What's up? Mobile instant messaging apps and the truckers′ uprising in Brazil

This study aims to investigate the trajectory of the truckers' uprising in Brazil in May 2018 - one of the hitherto largest social movement enabled by WhatsApp in the world - in order to understand its relationship with the use of this mobile instant messaging app. Thus, by applying the Actor-Network Theory as the theoretical and methodological lens associated with a single case study, one identified the relevant actors in the development of the socio-technical network related to this truckers' strike that occurred in Brazil. The study sets forth that this movement comprised sundry actors aligned around a single obligatory passage point – the social networks enabled by WhatsApp – and reveals that WhatsApp, with its specific dynamics, played a crucial role in an uprising that took place in a country of continental dimensions such as Brazil, favoring change in the way the demonstrators gathered and interacted, leading formal organizations to be less necessary and broadening the political participation of the society. Finally, one concluded that WhatsApp enabled a power shift from government to truck drivers and their unions, which indicates the need for a new digital governance based on distribution and transparency of information, accountability, and participation of citizens in government decisions to curb new uprisings enabled by mobile instant messaging apps.     

Assessing the Potential of Cross-border regional innovation Systems:A case study of the Hong Kong -Shenzhen region

While technological transformations have redrawn the boundaries of regional innovation systems, the effect of such transformations on the development of cross-border regional innovation systems (CBRISs) – defined here as the innovation system spanning neighboring territories belonging to different nation-states – has not been well understood. The Hong Kong-Shenzhen region presents a unique opportunity to study the potential of CBRISs, as Hong Kong and Shenzhen are adjacent to each other though there is a border between them and the two cities have their own political and legal systems under the “one country, two systems” arrangement. In this research, we examine the development of the Hong Kong-Shenzhen region by focusing on cognitive proximity, innovation actors, collaboration, and global connectivity through an analysis of patent and publication data for the 2001–2015 period. Our findings point to a good potential given the growing convergence in scientific research among the two cities, steadily increasing collaboration output, and a complementarity among innovation actors. However, compared with other cross-border regions, the technological linkages between Hong Kong and Shenzhen remain limited.     

Efficient reliability analysis of dynamic k-out-of-n phase-AND mission systems

Motivated by real-world applications of satellites and wireless sensor networks, this paper models and evaluates a dynamic k-out-of-n phase-AND mission system (k/n-PAMS). The mission task conducted by a k/n-PAMS involves multiple consecutive phases; the mission is successful as long as the task is successful in any of the phases. Due to factors, such as scheduled maintenance, location changes in task execution during different phases, and resource sharing with other tasks, the total number of available components n for the considered mission task and the required number of working components k may change from phase to phase. In addition, due to varying load and working environments, component failure time distributions are also phase dependent. This paper proposes an analytical modeling approach based on multivalued decision diagrams (MDDs) for assessing reliability of the considered k/n-PAMS. The approach encompasses a new and fast MDD model generation algorithm that considers behaviors of all the mission phases simultaneously based on node labeling. As demonstrated through empirical studies on k/n-PAMSs with different sizes (different numbers of phases and different numbers of system components), the proposed algorithm is more efficient than the traditional phase-by-phase model generation method.     

Self-information-based weighted CUSUM charts for monitoring Poisson count data with varying sample sizes

In many applications, the Poisson count data with varying sample sizes are monitored using statistical process control charts. Among these applications, the weighted CUSUM charts are developed to deal with the effect of the varying sample sizes. However, some of them use limited information of the sample size or the count data while assigning the weights. To gain more information of the process, the self-information weight functions are developed based on both the sample size and the observed count data. Then, the weighted CUSUM charts are proposed with the self-information-based weight. Simulation studies show the self-information-based weighted CUSUM charts perform better than the benchmark methods in detecting small shifts. Moreover, the performance of proposed method with estimated parameters is investigated via simulation. Finally, an example is given to illustrate the application of the proposed weighted CUSUM charts.     

Dynamics and control of a thermally self-sustaining energy storage system using integrated solid oxide cells for an islanded building

A solid oxide cell-based energy system is proposed for a solar-powered stand-alone building. The system is comprised of a 5 kW_el solid oxide fuel cell (SOFC), a 9.5 kW_el solid oxide electrolysis cell (SOEC), and the required balance of plant. The SOFC supplies: 1- building demand in the absence of sufficient solar power, 2- heat for SOEC in endothermic and standby modes. Thermal integration of SOFC and SOEC is implemented through a network of heat exchangers, combined with set of control algorithms. Two control strategies were implemented to actuate the SOFC in response to endothermic heat demands of SOEC by manipulating: 1- electric power, 2- fuel utilization. The results of dynamic simulation of system for two scenarios (sunny day and cloudy day) showed successful compliance of temperature constraints with both methods. Manipulation of fuel utilization, however, resulted in better system performance in terms of efficiency and H₂ balance.     

动态调湿控温立体针织物拓扑优化设计

为研发具有优良动态调湿控温性能的立体针织物,采用双股70 dtex(72 f)的DRYARN^®纱线与30 dtex DRYARN^®纱线包覆30 dtex氨纶长丝为原料,提出了立体针织物拓扑优化设计方法。通过优化设计各控温单元的排列方式,使控温单元按V字、Y字等斜向错位排列,然后采用单面提花工艺对织物进行局部拉伸与压褶以形成褶皱片状结构。结果表明:当人体流汗时,褶皱片状结构里层吸收汗滴并转移至面层以快速蒸发,当人体感到寒冷时,内部空气通道储藏静止空气有效控温;所制备织物的保温率均大于54%,具有出色的控温性能,整体液态水分传递能力达到3级以上,液态水动态传递性能良好,同时保持优良的拉伸回复性能,为冬季运动保暖内衣用立体针织物的研发提供了新思路。     

Formal Approach to Workflow Application Fragmentations Over Cloud Deployment Models

Workflow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments. Especially, such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workflow processes and applications with scalable on-demand services. In this paper, we focus on the distribution paradigm and its deployment formalism for such very large-scale workflow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments. We propose a formal approach to vertically as well as horizontally fragment very large-scale workflow processes and their applications and to deploy the workflow process and application fragments over three types of cloud deployment models and architectures. To concretize the formal approach, we firstly devise a series of operational situations fragmenting into cloud workflow process and application components and deploying onto three different types of cloud deployment models and architectures. These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workflow process and applications as an implementing component for cloud workflow management systems. Finally, we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workflow processes and applications to be deployed on cloud deployment models and architectural computing environments as well.     

Regression-adjusted Poisson EWMA control chart

Exponentially weighted moving average (EWMA) control chart has a significant effect in improving product quality and is widely used in various fields. In addition to continuous data, there are many Count Data in life that need to be monitored. Poisson distribution is one of the models that study the probability distribution of discrete data, and has a wide range of applications. In previous monitoring, it was considered that the mean value of Poisson distribution in normal state was a constant value after it was determined. But in the actual situation, there are many unavoidable objective conditions that will affect the final results. We cannot monitor all situations according to the same criteria. If we ignore the conditions that affect the occurrence of the event and directly monitor the final result, on the one hand, it will increase the probability of false alarms from the control chart. On the other hand, the control chart will not be able to detect problems in time due to the untimely update of conditions. In response to this situation, this paper proposes a regression-adjusted EWMA control chart to monitor the Poisson process. The control chart can continuously adjust and update the expected values according to the actual situation. It can make the monitoring process more reasonable and the monitoring results more valuable.