Agricultural Traceable and Marketing System Based on iOS-Platform and Wireless Sensor Network

With the rapid development of Things of Internet and its related technologies such as Wireless Sensor Network and remote access system development, its application on agricultural and farming domain has become a new research direction. This paper discusses a practical and efficient system by applying mobile software development technologies and ZigBee communication network. To achieve a compact and flexible iOS-based client, this study takes advantage of Model-View-ViewModel pattern as well as Client/Server style. Several encryption technologies such as Message Digest algorithm and Rivest Shamir Adleman algorithm are combined to ensure information security. Compared to the common portable marketing system, the proposed system also provides accurate growth information which is collected by wireless sensor network, and proposes a more flexible design pattern for mobile client hence obtaining a better performance on iOS devices.

Network marketing strategy research for e-business of maternal and baby＇s products

Maternal and baby＇s product is a special needed class in our country, and the safety of the product quality is very high. Mothers aged at 70s, 80s and 90s have the unique consumption idea and consumption behavior characteristics which have profound significance for merchants. These all have significant influence to enterprise＇s marketing channel construction. Present market situation of Maternal and infant＇s products were summarized in this paper, then consumption psychology and behavior of the buyer are analyzed, and finally put forward the strategy of channel construction of network marketing of Maternal and infant＇s product＇s enterprise.

Network efficiency analysis of Chinese inter-bank market

The inter-bank market network models are constructed based on the inter-bank credit lending relationships, and the network efficiency characters of the Chinese inter-bank market are studied. Since it is impossible to obtain the specific credit data among banks, this paper estimates the inter-bank lending matrix based on the partial information of banks. Thus, directed network models of the Chinese inter-bank market are constructed by using the threshold method. The network efficiency measures and the effects of random attacks and selective attacks on the global efficiency of the inter-bank network are analyzed based on the network models of the inter-bank market. Empirical results suggest that the efficiency measures are sensitive to the threshold, and that the global efficiency is little affected by random attacks, while it is highly sensitive to selective attacks. Properties such as inter-bank market network efficiency would be useful for risk management and stability of the inter-bank market.

食用菌产业前景分析及E-Marketing体系构建

随着新的市场环境的变化,在销售食用菌的过程中,传统的销售模式已经不能满足竞争压力逐渐加大的营商环境。在新时期的营销环境下,食用菌企业必须依托互联网平台,大力发展电商营销,形成更加高效和稳固的网络营销体系。企业应顺应时代发展趋势,以互联网为契机,加深研究与实践,促进体系完善。在分析食用菌营销体系时,从互联网+这一核心要义出发,对食用菌网络营销体系的构建路径进行全面探究。

ST-Trader:A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement

Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

General election effect on the network topology of Pakistan’s stock market: network-based study of a political event

To examine the interdependency and evolution of Pakistan’s stock market,we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange(KSE-100)index.Using the minimum spanning tree network-based method,we extend the financial network literature by examining the topological properties of the network and generating six minimum spanning tree networks around three general elections in Pakistan.Our results reveal a star-like structure after the general elections of 2018 and before those in 2008,and a tree-like structure otherwise.We also highlight key nodes,the presence of different clusters,and compare the differences between the three elections.Additionally,the sectorial centrality measures reveal economic expansion in three industrial sectors—cement,oil and gas,and fertilizers.Moreover,a strong overall intermediary role of the fertilizer sector is observed.The results indicate a structural change in the stock market network due to general elections.Consequently,through this analysis,policy makers can focus on monitoring key nodes around general elections to estimate stock market stability,while local and international investors can form optimal diversification strategies.

Study of Enterprises Marketing Risk Early Warning System Based on BP Neural Network Model

For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The＇principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning resuits of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.

Analysis on the Transformation of Marketing Strategy and the Countermeasures under the Network Economic Times

In this paper, we theoretically analyze the transformation of marketing strategy and the countermeasures under the network economic times. Network marketing is based on the network technology, including the whole process of marketing activities as a new form of marketing. In this paper, we analyze the issue from the listed perspectives. （1） Ultra space-and-time. The traditional marketing has very strong boundedness to the region, and this boundedness displays, the specifi c transaction can only be closed in the specific region, if the enterprise wants to expand the market share, only then establishes the retailing organization. （2） Interactivity. Network as a media, with the one-on-one interaction, this feature allows companies to basic communicate with consumers, and strengthen consumer participation, to determine the product form, function and the price. （3） The symmetry of information. Consumers can fi nd all kinds of that related products on the Internet information, there are plenty of time to judge the various products and prices. Companies will also be released in a timely manner on the Internet the company’s latest product information. Under this basis, we propose the new idea on the network marketing development direction that will help to build up the more efficient marketing system.

The Network Marketing of Fresh Agricultural Products in China

The paper introduced the connotation of network marketing of fresh agricultural products and analyzed the fundaments of launching the C2C marketing of fresh agricultural products in China.The fundaments mainly cover the development of e-commerce,the transformation of consumption concept,the change of life style and the features of Chinese agricultural production.The developmental status of the C2C marketing of Chinese fresh vegetables and fresh fruits is introduced and the plights,including imperfect rural network infrastructure,inadequate talents specialized in network marketing of fresh agricultural products,uneven quality of agricultural products,immature logistics system,uncertainty existed in faced by the C2C marketing of fresh agricultural products and imperfect governmental protection system,are pointed out.In the end,the developmental trend of Chinese network marketing of fresh agricultural products on the basis of C2C mode is described.

Marketing as Innovation Strategy in Non Profit Sector in China： The Network of Influence ＂Guanxi＂

In a globalized world, not only the business market is overlapped but also marketing models take shape within economic systems. While there has been a proliferation in the implementation of marketing in the private sector, this paper provides a useful entry point for the understanding role of marketing within the nonprofit sector focused on China, the second worldwide economic power. A number of senior managers and directors from different Non-Governmental Organizations （NGOs） were interviewed of what they understood marketing to be and how they considered and implemented marketing within organizational strategy. The findings showed that while marketing was identified as an interesting and influential concept, Chinese nonprofit managers generally had a limited understanding of a marketing theory. The article further discusses reasons behind the dynamic based on personalized networks of influence used and the potential implications for nonprofit entities in China, a sector that plays a very noteworthy role in transformative economies.

How Do Companies Achieve Their Marketing Goals With Social Networks？

This work examines the role of social network sites as a tool used by companies to achieve marketing goals. As known from the main business literature, the social network represents one of the most important instrument to improve the company fame by strengthening the affection of customers to the brand. For this reason, some companies use these tools to build relations and contacts with customers all over the world. The population of social networks users is made, for the most parts, of youngsters （people belonging to the 13-30 years old cluster）. In the last years, with the social web networking, social communication lost the exclusive social meaning and social network sites become strategic instruments for the construction of powerful relations that connect people with people and people with firms. This work is aimed at clarifying the genesis and the evolution of the relations between companies and potential customers, focusing on the tools used by the firm to achieve their marketing goals through social network sites （SNSs）. First of all, the work proposes the recognition of some studies about the origin of web social network and their links with marketing strategies. Secondly, it considers marketing goals achieved from any companies through social networking with a particular focus on advertising through web social networking.

Application of Data mining in Enterprise Network Marketing

With the rapid development of the Internet, market has been increasingly competitive and competition means are various. Internet Marketing has become a new way for enterprises to grow. Data mining of enterprise network marketing has become the new darling of many business managers.The marketing data will become a key to develop a corporate marketing strategy as an important basis tool. However, many companies now make mistakes in marketing data. They can＇t fully exploit the marketing data.lt can affect the development of enterprise network marketing strategy. This paper is based on the concept of marketing data and outline the importance of data mining for network marketing, then analyze a significant impact on the entemrise network marketin~ strate^w made by marketinR data mininR.

Question of the status quo and countermeasure exit in the China Business Network Marketing

In the application process of the Internet and network marketing, many Chinese companies are actively engaged in practice, and after an exploratory period of time, companies accumulated some experience in a representative model. Such as： Chinese companies have a combination with traditional marketing and purchase advanced software model and then implemented through secondary development, they also change management ideas and systems, etc. These experiences and models can provide an effective and common reference for enterprise＇ s development of China＇ s e-commerce.

Research on the application of the data mining technology in the electronic commerce network marketing

With the rapid development of China＇s reform and opening up and the socialist market economy, the development of Internet technology has promoted the prosperity of e-commerce, and further promoted the rapid development of China＇s economy. Data mining technology is an advanced science and technology, which has important implications for the e-commerce data processing. Through the summary of the data mining technology, this article puts forward the application of data mining technology in electronic commerce, in order to better promote the development of electronic commerce.

Network Forum Affects the Stock Market： An Empirical Analysis Based on Multivariate GARCH-BEKK Model

Based on the text of posing on the network forum, the authors establish a set of keyword dictionary to measure the long and short investors＇ sentiment effectively, then they investigate the mutual relations between investor sentiment and the trading market through a Multivariable BEKK-GARCH model of abnormal long and short investor sentiment, returns, and abnormal trading volume. The results show that abnormal sentiment of long investor has a negative impact on the returns and a positive impact on the abnormal trading volume; while abnormal sentiment of short investor has no impact on the return and a negative impact on the abnormal trading volume. Otherwise, there is the negative volatility effect from abnormal sentiment of long investor to returns and abnormal trading volume, the positive volatility effects from abnormal sentiment of short investor to returns, and no volatility effects from abnormal sentiment of short investor to abnormal trading volume. In addition, network forum investor sentiment is a factor affecting the trading market. The analysis of forum information plays a certain role in presenting market risk and improving efficiency in making investment decision.

The marketing situation and measures of the network sporting goods

This article describes the present situation of network sporting goods and analyzes the problem of sporting goods that exists on the network marketing, in addition, we propose marketing strategies, also, we have some suggestions on the sustainable developmem of the network marketing of sporting goods.

Market presence in China Strengthened Network Growth and Dual-Chinese-Hub Strategy

In the end of 2007, Air China Limited (hereinafter "Air China") and Shanghai Airlines, formally joined Star Alliance at a ceremony held in the new Terminal Three at Beijing Capital International Airport. With the addition of the two Chinese airlines, Star Alliance now has 19 member carriers operating 17,000 daily flights to 897 destinations in 160 countries.……

基于DPSO-LSTM超参数调优的股市价格预测

长短期记忆网络(Long Short-Term Memory,LSTM)适合处理和预测时间序列中间隔和延迟较长的重要事件。由于其复杂的网络结构、不确定的超参数和耗时的网络训练,使得人工寻找高效的网络配置成为一项具有挑战性的工作。文中采用分布式粒子群算法(Distributed Particle Swarm Optimization,DPSO)来有效解决LSTM的超参数调优问题,研究LSTM中最优的隐藏元个数、激活函数以及学习率等超参数的选择,寻找高性能的LSTM。基于沪深300历史交易数据进行价格预测,实验结果表明该方法是有效的,这为超参数调优与股市价格预测提供了新的思路和方法。

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.