A two-year-old castrated male Pomeranian dog was referred with the chief complaints of coughing and subcutaneous emphysema. On physical examination, the crepitant areas were palpable. When auscultated, the right chest was absent of respiratory sound, while the sound of the opposite side was enhanced. Radiographs presented pneumothorax and pneumomediastinum. On computed tomography, hypoattenuated bulla-like lesion at right middle lung lobe and trapped air in mediastinum were shown. After patient stabilization, surgery for excision of affected lobe was performed. During follow-up period, there were no recurrence and complication on radiographic examination. Based on clinical and pathological findings, the dog was diagnosed as congenital lobar emphysema.

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Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant.