中国矿业城市经济发展状况分析

本文选择了我国 51个矿业城市 ,以现有统计资料为依据 ,从经济发展水平、产业结构、所有制结构与外向度、经济发展效率、技术进步、财政状况等几个方面系统分析了矿业城市的动态特性、演变规律及其形成的原因。结果显示 ,矿业城市与一般城市之间、不同类型矿业城市之间 ,其经济发展的效益与效率存在很大的差异性。     

Key Determinants of the Stage of Internationalisation of Australian Wineries

A four stage measure capturing, awareness, interest, trial and adoption is employed to identify the key determinants of the stage of internationalisation of Australian wineries. Drawing from data from 292 wineries and using structural equation modelling techniques, this paper analyses the effects of a multitude of factors previously hypothesised to influence the stage of internationalisation. The results indicate that while these wineries exhibit characteristics described in the extant literature, significant differences remain. The paper concludes by discussing the limitations and areas for further research.     

Intelligent agents for adaptive security market surveillance

Market surveillance systems have increasingly gained in usage for monitoring trading activities in stock markets to maintain market integrity. Existing systems primarily focus on the numerical analysis of market activity data and generally ignore textual information. To fulfil the requirements of information-based surveillance, a multi-agent-based architecture that uses agent intercommunication and incremental learning mechanisms is proposed to provide a flexible and adaptive inspection process. A prototype system is implemented using the techniques of text mining and rule-based reasoning, among others. Based on experiments in the scalping surveillance scenario, the system can identify target information evidence up to 87.50% of the time and automatically identify 70.59% of cases depending on the constraints on the available information sources. The results of this study indicate that the proposed information surveillance system is effective. This study thus contributes to the market surveillance literature and has significant practical implications.     

The public R&D and productivity growth in Australia's broadacre agriculture: is there a link?

This paper investigates the dynamic relationships between research and development (R&D) expenditure and productivity growth in Australian broadacre agriculture using aggregate time series data for the period 1953 to 2009. The results show a cointegrating relationship between R&D and productivity growth and a unidirectional causality from R&D to TFP (total factor productivity) growth in Australian broadacre agriculture. Using the dynamic properties of the model, data from beyond the sample period are analysed by employing the variance decomposition and the impulse response function. The findings reveal that R&D can be readily linked to the variation in productivity growth beyond the sample period. Furthermore, the forecasting results indicate that a significant out‐of‐sample relationship exists between public R&D and productivity in broadacre agriculture.     

Tourism-based heritage conservation in Murshidabad: an appraisal

The article appraises the outcome of promoting tourism as a strategy for heritage conservation in the case of Murshidabad, an archetypal historic town in decline in India. A relational maturity-based framework has been used to examine the complementarities and conflicts between tourism and heritage conservation in the town. The prevailing nature of tourism conservation relationship in Murshidabad upholds the criticality characteristically observed in developing countries, where governments focus primarily on the revenue generating potential of built-heritage centric tourism ignoring the conservation attention necessary to retain authenticity and meaningful existence of historic structures. The overarching issues emerging from the current level of relational maturity in Murshidabad are identified with suggestions for possible policy implications of the study.     

Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology

This research proposes a novel method of identifying and understanding the holistic overview of emerging technologies’ unintended consequences. Latent Semantic Analysis (LSA) text mining technique is employed to yield multiple groups of contextually similar terms from future-oriented data sources, comprising both experts’ and the public's concerns regarding future technologies. Resulting term clusters are considered as new abstractions, or so-called scenarios, of future social impacts. Furthermore, the study acquires greater depth and breadth in conceptualizing social impacts through considering condition- and value-related terms as key linking factors to previous social impact-related literature. Our proposed methodology seeks to gain insights into the utilization of future-oriented data sources for the foresight activity, hoping to mitigate public skepticism and pursue a better social acceptance of emerging technologies.     

Australia's Carbon Tax: An Economic Evaluation

Australia's carbon tax has been in place since July 2012. Following the 2013 federal election and change of government, it is likely that the tax will be abolished. This paper evaluates Australia's carbon tax experience and draws lessons for policymakers in other jurisdictions who may be considering following the Australian example and implementing their own carbon taxes or cap and trade schemes. Overall, the policy was poorly thought through, badly implemented, and lacked majority public support before it began. Australia's carbon tax experience is an interesting case study in how not to go about implementing climate change policy.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

时空大数据支持的土地储备智能决策体系与应用研究

研究目的:设计土地储备智能决策体系,将土地储备决策环节进行科学化、定量化和模型化,为政府合理开展土地储备提供决策依据。研究方法:遵循科学性、实用性、先进性、开放性原则,以数据为驱动,人工智能为核心,通过基于Hadoop/Spark的大数据处理技术、多源异构数据融合技术、智能决策模型集对多源时空大数据挖掘分析。研究结果:提出了土地储备智能决策体系,研究了一套面向土地储备的决策方法集,实现对现状条件的精准刻画,对土地储备规模、结构、时序等内容的科学决策;在此基础上,研发了智能决策支持平台,将数据资源共享、现状条件评估、智能决策专题等功能集成,构建土地储备智能决策"一张图",并实现平台的自我学习和更新。研究结论:应用于宁波市实例验证,该体系有效地促进了土地储备科学智慧决策,对优化土地资源配置具有参考意义。     

关于我国瓦斯治理现状及存在问题的分析

随着我国煤矿开采深度的不断加大,煤层的透气性逐渐降低,而瓦斯的压力、浓度、放散量和速度则不断升高,因此加大瓦斯治理力度尤为关键。本文我们将针对当前我国煤矿瓦斯治理的现状,针对当前工作中存在的问题,提出煤矿瓦斯治理工作方面的意见和措施,以期能够提高煤矿的安全性,减少煤矿瓦斯事故的发生。