Persistent surgical wound bleeding: A rare condition related to acquired hemophilia A

SummaryAcquired hemophilia A (AHA) is a rare condition that predisposes affected patients to a bleeding tendency, even after a trivial physical insult. We present our experience with a 45-year-old male patient who was referred to our institute because of persistent bleeding from a left forearm surgical wound after fasciotomy. He was diagnosed as having AHA. Surgical treatment in combination with recombinant activated factor VII (rFVIIa) led to a satisfactory result. Clinical awareness and multidisciplinary professional connections are necessary in the treatment of AHA. Acquired hemophilia should be considered in the differential diagnosis of patients with uncontrolled bleeding episodes

Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan

AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities

Design of Automatic Correction System for UAV’s Smoke Trajectory Angle Based on KNN Algorithm

Unmanned aerial vehicles (UAVs) have evolved with the progress of science and technology in recent years. They combine high-tech, such as information and communications technology, mechanical power, remote control, and electric power storage. In the past, drones could be flown only via remote control, and the mounted cameras captured images from the air. Now, UAVs integrate new technologies such as 5G, AI, and IoT in Taiwan. They have a great application value in a high-altitude data acquisition, entertainment performances (such as night light shows and UAV shows with smoke), agriculture, and 3D modeling. UAVs are susceptible to the natural wind when spraying smoke into the air, which leads to a smoke track offset. This study developed an autocorrect system for UAV smoke tracing. An AI model was used to calculate smoke tube angle corrections so that smoke tube angles could be immediately corrected when smoke is sprayed. This led to smoke tracks being consistent with flight tracks

An ARM-Based Q-Learning Algorithm

Abstract. This article presents an algorithm that combines a FAST-based algorithm (Flexible Adaptable-Size Topology), called ARM, and Q-learning algorithm. The ARM is a self organizing architecture. Dynamically adjusting the size of sensitivity regions of each neuron and adaptively pruning one of the redundant neurons, the ARM can preserve resources (available neurons) to accommodate more categories. The Q-learning is a dynamic programming-based reinforcement learning method, in which the learned action-value function, Q, directly approximates Q*, the optimal action-value function, independent of the policy being followed. In the proposed method, the ARM acts as a cluster to categorize input vectors from the outside world. Clustered results are then sent to the Q-learning architecture in order that it learns to present the best actions to the outside world. The effect of the algorithm is shown through computer simulations of the well-known control of balancing an inverted pendulum on a cart.