The authors studied pediatric epilepsy and first afebrile seizure at presentation in Singapore. A total of 211 participants aged 1 month to 15 years with first presentation for afebrile seizures were recruited from November 2002 to May 2004; 108 with ≥2 prior afebrile seizures (newly diagnosed epilepsy) and 103 with first afebrile seizures. A χ(2) analysis of demographics, risk factors, examination, and investigation findings showed significant differences in development (normal in 87% [newly diagnosed epilepsy] and 93% [first afebrile seizure], P = .046), neurological examination (normal in 92% [newly diagnosed epilepsy] and 98% [first afebrile seizure], P = .016), and electroencephalogram findings (abnormal in 75% [newly diagnosed epilepsy] and 36.9% [first afebrile seizure], P < .005). Pediatric epilepsy incidence at our institution is 24 per 100 000 person-years and is highest in early childhood. Focal epilepsy is more common than generalized epilepsy. Patients with first afebrile seizure and abnormal development, neurological examination, and electroencephalogram findings should be monitored for future development of epilepsy. Population-based studies are recommended.

In this work we propose a non-intrusive video analytic system for patient's body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head/face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients' heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally non-intrusive. The user initializes the person-specific skin color and selects few face/head poses in the initial few frames. The system then tracks the head/face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection.

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Purpose This study aims to propose an integrated moderated mediation model to examine the process by which hotels obtain competitive advantage from the perspective of hotel managers. Design/methodology/approach After obtaining the agreement of the participants, a total of 560 candidates, all of whom were hotel managers, completed a survey administered online over a three-month period from November 2020 to January 2021. Ultimately, 257 valid responses were obtained after data screening by research assistants, for a response rate of 45.89%. Findings By reference to the concepts of big data (BD) and sustainability, the results show that BD management is a foundational attribute with respect to the indirect effects of BD analytics capabilities via proactive market orientation and social media collaboration. Furthermore, sustainability commitments and marketing are found to affect the relationship between such analytics capabilities and competitive advantage. Additionally, information sharing and food services have positive moderating effects that strengthen the changes in critical attributes during the process of obtaining competitive advantage. Originality/value To date, despite the proliferation of the concepts of BD, social media and sustainability, there is a lack of adequate empirical evidence and systematic literature reviews to comprehensively synthesize the emerging body of literature in the fields of tourism and hospitality research.

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