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Almenara, J. M., Bonfils, X., Bryant, E. M., Jordan, A., Hebrard, G., Martioli, E., et al. (2024). TOI-4860 b, a short-period giant planet transiting an M3.5 dwarf. Astron. Astrophys., 683, A166.
Abstract: We report the discovery and characterisation of a giant transiting planet orbiting a nearby M3.5V dwarf (d = 80.4pc, G = 15.1 mag, K=11.2mag, R-* = 0.358 +/- 0.015 R-circle dot, M-* = 0.340 +/- 0.009 M-circle dot). Using the photometric time series from TESS sectors 10, 36, 46, and 63 and near-infrared spectrophotometry from ExTrA, we measured a planetary radius of 0.77 +/- 0.03 R-J and an orbital period of 1.52 days. With high-resolution spectroscopy taken by the CFHT/SPIRou and ESO/ESPRESSO spectrographs, we refined the host star parameters ([Fe/H] = 0.27 +/- 0.12) and measured the mass of the planet (0.273 +/- 0.006 M-J). Based on these measurements, TOI-4860 b joins the small set of massive planets (>80 M-E) found around mid to late M dwarfs (<0.4 R-circle dot), providing both an interesting challenge to planet formation theory and a favourable target for further atmospheric studies with transmission spectroscopy. We identified an additional signal in the radial velocity data that we attribute to an eccentric planet candidate (e = 0.66 +/- 0.09) with an orbital period of 427 +/- 7 days and a minimum mass of 1.66 +/- 0.26 M-J, but additional data would be needed to confirm this.
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Jones, M. I., Reinarz, Y., Brahm, R.., Tala Pinto, M., Eberhardt, J., Rojas, F., et al. (2024). A long-period transiting substellar companion in the super-Jupiters to brown dwarfs mass regime and a prototypical warm-Jupiter detected by TESS. Astron. Astrophys., 683, A192.
Abstract: We report on the confirmation and follow-up characterization of two long-period transiting substellar companions on low-eccentricity orbits around TIC 4672985 and TOI-2529, whose transit events were detected by the TESS space mission. Ground-based photometric and spectroscopic follow-up from different facilities, confirmed the substellar nature of TIC 4672985 b, a massive gas giant in the transition between the super-Jupiters and brown dwarfs mass regime. From the joint analysis we derived the following orbital parameters: P = 69.0480(-0.0005)(+0.0004) d, M-p = 12.74(-1.01)(+1.01) M-J, R-p = 1.026(-0.067)(+0.065) R-J and e = 0.018(-0.004)(+0.004). In addition, the RV time series revealed a significant trend at the similar to 350 m s(-1) yr(-1) level, which is indicative of the presence of a massive outer companion in the system. TIC 4672985 b is a unique example of a transiting substellar companion with a mass above the deuterium-burning limit, located beyond 0.1 AU and in a nearly circular orbit. These planetary properties are difficult to reproduce from canonical planet formation and evolution models. For TOI-2529 b, we obtained the following orbital parameters: P = 64.5949(-0.0003)(+0.0003) d, M-p = 2.340(-0.195)(+0.197) M-J, R-p = 1.030(-0.050)(+0.050) R-J and e = 0.021(-0.015)(+0.024), making this object a new example of a growing population of transiting warm giant planets.
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Selvamani, M., Kesavan, A., Arulraj, A., Ramamurthy, P. C., Rahaman, M., Pandiaraj, S., et al. (2024). Microwave-Assisted Synthesis of Flower-like MnMoO4 Nanostructures and Their Photocatalytic Performance. Materials, 17(7), 1451.
Abstract: This article describes an affordable method for the synthesis of MnMoO4 nanoflowers through the microwave synthesis approach. By manipulating the reaction parameters like solvent, pH, microwave power, and irradiation duration along this pathway, various nanostructures can be acquired. The synthesized nanoflowers were analyzed by using a powder X-ray diffractometer (XRD), field emission scanning electron microscopy (FE-SEM) with energy dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FT-IR), and UV-vis diffuse reflectance spectroscopy (UV-DRS) to determine their crystalline nature, morphological and functional group, and optical properties, respectively. X-ray photoelectron spectroscopy (XPS) was performed for the examination of elemental composition and chemical states by qualitative and quantitative analysis. The results of the investigations demonstrated that the MnMoO4 nanostructures with good crystallinity and distinct shape were formed successfully. The synthesized MnMoO4 nanoflowers were tested for their efficiency as a photocatalyst in the degradation studies of methylene blue (MB) as model organic contaminants in an aqueous medium under visible light, which showed their photocatalytic activity with a degradation of 85%. Through the band position calculations using the electronegative value of MnMoO4, the photocatalytic mechanism of the nanostructures was proposed. The results indicated that the effective charge separation, and transfer mechanisms, in addition to the flower-like shape, were responsible for the photocatalytic performance. The stability of the recovered photocatalyst was examined through its recyclability in the degradation of MB. Leveraging MnMoO4's photocatalytic properties, future studies may focus on scaling up these processes for practical and large-scale environmental remediation.
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Perez-Quezada, J. F., Trejo, D., Lopatin, J., Aguilera, D., Osborne, B., Galleguillos, M., et al. (2024). Comparison of carbon and water fluxes and the drivers of ecosystem water use efficiency in a temperate rainforest and a peatland in southern South America. Biogeosciences, 21(5), 1371–1389.
Abstract: The variability and drivers of carbon and water fluxes and their relationship to ecosystem water use efficiency (WUE) in natural ecosystems of southern South America are still poorly understood. For 8 years (2015-2022), we measured carbon dioxide net ecosystem exchange (NEE) and evapotranspiration (ET) using eddy covariance towers in a temperate rainforest and a peatland in southern Chile. NEE was partitioned into gross primary productivity (GPP) and ecosystem respiration ( R eco ), while ET was partitioned into evaporation ( E ) and transpiration ( T ) and used to estimate different expressions of ecosystem WUE. We then used the correlation between detrended time series and structural equation modelling to identify the main environmental drivers of WUE, GPP, ET, E and T . The results showed that the forest was a consistent carbon sink ( – 486 +/- 23 g C m – 2 yr – 1 ), while the peatland was, on average, a small source (33 +/- 21 g C m – 2 yr – 1 ). WUE is low in both ecosystems and likely explained by the high annual precipitation in this region ( similar to 2100 mm). Only expressions of WUE that included atmospheric water demand showed seasonal variation. Variations in WUE were related more to changes in ET than to changes in GPP, while T remained relatively stable, accounting for around 47 % of ET for most of the study period. For both ecosystems, E increased with higher global radiation and higher surface conductance and when the water table was closer to the surface. Higher values for E were also found with increased wind speeds in the forest and higher air temperatures in the peatland. The absence of a close relationship between ET and GPP is likely related to the dominance of plant species that either do not have stomata (i.e. mosses in the peatland or epiphytes in the forest) or have poor stomatal control (i.e. anisohydric tree species in the forest). The observed increase in potential ET in the last 2 decades and the projected drought in this region suggests that WUE could increase in these ecosystems, particularly in the forest, where stomatal control may be more significant.
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Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Miralles, D. G., Beck, H. E., Siegmund, J. F., Alvarez-Garreton, C., et al. (2024). On the timescale of drought indices for monitoring streamflow drought considering catchment hydrological regimes. Hydrol. Earth Syst. Sci., 28(6), 1415–1439.
Abstract: There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12-24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70-0.75 and 0.58-0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.
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Holguin-Garcia, S. A., Guevara-Navarro, E., Daza-Chica, A. E., Patiño-Claro, M. A., Arteaga-Arteaga, H. B., Ruz, G. A., et al. (2024). A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure. BMC Med. Inform. Decis. Mak., 24(1), 60.
Abstract: IntroductionEpilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy.MethodTo optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis.ResultIn this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%.Conclusion Artificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.
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Severino, G., Fuentes, A., Valdivia, A., Auat-Cheein, F., & Reszka, P. (2024). Assessing wildfire risk to critical infrastructure in central Chile: application to an electrical substation. Int. J. Wildland Fire, 33(4), SI.
Abstract: Background. Wildfires have caused significant damage in Chile, with critical infrastructure being vulnerable to extreme wildfires. Aim. This work describes a methodology for estimating wildfire risk that was applied to an electrical substation in the wildland-urban interface (WUI) of Valparaiso, Chile. Methods. Wildfire risk is defined as the product between the probability of a wildfire reaching infrastructure at the WUI and its consequences or impacts. The former is determined with event trees combined with modelled burn probability. Wildfire consequence is considered as the ignition probability of a proxy fuel within the substation, as a function of the incident heat flux using a probit expression derived from experimental data. The heat flux is estimated using modelled fire intensity and geometry and a corresponding view factor from an assumed solid flame. Key results. The probability of normal and extreme fires reaching the WUI is of the order of 10(-4) and 10(-6) events/year, respectively. Total wildfire risk is of the order of 10(-5) to 10(-4) events/year Conclusions. This methodology offers a comprehensive interpretation of wildfire risk that considers both wildfire likelihood and consequences. Implications. The methodology is an interesting tool for quantitatively assessing wildfire risk of critical infrastructure and risk mitigation measures.
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Hernandez-Rocha, C., Chahuan, J., Uslar, T., Salas, R., Sepúlveda, I., Pavez, C., et al. (2024). Relative survival and cause-specific mortality of a Chilean Inflammatory Bowel Disease cohort. In Journal of Crohns and Colitis (Vol. 18, p. I2016).
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Franchi, O., Alvarez, M. I., Pavissich, J. P., Belmonte, M., Pedrouso, A., del Rio, A. V., et al. (2024). Operational variables and microbial community dynamics affect granulation stability in continuous flow aerobic granular sludge reactors. J. Water Process Eng., 59, 104951.
Abstract: Retrofitting wastewater treatment plants with continuous aerobic granular sludge reactors is a promising alternative to enhance treatment capacities and reduce footprint. This study investigates the main variables influencing granulation and microbial dynamics in two reactor configurations (25 L): stirred tanks in series (R1) and a plug-flow-like system (R2). Granule formation was achieved by increasing the organic loading rate (OLR) from 0.7 to 4.1 kg COD/(m3 & sdot;d) and the up-flow velocity in the biomass selector from 1.4 to 6.9 m/h. However, irreversible granule destabilization occurred at day 68 for R1 and day 108 for R2. Principal component analysis and examination of food-to-microorganisms (F/M) ratio medians identified the F/M ratio as the primary variable associated with instability. Microbial analysis revealed that a high F/M ratio induced significant increases in the abundance of specific genera such as Arcobacter, Cloacibacterium, Rikenella, Aquaspirillum and Sphaerotillus, whose overgrowth may negatively impact granule stability. Based on these findings, maximum F/M ratio thresholds were obtained to establish operational conditions allowing the maintenance of stable aerobic granules on continuous flow reactor configurations.
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Torres, R., & Villena, M. (2024). On the empirical performance of different covariance-matrix forecasting methods. Neural Comput. Appl., Early Access.
Abstract: In the study of financial time series, covariance/correlation matrices play a central role in risk-related applications, including financial contagion and portfolio selection. Different methodologies have been used in their prediction, from methods based on Financial Econometrics DCC-GARCH (Engle in J Bus Econ Stat 20(3):339-350, 2002), to others linked to Ecophysics like Random Matrix Theory (Wang et al. in Comput Econ 51:607-635, 2018), and more recently to Machine Learning (Fiszeder and Orzeszko in Appl Intell 51(10):7029-7042, 2021). Despite these developments, there is no state-of-the-art study that compares all these methods and assesses their predictive power in an out-of-sample setting. Indeed, in this work, we focus on measuring the out-of-sample predictive power of correlation matrices of these different statistical methods, in particular from three different fields that have converged in recent years in the analysis of financial data: Econometrics, Econophysics, and Machine Learning. Thus, using a moving window scheme, we studied the correlation matrices of 29 stock market indexes from different latitudes of the world. Among our findings, we see the relationship between the measures of Eigen Entropy found in the market, with the error found in the forecast of each method in the form of Square Forecast Error. We find that in the period from 2008 to 2022, considering 2608 moving windows, the out-of-sample error tends to converge between the different methods, highlighting the performance of DCC-GARCH.
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Asenjo, F. A., Hojman, S. A., Villegas-Martinez, B. M., Moya-Cessa, H. M., & Soto-Eguibar, F. (2024). Supersymmetric behavior of polarized electromagnetic waves in anisotropic media. Mod. Phys. Lett. A, 39(06), 2450013.
Abstract: A medium with specific anisotropic refractive indices can induce a supersymmetric behavior in the propagation of polarized electromagnetic waves, in an analog fashion to a quantum mechanical system. The polarizations of the wave are the ones which behave as superpartners from each other. For this to happen, the anisotropy of the medium must be transverse to the direction of propagation of the wave, with different refractive indices along the direction of each polarization, being in this way a biaxial medium. These refractive indices must be complex and follow a very specific relation in order to trigger the supersymetric response of the electromagnetic wave, each of them with spatial dependence on the longitudinal (propagation) direction of the wave. In this form, in these materials, different polarized light can be used to test supersymmetry in an optical fashion.
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Ruiz, E., Yushimito, W. F., Aburto, L., & de la Cruz, R. (2024). Predicting passenger satisfaction in public transportation using machine learning models. Transp. Res. A Policy Pract., 181, 103995.
Abstract: Enhancing the understanding of passenger satisfaction in public transportation is crucial for operators to refine transit services and to establish and elevate quality standards. While many researchers have tackled this issue using diverse tools and methods, the prevalent approach involves surveys with discrete choice models or structural equations. However, a common limitation of these models lies in their inherent assumptions and predefined relationships between dependent and independent variables. To address these limitations, we introduce a novel perspective by harnessing machine learning (ML) models to gauge and predict passenger satisfaction. ML models are advantageous when dealing with complex, non-linear relationships and massive datasets, and do not rely on predefined assumptions. Thus, in this paper, we evaluate four ML models for the prediction of ratings of the quality of transit service. These models were calibrated using data from the Transantiago bus system in Chile. Among the ML models, the Random Forest model emerges as the most effective, showcasing its ability to analyze and predict passengers' satisfaction levels. We delve deeper into its capabilities by examining the impact of three pivotal variables on passengers' score ratings: waiting time, bus occupation, and bus speed. The Random Forest model is able to capture threshold values for these variables that significantly influence or have no effect on passenger preferences.
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Trewhela, T. (2024). Segregation-rheology feedback in bidisperse granular flows: a coupled Stokes' problem. J. Fluid Mech., 983, A45.
Abstract: The feedback between particle-size segregation and rheology in bidisperse granular flows is studied using the Stokes' problem configuration. A method of lines scheme is implemented to solve the coupled momentum and segregation equations for a normally graded particle size distributed bulk at constant solids volume fraction. The velocity profiles develop quickly into a transient state, decoupled from segregation yet determined by the particle size. From this transient state, the velocity profile changes due to the particles' relative movement, which redistributes the frictional response, hence its rheology. Additionally, the particles' relative friction is modified via a frictional coefficient ratio, by analogy with the particles' size ratio. While positive values of this coefficient exacerbate the nonlinearity of the velocity profiles induced by size differences, negative values dampen this behaviour. The numerical solutions reproduce well the analytical solutions for the velocity profile, which can be obtained from the steady-state conditions of the momentum and segregation equations for the transient and steady states, respectively. Segregation-momentum balances and four characteristic time scales can be established to propose two non-dimensional quantities, including specific Schmidt and Peclet numbers that describe broadly the segregation-rheology feedback. The proposed scheme, theoretical solutions and non-dimensional numbers offer a combined approach to understand segregation and flow dynamics within a granular bulk, extensible across many flow configurations.
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Hojman, S. A., & Asenjo, F. A. (2024). Cosmological electromagnetic Hopfions. Phys. Scr., 99(5), 055514.
Abstract: It is shown that any mathematical solution for null electromagnetic field knots in flat spacetime is also a null field knotted solution for cosmological electromagnetic fields. This is obtained by replacing the time t -> tau = integral dt/a, where a = a(t) is the scale factor of the Universe described by the Friedman-Lemaitre-Robertson-Walker (FLRW) cosmology, and by adequately rewriting the (empty flat spacetimes) electromagnetic fields solutions in a medium defined by the FLRW metric. We found that the dispersion (evolution) of electromagnetic Hopfions is faster on cosmological scenarios. We discuss the implications of these results for different cosmological models.
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Asenjo, F. A. (2024). Accelerating self-modulated nonlinear waves in weakly and strongly magnetized relativistic plasmas. J. Plasma Phys., 30(1).
Abstract: It is known that a nonlinear Schrodinger equation describes the self-modulation of a large amplitude circularly polarized wave in relativistic electron-positron plasmas in the weakly and strongly magnetized limits. Here, we show that such an equation can be written as a modified second Painleve equation, producing accelerated propagating wave solutions for those nonlinear plasmas. This solution even allows the plasma wave to reverse its direction of propagation. The acceleration parameter depends on the plasma magnetization. This accelerating solution is different to the usual soliton solution propagating at constant speed.
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Chaigneau, S. E., Marchant, N., Canessa, E., & Aldunate, N. (2024). A mathematical model of semantic access in lexical and semantic decisions. Lang. Cogn., Early Access.
Abstract: In this work, we use a mathematical model of the property listing task dynamics and test its ability to predict processing time in semantic and lexical decision tasks. The study aims at exploring the temporal dynamics of semantic access in these tasks and showing that the mathematical model captures essential aspects of semantic access, beyond the original task for which it was developed. In two studies using the semantic and lexical decision tasks, we used the mathematical model's coefficients to predict reaction times. Results showed that the model was able to predict processing time in both tasks, accounting for an independent portion of the total variance, relative to variance predicted by traditional psycholinguistic variables (i.e., frequency, familiarity, concreteness imageability). Overall, this study provides evidence of the mathematical model's validity and generality, and offers insights regarding the characterization of concrete and abstract words.
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Heredia, C., Moreno, S., & Yushimito, W. (2024). ODMeans: An R package for global and local cluster detection for Origin–Destination GPS data. SoftwareX, 26, 101732.
Abstract: The ODMeans R package implements the OD-Means model, a two-layer hierarchical clustering algorithm designed for extracting both global and local travel patterns from Origin–Destination Pairs (OD-Pairs). In contrast to existing models, OD-Means automates cluster determination and offers advantages such as smaller Within-Cluster Distance (WCD) and dual hierarchies. The package includes functions for applying the model and visualizing the results on maps. Using real taxi data from Santiago, Chile, we demonstrate the package’s capabilities, showcasing its flexibility and impact on understanding urban mobility patterns.
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Lagos, F., Moreno, S., Yushimito, W. F., & Brstilo, T. (2024). Urban Origin–Destination Travel Time Estimation Using K-Nearest-Neighbor-Based Methods. Mathematics, 12(8), 1255.
Abstract: Improving the estimation of origin�destination (O-D) travel times poses a formidable challenge due to the intricate nature of transportation dynamics. Current deep learning models often require an overwhelming amount of data, both in terms of data points and variables, thereby limiting their applicability. Furthermore, there is a scarcity of models capable of predicting travel times with basic trip information such as origin, destination, and starting time. This paper introduces novel models rooted in the k-nearest neighbor (KNN) algorithm to tackle O-D travel time estimation with limited data. These models represent innovative adaptations of weighted KNN techniques, integrating the haversine distance of neighboring trips and incorporating correction factors to mitigate prediction biases, thereby enhancing the accuracy of travel time estimations for a given trip. Moreover, our models incorporate an adaptive heuristic to partition the time of day, identifying time blocks characterized by similar travel-time observations. These time blocks facilitate a more nuanced understanding of traffic patterns, enabling more precise predictions. To validate the effectiveness of our proposed models, extensive testing was conducted utilizing a comprehensive taxi trip dataset sourced from Santiago, Chile. The results demonstrate substantial improvements over existing state-of-the-art models (e.g., MAPE between 35 to 37% compared to 49 to 60% in other methods), underscoring the efficacy of our approach. Additionally, our models unveil previously unrecognized patterns in city traffic across various time blocks, shedding light on the underlying dynamics of urban mobility.
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Carleo, I., Malavolta, L., Desidera, S., Nardiello, D., Wang, S., Turrini, D., et al. (2024). The GAPS programme at TNG. Astron. Astrophys., 682, A135.
Abstract: Context. Different theories have been developed to explain the origins and properties of close-in giant planets, but none of them alone can explain all of the properties of the warm Jupiters (WJs, Porb = 10-200 days). One of the most intriguing characteristics of WJs is that they have a wide range of orbital eccentricities, challenging our understanding of their formation and evolution. Aims. The investigation of these systems is crucial in order to put constraints on formation and evolution theories. TESS is providing a significant sample of transiting WJs around stars bright enough to allow spectroscopic follow-up studies. Methods. We carried out a radial velocity (RV) follow-up study of the TESS candidate TOI-4515 b with the high-resolution spectrograph HARPS-N in the context of the GAPS project, the aim of which is to characterize young giant planets, and the TRES and FEROS spectrographs. We then performed a joint analysis of the HARPS-N, TRES, FEROS, and TESS data in order to fully characterize this planetary system. Results. We find that TOI-4515 b orbits a 1.2 Gyr-old G-star, has an orbital period of Pb = 15.266446 +/- 0.000013 days, a mass of Mb = 2.01 +/- 0.05 MJ, and a radius of Rb = 1.09 +/- 0.04 RJ. We also find an eccentricity of e = 0.46 +/- 0.01, placing this planet among the WJs with highly eccentric orbits. As no additional companion has been detected, this high eccentricity might be the consequence of past violent scattering events.
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Vera-Maldonado, P., Aquea, F., Reyes-Díaz, M., Cárcamo-Fincheira, P., Soto-Cerda, B., Nunes-Nesi, A., et al. (2024). Role of boron and its interaction with other elements in plants. Front. Plant Sci., 15, 1332459.
Abstract: Boron (B) is an essential microelement for plants, and its deficiency can lead to impaired development and function. Around 50% of arable land in the world is acidic, and low pH in the soil solution decreases availability of several essential mineral elements, including B, magnesium (Mg), calcium (Ca), and potassium (K). Plants take up soil B in the form of boric acid (H3BO3) in acidic soil or tetrahydroxy borate [B(OH)4]- at neutral or alkaline pH. Boron can participate directly or indirectly in plant metabolism, including in the synthesis of the cell wall and plasma membrane, in carbohydrate and protein metabolism, and in the formation of ribonucleic acid (RNA). In addition, B interacts with other nutrients such as Ca, nitrogen (N), phosphorus (P), K, and zinc (Zn). In this review, we discuss the mechanisms of B uptake, absorption, and accumulation and its interactions with other elements, and how it contributes to the adaptation of plants to different environmental conditions. We also discuss potential B-mediated networks at the physiological and molecular levels involved in plant growth and development.
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