: Recent facts and figures published in various studies in the US show that approximately 27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that the mortality rate is quite high in diagnosed cases. The early detection of these infections can save precious human lives. As the manual process of these infections is timeconsuming and expensive, therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy specialists in their clinics. Generally, an automated method of gastric infection detections using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing, feature extraction, segmentation of infected regions, and classification into their relevant categories. These steps consist of various challenges that reduce the detection and recognition accuracy as well as increase the computation time. In this review, authors have focused on the importance of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and the scope of endoscopy. Further, the general steps and highlight the importance of each step has presented. A detailed discussion and future directions have provided in the last.

In this paper, a pattern synthesis based on a multiobjective optimization algorithm is proposed for the generation of a reconfigurable pencil/flat top dual-beam planar antenna array built using isotropic antenna elements in selected phi cuts. These beams claim the same amplitude excitations and differ from each other in phase excitations. Zero-phase excitations are used in pencil beam and these phases are updated with optimum phases for the flat top beam. All the excitations are obtained using Moth-flame optimization algorithm. With the support of the fitness functions, care is taken to control the expected values of the radiation pattern parameters to remain under certain fixed limit. In addition, synthesis is also done for the provision of a null in a particular direction for rejection of interference in the pencil beam in two different phi cuts. To suppress the mutual coupling effects, dynamic range ratio is kept under a threshold limit. Simulation results show the effectiveness of this proposed synthesis for phi cut planes. This algorithm is compared and proved to be better in many aspects over the standard meta-heuristic algorithms like Artificial Bee Colony and Imperialist Competitive algorithms in terms of performance parameters.

Background: Epidemiologic studies have shown that persons suffering from psychotic disorders are at increased risk of violent behavior. Several factors have been shown to predict violent behavior among persons with psychosis. However, prior research is limited in that these factors have not been explored simultaneously within the same study. Methods: The current study, therefore, aimed to determine which demographic, clinical, cognitive, and developmental characteristics were associated with an increased likelihood of violence among patients diagnosed with a psychotic disorder and which combination of these best predicted a history of violence. Participants (n=53) completed measures of demographics, violence risk, psychotic and personality symptoms, trauma, psychopathy and cognitive functioning. Results: Bivariate relationships were conducted to compare history of violent behavior between all variables. Additionally, a binary logistic regression was run predicting participants’ history of violence. Several demographic, cognitive, clinical, and developmental factors were associated with increased odds of having a history of violence. The overall correct classification rate for the model was 92.2%, with 87.5% of participants without a history of violence and 91.4% with a history of violence being correctly classified. The model included antisocial personality traits, poor behavioral controls, head injury, not accepting responsibility, lacking goals, prior supervision failures, and HCR-20 total score. Conclusion: The binary logistic regression model showed good accuracy in predicting a history of violence in persons with psychosis. These findings are consistent with prior research and can inform efforts at risk assessment and identification of treatment targets for people with a psychotic disorder who are at highest risk of violence.