A case of perforation of rectum due to self-administered enema in a pregnant woman

A 23 year old primigravida at 35 weeks gestation presented with pain abdomen, fever and dissension of abdomen. Initially patient was managed conservatively at peripheral centre for two days and then reported to our hospital. An ultrasound done which shows collection inside peritoneal cavity and perforation was suspected. Decision of laparotomy was done. First caesarean section was done and a single live male baby born weighing 2.4 kg born. Then on exploration a small perforation in rectum was found and it was repaired by surgeon. But they found two more perforations and colostomy was done. On taking detailed history patient told that she-herself administered enema because of constipation. In post op period patient expired because of septicemia and ARDS

Detecting Missing Capacitors by Load Sensing

Factory testing on a production line can uncover several types of device failures. However, certain subtle failures can be masked from test procedures only to be discovered too late, e.g., by the customer. An example of such a failure is a missing bulk capacitor in circuits such as amplifiers, regulators, etc. Such a failure can go undetected at a factory due to the presence of smaller capacitors that have a marginally compensating effect. This disclosure describes techniques to detect missing bulk capacitors on circuit boards by inserting a simulated load resistance and measuring the decay time of an injected test voltage. A missing bulk capacitor is detected by decay times that are much faster than normal or expected

Training Neural Networks for Execution on Approximate Hardware

Approximate computing methods have shown great potential for deep learning. Due to the reduced hardware costs, these methods are especially suitable for inference tasks on battery-operated devices that are constrained by their power budget. However, approximate computing hasn't reached its full potential due to the lack of work on training methods. In this work, we discuss training methods for approximate hardware. We demonstrate how training needs to be specialized for approximate hardware, and propose methods to speed up the training process by up to 18X