Domain Of Interest Research Articles

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Abstract. Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

Prospective memory performance in veterans with and without histories of mild traumatic brain injury: effect of the apolipoprotein E (APOE) ε4 genotype

ABSTRACT Objective Identifying factors that moderate cognitive outcomes following mild traumatic brain injury (mTBI) is crucial. Prospective memory (PM) is a cognitive domain of interest in mTBI recovery as it may be especially sensitive to TBI-related changes. Since studies show that genetic status – particularly possession of the apolipoprotein E (APOE) ε4 allele – can modify PM performance, we investigated associations between mTBI status and APOE-ε4 genotype on PM performance in a well-characterized sample of Veterans with neurotrauma histories. Methods 59 Veterans (mTBI = 33, Military Controls [MCs] = 26; age range: 24–50; average years post-injury = 10.41) underwent a structured clinical interview, neuropsychological assessment, and genotyping. The Memory for Intentions Test (MIST) measured PM across multiple subscales. ANCOVAs, adjusting for age and posttraumatic stress symptoms, tested the effects of mTBI status (mTBI vs. MC) and ε4 status (ε4+ vs. ε4−) on MIST scores. Results Veterans with mTBI history performed more poorly compared to MCs on the MIST 15-min delay (p=.002, ηp 2 =.160), Time Cue (p = .003, ηp 2 =.157), and PM Total (p = .016, ηp 2 =.102). Those with at least one copy of the ε4 allele performed more poorly compared to ε4- Veterans on the MIST 15-min delay (p = .011, ηp 2 =.113) and PM Total (p = .048, ηp 2 = .071). No significant interactions were observed between mTBI and APOE-ε4 status on MIST outcomes (ps>.25). Within the mTBI group, APOE-ε4+ Veterans performed worse than APOE-ε4− Veterans on the MIST 15-min delay subscale (p = .031, ηp 2 = .150). Conclusions mTBI history and APOE-ε4 genotype status were independently associated with worse PM performance compared to those without head injury histories or possession of the APOE-e4 genotype. Performance on the MIST 15-min delay was worse in Veterans with both risk factors (mTBI history and APOE-ε4 positivity). Findings suggest that genetic status may modify outcomes even in relatively young Veterans with mTBI histories. Future research examining longitudinal associations and links to neuroimaging and biomarker data are needed.

A Helpful Measure to Measure Help: The Construction and Validation of the Intergroup Giving and Intergroup Acting in Favor of Refugees Scale (IGIAF)

Since 2015, Europe has experienced two important influxes of vulnerable migrants, refugees, and asylum seekers. Despite heterogenous reactions within and across countries, acts of humanitarianism and solidarity have occurred in a diverse range of behaviors. Given the particularities of Intergroup Helping in favor of refugees, a more nuanced understanding of intentions to engage in helping behaviors would enrich theoretical and applied research. We developed and validated a scale of Intergroup Helping in favor of refugees, that covers the two dimensions of help, i.e., Intergroup Giving (alleviating the suffering of others) and Intergroup Acting (addressing injustices and inequalities). Following scale construction practices, we proceeded in three phases. First, we identified and defined our domains of interest, and proceeded to collect representative helping behaviors, using secondary data of semi-structured interviews on volunteers. These behaviors were pre-tested. Then, across two studies, we examined the scale’s reliability, dimensionality, and validity qualities. Study 1 was distributed among a student sample at two time-points. Results yielded robust internal consistency, test-retest reliability, and predictive validity, and displayed preliminary evidence of the bidimensional structure. Study 2 was distributed among a non-student sample and supported the bidimensional structure of the scale. This research demonstrates that Intergroup Helping intentions in favor of refugees can be classified into Intergroup Giving and Intergroup Acting behaviors and offers a predictive tool to investigate these behaviors in an intergroup context.

A general ray tracing approach for solving thermal radiation in regular one-dimensional variable index media via the Monte Carlo method

In the heat transfer community, one-dimensional media refers to some media where the temperature and other thermophysical properties vary along one direction. Plane-parallel, concentric cylindrical, and concentric spherical geometries are among of regular one-dimensional media. In this paper, a general ray tracing approach is presented to track the curve paths of energy particles in regular one-dimensional variable index media. The domain of interest is limited by specular sidewalls and the medium is divided into slices with constant temperature and radiative properties. Then, the path of random energy particles emitted from boundary surfaces and volume elements are traced, element by element, until they absorb by diffuse-gray surfaces or the gray medium. After counting the number of absorbed particles by each boundary surface and volume element, the Monte Carlo method is performed to simulate thermal radiation heat transfer. The results are presented for three regular geometries, including plane-parallel, concentric cylindrical, and concentric spherical media, and the effects of radiative properties are investigated by some numerical experiments.

Antecedents of Independent Directors on Joint Venture Boards

The presence of independent directors on corporate boards is often seen as an important means of monitoring to address principal-agent problems and of providing external resources and advice to management. In joint ventures (JVs), however, shareholder-management frictions can be lessened by appointing insiders to management and board positions, whereas access to valuable expertise, resources, and networks are provided by the partners themselves. A natural question, then, is why and when do partners appoint independent directors to JV boards? We argue that the appointment of independent directors to joint venture boards is primarily driven by principal-principal conflict considerations, which are unique in the joint venture context compared with conventional widely held corporations. Consistent with this argument, we find that the likelihood of appointing independent directors increases when JVs face more exchange hazards due to the competitive overlap between partners and the broader functional scope of the JV. However, given that JVs also have alternative governance mechanisms to mitigate shareholder conflicts, we also find that more complex contractual agreements can potentially substitute for independent directors on JV boards. Although relational governance is often highlighted as a key facet of JV governance, we do not find such a substitution effect for this supporting governance mechanism. Overall, our research therefore highlights several interesting domain translation issues when applying existing corporate governance insights to the joint venture setting. Our paper concludes with a call for future research on independent directors serving on JV boards, as JVs represent an organizational form that has been neglected in corporate governance research.

The Effect of an Educational Intervention on Nurses' Knowledge, Beliefs, and Actions to be Taken, Regarding Suicide.

The aim of this study was to examine the effect of an educational intervention on nurses' knowledge defined as understanding, beliefs, and actions to be taken, regarding nurse suicide. Between 2007 and 2018, nurses were 18% more likely to die by suicide than the general public. As a result, an assessment of nurses' knowledge regarding suicide has become an important issue for nursing administration. A quasi-experiment (N = 225) was conducted. Variables of interest were assessed pre and post an educational intervention on 3 domains related to suicide. Significant and meaningful differences were found regarding 2 domains of interest in nurses' understanding related to nurse suicide, and actions to be taken, pre and post intervention. A significant change occurred regarding beliefs; however, the difference was small and therefore not meaningful. Finding demonstrate that educational interventions focused on issues related to suicide can enhance nurses' knowledge of the challenges implicit when one considers taking one's own life.

Inflow stabilization for hemodynamic simulations using Stokesian regions

Blood flow simulations in three-dimensional space pose the challenge of isolating the vascular domain of interest, which introduces artificial interfaces, both upstream and downstream, where boundary conditions must be prescribed. Vessel curvature, tortuosity, and blood flow pulsatility contribute to the complexity of performing these simulations. A common practice in defining boundary conditions is to prescribe Neumann-like boundary conditions over these boundaries. A typical numerical problem in such a setting is associated with the unbounded nature of the kinetic energy that enters into the system, either through antegrade upstream flow or through downstream retrograde flow. Lack of energy control in the continuum problem may become a source of numerical instability, resulting in corrupted simulations. In this work, we propose a novel approach to avoid these instabilities by considering a bulk fluid with specific properties in a small portion of the domain in the vicinity of such boundaries. More precisely, the convective term from the governing equations is nullified in these domain extensions, resulting in Stokesian regions. In contrast to the classical approach based on the inclusion of straight long extensions to allow flow development, the proposed approach involves dealing with small regions whose length is smaller than the vessel diameter, to control the kinetic energy with near zero added computational cost. This stabilization strategy is investigated within the context of the Transversally Enriched Pipe Element Method, which makes the implementation straightforward. Academic examples and patient-specific vascular geometries are employed to illustrate the performance of the proposed stabilization strategy.

Stability analysis and numerical evaluations of a COVID-19 model with vaccination

A novel (nonlinear) mathematical model for the transmission of Coronavirus 19 (COVID-19) with eight compartments and considering the impact of vaccination is examined in this manuscript. The qualitative behavior of the system such as the boundedness of solutions, the basic reproduction number, and the stability of the equilibrium points is investigated in detail. Some domestic real data collected from the Kerman University of Medical Science (KUMC) is used to estimate the parameters of the proposed model. We predict the dynamical behavior of the system through numerical simulations based on a combined spectral matrix collocation methodology. In this respect, we first linearize the nonlinear system of equations by the method of quasilinearization (QLM). Hence, the shifted version of Chebyshev polynomials of the second kind (SCPSK) is utilized along with the domain-splitting strategy to acquire the solutions of the system over a long time interval. The uniform convergence and upper bound estimation of the SCPSK bases are proved in a rigorous manner. Moreover, the technique of residual error functions is used to testify the accuracy of the QLM-SCPSK method. The presented numerical results justify the robustness and good accuracy of the QLM-SCPSK technique. The achieved numerical orders of convergence indicate that the QLM-SCSK algorithm has exponential rate of convergence. Using the linearization technique in one hand and the domain-splitting strategy on the other hand, enable us to predict the behaviour of similar disease problems with high accuracy and maximum efficiency on an arbitrary domain of interest.

Integrating the patient voice: patient-centred and equitable clinical risk prediction for kidney health and disease.

Personalized approaches to care are increasingly common in clinical nephrology. Although risk prediction models are developed to estimate the risk of kidney-disease related outcomes, they infrequently consider the priorities of patients they are designed to help. This review discusses certain steps in risk prediction tool development where patients and their priorities can be incorporated. Considering principles of equity throughout the process has been the focus of recent literature. Applying a person-centred lens has implications for several aspects of risk prediction research. Incorporating the patient voice may involve partnering with patients as researchers to identify the target outcome for the tool and/or determine priorities for outcomes related to the kidney disease domain of interest. Assessing the list of candidate predictors for associations with inequity is important to ensure the tool will not widen disparity for marginalized groups. Estimating model performance using person-centred measures such as model calibration may be used to compare models and select a tool more useful to inform individual treatment decisions. Finally, there is potential to include patients and families in determining other elements of the prediction framework and implementing the tool once development is complete.

CovXtreme : MATLAB software for non-stationary penalised piecewise constant marginal and conditional extreme value models

The covXtreme software provides functionality for estimation of marginal and conditional extreme value models, non-stationary with respect to covariates, and environmental design contours. Generalised Pareto (GP) marginal models of peaks over threshold are estimated, using a piecewise-constant representation for the variation of GP threshold and scale parameters on the (potentially multidimensional) covariate domain of interest. The conditional variation of one or more associated variates, given a large value of a single conditioning variate, is described using the conditional extremes model of Heffernan and Tawn (2004), the slope term of which is also assumed to vary in a piecewise constant manner with covariates. Optimal smoothness of marginal and conditional extreme value model parameters with respect to covariates is estimated using cross-validated roughness-penalised maximum likelihood estimation. Uncertainties in model parameter estimates due to marginal and conditional extreme value threshold choice, and sample size, are quantified using a bootstrap resampling scheme. Estimates of environmental contours using various schemes, including the direct sampling approach of Huseby et al. 2013, are calculated by simulation or numerical integration under fitted models. The software was developed in MATLAB for metocean applications, but is applicable generally to multivariate samples of peaks over threshold data. The software and case study data can be downloaded from GitHub, with an accompanying user guide.

Optimal decay rates in Sobolev norms for singular values of integral operators

The regularity of integration kernels forces decay rates of singular values of associated integral operators. This is well-known for symmetric operators with kernels defined on (a,b)×(a,b), where (a,b) is an interval. Over time, many authors have studied this case in detail [9,10,4,15,12]. The case of spheres has also been resolved [3]. A few authors have examined the higher dimensional case or the case of manifolds [1,6]. Typically, these authors have provided decay estimates of singular values in lp norms, p≥1 or in case of faster decay due to regularity, lp quasi-norms, 0<p<1. With that approach, it is straightforward to show that their estimates are optimal using periodic kernels obtained from Fourier series. Our new approach for deriving decay estimates of these singular values uses Weyl's asymptotic formula for Neumann eigenvalues [21] that we combine to an appropriately defined inverse Laplacian. We obtain decay estimates in the form nα where for the n -th singular value where α depends on dimension and on the Sobolev regularity of the kernel. Since we are interested in optimal estimates in case of regular kernels, instead of writing an upper bound by a constant times nα, we use the singular values of the kernel obtained by differentiation. While in [1,6]lp estimates were proven to be optimal by simply considering periodic Fourier series, these Fourier series do not provide sharp results for our estimates. Instead, series of Neumann eigenfunctions for the Laplacian that are specific to the domain of interest are used to prove that our decay estimates are optimal. Finally, we cover the case of real analytic kernels where we are also able to derive optimal estimates.

An offline coupling approach for efficient SPH simulations of long-distance tsunami events using wave source boundary condition

The entire procedure of tsunami events consists of wave generation and propagation toward the coastal structures encompassing the long-range system domain. Therefore, instead of an integrated continuous simulation, two or several consecutive simulations are preferred for numerical efficiency, where the hydrodynamic information within a particular domain from the preceding simulation can be utilized for the wave sources in the subsequent simulation. In this study, as an offline coupling approach, the wave source boundary condition is proposed based on the particle-based Lagrangian framework. This boundary condition is integrated with the solution algorithm of the smoothed particle hydrodynamics (SPH) in-house parallel code. In the dambreak simulation, it is validated that the wave source boundary condition can encompass both the forward and reverse flows, where the numerical results of the full and partial simulations show good agreement in terms of the wave profile, velocity distributions, and measured pressure histories. Thereafter, the full and partial simulations of a tsunami experiment are carried out. The numerical results in the partial simulation agree well with those obtained in the full simulation in terms of the particle distributions, physical quantities, and loading histories (e.g., pressure, acceleration) on the shoreline structure. It is demonstrated that the proposed offline coupling approach using the wave source boundary condition is compatibly utilized with the developed in-house SPH parallel solver, facilitating the detailed FSI simulations within a reduced domain of interest for long-distance tsunami-like phenomena.

GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN

Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers have used traditional data augmentation techniques to increase the amount of training data available to them. A recent trend in object detection is to use generative models to automatically create annotated data that can enrich a training set and improve the performance of the target model. This paper presents a method of training the proposed ColorGAN network, which is used to generate augmented data for the target domain of interest with the least compromise in quality. We demonstrate a method to train a GAN with images of vehicles in different colors. Then, we demonstrate that our ColorGAN can change the color of vehicles of any given vehicle dataset to a set of specified colors, which can serve as an augmented training dataset. Our experimental results show that the augmented dataset generated by the proposed method helps enhance the detection performance of a CNN for applications where the original training data are limited. Our experiments also show that the model can achieve a higher mAP of 76% when the model is trained with augmented images along with the original training dataset.

Financial assets prediction based on ARIMA, Random Forest and GRU

As a matter of fact, financial asset prediction is a domain of great interest because of its potential to generate revenue. In reality, financial asset prediction models have evolved from basic time series analysis models to contemporarily hybrid models with the help of machine learning algorithms. To be specific, this study will introduce and analyse three popular financial asset forecasting models and their hybrids in terms of the properties they have demonstrated in the completed studies. In reality, good results have been achieved using a time series analysis method named autoregressive integrated moving average (ARIMA) for capturing linear elements. According to the analysis, in dealing with data noise and interpretability, Random Forest (RF) algorithm, a machine learning technique, produced positive outcomes. Deep learning technique gate recurrent unit (GRU) produced positive outcomes in terms of prediction accuracy. Based on the evaluations, this study indicates future research directions in the field of financial asset forecasting by analysing and organizing the characteristics of three different mainstream models.

Performance and CO2 emission of a single cylinder compression ignition engine powered by Khaya senegalensis non-edible seeds fuel blends

This work aimed at investigating blends of Khaya senegalensis biodiesel in a compression ignition engine, attempting to improve engine performance and reduce CO2 emission compared with conventional diesel. Analysis of System (ANSYS) was used to predict in-cylinder behavior of the fuel. ANSYS SpaceClaim generated the geometric model on which 5° sector and mesh refinement was on ANSYS Internal Combustion Engine Modeler (ICEM). Computational domain of interest lies within the compression and expansion strokes. Experimental validation followed: 5% biodiesel, 95% diesel (B5); 15% biodiesel, 85% diesel (B15); 25% biodiesel, 75% diesel (B25); pure diesel (D100); pure biodiesel (B100) in volume proportions. B15 has the highest brake mean effective pressure (BMEP) of 4 bar as load increases. An experimental and numerical comparison reveals pressure declination against speed increment. Ignition temperature fluctuated between 799.76 and 806.256 K for D100 and 760.73–790.62 K for B100 within 1800–2800 rpm speed limit prediction. Power and brake thermal efficiency (BTE) had parallel load increment with all blends. CO2 emission on increasing load conditions were 47.01%, 8.07%, 21.72% and 6.06% for B5, B15, B25, and B100 respectively lower than D100. Pressure and temperature contours gave proper combustion predicted behaviors. All blends possess replaceable performance potential for D100 however, B5 offers better reliable potentials.

Facile synthesis of CuO-Ag2O hybrid metal oxide composite using carica papaya, cocooning with hydroxyapatite, and photocatalytic degradation of organic dyes

The synthetic strategy of mixed metal oxide nanoparticle has emerged to be one of the interesting domains in material science. Here carica papya fruit extract assisted synthesis of silver copper oxide is reported via co precipitation method. For the comparative study, using similar precursor material silver copper mixture composite is synthesized using conventional base via coprecipitation method. The composites were subjected to degradation of methylene blue for observation of its efficiency as a photocatalyst. Hydroxyapatite was obtained from fish bones and was used for encapsulating the composite material. The resultant composite was analysed using XRD, SEM-EDS, TEM, XPS and FT-IR techniques. The uncapped composites reported to degrade 90.12 % and 91.5 % of methylene blue within 60 min. The conventionally synthesized composite degraded 30 ppm Bibersch scarlet up to 86.04 % in 40 min under assistance of sunlight. The encapsulated composite was successful in degradation of Brilliant green 89.9 % in 140 min and eosin yellow 92.2 % in 120 min. The catalyst in encapsulated and un encapsulated form reveals its efficiency to be a potential photo catalyst.

Abstract 7055: Elucidation of the interplay between phospholipid byosynthesis and oxidative stress in the pathogenesis of renal cell carcinoma

Abstract Introduction: Renal cell carcinoma (RCC) is a primary malignant kidney tumor. We reported on the critical role of Protein of Relevant Evolutionary and Lymphoid Interest Domain 2 (PRELID2) in cell proliferation, emphasizing its regulatory function on mitochondrial ROS and maintenance of mitochondrial spare respiratory capacity. Recent studies have begun to clarify the molecular mechanism of phospholipid biosynthesis by PRELID1 and PRELID3 under oxidative stress conditions. Inspired by this evidence, we analyzed the relationship between PRELID2 and phospholipid biosynthesis, aiming to elucidate the molecular mechanism of PRELID2 related to the control of mitochondrial ROS. Methods: To analyze the dynamics of phospholipids and related mitochondrial molecules induced by oxidative stress, we examined the expression of YME1L and its substrates (OPA1, PRELID1) using Western blotting, which has been shown to be related to the turnover of other PRELI family proteins. Lipidomic analysis was performed using LC-MS on samples from HEK293 cells with stable expression of PRELID2 and control cells, as well as renal carcinoma cell lines with suppressed PRELID2 expression using siRNA and control cells. Additionally, we analyzed the expression of mitochondrial respiratory chain complexes Cytochrome C and COX4 and their relationship with PRELID2 expression via Western blotting. Results: Oxidative stress led to a noticeable decline in YME1L expression, which was correlated with decreased OPA1 and PRELID1 levels in control cells. Conversely, PRELID2-overexpressing cells retained stable expression of OPA1 and PRELID1. Furthermore, these cells exhibited a significant reduction in phosphatidyl glycerol (PG) levels. Suppressing PRELID2 expression in 786-O cells using siRNA resulted in significantly higher PG levels. When analyzing the molecules of the mitochondrial respiratory chain during oxidative stress induction, we observed that although oxidative stress induced a decline in cytochrome C expression in control cells, it remained consistent in PRELID2-expressing cells. However, PRELID2 expression did not affect COX4 levels. Conclusions: Our findings underscore the pivotal role of PRELIDs in maintaining mitochondrial homeostasis, proving instrumental in the oxidative stress-driven progression of renal cancer. Citation Format: Renpei Kato, Daiki Ikarashi, Shigekatsu Maekawa, Mitsugu Kanehira, Ryo Takata, Yosuke Matsushita, Tetsuro Yoshimaru, Tomoya Fukawa, Toyomasa Katagiri, Wataru Obrara. Elucidation of the interplay between phospholipid byosynthesis and oxidative stress in the pathogenesis of renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7055.

On the long-time behaviour of solutions to unforced evolution Navier–Stokes equations under Navier boundary conditions

We study the asymptotic behaviour of the solutions to Navier–Stokes unforced equations under Navier boundary conditions in a wide class of merely Lipschitz domains of physical interest. The paper draws its main motivation from celebrated results by Foias and Saut (1984) under Dirichlet conditions; here the choice of the boundary conditions requires carefully considering the geometry of the domain Ω, due to the possible lack of the Poincaré inequality in presence of symmetries. In non-axially symmetric domains we show the validity of the Foias–Saut result about the limit at infinity of the Dirichlet quotient, in axially symmetric domains we provide two invariants of the flow which completely characterize the motion and we prove that the Foias–Saut result holds for initial data belonging to one of the invariants.

Reproducing Popularity Bias in Recommendation: The Effect of Evaluation Strategies

The extent to which popularity bias is propagated by media recommender systems is a current topic within the community, as is the uneven propagation among users with varying interests for niche items. Recent work focused on exactly this topic, with movies being the domain of interest. Later on, two different research teams reproduced the methodology in the domains of music and books, respectively. The results across the different domains diverge. In this paper, we reproduce the three studies and identify four aspects that are relevant in investigating the differences in results: data, algorithms, division of users in groups and evaluation strategy. We run a set of experiments in which we measure general popularity bias propagation and unfair treatment of certain users with various combinations of these aspects. We conclude that all aspects account to some degree for the divergence in results, and should be carefully considered in future studies. Further, we find that the divergence in findings can be in large part attributed to the choice of evaluation strategy.

Evidence of large-scale conceptual disarray in multi-level taxonomies in Wikidata

The distinction between types and individuals is key to most conceptual modeling techniques and knowledge representation languages. Despite that, there are a number of situations in which modelers navigate this distinction inadequately, leading to problematic models. We show evidence of a large number of representation mistakes associated with the failure to employ this distinction in the Wikidata knowledge graph, which can be identified with the incorrect use of instantiation, which is a relation between an instance and a type, and specialization (or subtyping), which is a relation between two types. The prevalence of the problems in Wikidata’s taxonomies suggests that methodological and computational tools are required to mitigate the issues identified, which occur in many settings when individuals, types, and their metatypes are included in the domain of interest. We conduct a conceptual analysis of entities involved in recurrent erroneous cases identified in this empirical data, and present a tool that supports users in identifying some of these mistakes.