Modelling the Mechanisms of Ice Crystal Growth at the Molecular Scale

Our planet has massive ice resources that play a crucial role in mitigating the greenhouse effect and providing an environment for atmospheric chemical reactions. These reactions usually occur on the ice surfaces and are accompanied by the "quasi-liquid layer" (QLL) generated below the ice melting temperature. This QLL is also involved in the ice nucleation and growth on different ice surfaces at low vapour pressures. The most common ice crystal in our ambient environment is hexagonal ice (Ih). The growth competition between ice Ih basal and prismatic surfaces results in the ice morphologies varying between plates and columns. Therefore, our primary goals are to characterize the properties of the QLL and investigate the ice crystal growth on different ice surfaces.To unravel the mechanisms of ice growth from the vapour at the molecular scale, the initial work of this project was to systematically study the characteristics of the QLL on ice surfaces in a wide range of supercooling temperatures using molecular dynamics (MD) simulations. It analyzed the results using a range of order parameters and a deep-learning neural network framework (DeepIce) to distinguish the ice-like and liquid-like atomic environments. Then, the ice crystallisation process was investigated using the MD and well-tempered metadynamics methods to give insights into the QLL/ice interface dynamics. Through selecting an efficient collective variable (environment similarity), the free energy surfaces associated with QLL melting and recrystallising were recovered. The relationship between ice growth rates on different ice surfaces and a range of temperatures was obtained. The growth kinetics of varying ice surfaces were compared and discussed. Based on the direct coexistence MD method, the free energy and chemical potential differences between ice Ih and water in the ice bulk coexistence systems were investigated using the on-the-fly probability enhanced sampling (OPES) simulations. The melting temperature of ice Ih was also confirmed by fitting the curve of the chemical potential differences. In addition, by combining the direct coexistence MD technique with an off-the-shelf deep learning neural network potential for water (DNNP), the ice Ih melting temperature for this DNNP model was validated and compared with the TIP4P/Ice water model. The investigation of the QLL/ice interface’s dynamic equilibrium and the ice crystal growth mechanisms was expected to support our community in understanding ice further.<br/

A Grey Interval Relational Degree-Based Dynamic Multiattribute Decision Making Method and Its Application in Investment Decision Making

The purpose of this paper is to propose a three-dimensional grey interval relational degree model for dynamic Multiattribute decision making. In the model, the observed values are interval grey numbers. Elements are selected in the system as the points in an m-dimensional linear space. Then observation data of each element to different time and objects are as the coordinates of point. An optimization model is employed to obtain each scheme’s affiliate degree for the positive and negative ideal schemes. And a three-dimensional grey interval relational degree model based on time, index, and scheme is constructed in the paper. The result shows that the three-dimensional grey relational degree simplifies the traditional dynamic multiattribute decision making method and can better resolve the dynamic multiattribute decision making problem of interval numbers. The example illustrates that the method presented in the paper can be used to deal with problems of uncertainty such as dynamic multiattribute decision making

Rumor Evolution in Social Networks

Social network is a main tunnel of rumor spreading. Previous studies are concentrated on a static rumor spreading. The content of the rumor is invariable during the whole spreading process. Indeed, the rumor evolves constantly in its spreading process, which grows shorter, more concise, more easily grasped and told. In an early psychological experiment, researchers found about 70% of details in a rumor were lost in the first 6 mouth-to-mouth transmissions \cite{TPR}. Based on the facts, we investigate rumor spreading on social networks, where the content of the rumor is modified by the individuals with a certain probability. In the scenario, they have two choices, to forward or to modify. As a forwarder, an individual disseminates the rumor directly to its neighbors. As a modifier, conversely, an individual revises the rumor before spreading it out. When the rumor spreads on the social networks, for instance, scale-free networks and small-world networks, the majority of individuals actually are infected by the multi-revised version of the rumor, if the modifiers dominate the networks. Our observation indicates that the original rumor may lose its influence in the spreading process. Similarly, a true information may turn to be a rumor as well. Our result suggests the rumor evolution should not be a negligible question, which may provide a better understanding of the generation and destruction of a rumor.Comment: a regular paper with 6 pages, 3 figure

Traffic Fluctuations on Weighted Networks

Traffic fluctuation has so far been studied on unweighted networks. However many real traffic systems are better represented as weighted networks, where nodes and links are assigned a weight value representing their physical properties such as capacity and delay. Here we introduce a general random diffusion (GRD) model to investigate the traffic fluctuation in weighted networks, where a random walk's choice of route is affected not only by the number of links a node has, but also by the weight of individual links. We obtain analytical solutions that characterise the relation between the average traffic and the fluctuation through nodes and links. Our analysis is supported by the results of numerical simulations. We observe that the value ranges of the average traffic and the fluctuation, through nodes or links, increase dramatically with the level of heterogeneity in link weight. This highlights the key role that link weight plays in traffic fluctuation and the necessity to study traffic fluctuation on weighted networks.Comment: a paper with 11 pages, 6 figures, 40 reference

Investigating the quasi-liquid layer on ice surfaces: a comparison of order parameters

Ice surfaces are characterized by pre-melted quasi-liquid layers (QLLs), which mediate both crystal growth processes and interactions with external agents. Understanding QLLs at the molecular level is necessary to unravel the mechanisms of ice crystal formation. Computational studies of the QLLs heavily rely on the accuracy of the methods employed for identifying the local molecular environment and arrangements, discriminating between solid-like and liquid-like water molecules. Here we compare the results obtained using different order parameters to characterize the QLLs on hexagonal ice (Ih) and cubic ice (Ic) model surfaces investigated with molecular dynamics (MD) simulations in a range of temperatures. For the classification task, in addition to the traditional Steinhardt order parameters in different flavours, we select an entropy fingerprint and a deep learning neural network approach (DeepIce), which are conceptually different methodologies. We find that all the analysis methods give qualitatively similar trends for the behaviours of the QLLs on ice surfaces with temperature, with some subtle differences in the classification sensitivity limited to the solid-liquid interface. The thickness of QLLs on the ice surface increases gradually as the temperature increases. The trends of the QLL size and of the values of the order parameters as a function of temperature for the different facets may be linked to surface growth rates which, in turn, affect crystal morphologies at lower vapour pressure. The choice of the order parameter can be therefore informed by computational convenience except in cases where a very accurate determination of the liquid-solid interface is important

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Pricing and Collection Rate for Remanufacturing Industry considering Capacity Constraint in Recycling Channels

This paper explores the decision-making and coordination mechanism of pricing and collection rate in a closed-loop supply chain with capacity constraint in recycling channels, which consists of one manufacturer and one retailer. On the basis of game theory, the equilibriums of decisions and profits in the centralized and decentralized scenarios are obtained and compared. Through the performance analysis of a different scenario, a higher saving production cost and lower competition intensity trigger the members to engage in remanufacturing. Furthermore, we try to propose a two-part tariff contract through bargaining to coordinate supply chain and achieve a Pareto improvement. The results show that when the capacity constraints in recycling channels exceed a threshold, the decisions and profit will change. Additionally, for closed-loop supply chain, the selling price is more susceptible to the influence of capacity constraint in recycling channel than the members’ profit