Miniature distributed filters for software re-configurable radio applications

This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available

The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time

Parents and teachers often express concern about the extensive use of social media by youngsters. Some of them see emoticons, undecipherable initialisms and loose grammar typical for social media as evidence of language degradation. In this paper, we use a simple measure of text complexity to investigate how the complexity of public posts on a popular social networking site changes over time. We analyze a unique dataset that contains texts posted by 942, 336 users from a large European city across nine years. We show that the chosen complexity measure is correlated with the academic performance of users: users from high-performing schools produce more complex texts than users from low-performing schools. We also find that complexity of posts increases with age. Finally, we demonstrate that overall language complexity of posts on the social networking site is constantly increasing. We call this phenomenon the digital Flynn effect. Our results may suggest that the worries about language degradation are not warranted

Antibiotic Prescribing Practices of Filipino Dentists

There are reports that dentists overprescribe antibiotics which may contribute to antibiotic resistance. This is an exploratory study on antibiotic prescribing practices of Filipino dentists using an online platform to form a basis for antimicrobial stewardship policy for dentists. A link to an online questionnaire using Survey Monkey was posted in a Closed Group Facebook account of Filipino dentists. Two hundred thirty (230) dentists participated. Data was analyzed by Survey Monkey. Amoxicillin is the first choice of antibiotics(71.18%), andclindamycin is the second (57.27%). Most respondents follow the indications for antibiotic therapy, however, some will prescribe antibiotics for conditions without indications. For dental procedures, 88.99% will prescribe for periodontal surgery, 75.45% for endodontic surgery, 68.3% for extraction of a tooth with chronic infection, 87.17% for third molar surgery, 26.7% for routine endodontics, and 23.56% for periodontal treatment without surgery. Not all of the respondents would prescribe for medical conditions that require antibiotic prophylaxis, while 60.36% will prescribe when in doubt in diagnosis, under time pressure (25.68%), and 48.67% considers patient preference. Only 10.48% of the respondents are very familiar with antimicrobial stewardship, while majority (69.74%) have not attended a lecture for antimicrobial stewardship for dentists.There is inappropriate antibiotic prescribing of participants on certain dental diseases, procedures, and medical conditions. Most respondents are not very familiar and have not attended a lecture on antimicrobial stewardship for specifically for dentists

A Neural Attention Model for Categorizing Patient Safety Events

Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.Comment: ECIR 201

Muscle Fatigue Analysis Using OpenSim

In this research, attempts are made to conduct concrete muscle fatigue analysis of arbitrary motions on OpenSim, a digital human modeling platform. A plug-in is written on the base of a muscle fatigue model, which makes it possible to calculate the decline of force-output capability of each muscle along time. The plug-in is tested on a three-dimensional, 29 degree-of-freedom human model. Motion data is obtained by motion capturing during an arbitrary running at a speed of 3.96 m/s. Ten muscles are selected for concrete analysis. As a result, the force-output capability of these muscles reduced to 60%-70% after 10 minutes' running, on a general basis. Erector spinae, which loses 39.2% of its maximal capability, is found to be more fatigue-exposed than the others. The influence of subject attributes (fatigability) is evaluated and discussed