蕅益智旭与《占察善恶业报经》

《占察善恶业报经》自隋代问世以来,由于种种原因,历代乏有注释者。直到明末的藕益智旭著《占察善恶业报经义疏》《占察善恶业报经玄义》和《占察善恶业报经行法》,《占察善恶业报经》的思想内涵和修行方法才首次得到系统阐发,此经作为忏法经典和地藏菩萨信仰经典的地位才得到确立。智旭在对此经的注疏中,将佛教占察法定位于佛教忏法的一部分,并与地藏菩萨信仰密切结合起来,从而使其与世间的卜筮相法严格区分开来。这是智旭在佛教史上一大贡献。智旭对于《占察善恶业报经》的注疏书对近代的弘一法师和当代的梦参法师的思想产生了直接的影响,是承前启后的重要文献。

《父母恩难报经》与《父母恩重经》的研究

一、前言在现存《大正藏》中,我们看到以讲父母恩重思想为主题的经典有两个,它们是世传安世高翻译的《佛说父母恩难报经》,编号为No.684和从敦煌收集来的《佛说父母恩重经》编号为No.2887,他们分别被收在《大正藏》的第16和85册。学者们普遍认为后者是伪经,完全是中国人伪造的,而前者则几乎没有人研究过。据我个人的研究,《佛说父母恩难报经》是真正从印度翻译过来的经典,它与现存《增一阿含经》

西夏文“地藏三经”综考

本文全面系统地梳理考证了有'地藏三经'之称的《地藏菩萨本愿经》《占察善恶业报经》和《地藏菩萨十轮经》的西夏文译本,希望为学界了解地藏菩萨信仰在西夏的传承提供帮助。

翻译佛经语料年代的语言学考察——以《大方便佛报恩经》为例

《大方便佛报恩经》,七卷。大藏经题署“失译,附《后汉录》”。学者通常把它当作东汉语料来用,值得商榷。本文尝试从语法(判断句、被动句和疑问句)、词汇(一般语词和“～切”式复音词)的某些角度,对其翻译年代进行推定。本文认为,《大方便佛报恩经》所反映的语法词汇面貌与可靠的东汉译经不同,应该是魏晋以后的译经。

TIME SERIES NEURAL NETWORK MODEL FOR HYDROLOGIC FORECASTING

Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.

Projectile impact point prediction method based on GRNN

In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.

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Water quality forecast through application of BP neural network at Yuqiao reservoir

This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value,the model adopts LM (Leven-berg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.

Biloma following repeated transcatheter arterial embolization and complicated by intrahepatic duct stones: A case report

Biloma is an encapsulated bile collection outside the biliary tree due to a bile leak. It is occasionally found following traumatic liver injury or iatrogenic injury to the biliary tract, induced either during an endoscopic or surgical procedure. It is a rare complication of transcatheter arterial embolization (TAE). Although biloma can be shrunk by appropriate aspiration or drainage in majority of cases,we report a case of intrahepatic biloma following repeated TAE for hepatocellular carcinoma (HCC) and complicated by infection and intrahepatic stones. This particular constellation of problems has not been reported before and the intrahepatic stones need to be removed by percutaneous procedure.

Primary hepatic carcinoid:A case report and literature review

Carcinoids are tumors derived from neuroendocrine cells and often produce functional peptide hormones.Approximately 54.5% arise in the gastrointestinal tract and frequently metastasize to the liver.Primary hepatic carcinoid tumors(PHCT) are extremely rare;only 95 cases have been reported.A 65-year-old man came to our attention due to occasional ultrasound findings in absence of clinical manifestations.His previous medical history,since 2003,included an echotomography of the dishomogeneous parenchymal area but no focal lesions.A computed tomography scan performed in 2005 showed an enhanced pseudonodular-like lesion of about 2 cm.Cholangio-magnetic resonance imaging identified the lesion as a possible cholangiocarcinoma.No positive findings were obtained with positron emission tomography.Histology suggested a secondary localization in the liver caused by a low-grade malignant neuroendocrine tumor.Immunohistochemistry was positive for anti chromogranin antibodies,Ki67 antibodies and synaptophysin.Octreoscan scintigraphy indicated intense activity in the lesion.Endoscopic investigations were performed to exclude the presence of extrahepatic neoplasms.Diagnosis of PHCT was established.The patient underwent left hepatectomy,followed by hormone therapy with sandostatine LAR.Two months after surgery he had a lymph nodal relapse along the celiac trunk and caudate lobe,which was histologically confirmed.The postoperative clinical course was uneventful,with a negative follow-up for hematochemical,clinical and radiological investigations at 18 mo post-surgery.Diagnosis of PHCT is based principally on the histopathological confi rmation of a carcinoid tumor and the exclusion of a non-hepatic primary tumor.Surgical resection is the recommended primary treatment for PHCT.Recurrence rate and survival rate in patients treated with resection were 18% and 74%,respectively.

Prediction of Gas Emission Based on Infor-mation Fusion and Chaotic Time Series

In order to make more exact predictions of gas emissions, information fusion and chaos time series are com- bined to predict the amount of gas emission in pits. First, a multi-sensor information fusion frame is established. The frame includes a data level, a character level and a decision level. Functions at every level are interpreted in detail in this paper. Then, the process of information fusion for gas emission is introduced. On the basis of those data processed at the data and character levels, the chaos time series and neural network are combined to predict the amount of gas emission at the decision level. The weights of the neural network are gained by training not by manual setting, in order to avoid subjectivity introduced by human intervention. Finally, the experimental results were analyzed in Matlab 6.0 and prove that the method is more accurate in the prediction of the amount of gas emission than the traditional method.

Sigmoid schwannoma: A rare case

Schwannomas are rare tumors derived from the cells of Schwann that form the neural sheath. When located in the gastrointestinal tract, they constitute together with leiomyoma, leiomyoblastoma, and leiomyosarcoma, the gastrointestinal stromal tumors (GIST). Peripheral nerve sheath tumors represent 2-6% GIST with most common location, the stomach and the small intestine. Schwannomas of the colon and rectum are extremely rare and radical excision with wide margins is mandatory, due to their tendency to recur locally and become malignant, if left untreated. In the present study, we report a rare case of a sigmoid schwannoma, which was successfully treated in our department and reviewed the literature.

Managerial Ability and Forecast Accuracy

We examine the relation between managerial ability and management forecast accuracy. We base our analysis on S＆P 500 Composite Index constituents for the period of 2006-2012. Data were collected from Thomson Reuteurs, Compustat and Demerjian, Lev, and McVay （2012）. We find that forecast accuracy is positively associated with managerial ability in the case of sales forecasts. Specifically, more able managers are associated with lower magnitude＇s forecast errors in the case of sales forecasts. Additional analysis finds that managerial ability is immaterial to EPS figures＇ forecast accuracy, i.e., EPS forecasts appear not to be affected by manager＇s superiority. Regarding sales forecasts, the results are consistent with the assertion that managers impact the quality of the delivered management forecasts. Regarding EPS forecasts, the results are in alignment with Demerjian, Lev, Lewis, and McVay （2013） who highlighted that managerial ability is an ability score related to the entire management team.

Artificial network prediction on degradable properties of coal-filled films

Utilized degradable data of coal-filled films from the accelerated UV chamber ageing degradation experiments, and on the basis of control factors’ analysis, presented a predicting model on degradable properties of this film in photo-degradation according to back-propagation artificial neural network (BP ANN). 4 controlling factors in films degrada-tion, including temperature, the time of UV irradiation, the concentration and the type of coals were used as input parameters in the ANN model. While the degradable properties after film degradation, including the mechanical properties and carbonyl index, were used as output parameters. It was carried out by the neural network toolbox of Matlab 6.5 soft-ware and Visual Basic 6.0. Discussed partition of sample data and model’s parameters, and then selected the best configuration of ANN network. The accurate scope of predicting results was analyzed. This model has a high precision in predicting on properties of the coal-filled film degradation.

Equation Discovery for Financial Forcasting in Context of Islamic Banking

This paper describes an equation discovery approach based on machine learning using LAGRAMGE as an equation discovery tool, with two sources of input, a dataset and model presented in context-free grammar. The approach is searching a large range of po- tential equations by a specific inodel. The parameters of the equation are fitted to find the best equations. The experiments are illustratedwith commodity prices from the London Metal Exchange for the period of January-October 2009. The outputs of the experiments are a large mumber of equations; some of the equations display that the predicted prices are following the market trends in perfect patterns.

Research of deformation prediction method of soft soil deep foundation pit

In view of the characteristics of soft soil deep foundation pit for the construction and geotechnical characteristics of the special medium,it is difficult to calculate theoreti- cally accurately structural deformation of the foundation pit,so in the course of excavation on the construction of the information is particularly important.The analysis and compari- son of several popular non-linear forecasting methods,combined with the actual projects, set up a grey theoretical prediction model,time series forecasting model,improved neural network model to predict deformation of the foundation pit.The results show that the use of neural network to predict with high accuracy solution,it is the foundation deformation prediction effective way in underground works with good prospects.

Pancreatic schwannoma:Case report and an updated 30-year review of the literature yielding 47 cases

Pancreatic schwannomas are rare neoplasms. Authors briefly describe a 64-year-old female patient with cystic pancreatic schwannoma mimicking other cystic tumors and review the literature. Databases for PubMed were searched for English-language articles from 1980 to 2010 using a list of keywords, as well as references from review articles. Only 41 articles, including 47 cases, have been reported in the English literature. The mean age was 55.7 years （range 20-87 years）, with 45% of patients being male. Mean tumor size was 6.2 cm （range 1-20 cm）. Tumor location was the head （40%）, head and body （6%）, body （21%）, body and tail （15%）, tail （4%）, and uncinate process （13%）. Thirty-four percent of patients exhibited solid tumors and 60% of patients exhibited cystic tumors. Treatment included pancreati- coduodenectomy （32%）, distal pancreatectomy （21%）, enucleation （15%）, unresectable （4%）, refused opera- tion （2%） and the detail of resection was not specified in 26% of patients. No patients died of disease with a mean follow-up of 15.7 mo （range 3-65 too）, although 5 （11%） patients had a malignancy. The tumor size was significantly related to malignant tumor （13.8 ＋ 6.2 cm for malignancy vs 5.5 ＋ 4.4 cm for benign, P = 0.001） and cystic formation （7.9 ~ 5.9 cm for cystic tumor vs 3.9 ＋ 2.4 cm for solid tumor, P = 0.005）. The preoperative diagnosis of pancreatic schwannoma remains difficult. Cystic pancreatic schwannomas should be considered in the differential diagnosis of cystic neoplasms and pseudocysts. In our caset intraoperative frozen sec- tion confirmed the diagnosis of a schwannoma. Simple enudeation may be adequate, if this is possible.

An influence function method based subsidence prediction program for longwall mining operations in inclined coal seams

The distribution of the final surface subsidence basin induced by longwall operations in inclined coal seam could be significantly different from that in flat coal seam and demands special prediction methods. Though many empirical prediction methods have been developed, these methods are inflexible for varying geological and mining conditions. An influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, significant modifications have been made to the original Knothe function to produce an asymmetrical influence function. The empirical equations for final subsidence parameters derived from US subsidence data and Chinese empirical values have been incorpo- rated into the mathematical models to improve the prediction accuracy. A corresponding computer program is developed. A number of subsidence cases for longwall mining operations in coal seams with varying inclination angles have been used to demonstrate the applicability of the developed subsidence prediction model.

The risk early-warning of gas hazard in coal mine based on Rough Set-neural network

This article proposed the risk early-warning model of gas hazard based on Rough Set and neural network. The attribute quantity was reduced by Rough Set, the main characteristic attributes were withdrawn, the complexity of neural network system and the computing time was reduced, as well. Because of fault-tolerant ability, parallel processing ability, anti-jamming ability and processing non-linear problem ability of neural network system, the methods of Rough Set and neural network were combined. The examples research indicate： applying Rough Set and BP neural network to the gas hazard risk early-warning coal mines in coal mine, the BPNN structure is greatly simplified, the network computation quantity is reduced and the convergence rate is speed up.

Effect of School-Based Income Generating Activities on the Financial Performance of Public Secondary Schools in Kenya

This study was conducted to determine the value added by Income-Generating Activities （IGAs） on the financial performance of public secondary schools, in terms of assets, liability portfolio, and net worth. Secondary school managers have the gigantic task of balancing meager resources between subsistence and development needs as well as good performance in national examinations. However, macro-economic shocks such as inflation, fuel shortage, and crop failure, among others, often militate against the success of public schools. School-based IGAs enable public schools to cope with external economic shocks, without necessarily passing down budgetary adjustments to parents. However, the country lacks a clear policy guideline to facilitate the initiation, management, accounting, reviewing, and financial reporting of IGA projects. Besides, there is no documented information regarding the value added by IGA initiatives to the financial performance of public secondary schools. The study found that IGA and non-IGA schools were significantly different in terms of category, student population, age, annual income, and number of paid workers. Schools having IGAs were 1.9 times more likely to own as many assets as schools not having IGAs. Besides, IGA schools were about 2.2 times less likely to have their liability in excess of the median threshold. Regarding net worth, the study found that schools having IGAs were about 2.1 times more likely to be operating above the median threshold; suggesting that schools having IGAs were wealthier than non-IGA schools. Based on the findings, this study concludes that IGA projects were beneficial to schools by improving the ability of schools to accumulate assets and manage their liabilities. The study recommends the need to： formulate an appropriate policy framework to guide and standardize IGA activities; initiate suitable training programs for school IGA managers; as well as engage business development managers to advice schools on IGA matters.