Chryseobacterium meningosepticum in a parapneumonic effusion in a chronic kidney disease patient on hemodialysis

Chryseobacterium meningosepticum is an uncommon human pathogen, which is an inhabitant of soil and water. It should be included in the list of suspected nosocomial infections, especially in patients with immunocompromised status. C. meningosepticum infections are not common but are clinically important because the organism is naturally resistant to multiple antibiotics. We report a case where the bacterium was isolated from the pleural fluid from a chronic kidney disease patient on hemodialysis, who developed pneumonia with complicated parapneumonic effusion. To the best of our knowedge, this is the first case where C. meningosepticum is isolated from the pleural effusion, from India.     

Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence. In this paper, the rendering of different machine learning algorithms such as Decision Tree, Random Forest, linear regression, and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations. Further model accuracy metric have been performed with confusion matrix and calculation of accuracy, precision, recall and F-1 score along with Receiver Operating Characteristic (ROC )curves.     

Reconfigurable bow tie‐based filtering antenna for cognitive radio applications

This article presents the design and implementation of an 88 × 85 mm reconfigurable filtering bow‐tie antenna with four operating states. Two PIN diodes are deployed to reconfigure the transmission zeros to realize ultra‐wide band (UWB), 3.5/5.5 GHz with independent switching. This switching prevents the interference of the primary user into the secondary user thereby making the filtenna suitable for cognitive radio applications. The proposed filtenna consists of a UWB bow‐tie antenna integrated with a band‐pass filter with reconfigurable property enabled using two PIN diodes. The prototype‐filtering antenna is fabricated and the simulated results are validated using measurements. The presented results show that the antenna exhibits good impedance and radiation characteristics.     

Effect of seed layer on the ferroelectric properties and leakage current characteristics of sol–gel derived vanadium doped PbZr0.53Ti0.47O3 films

In this paper, we report the ferroelectric properties and leakage current characteristics of vanadium-doped PbZr_0.53Ti_0.47O₃ (PZTV) films grown on various seed layers prepared by a sol–gel process. The PZTV multilayered film of ~250-nm-thick showed excellent ferroelectric properties, with a large remnant polarization (P _r) of ~30 μC/cm² (E _c ~ 90 kV/cm), a high saturation polarization (P _s) of ~85 μC/cm² for an applied field of 1,000 kV/cm, fatigue-free characteristics of up to ≥ 10¹⁰ switching cycles, and a low leakage current density of 7 × 10⁻⁸ A/cm² at 100 kV/cm. X-ray diffraction (XRD), atomic force microscopy (AFM), and scanning electron microscopy (SEM) investigations indicated that PZTV films grown on PbZr_0.53Ti_0.47O₃/PbLa_0.05TiO₃ (PZT/PLT) seed layers exhibited a dense, well-crystallized microstructure with random orientations and a rather smooth surface morphology.