Discretization Research Articles

Discretisation strategies in architectural design process: a procedural classification system

This study addresses the issue of optimizing architectural production processes through discretisation methods. The primary aim is to develop a classification system for these methods, facilitating their application in digital design and robotic assembly. The central research questions are: What are the fundamental discretisation methods in architecture? How can these methods be classified for practical application? To answer these questions, we conduct a comprehensive review of existing discretisation methods and evaluate their core attributes. Our methodology involves a detailed evaluation of these methods, focusing on their adaptability, geometric predictability, and broadly repeatability. The findings highlight the potential for digitally controlled discretized design processes to innovate architectural practices, making construction faster, more affordable, and capable of producing complicated geometries. The significance of this study lies in its contribution to integrating digital design and modular discretisation in architectural production, moving toward advanced and adaptable production systems.

Efficient and High-Precision Method of Calculating Maximum Singularity-Free Space in Stewart Platform Based on K-Means Clustering and CNN-LSTM-Attention Model

The determination of maximum singularity-free space is critical to structural design and motion control strategy in the Stewart platform. Nevertheless, in practical applications, there exist several limitations such as computational efficiency, calculation precision, and the reliability of computational results. To overcome those shortcomings, this work proposes an efficient and high-precision method for computing the maximum singularity-free space within the Stewart platform. Firstly, apply K-Means clustering to group the variables, including the range, mean, and standard deviation of driving rod lengths, and the clustering centroids and extreme rod lengths collectively form a set of scenarios to avoid large-scale searching. An additional sorting methodology with a specific parameter is proposed for sorting the aforementioned scenarios in descending order and detecting singular-prone cases. Secondly, compute the initial solution for maximum singularity-free length without gimbal lock through an analytical solution formula, enabling reduction in the search scope. Thirdly, introduce a novel scaling factor to resolve the problem of dimensional inconsistency between rotation and translation within the Jacobian matrix using dual quaternions, and determine the singularity based on the determinant of the newly proposed Jacobian matrix. Finally, employ a CNN-LSTM-Attention model for a secondary verification procedure, specifically targeting the challenge of singularities encountered when solving the forward kinematics of the Stewart platform using zero-position values. The experiments demonstrate that the accelerated discretization method for maximum singularity-free joint space and workspace is applicable to devices with diverse geometric configurations. For two practical Stewart platforms, compared with two conventional methods, this method improves computational efficiency and precision significantly. The computation time of the first platform is reduced by 97.54% and 98.07% respectively, while that of the second platform is cut by 80.84% and 81.80% respectively. In terms of precision, the first platform demonstrates 95.83% and 78% improvement respectively, and the second platform attains 99.99% improvement over two conventional methods.

Robustness of 18F-FDG PET Radiomic Features in Lung Cancer: Impact of Advanced Reconstruction Algorithm.

18F-FDG PET radiomics is emerging as a promising tool to identify imaging biomarkers for quantifying intratumor heterogeneity in lung cancer. However, the robustness of PET radiomic features (RFs) is influenced by factors such as image reconstruction algorithms, tumor segmentation, and discretization. Although the impact of these factors on RFs has been explored, the specific influence of the advanced block sequential regularized expectation maximization (BSREM) reconstruction algorithm remains unclear. This study investigated the potential variations in PET RFs associated with different factors when using BSREM. Methods: Retrospective data of 18F-FDG PET from 120 lung cancer patients were reconstructed twice using advanced BSREM and conventional ordered-subset expectation maximization methods. For each reconstruction set, 3 tumor segmentations were performed, including manual, 40% threshold, and Nestle methods. Two discretization methods using absolute and relative settings were applied for each dataset before RF extraction. Stable and robust RFs were assessed by the coefficient of variance and intraclass correlation coefficient, respectively. Results: High instability was exhibited by 19%, 33%, and 36% of RFs, with a coefficient of variation of more than 20% for reconstruction, segmentation, and discretization, respectively. Conversely, 60%, 19%, and 35% of RFs demonstrated robustness against these factors, with an intraclass correlation coefficient of more than 0.90. The comparative evaluation revealed significantly greater robustness for most RF subtypes in BSREM than in ordered-subset expectation maximization under varying segmentation and discretization conditions (P < 0.05). Conclusion: The stability and robustness of PET RFs are enhanced if BSREM is applied rather than the conventional method. Study results suggest that the advanced reconstruction method could offer potential benefits in providing consistent PET-based radiomic analysis for improving diagnostic and prognostic value.

ACTIVE TRANSFORMATION OF PLANES DURING FORMATION OF DISCRETE FRAMES WHITH A STATIC-GEOMETRIC METHOD

Various transformations have found widespread use in applied geometry problems in simulating discrete frameworks of different surfaces with given properties. The paper presents a mathematical apparatus of active coordinate transformation, designed to solve problems of discrete geometric modeling. Under the active transformation of coordinates, the authors propose to consider a clear match between the points of the original and new coordinate systems, while maintaining their numerical correspondence, when the numerical values of the parameters of the original coordinate system will transition to the numerical values of the parameters of the new coordinate system. The paper describes the peculiarities of using active coordinate transformation. In the study is proposes to use the properties of the active plane transformation when switching from The work a rectangular Cartesian coordinate system to a rectangular cylindrical coordinate system for further modeling of discrete frameworks of curved surfaces. The authors of the article focus on that there are many discrete modeling methods among which the generalized static-geometric method of Professor Kovalev S.M. The work demonstrates the possibilities of this method by additional use of active coordinate transformation. It has been proved that the advantages and features of active transformation of coordinates can be used in providing the modeled surface with certain properties necessary for the designer or architect in the process of creating a particular modeled image. Using an active coordinate transformation will simplify the process of forming new geometric shapes of curved surfaces, which are quite difficult to describe analytically. The information presented in the work will be useful for architects and designers in the formation of discrete frames of various surfaces

Collecting Real-World Data via an In-Home Smart Medication Dispenser: Longitudinal Observational Study of Survey Panel Persistency, Response Rates, and Psychometric Properties.

A smart medication dispenser called "spencer" is a novel generator of longitudinal survey data. The patients dispensing medication act as a survey panel and respond to questions about quality of life and patient-reported outcomes. Our goal was to evaluate panel persistency, survey response rates, reliability, and validity of surveys administered via spencer to 4138 polychronic patients residing in the United States and Canada. Patients in a Canadian health care provider's program were included if they were dispensing via spencer in the June 2021 to February 2024 time frame and consented to have their data used for research. Panel persistency was estimated via discrete survival methods for 2 years and survey response rates were computed for 1 year. Patients were grouped by mean response rates in the 12th month (<90% vs ≥90%) to observe differential response rate trends. For reliability and validity, we used a spencer question about recent falls with ternary responses value-coded -1, 0, and 1. For reliability, we computed Pearson correlation between mean scores over 2 years of survey responses, and transitions between mean score intervals of [0, 0.5), [-0.5, 0.5), and [0.5, 1]. For validity, we measured the association between the falls question and known factors influencing fall risk: age, biological sex, quality of life, physical and emotional health, and use of selective serotonin reuptake inhibitors or serotonin-norepinephrine reuptake inhibitors, using repeated-measures regression for covariates and Kendall τ for concomitant spencer questions. From 4138 patients, dispenser persistency was 68.3% (95% CI 66.8%-69.8%) at 1 year and 51% (95% CI 49%-53%) at 2 years. Within the cohort observed beyond 1 year, 82.3% (1508/1832) kept surveys enabled through the 12th month with a mean response rate of 84.1% (SD 26.4%). The large SD was apparent in the subgroup analysis, where a responder versus nonresponder dichotomy was observed. For 234 patients with ≥5 fall risk responses in each of the first 2 years, the Pearson correlation estimate between yearly mean scores was 0.723 (95% CI 0.630-0.798). For mean score intervals [0, 0.5), [-0.5, 0.5), and [0.5, 1], self-transitions were the most common, with 59.8% (140/234) of patients starting and staying in [0.5, 1]. Fall risk responses were not significantly associated with sex (P=.66) or age (P=.76) but significantly related to selective serotonin reuptake inhibitor or serotonin-norepinephrine reuptake inhibitor usage, quality of life, depressive symptoms, physical health, disability, and trips to the emergency room (P<.001). A smart medication dispenser, spencer, generated years of longitudinal survey data from patients in their homes. Panel attrition was low, and patients continued to respond at high rates. A fall risk measure derived from the survey data showed evidence of reliability and validity. An alternative to web-based panels, spencer is a promising tool for generating patient real-world data.

Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High-Risk Individuals.

Lung cancer (LC) is the top cause of cancer deaths globally, prompting many countries to adopt LC screening programs. While screening typically relies on age and smoking intensity, more efficient risk models exist. We devised a Bayesian network (BN) for LC detection, testing its resilience with varying degrees of missing data and comparing it to a prior machine learning (ML) model. We analyzed data from 9940 patients referred for LC assessment in Southern Denmark from 2009 to 2018. Variables included age, sex, smoking, and lab results. Our experiments varied missing data (0%-30%), BN structure (expert-based vs. data-driven), and discretization method (standard vs. data-driven). Across all missing data levels, area under the curve (AUC) remained steady, ranging from 0.737 to 0.757, compared to the ML model's AUC of 0.77. BN structure and discretization method had minimal impact on performance. BNs were well calibrated overall, with a net benefit in decision curve analysis when predicted risk exceeded 5%. BN models showed resilience with up to 30% missing values. Moreover, these BNs exhibited similar performance, calibration, and clinical utility compared to the machine learning model developed using the same dataset. Considering their effectiveness in handling missing data, BNs emerge as a relevant method for the development of future lung cancer detection models.

Multiphysics coupling particle dissolution in aluminum electrolysis cells based on computational fluid dynamics and discrete element model

Particle dissolution is a common phenomenon in the metallurgical and chemical industries, involving heat and mass transfer. A user-defined solver toward the computational fluid dynamics and discrete element model method with particle dissolution sub-models integrating was developed based on the open-source software OpenFOAM-LIGGGHTS, which can simulate the alumina and spent refractory material (SRM) dissolution in electrolysis cells. This approach solves the fluid phase in the Eulerian framework and the particle motion in the Lagrangian framework, comprehensively considering the particle size shrinkage, bath temperature response, and concentration diffusion during particle dissolution. The model's accuracy was verified against experimental results. The dissolution of the alumina and SRM mixture at different mixing ratios was simulated. The results show that as the proportion of SRM in the mixture increases, the mixture's dissolution rate decreases, and the remaining undissolved mass increases significantly. The SRM content in the mixture will not affect the operation stability of aluminum electrolysis cells when it is 10%, and the maximum content cannot exceed 30%. The mixture particles and the dissolved alumina and silica first move around along the inter-anode gap and center channel and then gradually transport and diffuse to the anode–cathode distance region to replenish the consumed alumina and silica. The established model and method could provide guidance for optimizing the process parameters and feeding strategies of the electrolysis cell, further improving the dissolution rate of the mixtures and increasing the SRM consumption.

Discrete transfer method for radiative transfer in one-dimensional isotropically scattering variable index media: Part I – Concentric-cylindrical geometry

Discrete transfer method for radiative transfer in one-dimensional isotropically scattering variable index media: Part I – Concentric-cylindrical geometry

Assessment of vegetation restoration potential in central Asia.

Vegetation restoration potential (VRP) assessment is an important aspect and foundation of ecological restoration projects. Neglecting the carrying capacity of the natural environment in the formulation and implementation of ecological restoration projects often leads to diminished effectiveness or even environmental damage. Existing models for VRP either overly rely on empirical knowledge, resulting in low efficiency and reproducibility, or fail to consider the nonlinear relationship between the natural environment and vegetation cover, leading to low accuracy in assessment results. Building upon existing models, this study proposes a new Vegetation Restoration Potential Mapping (VRPM) model based on a dual-variable discretization method for habitat similarity division and machine learning. Focused on Central Asia as the research area, the study evaluates the vegetation restoration potential of the region and validates the model. The results demonstrate that this model efficiently produces high-resolution and high-precision vegetation restoration potential maps. The average VRP in Central Asia is relatively low, around 36%, with most areas already having vegetation cover close to or reaching their restoration potential The regions with a higher degree of unrealized vegetation restoration potential (VRPU) are mainly distributed near human settlements, while VRPU is negative in some areas around the desert-oasis boundaries and artificial structures in the desert. The findings of this research demonstrate that the model can provide a basis for planning and implementing ecological restoration projects, thereby aiding in the health and sustainable development of ecosystems in arid regions.

Approximation properties relative to continuous scale space for hybrid discretisations of Gaussian derivative operators

This paper presents an analysis of properties of two hybrid discretisation methods for Gaussian derivatives, based on convolutions with either the normalised sampled Gaussian kernel or the integrated Gaussian kernel followed by central differences. The motivation for studying these discretisation methods is that in situations when multiple spatial derivatives of different orders are needed at the same scale level, they can be computed significantly more efficiently, compared to more direct derivative approximations based on explicit convolutions with either sampled Gaussian derivative kernels or integrated Gaussian derivative kernels. We characterise the properties of these hybrid discretisation methods in terms of quantitative performance measures, concerning the amount of spatial smoothing that they imply, as well as the relative consistency of the scale estimates obtained from scale-invariant feature detectors with automatic scale selection, with an emphasis on the behaviour for very small values of the scale parameter, which may differ significantly from corresponding results obtained from the fully continuous scale-space theory, as well as between different types of discretisation methods. The presented results are intended as a guide, when designing as well as interpreting the experimental results of scale-space algorithms that operate at very fine scale levels.

Geometrically constrained quantum dynamics: numerical solution of the Schrödinger equation on a comb

Abstract The comb model constitutes a simplified model for investigation of the dynamics in heterogeneous and anisotropic media and its implementation in the Schrödinger equation enables understanding of the wave packet spreading in such media. In this article, the time-dependent Schrödinger equation (TDSE) in the presence of comb-like geometric constraints is numerically solved. The comb-like constraints in the Hamiltonian are tailored by a modified Laplacian operator, which incorporates the Dirac delta function. To devise an appropriate numerical scheme, we regularize the Dirac delta function by its approximation of Gaussian form. Further, the finite difference method for spatial discretization and the fourth order Runge-Kutta method for the time evolution are employed. The obtained results for the wave function are compared with the analytical solutions, previously obtained in an infinite domain and a thorough analysis is carried out. The best comparison is found for short and intermediate time intervals. After validating the numerical scheme, we investigate the influence of the initial conditions on the probability density function (PDF). The TDSE was first solved with the initial conditions of Gaussian form, and then a comb-like shape of the initial wave function was proposed. The latter leads to pronounced localization in the narrow regions along the backbone, which disappears after a long time and resembles the similar behaviour of the PDF, as in the case of Gaussian initial condition. The numerical scheme developed in this work widens the applicability of the comb model for finite domains and nontrivial initial conditions, where analytical solution is not possible. Therefore, this improves the perspectives for modeling of the realistic experimental situations, where the domains are finite.

Model Selection for Multivalued-Treatment Policy Learning in Observational Studies

This study investigates the policy learning problem in observational studies, where the treatment variable can be multivalued and the propensity scores are unknown. We approximate the optimal policy in a global policy class with infinite complexity (VC/Natarajan) dimension, using a sequence of sieve policy classes with finite complexity dimension. The optimal policy within each sieve class is estimated by maximizing the empirical welfare, constructed through the doubly robust moment condition and cross-fitting method. To select the suitable sieve space, we maximize the penalized empirical welfare, with the penalty determined by either the Rademacher complexity or a holdout method. We establish oracle inequalities that demonstrate the bias and variance tradeoff achieved by the data-driven policy estimator. We also investigate two specific sieve selections: (a) a monotone single index model and (b) a systematic discretization method, which uses conventional sieve results for smooth functions such as linear sieves and deep neural networks. In the empirical study, we apply our method to examine the policy of assigning individuals to job training of different lengths.

Neutronics Analysis of Pressurized Water Reactor Cores using IDEA Code

Background This paper discusses the development and benchmarking of IDEA (Indigenous Diffusion Equation Analyzer), an in-house transient, multi-group, 3-dimensional neutron diffusion equation (NDE) code. IDEA can perform both steady-state criticality calculations and reactivity transient analysis for 3-dimensional rectangular geometries, with potential extension to cylindrical geometries. Methods The IDEA code employs the finite difference method (FDM) for spatial and temporal discretization, enhanced by source extrapolation-based acceleration. Its performance was evaluated by comparing its results with those of established NDE-based codes, PRIDE and CITATION. Various 2D and 3D steady-state and transient reactivity benchmark problems were used for this comparison. Results The results obtained from IDEA were found to be in close agreement with reference values from publicly available literature. In addition, the IDEA code demonstrated superior accuracy and consistency compared to PRIDE and CITATION in both steady-state and transient reactivity analyses. A neutronic analysis of a typical small PWR core under clean and cold conditions also showed validation against reference results. Conclusions The IDEA code has shown promising figures of merit to be used for practical neutronic calculations. Moreover, a multitude of users can benefit from its further development due to ease of access and lack of proprietary restrictions.

Design of polarization-maintaining fiber with uniform doping concentration supporting 14 weakly coupled modes based on discrete point configuration optimization

In this work, we propose a polarization-maintaining, weakly coupled few-mode fiber with a uniform doping concentration, designed via a particle swarm optimization algorithm and a discrete point configuration method. The design features a circular central air hole and an irregular doped boundary. Across the C+L band, all 14 modes exhibit an effective refractive index difference exceeding 1.43×10−4. Compared to conventional polarization-maintaining few-mode fibers, the proposed design offers enhanced flexibility, ensuring mode isolation with a very non-complicated process. This approach can be readily extended to other inhomogeneous optical waveguides, offering a valuable framework for intelligent design in non-uniform waveguide systems.

End-to-end hybrid infrared imaging system design with thermal analysis

The hybrid refractive-diffractive optical system exhibits strong capabilities in achromatic and athermal imaging, as well as in information encoding. This paper presents a novel end-to-end design framework for refractive-diffractive hybrid optical imaging systems. Utilizing a differential hybrid ray-tracing model, the framework simultaneously optimizes optical and neural network parameters. It allows for the design of diffractive optical elements (DOE) on aspheric substrates, enhancing flexibility and enabling applications in infrared optics. The integrated thermal analysis facilitates the development of athermal hybrid optical systems by combining them with an advanced restoration network. When applied to a single-lens short-wave infrared (SWIR) system, this approach outperforms traditional discrete design methods in both simulations and experiments, demonstrating its significant potential for future optical applications.

A Comparative Study of Differential Quadrature Methods for METE Nanobeam Vibrations

This study investigates the use of three different quadrature schemes, as well as an iterative quadrature methodology, to analyze vibrations in magneto-electro-thermo-elastic nanobeams. Individual MATLAB programs for each method are developed with the goal of minimizing errors in comparison to accurate findings, as well as determining the execution time for each strategy. This study shows that the Discrete Singular-Convolution Differential Quadrature Method with a Regularized Shannon Kernel (DSCDQM-RSK) and specified parameters produces the best accurate and efficient results for this particular situation. A subsequent parametric study is carried out to determine the effect of various factors on the vibrated nanobeam, including boundary conditions, material types, linear and nonlinear elastic foundation properties, nonlocal parameters, length-to-thickness ratios, external electric and magnetic potentials, axial forces, and temperature variations. Important discoveries include insights into the relationship between fundamental frequency, linear elastic foundation features, axial loads, external magnetic fields, temperature fluctuations, and material types. According to this study, these findings could be critical in the development of sophisticated nanostructures made from magneto-electro-thermo-elastic materials for use in a variety of electromechanical applications. This would entail utilizing nanobeams’ unique properties in applications such as sensors, resonators, and transducers for nanoelectronics and biology.

Application of Discrete Exterior Calculus Methods for the Path Planning of a Manipulator Performing Thermal Plasma Spraying of Coatings

This paper presents a new method of path planning for an industrial robot manipulator that performs thermal plasma spraying of coatings. Path planning and automatic generation of the manipulator motion program are performed using preliminary 3D surface scanning data from a laser triangulation distance sensor installed on the same robot arm. The new path planning algorithm is based on constructing a function of the geodesic distance from the starting curve. A new method for constructing a geodesic distance function on a surface is proposed, based on the application of Discrete Exterior calculus methods, which is characterized by a high computational efficiency. The developed algorithms and their software implementation were experimentally tested with the robotic microplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral-based raw materials.

A Thermo‐Mechanical Localizing Gradient Damage Model With Modified Mazars Strain

ABSTRACTThis work establishes a thermo‐mechanical coupling model for thermally induced cracks in quasi‐brittle materials within the framework of localizing gradient damage theory. The constitutive relation considering the thermal expansion effect, and the heat conduction equation integrating the volumetric strain and damage evolution, are formulated based on the Clausius–Duhem inequality to ensure thermodynamic consistency. In particular, to accommodate the tension‐compression strength asymmetry across different quasi‐brittle materials, a modified Mazars strain is proposed as the driving force for the micro equivalent strain, where the first invariant of strain tensor and an adjustable ratio of compressive strength to tensile strength () are introduced. The numerical implementation employs the generalized‐α method for time domain discretization and the staggered scheme for decoupling. Numerical simulations demonstrate a nice feasibility of the modified Mazars strain in capturing both tensile and mixed‐mode failures, and the proposed model proves capable of characterizing complex crack behaviors under thermal loading in both quasi‐static and dynamic scenarios. The quenching test simulations of ceramic plates highlight the bidirectional coupling effects on crack patterns. Unidirectional coupling models that ignore the influence of volumetric strain and damage evolution on the temperature field would underestimate the temperature gradient, resulting in larger crack spacing and fewer cracks. In the thermal shock tests of cylindrical specimens, the transition in crack patterns from spiral shear bands to an annular damage zone with increasing compression‐tension strength ratio () is precisely captured, demonstrating the generality of the proposed model for various quasi‐brittle materials with tension‐compression strength asymmetry.

Parental perception of treatment options for mucopolysaccharidosis: a survey to bridge the gap for personalized medicine

BackgroundMucopolysaccharidosis (MPS) are a group of lysosomal storage diseases with substantial unmet medical needs—for both patients and caregivers. Approved therapies are limited, and the perception of investigative ones remains enigmatic.MethodUsing an innovative survey concept based on the discrete choice experiment method (DEC) with neuronopathic and non-neuronopathic patient scenarios, we aimed to evaluate how parents of children with MPS perceive different approved and innovative therapies. The questionnaire was distributed via patient organizations in Germany, Switzerland, and Austria.ResultsMost likely parents would choose an approach to repurposed treatments for their child (neuronopathic: 82%, 14/17 and non-neuronopathic: 94%, 16/17), followed by enzyme replacement therapy (ERT, both 88%, 15/17), hematopoietic stem cell therapy (HSCT, 70%, 12/17 and 76%, 13/17), and finally gene therapy (GT, 58%, 10/17 and 53%, 9/17). The general attitudes strongly influenced decision-making regarding treatment options. While over 80% of respondents who held a positive attitude toward ERT, HSCT, and ITTs indicated they would opt for these therapies in both neuronopathic and non-neuronopathic cases, only about half of the parents with a favorable general view of gene therapy (GT) expressed a positive perception of its likelihood as a treatment option. Furthermore, most parents found mild infections, injection site reactions (ISRs), hypertonia, and treatment-related hospitalizations acceptable and indicated patient organizations as their main source of information.ConclusionsThis study provides an innovative survey method, thereby offering the rationale for a quantitative risk–benefit model and the importance of patient and caregiver-centered information dissemination, especially for innovative therapies.

Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation

Abstract. The urban canopy model Town Energy Balance (TEB) is coupled with the radiation model SPARTACUS-Urban to improve the urban geometry simplification and the radiative transfer calculation. SPARTACUS-Urban assumes that the probability density function of wall-to-wall and ground-to-wall distances follows a decreasing exponential. This better matches the distributions in real cities than in the infinitely long street canyon employed by the classical TEB. SPARTACUS-Urban solves the radiative transfer equation using the discrete ordinate method. This allows us to take into account physical processes such as the interaction of radiation with the air in the urban canopy layer and the spectral dependence of urban material reflectivities or specular reflections. Such processes would be more difficult to account for with the radiosity method used by the classical TEB. With SPARTACUS-Urban, the mean radiant temperature, a crucial parameter for outdoor human thermal comfort, can be calculated from the radiative fluxes in the vertical and horizontal directions incident on the human body in an urban environment. TEB–SPARTACUS is validated by comparing the solar and terrestrial urban radiation budget observables with those simulated by the Monte-Carlo-based HTRDR-Urban reference model for procedurally generated urban districts that mimic the local climate zones. Improvement is found for almost all radiative observables and urban morphologies for direct solar, diffuse solar, and terrestrial infrared radiation. The TEB mean radiant temperature diagnostic for a person in the urban environment is also improved with TEB–SPARTACUS compared with the classical TEB. Based on these results, TEB–SPARTACUS could lead to more realistic results for building energy consumption, outdoor human thermal comfort, or the urban heat island effect.