The problems that arise while developing a real-time distributed information-processing software system are studied. And based on the TCP/IP protocols and socket, for its facility in client/server （C/S） model networ...The problems that arise while developing a real-time distributed information-processing software system are studied. And based on the TCP/IP protocols and socket, for its facility in client/server （C/S） model networking programming, a prototype is designed for data transmission between the server and clients and it is applied on an on-line products automatic detection system. The probability analysis on network congestion was also made. A proper mechanism based on the ARCC （adapted RTT congestion control） algorithm is employed for detecting and resolving congestion, the purpose of which is mainly to achieve congestion avoidance under the particular conditions in this network-based system and reach the desired performance. Furthermore, a method is proposed for a client to diagnose automatically the connection status between the server and the client and to re-connect to the server when the disconnection is detected.展开更多
Background： Medical students have different perception of symptoms and illness. Moreover, medical students reportbarriers to seeking help about their health, and are more likely to seek advice informally from friends...Background： Medical students have different perception of symptoms and illness. Moreover, medical students reportbarriers to seeking help about their health, and are more likely to seek advice informally from friends and/or family. It is important toidentify health seeking behaviors among medical students to be able to modify and interfere accordingly. Objectives： To describe thehealth seeking behavior of medical students in UOS and identify the factors affecting those behaviors. Methods： A cross-sectionalstudy was conducted at the University of Sharjah during the spring semester of the academic year 2012-2013. Self-administeredquestionnaires were distributed to all medical students from all 5 years. Questions were related to physical health seeking behaviorsonly. Data was analyzed using the SPSS21 software. Results： We have found that self-prescription was the most common practicedhealth seeking behavior among 91.8%-96.6% of UOS medical students （CI of 95%） followed by the order ignoring a health problem,seeking immediate care, using the internet, reading more about the problem and self diagnosis ＆ management. All are practiced bymore than 50% of the students. A set of other behaviors was identified as well. Factors affecting these behaviors mainly included：self-care orientation ＆ medical education. Other factors that had a role as well were： gender, stage of studying, having a chronic illnessand lack of knowledge about the health services available. Conclusions： Medical students in the University of Sharjah have a high levelof self-care orientation and accordingly, tend to react to their illness in a variety of ways. The most common of these is self-prescription.Studying medicine is the 2nd major factor that influences their health seeking behaviors. Sufficient guidance about the consequencesassociated with certain behaviors may be required.展开更多
Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include...Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include optical coherence tomography and digital fundus imaging.If digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties involved.Optometrists and their patients will find this useful.Using deep convolutional neural networks(DCNNs),we provide a novel approach to this problem.Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones.In order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are trained.To establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine classifier.In our experiments,we used information from Kaggle’s open DR detection database.We fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test it.Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4.Experimental results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be effective.We have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep retina.Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology.This technology might be used for self-diagnosis,at-home care,and telemedicine.展开更多
基金The National Natural Science Foundation of China(No60474021)
文摘The problems that arise while developing a real-time distributed information-processing software system are studied. And based on the TCP/IP protocols and socket, for its facility in client/server （C/S） model networking programming, a prototype is designed for data transmission between the server and clients and it is applied on an on-line products automatic detection system. The probability analysis on network congestion was also made. A proper mechanism based on the ARCC （adapted RTT congestion control） algorithm is employed for detecting and resolving congestion, the purpose of which is mainly to achieve congestion avoidance under the particular conditions in this network-based system and reach the desired performance. Furthermore, a method is proposed for a client to diagnose automatically the connection status between the server and the client and to re-connect to the server when the disconnection is detected.
文摘Background： Medical students have different perception of symptoms and illness. Moreover, medical students reportbarriers to seeking help about their health, and are more likely to seek advice informally from friends and/or family. It is important toidentify health seeking behaviors among medical students to be able to modify and interfere accordingly. Objectives： To describe thehealth seeking behavior of medical students in UOS and identify the factors affecting those behaviors. Methods： A cross-sectionalstudy was conducted at the University of Sharjah during the spring semester of the academic year 2012-2013. Self-administeredquestionnaires were distributed to all medical students from all 5 years. Questions were related to physical health seeking behaviorsonly. Data was analyzed using the SPSS21 software. Results： We have found that self-prescription was the most common practicedhealth seeking behavior among 91.8%-96.6% of UOS medical students （CI of 95%） followed by the order ignoring a health problem,seeking immediate care, using the internet, reading more about the problem and self diagnosis ＆ management. All are practiced bymore than 50% of the students. A set of other behaviors was identified as well. Factors affecting these behaviors mainly included：self-care orientation ＆ medical education. Other factors that had a role as well were： gender, stage of studying, having a chronic illnessand lack of knowledge about the health services available. Conclusions： Medical students in the University of Sharjah have a high levelof self-care orientation and accordingly, tend to react to their illness in a variety of ways. The most common of these is self-prescription.Studying medicine is the 2nd major factor that influences their health seeking behaviors. Sufficient guidance about the consequencesassociated with certain behaviors may be required.
文摘Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include optical coherence tomography and digital fundus imaging.If digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties involved.Optometrists and their patients will find this useful.Using deep convolutional neural networks(DCNNs),we provide a novel approach to this problem.Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones.In order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are trained.To establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine classifier.In our experiments,we used information from Kaggle’s open DR detection database.We fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test it.Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4.Experimental results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be effective.We have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep retina.Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology.This technology might be used for self-diagnosis,at-home care,and telemedicine.
