Clinical analysis of uncinate process carcinoma of the pancreas

OBJECTIVE: To analyse the clinical features of uncinate process carcinoma of the pancreas and the diagnosis and treatment of this malignancy. METHOD: Fifty-nine patients with pancreas uncinate process carcinoma treated from January 1998 to September 2002 at our hospital were analysed retrospectively. RESULTS: Major symptoms of these patients were upper abdominal pain accompanied with lumbar pain, body weight loss and jaundice. Thirty-seven patients received regional pancreaticoduodenectomy (RP), 16 partial resection of the superior mesenteric vein-portal vein (SMV-PV) or superior mesenteric artery (SMA) and reconstruction, 1 anhydrous alcohol injection in the celiac nerve plexus, regional chemotherapy via a chemotherapy pump, and liver biopsy, and 5 no operation. The survival of the patients after operation was 2-46 months (median 12.1 months). Eleven patients are still alive with a longest survival of 46 months. The 1- and 3-year survival rates were 37.7% and 5.6%. CONCLUSIONS: Pancreas uncinate process carcinoma invading the adjacent SMV/SMA-PV causes difficulty in early diagnosis and poor prognosis, which are related to its location, not tumor's aggressive nature. This carcinoma has a high resection rate of 89.8%.

Experimental Analysis of the Flow Characteristics of an Adjustable Critical-Flow Venturi Nozzle

The response of an adjustable critical-flow Venturi nozzle is investigated through a set indoor experiments aimed to determine the related critical flow rate,critical pressure ratio,and discharge coefficient.The effect of a variation in the cone displacement and liquid content on the critical flow characteristics is examined in detail and it is shown that the former can be used to effectively adjust the critical flow rate.The critical pressure ratio of the considered nozzle is above 0.85,and the critical flow control deviation of the gas flow is within±3%.Liquid flow can reduce the gas critical mass flow rate accordingly,especially for the cases with larger liquid volume and lower inlet pressure.The set of results and conclusions provided are intended to support the optimization of steam injection techniques in the context of heavy oil recovery processes.

Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Long-term joint effect of nutrients and temperature increase on algal growth in Lake Taihu,China

To study how global warming and eutrophication affect water ecosystems, a multiplicative growth Monod model, modified by incorporating the Arrhenius equation, was applied to Lake Taihu to quantitatively study the relationships between algal biomass and both nutrients and temperature using long-term data. To qualitatively assess which factor was a limitation of the improved model, temperature variables were calculated using annual mean air temperature （AT）, water temperature （WT）, and their average temperature （ST）, while substrate variables were calculated using annual mean total nitrogen （TN）, total phosphorus （TP）, and their weighted aggregate （R）, respectively. The nine fitted curves showed that TN and AT were two important factors influencing algal growth; AT limited growth as algal photosynthesis is mainly carried out near the water surface; N leakage of phytoplankton and internal phosphorus load from sediment explains why TN was the best predictor of peak biomass using the Monod model. The fitted results suggest that annual mean algal biomass increased by 0.145 times when annual mean AT increased by 1.0℃. Results also showed that the more eutrophic the lake, the greater the effect AT had on algal growth. Subsequently, the long-term joint effect of annual temperature increase and eutrophication to water ecosystems can be quantitatively assessed and predicted.