Simple Representation Research Articles

Method for generating kinetically relevant fuel surrogates based on chemical functional group compositions

Fuel surrogates, simplified representations of complex fuels, play a crucial role in understanding fuel behavior and developing efficient combustion technologies. Previously published surrogates, specifically those designed to model shock tube speciation data, exhibit chemical functional group fragmentation similar in both type and quantity to their parent fuels, suggesting the importance of matching chemical functional groups for surrogates. This paper presents a data-driven optimization procedure for formulating fuel surrogates exclusively by matching chemical functional groups to their parent fuels. The resulting surrogates enable accurate modeling of speciation results, provide valuable insights into reaction kinetics, and are shown to consistently reproduce the ignition quality of their parent fuels, as represented by the Derived Cetane Number (DCN). The method automatically selects the appropriate number and type of components and optimizes their compositions to achieve similar chemical functional group compositions as the target fuel. The applicability of the method is demonstrated by formulating surrogates for two reference jet fuels (Jet-A POSF-10325, F-24), one synthetic alcohol-to-jet fuel (ATJ POSF11498), and a blend of ATJ/F-24 (50/50 vol ratio). Additionally, comparisons of rate of production, rate of progress, and sensitivity analyses in chemical kinetics modeling of the fuels and their surrogates highlight the consistency in outputs between surrogates with different compositions but similar chemical functional group fragmentation.

Shape-changing particles for locally-resolved particle geometry in DEM simulations

Taking into account the complex shape of particles in discrete element method (DEM) simulations of large-scale granular systems is computationally demanding due to the time-consuming contact detection algorithms for polyhedral particles. In this short communication, a novel approach that locally resolves the particle shapes where needed and uses a simplified representation elsewhere, to accelerate simulations without compromising accuracy, is presented. For this purpose, a method employing a smooth transition of the particle shape representation from analytical spheres to shape-resolving polyhedra is introduced in DEM. The feasibility and correct implementation of this approach are demonstrated through simulations of hopper discharge involving spherical and dodecahedral particles from a flat bottom silo or shaft kiln. The model capabilities, in terms of accuracy as well as reduction in computational effort, are quantified for a moving bed with continuous outflow.

Utilising personas as a methodological approach to support prospective mathematics teachers’ adaptation and development of digital mathematics learning resources

AbstractPersonas, initially originated in user experience research, are short and simplified representations of particular user groups, and this methodological approach has recently gained ground in educational research. This study aims to explore aspects of personas that may be beneficial for prospective mathematics teachers when they develop digital learning resources. To explore such aspects, we employed qualitative interviews, thinking-out-loud techniques, and jointly developed learning resources with prospective mathematics teachers, and analysed this diverse data with a combination of case study and grounded theory approaches. Consequently, we were able to identify the following essential aspects of using personas in our study: (A) personas as representatives of real people, (B) personas as planning & feedback tools for material development, (C) professionalisation of prospective mathematics teachers (by using personas), (D) differentiation/individualisation for personas through digital learning resources, and (E) motivational elements of digital mathematics learning resources. Based on our results, we concluded that using personas could broaden prospective mathematics teachers’ views on student characteristics and demands that may enable teachers to facilitate the development of differentiated and individualised digital mathematics learning resources.

Examining the Potential of Cartoon-Based Simulations for Studying Mathematics Teachers’ Handling of Student Emotions: A Replication Study

Abstract Technology-mediated simulations of teaching are used increasingly to represent practice in the context of professional development interventions and assessment. Some such simulations represent students as cartoon characters. An important question in this context is whether simplified cartoon representations of students can convey similar meanings as real facial expressions do. Here, we share results from an implementation and replication study designed to observe whether and how (1) cartoon-based representations of emotion using graphical facial expressions can be interpreted at similar levels of accuracy as photo representations of emotions using actors and (2) the inclusion of markers of student emotions in storyboard-based scenarios of secondary mathematics teaching affects teachers’ appropriateness rating of the actions taken by a teacher represented in the storyboard. We show graphical representations of emotions can evoke particular intended emotions and that markers of student emotions in representations of practice could cue mathematics teachers into particular judgments of action. The Impact Sheet to this article can be accessed at 10.6084/m9.figshare.24219964

Nonlinear cable computations using high‐order solid elements with hp‐adaptive refinement for anisotropic plasticity

AbstractCables are slender structural elements, that are found in many engineering structures and are exposed to large displacements while experiencing small or large local strains during their installation or in dynamic environments. To address the complexity of their inner structure, an effective material model with anisotropic properties in the elastic and elastoplastic domain is utilized, allowing for a simplified representation that overcomes numerical challenges associated with a fully resolved model. This article focuses on enhancing the representation of local discontinuities arising from the plastic front using ‐refinement. The described workflow is applied to a perforated plate benchmark example and a cable subjected to torsion. Results indicate that the ‐refinement achieves a fast convergence and on average uses fewer degrees of freedom to achieve similar accuracy as compared to pure h‐refinement or pure p‐refinement.

Large constellations for fast acquisition and delivery of information: Design and analysis by stereographic projection onto the equatorial plane

SummaryRecent space projects are designed by satellite constellations with a large number of spacecraft, a global character (i.e., from equatorial to high inclination orbits), and the possibility to transfer information to different satellites of the constellation (inter satellite link) in order to deliver the information to the ground as soon as possible. To cope with a large number of parameters, a fast tool for constellation design, performance evaluation and networking strategies is needed. The aim of this paper is to obtain the performance of any constellation in less than 1 s, even when the number of satellites in the constellation and the duration of the analysis is large (e.g., more than 200 satellites in a period of some days). The proposed algorithm is based on analytical formulae obtained by using the stereographic projection on the equatorial plane of the satellite orbits and the projection of the target and ground station orbits. It is believed that the two‐dimensional projection proposed here can offer some advantages with respect to the spatial analysis of satellite orbits and their ground tracks, such as the reduction in time required to calculate station/satellite and satellite/satellite encounter conditions, and the clear and simple representation of the motion of the satellite of the constellation and of the ground stations of interest.

An improved effectiveness-NTU method for streamlining the design and optimization of packed bed latent thermal energy storage

The packed bed thermal energy storage system (PBLTES) is one of the promising and effective ways to solve the contradiction between the supply and demand of renewable energy. As a simplified mathematical representation, the effectiveness-number of transfer units (NTU) method can characterize the PBLTES faster than numerical simulation, which is convenient for PBLTES design, analysis, and optimization. The p factor, which is a key parameter for calculating the equivalent thermal resistance in the effectiveness-NTU method, is used to define the path of charging and discharging of the PBLTES, that is, the synchronization of phase change processes of phase change materials in different layers, which determines the accuracy of the model. In this paper, the calculation method of average effectiveness is further refined based on existing studies and mathematical derivations. The phase change duration characteristic of the PBLTES is incorporated into the method. Then, a numerical simulation model of PBLTES with spherical capsules was established to investigate the influence of relevant parameters on the p-factor and the impact mechanism. Finally, the p-factor was fitted based on numerical simulation. The results show that the effectiveness solved by the fitting p-factor equation has a maximum error of less than 10 % and an average error of 5.11 % compared to the experimental results. The establishment of the p-factor calculation equation allows the effectiveness-NTU method to be used more accurately and quickly for the design and optimization of the structural and operating parameters of the PBLTES, which can advance the engineering applications of PBLTES.

Two modes of social impressions and their effects on choice.

Our memories of other people shape how we interact with them. Yet, even when we forget exactly what others said or did, we often remember impressions that capture a general gist of their behavior-whether they were forthright, friendly, or funny. Drawing on fuzzy trace theory, we propose two modes of social impression formation: impressions formed based on ordinal gist ("more competent," "less competent") or categorical gist ("competent," "incompetent"). In turn, we propose that people gravitate toward the simplest representation available and that different modes of memory have distinct consequences for social decisions. Specifically, ordinal impressions lead people to make decisions based on an individual's standing relative to others, whereas categorical impressions lead people to make decisions based on discrete classifications that interpret behavior. In four experiments, participants learned about two groups of individuals who differed in their competence (Studies 1a, 2, and 3) or generosity (Study 1b). When participants encoded impressions as ordinal rankings, they preferred to hire or help a relatively good target from a low-performing group over a relatively bad target from a high-performing group, even though both targets behaved identically and accuracy was incentivized. However, when participants could use categorical boundaries to interpret behavior, this preference was eliminated. In a final experiment, changing the category participants used to encode others' generosity changed their impressions, even when accounting for memory for verbatim details. This work links social impressions to theories of mental representation in memory and judgment, highlighting how distinct representations support divergent patterns of social decision-making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Performance gaps between energy system planning and operation: a study exploring the impacts of model fidelity and dispatch strategy

Effective integration of renewables is essential in the energy transition, which necessitates efficient management of intermittent renewable energy generation, system cost minimization and continuous balance between supply and demand. Due to the problem’s multifaceted nature, tools with a simplified representation of energy system operation are often used to ensure computational tractability, causing performance gaps between planning and operation stages. This work quantifies the gaps focusing on the impacts of model fidelity and dispatch strategy, such as the representation of physical constraints at different levels and varying forecast horizons. More specifically, a real-world case study with three energy hubs is considered. A Pareto front is first obtained considering the trade-offs between cost and carbon footprint using the Ehub tool, a state-of-the-art energy system planning tool. Following that, the cost-optimal and the emission-optimal designs are selected for evaluating the performance gaps, using the dispatch strategies obtained from the Ehub tool as the baseline. Results show that detailed considerations of physical constraints influence grid dependencies and fuel consumption but there are no significant impacts on resultant total costs. The cost increase due to detailed physical constraints is higher for the cost-optimal system than for the emission-optimal system. Moreover, limiting the forecast horizon to 24 hours has significant impacts on the emission-optimal system with an increase in total system cost by 20.3 %. In contrast, there is only a marginal increase of 0.8 % for the cost-optimal system.

A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx

Abstract. Satellites monitoring air pollutants (e.g., nitrogen oxides; NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability in and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic hotspots. Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide; CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT–NOx) and to investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO2). Interpreting emission signals from tNO2 commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three United States (US) power plants and two urban areas across seasons. Using the U.S. Environmental Protection Agency (EPA)-reported emissions for power plants with non-linear NOx chemistry improves the model–data alignment in tNO2 (a high bias of ≤ 10 % on an annual basis), compared to simulations using either the Emissions Database for Global Atmospheric Research (EDGAR) model or without chemistry (bias approaching 100 %). The largest model–data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO2 can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of NOx and CO2, which laid a foundation for a local-scale multi-tracer emission optimization system.

Evaluation of 30 urban land surface models in the Urban‐PLUMBER project: Phase 1 results

AbstractAccurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban‐focussed land surface models in over a decade. Here, in Phase 1 of the Urban‐PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information‐limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short‐wave radiation, sensible and latent heat fluxes, but little or no improvement in long‐wave radiation and momentum fluxes. Models with a simple urban representation (e.g., ‘slab’ schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid‐complexity models (e.g., ‘canyon’ schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three‐dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation‐only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of Urban‐PLUMBER will evaluate models across 20 sites in different urban and regional climate zones.

Rational Simplification and Rigidity in Human Planning.

Planning underpins the impressive flexibility of goal-directed behavior. However, even when planning, people can display surprising rigidity in how they think about problems (e.g., "functional fixedness") that lead them astray. How can our capacity for behavioral flexibility be reconciled with our susceptibility to conceptual inflexibility? We propose that these tendencies reflect avoidance of two cognitive costs: the cost of representing task details and the cost of switching between representations. To test this hypothesis, we developed a novel paradigm that affords participants opportunities to choose different families of simplified representations to plan. In two preregistered, online studies (Ns = 377 and 294 adults), we found that participants' optimal behavior, suboptimal behavior, and reaction time were explained by a computational model that formalized people's avoidance of representational complexity and switching. These results demonstrate how the selection of simplified, rigid representations leads to the otherwise puzzling combination of flexibility and inflexibility observed in problem solving.

Nanocomposite physics and structure considerations in MCNP proton shielding models

Background Deep space crewed missions require unique radiation shielding considerations. Due to mission duration and the heavy, high-energy nature of deep space radiation, the aluminum alloys that have historically been used for spacecrafts do not provide sufficient protection for crew members. Polymer-based nanocomposites have been proposed as potential radiation shielding materials for deep space applications; however, the high tunability of nanocomposites and the limited number of facilities able to produce representative radiation energies presents barriers to nanocomposite testing. Computational approaches may allow for preliminary investigation and discrimination of nanocomposite parameters like polymer and filler composition, loading percentage, and filler size and structure. Methods This work aims to investigate several modeling methods to perform radiation transport simulations for a nanocomposite, particularly with regards to modeling the filler nanostructure and the involved physics. Published experiments of a polydimethylsiloxane-carbon nanotube nanocomposite in 63 MeV and 105 MeV proton beams were digitally recreated in MCNP6.2 with three different nanocomposite structures. Additionally, several simulations were performed under various physics assumptions. The computationally determined water equivalent thicknesses are compared between modeling methods, as well as between the computational and the published experimental work. Results Nanostructure model complexity had no impact on the computed water equivalent thickness, with <1% difference between the three modeling methods. The inclusion of delta ray production and tracking physics produced a slight decrease in the computed water equivalent thickness and significantly increased the computational time for a single simulation by a factor of 35.7. Computationally determined water equivalent thicknesses are within 5% of published experimentally determined values. Conclusions Shielding capabilities of a polymer/carbon nanotube nanocomposite estimated using Monte Carlo radiation transport models agree well with published experimental results, even with a simplistic representation of the nanocomposite material. Radiation transport settings influenced the resulting water equivalent thickness as well as computational resource needs.

Fluoroethylnormemantine (FENM) shows synergistic protection in combination with a sigma-1 receptor agonist in a mouse model of Alzheimer's disease

Fluoroethylnormemantine (FENM) is a Memantine derivative with anti-amnesic and neuroprotective activities showed in the Aβ25-35 pharmacological mouse model of Alzheimer's disease (AD). As AD is a complex multi-factorial neurodegenerative pathology, combination therapies relying on drugs acting through different pathways, have been suggested to more adequately address neuroprotection. As several agonists of the sigma-1 receptor (S1R), an intracellular chaperone, are presently in phase 2 or 3 clinical trials in neurodegenetrative diseases including AD, we examined the potentialities of S1R drug-based combinations with FENM, or Memantine. Aβ25-35-treated mice were treated with S1R agonists (PRE-084, Igmesine, Cutamesine) and/or FENM, or Memantine, during 7 days after intracerebroventricular administration of oligomerized Aβ25-35. Mice were then tested for spatial short-term memory on day 8 and non-spatial long-term memory on days 9–10, using the spontaneous alternation or passive avoidance tests, respectively. The FENM or Memantine combination with Donepezil, that non-selectively inhibits acetylcholinesterase and activates S1R, was also tested. The efficacy of combinations using maximal non-active or minimal active doses of S1R agonist or FENM was analyzed using calculations of the combination index, based on simple isobologram representation. Data showed that most of the FENM-based combinations led to synergistic protection against Aβ25-35-induced learning deficits, for both long- and short-term memory responses, with a higher efficiency on the latter. Memantine led to synergistic combination in short-term memory but poorly in long-term memory responses, with either PRE-084 or Donepezil. These study showed that drug combinations based on FENM and S1R agonists may lead to highly effective and synergistic protection in AD, particularly on short-term memory.

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework – which we call HydroGraphs – for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Model-free prediction of time series: a nonparametric approach

We propose a novel approach for model-free time series forecasting. Unlike most existing methods, the proposed method does not rely on parametric error distributions nor assume parametric forms of the mean function, leading to broad applicability. We achieve such generality by establishing a simple but powerful representation of a time series { X t ; t ∈ Z } with sup t E | X t | < ∞ , that is, X t has a solution which is a linear combination of infinite past values. Then using the obtained solution a prediction algorithm is presented, with large sample theoretical guarantees. Simulation studies show favourable performance of the proposed method compared with popular parametric and neural networks methods, and suggest its superiority when the sample size is small. An application to practical time series is discussed.

Modelling and Analysis of Dynamic Servo Error of Heavy Vertical Machining Centre Considering Nonlinear Factors

The dynamic servo error of heavy-duty vertical machining centres is one of the decisive factors affecting the machining accuracy of large and complex parts. Due to the characteristics of large mass, large load, and the large travel distance of the machine tool, non-linear factors such as friction, backlash, and lateral shift are more likely to cause unstable behaviours such as stick-slip and oscillation of the servo feed system of the machine tool, and reduce the performance and servo accuracy of the motion axis. In this paper, to consider the influence of non-linear factors such as friction, backlash, and lateral shift, an appropriately simplified representation of the mechanical transmission system of the ball screw has been used. According to the control structure of the Siemens 840D numerical control system, a theoretical model of the servo feed system for the heavy-duty vertical machining centre was established based on three-loop control. Then, the single-axis and double-axis closed-loop simulation models of the servo feed system were built in Simulink, and the influence pattern of control parameters and nonlinear factors on the dynamic servo error was obtained through simulation analysis. Finally, the validity of the theoretical model for the servo feed system was verified through a comprehensive comparison of simulation and experimental outcomes. This encompasses an analysis of the control system Bode plots, critical stick-slip velocity, and tracking errors in the X-axis with linear motion. The validation provides theoretical guidance for parameter design and mechanical adjustments of the servo feed system in heavy-duty vertical machining centres.

Experimental and numerical modelling of water waves in sewer networks during sewer/surface flow interaction using a coupled ODE‐SWE solver

AbstractFlooding in urban areas is expected to increase its magnitude and frequency in the future. Therefore, there is a strong need to better model sewer–surface flow interactions. Existing numerical methods are commonly based on simplified representations of sewer/surface mass exchange, and mainly validated in steady flow conditions. Current methodologies describing the propagation of transient conditions/waves through interaction nodes are simplified, rely on empirical coefficients and/or lack detailed validation. In this paper, an integrated numerical approach for modelling the propagation of water waves through interaction nodes (e.g., manholes) is presented. In this solution, the shallow water equations are used to simulate the free‐surface propagation inside the sewer network, and an ordinary differential equation is employed for modelling flow regimes through pipes and manholes. The model proposed is validated against the well‐known STAR‐CD modelling software for a number of test cases. Finally, further validation is performed against experimental data describing the evolution of water depth around a manhole in unsteady surcharging conditions.

Freight activity-travel pattern generation (FAPG) as an enhancement of freight (trip) generation modelling: Methodology and case study

Trip-based models and activity-based models represent two extreme ends of the spectrum of travel demand models in data granularity requirement and ability to reflect the underlying motivation to travel. Modelling of representative freight activity-travel patterns (RFAPs) has the potential to serve as the bridge between these approaches. RFAP clusters represent homogeneous groups of establishments, where utility maximization models predict the probability that an establishment belongs to a particular cluster. However, it is still an open question how to define, interpret and model activity-travel patterns in the context of freight system. To answer this question, this study conducted a large-scale establishment-based freight survey (EBFS) in seven cities of India and resulted in a sample of 432 establishments and their 1613 shipment records. In the first part, this paper proposes a novel approach for identifying RFAPs based on the notion that “activities” that inspire trip-making for passenger is equivalent to “freight orders” in the case of establishments. The cluster analyses revealed the presence of three well separated main clusters and nine less separated nested clusters. Through interpretation and labelling of these RFAPs, freight travel market is categorized into useful segments. The results suggested that a priori industrial classification systems used in trip-based models are overly simplified representations of the complex structure of the travel patterns. In the last part, freight activity-travel pattern generation (FAPG) models are developed which predicts the probability that an establishment exhibits a particular RFAP. The FAPG models developed using these RFAPs could replace the traditional freight generation (FG) and freight trip generation (FTG) models due to its ability to convert the assigned activity-patterns to trips or tonnage. For example, FAPG model suggest that at an employment level of 120, there is a 56% probability that establishments will exhibit MDV-HFMH (medium duty vehicles - high frequency medium haul) pattern which, in turn, implies that FP = 1630 tons/year; shipment frequency, i.e., FTP = 8 trips/week; length of haul = 240.6 km and commercial vehicle type choice = MDV. Thus, FAPG models can present an enhanced representation of freight flows since both FG and FTG are jointly modeled in this approach. That is, the best features of both commodity-based modelling (i.e., ability to capture the fundamental mechanism that drives freight demand) and vehicle-based modelling (i.e., ability to capture freight traffic implications) are included in FAPG models. The study findings are expected to assist in identifying the variations in establishments’ preferences so that it is possible to identify the type of transport supply improvements that the establishments will respond to accurately, and thus prioritize the infrastructure investments. Moreover, the discussions on these findings are expected to improve the behavioral and spatial foundations of traditional freight models.

Automatic Mesh and Shader Level of Detail.

The level of detail (LOD) technique has been widely exploited as a key rendering optimization in many graphics applications. Numerous approaches have been proposed to automatically generate different kinds of LODs, such as geometric LOD or shader LOD. However, none of them have considered simplifying the geometry and shader at the same time. In this paper, we explore the observation that simplifications of geometric and shading details can be combined to provide a greater variety of tradeoffs between performance and quality. We present a new discrete multiresolution representation of objects, which consists of mesh and shader LODs. Each level of the representation could contain both simplified representations of shader and mesh. To create such LODs, we propose two automatic algorithms that pursue the best simplifications of meshes and shaders at adaptively selected distances. The results show that our mesh and shader LOD achieves better performance-quality tradeoffs than prior LOD representations, such as those that only consider simplified meshes or shaders.