Integrating classification and regression learners with bioimpedance methods for estimating weight status in infants and juveniles from the southern Cuba region.

OBJECTIVE: The search for other indicators to assess the weight and nutritional status of individuals is important as it may provide more accurate information and assist in personalized medicine. This work is aimed to develop a machine learning predictions of weigh status derived from bioimpedance measurements and other physical parameters of healthy younger volunteers from Southern Cuba Region. METHODS: A pilot random study at the Pediatrics Hospital was conducted. The volunteers were selected between 2002 and 2008, ranging in age between 2 and 18 years old. In total, 776 female and male volunteers are studied. Along the age and sex in the cohort, volunteers with class I obesity, overweight, underweight and with normal weight are considered. The bioimpedance parameters are obtained by measuring standard tetrapolar whole-body configuration. The bioimpedance analyser is used, collecting fundamental bioelectrical and other parameters of interest. A classification model are performed, followed by a prediction of the body mass index. RESULTS: The results derived from the classification leaner reveal that the size, body density, phase angle, body mass index, fat-free mass, total body water volume according to Kotler, body surface area, extracellular water according to Kotler and sex largely govern the weight status of this population. In particular, the regression model shows that other bioparameters derived from impedance measurements can be associated with weight status estimation with high accuracy. CONCLUSION: The classification and regression predictive models developed in this work are of the great importance to assist the diagnosis of weigh status with high accuracy. These models can be used for prompt weight status evaluation of younger individuals at the Pediatrics Hospital in Santiago de Cuba, Cuba.

The role of various physiological and bioelectrical parameters for estimating the weight status in infants and juveniles cohort from the Southern Cuba region: a machine learning study.

OBJECTIVE: The search for other indicators to assess the weight status of individuals is important as it may provide more accurate information and assist in personalized medicine.This work is aimed to develop a machine learning predictions of weigh status derived from bioimpedance measurements and other physical parameters of healthy infant juvenile cohort from the Southern Cuba Region, Santiago de Cuba. METHODS: The volunteers were selected between 2002 and 2008, ranging in age between 2 and 18 years old. In total, 393 female and male infant and juvenile individuals are studied. The bioimpedance parameters are obtained by measuring standard tetrapolar whole-body configuration. A classification model are performed, followed by a prediction of other bioparameters influencing the weight status. RESULTS: The results obtained from the classification model indicate that fat-free mass, reactance, and corrected resistance primarily influence the weight status of the studied population. Specifically, the regression model demonstrates that other bioparameters derived from impedance measurements can be highly accurate in estimating weight status. CONCLUSION: The classification and regression predictive models developed in this work are of the great importance for accessing to the weigh status with high accuracy of younger individuals at the Oncological Hospital in Santiago de Cuba, Cuba.

Theoretical approaches to defect mechanisms and transport properties of compounds used for electrodes and solid-state electrolytes in alkali-ion batteries.

The transition from fossil fuels to cleaner energies employing different renewable sources constitutes one of the primary worldwide challenges. The search for appropriate solutions is becoming more urgent in view of the severe consequences of climate change. As for a perspective, stationary energy storage, alkali-ion batteries and hybrid supercapacitors are, among others, considered as efficient and affordable solutions. Alkali-ion batteries have proved to be the most investigated products in the past decade including optimizations for cost, energy density and safety. In this Perspective, a computational approach and its applicability in the inverse material design are presented. This approach includes density functional theory calculations, force field-based determinations and both static and molecular dynamics simulations. As for an illustration, the main properties of a selected series of battery materials, including oxides and sulfides Li2SiO3, Li2SnO3, SrSnO3, and A2B6X13 (A = Li+, Na+, K+; B = Ti4+, Sn4+; X = O2-, S2-), and mixed halide antiperovskite A3OX (A = Li+, Na+; X = Cl-, Br-) are explored in depth using these theoretical approaches. Doping strategies, new dopant incorporation mechanism, treatment with alkali insertion/de-insertion cycle in electrodes, transport properties, as well as thermodynamic stability, are discussed. Theoretical approaches reveal that the oxygen-sulfur exchange in alkali hexatitanates and hexastannates induces remarkable improvement of the required properties for electrode and electrolyte materials. In addition, doping of Li2SiO3 with low Na-concentration enhances the room temperature Li-diffusivity by a reduction of the activation energy. The effects of transition-metal and divalent dopants on the defect chemistry and transport properties of Li2SnO3 are also disclosed. The interstitial trivalent doping mechanism is a friendly synthesis strategy to improve the large-scale diffusion in Li2SnO3. The potential of SrSnO3 as an anode in alkali-ion batteries, and the influence of a particular grain boundary in nanocrystalline antiperovskite A3OX are also revealed by using advanced atomistic simulations. The computational approaches described here provide us with a convenient tool for the determination of the properties of battery materials with high accuracy and for the prediction of characteristics of a new generation of alkali battery materials that could be used in improved technologies.

Unravelling the alkali transport properties in nanocrystalline A3OX (A = Li, Na, X = Cl, Br) solid state electrolytes. A theoretical prediction.

Transport properties of the halogeno-alkali oxides A3OX (A = Li, Na, X = Cl, Br) nanocrystalline samples with the presence of ∑3(111) grain boundaries were computed using large-scale molecular dynamic simulations. Results on the diffusion/conduction process show that these nanocrystalline samples are characterized with higher activation energies as compared to previous theoretical studies, but closer to experiment. Such a performance can be attributed to the larger atomic density at the ∑3(111) grain boundary regions within the nanocrystals. Despite a minor deterioration of transport properties of the mixed cation Li2NaOX and Na2LiOX samples, these halogeno-alkali oxides can also be considered as good inorganic solid electrolytes in both Li- and Na-ion batteries.

Enhanced Li-ion transport in divalent metal-doped Li2SnO3.

The improvement of Li-ion transport properties and doping engineering in Li-ion batteries are currently active research topics in the search for next-generation energy storage devices. In this theoretical work, the intrinsic defect formation and transport properties of divalent metal-doped Li2SnO3, which is being considered as an electrode and coating electrode material, are explored using atomistic simulations. Defect formation simulations reveal that all divalent dopants (Zn, Sc, Cd and Eu) occupy the Li site with charge compensation through Li vacancies. Molecular dynamics simulations show that the divalent dopants significantly reduce the activation energy for ionic diffusion and conduction compared to the undoped sample. The effects of both grains and grain boundaries on the Li-ion transport properties are investigated. Our calculated results demonstrate a marked improvement in the properties of Li2SnO3 that can be achieved either in current commercial and next-generation Li-ion battery technologies through divalent doping in mono- and polycrystalline Li2SnO3 samples.

Boosting Li-Ion Transport in Transition-Metal-Doped Li2SnO3.

Lithium stannate (Li2SnO3) is currently being considered as a material for electrode and electrode coating applications in Li-ion batteries. The intrinsic defect formation and Li-ion transport properties of Li2SnO3 doped with divalent and trivalent transition-metal dopants (Mn, Fe, Co, and Ni) are explored in this work using atomistic simulations. Defect formation simulations reveal that all divalent dopants occupy the Li site with charge compensation through Li vacancies. For trivalent doping, occupation of the Sn site is energetically preferred with charge compensation from Li interstitials. Molecular dynamics simulations reveal that divalent and trivalent dopants increase Li-ion diffusion and reduce its activation energy compared with the undoped system. We show that Li2SnO3 with Li excess or deficiency as a result of doping has improved Li-transport properties. This study highlights the substantial improvement in Li-ion diffusion of Li2SnO3 for both current commercial and next-generation Li-ion battery technologies that can be achieved through transition-metal doping.