Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.     

Tourism as a discipline in light of the non-classical sociology of science

Embedding the issue of the recognition of tourism by the academia in the context of the scientific identity of the researchers of tourism can bring valuable insights into the debate. Moreover, such an approach is aligned with the main assumptions of social constructionism and the non-classical sociology of science (knowledge) stemming from the paradigm theory of science formulated by Kuhn. These concepts served as the foundations for the present research, allowing for the formulation of its main hypothesis: that every academic community has the right to decide whether it accepts tourism as a separate academic discipline or not, and that there is no reason why any opinion on this matter should be imposed on everyone. Another useful piece of information can be gained by adopting an epistemological perspective in the form of the questions: will separating tourism from other disciplines make it possible for us to learn more about it? Or should we continue considering it through the prisms of other disciplines? These were the premises which drove both parts of the present research: literature query and empirical study (which featured results from researchers from all around the world), and were addressed in the conclusion.     

Innovation in the bioeconomy – dynamics of biorefinery innovation networks

The bioeconomy has become a central concept in many strategies for future economic development, emphasising an increasing need for collaboration across industries and sectors for innovation. This paper unpacks aspects of collaboration in the bioeconomy by looking at the development of innovation networks for biorefinery technologies from 2004 to 2014 based on innovation project data from Swedish public funding agencies using a stochastic actor-oriented model for network analysis. The analysis shows that although the network grew significantly during the time period, indicating an increasing interest in biorefinery technology innovation, inter-sectoral collaboration is not favoured over intra-sectoral collaboration. As is known from previous work on social networks trust-building is a key driver for collaboration, as actors tend to form collaborations with previous partners or indirectly connected partners, creating clustered networks.     

Growth machines and social movements in mature tourist destinations Costa del Sol-Málaga

Abstract

The purpose of this study is to analyse the new processes of tourism growth and its conflicts from the perspective of social movements. First, the urban growth machine analysis model is applied by the systematisation of six projects. Second, the resistance movements against those projects and whether this resistance could be the start of local tourism degrowth policies are examined. The methodology is qualitative, based on documentary analysis, participatory observation, discussion groups and interviews. The case study is the destination of Costa del Sol-Málaga. The results enable the development of the urban growth machine model in tourist destinations. Meanwhile, social movements demystify the argument based on neoclassical economic progress. The social movements condemn the effects of large-scale top-down projects, and implement alternative bottom-up proposals. Although the social movements do not reject tourism, they call for greater control over its impact, denounce unlimited growth, overtourism and the loss of urban quality of life. These movements advocate a lifestyle linked to the everyday space, which they believe is threatened by excessive urban-tourism growth. They are a symptom of the need to devise a proposal using the principles of degrowth.     

水生态系统服务供需视角下的水网乡村 适应性规划策略——以吴江张鸭荡片区为例

苏南乡村地区一直是中国乡村建设的先行区域，特殊的纵横交错的水网结构构成了一个错综复杂的系统，呈现出其他地区不具有的复杂性和生态特殊性。随着城镇的扩张，乡村的发展建设使其水网空间的平衡发展面临极大的挑战。传统单一静态的规划方法逐渐显示出无法适应经济、社会等发展要求的缺陷。苏南乡村地区以水为核心，从水生态系统服务供需关系的视角下探究水网乡村的适应性规划策略更加适应当前的乡村现状和需求。以传统水网乡村空间形态转译为基础，建立水生态系统供需服务评估体系，在评估水生态系统服务供需能力的基础上，分析供需分异模式及供需矛盾。从构建乡村水域空间生态格局、乡村水域空间功能分区规划，以及乡村水域空间多情景预判3个方面，提出苏南水网乡村的适应性规划策略，并为水网乡村的生态实践提出新思路。     

公民参与网络反腐的意愿及影响因素研究——基于武汉市调研数据的分析

文章运用Logistic回归模型,基于武汉市调研数据,从实践角度透视和思考公民参与网络反腐的意愿及影响因素。研究发现,大部分接受调研公民参与网络反腐的意愿较高;政府信任对公民参与网络反腐意愿的负向影响非常显著;年龄的负向影响比较显著;月收入的正向影响一般显著;性别、受教育程度和单位类别对其参与意愿的影响不显著。     

A practical road map for assessing cyber risk

The increase in interconnectivity and developments in technology have caused cyber security to become a universal concern. This paper highlights the dangers of the evolution of cyber risk, the challenges of quantifying the impact of cyber-attacks and the feasibility of the traditional actuarial methodologies for quantifying cyber losses. In this paper, we present a practical roadmap for assessing cyber risk, a roadmap that emphasizes the importance of developing a company and culture-specific risk and resilience model. We develop a structure for a Bayesian network to model the financial loss as a function of the key drivers of risk and resilience. We use qualitative scorecard assessment to determine the level of cyber risk exposure and evaluate the effectiveness of resilience efforts in the organization. We highlight the importance of capitalizing on the knowledge of experts within the organization and discuss methods for aggregating multiple assessments. From an enterprise risk management perspective, impact on value should be the primary concern of managers. This paper uses a value-centric/reputational approach to risk management rather than a regulatory/capital-centric approach to risk.     

让自然参与设计：一种基于自然演替的 城市自生群落改良设计途径

城市自生群落具有重要的生态服务功能与潜力，但因其外貌不被公众所接受而难以在城市公共空间中应用。将群落生态学和设计学相结合，探索一种介入栽培植物对城市自生群落进行改良，最终形成具有一定美学价值的植物群落的途径。实验以植物群落设计美学原理和种间竞争原理为依据，向已经演替1年的城市自生群落中引入63种栽培植物，按照分层的方式对群落进行改良。3年的观测结果表明，有61.9%的栽培植物可以在城市自生群落中定居并建立优势，能对76.7%的自生植物起到抑制作用，使55.8%的自生植物消失。相比城市自生群落，改良后的群落美观度提高29.4%，公众接受度提高24%，群落的物种丰富度提高至2.29倍。相比一般的人工植物群落，改良后的群落可以节约58.2%的建植成本，降低83.9%的浇水成本和67.8%的除草成本。实验证明改良设计是一种可行的群落设计途径，能有效提高群落的物种丰富度和美学价值，同时让自然参与群落设计过程，实现人与自然的合作。     

The evolution of world trade from 1995 to 2014: A network approach

This paper employs network analysis to study world trade from 1995 to 2014. We focus on the main connective features of the world trade network (WTN) and their dynamics. Results suggest that countries’ efforts to attain the benefits of trade have resulted in an intertwined network that is increasingly dense, reciprocal, and clustered. However, these features do not correspond to a linear aggregation of the characteristics of its constituents (trade sectors). Trade linkages are distributed homogeneously among countries, but their intensity (i.e. their value) is highly concentrated in a small set of countries. The main connective features of the WTN were not affected by the 2007–2008 international financial crisis. However, we find that the crisis marks a turning point in the evolution of the WTN from a two-group (led by the US and Germany) to a three-group (led by the US, Germany, and China) hierarchical structure.