replica was evaluated by creating surface molds of several locations on surface roughness standards representing ISO grade surfaces N3, N5, N7, and N8. Overall, the molds were able to accurately reproduce the expected roughness average (Ra) values, but systematically over-estimated the peak-valley maximum roughness (Rz) values. Using a 3D printed sample cell, several locations across the stainless steel spent nuclear fuel canister were sampled to determine the surface roughness. These measurements provided information regarding variability in normal surface roughness across the canister as well as a detailed evaluation on specific surface features (e.g., welds, grind marks, etc.). The results of these measurements can support development of dry storage canister ageing management programs, as surface roughness is an important factor for surface dust deposition and accumulation. This method can be applied more broadly to different surfaces beyond stainless steel to provide rapid, accurate surface replications for analytical evaluation by profilometry.

Measuring technological development and sovereignty

The article discusses the issue of measuring technological sovereignty, which is essential for developing a scientific and technical strategy to enhance Russia’s technological independence. Index methods do not provide an accurate representation of the level of technological sovereignty. Solving this problem requires assessing the efficiency of economic technologies. The aim of the study is to explain the direct (non-indexed) method of measuring technology sovereignty, taking into account qualitative features of technologies. The study employs the theory of technological change and empirical regression analysis and uses statistical data from the Russian economy as its information base. A general algorithm for measuring technological independence is proposed. It is applied to each technological area separately, considering the qualitative characteristics of the technologies used. An indicator for assessing the level of producibility is calculated based on the ratio of innovative to non-innovative products and services. The impact of investments in new and old technologies on this indicator is assessed, allowing to obtain a structural picture of Russia’s technological development and renewal. The general conclusion is that investments in new technologies have not been a determining factor in increasing the level of producibility for the Russian economy. The measurement of technological sovereignty requires changes to the federal statistical system for tracking technological development. This would not only allow for a more accurate quantification of the superiority of domestically produced technologies over imported ones but would also reflect the qualitative aspects of potential dependence on foreign technologies in a more comprehensive way. It is important to identify the balance between new and obsolete technologies, as well as between domestic and foreign-produced technologie

Data Fusion for Enhancing Urban Air Quality Modeling using Large-Scale Citizen Science

Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality management by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the priori dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.

Evaluation of Academic Enhancement Program for Low-Income Students in Rural Malaysia

Malaysia National Science, Technology and Innovation policy 2021-2023highlighted thatScience, Technology, Engineering and Mathematics (STEM) Education plays an important role for Technical and Vocational Education and Training (TVET). Many countries are now keen on integrating STEM into TVET education to maintain competitiveness and prepare the workforce to be more effective and productive in the knowledge-based economy. Penang Regional Development Authority (PERDA) initiated the B40@PERDA Academic Enhancement Programme to enhance the academic performance of primary and secondary school students from the B40 group, focusing on critical subjects.The study aims to evaluate the program's effectiveness and gauge student and teacher satisfaction. Using a quantitative approach, data were collected via questionnaires from students and teachers at various educational institutions. Respondents were randomly selected to ensure accurate representation. Analysis results showed that more than 95% of primary school students achieved the minimum proficiency level in EMAS with a minimum of 10% reaching the highest proficiency. For secondary school students, at least 50% attained at least a C grade in EMAS and 45.77% did so in Elective Science subjects (Physics, Chemistry, Biology, and Additional Mathematics). This study concluded that the B40@PERDA programme significantly improves EMAS proficiency among rural students and helps reduce dropout rates in these subjects. Furthermore, this programme is crucial for fulfilling the minimum requirements for TVET. By enhancing proficiency in critical subjects such as English, Mathematics, and Science, the B40@PERDA Programme ensures that students are better prepared for TVET pathways, ultimately contributing to their future career opportunities and the development of skilled labor in the region.

Application of the Soil and Water Assessment Tool (SWAT) Model for hydrological modelling in the Upper Oum Er Rbia watershed (Middle Atlas, Morocco)

Abstract In the study assessing the impact of global change on hydrosystems, particularly water resources, calibrated and validated tools are deemed crucial for accurate representation of the area’s characteristics. The Soil and Water Assessment Tool “SWAT” model is identified through literature review as well-suited for evaluating changes in land use and climate effects on water balance components. Applied to the Upper Oum Er Rbia watershed and its sub-basins, namely Oum Er Rbia, Srou, and Ouaoumana, the SWAT model demonstrates its effectiveness over the 1982-2019 period. Employing the sequential uncertainty adjustment method (SUFI-2) in SWAT-CUP software for calibration and validation, the model achieves good performance in simulating river flows. Statistical tests, including “R²,” “NSE,” “PBIAS,” and “RSR,” reveal consistent accuracy across various stations. The positive outcomes affirm the SWAT model’s operational capability for diagnostic assessments, risk evaluation, adaptation measures, and potential future projections in these three sub-basins.

Simple and Accurate Representation of Cumulative Nighttime Leaf Respiratory CO2 Efflux.

Leaf respiratory carbon loss decreases independent of temperature as the night progresses. Detailed nighttime measurements needed to quantify cumulative respiratory carbon loss at night are challenging under both lab and field conditions. We provide a simple yet accurate approach to represent variation in nighttime temperature-independent leaf respiratory CO2 efflux in environments with both stable and fluctuating temperatures, which requires no detailed measurements throughout the night. We demonstrate that the inter- and intraspecific variation in the cumulative leaf respiratory CO2 efflux at constant temperature, at any length of night, scales linearly with the inter- and intraspecific variation in initial measurement of leaf respiratory CO2 efflux at the same temperature at the beginning of the night. This approach informs large-scale predictions of cumulative leaf respiratory CO2 efflux, which is needed to understand plant carbon economy in global change studies as well as in global modeling and eddy covariance monitoring of the land-atmosphere exchange of CO2.

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the Allen–Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400 °C at an average rate of 30 °Cmin, traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal’s microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. These relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

Associations of heat with diseases and specific symptoms in Flanders, Belgium: An 8-year retrospective study of general practitioner registration data

IntroductionGlobal temperature rise has become a major health concern. Most previous studies on the impact of heat on morbidity have used hospital data. ObjectiveThis study aimed to quantify the association between ambient temperature and a variety of potentially heat-related medical conditions and symptoms using general practitioner (GP) data, in Flanders, Belgium. MethodsWe used eight years (2012–2019) of aggregated data of daily GP visits during the Belgian summer period (May-September). A distributed lag nonlinear model (DLNM) with time-stratified conditional quasi-Poisson regression was used to account for the non-linear and delayed effect of temperature indicators (minimum, mean and maximum). We controlled for potential confounders such as particulate matter, humidity, and ozone. ResultsThe overall (lag0-14) association between heat and most of the outcomes was J-shaped, with an increased risk of disease observed at higher temperatures. The associations were more pronounced using the minimum temperatures indicator. Comparing the 99th (20 °C) to the minimum morbidity temperature (MMT) of the minimum temperature distribution during summer, the relative risk (RR) was significantly higher for heat-related general symptoms (RR = 1.30 [95 % CI: 1.07, 1.57]), otitis externa (RR = 4.87 [95 % CI:2.98, 7.98]), general heart problems (RR = 2.43 [95 % CI: 1.33, 4.42]), venous problems (RR = 2.48 [95 % CI:1.55, 3.96]), respiratory complaints (RR = 1.97 [95 % CI: 1.25, 3.09]), skin problems (RR = 3.26 [95 % CI: 2.51, 4.25]), and urinary infections (RR = 1.37 [95 % CI: 1.11, 1.69]). However, we did not find evidence for heat-related increases in gastrointestinal problems, cerebrovascular events, cardiovascular events, arrhythmia, mental health problems, upper respiratory problems and lower respiratory problems. An increased risk of allergy was observed when the minimum temperature reached 17.8 °C (RR = 1.50 [95 % CI: 1.23, 1.83]). Acute effects of heat were observed (largest effects at the first few lags). SummaryOur findings indicated that the occurrence of certain symptoms and illnesses during summer season is associated to high temperature or environmental exposures that are augmented by elevated temperatures. Overall, unlike hospitalization data, GP visits data provide broader population coverage, revealing a more accurate representation of heat-health association.

Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics

Textile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 214=16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics–woven or knitted, weft knit or warp knit, polymeric or metallic–and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN. Statement of significanceTextile structures are rapidly gaining popularity in many biomedical applications including tissue engineering, wound healing, and surgical repair. A precise understanding of their unique mechanical properties is critical to tailor them to their specific functions. Here we integrate mechanical testing and machine learning to automatically discover the best models for knitted polypropylene fabrics. We show that warp knitted fabrics possess a complex symmetry with three distinct microstructural directions. Along these, the behavior is dominated by an exponential linear term that characterize the initial flexibility of the virgin mesh and the nonlinear stiffening as the loops of the fabric tighten. We expect that our technology will generalize naturally to other fabrics and enable the robust discovery of complex anisotropic models for a wide variety of textile structures.

Removal of 4-(2-pyridylazo) resorcinol and Arsenazo-III from liquid waste solutions using Penicillium oxalicum: Gamma irradiation studies

The current investigation involved the isolation of a fungal strain from liquid radioactive wastewater, which was then identified as Penicillium oxalicum and was employed to remove 4-(2-pyridylazo) Resorcinol (PAR) and Arsenazo-III (Ar-III) from liquid waste solutions. The physicochemical properties of the biosorbent fungi were analyzed using SEM and FT-IR. The biosorption effectiveness of PAR and Ar-III was investigated under several experimental settings, including pH values, adsorption times, biosorbent weight, initial dye concentrations, and reaction temperatures. The Langmuir isotherm model describes the adsorption isotherms well, with capacities of 10.88 and 11.96mgg-1 for PAR and Ar-III, respectively. The sorption of PAR and Ar-III exhibited E values of 19.84 and 17.84kJ/mol, respectively, indicating that the process is chemical adsorption. The pseudo-second-order kinetic model was determined as the most accurate representation of the biosorption kinetics. Both film diffusion and intra-particle diffusion describe the diffusion of coloring reagents through the biosorbent material. The thermodynamic analysis showed that chemical adsorption regulated the biosorption of the coloring reagent on the biosorbent material, while exothermic and spontaneous biosorption of PAR and Ar-III was observed. The extensive decolorization and easy operating circumstances demonstrated the promise of Penicillium oxalicum for the biological treatment of textile dyes.

A Distinct Role for Aerosol and GHG Forcing in Historical CMIP6 Evapotranspiration Trends

AbstractEvapotranspiration (ET) is crucial for the global water balance, plant growth, and freshwater availability. It connects the surface water balance with surface energy fluxes, making its accurate representation vital for climate projections. However, global climate models (GCMs) struggle with ET representation due to resolution limitations and simplified depictions of soil, plant, and atmosphere interactions. Simulated future changes in ET are uncertain, and the role of driving processes remain unclear. Here, we explore the utility of a simple and interpretable method to disentangle these varying drivers. We investigate the sensitivity of JJA ET to different atmospheric variables through simple linear models predicting ET from atmospheric variables only. The model consistently yields good results across GCMs or forcing scenarios. We find that GCMs have shown strong decreases and subsequent increases in ET over the historical period, related to changes in net surface radiation. For future climate projections, decreases in water availability compete with higher available surface radiation, making future projections uncertain. Single forcing GCM realizations show that historical ET trends in densely populated regions have been more influenced by aerosol emissions than greenhouse gases. Finally, we investigate which atmospheric variables explain most short‐term (year‐to‐year) and long‐term (decadal) changes. While water availability may be the most important driver of short‐term variability, for certain regions, radiation trends dominate long‐term forcing. This paper leverages a simple approach to provide a comprehensive and understandable view into recent and future changes in ET, reconciling the evidence provided by more complex case studies.

A study on the fractional-order COVID-19 SEIQR model and parameter analysis using homotopy perturbation method

In this article, we present a fractional-order susceptible-exposed-infected-quarantine-recovered (SEIQR) model to analyze the dynamics of the COVID-19 pandemic. The model includes susceptible (S), exposed (E), infected (I), quarantined (Q), and recovered (R) populations and uses a fractional-order differential equation to provide a further accurate representation of the disease's progression. We employ the homotopy perturbation method (HPM) to derive analytical solutions and the Runge-Kutta fourth-order (RK4) method to obtain numerical solutions. The results indicate that the fractional-order model, particularly for a fractional parameter α = 0.40, provides better accuracy and stability compared to the classical integer-order model. This study highlights the importance of fractional-order modeling in understanding the spread of COVID-19 and suggests its potential application in predicting and controlling future epidemics.

BIPOLAR PYTHAGOREAN FUZZY NEUTROSOPHIC SET (BPNS) INTEGRATED WITH AHP EXPRESS (BPNS-AHP EXPRESS) WITH LINGUISTIC VARIABLE: A NEW APPROACH

Decision-making in real-world situations inherently involves uncertainty and insufficient information. To overcome these challenges, decision-makers must adopt techniques that recognize and mitigate the influence of unknown or insufficiently known elements in the decision environment. A single technique often does not address these diverse scenarios, as multiple perspectives may emerge. AHP-express, a simplified form of the Analytic Hierarchy Process (AHP), has recently been introduced; however, the development of a new linguistic variable for AHP-express has not been thoroughly investigated. This study initiates the integration of the newly generalized fuzzy set, known as Bipolar Pythagorean Neutrosophic Set (BPNS), with AHP-express (BPNS-AHP express). We develop linguistic variables within the BPNS-AHP express framework using a 7-point linguistic scale, ensuring that BPNS-outlined requirements are met. This articles highlights the six step BPNS-AHP express procedure and demonstrates how it can enhance decision-makers effectiveness across various application domains by providing a more thorough and accurate representation of their thoughts and judgments.

Impact of ice growth on the physical and chemical properties of dense cloud cores

We investigated the effect of time-dependent ice growth on dust grains on the opacity and hence on the dust temperature in a collapsing molecular cloud core, with the aim of quantifying the effect of the dust temperature variations on ice abundances as well as the evolution of the collapse. To perform the simulations, we employed a one-dimensional collapse model that self-consistently and time-dependently combines hydrodynamics with chemical and radiative transfer simulations. The dust opacity was updated on the fly based on the ice growth as a function of the location in the core. The results of the fully dynamical model were compared against simulations run with different values of fixed ice thickness. We found that the ice thickness increases quickly and reaches a saturation value (as a result of a balance between adsorption and desorption) of approximately 90 monolayers in the central core (volume density ~104 cm−3), and several tens of monolayers at a volume density of ~103 cm−3, after only a few 105 yr of evolution. The results thus exclude the adoption of thin (approximately ten monolayer) ices in molecular cloud simulations except at very short timescales. However, the differences in abundances and the dust temperature between the fully dynamic simulation and those with a fixed dust opacity are small; abundances change between the solutions generally within a factor of two only. The assumptions on the dust opacity do have an effect on the collapse dynamics through the influence of the photoelectric effect on the gas temperature, and the simulations take a different time to reach a common central density. This effect is, however, small as well. In conclusion, carrying out chemical simulations using a dust temperature corresponding to a fixed opacity seems to be a good approximation. Still, although at least in the present case its effect on the overall results is limited – as long as the grains are monodisperse – ice growth should be considered to obtain the most accurate representation of the collapse dynamics. We have found in a previous work that considering a grain size distribution leads to a complicated ice composition that depends on the grain size nonlinearly. With this in mind, we will carry out a follow-up study where the influence of the grain size on the present simulation setup is investigated.

Brushstrokes of Change: Hieronymous Bosch and Rachel Gadsden's Evolving Portrayals of Disability

Historically, there has been a notable lack of representation of disability in western art history. Where disabilities are displayed, the artists’ intentions were often to convey moral corruption, religious infidelity, and the faults of society. Other times, the representations functioned scientifically as medical records. In fact, one of the most anatomically accurate representations of physical disability is credited to the early modern artist Hieronymous Bosch, who was known for his unfiltered commentary on the flaws of human society. Contrary to his typical surreal, vibrant, and sarcastic style, Cripples and Beggars (after 1570) (Fig. 1), a naturalistic and observational piece, offers a rare lens into how disability was portrayed during the early modern period. In contrast, Rachel Gadsden, a contemporary, disabled artist, foregrounds her identity as a disabled artist in her work Ubuntu (2012) (Fig. 2) and utilizes an expressive, abstract style to convey the fluidity of the disabled human form. Cripples and Beggars and Ubuntu, separated by centuries, provide a captivating shared subject for comparative analysis that transcends temporal boundaries. Cripples and Beggars provides an early explicit and moralizing depiction of physical disability as an isolating scourge while Rachel Gadsden focuses on abstract, yet recognizable human bodies in optimistic motion and community. Analysis of the varying style, medium, and intention reveals the evolved reception and, thus, depiction of disability. The evolution of the reception and depiction of disability from Hieronymous Bosch's Cripples and Beggars to Rachel Gadsden's "Ubuntu" signifies a shift from viewing disability as a symbol of moral corruption and societal decay to an expression of identity, resilience, and community.