Complex network states are characterized by the interplay between system’s structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy ...characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize.
The final thermo-mechanical properties of structural parts fabricated by masked stereolithography (MSLA) are highly determined not only by the processing parameters, but also by the post-processing ...methods. Improper implementation of post-treatment often leads to underperforming printouts. A novel tool for complex characterization of 3D printed bodies was developed and systematically demonstrated on a commercial free-radical photopolymerization (FRP) resin. The method relies on superimposed static and oscillatory mechanical test combining the heat deflection temperature (HDT) measurement together with the dynamic mechanical analysis (DMA) in a single test for fast and reliable characterization of parameters determining the curing behaviour of the photopolymer. The influence of post-curing time was addressed with a special focus on network density. Furthermore, the print orientation, having a high impact on mechanical properties, is discussed with a particular regard on the residual stress mitigation in future applications, such as 3D-printed cellular bodies.
•Development of a combined DMA-HDT technique for photo-cured resin characterization.•Network density as the key parameter to control thermomechanical properties.•Evaluation of the curing time, print orientation and sample thickness influence on final properties.•Conversion profile along the specimen depth determined by FTIR-ATR.
This research examines the effects of alliance portfolios – the collection of alliances a firm undertakes – on joint R&D project performance. Drawing on the exploration-exploitation framework, we ...examine how alliance portfolio exploration (alliances across product-markets) and portfolio exploitation (alliances within product-markets) influence alliance partners' R&D projects. Moreover, we conceptualize alliance portfolio ambidexterity as a balanced portfolio of alliances across and within product-markets, and examine its effect on project performance. Finally, we hypothesize how factors at two different levels – alliance type (scale or link), and network density – moderate the relationships between firms' alliance portfolios and performance. We collect data on nearly 600 alliances over 12 years and find that alliance portfolio exploration and exploitation increase joint R&D project performance, and that alliance portfolio ambidexterity enhances performance to a greater extent. We also find that both scale alliances and network density reinforce the relationship between alliance portfolio exploration and project performance, and they diminish the relationship between alliance portfolio exploitation and performance.
•Alliance portfolio exploration and exploitation improve joint R&D project performance.•Alliance portfolio ambidexterity generates better outcomes than either portfolio exploration or exploitation.•Scale alliances and network density reinforce the positive effect of portfolio exploration on R&D project performance.•However, scale alliances and network density diminish the impact of portfolio exploitation on R&D project performance.
This study investigated the differential effects of the ego-network density (i.e., ego-density) of the focal firm and its partner on the focal firm's competitive behavior toward its partner in ...technological areas, as well as the moderating role of relative structural holes in the whole alliance network. The empirical findings indicate that the ego-density of the focal firm has an inverted U-shaped relationship with technological invasion, whereas the ego-density of the partner firm has a negative effect. Relative structural holes also appear to strengthen the inverted U-shaped relationship between the focal firm's ego-density and technological invasion while attenuating the negative effect of the partner firm's ego-density on technological invasion. By concurrently considering both participants in this allying relationship and the synergistic interaction of the ego and whole alliance network, these results add to our understanding of the coopetition relationship within such networks.
•Firms may compete in the technological domains after allying with each other.•The effects of the focal firm's and its partner's ego-network density are differential.•An inverted U-shaped relationship exists between the focal firm's ego-network density and technological invasion.•The characteristics of the whole network have a moderating effect on the effect of ego-network density.
This study aimed to assess whether adding information on psychological experiences derived from a daily diary to baseline cross-sectional data could improve short- (1-year) and long-term (3-years) ...prediction of psychopathology and positive psychotic experiences (PEs). We used 90-day daily diary data from 96 individuals in early subclinical risk stages for psychosis. Stepwise linear regression models were built for psychopathology and PEs at 1- and 3-years follow-up, adding: (1) baseline questionnaires, (2) the mean and variance of daily psychological experiences, and (3) individual symptom network density. We assessed whether similar results could be achieved with a subset of the data (7-14- and 30-days). The mean and variance of the diary improved model prediction of short- and long-term psychopathology and PEs, compared to prediction based on baseline questionnaires solely. Similar results were achieved with 7-14- and 30-day subsets. Symptom network density did not improve model prediction except for short-term prediction of PEs. Simple metrics, i.e., the mean and variance from 7 to 14 days of daily psychological experiences assessments, can improve short- and long-term prediction of both psychopathology and PEs in individuals in early subclinical stages for psychosis. Diary data could be a valuable addition to clinical risk prediction models for psychopathology development.
The long convergence time required to achieve high-precision position solutions with integer ambiguity resolution-enabled precise point positioning (PPP-RTK) is driven by the presence of ionospheric ...delays. When precise real-time ionospheric information is available and properly applied, it can strengthen the underlying model and substantially reduce the time required to achieve centimeter-level accuracy. In this study, we present and analyze the real-time PPP-RTK user performance using ionospheric corrections from multi-scale regional networks during a day with medium ionospheric disturbance. It is the goal of this contribution to measure the impact the network dimension has on the ambiguity-resolved user position through the predicted ionospheric corrections. The user-specific undifferenced ionospheric corrections are computed at the network side, along with the satellite phase biases needed for single-receiver ambiguity resolution, using the best linear unbiased predictor. Such corrections necessitate the parameterization of an estimable user receiver code bias, on which emphasis is given in this study. To this end, we process GPS dual-frequency data from four four-station evenly distributed CORS networks in the United States with varying station spacings in order to evaluate if and to what extent the ionospheric corrections from multi-scale networks can improve the user convergence times. Based on a large number of samples, our experimental results showed that sub-10 cm horizontal accuracy can be achieved almost instantaneously in the ionosphere-weighted partially-ambiguity-fixed kinematic PPP-RTK solutions based on corrections from a network with 68 km spacing. Most of the solutions (90%) were shown to require less than 6.0 min, compared to the ionosphere-float PPP solutions that needed 68.5 min. In case of sparser networks with 115, 174 and 237 km spacing, 50% of the horizontal positioning errors are shown to become less than one decimeter after 1.5, 4.0 and 7.0 min, respectively, while 90% of them require 10.5, 16.5 and 20.0 min. We also numerically demonstrated that the user's convergence times bear a linear relationship with the network density and get shorter as the density increases, for both full and partial ambiguity resolution.
The knowledge gap hypothesis predicts that information inequity will be amplified rather than attenuated by the media. Previous research has focused on the role of mass media exposure and has not ...examined the roles of social media and social networks in mitigating the gap. This study investigated the potential moderating roles of social media engagement, social networks, and the interaction between engagement and online and offline networks. Hypotheses were tested with data from a national sample survey (N = 991) concerning political and health knowledge. More social media engagement predicted a smaller knowledge gap in the political domain but not the health domain. More diverse and denser social media networks predicted a smaller political, but not health, knowledge gap. Social media engagement interacted with mixed-media relationships to predict the political knowledge gap. More engagement with mixed-media relationships was associated with a smaller political knowledge gap.
The digitization of channel management changes how firms acquire, interpret, and analyze information, which needs to be considered when evaluating the efficacy of exercising power strategies on ...interfirm cooperation. Drawing insights from the three mechanisms of interorganizational information processing capability, we identify contingent factors that may affect the relationship between firms' exercise of power strategies and interfirm cooperation at the firm (i.e., IT capability), dyadic (i.e., relationship quality), and network (i.e., network density) levels. We analyzed data sampled from 288 manufacturing firms and found that manufacturers' IT capability weakens the negative effect of exercising coercive power and the positive effect of exercising noncoercive power on interfirm cooperation. At the dyadic level, relationship quality between manufacturer and distributor weakens the negative effect of exercising coercive power on interfirm cooperation. Finally, at the network level, distributors' network density strengthens the positive effect of exercising noncoercive power on interfirm cooperation. Our findings contribute to the marketing channel literature by shedding new light on the effects of the exercise of power on interfirm cooperation in this digital era and offer actionable managerial insights.
•We introduce the information processing view in examining the efficacy of exercising power strategies.•IT capability weakens the negative (positive) effect of exercising coercive (noncoercive) power on cooperation.•Relationship quality between partners weakens the negative effect of exercising coercive power on cooperation.•Distributors’ network density strengthens the positive effect of exercising noncoercive power on cooperation.
Ecological environment conditions (EEC) assessment plays an important role in watershed management. However, due to insufficient field data, EEC assessment in large-scale watersheds faces challenges. ...Our study was conducted to develop an effective EEC assessment method framework that was capable of reducing the use of field data. Three indicators were developed from multisource data, including landscape ecological risk index (LERI), road network density (RND), and industry density (ID). The knowledge-based raster mapping approach integrated the three indicators into an overall score of the EEC. Then model validation was conducted with principal components of water quality from field sampling data by Pearson correlation analysis methods. Finally, we applied and demonstrated the constructed method framework in the EEC assessment of the YRB.The results showed that bad EEC (0.5326 < Overall score ≤ 0.7679) areas were mainly distributed in the northern part of the YRB, showing a circular distribution pattern. The areas with bad EEC were 15.84 million km2, accounting for 19.87 % of the YRB. The area of the highest LERI (0.157 < LERI≤0.246), the highest RND (4.4435 < RND ≤ 8.5574), and the highest ID (0.1403 < ID≤0.2597) finally converted to bad EEC was 7.22 million km2, 0.78 million km2, and 0.91 million km2, respectively. The results indicated that the ecological risk factors were the primary challenges for improving EEC, followed by industrial agglomeration and road network factors. The primary factors affecting EEC varied between the provinces in the YRB, suggesting that provinces take the management strategies and measures should be adaptive. The correlation coefficients between EEC and the principal components of water quality characteristics were between 0.022 and 0.241, P < 0.05. These findings validated that our method framework could distinguish the spatial variation of EEC in detail and further provide effective support for watershed management.
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•Indicators were developed with LCLU, road network, and industry-related POI data.•Indicators were integrated into an overall score of EEC by the KBRM approach.•Bad EEC areas showed a circular distribution pattern in the northern of the YRB.•The LERI had the primary effect on the EEC of YRB, followed by ID and RND.•Principal components of water quality validated the effectiveness of our method.
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