Microfinance and social capital formation- a social network analysis approach

The microcredit program has emerged as an important poverty alleviation strategy over the last three decades, and several studies have examined its economic impacts on the community well-being. However, far too little attention has been paid to the effects of micro credits on community social connection and solidarity. This paper aims to examine the application of Social Network Analysis (SNA) to explore the impact of the rural microcredit fund on community social capitals. In doing so, the data on interactions of four rural development groups' members before and after the microcredit project implementation were collected using participatory workshops in Neyzar village of Qom province in Iran. The data were analyzed by Ucinet software, and the socio-graphs were produced by the NetDraw application. The results show that, more people have been involved in the social interactions after the project implementation and there was statistically significant increase in density and decrease in centralization of cooperation network. Furthermore, there were no important distinctions in centrality of people with various educational levels before and after the project implementation. Overall, it can be concluded that, the microfinance initiative considerably promotes the community social capital and participation in the rural development activities. Moreover, the SNA techniques are applicable as an impact assessment tool to investigate changes in community social capital.     

Ratio-based design hour determination for airport passenger terminal facilities

To avoid both over-design and under-sizing of airport passenger terminal facilities such as security checkpoints, the infrastructure is designed for a specifically determined design load. As such, the design load is considered for a short period of time, usually an hour of operation, during which peak, though not necessarily maximum, demand occurs. For strategic planning applications, future design loads can be determined by either fictitious flight schedules or ratio-based models which forecast the relationship between design load and annual demand. This study presents two ratio-based methods which allow the direct determination of design hour loads (DHL) for passenger terminal facilities. The unsaturated DHL model considers the relationship between observed passenger flows in the terminal and aggregated annual demand data. The saturated DHL model includes several operational constraints which limit the actual DHL, such as limitations in the runway system or the fleet mix operating at an airport. Both models are applied to two real-world airports, for which the DHL of the security checkpoint facilities is estimated from large datasets covering multiple years. Results are significant at the 5 % level and suggest that the proposed ratio-based methods are appropriate for airport strategic planning applications.     

移动网络数据中大数据处理的关键技术探索

最近几年以来,我国科技水平持续提升,计算机网络技术也逐渐在各行各业中获得了广泛运用,这也使得信息技术的深度融合发展受到一定的促进,数据规模愈发庞大,在此背景下,开始演变成大数据供不应求的状况。为此,国内陆续推出了一系列相关的管理法规与条例,同时扩大了对大数据的探究创新力度,有效提升了移动通信网络处理数据的速度,这同时促使国内移动通信网络中有关大数据的研究内容更为明晰,由此可知,针对大数据的相关研究具有一定必要性。     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

基于MBP算法和深度学习的人脸识别

为了解决在深度学习提取人脸图像特征时,易忽略其局部结构特征和缺乏对其旋转不变性学习的问题,提出了一种基于单演局部二值模式(MBP)与深度学习相结合的高效率人脸识别方法。首先,用多尺度单演滤波器对图像进行滤波,得到幅值和方向信息;其次,用LBP算法和象限比特的方法进行编码,分块计算组合其直方图特征;然后,将提取的单演特征作为深度信念网络(DBN)的输入,逐层训练优化网络参数,得到优异的网络模型;最后,将训练好的DBN网络在ORL人脸数据库上进行人脸识别实验,进行识别率计算,其识别率为98.75%。所提出的方法使用无监督的贪婪算法,隐藏层设定为2层,使用反向传播算法优化网络。相较于已知的人脸识别方法,MBP+DBN算法对光照、表情和部分遮挡变化具有较好的鲁棒性,在人脸识别中识别率较高,具有一定的优势,为图像特征提供了一种新的识别方法。     

Secrets to design an effective message on Facebook: an application to a touristic destination based on big data analysis

The objective of this research is to identify which are the key variables for designing a message in a social network that can be used by an advertiser to generate Positive/Negative Engagement. The message’s design variables have been classified into four main groups: (i) Message Tools (presence of text, images, video, labels, applications, interactive games and events calendar or others), (ii) Appropriate Message Structure (length and intelligibility), (iii) Informative Cues (links to the brand, orientation towards the product or the brand, topics relevant to the audience, and a remuneration’s promise) and (iv) Persuasive and Emotional Cues (emotional signals, valence, endorsement and influencer mentions). The focus has been a tourist destination: Brand Spain that is advertised through Facebook. A content analysis was carried out and regression analysis with optimal scaling was used on 180 Spain brand’s publications; 57,626 audience reactions to such publications; and 1361 audience comments on the Brand Spain Official Fan Page. According to our results, from the four blocks of predictive variables, only two of them are useful to predict Positive/Negative Engagement: (i) the use of Message Tools (videos), and (ii) the use of informative cues (relevant topics, links on posts, and post’s orientation towards product).     

基于PSO的信息熵数据融合非均匀分簇路由算法

针对无线传感器网络分簇算法中能量分布不均衡导致的"热区"和簇头负载过重问题,提出了一种基于PSO算法优化簇头选举的非均匀分簇算法。在候选簇头选举和竞争半径计算过程中综合考虑节点动态能量、节点密度和节点距基站距离,将网络进行非均匀分簇,并引入PSO算法进行最终簇头选举。根据节点能量、节点密度和距基站距离确定簇间单跳多跳结合的路由规则,选取代价函数小的节点作为下一跳节点。基于节点信息熵确定融合阈值,进行簇内数据融合剔除冗余数据。仿真结果表明,改进算法的数据传输量比EEUC算法和UCRA算法分别提高了20%和10%,提升了数据的融合效率,有效延长了网络生命周期,簇头能量消耗得到均衡,减少了网络能量消耗,网络的整体性能显著优于其他对比算法。     

Gender wage gaps and economic crisis in Greece

ABSTRACT

We examine gender wage gap (GWG) in Greece for 2013, by using a survey data set. Our findings show first, that the unadjusted GWG is 15.3%, while European Commission reports a value of 15%. Secondly, we derive the ‘adjusted’ GWG, using the Oaxaca and Ransom (OR) and the Juhn, Murphy and Pierce (JMP) methods to be ranging from 10% to 13.6%. Thirdly, looking into the behaviour of the full population, we find a decreasing trend for the discrimination effects, an increasing trend for the residuals effects and a ‘random’ endowments effects while moving to higher deciles. These three effects are associated to the economic crisis 2008–2015. Fourthly, our findings do not show evidence of either a ‘glass ceiling’ effect or a ‘sticky floor’ effect. Finally, that there is strong evidence that investing in higher education reduces the wage discrimination between sexes.     

What determines international tourist arrivals in India?

Using data for 18 major tourist originating countries to India from 2001 to 2015, this study examines the major determinants of international tourist arrivals in India. The results indicate that past experiences of the tourists, per capita income in the tourist originating country, relative costs of living between India and the country of origin, and the level of infrastructure development in India are key determinants of international tourist arrivals in India. Furthermore, both transportation and communication infrastructure are important in attracting tourists to India. In particular, evidence suggests that availability of road and air network and telephone connections has favourable impacts on international tourist arrivals in India. These results are robust to the inclusion of additional variables. These results have important policy implications.     

Do special issues offer something special? An overview of research trends in Telecommunications Policy special issues

This study explores the value of special issues by analyzing the characteristics of special issues of Telecommunications Policy (TP). Specifically, this study examines the status of special issue publications, the characteristics of their articles compared with those of general issues, and the factors affecting article citations. For the analysis, all information on TP articles published from 1976 to 2018 was collected from the Scopus database. A comparative analysis as well as a negative binomial regression were conducted on papers published after 1994, when the first special issue was published. The results show that special issues are distinguished from general issues by various characteristics, such as multinational author collaboration, number of references, keywords, and funding. While general issue papers are more frequently cited in academia, special issue papers show better performance in terms of online captures and social media sharing. Moreover, the smaller number of variables affects the frequency of citations in the case of special issues, revealing that content and format are more influential than author factors.