A 21-year-old woman presented to the hospital with 3 days of headache, fever, mood disturbance and nausea. She had recently emigrated from India, and was noted to have a positive screening purified protein derivative tuberculosis skin test with normal chest x-ray. Meningeal signs were noted prompting lumbar puncture and initiation of presumptive treatment for bacterial meningitis. While tuberculous meningitis (TM) was entertained at admission, diagnosis was clouded by the rapid onset of symptoms and recent major psychosocial stressors. She developed severe hyponatremia. Brain MRI revealed tuberculomas, and she was started on treatment for TM, a diagnosis confirmed by culture. On review, several lessons were learned: (1) globalisation of society makes uncommon diagnoses present in unlikely locations, (2) hyponatremia is a common complication of TM, (3) MRI can aid in diagnosis of TM and (4) cognitive and mood changes can be prodromal symptoms of TM.

The antenna design for a specified resonant frequency necessitates the computation of optimal values of different sizes. This is a harder task for microstrip patch antenna (MPA) since there is no precise numerical formula that leads to accurate solutions for designing these antennas. Presently, bio‐inspired approaches are widely deployed in numerous antenna designs and it has revealed an immense assurance in handling the rising necessities of antenna engineering for overall cost, reduced size, and enhanced performances. This work aims to introduce an optimal MPA design, where the antenna elements like patch length, patch height, substrate width, and substrate length are optimally tuned by a new hybrid optimization model. For this, a new hybridized model known as shark smell integrated EHO is proposed. Eventually, the primacy of the suggested model is scrutinized via varied assessments.

The main intension of this work is to find the warhead and decoy classification and identification. Classification of radar target is one of the utmost imperatives and hardest practical problems in finding out the missile. Detection of target in the pool of decoys and debris is one of the major radas technologies widely used in practice. In this study we mainly focus on the radar target recognition in different shapes like cone, cylinder and sphere based on radar cross section (RCS). RCS is a critical element of the radar signature that is used in this work to identify the target. The concept is to focus on new technique of ML for analyzing the input data and to attain a better accuracy. Machine learning has had a significant impact on the entire industry as a result of its high computational competency for target prediction with precise data analysis. We investigated various machine learning classifiers methods to categorize available radar target data. This chapter summarizes conventional and deep learning technique used for classification of radar target.