Mining citizen emotions to estimate the urgency of urban issues

Crowdsourcing technology offers exciting possibilities for local governments. Specifically, citizens are increasingly taking part in reporting and discussing issues related to their neighborhood and problems they encounter on a daily basis, such as overflowing trash-bins, broken footpaths and lifts, illegal graffiti, and potholes. Pervasive citizen participation enables local governments to respond more efficiently to these urban issues. This interaction between citizens and municipalities is largely promoted by civic engagement platforms, such as See-Click-Fix, FixMyStreet, CitySourced, and OpenIDEO, which allow citizens to report urban issues by entering free text describing what needs to be done, fixed or changed. In order to develop appropriate action plans and priorities, government officials need to figure out how urgent are the reported issues. In this paper we propose to estimate the urgency of urban issues by mining different emotions that are implicit in the text describing the issue. More specifically, a reported issue is first categorized according to the emotions expressed in it, and then the corresponding emotion scores are combined in order to produce a final urgency level for the reported issue. Our experiments use the SeeClickFix hackathon data and diverse emotion classification algorithms. They indicate that (i) emotions can be categorized efficiently with supervised learning algorithms, and (ii) the use of citizen emotions leads to accurate urgency estimates. Further, using additional features such as the type of issue or its author leads to no further accuracy gains.     

PCC载体氧化剂的制备及应用研究

制备得到各种有代表性的氯铬酸吡啶(PCC)无机载体氧化试剂,以苯甲醇选择性氧化成苯甲醛为探针反应,考察了载体、反应条件对氧化反应效率的影响,控制各载体氧化剂中PCC的负载量均为1.8 mmol/g。室温下反应,搅拌时间为30 m in,苯甲醇的氧化反应产率为:分子筛65.8%,活性炭75.4%,氧化铝94.6%,硅胶96.9%,相同条件下PCC的产率为65.8%。硅胶是制备负载PCC的优良载体,在载体氧化剂对苯甲醇的氧化反应中,一定范围内增加反应时间和载体加入量都可以提高氧化产率。     

Supercapacitors based on conducting polymers/nanotubes composites

Three types of electrically conducting polymers (ECPs), i.e. polyaniline (PANI), polypyrrole (PPy) and poly-(3,4-ethylenedioxythiophene) (PEDOT) have been tested as supercapacitor electrode materials in the form of composites with multiwalled carbon nanotubes (CNTs). The energy storage in such a type of composite combines an electrostatic attraction as well as quick faradaic processes called pseudo-capacitance. It has been shown that carbon nanotubes play the role of a perfect backbone for a homogenous distribution of ECP in the composite. It is well known that pure conducting polymers are mechanically weak, hence, the carbon nanotubes preserve the ECP active material from mechanical changes (shrinkage and breaking) during long cycling. Apart of excellent conducting and mechanical properties, the presence of nanotubes improves also the charge transfer that enables a high charge/discharge rate. For an optimal use of ECPs in electrochemical capacitors, a special electrode composition with ca. 20 wt.% of CNTs and a careful selection of the potential range is necessary. The capacitance values ranging from 100 to 330 F g⁻¹ could be reached for different asymmetric configurations with a capacitor voltage from 0.6 to 1.8 V. It is also noteworthy that such a type of ECP/CNTs composite does not need any binding substance that is an important practical advantage.     

Project-based mechanisms for emissions reductions: balancing trade-offs with baselines

Project-based mechanisms for emissions reductions credits, like the Clean Development Mechanism, pose important challenges for policy design because of several inherent characteristics. Participation is voluntary, so it will not occur without sufficient credits. Evaluating reductions requires assigning an emissions baseline for a counterfactual that cannot be measured. Some investments have both economic and environmental benefits and might occur anyway. Uncertainty surrounds both emissions and investment returns, and parties to the project are likely to have more information than the certifying authority. The certifying agent is limited in its ability to design a contract that would reveal investment intentions. As a result, rules for benchmarking emissions may be systematically biased to overallocate, and they also risk creating inefficient investment incentives. This paper evaluates, in a situation with asymmetric information, the efficacy of the main baseline rules currently under consideration: historical emissions, an average industry emissions standard, and expected emissions.     

信息技术课堂中情境教学法的应用

情境教学法是教学中常的一种教学方法,本文根据信息技术的学科特点,结合教师创设的情景,阐述了信息技术课堂教学中情境的创设,以及在信息技术教学中的应用应该注意的几个问题。     

Financial distress prediction using support vector machines: Ensemble vs. individual

Financial distress prediction (FDP) is of great importance to both inner and outside parts of companies. Though lots of literatures have given comprehensive analysis on single classifier FDP method, ensemble method for FDP just emerged in recent years and needs to be further studied. Support vector machine (SVM) shows promising performance in FDP when compared with other single classifier methods. The contribution of this paper is to propose a new FDP method based on SVM ensemble, whose candidate single classifiers are trained by SVM algorithms with different kernel functions on different feature subsets of one initial dataset. SVM kernels such as linear, polynomial, RBF and sigmoid, and the filter feature selection/extraction methods of stepwise multi discriminant analysis (MDA), stepwise logistic regression (logit), and principal component analysis (PCA) are applied. The algorithm for selecting SVM ensemble's base classifiers from candidate ones is designed by considering both individual performance and diversity analysis. Weighted majority voting based on base classifiers’ cross validation accuracy on training dataset is used as the combination mechanism. Experimental results indicate that SVM ensemble is significantly superior to individual SVM classifier when the number of base classifiers in SVM ensemble is properly set. Besides, it also shows that RBF SVM based on features selected by stepwise MDA is a good choice for FDP when individual SVM classifier is applied.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.     

聚酰亚胺/二氧化硅杂化膜的制备与介电性能的研究

采用溶胶-凝胶法制备了BTDA-ODA聚酰亚胺/SiO2杂化膜,利用红外分光光度计（FTIR）、热重分析仪（TGA）和透射电镜（TEM）研究了杂化膜的微观结构和热性能,并对杂化膜的介电常数（e）和介电损耗（tand）随SiO2粒子含量和电场频率的变化进行了分析和讨论。结果表明：杂化膜的介电常数和介电损耗随SiO2粒子含量的增加而增大,随电场频率的升高而逐渐降低,用考虑到粒子的形状因素和两相间相互作用的EMT模型可以预测聚酰亚胺/SiO2杂化膜的介电常数。     

Simulating the Evolution of Market Shares: The Effects of Customer Learning and Local Network Externalities

This paper develops a simulation model to investigate competitive technology diffusion, and focuses on examining the influence of customer learning on the evolution of market share when both technologies exhibit local network externalities. Results show that: (1) the equilibrium market share of the new technology is determined by two key factors: the characteristics of customer learning behavior and the strength of local network externalities; (2) moderate network externalities can be beneficial for new technology to dominate the entire market when customers adopt belief-based learning rule; (3) moderate learning rate would facilitate the diffusion of new technology when customers make their decisions based on reinforcement learning; (4) the decay of customer learning and the proportions of imitators in market would help the old technology establish advantage by maintaining demand inertia of customer. The joint effects of psychology of customer behavior and local interactions of customers offer a new mechanism to explain the diffusion of technology in a competitive market with network externalities.     

Toluene oxidation on Pd catalysts supported by CeO2–Y2O3 washcoated cordierite honeycomb

A novel CeO₂–Y₂O₃ (CY) washcoat on cordierite honeycomb was prepared by an impregnation method, which was used as a support to prepare a Pd catalyst. A model reaction of the complete combustion of toluene was conducted to evaluate the performance of the developed Pd/CY catalyst. The CY washcoat support and the Pd/CY catalyst were characterized by XRD, Raman spectroscopy, H₂-TPR and SEM techniques. The results show that compared with conventional washcoat the CY washcoat has better adhesion and higher vibration- and heat-resistance. The CY washcoat can anchor well Pd onto the cordierite honeycomb substrate. The formation of a CeO₂–Y₂O₃ solid solution and the steady present of PdO occur at high calcination temperatures, resulting in a better thermal stability. On a Pd/CY catalyst calcined at 500 °C, a 99% of toluene conversion was obtained at 210 °C, and it was stable for reaction time up to 30 h.