区块链技术与国家财政治理

政府权责边界清晰、信息公开、高效的绩效监督是发展符合市场经济规律的现代财政治理体系的关键。我国财政治理体系在围绕关键点的发展过程中,面临财政资金使用效率低下、财政征管机制不健全、预算管理体系不完善、财政透明度不高等治理困境问题。以信息透明可追溯、数据不可篡改、分布式记账为特点的区块链技术,与财政治理中对数据公开、安全、准确的要求之间有着内在的耦合性。区块链技术有助于提升财政治理能力和优化财政治理体系,但还面临财政金融领域成熟应用较少、数据处理能力有限、存在数据安全隐患、灵活性不足,与政府财政治理理念存在差异等方面的挑战。为了应对这些挑战,应加强区块链在财政治理场景的尝试,优先发展跨机构的联盟链,强化中心化核心应用与非中心化区块链应用的混合模式发展,以及推动政府财政治理理念的创新。     

动画衍生产品的设计与开发研究

论述了动画衍生产品及其产业链之间的内在关系,总结了当前动画衍生产品设计与开发中存在的不足问题。在此基础上,分别从充分挖掘民族文化,提高原创能力、结合中国传统元素与现代科技、加强现代媒体传播等方面,提出了动画衍生产品设计与开发的有效策略。     

The inherent tensions within sustainable supply chains: a case study from Bangladesh

Abstract

The complexities surrounding the supply chain logistics for perishable commodities within Bangladesh are extensive. Poor infrastructure, fragmented transportation and corruption compound the operational complexities within this emerging market. This case study analyses many of the day-to-day operational challenges and tensions inherent within Micro-Small-Medium Enterprises (MSMEs) forming the backbone of the Bangladesh socio-economic structure. The drive for transition toward greater levels of sustainability and corporate responsibility is problematic, affecting many levels within an extended and fragmented supply chain. The selected case study highlights the ‘lived in’ geographical, environmental, economic and cultural factors that impact the ability of emerging market enterprises to remain profitable within emergency scenarios whilst transitioning toward a more sustainable model. This study, whilst detailing many of the tensions and critical issues facing MSMEs, highlights the benefits of direct Government intervention, criticality of a leaner and more efficient supply chain and reassessment of financial incentives to drive the transition to a more efficient and sustainable economy.     

碳交易机制下供应链CCS投资时机研究

在碳交易机制下,碳价格不确定性对发电商的碳捕获与封存(Carbon Capture and Storage,CCS)投资时机具有重要影响。基于实物期权理论,考虑碳价格不确定性及CCS投资成本递减情况下CCS投资的期权价值,建立集碳捕获-运输-封存于一体的CCS投资时机决策基本模型。在此基础上,进一步构建考虑供应链内发电商与CCS运营商合作的CCS投资博弈模型,分析发电商与CCS运营商在收益转移比例与投资时机之间进行决策的均衡条件。基于上述模型,结合数值算例进一步分析了CCS投资时机的影响因素。结果表明:碳价格波动率、政府对CCS投资补贴系数、CCS技术学习效应以及供应链内部企业间的收益转移比例等都将对CCS投资门槛产生影响,供应链企业应综合考虑各因素的影响,科学选择投资时机与收益转移比例,以实现CO2减排的同时保证各方的经济利益,保障供应链的协调运行与CCS投资优化。     

Factors Constraining Building Effective and Fair Geographical Indications for Coffee: Insights from a Dominican Case Study

The commodity nature of green coffee is the main cause of the ‘coffee paradox’ (falling producer prices and rising consumer prices). Geographical Indications (GIs) may be an effective ‘decommodifier’ of the coffee market, but many constraints have to be overcome. Based on an analysis of how the characteristics of the coffee production system shape the positions of local stakeholders and, thus, the GI building process, this article identifies some constraining factors that can seriously undermine the capacity the GI has to decommodify the market and attain a fairer distribution of the benefits for local producers.     

The Bayesian approach for highly reliable electro-explosive devices using one-shot device testing

Due to the high reliability and high testing cost of electro-explosive devices, even though an accelerated test is performed, one may observe very few failures or even no failures at all due to censoring. In this paper, we consider modelling the reliability of such devices by an exponential lifetime distribution in which the failure rate is assumed to be a function of some covariates and that the observed data are binary. The Bayesian approach, with three different prior settings, is used to develop inference on the failure rate, lifetime and the reliability under some settings. A Monte Carlo simulation study is carried out to show that this approach is quite useful and suitable for analysing data of the considered form, especially when the failure rates are very small. Finally, illustrative data are analysed using this approach.     

Exact Sampling from a Continuous State Space

Propp & Wilson (1996) described a protocol, called coupling from the past, for exact sampling from a target distribution using a coupled Markov chain Monte Carlo algorithm. In this paper we extend coupling from the past to various MCMC samplers on a continuous state space; rather than following the monotone sampling device of Propp & Wilson, our approach uses methods related to gamma-coupling and rejection sampling to simulate the chain, and direct accounting of sample paths.     

Some Estimates for the Rate of Convergence in the Multidimensional Central Limit Theorem for Homogeneous Markov Chains

This paper obtains some estimates for the rate of convergence in the multi-dimensional central limit theorem for vector-valued functions of a homogeneous Markov chain without assuming the finiteness of their absolute third moment. These estimates have a universal character and generalize the results that hold when the third moments are finite.     

Joint analysis of longitudinal data comprising repeated measures and times to events

In biomedical and public health research, both repeated measures of biomarkers Y as well as times T to key clinical events are often collected for a subject. The scientific question is how the distribution of the responses [ T , Y | X ] changes with covariates X . [ T | X ] may be the focus of the estimation where Y can be used as a surrogate for T . Alternatively, T may be the time to drop-out in a study in which [ Y | X ] is the target for estimation. Also, the focus of a study might be on the effects of covariates X on both T and Y or on some underlying latent variable which is thought to be manifested in the observable outcomes. In this paper, we present a general model for the joint analysis of [ T , Y | X ] and apply the model to estimate [ T | X ] and other related functionals by using the relevant information in both T and Y . We adopt a latent variable formulation like that of Fawcett and Thomas and use it to estimate several quantities of clinical relevance to determine the efficacy of a treatment in a clinical trial setting. We use a Markov chain Monte Carlo algorithm to estimate the model's parameters. We illustrate the methodology with an analysis of data from a clinical trial comparing risperidone with a placebo for the treatment of schizophrenia.     

Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag–lead structure

Multiple time series of scalp electrical potential activity are generated routinely in electroencephalographic (EEG) studies. Such recordings provide important non-invasive data about brain function in human neuropsychiatric disorders. Analyses of EEG traces aim to isolate characteristics of their spatiotemporal dynamics that may be useful in diagnosis, or may improve the understanding of the underlying neurophysiology or may improve treatment through identifying predictors and indicators of clinical outcomes. We discuss the development and application of non-stationary time series models for multiple EEG series generated from individual subjects in a clinical neuropsychiatric setting. The subjects are depressed patients experiencing generalized tonic–clonic seizures elicited by electroconvulsive therapy (ECT) as antidepressant treatment. Two varieties of models—dynamic latent factor models and dynamic regression models—are introduced and studied. We discuss model motivation and form, and aspects of statistical analysis including parameter identifiability, posterior inference and implementation of these models via Markov chain Monte Carlo techniques. In an application to the analysis of a typical set of 19 EEG series recorded during an ECT seizure at different locations over a patient's scalp, these models reveal time-varying features across the series that are strongly related to the placement of the electrodes. We illustrate various model outputs, the exploration of such time-varying spatial structure and its relevance in the ECT study, and in basic EEG research in general.