MARS Model Research Articles

Mantle–melt partitioning of the highly siderophile elements: New results and application to Mars

AbstractTrace elements and extant and extinct isotopic attributes in Martian meteorites have been used to argue that Mars accreted quickly, differentiated into core and mantle, and established several mantle reservoirs, possibly within 10 Ma of T0. The partitioning of trace elements in the deep mantle has been relatively unstudied, despite the need for such knowledge in understanding magma ocean crystallization and the origin of depleted and enriched mantle reservoirs. The siderophile element composition of the Martian mantle and lithophile isotopic systems such as Sr, Hf, and Nd are thought to record evidence for early metal–silicate equilibrium and deep magma ocean at an intermediate depth and pressure of 800 km or 14 GPa. We have carried out experiments across this pressure range to better understand the mineral/melt partitioning of a wide range of elements. These new data are used to evaluate differentiation models for Mars and to help interpret the available isotopic data. The relatively incompatible nature of Re compared to mildly compatible Os means that the crystallization of a deep magma ocean will lead to residual liquids with superchondritic Re/Os, and solids with subchondritic Re/Os. Such material available in the mantle could be the source of enriched isotopic reservoir that produced shergottites with +γOs values. Slightly subchondritic Re/Os ratios in the crystallizing solids would provide a reservoir that could produce −γOs values. Melting of mixtures of these two enriched and depleted endmembers could explain the Nd‐Os isotopic correlations and systematics of shergottites.

MSS/1: Single‐Station and Single‐Event Marsquake Inversion

AbstractSEIS, the seismometer of the InSight mission, which landed on Mars on 26 November 2018, is monitoring the seismic activity of the planet. The goal of the Mars Structure Service (MSS) is to provide, as a mission product, the first average 1‐D velocity model of Mars from the recorded InSight data. Prior to the mission, methodologies have been developed and tested to allow the location of the seismic events and estimation of the radial structure, using surface waves and body waves arrival times, and receiver functions. The paper describes these validation tests and compares the performance of the different algorithms to constrain the velocity model below the InSight station and estimate the 1‐D average model over the great circle path between source and receiver. These tests were performed in the frame of a blind test, during which synthetic data were inverted. In order to propagate the data uncertainties on the output model distribution, Bayesian inversion techniques are mainly used. The limitations and strengths of the methods are assessed. The results show the potential of the MSS approach to retrieve the structure of the crust and underlying mantle. However, at this time, large quakes with clear surface waves have not yet been recorded by SEIS, which makes the estimation of the 1‐D average seismic velocity model challenging. Additional locatable events, especially at large epicentral distances, and development of new techniques to fully investigate the data, will ultimately provide more constraints on the crust and mantle of Mars.

Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model

The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is investigated in rainfall-runoff modeling at hourly timescale. The results are compared with the conceptual EBA4SUB model using rainfall and runoff data from Samoggia River basin, Italy. The capability of the methods is measured using five statistics, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error, scatter index, and adjusted index of agreement. Comparison of single ML reveals that the ANFIS-PSO, ANFIS-FCM and MARS produce similar accuracy which is better than the M5Tree model. MM-SA ensemble model improves the accuracy of ANFIS-PSO, ANFIS-FCM, MARS and M5Tree models with respect to RMSE by 8.5%, 5%, 7.4% and 28.8%, respectively. Comparison with the conceptual event-based method indicates that the ML methods generally performs superior to the EBA4SUB; however, latter method provides better accuracy than the M5Tree and MARS in some cases.

Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

Yumurta Verimi Üzerine Bazı Özelliklerin Etkisinin Regresyon Analiz Yöntemlerinden Bagging Mars ile Belirlenmesi ve R Uygulaması

Çalışmada, BAGGING ile MARS birleştirilerek elde edilen hibrid bir yaklaşımın yumurta tavuklarından elde edilen bir veri setinde sıcaklık ve nemin yumurta verimi, kırık/çatlak yumurta, ölen hayvan sayısı ve yem tüketimi üzerine etkisinin olup olmadığı test edilmiştir. Çalışmada Adana ili Çukurova Bölgesinde bulunan özel bir hayvancılık işletmesinden 2018 yılına ait yumurta üretim işletmesinden elde edilen veriler kullanılmıştır. Araştırmada, ortalama 60 haftalık yaşta olan Lohman ırkı tavuklardan elde edilen veri seti kullanılmıştır. Oluşan MARS ve Bagging MARS algoritmasında regresyon problemlerinin daha güçlü bir çözümünü sunmak için R STUDIO programındaki earth (enhanced adaptive regression through hinges) ve caret (classification and regression training), mda (Mixture Discriminant Analysis) paketleri kullanılmıştır. Bagging MARS tekniğinin tahmin performansı, bootstrap örnek sayısı olan B değeri 3 alınarak uyum iyiliği kriterleri ile değerlendirilmiştir. Çalışmada, MARS ve bagging MARS analizi kullanarak sıcaklık ve nemin yumurta verimi, kırık/çatlak yumurta, ölen hayvan sayısı ve yem tüketimi üzerine etkisi araştırılmıştır. Yumurta verimi üzerine, akşam sıcaklığının(t3) etkisinin olduğu fakat sabah(t1) ve öğle(t2) sıcaklıklarının ise etkisinin olmadığı belirtilmiştir. Kırık/çatlak yumurta ve ölen hayvan hayvan sayısı 5 den az/ hafta olmasından dolayı, MARS ve Bagging MARS modellerinde bu değişkenlere tahmin denkleminde yer verilmemiştir. Yem tüketiminin ise, her iki modelde de pozitif bir katkısının olduğu görülmüştür.

Ultraviolet glow reveals Mars weather patterns

NASA’s Mars Atmosphere and Volatile Evolution (MAVEN) orbiter has mapped seasonal wind patterns at the red planet’s equator and poles using the ultraviolet nightglow produced by atmospheric reactions ( 2020, DOI: ). Solar radiation splits CO and N molecules in Mars’s atmosphere. When N and O atoms combine in the cooler, denser night air to make NO, they release a UV photon. The atmospheric waves and tidal action MAVEN observed mostly match scientists’ existing models of Mars’s atmosphere and its seasonal variations. But the satellite did find stronger NO signals at the equator than expected, and a surprising spiral wave at the south pole (shown).—

Preliminary assessment of the nuclear thermal–hydraulic system code MARS for the application to a refrigeration cycle

MARS is a nuclear thermal–hydraulic system code. It has been developed for the design and safety analysis of a water-cooled nuclear power plant and has been validated using various experimental data. It can realistically predict two-phase flow behaviors in both a steady-state and a transient of a complicated thermal–hydraulic system with robustness and versatility. This study is an attempt to fully utilize the advantages of MARS for the simulation of a refrigeration cycle, such as a multi-split system air conditioner. However, since most models and correlations in the MARS code have been developed for water-cooled nuclear systems, some code modification and assessment were required. First, we have implemented the thermodynamic properties of a refrigerant R-410A and a compressor model into the code. Then we assessed the heat transfer and pressure drop models of MARS using a set of R-410A boiling and condensation experiments. To assess the applicability of the MARS code to a refrigeration cycle, we have simulated a multi-split system air conditioner experiment. The results showed that the nuclear thermal–hydraulic system code can predict the transient behavior of a refrigeration cycle reasonably well. The limitations and further improvements are also identified from the results.

A prediction model for the critical liquid-carrying velocity of gas–liquid stratified flow in micro-tilting line pipes with low liquid contents

Wet gas can form liquid loading at the lower line pipe sections, so the transportation efficiency will be impacted and the line pipes will be corroded and even blocked. Therefore, to accurately predict the critical liquid-carrying velocity of gas is of great significance to preventing the liquid loading in wet gas line pipes. In view of the gas–liquid two-phase stratified flow in micro-tilting line pipes with low liquid content, this paper newly established a critical liquid-carrying velocity prediction model considering droplet entrainment according to the momentum balance equation of a gas–liquid two-phase flow and the closure relationship of a new gas–liquid interface shape. Then, based on the experimental data, the new model, FLAT model, ARS model, double-circle model and MARS model were verified and their prediction results were compared. Finally, the new model was applied to analyze the effects of pipe dip, operational pressure, liquid density and gas component on the critical liquid-carrying velocity and critical liquid content of natural gas–water and natural gas–60% glycerine with water stratified flow in a micro-tilting line pipe. And the following research results were obtained. First, with the increase of pipe dip and liquid density, the critical liquid-carrying velocity increases continuously and the critical liquid content decreases gradually. Second, with the increase of operational pressure and heavy component content, the critical liquid-carrying velocity decreases continuously and the critical liquid content increases gradually. In conclusion, the new model is higher in prediction accuracy and its prediction result is better accordant with the experimental value, so it can be used to predict the critical liquid-carrying velocity in wet gas line pipes.

An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils

This study is attempted to propose a new hybrid artificial intelligence model called integrative genetic algorithm with multivariate adaptive regression splines (GA-MARS) for settlement prediction of shallow foundations on sandy soils. In this hybrid model, the evolution algorithm – Genetic Algorithm (GA) was used to search and optimize the hyperparameters of multivariate adaptive regression splines (MARS). For this purpose, a total of 180 experimental data were collected and analyzed from available researches with five-input variables including the bread of foundation (B), length to width (L/B), embedment ratio (Df/B), foundation net applied pressure (qnet), and average SPT blow count (NSPT). In further analysis, a new explicit formulation was derived from MARS and its accuracy was compared with four available formulae. The attained results indicated that the proposed GA-MARS model exhibited a more robust and better performance than the available methods.

Structural connectivity to reconstruct brain activation and effective connectivity between brain regions

Objective. Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two non-invasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering spatio-temporal constraints but only few of them use structural information to do so. Approach. In this work, we present a source reconstruction algorithm that uses brain structural connectivity, estimated from diffusion MRI (dMRI), to constrain the EEG/MEG source reconstruction. Contrarily to most source reconstruction methods which reconstruct activation for each time instant, the proposed method estimates an initial reconstruction for the first time instants and a multivariate autoregressive model that explains the data in further time instants. This autoregressive model can be thought as an estimation of the effective connectivity between brain regions. We called this algorithm iterative Source and Dynamics reconstruction (iSDR). Main results. This paper presents the overall iSDR approach and how the proposed model is optimized to obtain both brain activation and brain region interactions. The accuracy of our method is demonstrated using synthetic data in which it shows a good capability to reconstruct both activation and connectivity. iSDR is also tested with real data obtained from (Wakeman D and Henson R 2015 A multi-subject, multi-modal human neuroimaging dataset Scientific Data 2 15001) (face recognition task). The results are in phase with other works published with the same data and others that used different imaging modalities with the same task showing that the choice of using an autoregressive model gives relevant results. Significance. This work shows that complex EEG/MEG datasets can be explained by an initial state and a MAR model for effective connectivity. This is a compact way to describe brain dynamics and offers a direct access to effective connectivity.

The potential impacts of automated cars on urban transport: An exploratory analysis

ObjectiveThe concept of automated cars is rapidly becoming a reality. Yet there has been very little analysis of the impacts of such developments on the performance of urban transport systems. These impacts are potentially complex. On the positive side, automation has the potential to increase road capacity, make driving available to more people, and reduce accidents and emissions. On the negative side, it could attract users away from public transport, walking and cycling, substantially increase traffic levels and stimulate urban sprawl. These impacts cannot currently be measured empirically and, by the time that they can, it will be too late to change the implementation model to rectify any resulting problems. Predictive assessments are therefore needed. This paper considers the possible impacts of automated vehicles, predicts their effects on the urban land use and transport system, and discusses the policy implications. We focus on automation of the car fleet, and do not consider the potential of automation of public transport or freight. MethodsWe consider the literature on the range of attributes of automated vehicles which might affect transport and land use patterns, and suggest potential outcomes for each over the period to 2050. These include the proportion of automated vehicles in the car fleet, whether automated vehicles are privately purchased or publicly shared, the impacts on network capacity, the reduced need to pay for and walk from parking places, the potential reduction in the value of in-vehicle time and the potential use by current non-drivers. We represent these attributes in an expanded causal link diagram of the urban land use and transport system and import those causal links into the MARS system dynamics model. We determine from the literature a level for each attribute, and test the impacts in a set of ten scenarios using an updated MARS model of Leeds. ResultsBased on our input assumptions, we find that car-km in 2050 could be over 50% higher than in the business as usual scenario. Public transport use could fall by 18%, threatening accessibility for those dependent on it, while walking and cycling could fall by 13%, reducing their health benefits. Overall person-km would rise, suggesting a reduction in sustainability. A requirement that all automated cars are shared vehicles could reduce these adverse impacts somewhat, but the effects are sensitive to the charge per km. DiscussionOur use of a single value for each attribute means that our analysis is exploratory, but the size of the resulting impacts demonstrates the importance of understanding the scale of systems response to each of the attributes which we have considered. It will be important to manage the way in which automated cars are introduced into urban areas, if they are not to lead to a worsening of the urban environment, accessibility and health. A requirement to make all such vehicles part of shared fleets offers one way forward, but more work is needed to understand the way in which use of such fleets should be charged.

A machine-learning-based surrogate model of Mars’ thermal evolution

SUMMARY Constraining initial conditions and parameters of mantle convection for a planet often requires running several hundred computationally expensive simulations in order to find those matching certain ‘observables’, such as crustal thickness, duration of volcanism, or radial contraction. A lower fidelity alternative is to use 1-D evolution models based on scaling laws that parametrize convective heat transfer. However, this approach is often limited in the amount of physics that scaling laws can accurately represent (e.g. temperature and pressure-dependent rheologies or mineralogical phase transitions can only be marginally simulated). We leverage neural networks to build a surrogate model that can predict the entire evolution (0–4.5 Gyr) of the 1-D temperature profile of a Mars-like planet for a wide range of values of five different parameters: reference viscosity, activation energy and activation volume of diffusion creep, enrichment factor of heat-producing elements in the crust and initial temperature of the mantle. The neural network we evaluate and present here has been trained from a subset of ∼10 000 evolution simulations of Mars ran on a 2-D quarter-cylindrical grid, from which we extracted laterally averaged 1-D temperature profiles. The temperature profiles predicted by this trained network match those of an unseen batch of 2-D simulations with an average accuracy of $99.7\, {\rm per~cent}$.

Longomontanus’ Model for the Longitudes of Mars

When at the beginning of 1600, Kepler arrived to work with Tycho Brahe, Longomontanus, Tycho’s principal assistant, who was working with a model for Mars that predicted with remarkable accuracy its longitudes at oppositions. According to Kepler, this hypothesis “represented all these oppositions within a distance of two minutes in longitude.” The model, however, was unsuccessful in predicting longitudes at other elongations from the Sun, and latitudes even at opposition. Much has been said on how Kepler developed his model after this meeting, arriving finally at the so-called first two laws published in his Astronomia Nova in 1609. By contrast, Longomontanus’ attempt, published as a final model in his Astronomia Danica, has received little scholarly attention. In this paper, I will systematically analyse and explain this model. Even if Longomontanus’ solution is not as elegant as Kepler’s, it deserves scholarly attention, both because it solves the problems posed by the model that he and Tycho were working on when Kepler arrived and because it offers an interesting though heterodox solution that, by contrast, helps to highlight the elegance and simplicity of Kepler’s own solution.

Seafloor geomorphic features as an alternative approach into modelling the distribution of cetaceans

Bathymetric proxies tend to indicate the relationships between the distribution of cetaceans and the seafloor in species distribution models (SDMs). Usually, seafloor features are described arbitrary to explain these relationships. Currently, a global seafloor geomorphic features dataset is available, with an objective identification and full representation for each seafloor geomorphic feature. Models based on common bathymetric proxies were compared with models with seafloor predictors, by a standardized ensemble SDM framework. Occurrences of two species with different foraging strategies and relationships with the seabed were selected: the sperm whale (Physeter macrocephalus) and the striped dolphin (Stenella coeruleoalba).. For sperm whales, GLM models had a significant better performance with seageomorphic features than with the common bathymetric proxies. In GAM and MARS models, the performance was similar for both seabed features and bathymetric proxies' models. In relation to striped dolphins, all models obtained had a significant better performance with seafloor features than with bathymetric proxies, regardless the modelling technique. The models showed preference of sperm whales for areas distant from the continental shelf and continental rise areas which consists of submarine canyons with adjacent submarine fans. Striped dolphins showed a clear preference for continental slope areas. The modelling based on seafloor geomorphic features support the hypothesis that the use of this type of predictors can improve the modelling performance and provide a complementary understanding of the relationship between cetaceans and the seafloor.

Influence of Lecturers' Competence on Students' Satisfaction of Lecturing: Evidence for Mediating Role of Lecturing Behaviour

Reviewed literature reveals that how lecturing behaviour mediates the relationship between lecturers' competence and students' satisfaction of lecturing has neither been theoretically argued nor empirically been tested. This paper pursues to investigate empirically how lecturing behaviour could influence the relationship between lecturers' competence and students' satisfaction of lecturing. An integrated, three-construct conceptual model was developed based on the research gap identified for this research paper, and four hypotheses (H1 to H4) were developed through the deductive approach, under which the two general theories (General System Theory and MARS Model of Individual Behavior and Results), arguments, empirical evidence, and logical beliefs of the researcher were used. For the purpose of testing the conceptual model, 100 graduate students who were professional in doing managerial jobs or equivalents reading for MBA in a leading state university in Sri Lanka were considered as respondents through a self-administered survey consisting of three instruments, which possessed validity and reliability adequately. The correlation analysis and multiple regression analysis following the procedure recommended by Frazier, Tix and Barron in 2004 supported substantially the acceptance of the four hypotheses. According to the statistical results of the mediation analysis, lecturing behaviour significantly and fully mediated the relationship between lecturers' competence and students' satisfaction of lecturing.

Causes and Impact of Law Avoidance and Disobedience in the Light of Mars Model with Special Reference to Laws for Special Need Persons

To determine reasons and the influence of escaping and noncompliance of laws; data were collected through a mix method approach. Questionnaires and focus group discussion from special needs persons (physically challenged, visually impaired), their parents in case of hearing-impaired and intellectually challenged persons, professional law practitioners and employers of the business organization were taken. The result indicates that most special persons and their parents are unaware of the opportunities and laws related to their special needs. But on the part of employers' willful neglect and avoidance of laws is present because of loopholes available for not complying with these laws. Another reason is non-availability of any motivation for obeying laws, whether obedience of individuals or groups. It was also indicated that employers are not very clear about their roles related to the employment of special need persons.

Effects of Global and Regional Dust Storms on the Martian Hot O Corona and Photochemical Loss

AbstractWe examine here for the first time the effects of both global and regional dust storms on the formation of the Martian hot O corona and associated photochemical loss of O. Our study is conducted by utilizing our integrated model framework, which couples our Martian hot O corona model with a multifluid magnetohydrodynamic model for Mars for the dusty and clear atmospheric condition cases. We present our results with the most up‐to‐date cross sections for the O(3P)‐CO2 collisions. The main effect of dust storms on the ionosphere is the upward shift of the ionosphere on the dayside, which results in an increase in production of hot O at all altitudes above the ionospheric peak. However, the dust‐induced inflation of the neutral upper atmosphere results in an enhancement in collisional loss of hot O and thus effectively suppresses the hot O density, reducing the global photochemical loss rate by ~28% for the global dust storm scenario. The relative density structure of the hot O corona does not show any significant changes, while its magnitude decreases at all altitudes.

State‐of‐charge prediction of lithium ion battery through multivariate adaptive recursive spline and principal component analysis

AbstractThe main aim of this research work is to provide a comprehensible state of art for the intensification of the utmost decisive task performed by a modern BMS system to monitor and estimate battery states through a well‐entrenched statistical analysis method. In the present work, “multivariate adaptive regression splines” (MARS) method along with principal component analysis (PCA) has been used to develop a predictive model‐based state of charge (SoC) estimator for an NCR 18650PF Lithium ion battery at constant charging c‐rate of 0.3 C and 0.3 C and 0.5 C constant discharge profiles. Time‐weighing factors, that is, voltage‐current and temperature are employed as training datasets, to provide greater impact for developing a SoC MARS model of with high coefficient of correlation R2 (0.9984). The SoC MARS model adequacy is then validated for voltage prediction of the same battery for two different profiles of discharging using NIPALS algorithm for principal component analysis (PCA) with SS2 of 93.69% and 94.23% for profile A and profile B, respectively.

The composition of Mars

Comparing compositional models of the terrestrial planets provides insights into physicochemical processes that produced planet-scale similarities and differences. The widely accepted compositional model for Mars assumes Mn and more refractory elements are in CI chondrite proportions in the planet, including Fe, Mg, and Si, which along with O make up >90% of the mass of Mars. However, recent improvements in our understandings on the composition of the solar photosphere and meteorites challenge the use of CI chondrite as an analog of Mars. Here we present an alternative model composition for Mars that avoids such an assumption and is based on data from Martian meteorites and spacecraft observations. Our modeling method was previously applied to predict the Earth’s composition. The model establishes the absolute abundances of refractory lithophile elements in the bulk silicate Mars (BSM) at 2.26 times higher than that in CI carbonaceous chondrites. Relative to this chondritic composition, Mars has a systematic depletion in moderately volatile lithophile elements as a function of their condensation temperatures. Given this finding, we constrain the abundances of siderophile and chalcophile elements in the bulk Mars and its core. The Martian volatility trend is consistent with ⩽7 wt% S in its core, which is significantly lower than that assumed in most core models (i.e., >10 wt% S). Furthermore, the occurrence of ringwoodite at the Martian core-mantle boundary might have contributed to the partitioning of O and H into the Martian core.

A Scoring Rule for Factor and Autoregressive Models Under Misspecification

Factor models (FM) are now widely used for forecasting with large set of time series. Another class of models, which can be easily estimated and used in a large dimensional setting, is multivariate autoregressive models (MAR), where independent autoregressive processes are assumed for the series in the panel. We compare the forecasting abilities of FM and MAR models when assuming both models are misspecified and the data generating process is a vector autoregressive model. We establish which conditions need to be satisfied for a FM to overperform MAR in terms of mean square forecasting error. The condition indicates in presence of misspecification that FM is not always overperforming MAR and that the FM predictive performance depends crucially on the parameter values of the data generating process. Building on the theoretical relationship between FM and MAR predictive performances, we provide a scoring rule which can be evaluated on the data to either select the model, or combine the models in forecasting exercises. Some numerical illustrations are provided both on simulated data and on wel-known large economic datasets. The empirical results show that the frequency of the true positive signals is larger when FM and MAR forecasting performances differ substantially and it decreases as the horizon increases.