We propose a fully-distributed peer-to-peer architecture to solve the scalability problem of Networked Virtual Environment in a simple and efficient manner. Our method exploits locality of user interest inherent to such systems and is based on the mathematical construct Voronoi diagram. Scalable, responsive, fault-tolerant NVE can thus be constructed and deployed in an affordable way.
Background: Stress urinary incontinence (SUI) is a major health problem affecting approximately 50% of the female population over 45 years of age. We evaluated the therapeutic effects of a home-based non-invasive wireless sensor pelvic floor muscle training (PFMT) device with assisted Kegel exercise for SUI. Methods: We included 60 women 40 to 60 years of age who were diagnosed with urodynamic SUI (mean pad test, 10.52 g). The PFMT device applicator was clamped on the upper inner thigh, and the patients could self-train at home. The signal was recorded and delivered to a 3G/4G smartphone via Bluetooth, which also allows guided feedback via the smartphone's voice. To evaluate the therapeutic effect, all patients completed the following questionnaires: a 3-day bladder diary, the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), the Urogenital Distress Inventory-Short Form, and the Incontinence Impact Questionnaire-7 (IIQ-7). One-hour pad test measurements were performed before the test (M0) and at 1 (M1), 2 months (M2), and 3 months (M3) after the PFMT device-assisted Kegel exercise. Results: The 1-hour pad test and the scores of the ICIQ-SF, UDI-6, and IIQ-7 questionnaires were improved at M1, M2, and M3, compared with the M0 values. The mean value of the post-voiding residual urine (PVR) significantly decreased at M2 and M3. The subjective and objective improvement rates at M3 were 80% and 72%, respectively. Conclusion: The data demonstrated that 3 months of Kegel exercise assisted with a home-based PFMT device improved the number and severity of episodes, PVR, and quality of life in patients with SUI, suggesting that this device might serve as an alternative non-invasive therapy for mild and moderate SUI.
The current and developing trend for consumers is to access web contents and applications anytime, anywhere, and on any devices. Most of Internet services and most of web contents have been designed for desktop computers, and often contain rich medias, such as images, audios, and videos. However, some devices are different from network connectivity, processing power, storage capacity, display size, and formative handling capability. In many cases, the content designed for computers is not suitable for new (and often mobile) devices. Therefore, content adaptation is needed in order to optimize the service for different devices and access methods. This research discusses the context issues for web content adaptation. The CC/PP and UAProf are two related standards that define the format to describe the capabilities of devices for accessing content. A context-aware environment should allow adaptive access to context information. In this paper, first we proposed an inference mechanism for context-aware service. Through this inference mechanism, users using different devices can get appropriate contents based on inference results. Second, we can demonstrate the correlation between classes and individuals, and provide better scalability by means of building ontologies. Lastly, SWRL depends on ontology based rule languages. Rules written based on SWRL can directly use an established object relationship from ontology
Urinary incontinence is a common problem among adults. Studies have shown up to 70% improvement in stress incontinence symptoms following appropriately performed pelvic floor exercise. This improvement is evident across all age groups. In this study, we cooperated with a doctor of the Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taiwan. We developed a noninvasive device with the purpose of helping patients commence and perform pelvic floor muscle training (PFMT). This device consists of a PFMT device, an Arduino control board, a force sensor, a Bluetooth device, and an SD card. The objectives of this study are to train patients to inhibit detrusor contraction voluntarily and to contract periurethral muscles selectively. The system records and analyzes sensor data and provides voice prompts during PFMT exercise for patients at hospitals or their homes. Meanwhile, it tracks patients' PFMT exercise at home, and doctors can contact patients for additional visitation(s) if necessary.
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