Suchergebnisse

Many complex networks demonstrate a phenomenon of striking degree correlations: that is, a node tends to link to other nodes with similar (or dissimilar) degrees. From the perspective of degree correlations, in this paper we attempt to characterize topological structures of urban street networks. We adopted six urban street networks (three European and three North American), and converted them into network topologies in which nodes and edges represent individual streets and street intersections, respectively, and compared the network topologies with three reference network topologies (biological, technological, and social). The urban street network topologies (with the exception of Manhattan) showed a consistent pattern that distinctly differs from the three reference networks. The topologies of urban street networks lack striking degree correlations in general. Through reshuffling the network topologies towards, for example, maximum or minimum degree correlations while retaining the initial degree distributions, we found that all the surrogate topologies of the urban street networks, as well as the reference ones, tended to deviate from small-world properties. This implies that the initial degree correlations do not have any positive or negative effect on the networks' performance or functions.

We examine the evolution of economics research using a machine-learning-based classification of publications into fields and styles. The changing field distribution of publications would not seem to favor empirical papers. But economics' empirical shift is a within-field phenomenon; even fields that traditionally emphasize theory have gotten more empirical. Empirical work has also come to be more cited than theoretical work. The citation shift is sharpened when citations are weighted by journal importance. Regression analyses of citations per paper show empirical publications reaching citation parity with theoretical publications around 2000. Within fields and journals, however, empirical work is now cited more.

Abstract
Powered air-purifying respirator (PAPR) is gradually being widely used in many working environments. During the coronavirus disease 2019 (COVID-19) pandemic, many powered air-purifying respirators (PAPRs) were used to protect against bioaerosol transmission. However, according to the fit-testing requirements of the US 29 CFR, It only applies to tight-fitting PAPRs, but not applies to loose-fitting hooded PAPRs.
The study was to test commercially available loose-fitting hooded PAPRs by using quantitative testing method. In the experimental chamber (2.0*2.0*2.1 m3), a 3M Air-Mate PAPR with S-757 high-end lined hood (Versaflo) was used to perform the fit-testing. A TSI Portacount was used to test the real-time fit factor (FF) of the volunteer male and female subjects. Positive pressure air supply was maintained in the hood, 10 steps of ISO-16900 standard exercises were performed for 20 minutes.
The results showed that when male subjects were standing still and not talking (the 10th exercise), the test achieved the highest FF value as averaged 54849; and when doing the 8th exercise (walk, speaking), the averaged FF was the lowest one, which was averaged 621. The highest FF of female subjects was averaged 776 when doing the 5th exercise (hands and knees, head rotation), and the lowest FF was averaged 184 when doing the 10th exercise (standing still). Most of the FF values of male subjects were higher than that of female subjects (up to averaged 297 times), which might be because of the head shape, hairstyle, or wearing glasses of the subjects.

AbstractEnergy storage can further reduce carbon emission when integrated into the renewable generation. The integrated system can produce additional revenue compared with wind-only generation. The challenge is how much the optimal capacity of energy storage system should be installed for a renewable generation. Electricity price arbitrage was considered as an effective way to generate benefits when connecting to wind generation and grid. This wind-storage coupled system can make benefits through a time-of-use (TOU) tariff. A proportion of electricity is stored from the wind power system at off-peak time (low price), and released to the customer at peak time (high price). Thus, extra benefits are added to the wind-storage system compared with wind-only system. A Particle Swarm Optimization (PSO) algorithm based optimization model was constructed for this integrated system including constraints of state-of-charge (SOC), maximum storage and release powers etc. The proposed optimization model was to obtain the optimal capacity of energy storage system and its operation control strategy of the storage-release processes, to maximize the revenue of the coupled system considering the arbitrage. Furthermore, the energy storage can provide reserve ancillary services for the grid, which generates benefits. The benefits of energy storage system through reserve ancillary services were also calculated. A case study was analyzed with respect to yearly wind generation and electricity price profiles. The benefit compared with no energy storage scenario was calculated. The impact of the energy storage efficiency, cost and lifetime was considered. The sensitivity and optimization capacity under various conditions were calculated. An optimization capacity of energy storage system to a certain wind farm was presented, which was a significant value for the development of energy storage system to integrate into a wind farm.

AbstractThe supply chain is vulnerable to disruptions, which can lead to operational failure. Therefore, it is crucial to investigate and monitor the risks associated with such disruptions. Epidemics, including the profound impact of COVID‐19, exemplify disruptive risks that engender complexities throughout the value chain. COVID‐19 has caused significant disruptions to the global supply chain due to the preexisting fragile supply chains, resulting in the unavailability of essential raw materials, halts in production, and imbalances between supply and demand. It has forced the supply chain community to rethink their risk mitigation strategies. Despite its significance, there are a few studies on the existing options and strategies to mitigate the risks associated with epidemics such as COVID‐19. This special issue explores state‐of‐the‐art knowledge of various risks and their mitigation strategies, presenting optimized models and solutions. This editorial seeks to introduce valuable insights and illuminate promising avenues for future research by reviewing existing studies. The comprehensive understanding derived from this editorial and the accompanying articles of this special issue will benefit both researchers and practitioners, enabling them to navigate the multifaceted landscape of epidemic‐induced disruptive risks in the supply chain and adopt suitable mitigation strategies. Furthermore, this special issue lays a solid foundation for implementing advanced methods for managing and mitigating the disruptive risks that epidemics pose to the supply chain.