Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain.In an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain.As an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative study.The root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,respectively.Among these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,respectively.The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more accurate.The prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.

Sustainability of Transhumance Grazing Systems under Socio-economic Threats in Langtang,Nepal

The decline or loss of traditional socialecological systems may induce adverse effects to the societies and ecosystems. Transhumance, the recurring and seasonal movement of grazing livestock, is increasingly constrained by a numbers of factors including policy, land use and socio-economic changes in Nepal. To explore how these changes have affected the transhumance, this study investigated transhumance at the Langtang valley in central Nepal. The specific objectives of this study were to determine the herd size and composition, spatial-temporal patterns and to identify the major drivers of the system and the system changes. Data were collected from field study comprising semi-structured interviews with the herders, focus group discussions, key informants survey, and observations of rangeland and livestock management systems. The study revealed that the transhumance system in the Langtang is influenced by two types of drivers. In one hand, traditional practices are contributing to the sustainability of the system. On the other hand, the grazing patterns and adaptive responses are strongly influenced by changes in government policies, socioeconomic and cultural transformation, livestock productivity, markets, rangeland conditions and climate change. The findings of this study help with the development and implementation of transhumance management policy for the sustainability.

Image Processing of Manganese Nodules Based on Background Gray Value Calculation

To troubleshoot two problems arising from the segmentation of manganese nodule images-uneven illumination and morphological defects caused by white sand coverage,we propose,with reference to features of manganese nodules,a method called“background gray value calculation”.As the result of the image procession with the aid this method,the two problems above are solved eventually,together with acquisition of a segmentable image of manganese nodules.As a result,its comparison with other segmentation methods justifies its feasibility and stability.Judging from simulation results,it is indicated that this method is applicable to repair the target shape in the image,and segment the manganese nodule image in a short time.Also,it could be used to synchronously process a large number of manganese nodules on different conditions in an image,laying a good foundation for automatic underwater manganese nodule survey.Even if the target in the image is slightly distorted,the statistical data of manganese nodules are still accurate.Moreover,other methods cannot be fully applied to the segmentation of manganese nodule images;in another word,the effectiveness and stability of this method are proved.

At what levels of financial development does information sharing matter?

Background:The purpose of this study is to investigate how an increase in information-sharing bureaus affects financial access.Methods:We employed contemporary and non-contemporary interactive quantile regressions in 53 African countries for the period 2004–2011.Information-sharing bureaus are proxied with public credit registries and private credit offices.Financial development dynamics involving depth(at overall economic and financial system levels),efficiency(at banking and financial system levels),activity(from banking and financial system perspectives),and size are used.Results:Two key findings are established.First,the effect of an increase in private credit bureaus is not clearly noticeable on financial access,probably because private credit agencies are still to be established in many countries.Second,an increase in public credit registries for the most part improves financial allocation efficiency and activity(or credit)between the 25th and 75th quartiles.Conclusions:As a main policy implication,countries in the top and bottom ends of the financial efficiency and activity distributions are unlikely to benefit from enhanced financial allocation efficiency as a result of an increase in public credit registries.

Development of Sustainable Tourism on the Example of the Montenegrin Coast

Sustainable tourism represents a form of tourism which generates and creates economic benefits for the local community and at the same time takes care of the local ecosystem as well as the culture and customs of the local community.This form of tourism is mostly convenient for rural areas which have contained their natural and socio-cultural uniqueness.Unfortunately,the potentials of Montenegro for developing this form of tourism are slightly or almost hardly exploited.A crucial limiting factor for developing ecotourism is a lack of an overall strategy which would stimulate and enhance the development of many potential tourist destinations which did not valorise their tourists during the development of ecotourism,as well as those tourist destinations which did so but in an improper and insufficient way.The aim of this paper is to point out the state and possibilities for developing ecotourism based on sustainable tourism in selected areas along the Montenegrin Coast and to emphasise guidelines for sustainable development of these areas in the future.This is why an empirical research has been conducted based on the method of semi-structured interviews.By developing ecotourism and managing resources in accordance with sustainable development of this area,a high quality tourist product could be offered to the market.

排污权交易条件下污水处理商投资行为分析

基于排污权交易制度,运用Hotelling模型,在双寡头垄断专业污水处理市场中引入竞优机制,分析专业污水处理企业的投资动机。结果表明:在条状城市中,两企业的建厂选址策略受双方边际成本及单位污水运输费的影响;两寡头垄断企业竞优的结果存在两种情形,一种是两企业分别选择在城市两端建厂的策略,一种是边际成本较小的企业在选址上具有主动权、有多种选择策略;双方关于边际成本的竞优博弈具有直接效应和策略效应。

Optimal Inspection and Replacement Strategy for Systems Subject to Two Types of Failures with Adjustable Inspection Intervals

The inspection activities are often carried out to detect possible indication of failures in plant systems.This paper considers a single unit system subject to two types of failures, where one failure mode is the traditional0-1 logic failure and the other failure mode is described by a two-stage failure process. Adjustable inspections are used to detect the defective stage of the latter. We assume that the inspection duration gets shorter and shorter with a constant ratio. At the same time, preventive replacement is used to avoid the possible failure due to the former failure mode. The renewal process of this system is analyzed and the expected long-run cost per unit time(ELRCUT) is derived. The optimal inspection period and the preventive replacement interval to minimize ELRCUT are studied. At last, a numerical example is presented to illustrate the proposed model.

Transfer Pricing,Taxing Rights over Cross-Border Sales Income and the OECD Two-Pillar Response

The confluence of several factors including government fiscal pres­sures and growing opportunities for tax minimization in a digital economy prompted the OECD to embark on a program to reduce base erosion and profit shifting(BEPS).At the same time,countries became increasingly unhappy about international tax rules that left them unable to impose income taxes on companies earning profits by selling to customers inside their country without establishing a taxable presence there.The frustration led to unilateral responses that prompted the OECD to devel­op proposals to address this problem as well.The OECD’s proposals to reduce profit shifting and enhance taxing rights of sales destination countries evolved into what are now known as Pillar One and Pillar Two international tax reforms.This paper provides an overview of the operation of each of the Pillars and notes the limitations that prevent them from addressing the underlying causes of profit shifting and pro­viding full taxing rights to sales destination jurisdictions.

Banking with blockchain-ed big data

Blockchain is disrupting the banking industry and contributing to the increased big data in banking.However,there exists a gap in research and development into blockchain-ed big data in banking from an academic perspective,and this gap is expected to have a significant negative impact on the adoption and development of blockchain technology for banking.In hope of motivating more active engagement by academics,researchers and bankers alike,we present the most comprehensive review of the impact of blockchain in banking to date by summarizing the opportunities and challenges from a bankers perspective.In addition,we also discuss the impact that big data from blockchain will have on banking data analytics in future and show the increasing importance of filtering and signal extraction for the banking industry.Whilst there is evidence of selected banks adopting blockchain technology in isolation or small groups,we find the need for extensive research and development into several aspects of banking with blockchain to overcome the challenges which are currently hindering its adoption in banking across the globe.

Do Chinese Retail Option Traders Know Anything about Market Volatility？

这篇论文在中国盖住的保证市场检验暗示轻快的信息内容并且发现暗示轻快比为所有保证并且越过所有成熟的认识到的轻快一致地高。暗示轻快在被零卖投资者统治的中国保证市场为未来轻快有很小的信息内容。对结果的可能的解释是象限制在上那样的规章的问题保证，股票和保证的微分做贸易规则，高力量和低做贸易费用和零卖投资者统治的一个市场短卖。

Enhancing pedestrian mobility in Smart Cities using Big Data

Smart City is an emerging concept in global urban development.A Smart City applies ICT technologies to provide greater efficiencies for its urban areas and civilian population.One of the key requirements for a Smart City is to exploit data from its ICT infrastructure(such as Internet of Things connected sensors)to improve city services and features such as accessibility and sustainability.To address this requirement,the City of Melbourne(COM)Smart City office maintains several hundred data sets relating to urban activity and development.These datasets address parking,mobility,land use,3D data,statistics,environment,and major city developments such as rail projects.One promising dataset relates to pedestrian traffic.Data are obtained from sensors and updated on the COM website(City of Melbourne Open Data Platform:https://data.melbourne.vic.gov.au/.)at regular intervals.These data include the number of pedestrians passing 53 specific locations in the central business district and also their times and directions of travel.In a 24 h period,over 650,000 pedestrians were counted passing all locations.Peak rates of several thousand pedestrians per minute are regularly recorded during city rush hours at hotspots making the data amenable to Big Data analysis techniques.Results are obtained in graphical format as heatmaps and charts of city pedestrian traffic using both Microsoft Excel^(■)for static analysis and PowerBI^(■)for more advanced interactive visualisation and analysis.These findings can identify pedestrian hotspots and inform future locations of traffic lights and street configurations to make the city more pedestrian friendly.Further,the experience gained can be used to examine other data sets such as bicycle traffic that can be analysed to inform city infrastructure projects.Future work is suggested that could link these pedestrian flow data with social media data from smartphones and potentially wearable devices such as fitness monitors to correlate pedestrian satisfaction with traffic flow.The‘happiness’effect of pedestrians passing through green areas such as city parks can also be quantified.This research was undertaken with the assistance of Swinburne University under its Capstone Project scheme.