Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

The epidemiology of gastrointestinal stromal tumors in Taiwan, 1998–2008: a nation-wide cancer registry-based study

BACKGROUND: To investigate the incidence of gastrointestinal stromal tumors (GISTs) in Taiwan and the impact of imatinib on the overall survival (OS) of GIST patients. METHODS: GISTs were identified from the Taiwan Cancer Registry (TCR) from 1998 to 2008. The age-adjusted incidence rates and the observed OS rates were calculated. Cox proportional hazards models were applied to examine the mortality risk in three time periods (1998–2001, 2002–2004, 2005–2008) according to the application and availability of imatinib. RESULTS: From 1998 to 2008, 2,986 GISTs were diagnosed in Taiwan. The incidence increased from 1.13 per 100,000 in 1998 to 1.97 per 100,000 in 2008. The most common sites were stomach (47-59%), small intestine (31-38%), and colon/rectum (6-9%). The 5-year observed OS was 66.5% (60.3% for men, 74.2% for women, P < .0001). GISTs in the stomach had a better 5-year observed OS (69.4%) than those in the small intestine (65.1%) (P < .0001). The outcome of GIST improved significantly after the more widespread use of imatinib; the 5-year observed OS increased from 58.9% during 1998–2001 to 70.2% during 2005–2008 (P < .0001). Younger age, female sex, stomach location, and later diagnostic years were independent predictors of a better survival. CONCLUSIONS: The incidence of GIST has been increasing in Taiwan, partially due to the advancement of diagnostic technology/method and the increased awareness by physicians. The outcome of GIST has improved significantly with the availability and the wider use of imatinib

Knowing who to know in Knowledge Sharing Communities: A Social Network Analysis Approach

Information stored in online communities consist not only knowledge contents, but also the information of knowledge providers and searchers‟ connective relationships, and network structures. Online Communities provide effective platforms for interaction and play pivotal roles in making provision for the basis of analysis as all the ask-response paired relationships are automatically recorded. This paper demonstrates how to apply social network analysis to analyze the interaction data for generating the “role information” of the knowledge searchers and providers. Integrating concepts of uncertainty in knowledge searching and sociometric used in social network analysis, we develop a mechanism for role matching in knowledge search for each questions posed. Roles identified in this approach including central, network entrepreneur (e.g. spanning structural holes), neighboring mediate (e.g. knowledge gate keeper), and resource competitor (e.g. structural equivalent players). The result is demonstrated and visualized in a web-based community platform and tested in a real-world programmer forum-based community

Isoflavones prevent bone loss following ovariectomy in young adult rats

Soy protein, a rich source of phytoestrogens, exhibit estrogen-type bioactivity. The purpose of this study was to determine if ingestion of isoflavones before ovariectomy can prevent bone loss following ovariectomy. Twenty-four nulliparous Wistar rats were randomly divided into four groups. In the normal diet groups, a sham operation was performed on Group A, while ovariectomy was performed on Group B. For Groups C and D, all rats were fed with an isoflavone-rich (25 mg/day) diet for one month, then bilateral ovariectomy were performed. In the rats in Group C, a normal diet was begun following the ovariectomy. The rats in Groups D continued to receive the isoflavone-rich diet for two additional months postoperatively. All rats were sacrificed 60 days after surgery. The weight of bone ash of the long bones and whole lumbar spine were determined. A histological study of cancellous bone was done and biochemical indices of skeletal metabolism were performed and analyzed. The markers of bone metabolism exhibited no significant changes. When compared with the sham-operated rats fed a normal diet, the bone mass of ovariectomized rats decreased significantly; pre-ovariectomy ingestion of an isoflavone-rich diet did not prevent bone loss. The bone mass of rats treated with an isoflavone-rich diet for three months was higher than controls two months after ovariectomy

Traditional Chinese Medicine ZHENG Identification Provides a Novel Stratification Approach in Patients with Allergic Rhinitis

Background. We aimed to apply the ZHENG identification to provide an easy and useful tool to stratify the patients with allergic rhinitis (AR) through exploring the correlation between the quantified scores of AR symptoms and the TCM ZHENGs. Methods. A total of 114 AR patients were enrolled in this observational study. All participants received the examinations of anterior rhinoscopy and acoustic rhinometry. Their blood samples were collected for measurement of total serum immunoglobulin E (IgE), blood eosinophil count (Eos), and serum eosinophil cationic protein (ECP). They also received two questionnaire to assess the severity scores of AR symptoms and quantified TCM ZHENG scores. Multiple linear regression analysis was used to determine explanatory factors for the score of AR manifestations. Results. IgE and ECP level, duration of AR, the 2 derived TCMZHENG scores of “Yin-Xu − Yang-Xu”, and “Qi-Xu + Blood-Xu” were 5 explanatory variables to predict the severity scores of AR symptoms. The patients who had higher scores of “Yin-Xu − Yang-Xu” or “Qi-Xu + Blood-Xu” tended to manifest as “sneezer and runner” or “blockers,” respectively. Conclusions. The TCM ZHENG scores correlated with the severity scores of AR symptoms and provided an easy and useful tool to stratify the AR patients