A firearm bullet lodged into the thoracic spinal canal without vertebral bone destruction: a case report

<p>Abstract</p> <p>Introduction</p> <p>Firearm injuries account for 13% to 17% of all spinal cord injuries, and are generally caused during warfare or assault with intent to kill. Spinal cord injuries caused by firearms are usually observed in patients aged 15 to 34 years old, and are especially common among men.</p> <p>Case presentation</p> <p>We report the case of a 28-year-old Iraqi man who was referred to our radiology department with lower limb paraplegia secondary to a gunshot wound. We performed 64-slice computerized tomography with two-dimensional and three-dimensional reconstruction of the thoracolumbar spine. On the two-dimensional and three-dimensional reconstructed axial images of the thoracolumbar spine, an intra-canalicular bullet nucleus was found at the mid-spinal cord at the T8 level, with no evidence of vertebral bone destruction.</p> <p>Conclusions</p> <p>To the best of our knowledge, there is only one previous report in the literature describing a case of a bullet nucleus lodged into the inferior epidural spinal canal without destruction of the vertebral bone. With the rise of violence worldwide the incidence of gunshot injuries continues to increase, and, thus, it is essential for radiologists to have a clear understanding of gunshot injuries and the findings on radiographic images.</p

Hybrid Islanding Detection in Microgrid with Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network

© 1969-2012 IEEE. This paper presents a new hybrid islanding detection approach for microgrids (MGs) with multiple connection points to smart grids (SGs) which is based on the probability of islanding (PoI) calculated at the SG side and sent to the central control for microgrid (CCMG). The PoI values are determined using a combination of passive, active, and communication islanding detection approaches based on the utility signals measured at the SGs sides which are processed by discrete wavelet transform using an artificial neural network (ANN). If {\text{PoI}}-{{\rm{ANN}}} is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it ({\text{PoI}}-{{\rm{FUZZY}}}) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to {\text{PoI}}-{{\rm{FUZZY}}}. Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method