Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be used to identify hidden trends in the data. However, these methods rely on large datasets and often exhibit overfitting when used with sparse dataset. Further, assessing the uncertainty in predictions for a new dataset or an extrapolation of the present dataset is challenging. Herein, using Gaussian process regression (GPR), we predict Young's modulus for silicate glasses having sparse dataset. We show that GPR significantly outperforms NN for sparse dataset, while ensuring no overfitting. Further, thanks to the nonparametric nature, GPR provides quantitative bounds for the reliability of predictions while extrapolating. Overall, GPR presents an advanced ML methodology for accelerating the development of novel functional materials such as glasses.

CrGeTe 3 (CGT) is a semiconducting vdW ferromagnet shown to possess magnetism down to a two-layer thick sample. Although CGT is one of the leading candidates for spintronics devices, a comprehensive analysis of CGT thickness dependent magnetization is currently lacking. In this work, we employ scanning SQUID-on-tip (SOT) microscopy to resolve the magnetic properties of exfoliated CGT flakes at 4.2 K. Combining transport measurements of CGT/NbSe 2 samples with SOT images, we present the magnetic texture and hysteretic magnetism of CGT, thereby matching the global behavior of CGT to the domain structure extracted from local SOT magnetic imaging. Using this method, we provide a thickness dependent magnetization state diagram of bare CGT films. No zero-field magnetic memory was found for films thicker than 10 nm, and hard ferromagnetism was found below that critical thickness. Using scanning SOT microscopy, we identify a unique edge magnetism, contrasting the results attained in the CGT interior.

Study DesignThis is a retrospective study.PurposeTo determine the efficacy and safety of a posterior transpedicular approach with regard to functional and radiological outcomes in people with thoracic and thoracolumbar spinal tuberculosis.Overview of LiteratureSpinal tuberculosis can cause serious morbidity, including permanent neurological deficits and severe deformities. Medical treatment or a combination of medical and surgical strategies can control the disease in most patients, thereby decreasing morbidity incidence. A debate always existed regarding whether to achieve both decompression and stabilization via a combined anterior and posterior approach or a single posterior approach exists.MethodsThe study was conducted at the Indian Spinal injuries Centre and included all patients with thoracic and thoracolumbar Pott's disease who were operated via a Posterior transpedicular approach. Data regarding 60 patients were analyzed with respect to the average operation time, preoperative and postoperative, 6 months and final follow-up American Spinal Injury Association (ASIA) grading, bony fusion, implant loosening, implant failure, preoperative, postoperative, 6 months and final follow-up kyphotic angles, a loss of kyphotic correction, Oswestry disability index (ODI) score, and visual analog scale (VAS) score. Data were analyzed using either a paired t -test or a Wilcoxon Signed Rank test.ResultsThe mean operation time was 260±30 minutes. Fifty-five patients presented with evidence of successful bony fusion within a mean period of 6±1.5 months. Preoperative dorsal and lumbar angles were significantly larger than postoperative angles, which were smaller than final follow-up angles. The mean kyphotic correction achieved was 12.11±14.8, with a mean decrease of 5.97 and 19.1 in VAS and ODI scores, respectively.ConclusionsAnterior decompression and posterior stabilization via a posterior transpedicular approach are safe and effective procedures, with less intraoperative surgical duration and significant improvements in clinical and functional status.

The progress of human civilization has always been closely associated with the discovery of new materials. This is probably why the tripartite classification of historical periods is also based on materials-stone, bronze, and iron age.Beyond these materials, there are several others which have significantly improved the quality of human life, namely, steel, aluminum, glass, plastics, the latest in the list being nanomaterials. Among these materials, glasses hold a unique place in human lives, considering their applications ranging from everyday glass utensils and kitchen-wares to

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Due to their excellent optical properties, glasses are used for various applications ranging from smartphone screens to telescopes. Developing compositions with tailored Abbe number (Vd) and refractive index at 587.6 nm (nd), two crucial optical properties, is a major challenge. To this extent, machine learning (ML) approaches have been successfully used to develop composition–property models. However, these models are essentially black boxes in nature and suffer from the lack of interpretability. In this paper, we demonstrate the use of ML models to predict the composition‐dependent variations of Vd and nd. Further, using Shapely additive explanations (SHAP), we interpret the ML models to identify the contribution of each of the input components toward target prediction. We observe that glass formers such as SiO2, B2O3, and P2O5 and intermediates such as TiO2, PbO, and Bi2O3 play a significant role in controlling the optical properties. Interestingly, components contributing toward increasing the nd are found to decrease the Vd and vice versa. Finally, we develop the Abbe diagram, using the ML models, allowing accelerated discovery of new glasses for optical properties beyond the experimental pareto front. Overall, employing explainable ML, we predict and interpret the compositional control on the optical properties of oxide glasses.

The SARS-CoV-2 driven disease COVID-19 is pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. To launch both containment and mitigation measures, each country requires estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and to design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory of COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India. We show that our approach predicts state-wise COVID-19 spread for each country with reasonable accuracy. We show that R t, as the effective reproduction number, exhibits significant spatial variations in these countries. However, by accounting for the spatial variation of R t in an adaptive fashion, the predictive model provides estimates of the possible asymptomatic and undetected COVID-19 cases, both of which are key contributors in COVID-19 transmission. We have applied our methodology to make detailed predictions for COVID19 incidences at the district and state level in India. Finally, to make the models available to the public at large, we have developed a web-based dashboard, namely “Predictions and Assessment of Corona Infections and Transmission in India” (PRACRITI, see http://pracriti.iitd.ac.in ), which provides the detailed R t values and a three-week forecast of COVID cases.