Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors

Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the glass properties using machine learning techniques. However, these techniques have the limitations, namely, (i) predictions are limited to the components that are present in the original dataset, and (ii) predictions towards the extreme values of the properties, important regions for new materials discovery, are not very reliable due to the sparse datapoints in this region. To address these challenges, here we present a low complexity neural network (LCNN) that provides improved performance in predicting the properties of oxide glasses. In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set. By training on a large dataset (~50000) of glass components, we show the LCNN outperforms state-of-the-art algorithms such as XGBoost. In addition, we interpret the LCNN models using Shapely additive explanations to gain insights into the role played by the descriptors in governing the property. Finally, we demonstrate the universality of the LCNN models by predicting the properties for glasses with new components that were not present in the original training set. Altogether, the present approach provides a promising direction towards accelerated discovery of novel glass compositions.Comment: 15 pages, 3 figure

Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks

The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derived as functionals based on observables such as positions and velocities. Discovering these governing symbolic laws is the key to comprehending the interactions in nature. Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory. We demonstrate the performance of HGNN on n-springs, n-pendulums, gravitational systems, and binary Lennard Jones systems; HGNN learns the dynamics in excellent agreement with the ground truth from small amounts of data. We also evaluate the ability of HGNN to generalize to larger system sizes, and to hybrid spring-pendulum system that is a combination of two original systems (spring and pendulum) on which the models are trained independently. Finally, employing symbolic regression on the learned HGNN, we infer the underlying equations relating the energy functionals, even for complex systems such as the binary Lennard-Jones liquid. Our framework facilitates the interpretable discovery of interaction laws directly from physical system trajectories. Furthermore, this approach can be extended to other systems with topology-dependent dynamics, such as cells, polydisperse gels, or deformable bodies

Effect of mattress deflection on CPR quality assessment for older children and adolescents

Appropriate chest compression (CC) depth is associated with improved CPR outcome. CCs provided in hospital are often conducted on a compliant mattress. The objective was to quantify the effect of mattress compression on the assessment of CPR quality in children. Methods: A force and deflection sensor (FDS) was used during CPR in the Pediatric Intensive Care Unit and Emergency Department of a children's hospital. The sensor was interposed between the chest of the patient and hands of the rescuer and measured CC depth. Following CPR event, each event was reconstructed with a manikin and an identical mattress/backboard/patient configuration. CCs were performed using FDS on the sternum and a reference accelerometer attached to the spine of the manikin, providing a means to Calculate the mattress deflection. Results: Twelve CPR events with 14,487 CC (11 patients, median age 14.9 years) were recorded and reconstructed: 9 on ICU beds (9296 CC), 3 on stretchers (5191 CC). Measured mean CC depth during CPR was 47 +/- 8 mm on ICU beds, and 45 +/- 7 mm on stretcher beds with overestimation of 13 +/- 4 mm and 4 +/- 1 mm, respectively, due to mattress compression. After adjusting for this, the proportion of CC that met the CPR guidelines decreased from 88.4 to 31.8% on ICU beds (p < 0.001), and 86.3 to 64.7% on stretcher (p < 0.001 The proportion of appropriate depth CC was significantly smaller on ICU beds (p < 0.001). Conclusion: CC conducted on a non-rigid surface may not be deep enough. FDS may overestimate CC depth by 28% on ICU beds, and 10% on stretcher beds

Antibiotic evaluation of odontogenic microbiological spectrum of orofacial infection

The present study aimed towards the development of active delivery system for management of hypertension. The Orodispersible tablets (ODTs) containing Telmisartan was developed in order to accomplish enhanced solubility leading to better bioavailability profile. Different ratios, of Telmisartan and PEG 6000 i.e. 1:1, 1:2, 1:3, 1:4 and 1:5 were selected for the formulation of ODT system. A batch process was adopted for the preparation of solid dispersion with each combination of drug and polymer and the finally compressed as tablets by direct compression technique. For the preformulation perspective materials were scrutinized on the basis of solubility profile, drug content, Fourier Transform Infrared (FTIR) spectroscopy and Differential scanning calorimetry (DSC). The drug polymer ratio 1:4 was selected for further compression process. The prepared batches of ODTs were characterized for micromeritic study, thickness, hardness, weight variation, wetting time, disintegration time, drug content and in vitro drug release profile. The evaluation data for all batches was satisfactory out of them formulation TF3 containing 6% kyron T-314 showed the best results with a value of 29.3 sec and 24.1 sec for wetting and disintegration, respectively. This formulation showed superior drug release of 99.93% over a period of 30 minutes. Keywords: Telmisartan, PEG 6000, Angioten receptor-II antagonist, Solid dispersion, Kyron T-31