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Query: UMLS:C0015672 (fatigue)
 51,768 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A total of 80 freshly extracted human molars, free from caries, cracks & decalcifications, were used in this study. Conservative class I cavities were prepared in the occlusal surface. Two types of amalgam alloys were used, high copper (Dispersalloy) & conventional (Velvalloy). The prepared cavities were classified into 5 groups, 16 each carve (C), carve & polish (CP), precarve burnish (BC), past-carve (CB) & pre post carve burnish (BCB). The specimens were thermally stressed using the stress fatigue device. The marginal integrity of the amalgam enamel interface were evaluated using SEM, for the four marginal quantities: 1--excellent margin, 2--open margins, 3--enamel fracture, and 4--amalgam fracture. The results of this study revealed that higher copper amalgam demonstrated superior marginal integrity than the conventional one. The pre-post carve burnish group showed the highest percentage of excellent margin than the other groups.
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PMID:Effect of surface treatment on marginal integrity of amalgam restorations (in vitro study). 949 75

Stress can lead to headaches and fatigue, precipitate addictive behaviors (e.g., smoking, alcohol and drug use), and lead to cardiovascular diseases and cancer. Continuous assessment of stress from sensors can be used for timely delivery of a variety of interventions to reduce or avoid stress. We investigate the feasibility of continuous stress measurement via two field studies using wireless physiological sensors - a four-week study with illicit drug users (n = 40), and a one-week study with daily smokers and social drinkers (n = 30). We find that 11+ hours/day of usable data can be obtained in a 4-week study. Significant learning effect is observed after the first week and data yield is seen to be increasing over time even in the fourth week. We propose a framework to analyze sensor data yield and find that losses in wireless channel is negligible; the main hurdle in further improving data yield is the attachment constraint. We show the feasibility of measuring stress minutes preceding events of interest and observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.
ACM BCB
PMID:Are We There Yet? Feasibility of Continuous Stress Assessment via Wireless Physiological Sensors. 2582 61

Brain fog, also known as confusion, is one of the main reasons for low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in a human's mind in real time is a challenging and important task that can be applied to online education, driver fatigue detection and so on. In this paper, we apply Bidirectional LSTM Recurrent Neural Networks to classify students' confusion in watching online course videos from EEG data. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is the gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activity.
ACM BCB 2017 Aug
PMID:Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks. 2896 96