A 16-year-old girl with a background of childhood trichophagia presented with a 2-day history of epigastric pain and associated anorexia with vomiting. An epigastric mass was palpable on examination. A CT scan revealed an intragastric trichobezoar, extending into the duodenum consistent with Rapunzel syndrome with evidence of partial gastric outlet obstruction and a possible perforation. The patient underwent an urgent laparotomy and extraction of the trichobezoar. The bezoar was removed without complication and no intraoperative evidence of perforation was detected. After an uncomplicated postoperative recovery, she was discharged home with psychiatric follow-up.
Load testing is crucial to uncover functional and performance bugs in large-scale systems. Load tests generate vast amounts of performance data, which needs to be compared and analyzed in limited time across tests. This helps performance analysts to understand the resource usage of an application and to find out if an application is meeting its performance goals. The biggest challenge for performance analysts is to identify the few important performance counters in the highly redundant performance data. In this paper, we employed a statistical technique, Principal Component Analysis (PCA) to reduce the large volume of performance counter data, to a smaller, more meaningful and manageable set. Furthermore, our methodology automates the process of comparing the important counters across load tests to identify performance gains/losses. A case study on load test data of a large enterprise application shows that our methodology can effectively guide performance analysts to identify and compare top performance counters across tests in limited time.
In an environment where node density is massive, placement is heterogeneous and redundant sensory traffic is produced; limited network resources such as bandwidth and energy are hastily consumed by individual sensor nodes. Equipped with only a limited battery power supply, this minimizes the lifetime of these sensor nodes. At the network layer, many researchers have tackled this issue by proposing several energy efficient routing schemes. All these schemes tend to save energy by elevating redundant data traffic via in-network processing and choosing empirically good and shortest routing paths for transfer of sensory data to a central location (sink) for further, application-specific processing. Seldom has an attempt been made to reduce network traffic by moving the application-specific code to the source nodes. We unmitigated our efforts to augment the node lifetime within a sensor network by introducing mobile agents. These mobile agents can be used to greatly reduce communication costs, especially over low bandwidth links, by moving the processing function to the data rather than bringing the data to a central processor. Toward this end, we propose an agent-based directed diffusion approach to increase sensor node efficiency and we present the experimental results.
-The popularity of smartphones -small computers that run on battery power -has exploded in the last decade. Unsurprisingly, power consumption is an overarching concern for mobile app developers, who are anxious to learn about powerrelated problems that are encountered by others. In this paper, we present an empirical study exploring the characteristics of energy-related questions posed in StackOverflow, issues faced by the developers, and the most significantly discussed APIs. We extracted a sample of 5009 StackOverflow questions, and manually analyzed 1000 posts of Android-related energy questions. Our study shows that developers are most concerned about energy-related issues that concern improper implementations, sensor, and radio utilization.
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