研发市场界定的适用困境及应对策略

研发市场是经营者在特定产品和技术竞争中形成的市场范围。由于研发活动的不确定性，反垄断执法机关在界定研发市场时难以准确厘定市场边界，更深层次的原因在于维持竞争与激励研发的关系尚不明晰。研发竞争中的垄断行为难以通过相关市场界定的方式判断其违法性，需要结合具体案情和市场条件，动态分析涉案行为是否损害产品或技术研发竞争。     

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

战术面向服务架构概念及相关技术

战术面向服务架构（TSOA）是实现战术云平台服务基础设施的重要支撑概念。由于战术环境的复杂性，目前对TSOA实施过程中面临问题和相关实现技术的总结还较少。为此，开展了针对TSOA的概念以及相关技术的研究。首先，从战术环境下的TSOA架构需求背景出发，介绍了TSOA架构的组成以及适应性特征；然后，通过分析战术环境下设备和网络等多方面存在的限制性因素，明确实施TSOA架构所面临的挑战以及需要解决的关键问题，并对实现过程中涉及的服务发现、消息传输机制、网络状态自适应等相关技术进行归纳；最后，对国内外的相关研究工作进行总结，期望对推动TSOA架构的具体实施提供一定参考。     

Happiness and freedom in direct action: critical mass bike rides as ecstatic ritual,play, and temporary autonomous zones

Most leisure research does not consider collective action and social change, while most social movement research overlooks carnivality and spontaneity. A counter-example is the alternative bicycling movement critical mass (CM), a rebellious, liberatory leisure and cultural event. CM is goal-oriented and a libidinal expression of participant desire. This paper investigates CM’s production of happiness and freedom via three heterodox theories. Ecstatic rituals are repetitive, cathartic collective events that people engage in to express joy and flaunt convention. Political theories of play interpret individual’s physically-embodied, creative, and performative actions. Temporary autonomous zones are spatial locations of resistance to authority that evade state detection and suppression. CM – unlike standard bike races or conventional movement protest – transcends the limitations of physical exercise, lackadaisical leisure, and militant direct action. Instead it is a hybrid form of collective action, combining premeditated ritual, rebellious play, and mobile freedom-seeking.     

Nowcasting China’s GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated. Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises. In this study, we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data. We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors. We demonstrate the validity of our approach through simulation studies, and explore the applicability of our approach through an empirical study in nowcasting the China’s GDP using 117 monthly data series of several categories in the Chinese market. The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China’s GDP.     

Applying the counseling-learning approach to a tourism-related massive open online course

ABSTRACT

This study discusses how to apply counseling-learning (CL) principles to a particular e-learning solution for teaching a tourism-related subject via a massive open online course (MOOC). Several effective caring patterns in achieving learning-related goals and being part of a community according to the CL principles are presented and discussed using the MOOC “eTourism: Communication Perspectives” as a case study. The study underlines how the MOOC platform can act as a place for contents enjoinment and active learning. Moreover, the active role of social media for increasing the engagement of learners in the proposed activities and for developing a sense of the community is identified. In particular, Facebook and Twitter can act as places for community building and informal social interactions among learners that last beyond the completion of the course, which in turn can be a valid aid for continuing the relationships among instructors/learners and learners/learners, and for reaching new ones.     

A comparative study of eight COVID-19 protective measures and their impact on Swiss tourists’ travel intentions

A comparative vignette-based experimental survey design incorporating various socio-psychological factors, linked to the Theory of Planned Behavior (TPB), the Health Belief Model (HBM) and the Domain-Specific Risk-Taking scale (DOSPERT) was carried out to test variations in eight travel-related COVID-19 protective measures on Swiss tourists’ travel intentions. Among the tested measures, vaccination passports, surgical masks and quarantining are those that stand out the most, with surgical masks having the greatest acceptance and willingness to adopt while traveling. Quarantining, on the other hand, appears to have a deterrent influence on travel intentions, and vaccination passports have the lowest perceived barriers during travel, but the highest perceived benefits in mitigating the spread of the infection. The discussion of individual differences has specific implications for tourism management against the background of our empirical findings.     

Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process

The concept of User Driven Prioritisation Process (UDPP) was introduced to give Airspace Users (AUs) more flexibility under demand-capacity imbalance. This paper presents two UDPP-DCB models, built on the UDPP principle, using the Selective Flight Protection (SFP) approach to minimize the total delay cost. AUs are enabled to adapt their operations in a more cost-efficient way in the presence of capacity constraints in airspace, optimizing their flights to keep the priorities track. Then, ATFM integrates the AUs’ decision to reassign the time slots and the preferred rerouting trajectories. Results suggest that the delay cost for AUs can be largely reduced through applying the UDPP-DCB models proposed in this paper, while allowing rerouting proves effective in reducing the system delay cost.     

TrAffic LIght system for systemic Stress: TALIS3

For the purposes of financial stability, identifying financial institutions that, when in distress, could have a significant adverse impact on financial markets is important. A TrAffic LIght System for Systemic Stress (TALIS-cube) is proposed that provides a comprehensive color-based classification for grouping companies according to both the stress reaction level of the system when the company is in distress and the company’s stress level. TALIS³ can integrate multiple signals from the interaction between different risk metrics. Starting from specific risk indicators, companies are classified by combining two loss functions—one for the system and one for each company—evaluated over time and as a cross section. An aggregated index is also obtained from the color-based classification of companies. TALIS³ can be used to enhance the performance and robustness of existing systemic risk measures. An empirical analysis of the U.S. market is also provided.     

Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.