Addressing plastic pollution through green consumption: Predicting intentions to use menstrual cups in the Philippines

Plastic pollution is a global environmental crisis that poses a huge threat to the health of people and marine ecosystems worldwide. A significant source of plastic pollution is menstrual hygiene management, and an approach that can help address this crisis is the usage of washable and reusable menstrual cups. Using an extended theory of planned behavior model that includes self-identity and perceived quality, the study predicted intentions to use menstrual cups in the Philippines. Structural equation modeling results showed that perceived quality predicted attitudes towards menstrual cup usage. Moreover, attitudes, perceived behavioral control, and self-identity predicted intentions to use menstrual cups. There were also some differences in the factors and predictors of intentions between non-users and regular users of menstrual cups. Among regular users, perceived behavioral control did not predict intentions. These findings provide empirical support for the extended model, and provides insights for governments, non-government organizations, and corporations in promoting the usage of menstrual cups to address the global plastic pollution crisis.     

利用大数据分析技术的智能化网络故障诊断系统

随着网络技术的快速发展以及各种新网络业务的出现，网络的规模日益扩大和复杂。同时，各种网络服务对于网络可靠性的要求也逐渐提高，这就对网络的维护提出了更高的要求。在这种情况下，传统的网络维护模式逐渐不能适应现代网络发展的需求。针对现代大型网络的故障诊断问题，利用大数据分析技术，设计并完成了一套整体的智能化故障诊断系统。在此系统中，针对不同的故障类型，利用能够获取到的数据类型，针对性地设计了相关的故障诊断模块和算法。系统整体基于开源的数据处理平台构建，具备良好的可扩展性。通过实际案例数据的验证，结果表明此系统具备良好的处理实际网络故障的能力，将大数据分析技术应用于网络故障诊断领域也具备较强的可行性。     

Flexibility in the gig economy: managing time on three online piecework platforms

Gig economy platforms seem to provide extreme temporal flexibility to workers, giving them full control over how to spend each hour and minute of the day. What constraints do workers face when attempting to exercise this flexibility? We use 30 worker interviews and other data to compare three online piecework platforms with different histories and worker demographics: Mechanical Turk, MobileWorks, and CloudFactory. We find that structural constraints (availability of work and degree of worker dependence on the work) as well as cultural‐cognitive constraints (procrastination and presenteeism) limit worker control over scheduling in practice. The severity of these constraints varies significantly between platforms, the formally freest platform presenting the greatest structural and cultural‐cognitive constraints. We also find that workers have developed informal practices, tools, and communities to address these constraints. We conclude that focusing on outcomes rather than on worker control is a more fruitful way to assess flexible working arrangements.     

面源污染视角下江西省耕地利用效率研究

[目的]在面源污染视角下考察耕地利用效率,可以为推动耕地可持续利用提供理论和参考依据。[方法]文章基于2000—2014年我国13个粮食主产省(区)耕地投入产出的面板数据,运用SBM方向性距离函数将耕地面源污染因素纳入到传统的效率分析框架,从省际比较的维度对江西省耕地利用效率进行了测度与分析。[结果]考虑面源污染因素对耕地利用效率测算产生了一定的影响;江西省耕地利用效率值呈现阶段性特征,耕地利用效率排名比较稳定,近年来排名有上升趋势;江西省耕地利用绩效一般,且与最佳实践者存在较大的差距。[结论]忽视面源污染因素的耕地利用效率评估是失真和不符合实际的,宜将面源污染因素纳入到耕地利用效率的测度框架,以此来更科学地量化评估耕地利用绩效;设计和完善相关政策,以有效控制耕地面源污染;加强与先进省(区)的交流与合作,学习其农业可持续耕作技术与管理经验,进一步推动江西省耕地产出与资源、环境协调发展。     

水源地突发水污染政府应急预留水量需求预测

近年来,随着工业化和产业化的快速发展,水源地突发水污染事件频发。为应对紧急情况下的水资源非常规需求,保障经济社会稳定发展,政府需要安排一部分应急预留水量。如何合理预测政府应急预留水量的规模是一个值得探讨的课题。提出了一种基于案例推理技术的政府应急预留水量需求预测方法,具体思路是:借助于案例库,利用主成分分析法从众多影响水源地突发水污染事件的因素中提取出两两不相关的因子,以此作为案例的特征属性,基于加权曼哈顿距离测算相似度,依此得到匹配案例,进而测算政府应急预留水量。以太湖流域常州地区水源地为例,进行政府预留水量需求量的预测与分析。该方法可以为政府应急预留水量的配置、储备等提供相关理论知识。     

农户减少化肥用量和采用有机肥的意愿研究——基于洱海流域上游面源污染防控的视角

[目的]过量施用化肥导致水体富营养化是造成洱海流域上游面源污染的主要原因之一,调查分析农户减少化肥用量和采用有机肥的意愿有助于了解其调整施肥结构的行为。当前研究忽视了农户减少化肥施用强度和采用有机肥的意愿间的替代性,未能开展联立研究。[方法]基于洱海流域上游397个水稻种植户的问卷调查数据,通过Bivariate-Probit模型对农户减少化肥用量和采用有机肥两种意愿进行分析。[结果]年龄负向影响农户减少化肥量的意愿,农户对有机肥好处的认知正向影响其减少化肥量的意愿,农户参加农业社会化服务对其减少化肥用量和采用有机肥的意愿均产生正向影响,相比之下,对农户采用有机肥意愿的促进作用更大。[结论]引导农户参加农业社会化服务以及加强宣传和培训,提高农户对有机肥作用的认知可能是加强其减少化肥用量和增加有机肥施用量意愿的有效途径。     

Self serving reference points in double auctions

We present a novel approach for modelling self serving bias by way of reference dependence. Bias is modelled as a systematic individual level deviation of the endogenous reference point from the expected price of a good in a two person k-double auction. We find that bias decreases the efficiency of the model, and that efficiency is decreasing in both the level of bias and reference dependence.     

Investor behaviour and reaching for yield: Evidence from the sterling corporate bond market

We provide evidence on how corporate bond investors react to a change in yields, and how this behaviour differs in times of market‐wide stress. We also investigate ‘reaching for yield’ across investor types, as well as providing insights into the structure of the corporate bond market. Using proprietary sterling corporate bond transaction data, we show that insurance companies, hedge funds and asset managers are typically net buyers when corporate bond yields rise. Dealer banks clear the market by being net sellers. However, we find evidence for this behaviour reversing in times of stress for some investors. During the 2013 ‘taper tantrum’, asset managers were net sellers of corporate bonds in response to a sharp rise in yields, potentially amplifying price changes. At the same time, dealer banks were net buyers. Finally, we provide evidence that insurers, hedge funds and asset managers tilt their portfolios towards higher risk bonds, consistent with ‘reaching for yield’ behaviour.     

Cox Point Processes: Why One Realisation Is Not Enough

We review a rich class of point process models, Cox point processes, and illustrate the necessity of more than one observation (point patterns) in performing parameter estimation. Furthermore, we introduce a new Cox point process model by treating the intensity function of the underlying Poisson point process as a random mixture of normal components. The behaviour and performance of the new model are compared with those of popular Cox point process models. The new model is exemplified with an application that involves a single point pattern corresponding to earthquake events in California, USA.