A quad DC source switched three-phase multilevel DC-link inverter topology.

The concept of an isolated DC source cascaded multilevel inverter finds good solutions for generating quality output voltage for low-medium power applications. It shapes the output voltage from three levels into a number of steps closer to a sinusoidal shape using small DC sources or batteries. Several advantages have been sighted like lower voltage stress and bearing noise, and lesser THD. However, a common issue in the MLIs is the total components required which increase with the rise in voltage levels. This paper proposes a three-phase MLI design having several isolated quad voltage source modules including an H-Bridge inverter. The design suggested claims reduced switching components for current conduction paths showing improved output quality. The operational features of the suggested MLI have been analyzed using Matlab/Simulink software, furthermore, an experimental module is constructed for demonstrating the effectiveness of the simulated results.

Hybrid optimized feature selection and deep learning based COVID-19 disease prediction.

The COVID-19 virus has affected many people around the globe with several issues. Moreover, it causes a worldwide pandemic, and it makes more than one million deaths. Countries around the globe had to announce a complete lockdown when the corona virus causes the community to spread. In real-time, Polymerase Chain Reaction (RT-PCR) test is conducted to detect COVID-19, which is not effective and sensitive. Hence, this research presents the proposed Caviar-MFFO-assisted Deep LSTM scheme for COVID-19 detection. In this research, the COVID-19 cases data is utilized to process the COVID-19 detection. This method extracts the various technical indicators that improve the efficiency of COVID-19 detection. Moreover, the significant features fit for COVID-19 detection are selected using proposed mayfly with fruit fly optimization (MFFO). In addition, COVID-19 is detected by Deep Long Short Term Memory (Deep LSTM), and the Conditional Autoregressive Value at Risk MFFO (Caviar-MFFO) is modeled to train the weight of Deep LSTM. The experimental analysis reveals that the proposed Caviar-MFFO assisted Deep LSTM method provided efficient performance based on the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), and achieved the recovered cases with the minimal values of 1.438 and 1.199, whereas the developed model achieved the death cases with the values of 4.582 and 2.140 for MSE and RMSE. In addition, 6.127 and 2.475 are achieved by the developed model based on infected cases.

Geriatric Trauma Patients With Cervical Spine Fractures due to Ground Level Fall: Five Years Experience in a Level One Trauma Center.

BACKGROUND: It has been found that significantly different clinical outcomes occur in trauma patients with different mechanisms of injury. Ground level falls (GLF) are usually considered "minor trauma" with less injury occurred in general. However, it is not uncommon that geriatric trauma patients sustain cervical spine (C-spine) fractures with other associated injuries due to GLF or less. The aim of this study is to determine the injury patterns and the roles of clinical risk factors in these geriatric trauma patients. METHODS: Data were reviewed from the institutional trauma registry of our local level 1 trauma center. All patients had sustained C-spine fracture(s). Basic clinical characteristics, the distribution of C-spine fracture(s), and mechanism of injury in geriatric patients (65 years or older) were compared with those less than 65 years old. Furthermore, different clinical variables including age, gender, Glasgow coma scale (GCS), blood alcohol level, and co-existing injuries were analyzed by multivariate logistic regression in geriatric trauma patients due to GLF and internally validated by random bootstrapping technique. RESULTS: From 2006 - 2010, a total of 12,805 trauma patients were included in trauma registry, of which 726 (5.67%) had sustained C-spine fracture(s). Among all C-spine fracture patients, 19.15% (139/726) were geriatric patients. Of these geriatric patients 27.34% (38/139) and 53.96% (75/139) had C1 and C2 fractures compared with 13.63% (80/587) and 21.98% (129/587) in young trauma patients (P < 0.001). Of geriatric trauma patients 13.67% (19/139) and 18.71% (26/139) had C6 and C7 fractures compared with 32.03% (188/587) and 41.40% (243/587) in younger ones separately (P < 0.001). Furthermore, 53.96% (75/139) geriatric patients had sustained C-spine fractures due to GLF with more upper C-spine fractures (C1 and C2). Only 3.2% of those had positive blood alcohol levels compared with 52.9% of younger patients (P < 0.001). In addition, 6.34% of geriatric patients due to GLF had intracranial pathology (ICP) which was one of the most common co-injuries with C-spine fractures. Logistic regression analysis showed the adjusted odds ratios of 1.17 (age) and 91.57 (male) in geriatric GLF patients to predict this co-injury pattern of C-spine fracture and ICP. CONCLUSION: Geriatric patients tend to sustain more upper C-spine fractures than non-geriatric patients regardless of the mechanisms. GLF or less not only can cause isolated C-spines fracture(s) but also lead to other significant injuries with ICP as the most common one in geriatric patients. Advanced age and male are two risk factors that can predict this co-injury pattern. In addition, it seems that alcohol plays no role in the cause of GLF in geriatric trauma patients.