Exploring S-shape curves and heterogeneity effects of rumor spreading in online collective actions

<abstract> <p>Nowadays online collective actions are pervasive, such as the rumor spreading on the Internet. The observed curves take on the S-shape, and we focus on evolutionary dynamics for S- shape curves of online rumor spreading. For agents, key factors, such as internal aspects, external aspects, and hearing frequency jointly determine whether to spread it. Agent-based modeling is applied to capture micro-level mechanism of this S-shape curve. We have three findings: (a) Standard S-shape curves of spreading can be obtained if each agent has the zero threshold; (b) Under zero-mean thresholds, as heterogeneity (SD) grows from zero, S-shape curves with longer right tails can be obtained. Generally speaking, stronger heterogeneity comes up with a longer duration; and (c) Under positive mean thresholds, the spreading curve is two-staged, with a linear stage (first) and nonlinear stage (second), but not standard S-shape curves either. From homogeneity to heterogeneity, the spreading S-shaped curves have longer right tail as the heterogeneity grows. For the spreading duration, stronger heterogeneity usually brings a shorter duration. The effects of heterogeneity on spreading curves depend on different situations. Under both zero and positive-mean thresholds, heterogeneity leads to S-shape curves. Hence, heterogeneity enhances the spreading with thresholds, but it may postpone the spreading process with homogeneous thresholds.</p> </abstract>

Tissue- and ethnicity-independent hypervariable DNA methylation states show evidence of establishment in the early human embryo

We analysed DNA methylation data from 30 datasets comprising 3,474 individuals, 19 tissues and 8 ethnicities at CpGs covered by the Illumina450K array. We identified 4,143 hypervariable CpGs ('hvCpGs') with methylation in the top 5% most variable sites across multiple tissues and ethnicities. hvCpG methylation was influenced but not determined by genetic variation, and was not linked to probe reliability, epigenetic drift, age, sex or cell heterogeneity effects. hvCpG methylation tended to covary across tissues derived from different germ-layers and hvCpGs were enriched for associations with periconceptional environment, proximity to ERV1 and ERVK retrovirus elements and parent-of-origin-specific methylation. They also showed distinctive methylation signatures in monozygotic twins. Together, these properties position hvCpGs as strong candidates for studying how stochastic and/or environmentally influenced DNA methylation states which are established in the early embryo and maintained stably thereafter can influence life-long health and disease.

Gas-bearing sands appraisal for Zamzama Gas field in Pakistan through inverted elastic attributes assisted with PNN approximation of petrophysical properties

The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. The optimized production is limited by water encroachment in producing wells, thus it is required to distinguish the gas-sand facies from the remainder of the wet sands and shales for additional drilling zones. An approach is adopted based on a relation between petrophysical and elastic properties to characterize the prospect locations. Petro-elastic models for the identified facies are generated to discriminate lithologies in their elastic ranges. Several elastic properties, including p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62), are calculated from the simultaneous prestack seismic inversion, allowing the identification of gas sands in the field. Furthermore, inverted elastic attributes and well-based lithologies are incorporated into the Bayesian framework to evaluate the probability of gas sands. To better determine reservoir quality, bulk volumes of PHIE and clay are estimated using elastic volumes trained on well logs employing Probabilistic Neural Networking (PNN), which effectively handles heterogeneity effects. The results showed that the channelized gas-sands passing through existing well locations exhibited reduced clay content and maximum effective porosities of 9%, confirming the reservoir's good quality. Such approaches can be widely implemented in producing fields to completely assess litho-facies and achieve maximum production with minimal risk.

Adoption of complementary climate-smart agricultural technologies: lessons from Lushoto in Tanzania

Background Agriculture is important for economic growth and development in many countries in Sub-Saharan Africa, including Tanzania. However, agricultural production and productivity remain relatively low, with significant yield gaps attributed to factors such as limited access to and low adoption of appropriate agricultural technologies, and climate-related risks resulting from climate variability and change. This paper explores the drivers of adoption of climate-smart agricultural (CSA) technologies and practices, taking into account the complementarity among agricultural technologies and heterogeneity of the farm households, using data from Lushoto in Tanzania. Methods We use a Multivariate Probit analysis of cross-sectional data collected from 264 smallholder farmers in Lushoto—a climate hotspot in Tanzania—to understand the drivers of household decisions to adopt CSA technologies and practices. The technologies included diversification of multiple stress (drought, floods, pests, diseases)-tolerant crop varieties, use of fertilizers, and application of herbicides and pesticides. The Multivariate Probit model was preferred as it takes into account the inter-relationships of the technologies as well as heterogeneity of the smallholder farmers for more robust estimates. The independent variables used in the analysis included household socio-economic factors such as the relative importance of crop and livestock enterprises, household land size, social capital, access to agricultural credit and weather information, previous experience with fertilizer use and household characteristics (age, education and gender of household head, and household size). Results About 63% of the households diversified their crop enterprises, shifting to improved resilient crops and crop varieties. Another 37% adopted fertilizers, while 38% applied pesticides and herbicides. Conditional on the unobservable heterogeneity effects, the results show that household adoption decisions on diversification of multiple stress-tolerant crops and crop varieties, fertilizer, and pesticides and herbicides are complementary. In addition, the results confirm existence of unobserved heterogeneity effects leading to varying impact of the explanatory variables on adoption decisions among farmers with similar observable characteristics. Conclusions The findings indicate that any effective CSA technology adoption and diffusion strategies and policies should take into account the complementarity of the technologies and heterogeneity of the smallholder farmers. Therefore, inter-related technologies should be promoted as a package or bundled while taking into consideration household and farm-level constraints to adoption.

Two-Lane Highway Crash Severities: Correlated Random Parameters Modeling Versus Incorporating Interaction Effects

Two-lane highways represent the majority of highways in the U.S. and their safety is of crucial concern. Even though road safety researchers intensively evaluated two-lane highway safety, past studies were challenged by a methodological hindrance, namely that of correlated random parameters (CRP) modeling methods. Random parameters models capture unobserved heterogeneity effects of crash contributing factors, while CRP models offer the additional benefit of capturing correlations among variables inducing such unobserved heterogeneity effects. However, CRP models do not permit specifying pairs of regressors, with statistically insignificant correlations, to be uncorrelated. In this research, it was demonstrated that the conventional uncorrelated random parameters ordinal probit (URPOP) structure with interaction effects outperformed the correlated random parameters ordinal probit (CRPOP) structure when modeling injury severity risks of two-lane highway crashes in Wyoming. As per the former model’s results, speeding, head-on collisions, sideswipe opposite-direction collisions, intersecting-direction collisions, motorcycle involvement, impaired driving, distracted driving, the interaction effect of speeding with motorcycle involvement, that of head-on collisions with impaired driving, and that of head-on collisions with commercial vehicle involvement all raised the likelihood of sustaining severe injuries. Conversely, leaving the crash scene, proper seat belt use, wet road surfaces, and the interaction effect of impaired driving with motorcycle involvement alleviated the risk of incurring severe injuries. The superiority of the proposed model and its reduced computation time warrant its recommendation for implementation in future studies. Also, from a practical perspective, safety mitigation measures are suggested based on this research’s findings.

The Decoupling of China's Economy-Carbon Emission and Its Driving Factors

Decoupling between economic growth and carbon emission is a global hot topic. This paper studies China’s economic-carbon decoupling and its driving factors. By using the panel data of 30 Chinese provinces from 2000 to 2016, the decoupling index of each province is calculated with the Tapio model. It is found that many provinces have progressed from no decoupling to weak decoupling and then strong decoupling. Then, the econometric models are used to explore the driving factors. Results show that energy structure is the most important factor, followed by GDP per capita and energy intensity, which all increase CO2 emission significantly. The results are robust when tested with GMM, PCSE and FGLS estimation and LMDI decomposition. Further, we conduct a comparative analysis regarding the temporal and spatial characteristics of the above three driving factors to identify their relationship with decoupling, four groups of regions that represent different economic features are selected for the analysis. Heterogeneity effects of the factors among the regions has been observed, based on this we provide targeted strategies for different regions.

Quantifying how urban landscape heterogeneity affects land surface temperature: A comparison of discrete and continuous approaches

The present study examines the efficiency of discrete and continuous approaches to measuring urban heterogeneity effects on land surface temperature (LST). In the discrete approach, landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and alternative approaches to measuring urban heterogeneity effects on LST. We compared landscape metrics results with nine texture-based measures, and two local spatial autocorrelation indices (local Moran’s I and Gi statistics) applied to NDVI and BAI indices as a proxy of the spatial patterns of Tehran vegetation and built-up classes. The statistical results showed that urban landscape heterogeneity had significant impacts on the LST variations, and there was a compatibility between landscape metrics and alternative measures results. Overall results showed that the less-fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was between patch density (PD) and LST (r= -0.71). Higher values of PD have mostly been interpreted to show higher fragmentation, but other landscape metrics and alternative measures declined this conclusion. Our study demonstrated that PD was not a reliable metric and presented no information about the spatial distribution of landscape elements. This study confirms alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into the urban design.

Heterogeneity Effects in Highly Cross-Linked Polymer Networks

Despite their level of refinement, micro-mechanical, stretch-based and invariant-based models, still fail to capture and describe all aspects of the mechanical properties of polymer networks for which they were developed. This is for an important part caused by the way the microscopic inhomogeneities are treated. The Elastic Network Model (ENM) approach of reintroducing the spatial resolution by considering the network at the level of its topological constraints, is able to predict the macroscopic properties of polymer networks up to the point of failure. We here demonstrate the ability of ENM to highlight the effects of topology and structure on the mechanical properties of polymer networks for which the heterogeneity is characterised by spatial and topological order parameters. We quantify the macro- and microscopic effects on forces and stress caused by introducing and increasing the heterogeneity of the network. We find that significant differences in the mechanical responses arise between networks with a similar topology but different spatial structure at the time of the reticulation, whereas the dispersion of the cross-link valency has a negligible impact.