Enhanced Thermal Stability and Synergistic Effects of Magnesium and Iron Borate Composites against Pathogenic Bacteria.

A simple process based on the dual roles of both magnesium oxide (MgO) and iron oxide (FeO) with boron (B) as precursors and catalysts has been developed for the synthesis of borate composites of magnesium and iron (Mg2B2O5-Fe3BO6) at 1200°C. The as-synthesized composites can be a single material with the improved and collective properties of both iron borates (Fe3BO6) and magnesium borates (Mg2B2O5). At higher temperatures, the synthesized Mg2B2O5-Fe3BO6 composite is found thermally more stable than the single borates of both magnesium and iron. Similarly, the synthesized composites are found to prevent the growth of both gram-positive (Staphylococcus aureus) and gram-negative (Escherichia coli) pathogenic bacteria on all the tested concentrations. Moreover, the inhibitory effect of the synthesized composite increases with an increase in concentration and is more pronounced against S. aureus as compared to E. coli.

Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin.

The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f'c), splitting tensile strength (fst), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f'c, fst, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f'c and fst.

Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm.

Compressive strength (CS) and splitting tensile strength (STS) are paramount parameters in the design of reinforced concrete structures and are required by pertinent standard provisions. Robust prediction models for these properties can save time and cost by reducing the number of laboratory trial batches and experiments needed to generate suitable design data. Silica fume (SF) is often used in concrete owing to its substantial enhancements of the engineering properties of concrete and its environmental benefits. In the present study, the M5P model tree algorithm was used to develop models for the prediction of the CS and STS of concrete incorporating SF. Accordingly, large databases comprising 796 data points for CS and 156 data records for STS were compiled from peer-reviewed published literature. The predictions of the M5P models were compared with linear regression analysis and gene expression programming. Different statistical metrics, including the coefficient of determination, correlation coefficient, root mean squared error, mean absolute error, relative squared error, and discrepancy ratio, were deployed to appraise the performance of the developed models. Moreover, parametric analysis was carried out to investigate the influence of different input parameters, such as the SF content, water-to-binder ratio, and age of the specimen, on the CS and STS. The trained models offer a rapid and accurate tool that can assist the designer in the effective proportioning of silica fume concrete.

Size limits of magnetic-domain engineering in continuous in-plane exchange-bias prototype films.

Background: The application of superparamagnetic particles as biomolecular transporters in microfluidic systems for lab-on-a-chip applications crucially depends on the ability to control their motion. One approach for magnetic-particle motion control is the superposition of static magnetic stray field landscapes (MFLs) with dynamically varying external fields. These MFLs may emerge from magnetic domains engineered both in shape and in their local anisotropies. Motion control of smaller beads does necessarily need smaller magnetic patterns, i.e., MFLs varying on smaller lateral scales. The achievable size limit of engineered magnetic domains depends on the magnetic patterning method and on the magnetic anisotropies of the material system. Smallest patterns are expected to be in the range of the domain wall width of the particular material system. To explore these limits a patterning technology is needed with a spatial resolution significantly smaller than the domain wall width. Results: We demonstrate the application of a helium ion microscope with a beam diameter of 8 nm as a mask-less method for local domain patterning of magnetic thin-film systems. For a prototypical in-plane exchange-bias system the domain wall width has been investigated as a function of the angle between unidirectional anisotropy and domain wall. By shrinking the domain size of periodic domain stripes, we analyzed the influence of domain wall overlap on the domain stability. Finally, by changing the geometry of artificial two-dimensional domains, the influence of domain wall overlap and domain wall geometry on the ultimate domain size in the chosen system was analyzed. Conclusion: The application of a helium ion microscope for magnetic patterning has been shown. It allowed for exploring the fundamental limits of domain engineering in an in-plane exchange-bias thin film as a prototypical system. For two-dimensional domains the limit depends on the domain geometry. The relative orientation between domain wall and anisotropy axes is a crucial parameter and therefore influences the achievable minimum domain size dramatically.

Comparison of outcome between low and high thoracic trauma severity score in blunt trauma chest patients.

BACKGROUND: Blunt chest trauma is second leading cause of death among trauma patients. Early identification and aggressive management of blunt thoracic trauma is essential to reduce the significant rates of morbidity and mortality. Thoracic trauma severity score (TTS) is a better predictor of chest trauma related complications. The objective of the study was to compare outcomes between low-and high thoracic trauma severity score in blunt trauma chest patients. METHODS: A cross sectional descriptive study was carried out in public and private sector hospitals of Rawalpindi, Pakistan from 2008 to 2012 and 264 patients with blunt trauma chest who reported to emergency department of the hospitals, within 48 hrs of trauma were recruited. All patients were subjected to detailed history and respiratory system examination to ascertain fracture ribs, flail segment and hemopneumothorax. Written and informed consent was taken from each patient. Permission was taken from ethical committee of the hospital. RESULTS: The patients with blunt chest trauma had an array of associated injuries; however there were 70.8% of patients in low TTS group and 29.2% in high TTS group. Outcome was assessed as post trauma course of the patient. Outcome in low and high TTS group was compared using Chi square test which shows a significant relationship (p=0.000) between outcome and TTS, i.e., outcome worsened with increase in TTS. CONCLUSION: It is concluded that there is a significant relationship between outcome and thoracic trauma severity. Outcome of the patient worsened with increase in thoracic trauma severity score.