MARS Model Research Articles

Relationship Between Economic Growth And Macroeconomic Indicators In Turkey: MARS Model

Ekonomik büyüme ülkelerin birbiri ile kıyaslanması ve yurtiçindeki vatandaşların yaşam kalitesinin de bir göstergesi olarak önemli bir değişkendir. Bu çalışmada Türkiye’de ekonomik büyümeye etki eden makroekonomik değişkenlerin ve bu değişkenlerin ekonomik büyümeyi nasıl etkilediğinin belirlenmesi amaçlanmıştır. Bu düşünceden hareketle makroekonomik göstergelerin her değerinin ekonomik büyümeyi aynı düzeyde ve aynı yönde etkilemeyebileceği anlaşılmış ve bu durumu modellemeye imkân sağlayan MARS (Çok değişkenli Uyarlanabilir Regresyon Uzanımları) tekniği kullanılmıştır. Analiz sonuçlarına göre, Türkiye’de ekonomik büyümeyi etkileyen değişkenler önem düzeyine göre ithalat, işsizlik oranı, kredi hacmi, faiz oranı, döviz kuru ve enflasyon şeklinde sıralanmaktadır. Kurulan matematiksel model incelendiğinde, Türkiye’de ithalatın büyük değerlerinin ekonomik büyümeyi pozitif, küçük değerlerinin negatif, işsizlik oranının yüksek değerlerinin ekonomik büyümeyi negatif, kredi hacminin çok büyük değerlerinin ekonomik büyümeyi pozitif, küçük değerlerinin pozitif, aradaki değerlerinin ise negatif, faiz oranının küçük değerlerinin pozitif, döviz kurunun küçük değerlerinin pozitif, enflasyon oranının yüksek değerlerinin ekonomik büyümeyi negatif yönde etkilediği görülmektedir.

Spatial Variations in Terrestrial Water Storage with Variable Forces across the Yellow River Basin

Terrestrial water storage (TWS) variations are a result of the interconnected impact of various variables including climate, hydrology, ecology, and anthropogenic activities. Previous studies have indicated that climate factors (e.g., precipitation and potential evapotranspiration), vegetation restoration, and water withdrawals (irrigational and industrial water use) are the major determinants of TWS depletion across the Yellow River Basin (YRB). However, few studies have provided explicit information about the main forcing variables that determine spatiotemporal variations in TWS and the synergies among these factors. This study explored the explicit understanding of hydro-climatic and socio-ecological determinants and the key interacting processes that affected the TWS variations across the Yellow River Basin in northern China. The multivariate adaptive regression splines model was employed to establish the relationship function of the long-term trends for the dependent (TWS) and independent (explanatory) variables consisting of normalized difference vegetation index (NDVI), hydro-climate, and human water withdrawal. The long-term trends estimated from the MARS model reproduced the ones calculated by Gravity Recovery and Climate Experiment gravity satellites, with a determination coefficient (R2) of 0.83 and a mean absolute error (MAE) of 1.2 mm. The results showed that precipitation, minimum temperature, runoff, base flow, water withdrawal for electricity, and NDVI were the main drivers of the spatiotemporal variations in the TWS, of which minimum temperature and runoff played a considerable role in TWS variations through the interplay with other variables. The critical values of the trend for interactive variables, which could alter the acting direction of the synergy on the TWS, were also estimated. In view of the connotation of interactive variables, we suggested that spatiotemporal variations in TWS resulted from the coupling of the hydrological energy system, hydrological ecosystem, and hydrological system in the YRB, of which the hydrological system plays the most significant role, followed by the hydrological ecosystem.

Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests

This study presents a comparative assessment of conventional soft computing techniques in estimating the compressive strength (CS) of concrete utilizing two non-destructive tests, namely ultrasonic pulse velocity and rebound hammer test. In specific, six conventional soft computing models namely back-propagation neural network (BPNN), relevance vector machine, minimax probability machine regression, genetic programming, Gaussian process regression, and multivariate adaptive regression spline, were used. To construct and validate these models, a total of 629 datasets were collected from the literature. Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values. The results of the employed MARS and BPNN models are significantly better than those obtained in earlier studies. Thus, these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level.

Forward Modeling of the Phobos Tides and Applications to the First Martian Year of the InSight Mission

AbstractThe tidal response of Mars, due to the Sun and the Martian moons, Phobos and Deimos, provides information about the interior structure of Mars. By using the Very Broad Band (VBB) seismometer of Seismic Experiment for Interior Structure (SEIS) as a gravimeter on the surface of Mars, the InSight mission will provide the long‐period data suited to tidal analysis: most notably, the proximity of Phobos implies that degree 2, 3, 4, and further tides will be detectable by the VBB and are expected to provide information about the rheology at different depths within Mars. In order to expedite the recovery of these tidal signals in the SEIS measurements, we model the tides raised by Phobos using a tidal potential deduced from JPL Horizons ephemerides. From this potential, we calculate the expected tidal acceleration at InSight's location and gravimetric factors using a set of plausible interior models of Mars. To simulate the expected long period signal on the VBB seismometer, we use the InSight Auxiliary Payload Sensor Suite data to model the noise seen at low frequency by SEIS mainly due to temperature and pressure variations. Based on this synthetic signal, by applying filtering methods such as stacking and matched filtering to these synthetic data, we show that by recovering the gravimetric factors, it should be possible to constrain the state of the core and its size with an accuracy of 125 km after two Earth years.

Fisher’s z Distribution-Based Mixture Autoregressive Model

We generalize the Gaussian Mixture Autoregressive (GMAR) model to the Fisher’s z Mixture Autoregressive (ZMAR) model for modeling nonlinear time series. The model consists of a mixture of K-component Fisher’s z autoregressive models with the mixing proportions changing over time. This model can capture time series with both heteroskedasticity and multimodal conditional distribution, using Fisher’s z distribution as an innovation in the MAR model. The ZMAR model is classified as nonlinearity in the level (or mode) model because the mode of the Fisher’s z distribution is stable in its location parameter, whether symmetric or asymmetric. Using the Markov Chain Monte Carlo (MCMC) algorithm, e.g., the No-U-Turn Sampler (NUTS), we conducted a simulation study to investigate the model performance compared to the GMAR model and Student t Mixture Autoregressive (TMAR) model. The models are applied to the daily IBM stock prices and the monthly Brent crude oil prices. The results show that the proposed model outperforms the existing ones, as indicated by the Pareto-Smoothed Important Sampling Leave-One-Out cross-validation (PSIS-LOO) minimum criterion.

Lognormal Random Field models to identify temporal Land cover changes using full polarimetric L-Band SAR imagery

Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references.

Poverty in Central Java using Multivariate Adaptive Regression Splines and Bootstrap Aggregating Multivariate Adaptive Regression Splines

Population poverty is one of the serious problems in Indonesia. The percentage of population poverty used as a means for a statistical instrument to be guidelines to create standard policies and evaluations to reduce poverty. The aims of the research are to determine model population poverty using MARS and Bagging MARS then to understand the most influence variable population poverty of Central Java Province in 2018. The result of this research is the Bagging MARS model showed better accuracy than the MARS model. Since, GCV value in the Bagging MARS model is 0,009798721 and GCV value in the MARS model is 6,985571. The most influential variable poverty population of Central Java Province in 2018 in the MARS model is the percentage of the old school expectation rate (X9). Then, the most influential variable in the Bagging MARS model is the number of diarrhea (X1).

Stability of Atmospheric Redox States of Early Mars Inferred from Time Response of the Regulation of H and O Losses

Abstract The ratio of total losses of H and O from the atmosphere is crucial for determining the Martian atmospheric redox state. The H and O escapes are shown to be regulated in a stoichiometric 2:1 ratio in a converged model of present-day Mars over a timescale of ∼105 yr, which is called self-regulation. Self-regulation timescales under different atmospheric conditions on early Mars are not well understood. Here we use a 1D photochemical model to calculate the timescales of self-regulation for denser CO2 atmospheres with various surface temperatures as benchmark cases for early Mars. Self-regulation is driven by variations in the amount of O2 or CO in the atmosphere, depending on the atmospheric redox state. Self-regulation timescales are likely to be controlled by the net redox balance. A 1 bar CO2 atmosphere with a surface temperature of 240 K has a self-regulation timescale of a few million years. Denser atmospheres of early Mars have a longer regulation timescale and are less redox-stable than the atmosphere of present-day Mars. Obliquity variations cause atmospheric CO2 fluctuations, producing a difference in the self-regulation timescale between high and low obliquity. Because an increase in CO2 suppresses H escape, the net effect of the obliquity cycle could have driven the atmospheric redox states to be more reducing. Our results also suggest the possibility of a CO-dominated atmosphere of 10–100 mbars at 3 Ga. The redox state of ancient Mars might have fluctuated more easily than that of the present.

Prediction of live body weight based on body measurements in Thalli sheep under tropical conditions of Pakistan using cart and mars.

Thalli sheep is a significant breed reared under tropical region of Punjab province of Pakistan. The present study was conducted to predict live body weight (LBW) by means of from some body measurements, i.e., chest girth (CG), belly girth (BG), rump height (RH), withers height (WH), neck girth (NG), and body length (BL) taken from 155 Thalli indigenous sheep of Pakistan. Age factor is determined to be a significant source of variation for BL, BG, CG, BG, WH, and NG (p < 0.05). LBW is correlated significantly with BL (0.850), CG (0.825), BG (0.849), RH (0.579), WH (0.547), and NG (0.7760), respectively (p < 0.01). For LBW prediction, CART and MARS data mining algorithms were comparatively used based on ten cross-validation method. Among 185 candidate MARS models with 1-5 degrees of interaction and 2-38 terms, the MARS model with 7 terms and no interaction effect in R software was the best model for LBW prediction on the basis of the smallest cross-validated RMSE value. Also, the optimal CART tree structure was obtained with 9 terminal nodes for the smallest cross-validated RMSE value. MARS algorithm outperformed CART in LBW prediction and explained 90.3 (%) of variability in LBW of Thalli sheep. Results of the optimal CART structure reflected that Thalli sheep with BL > 75 cm, RH > 83 cm, and NG > 55 cm has the heaviest LBW of 72 kg. The optimal MARS model displays that the heaviest LBW can be produced by Thalli sheep with BL > 71.12 cm, BG > 106.68 cm, WH > 76.2 cm, NG > 50.8 cm in 5th age group. In conclusion, it coud be recommended that MARS predictive modeling may enable animal breeders to obtain elite Thalli sheep population and to detect body measurement positively influencing LBW as indirect selection criteria for not only describing breed characterization and developing flock management standards, but also ensuring sustainable meat production and rural development in Pakistan.

Mars’ Formation Can Constrain the Primordial Orbits of the Gas Giants

Abstract Recent high-precision meteoritic data infers that Mars finished its accretion rapidly within 10 Myr of the beginning of the Solar System and had an accretion zone that did not entirely overlap with the Earth’s. Here we present a detailed study of the accretion zone of planetary embryos from high-resolution simulations of planetesimals in a disk. We found that all simulations with Jupiter and Saturn on their current eccentric orbits (EJS) result in a similar accretion zone between fast-forming Mars and Earth-region embryos. Assuming more circular orbits for Jupiter and Saturn (CJS), on the other hand, has a significantly higher chance of forming Mars with an accretion zone not entirely dominated by Earth and Venus-region embryos; however, CJS in general forms Mars slower than in EJS. By further quantifying the degree of overlap between accretion zones of embryos in different regions with the average overlap coefficient (OVL), we found that the OVL of CJS shows a better match with the OVL from a chondritic isotopic mixing model of Earth and Mars, which indicates that the giant planets are likely to have resided on more circular orbits during gas disk dissipation than they do today, matching their suggested pre-instability orbits. More samples, including those from Mercury and Venus, could potentially confirm this hypothesis.

Spatial modelling of soil salinity: deep or shallow learning models?

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks-DCNNs, dense connected deep neural networks-DenseDNNs, recurrent neural networks-long short-term memory-RNN-LSTM and recurrent neural networks-gated recurrent unit-RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree-BCART, cforest, cubist, quantile regression with LASSO penalty-QR-LASSO, ridge regression-RR and support vectore machine-SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0-5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.

Forecasting Non-Stationary Time Series with the Multiscale Autoregressive (MAR) Model Approach Using the Haar Wavelet Filter at the Rupiah Exchange Rate Against the Dollar

Time series data is a collection of a phenomenon that occurs based on a fixed or at the same time index. Time series phenomena often exhibit non-stationary behavior. One of the time series analyses for non-stationary data is the Multiscale Autoregressive Model (MAR). The MAR model chosen is a model that meets the assumptions of normality and white noise. The predictors used in MAR modeling are wavelet coefficients and scales which are the result of decomposition using Maximal Overlap Discrete Wavelet Transform (MODWT), MODWT functions to decompose data based on the level of each wavelet filter (family). The wavelet filters used in this study are Haar. In this study, the model generated from the data that was stationary first was more accurate than the data that was not stationary first.

On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques

The main aim of the present study is to investigate the capabilities of four robust machine learning method - the Kernel Extreme Learning Machine (KELM), Adaptive Regression Spline (MARS), M5 Model Tree (M5Tree), and Gene Expression Programming (GEP) model in predicting specific heat capacity (SHC) of metal oxide-based nanofluids implemented in solar energy application. Sets of 1180 data of different metal oxide-based nanofluids containing Al2O3, ZnO, TiO2, SiO2, MgO, and CuO dispersed in various base fluids were collected from reliable literature to provide the predictive model of SHC of nanofluids. The volume fraction, temperature, SHC of the base fluid, and mean diameter of nanoparticles were used as an input variable to predict nanofluids' SHC as the output variable. The artificial intelligence (AI) models were validated using several statistical performance criteria, graphical devices, and conventional models. The results obtained from all datasets demonstrated that the KELM model significantly outperformed the MARS, M5Tree, and GEP model in predicting the SHC of nanofluid. Moreover, the sensitivity analysis showed that the mean diameter of the nanoparticle and SHC of the base fluid have the most considerable impact on estimating the SHC of metal oxide-based nanofluids.

Use of MARS algorithm for predicting mature weight of different camel (Camelus dromedarius) breeds reared in Pakistan and morphological characterization via cluster analysis.

Mature weight is a significant trait that can be influenced by age, sex, breed, production system, and climate conditions in camels. In camel breeding, it is essential to describe breed standards of the studied camel breeds as part of morphological characterization and to determine morphological traits positively influencing mature weight within the scope of indirect selection criteria. This study was to find the best one among candidate models in prediction of mature weight from several morphological traits measured for eight camel breeds (Bravhi, Kachi, Kharani, Kohi, Lassi, Makrani, Pishin, and Rodbari) raised under Pakistan conditions. The morphological measurements taken from the camels in the study were birth weight (BW), weaning weight (WW), mature weight (MW), age of ridding (ARD), face length (FL), face width (FW), head length (HL), head width (HW), ear length (EL), ear width (EW), neck length (NL), neck width (NW), hump length (HL), hump width (HuW), heart girth (HG), withers height (WH), body length (BL), fore leg length (FLL), and hind leg length (HLL), respectively. In the prediction of mature body weight as a response variable, the optimal MARS predictive model with 15 terms selected by train function of the caret package produced very high predictive performance without encountering overfitting problem. Goodness of fit criteria were estimated to measure predictive quality of the MARS model using ehaGoF package available in R environment. Morphological characterization of the camel breeds was performed with hierarchical cluster analysis (HCA) on the basis of Euclidean distance-Single linkage. At the first step of hierarchical cluster analysis, the similarity level of Bravhi and Kachi camel breeds was the highest with 85.3569 (%). At the second step, Makrani joined to new cluster of Bravhi and Kachi camels found at the first step, and the similarity level of the new cluster comprising Bravhi, Kachi, and Makrani breeds was found as 84.5562 (%). MW was significantly correlated with BW (0.677), WW (0.536), HL (0.524), HuW (0.529), and ARD (0.375) at P < 0.01, and there was the highest correlation of 0.994 between HHL and FLL (P < 0.01). As a result, it could be suggested that results of MARS modeling may help camel breeders to reproduce the elite camel populations and to describe characteristics associated positively with MW within the scope of indirect selection criteria.

Convolution‐based filtering and forecasting: An application to WTI crude oil prices

AbstractWe introduce new methods of filtering and forecasting for the causal–noncausal convolution model. This model represents the dynamics of stationary processes with local explosions, such as spikes and bubbles, which characterize the time series of commodity prices, cryptocurrency exchange rates, and other financial and macroeconomic variables. The convolution model is a structural mixture of independent latent causal and noncausal component series. We propose an algorithm that recovers the latent components by evaluating the filtering density of one component, conditional on the observed past, present, and future values of the time series. Forecasts of the observed time series are obtained as a combination of filtered causal and noncausal component forecasts. The new filtering and forecasting methods are illustrated in a simulation study and compared with the results obtained from the mixed causal–noncausal autoregressive MAR model in application to WTI crude oil prices.

Earth and Mars – Distinct inner solar system products

Composition of terrestrial planets records planetary accretion, core–mantle and crust–mantle differentiation, and surface processes. Here we compare the compositional models of Earth and Mars to reveal their characteristics and formation processes. Earth and Mars are equally enriched in refractory elements (1.9 × CI), although Earth is more volatile-depleted and less oxidized than Mars. Their chemical compositions were established by nebular fractionation, with negligible contributions from post-accretionary losses of moderately volatile elements. The degree of planetary volatile element depletion might correlate with the abundances of chondrules in the accreted materials, planetary size, and their accretion timescale, which provides insights into composition and origin of Mercury, Venus, the Moon-forming giant impactor, and the proto-Earth. During its formation before and after the nebular disk's lifetime, the Earth likely accreted more chondrules and less matrix-like materials than Mars and chondritic asteroids, establishing its marked volatile depletion. A giant impact of an oxidized, differentiated Mars-like (i.e., composition and mass) body into a volatile-depleted, reduced proto-Earth produced a Moon-forming debris ring with mostly a proto-Earth's mantle composition. Chalcophile and some siderophile elements in the silicate Earth added by the Mars-like impactor were extracted into the core by a sulfide melt (∼0.5% of the mass of the Earth's mantle). In contrast, the composition of Mars indicates its rapid accretion of lesser amounts of chondrules under nearly uniform oxidizing conditions. Mars’ rapid cooling and early loss of its dynamo likely led to the absence of plate tectonics and surface water, and the present-day low surface heat flux. These similarities and differences between the Earth and Mars made the former habitable and the other inhospitable to uninhabitable.

Critique of “Planetary Normal Mode Computation: Parallel Algorithms, Performance, and Reproducibility” by SCC Team From ETH Zurich

This report analyzes the reproducibility of the article “Computing Planetary Interior Normal Modes with A Highly Parallel Polynomial Filtering Eigensolver” by Jia Shi et al. (Shi, 2018). To reproduce the results we perform different weak and strong scaling studies using a series of Mars models. All experimental runs were performed during the SC19 Student Cluster Competition on a four node Intel Skylake cluster. We show that the findings of the original article can be reproduced in a different environment.

Seasonal and Spatial Variability of Carbon Monoxide (CO) in the Martian Atmosphere From PFS/MEX Observations

AbstractA subset of more than 100,000 nadir measurements covering more than 7 Martian years (MY 26–MY 33) recorded in the thermal part of the Short Wavelength Channel (SWC) from the Planetary Fourier Spectrometer (PFS) on board Mars Express is exploited to investigate the global distribution and the seasonal cycle of carbon monoxide (CO) on Mars. The retrieval of CO vertical profiles is successfully achieved using a methodology based on the optimal estimation but the low information content is such that we mainly discuss the variability in CO in terms of integrated columns (from the surface to 24 km) or the associated column‐averaged mixing ratio. We find a strong seasonality in CO, especially at mid and high‐latitudes, which confirms earlier work and the current knowledge of the CO2 condensation/sublimation cycles, as implemented for instance in the Global Environmental Multiscale (GEM) general circulation model for Mars, that we use as a basis for comparison. We report a general consistency between model and observation, with a tendency of the latter to provide lower CO volume mixing ratios (VMRs), except at low latitudes. The spatial distribution of the CO column‐averaged VMR is obtained on a seasonal basis and investigated in terms of large‐scale patterns but also local peculiarities. Finally, we show that the retrieved profiles systematically present strong CO vertical gradients close to the surface in mid‐ and equatorial latitudes, likely related to the vertical sensitivity of PFS rather than real near‐surface CO enrichment.

Warming-driven shifts in ecological control of fish communities in a large northern Chinese lake over 66 years

Warming, land-use change, and habitat loss are three major threats to aquatic biodiversity worldwide under the influences of anthropogenic disturbances. Positive feedback between warming and bottom-up regulation may cause irreversible ecological regime shifts. Threshold dynamics of interspecific interactions have been rarely studied in freshwater fish communities using threshold community models. Here we use 66 years (1950–2015) of data to link four ecological regime shifts of 9-species fish communities to climatic and land use changes in Lake Hulun, the largest freshwater lake of Northern China. Overfishing caused the collapse of piscivorous fish populations and an ecological regime shift of Lake Hulun in the late 1950s. The first recorded algal bloom of Lake Hulun took place in 1986, with accelerated warming and rapid increases in livestock grazing. The dominance of planktivorous minnow populations reduced fish biodiversity in a nonlinear, threshold manner when annual mean ambient temperature was >0.12 °C. Multivariate environmental vector regression demonstrated that warming, eutrophication, and water-storage reduction (i.e., habitat loss) were related to three ecological regime shifts of Lake Hulun from 1960 to 2015. Multivariate autoregressive models (MAR) did not detect predation by piscivorous fish in Lake Hulun after 1960. Threshold MAR models indicated that dominant minnow populations and other prey fish populations switched from top-down to bottom-up control during the 1980s. Sustained positive feedback between warming, the dominance of planktivorous fish populations, and bottom-up regulation caused predator-prey role reversal, and probably resulted in three regime shifts of Lake Hulun over 56 years. This study provides a comprehensive analysis of ecological regime shifts in Hulun Lake fish communities, and has potential implications for fish species living in similar environments that are subject to global warming, land-use changes, and overfishing.

Data-driven CFD scaling of bioinspired Mars flight vehicles for hover

One way to improve our model of Mars is through aerial sampling and surveillance, which could provide information to augment the observations made by ground-based exploration and satellite imagery. Flight in the challenging ultra-low-density Martian environment can be achieved with properly scaled bioinspired flapping wing vehicle configurations that utilize the same high lift producing mechanisms that are employed by insects on Earth. Through dynamic scaling of wings and kinematics, we investigate the ability to generate solutions for a broad range of flapping wing flight vehicles with masses ranging from insects O(10−3) kg to the Mars helicopter Ingenuity O(100) kg. A scaling method based on a neural-network trained on 3D Navier-Stokes solutions is proposed to determine approximate wing size and kinematic values that generate bioinspired hover solutions. We demonstrate that a family of solutions exists for designs that range from 1 to 1000 g, which are verified and examined using a 3D Navier-Stokes solver. Our results reveal that unsteady lift enhancement mechanisms, such as delayed stall and rotational lift, are present in the bioinspired solutions for the scaled vehicles hovering in Martian conditions. These hovering vehicles exhibit payloads of up to 1 kg and flight times on the order of 100 min when considering the respective limiting cases of the vehicle mass being comprised entirely of payload or entirely of a battery and neglecting any transmission inefficiencies. This method can help to develop a range of Martian flying vehicle designs with mission viable payloads, range, and endurance.