Hepatic epithelioid hemangioendothelioma (HEHE) is a rare tumor of vascular origin. Whether HEHE in Chinese patients exhibits similar characteristics compared with Western patients is not well known. The aim of the present study was to summarize the characteristics of HEHE in Chinese patients and identify its prognostic factors. In total, six patients diagnosed with HEHE at the Beijing Friendship Hospital between 2000 and 2012 were combined with 44 previously reported cases in China, retrieved from the literature between 1989 and mid-2012. These 50 cases from China were compared with 402 patients from Western populations. Prognostic factors were identified by the χ2 test and Cox regression analysis. The male to female ratio of the Chinese patients was 1:2.1 with the mean age of 44.2 years (range, 22–86 years). The percentage of asymptomatic Chinese patients was significantly higher than in the Western patients (40.0 vs. 24.8%; P=0.026), and that of extrahepatic metastasis (16.0 vs. 36.6%; P=0.005) was significantly lower in Chinese patients. On imaging study, capsular retraction (59.5%) and calcification (26.0%), as well as positivity of CD34 (93.5%) and CD31 (80.6%), were more frequently found in the Chinese patients. Management for the Chinese patients included liver resection (LRx; 45.7%), liver transplantation (LTx; 5.7%), trans-catheter arterial chemoembolization (14.3%) and palliative treatment (34.3%). Chinese patients with larger-sized tumor nodules [relative risk (RR), 1.58; 95% confidence interval (CI), 1.032–2.422; P=0.035) and diffuse type (RR, 12.17; 95% CI, 1.595–92.979; P=0.016) exhibited unfavorable outcomes. In contrast to Western patients with HEHE, a larger number of Chinese patients were asymptomatic with less extrahepatic metastasis. In China, LRx is widely adopted rather than LTx. Chinese patients with large tumor size or diffuse type may encounter a poorer prognosis.
Internet of Vehicles (IoV) is turning out to be one of the first impressive examples of Internet of Things (IoT). In IoV, the factors of connectivity and interaction/information dispersion are equally important as sensing/actuating, context-awareness, services provisioning, etc. However, most of the researches related to connectivity and interaction are constrained to physics of signaling and data science (semantics/contents), respectively. Very rapidly, the meanings of these factors are changing due to evolution of technologies from physical to social domain. For example, Social IoV (SIoV) is a term used to represent when vehicles build and manage their own social network. Hence, in addition to physical aspects, the social aspects of connectivity and information dispersion towards these systems of future should also be researched, a domain so far ignored in this particular context. In this paper, an agent-based model of information sharing (for context-based recommendations) of a hypothetical population of smart vehicles is presented. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts the dispersion of novel information (necessary for a recommender system) substantially. It is also observed that as the network evolves due to incremental interactions, the recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible.
There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, as well as text in image form cannot be undergo further processes like edit, search, copy, etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwritten or in printed form that needs to be converted into digital format for knowledge acquisition. Highly cursive, complex structure, bi-directionality, and compound in nature, etc. make the Urdu language too complex to obtain accurate OCR results. In this study, supervised learning-based OCR system is proposed for Nastalique Urdu language. The proposed system evaluations under a variety of experimental settings apprehend 98.4% training results and 97.3% test results, which is the highest recognition rate ever achieved by any Urdu language OCR system. The proposed system is simple to implement especially in software front of OCR system also the proposed technique is useful for printed text as well as handwritten text and it will help in developing more accurate Urdu OCR’s software systems in the future.
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