当下民间礼俗习传的应对策略

民间礼俗在传续文化血脉上具有异于典籍文化的影响力。习传当下民间礼俗,可从熟稔古人思维方式、常存敬畏之心、通晓人情交往准则、拷问礼俗文化蕴藉、辩证发展地认识五个方面进行。

桂西南壮民族风情的记录——壮族作家岑献青的散文

桂西南养育了当代知名作家岑献青,她的散文既闪现着桂西南左江流域神奇异样的自然风光,又并包壮民族色彩斑斓的世俗民情等文化习传,洋溢着浓郁的地域风情。因地域风情与岑献青的生命存在交织在一起,便摆脱了纯粹物理意义上的地方性,而成为岑献青传达审美理想的符号载体。

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

Adaptive Fuzzy Neural Control of Dynamic System

Aim To build an adaptive fuzzy neural controller and simulate it. Methods\ Fuzzy logic and back propagation(BP) algorithm are combined to utilize their advantages while avoiding the disadvantages. Results and Conclusion\ Simulation results of the third order plant with disturbances and dead times show the validity of the presented controller. The presented controller can control cases that preceding controllers were unable to control.

Characteristics and outcomes of cholangiocarcinoma by region in Thailand: A nationwide study

AIM To identify the potential risk factors of cholangiocarcinoma, we determined the characteristics of cholangiocarcinoma patients among 5 different regions of Thailand. METHODS All patients diagnosed with cholangiocarcinoma between 2008 and 2013 were identified using the Nationwide Hospital Admission Data registry(n = 39421). Baseline characteristics, comorbidities and survival were abstracted. RESULTS The annual incidence during the study period was stable in all regions. Most patients lived in the Northeast(62.8%), followed by the North(16.9%), Central(12.3%), Bangkok(5.4%), and South(n = 2.6%) regions(P < 0.0001). Significantly more cholangiocarcinoma patients had diabetes, cirrhosis, and chronic viral hepatitis B/C infection than noncholangiocarcinoma participants(diabetes: 11.42% vs 5.28%; cirrhosis: 4.81% vs 0.92%; hepatitis B: 0.74% vs 0.12%; and hepatitis C: 0.50% vs 0.10%, P < 0.0001 for all, respectively). The overall 1-year mortality rate was 81.7%, with a stable trend over time. CONCLUSION Diabetes and chronic liver diseases may be associated with cholangiocarcinoma in the Thai population.

An improved brain emotional learning algorithm for accurate and efficient data analysis

To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.

Q-learning-based energy transmission scheduling over a fading channel

To solve the problem of energy transmission in the Internet of Things(IoTs),an energy transmission schedule over a Rayleigh fading channel in the energy harvesting system(EHS)with a dedicated energy source(ES)is considered.According to the channel state information(CSI)and the battery state,the charging duration of the battery is determined to jointly minimize the energy consumption of ES,the battery's deficit charges and overcharges during energy transmission.Then,the joint optimization problem is formulated using the weighted sum method.Using the ideas from the Q-learning algorithm,a Q-learning-based energy scheduling algorithm is proposed to solve this problem.Then,the Q-learning-based energy scheduling algorithm is compared with a constant strategy and an on-demand dynamic strategy in energy consumption,the battery's deficit charges and the battery's overcharges.The simulation results show that the proposed Q-learning-based energy scheduling algorithm can effectively improve the system stability in terms of the battery's deficit charges and overcharges.

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.

Support Vector Machine Ensemble Based on Genetic Algorithm

Support vector machines （SVMs） have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble （DGE）, is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, hagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.

A Signal Recognition Algorithm Based on Compressive Sensing and Improved Residual Network at Airport Terminal Area

It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work.Firstly,the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals.And the compressed measurement signals are taken as the input of the network.Furthermore,based on a scaled exponential linear units activation function,the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics.Finally,the global residual is introduced into the training network to guarantee the convergence of the network.Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.

Rotation forest based on multimodal genetic algorithm

In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the feature space randomly.Thus,a large number of trees are required to ensure the performance of the ensemble model.This random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this problem.It is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature rotation.However,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method.The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets.Experimental results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.

Applying machine learning for cars’semi-active air suspension under soft and rigid roads

To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.

A Brief Analysis of Culture Aphasia in Higher English Teaching in China

Culture is a result of the accumulation of human society; language is the important carrier of culture. Infiltration of Western culture and the widely use of English seriously hinder the globalization of the Chinese culture and it results in ＂cultural aphasia＂ phenomenon. After having studied foreign language for several years, many foreign language learners still cannot express Chinese traditional culture correctly in international communication. This is mainly due to the neglect of Chinese culture in the process of foreign language teaching. With the development of globalization, the communication between different cultures has become more and more frequent It is necessary to pay more attention to the output of native language culture as well as the input of target language culture. As the main places of cultivating high-level talents in China, colleges and universities are focusing on how to change this phenomenon in the foreign language teaching reform. This paper begins with the introduction of ＂cultural aphasia＂ phenomenon in China, then analyzes the reasons, finally proposes suggestions and teaching strategies to overcome the culture aphasia in cross-cultural communication.

Rock burst prediction based on genetic algorithms and extreme learning machine

Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.

LEARNING ALGORITHM OF STAGE CONTROL NBP NETWORK

This letter analyzes the reasons why the known Neural Back Promulgation (NBP)network learning algorithm has slower speed and greater sample error. Based on the analysis and experiment, the training group descending Enhanced Combination Algorithm (ECA) is proposed.The analysis of the generalized property and sample error shows that the ECA can heighten the study speed and reduce individual error.

Times Series Prediction to Basis of a Neural Network Conceived by a Real Genetic Algorithm

Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.

Small intestine adenocarcinoma associated with Peutz-Jeghers syndrome: a report of 5 cases and literature review

Peutz-Jeghers syndrome(PJS) is a rare autosomal dominant inherited disorder, manifested as multiple hamartomatous polyps of gastrointestinal tract, mucocutaneous pigmentations and increased risk of cancers. In this manuscript, we reported five cases of small intestinal carcinoma associated with the PJS. All the five patients have a history of PJS and postoperative pathological examination confirmed the diagnosis of small intestinal carcinoma. Histopathological features and recommended surveillance were additionally discussed.

Factors Influencing on Drinking Water Source Selection in Rural Areas of the Bengal Region

The arsenic contamination of underground water is a serious problem in rural areas of the Bengal region, where the water pipeline supply is not equipped. Those, who suffer from the contamination are mainly in the poorest sector. In the region, the selection of drinking water sources is done by women. The local traditional custom of Parda may restrict women＇s behavior. In this paper, a quantitative way of evaluating women＇s psychological stress is proposed, and mechanisms of women＇s water selection are analyzed using behavioral model. Ultimately, the more acceptable ways of installing water facilities to local people in each village are discussed.

Traditional Agrodiversity Management:A Case Study of Central Himalayan Village Ecosystem

Environmental, biological, socio-cultural and economic status variation existing in the Central Himalaya have led to the evolution of diverse and unique traditional agroecosystems, crop species and livestock, which facilitate the traditional mountain farming societies to sustain themselves. Indigenous agroecosystems are highly site specific and differ from place to place, as they have evolved along divergent lines. For maintenance of traditional agrodiversity management the farmers of the Central Himalaya have evolved various types of crop rotations in consonance with the varied environmental conditions and agronomic requirements. In irrigated fiat lands two crops are harvested in a year with negligible fallow period but in rainfed conditions if a cropping sequence is presumed to be starting after winter fallow phase then four major cropping seasons can be identified namely first kharif season （first crop season）, first rabi season （second crop season）, second kharif season （third crop season） and second rabi season （fourth crop season）. Highest crop diversity is present in kharif season in comparison to rabi season. Traditionally the fields are left fallow after harvest of the second kharif season crop. Important characteristics of agrodiversity management are the use of bullocks for draughtpower, human energy as labour, crop residues as animal feed and animal waste mixed with forest litter as organic input to restore soil fertility levels. Women provide most of the human labour except for ploughing and threshing grain. The present study deals with assessment of traditional agrodiversity management such as （i） crop diversity, （ii） realized yield under the traditional practices and （iii） assess the differences of realized yields under sole and mixed cropping systems. It indicated that crop rotation is an important feature of the Central Himalayan village ecosystem which helps to continue the diversity of species grown, as are the distribution of crops in the growing period and the management of soil fertility. The cropping diversity existing and the sequences practiced by the traditional farmers seems to have achieved high degree of specialization and thus even when the yield/biomass variations are about 6o%, the farmers continue to practice these sequences as they need to maintain diversity and synergistic relationships of crops in addition to manage the food and labour requirements for crop husbandry. Crop yields are generally higher in irrigated systems than rainfed systems and in sole cropping as compared with mixed cropping. However, gross biological and economic yields are higher in mixed cropping than sole cropping systems.

LoColms： an innovative approach of enhancing traditional classroom form of education by promoting web-based distance learning in the poorer countries

There have been numerous attempts recently to promote technology based education (Shrestha, 1997) in the poorer third world countries, but so far all these have not provided a sustainable solution as they are either centered and controlled from abroad and relying solely on foreign donors for their sustenance or they are not web-based, which make distribution problematic, and some are not affordable by most of the local population in these places. In this paper we discuss an application, the Local College Learning Management System (LoColms) , which we are developing, that is both sustainable and economical to suit the situation inthese countries. The application is a web-based system, and aims at improving the traditional form of education by empowering the local universities. Its economicability comes from the fact that it is supported by traditional communication technology, the public switching telephone network system, PSTN, which eliminates the need for packet switched or dedicated private virtual networks (PVN) usually required in similar situations. At a later stage, we shall incorporate ontology and paging tools to improve resource sharability and storage optimization in the Proxy Caches (ProCa) and LoColms servers. The system is based on the client/server paradigm and its infrastructure consists of the PSTN, ProCa, with the learning centers accessing the universities by means of point-to-point protocol (PPP) .