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Effects of xanthene dyes and aerosil nanoparticles on the structure and dynamic characteristics of the cross-linked polyurethane were investigated by the IR-spectroscopy, dynamic-mechanical analysis, and electronic paramagnetic resonance methods. Dye molecule presence causes self-association of urethane groups within polymer rigid segments, weaker interactions between urethane groups and flexible oligoether parts and increased oligoether mobility. Increased mobility of flexible segments by xanthene dyes provides the necessary beam strength due to compensating the nanofiller network effect. The most efficient polyurethane material in the radiation resistance is expected to be a polymer composite with the highest permeability and the lowest modulus of elasticity. [ABSTRACT FROM AUTHOR]

The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability and Technology. The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance. [ABSTRACT FROM AUTHOR]

At present, two subsidy policies, which are based on electric vehicle (EV) consumers and EV charging station firms, have been widely implemented in different areas in China to promote EV adoption. However, the effectiveness of subsidy policies is significantly influenced by the indirect network and herd effects, depicting the positive impacts of the numbers of energy infrastructure firms and the sales of vehicles on consumers' utilities, respectively. Taking the aforementioned effects into consideration, this study examines the optimal subsidy policies by developing a Stackelberg game model. The game model analyzes the interaction and decisions of multiple stakeholders (i.e., consumers, energy infrastructure firms, EV and gasoline vehicle manufacturers, and the government). Interestingly, we find that a stronger herd effect will promote EV sales when the unit production cost of EVs is low; however, it will hinder EV sales when the unit production cost is high. Besides, we compare the effectiveness of the two subsidy policies for promoting EV adoption, reducing environmental impacts, and increasing the social welfare. Findings show that the effectiveness of subsidy policies highly depends on the extent of the indirect network and herd effects. Specifically, compared with EV consumers-based subsidy policy, EV charging station firms-based subsidy policy will be more effective in only two conditions: a weak indirect network effect and a strong herd effect. Otherwise, the EV consumers-based subsidy policy will be more effective. The findings of the paper are helpful in enhancing the effectiveness of subsidy policy in China. • Government subsidy cannot always promote electric vehicle adoption. • The indirect network and herd effects affect the effectiveness of subsidy policy. • A stronger herd effect will not always promote electric vehicle sales. • Either consumers or charging station investors can be the best subsidy object. [ABSTRACT FROM AUTHOR]

Firms are embedded in complex networks, where diverse ideas combine and generate new ideas. Shareholders of firms are often seen as critical external resources that have significant influence on firm innovation. The current literature tends to focus on the relationship between firms and their shareholders, while paying less attention to the connections between firms with the same shareholders. This article identifies two types of network spillover effects, intra-city network effect and inter-city network effect, by visualizing the co-ownership networks in China's electric vehicle (EV) industry. We find that firms with the same shareholders, which are defined as co-owned EV firms, are more innovative than non-co-owned ones. Furthermore, there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders. While corporate shareholders help exploiting local tacit knowledge, financial institutions are more active in bridging inter-city connections. The conclusion is confirmed at both firm and city levels. This paper theorizes the firm co-ownership network as a new form of institutional proximity and tested the result empirically. For policy consideration, we have emphasized the importance of building formal or informal inter-firm network, and the government should further enhance the knowledge flow channel by institutional construction. [ABSTRACT FROM AUTHOR]

Extant research has popularized the perspective that strong network effects produce "winner-take-all" outcomes, which leads platforms to invest in user growth and encourages investors to subsidize these platforms. However, user growth does not necessarily imply strong user stickiness. Without user stickiness, strong network effects in the current period may fade in future periods, thus rendering a user growth strategy ineffective. By adding a time dimension to network effects, we developed a model of cross-period and within-period network effects to explain how different types of network effects drive value. We emphasize that the cross-period same-side network effect contributes to user stickiness, while the within-period cross-side network effect persists conditional on user stickiness. We propose that one reason for platforms having heterogeneous cross-period same-side network effects is because of the "product learning" mechanism: it is expected that products with higher uncertainty have a stronger cross-period same-side network effect. Based on different drivers, we extend the customer lifetime value model (CLV2) to two-sided platform markets, allowing us to measure how different interventions drive platform value. Using Groupon data, we verify our insights and discuss platform design choices that enhance user stickiness when the cross-period same-side network effect is weak. [ABSTRACT FROM AUTHOR]

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance. [ABSTRACT FROM AUTHOR]

Traditionally regarded as one of the least digitised industries, agriculture is now transforming due to the emergence of digital platforms and ecosystems. Extant literature underscores Agtech platforms as business solutions capable of remedying operational inefficiencies, diminishing transaction costs, enhancing information exchange, mitigating spatiotemporal disparities, facilitating market integration, and generating novel forms of value. Nevertheless, persistent ambiguity surrounds the identification and assessment of the functional roles of peripheral actors, referred to as complementors, who play a pivotal role in engendering network effects. Grounded in network externality theory and platform value proposition framework, this study seeks to comprehensively discern complementors, lead firms, and end-users associated with Agtech platforms and their overarching value propositions. Employing the case research method, we synthesise existing literature and review archival sources while focusing on four illustrative cases: Dehaat, Agrielevate, IFFCO Bazar, and KRIBHCO-CSC-eGov. The analysis was further strengthened through consultation with domain experts, facilitating triangulation and validation. This study offers insights into complementor-core, lead firm, and end-user groups within Agtech platforms, thus deepening our epistemic comprehension. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, third-party platforms have emerged and become the important channels to promote collaborations among manufacturing enterprises. Under this background, this paper focuses on a third-party platform whose multilateral users are manufacturers, suppliers, and logistics providers. There exist various cross-side network effects (i.e., CNEs) and same-side network effects (i.e., SNEs) among users (including manufacturers, suppliers, and logistics providers) at different development stages of the platform, which may affect the multi-value chain collaboration of users positively or negatively. By building a system dynamics model, this paper examines the interactions of the network effects and analyzes their impacts on the multi-value chain collaboration and platform's revenues. Then, the key factors that affect the network effect interactions are explored. The results show that the network effect interactions are significant in the early development stage of the platform. The growth of the SNEs of suppliers and logistics providers has significant impacts on their own CNEs and profits. Interestingly, the increase of manufacturers' SNEs can affect the CNEs of their own and suppliers simultaneously, which may decrease the profits of other participants but does not change their own profits. Affected by multiple network effects, the platform needs to implement a differentiated pricing strategy for its users. That is, the platform should charge suppliers and logistics providers access fee while giving manufacturers a certain subsidy. These findings can provide some valuable insights to the operation of the third-party platform. [ABSTRACT FROM AUTHOR]

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems using a firing rate model framework, in which clusters of similarly responsive neurons are represented as single units, which interact with each other through independent random connections. We explore the range of conditions in which multistability arises via recurrent input from other units while individual units, typically with some degree of self-excitation, lack sufficient self-excitation to become bistable on their own. We find many cases of multistability—defined as the system possessing more than one stable fixed point—in which stable states arise via a network effect, allowing subsets of units to maintain each others' activity because their net input to each other when active is sufficiently positive. In terms of the strength of within-unit self-excitation and standard deviation of random cross-connections, the region of multistability depends on the response function of units. Indeed, multistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the response function rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which produces Zipf's Law when enumerating the proportion of trials within which random initial conditions lead to a particular stable state of the system. [ABSTRACT FROM AUTHOR]

This paper provides a thorough analysis of an oligopolistic market for a vaccine, characterized by negative (demand-side) network externalities, which stem from the free-riding behavior of individuals engaged in a vaccination game. We investigate industry viability in terms of a standard natural Cournot-type learning process for network industries and show that viability tends to favor monopoly or competition among few suppliers. We confirm that market performance is highly inefficient, due to the combination of three imperfections: market power, network effects and a health externality (i.e., contagion). We investigate the extent of these imperfections. Finally, we devise a two-part government subsidy scheme for producers and consumers that may restore social efficiency in such markets. [ABSTRACT FROM AUTHOR]

Objectives: To understand the distribution and secular trends of Chinese adult boys and girls and investigate their spatial network effects and determinants of spatial network effects. Methods: Our study extracted data of 18‐year‐old boys and 17‐year‐old girls in China from the national students fitness and health survey reports in 1985, 2000, 2010, and 2019. A total of 89 839 participants were selected. The growth range and growth rate in each period were calculated. A neighborhood relationship network was created to share the common boundary. Results: During the period 1985–2019, the average height of Chinese 18‐year‐old boys increased from 168.19 to 172.14 cm, and that of 17‐year‐old girls increased from 156.97 to 160.17 cm (all p trend <.05). Both boys and girls showed significant correlations in first‐ and second‐level neighbors (all p <.05) with the most significant correlations in first‐level neighbors (all p <.001). But there were no significant correlations in third‐level neighbors. Height of girls and boys in each region was correlated with gross domestic product (GDP) per capita, urbanization rate, population density, longitude, and latitude. After controlling for factors with significance, only the initial regional height of girls in 2000 was positively correlated with first level neighborhood (p <.05). Conclusion: Since 1985, the heights of Chinese boys aged 18 and girls aged 17 have been increasing and the increases accelerated. Height is related to nearer neighbors. After controlling GDP per capita, urbanization rate, population density, longitude and latitude, the correlations has almost disappeared. [ABSTRACT FROM AUTHOR]

Individuals who perceive greater support or approval for their relationships from friends and family also report greater relationship stability and commitment and better mental and physical health (known as the "social network effect"). These associations have been explained, in part, through three cognitive-affective processes: uncertainty reduction, cognitive balance, and dyadic identity formation. However, we know less about cognitive- behavioral mechanisms that might help explain the social network effect. In this study, we propose and test a model in which physical affection-sharing acts as one such behavioral mechanism. In a sample of 1848 individuals in same-sex (n = 696), mixed-sex (n = 1045), and gender-diverse (n = 107) relationships, we found support for our overall model. Our findings suggest that perceived support for one's relationships is a significant predictor of perceived support for physical affection-sharing, which in turn predicts the frequency of affection-sharing in private and public contexts and, ultimately, relationship well-being. However, we also found that relationship type moderates these associations, highlighting how the experience of sharing affection with one's partner changes for many in marginalized relationships, especially in public. We conclude by discussing how our findings contribute to theories of social support for relationships, underscoring the importance of considering affective, cognitive, and behavioral factors relevant to the process. We also emphasize the understudied role of context in shaping affection-sharing experiences across all relationship types. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of fair sharing of the total value of a crowd-sourced network system between major participants (founders) and minor participants (crowd) using cooperative game theory. We use the framework of a Shapley allocation which is regarded as a fundamental method of computing the fair share of all participants in a cooperative game when the values of all possible coalitions could be quantified. To quantify the value of all coalitions, we define a class of value functions for crowd-sourced systems which capture the contributions of the founders and the crowd plausibly and derive closed-form expressions for Shapley allocations to both. These value functions are defined for different scenarios, such as the presence of oligopolies or geographic spread of the crowd, taking network effects, including Metcalfe's law, into account. A key result we obtain is that under quite general conditions, the crowd participants are collectively owed a share between 1 2 and 2 3 of the total value of the crowd-sourced system. We close with an empirical analysis demonstrating the consistency of our results with the compensation offered to the crowd participants in some public internet content sharing companies. [ABSTRACT FROM AUTHOR]

The intermolecular interactions responsible for the microsolvation of the highly flexible trimethylene oxide (TMO) and trimethylene sulfide (TMS) rings with one and two water (w) molecules were investigated using rotational spectroscopy (8–22 GHz) and quantum chemical calculations. The observed patterns of transitions are consistent with the most stable geometries of the TMO–w, TMO–(w)2, and TMS–w complexes at the B2PLYP-D3(BJ)/aug-cc-pVTZ level and were confirmed using spectra of the 18O isotopologue. Due to its effectively planar backbone, TMO offers one unique binding site for solvation, while water can bind to the puckered TMS ring in either an axial or equatorial site of the heteroatom. In all clusters, the first water molecule binds in the σv symmetry plane of the ring monomer and serves as a hydrogen bond donor to the heteroatom. The second water molecule is predicted to form a cooperative hydrogen bonding network between the three moieties. Secondary C–H⋯O interactions are a key stabilizing influence in trimers and also drive the preferred binding site in the TMS clusters with the axial binding site preferred in TMS–w and the equatorial form calculated to be more stable in the dihydrate. Using an energy partition scheme from the symmetry-adapted perturbation theory for the O, S, and Se containing mono- and dihydrates, the intermolecular interactions are revealed to be mainly electrostatic, but the dispersive character of the contacts is enhanced with the increasing size of the ring's heteroatom due to the key role of longer-range secondary interactions. [ABSTRACT FROM AUTHOR]

Herein we address the measurable consequences of the network effect (NE) on time series generated by different parts of the brain, heart, and lung organ-networks (ONs), which are directly related to their inter-network and intra-network interactions. Moreover, these same physiologic ONs have been shown to generate crucial event (CE) time series, and herein are shown, using modified diffusion entropy analysis (MDEA) to have scaling indices with quasiperiodic changes in complexity, as measured by scaling indices, over time. Such time series are generated by different parts of the brain, heart, and lung ONs, and the results do not depend on the underlying coherence properties of the associated time series but demonstrate a generalized synchronization of complexity. This high-order synchrony among the scaling indices of EEG (brain), ECG (heart), and respiratory time series is governed by the quantitative interdependence of the multifractal behavior of the various physiological ONs' dynamics. This consequence of the NE opens the door for an entirely general characterization of the dynamics of complex networks in terms of complexity synchronization (CS) independently of the scientific, engineering, or technological context. CS is truly a transdisciplinary effect. [ABSTRACT FROM AUTHOR]

A/B testing is widely used for comparing two versions of a product and evaluating new proposed product features. It is of great importance for decision-making and has been applied as a golden standard in the IT industry. It is essentially a form of two-sample statistical hypothesis testing. Average treatment effect (ATE) and the corresponding p-value can be obtained under certain assumptions. One key assumption in traditional A/B testing is the stable-unit-treatment-value assumption (SUTVA): there is no interference among different units. It means that the observation on one unit is unaffected by the particular assignment of treatments to the other units. Nonetheless, interference is very common in social network settings where people communicate and spread information to their neighbors. Therefore, the SUTVA assumption is violated. Analysis ignoring this network effect will lead to biased estimation of ATE. Most existing works focus mainly on the design of experiment and data analysis in order to produce estimators with good performance in regards to bias and variance. Little attention has been paid to the calculation of p-value. We work on the calculation of p-value for the ATE estimator in network A/B tests. After a brief review of existing research methods on design of experiment based on graph cluster randomization and different ATE estimation methods, we propose a permutation method for calculating p-value based on permutation test at the cluster level. The effectiveness of the method against that based on individual-level permutation is validated in a simulation study mimicking realistic settings. [ABSTRACT FROM AUTHOR]

Channel selection is a critical trade-off for digital products firms whose products are characterized by network externality. This work develops the models of consumers' utility impacted by the network externality for two channel strategies of the digital product firms in the two-sided market: direct channel strategy and platform channel strategy. Deriving from the consumers' utility, the optimization models of the two channel strategies are presented. The optimization models are solved through the Lagrangian method, and the comparative statics analysis is conducted to investigate the effect of network externality on optimality. Mathematical results show that if the intensity of network externality in the online platform surpasses that in the direct channel, the platform channel strategy dominates the other channel strategy; otherwise, the direct channel strategy is the firms' optimal decision. In addition, the two channels share the equal optimal price, and the firms' profit (and demand) would be positively impacted by the network effect and the products' features but negatively impacted by the consumers' learning cost. This work provides decision support for the digital product firms on channel selection in the context of the two-sided market. [ABSTRACT FROM AUTHOR]

This study examines the effects of social networks on the disclosure of stigmatizing and traumatic sexual assault experiences. We analyzed publicly archived oral histories of Korean "comfort women" from World War II, employing an innovative method combining word embedding analysis, word frequency comparison, and grounded theory. By extracting their significant social relationships from narrated survivor stories, we parsed two distinctive disclosure patterns according to timing of disclosure: early disclosers and late disclosers. The latter were more socially embedded than the former, indicating the constraining aspect of social networks, in which the size of social networks was positively associated with delayed disclosure. Qualitative findings further elaborated that social networks have double-edged effects. Survivors' familial networks functioned as both social constraints and social support for public disclosure. Yet, the late disclosers tend to exploit it more as constraints for the fear of transgenerational transmission of social scorn and stigma. The findings contribute to enhancing a culturally relevant understanding of trauma and the repercussions of human trafficking. [ABSTRACT FROM AUTHOR]

Estimating the differences in the incubation-period, serial-interval, and generationinterval distributions of SARS-CoV-2 variants is critical to understanding their transmission. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection--for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we reanalyze incubationperiod and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same dataset reported shorter mean observed incubation period (3.2 d vs. 4.4 d) and serial interval (3.5 d vs. 4.1 d) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8 to 4.5 d) for both variants but a shorter mean generation interval for the Omicron variant (3.0 d; 95% CI: 2.7 to 3.2 d) than for the Delta variant (3.8 d; 95% CI: 3.7 to 4.0 d). The differences in estimated generation intervals may be driven by the "network effect"--higher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant. [ABSTRACT FROM AUTHOR]

In the two-sided market where the third-party platforms connect the providers and consumers, the online platforms become the significant distribution channel of digital products; therefore, the digital product firms face the hybrid channel pricing problem in the two-sided market in which the products are launched through the platform channel and the existing direct channel. Because the network externality effect is the significant economic characteristic of digital products and services, the current work presents the models of consumers' utility obtained by adopting digital products from the direct and platform channels, and the utility models use the network effect in the direct and platform channels as the parameters. The optimization model on pricing is derived from the utility models and solved mathematically. The closed-form solutions show that the price in the direct channel is supposed to be lower than that in the platform channel, while the prices of digital products would be affected by the network effect only when the products are distributed through the direct channel. The comparative statics analysis on the network effect illustrates that the network effect in the direct channel and the platform channel would, respectively, have the positive and negative impact on the products' prices and the firms' profit. The current work explores the hybrid channel pricing problem and provides insights for the digital product firms on the optimal pricing decision in the context of the emerging platform economy. [ABSTRACT FROM AUTHOR]

Estimates of the effect of social networks on consumer behavior may be biased, because social network formation is endogenous. Thus, it is hard to distinguish the impact of a social network from the impact of the factors leading to its formation. I exploit a machine learning technique to systematically search for groups of individuals who are similar to the extent that friendship variation within these groups is unlikely to be correlated with unobservables that affect the outcome. I apply the method using Yelp data to estimate the effect of social network friendship on restaurant choices. I find that compared to non-friends, social network friends are 64 percent more likely to visit the same restaurant. Additional exploration of the heterogeneous effects suggests that the social network effect takes place mostly through the information channel. [ABSTRACT FROM AUTHOR]

The effects of a gel network structure on purple membrane (PM) spontaneous stacking are discussed, with particular emphasis on the relationship between the PM periodic pre-structures before gelation. Such peculiar intermembrane pre-structures with a periodic distance of ∼20 nm have recently been found to provide an important key to PM stacking, which is critical for the engineering applications of PM. Here, we investigated how the PM stacking differs depending on the gel network of a poly(vinyl alcohol) gel. On the basis of the results, we propose a plausible interplay between the PM periodic pre-structures and the type of gel network for PM stacking. The results indicate that a porous gel network, in addition to the PM periodic pre-structure in solutions, is necessary for PM stacking in immobilized gel samples. [ABSTRACT FROM AUTHOR]

An experimental research on multi-stable mode transitions and hysteresis loops in a high magnetic field helicon wave plasma source is conducted by adjusting matching network parameters. The correspondence relation between the electric circuit and plasma parameters is explored by measuring the plasma absorbed power, plasma electron density, and power transfer efficiency. The details of mode transitions are recorded by measuring the transmission coefficient to understand the feedback effects on the electric circuit from the plasma. Three discharge modes are observed in helicon discharge: the capacitively coupling mode (E mode), the inductively coupling mode (H mode), and the wave coupling mode (W mode). When the plasma absorbed power increases, the discharge mode directly jumps from the E mode to the W mode, while the discharge mode jumps in the order of W–H–E when the plasma absorbed power decreases. In such multi-stable systems, the plasma may be in different modes under the same set of circuit conditions. Hysteresis loops exist even when the dissipative power in the matching network is subtracted, which indicates that the main cause of hysteresis is nonlinearities inside the plasma. [ABSTRACT FROM AUTHOR]

Recently, polymeric carbon nitride (nominally, g-C3N4) has attracted extensive attention due to its photoresponsive applications such as photocatalysis, photoluminescence, and photoelectrochemistry. Due to unique compositions and structures, strong excitonic effects of g-C3N4 network derived from low dielectric property have led to complicated photophysical processes. Studying exciton-related photophysical processes in g-C3N4 is of great significance for gaining in-depth understandings of the relationship between excitonic effects and photoresponsive behaviors. In this Perspective, we highlight the impacts of excitonic effects on photophysical processes of g-C3N4, in which excitonic behaviors like excitation, recombination, nonradiative relaxation, and annihilation are summed up. In addition, the key role of excitonic regulation in optimizing photoresponsive properties of g-C3N4 is also summarized, where the relevance between different photoinduced species and structural factors is emphasized. This Perspective will present a comprehensive understanding of excitonic effects-dominated photophysical processes of g-C3N4 and pave a new way for the design of novel photoresponsive polymeric semiconductors with strong excitonic effects. [ABSTRACT FROM AUTHOR]

To restrain the serious flux–steel reaction, CaO–Al2O3–La2O3‐based mold flux is designed for continuous casting of La‐bearing FeCrAl alloy. The properties and structure of slags with various alkali metal oxide contents are investigated. Results show that with the increase in Na2O or Li2O contents from 0 to 10 wt%, the melting temperature of slags decreased, and the fluxing effect of Li2O is improved. The addition of Na2O within 10 wt% reduces the slag viscosity at the temperature range of 1663–1823 K. While further increasing Na2O content to 15 wt%, the viscous behavior of the slag depends on the balance between the strong charge compensation effect of Na+ and the breaking network effect of O2−. Increasing Li2O contents from 0 to 15 wt% greatly reduces the slag viscosity, which is attributed to the reduction of the fraction of high‐polymerized QAl4 units from 23.55% to 9.70% and the increase of [AlO6] octahedrons from 0 to 9.02% caused by more O2−. The NBO/Al and NBO fraction is calculated, which is consistent with the viscosity variations. In the melts, the structural roles of Na+, Li+, Ca2+, and La3+ are elucidated. Moreover, the relationship between the variations of crystalline phases and slag structure is discussed. [ABSTRACT FROM AUTHOR]

Navigation is one of the most heavily studied problems in robotics and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation presents a complex set of physical challenges that defies simple geometric abstractions. Machine learning offers a promising way to go beyond geometry and conventional planning, allowing for navigational systems that make decisions based on actual prior experience. Such systems can reason about traversability in ways that go beyond geometry, accounting for the physical outcomes of their actions and exploiting patterns in real-world environments. They can also improve as more data is collected, potentially providing a powerful network effect. In this article, we present a general toolkit for experiential learning of robotic navigation skills that unifies several recent approaches, describe the underlying design principles, summarize experimental results from several of our recent papers, and discuss open problems and directions for future work. This article is part of the theme issue 'New approaches to 3D vision'. [ABSTRACT FROM AUTHOR]

The governmental subsidy is an important instrument for catalyzing the adoption of electric vehicles (EVs). A sustainable subsidy policy must balance between promoting EVs and expanding the charging infrastructure. This paper proposes a dynamic game approach for computing the optimal subsidies, taking into consideration the cross-side network effect between EV adoption and charging infrastructure expansion. The government's objective is to expedite the adoption of EV technology, maintain sustainable EV-to-CS ratios, and maximize cumulative social benefit in the face of uncertainty. The case study of China's EV industry indicates that the existing EV subsidy program, known as the Dual-Credit Policy (DCP), will not be sufficient to support EV growth by 2035 due to inadequate infrastructure investment. In contrast, the optimal subsidies will achieve the target EV market penetration rate and ensure the accessibility to charging infrastructure by 2035. This non-myopic strategy can prioritize the infrastructure expansion, eliminate policy enforcement's monitoring costs, prevent compensation evasion, and help achieve long-term targets by making proactive regulatory policies. [ABSTRACT FROM AUTHOR]

The use of an appropriate design is a very important aspect of agroforestry experimentation. Since different species of trees may interact spatially, plots in agroforestry could be connected through network of trees which would create non-directional adjacency effects on a plot. Hence, there is a need to take the "direct effect of trees" and "tree network effects" on the crop into account when designing an agroforestry experiment. Tree network effect refers to the influence a tree exerts on the surrounding plots as a result of adjacency of the plots. In this paper, a class of network designs incorporating effects of trees from adjacent plots has been obtained. The characterization properties of the designs have also been studied. The designs are found to be variance balanced for the estimation of direct as well as network effects of trees. They are also highly efficient designs, and hence, are recommended for agroforestry experimentation with multiple tree species and a single crop. [ABSTRACT FROM AUTHOR]

Existing studies mainly focus on exploring the ‘inclusive effect’ of digital finance in the process of technological innovation, but generally ignore the ‘network effect’ of digital finance empowerment. Based on China’s inter-provincial panel data and threshold regression technology, this paper empirically examined the nonlinear empowerment mechanism of digital finance to promote the development of green innovation. The results of this study showed that: during the study period, digital finance significantly drove China ’s green innovation and the empowerment effect of the eastern and central regions was much higher than that of the western regions, indicating that the ‘inclusive effect’ of digital finance was significant; the empowerment effect of digital finance on green innovation had a positive dynamic evolution process from weak to strong, but this kind of ‘network effect’ characteristic mainly appeared in the eastern and western regions, while the central region showed a positive ‘U’-shape nonlinear pattern; and the nonlinear empowerment effect of digital finance on green innovation had a heterogeneous and strengthened adjustment mechanism. It had positive and increasing marginal efficiency characteristics under the adjustment of urbanization, fiscal transparency, and residents’ income level. It was manifested under the constraints of urban-rural income gap and economic growth targets. Under the influence of technology introduction, it presented a positive inverted a ‘U’-shaped characteristic. At present, only by continuously improving fiscal transparency, residents’ income, and urbanization level, continuously narrowing the urban-rural income gap, and keeping the economic growth target and technology introduction at a reasonable level, can we maximize the green innovation effect empowered by digital finance. This article has discovered the coexistence of the ‘inclusive effect’ and ‘network effect’ of digital finance empowerment in the process of green innovation. The relevant conclusions may provide local governments with a ‘Digital Finance Express’ during the 14th Five-Year Plan period to accelerate the development of green innovation. [ABSTRACT FROM AUTHOR]

Disruptive technology theory receives a lot of attention and has a significant influence on scholars and managers in approaching technology competition. Some studies have formalized the disruptive technology, investigating the mechanism and determinants of disruption. While some modern technologies are understood to be disruptive, they also exhibit increasing return to the adoption properties or network effects. However, how the network effects influence the disruption mechanism has only received little attention in the previous formal disruptive technology models. Therefore, in this study, we developed a formal model to investigate the dynamics of disruption by exploring the interaction of technology development, the consumer's choice, firm's decision, and structure of demand under the influence of different degrees of network effects. The model is simulated using agent-based software of the Laboratory for Simulation Development (LSD). We argue that weak network effects allow for different competitive outcomes (i.e., competitive isolation, convergence, and disruption), where the heterogeneity of the consumers' preferences matters in determining the outcomes. Therefore, this study suggests that weak network effects enhance the likelihood of disruptive innovation, depending on the heterogeneity in customer preference. Strong network effects, however, lead to a winner-takes-all situation regardless of the heterogeneity in the consumers' preferences. [ABSTRACT FROM AUTHOR]

Background: Systematic reviews attempt to gather all available evidence. Controversy exists regarding effort and benefit of including study results presented at conferences only. We recently published a Cochrane network meta-analysis (NMA) including 585 randomized controlled trials comparing drugs for prevention of postoperative nausea and vomiting (PONV). Studies published as conference abstracts only were excluded. This study aimed to include all eligible studies published as abstracts only, assessing their added value regarding reporting quality and effect on the review's interpretation. Methods: Conference abstracts were searched in the review's excluded studies and conference proceedings of anaesthesiologic societies. We assessed their reporting quality regarding review's eligibility criteria, Cochrane 'risk of bias' assessment tool 1.0, and adherence to CONSORT (Consolidated Standards of Reporting Trials) for abstracts. Abstracts were included in sensitivity NMA, and impact on the NMA structure was investigated. Results: We identified 90 abstracts. A total of 14% (13/90) were eligible. A total of 86% (77/90) are awaiting classification due to insufficient reporting of review's eligibility criteria. In abstracts awaiting classification, sufficient information was missing on standardization of anaesthesia in 71% (55/77), age of participants in 56% (43/77), and outcome details in 46% (36/77). A total of 73% (66/90) of abstracts lacked sufficient information on 15/25 data extraction items. Reported study characteristics of abstracts were comparable to included studies of the review. A total of 62% (56/90) of abstract trials were assessed as overall high risk of bias due to poor reporting. Median adherence to CONSORT for abstracts was 24% (IQR, 18 to 29%). Six of the 13 eligible abstracts reported relevant outcome data in sufficient detail for NMA on seven outcomes of the Cochrane review. Inclusion of abstracts did not substantially change the network structure, network effect estimates, ranking of treatments, or the conclusion. Certainty of evidence for headache on palonosetron use was upgraded from very low to low. Conclusions: Most conference abstracts on PONV were insufficiently reported regarding review's narrow inclusion criteria and could not be included in NMA. The resource-intensive search and evaluation of abstracts did not substantially extent the full-text evidence base of the review, given the few adequately reported abstracts. Conferences should oblige authors to adhere to CONSORT for abstracts. [ABSTRACT FROM AUTHOR]

We draw parallels between the pandemic and foreclosure in network industries by applying "Susceptible, Infected, Recovered" (SIR) modelling to an antitrust setting. We consider a digital service seeking to grow into an addressable market occupied by an incumbent platform. The entrant can grow organically, but amassing more users allows it to spread faster as users invite friends or generate content increasing its attractiveness. We consider the impact of the incumbent taking steps (e.g. reducing interoperability) to make the entrant "less infectious" with three main implications for antitrust policy: conduct may have large effects even if the targeted service continues to grow; conduct is most effective when applied against nascent services before they can harness netwrok effects; and conduct can have non-linear effects, with the most "viral" services continuing to grow while others are eliminated. Each result has parallels with the experience of the pandemic and implications for innovation incentives. [ABSTRACT FROM AUTHOR]

The rapid technological revolution of Industry 4.0 has set off rapid advancement in the smart product development. Testing the smart product's system before release to market is crucial for ensuring proper functioning. Smart product manufacturers must carefully consider different testing modes to choose the optimal one based on product characteristics and consumer's shopping intention. Additionally, different testing modes have varying impacts on the environment and social welfare, as well as profitability of the product. This study utilizes a two-period game theory model to analyze a smart product manufacturer's optimal testing mode and pricing decision. Three testing mode are evaluated: manual testing mode (MT mode), artificial intelligence testing mode (AT mode), and public testing mode (PT mode). Consumers' utility is influenced by factors such as consumer preferences, product quality, usage duration, and network externality effect. Our findings indicate that the optimal testing mode depends on quality improvement and network effect coefficients. Specifically, when the degree of quality improvement is high and the network effect is moderate, MT mode is preferred; when the quality improvement is high but the network effect is small, AT mode dominates; under other conditions, PT mode is optimal. Quality improvement and network effect coefficients have similar effects on pricing decisions but impact the profits in different manners. Social welfare is the highest under MT mode when the network effect is high and quality improvement is low, under AT mode when network effect is low and quality improvement is high, and under PT mode when both factors are high or low. Finally we provide a numerical example of how profit-driven business decisions may conflict with social welfare, potentially leading to lower social welfare than the theoretical optimum under the chosen testing mode strategy. • AI testing mode dominates only with high quality improvement and low network effect. • The impact of network effect on profit trends varies across different modes. • Profit trends are influenced differently by quality improvement across three modes. • Social welfare under the chosen mode may be lower than the theoretical optimum. [ABSTRACT FROM AUTHOR]

Academic libraries fail to take advantage of the network effect because they manage too many digital repositories locally. While this argument applies to all manner of digital repositories, this article examines the fragmented environment of institutional repositories (IRs), in which effort and costs are duplicated, numerous software platforms and versions are managed simultaneously, metadata are applied inconsistently, users are served poorly, and libraries are unable to take advantage of collective data about content and users. In the meantime, commercial IR vendors and academic social networks have shown much greater success with cloud-based models. Collectively, the library profession has enough funding to create a national-level IR, but it lacks the willingness to abandon local control. [ABSTRACT FROM AUTHOR]

Background: Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs information is costly and time-consuming. Result: In this paper, SDNN-PPI, a PPI prediction method based on self-attention and deep learning is proposed. The method adopts amino acid composition (AAC), conjoint triad (CT), and auto covariance (AC) to extract global and local features of protein sequences, and leverages self-attention to enhance DNN feature extraction to more effectively accomplish the prediction of PPIs. In order to verify the generalization ability of SDNN-PPI, a 5-fold cross-validation on the intraspecific interactions dataset of Saccharomyces cerevisiae (core subset) and human is used to measure our model in which the accuracy reaches 95.48% and 98.94% respectively. The accuracy of 93.15% and 88.33% are obtained in the interspecific interactions dataset of human-Bacillus Anthracis and Human-Yersinia pestis, respectively. In the independent data set Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, all prediction accuracy is 100%, which is higher than the previous PPIs prediction methods. To further evaluate the advantages and disadvantages of the model, the one-core and crossover network are conducted to predict PPIs, and the data show that the model correctly predicts the interaction pairs in the network. Conclusion: In this paper, AAC, CT and AC methods are used to encode the sequence, and SDNN-PPI method is proposed to predict PPIs based on self-attention deep learning neural network. Satisfactory results are obtained on interspecific and intraspecific data sets, and good performance is also achieved in cross-species prediction. It can also correctly predict the protein interaction of cell and tumor information contained in one-core network and crossover network.The SDNN-PPI proposed in this paper not only explores the mechanism of protein-protein interaction, but also provides new ideas for drug design and disease prevention. [ABSTRACT FROM AUTHOR]

Assessing the impact of the coronavirus disease 2019 (COVID-19) on air transportation is essential for policymakers and airlines to prevent their widespread shutdown. The panel data observed from January 20, 2020, to April 30, 2020, were used to identify the impact of COVID-19 and the relevant control measures adopted on China's domestic air transportation. Hybrid models within negative binomial models were employed to separate the temporal and spatial effects of COVID-19. Temporal effects show that the number of new confirmed cases and the control measures significantly affect the number of operated flights. Spatial effects show that the network effect of COVID-19 cases in destination cities, lockdown, and adjustment to Level I in the early stages have a negative impact on the operated flights. Adjustment to Level II or Level III both has positive temporal and spatial effects. This indicates that the control measures adopted during the early stage of the pandemic positively impact the restoration of the aviation industry and other industries in the later stage. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop and empirically validate the research model based on value-attitude-behavior (VAB) framework in order to understand English learners' behavior in the context of augmented reality (AR). To this end, this study proposes four variables (learning connectivity, learning realism, immersive, and learning efficiency) as unique characteristics of AR English learning, which have a positive impact on the perceived value of AR learning. Then the perceived value has a positive impact on the attitude toward AR English learning, positively related to the intention to use AR for English learning. Also, the proposed research model includes the network effect as a moderator within the relationships among VAB variables. For a period of five months, a total of 184 responses is collected using a short survey from 5th and 6th grade students at local elementary schools, children's libraries, and child care centers participating in the Edutech program by the Ministry of Education. Using AMOS 24.0, the structural equational model was analyzed in order to test hypotheses. The results indicate that all proposed hypotheses were significant, which provides a clear understanding of English learners' behavior regarding AR. Based on the findings of the study, the implications are discussed with respect to the future direction of AR research in English education. [ABSTRACT FROM AUTHOR]

Pixel-level image fusion is an effective way to fully exploit the rich texture information of visible images and the salient target characteristics of infrared images. With the development of deep learning technology in recent years, the image fusion algorithm based on this method has also achieved great success. However, owing to the lack of sufficient and reliable paired data and a nonexistent ideal fusion result as supervision, it is difficult to design a precise network training mode. Moreover, the manual fusion strategy has difficulty ensuring the full use of information, which easily causes redundancy and omittance. To solve the above problems, this paper proposes a multi-stage visible and infrared image fusion network based on an attention mechanism (MSFAM). Our method stabilizes the training process through multi-stage training and enhances features by the learning attention fusion block. To improve the network effect, we further design a Semantic Constraint module and Push–Pull loss function for the fusion task. Compared with several recently used methods, the qualitative comparison intuitively shows more beautiful and natural fusion results by our model with a stronger applicability. For quantitative experiments, MSFAM achieves the best results in three of the six frequently used metrics in fusion tasks, while other methods only obtain good scores on a single metric or a few metrics. Besides, a commonly used high-level semantic task, i.e., object detection, is used to prove its greater benefits for downstream tasks compared with single-light images and fusion results by existing methods. All these experiments prove the superiority and effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

This is the third essay advocating the use the (non-integer) fractional calculus (FC) to capture the dynamics of complex networks in the twilight of the Newtonian era. Herein, the focus is on drawing a distinction between networks described by monfractal time series extensively discussed in the prequels and how they differ in function from multifractal time series, using physiological phenomena as exemplars. In prequel II, the network effect was introduced to explain how the collective dynamics of a complex network can transform a many-body non-linear dynamical system modeled using the integer calculus (IC) into a single-body fractional stochastic rate equation. Note that these essays are about biomedical phenomena that have historically been improperly modeled using the IC and how fractional calculus (FC) models better explain experimental results. This essay presents the biomedical entailment of the FC, but it is not a mathematical discussion in the sense that we are not concerned with the formal infrastucture, which is cited, but we are concerned with what that infrastructure entails. For example, the health of a physiologic network is characterized by the width of the multifractal spectrum associated with its time series, and which becomes narrower with the onset of certain pathologies. Physiologic time series that have explicitly related pathology to a narrowing of multifractal time series include but are not limited to heart rate variability (HRV), stride rate variability (SRV) and breath rate variability (BRV). The efficiency of the transfer of information due to the interaction between two such complex networks is determined by their relative spectral width, with information being transferred from the network with the broader to that with the narrower width. A fractional-order differential equation, whose order is random, is shown to generate a multifractal time series, thereby providing a FC model of the information exchange between complex networks. This equivalence between random fractional derivatives and multifractality has not received the recognition in the bioapplications literature we believe it warrants. [ABSTRACT FROM AUTHOR]

A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer proximity in the low-dimension projection corresponds to stronger connections. Interestingly, the convergence rate of our estimator is found to depend on a network effect factor, which is the smallest number that can partition a graph in a manner similar to the graph colouring problem. Our method has interesting connections to principal component analysis and linear discriminant analysis, which we exploit for clustering and community detection. The proposed approach is further illustrated by numerical experiments and analysis of a pulsar candidates dataset from astronomy. [ABSTRACT FROM AUTHOR]

The development of photovoltaic power generation projects is essential for China to achieve its targets of achieving peak carbon emissions before 2030 and carbon neutrality before 2060, and to better complete the task of low-carbon energy transformation. China should accelerate the development of photovoltaic power generation projects and make good use of abundant solar energy resources. This article studied the impact of social networks on residents' willingness to install photovoltaic power generation projects. Based on the questionnaire in Changping District in Beijing in 2019, this article used a spatial probit model and Bayesian estimation based on Markov chain Monte Carlo to study the impact of network effects on household distributed photovoltaic generation behavior. The results showed that: 1 The presence of residents who had installed photovoltaic power generation equipment would significantly reduce the participation probability of residents who had not installed photovoltaic power generation equipment. This indicated that most residents were not satisfied with photovoltaic power generation projects after installing the equipment, the reason of which was explored in this article. 2 Residents' understanding of subsidy policy would significantly improve their probability of participating in photovoltaic power generation. 3 The free riding psychology of residents would significantly reduce the probability of their participation in photovoltaic power generation. To this end, this article offers three policy suggestions: 4 Manufacturers should increase publicity, shorten the distance between the projects and the public, change the residents' misunderstanding of photovoltaic projects, and use network effect to promote the development of family-distributed photovoltaic projects. 5 Government departments should implement the existing subsidy policies, correct the adverse effects of the subsidy policies in the minds of residents, and especially improve the identity of installed families for photovoltaic power generation projects, in order to encourage residents to install the equipment. 6 Government departments should strengthen grassroots education in environmental protection, improve people's awareness and participation in environmental protection, and correct the negative influence of residents' free riding psychology. This article studies the impact of network effects on household distributed photovoltaic power generation behavior based on micro-level household survey data. Our study might contribute to the improvement of the 'Whole County Promotion' policy of household distributed photovoltaic projects. [ABSTRACT FROM AUTHOR]

This study analyses the changes in intra-city housing values in response to improved inter-city connection brought by high-speed rail (HSR), using the opening of the Hangzhou–Fuzhou–Shenzhen Passenger Dedicated Line (HFSL) in Shenzhen, China, as an example. The opening of the HFSL and its integration into the local metro network at Shenzhen North Station provide exogenous intra-city variations in access to the surrounding economic mass. With a difference-in-differences approach, we find that the HFSL showed a negative local effect as housing values declined by 11.5%–13.3% in the proximity of Shenzhen North Station relative to areas further from the station after the opening, possibly due to the negative externalities of the HFSL. The HFSL effect can spread along the metro network and lead to, on average, a 7% appreciation of housing values around metro stations (network effect). The direction and strength of the network effect vary by metro travel time between Shenzhen North Station and metro stations. Housing values decreased by 7.7% around metro stations within 5–15 minutes of metro travel time but increased by 63.6%, 16.6% and 29.2% around metro stations within 15–25, 25–35 and 35–45 minutes of metro travel time to Shenzhen North Station, respectively. The HFSL effect on housing values diminishes when the rail travel time is above 45 minutes. We interpret these findings as evidence of the redistribution effect in the city related to HSR connection. [ABSTRACT FROM AUTHOR]

The idea of establishing science and technology parks (STP) in the regions affiliated with universities, first launched forth in the USA in the 1950s. With the successful results of these regions, STPs have started to be established in many countries. While STPs have important contributions to the economy in some countries, it is observed that they do not have significant effects in some countries. Therefore, there are controversial results in the literature regarding the effects of STPs on firm performance, region, and national economy. Many new STPs have been established in Turkey in the last 20 years. The purpose of this study is to present a detailed evaluation of the studies examining the techno-economic effects of STPs in Turkey in terms of content, scope, method, and results. In this sense, content analysis of 55 articles and theses/dissertations published between 2004-2019 was conducted. The results obtained showed that most of the studies have reached the conclusion that TGBs positively affect both established companies and regional development. It was determined that the main effect mechanism for the regional development was the establishment of the regional innovation system while the network effect and the resources offered by STPs were the main effects on the firm performance. [ABSTRACT FROM AUTHOR]

Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storage is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. These memories are stored in a distributed fashion throughout connectivity matrix, and individual synaptic connections have only a small influence. Although memory-specific connections are increased in number, the total number of inputs and outputs of neurons undergo only small changes during stimulation. We find that homeostatic structural plasticity induces a specific type of "silent memories", different from conventional attractor states. Author summary: Memories are thought to be stored in groups of strongly connected neurons, or engrams. Much effort has been put into understanding engrams, but currently there is no definitive consensus about how they are formed. Hebbian plasticity was proposed to underlie engram formation. Hebbian plasticity occurs when synaptic weights are strengthened between pairs of neurons with correlated activity. Hebbian plasticity, therefore, is thought to rely on a mechanism that can detect correlation between neurons. However, the increased synaptic weight implies increased correlation, which creates a positive feedback loop that can lead to runaway growth. Avoiding such an unfavorable condition would require regulatory mechanisms that are much faster than the ones which have been observed in the brain. Here we show that a structural plasticity rule that is based on firing rate homeostasis can also lead to the formation of engrams. In this case, engram formation does not rely on a mechanism that traces correlation. Instead, stronger connectivity between correlated neurons emerges as a network effect, based on self-organized rewiring. Our work proposes a different possibility how engrams could be created. This new perspective could be instrumental for better understanding the process of memory formation. [ABSTRACT FROM AUTHOR]

• Neighborhood effect plays an important role in plug-in electric vehicle diffusion. • Peer effect at the workplace has an important impact on the diffusion process. • Positive effect of charging infrastructure on PEV sales. • Targeted policies like Clean Car 4All can encourage PEV adoption. The market for plug-in electric vehicles (PEVs) --including both battery electric vehicles (BEVs) and plug-in hybrid vehicles (PHEVs)--has been rapidly growing in California for the past few years. Given the targets for PEV penetration in the state, it is important to have a better understanding of the pattern of technology diffusion and the factors that are driving the process. Using spatial analysis and Poisson count models we identify the importance of a neighborhood effect (at home locations) and a workplace effect (at commute destinations) in supporting the diffusion of PEV technology in California between 2014 and 2016. In the case of new BEV sales, we find that exposure to one additional BEV or PHEV within a 1-mile radius of a census block group centroid is associated with a 0.2% increase in BEV sales in the block group. Interestingly, for new PHEV sales- the neighborhood effect of BEV sales is negative, suggesting that enhanced exposure to this type of technology (which is differentiated in distinctive ways from PHEVs) may impact new PHEV sales through a substitution effect. Specifically, higher BEV concentration in an area can have an overall negative effect on new PHEV sales. While the neighborhood effect at residential locations is important, a workplace effect also has a notably important influence on new PEV sales. Both effects work in combination with socioeconomic, demographic, policy, and built environment factors in encouraging PEV adoption. These results, insightful not just for California but other regions in early phases of the PEV market, suggest that policymakers should consider targeted programs and investments that can attract a wider group of early adopters (in terms of sociodemographic characteristics and spatial location) to boost the impact of neighborhood and workplace effects on PEV sales. [ABSTRACT FROM AUTHOR]

This paper discusses whether high‐speed rail (HSR) construction in China promotes urban economic growth, and it uses social network analysis (SNA) and a panel threshold model. We provide robust evidence that HSR networks have non‐linear effects on the urban economy. Urban location and transportation hub status change as the density of HSR networks increases, and from 2008 to 2017, the national average annual growth rate of the weighted degree centrality (WDC) was 44.93%. We find the WDC and betweenness centrality (BC) have positive influences on urban economic growth. However, HSR network expansion reduces the economic benefits of HSR because the marginal contribution of new HSR lines to economic productivity decreases and there is a crowding out effect caused by excessive agglomeration. HSR networks can promote the economy of Western China to a greater extent and can promote the economy more in small cities than in large and medium‐sized cities. These findings can help the government rationalize its strategy for designing HSR lines in various regions. [ABSTRACT FROM AUTHOR]