When multi-group selection meets mystery of cooperation in structured public goods games.

Cooperation is the cornerstone of social stability and human development. In order to promote mutual cooperation among individuals, some researchers analyzed the important factors influencing individual behavior from the perspective of group selection, while others revealed the evolutionary mechanism of cooperative behavior in groups from the perspective of network reciprocity. However, group selection and network reciprocity actually work together and simultaneously drive individuals to cooperate with each other. Analyzing each mechanism in isolation provides an incomplete understanding of the interaction process. Inspired by this, we integrate the coupled effects of both group selection and network reciprocity on the behavior of individuals. We develop a structured public goods game model to study the evolution of individual cooperative behavior in multiple groups, where each individual can interact not only with intra-group individuals but also with inter-group individuals. Based on the fixed probabilities of multi-group selection, including intra-group and inter-group selection, we derive a general condition that promotes cooperation among individuals. Besides, we discuss the effects of the number of neighbors in a group, group size, and group size on the selection of cooperative behavior. Finally, we systematically compare our model with the well-mixed case, and the results show that a structured population enhances cooperation. Increasing the number of populations boosts the fixation probability of cooperation. To the best of our knowledge, this paper is the first to study the cooperative evolutionary dynamics of multi-group selection in structured populations through public goods games.

Locating sources of Szegedy's quantum network.

Source location in quantum networks is a critical area of research with profound implications for cutting-edge fields such as quantum state tomography, quantum computing, and quantum communication. In this study, we present groundbreaking research on the technique and theory of source location in Szegedy's quantum networks. We develop a linear system evolution model for a Szegedy's quantum network system using matrix vectorization techniques. Subsequently, we propose a highly precise and robust source-location algorithm based on compressed sensing specifically tailored for Szegedy's quantum network. To validate the effectiveness and feasibility of our algorithm, we conduct numerical simulations on various model and real networks, yielding compelling results. These findings underscore the potential of our approach in practical applications.

NLSI: An innovative method to locate epidemic sources on the SEIR propagation model.

Epidemics pose a significant threat to societal development. Accurately and swiftly identifying the source of an outbreak is crucial for controlling the spread of an epidemic and minimizing its impact. However, existing research on locating epidemic sources often overlooks the fact that epidemics have an incubation period and fails to consider social behaviors like self-isolation during the spread of the epidemic. In this study, we first take into account isolation behavior and introduce the Susceptible-Exposed-Infected-Recovered (SEIR) propagation model to simulate the spread of epidemics. As the epidemic reaches a certain threshold, government agencies or hospitals will report the IDs of some infected individuals and the time when symptoms first appear. The reported individuals, along with their first and second-order neighbors, are then isolated. Using the moment of symptom onset reported by the isolated individuals, we propose a node-level classification method and subsequently develop the node-level-based source identification (NLSI) algorithm. Extensive experiments demonstrate that the NLSI algorithm is capable of solving the source identification problem for single and multiple sources under the SEIR propagation model. We find that the source identification accuracy is higher when the infection rate is lower, and a sparse network structure is beneficial to source localization. Furthermore, we discover that the length of the isolation period has little impact on source localization, while the length of the incubation period significantly affects the accuracy of source localization. This research offers a novel approach for identifying the origin of the epidemic associated with our defined SEIR model.

The locatability of Pearson algorithm for multi-source location in complex networks.

We study locating propagation sources in complex networks. We proposed an multi-source location algorithm for different propagation dynamics by using sparse observations. Without knowing the propagation dynamics and any dynamic parameters, we can calculate node centrality based on the character that positive correlation between inform time of nodes and geodesic distance between nodes and sources. The algorithm is robust and have high location accuracy for any number of sources. We study locatability of the proposed source location algorithm and present a corresponding strategy to select observer nodes based on greedy algorithm. All simulations on both model and real-world networks proved the feasibility and validity of this algorithm.

Scalable Preparation of Sub-Millimeter Double-Shelled Al2O3 Hollow Spheres and Their Rapid Separation from Wastewater after Adsorption of Congo Red.

Porous double-shelled ceramic hollow spheres (PDSs) have attracted extensive attention due to their high specific surface areas and multifunctional designs. When used in wastewater treatment, millimeter or sub-millimeter spheres can be quickly separated from water by commercial sieves. However, the simple, scalable, and low-cost preparation of sub-millimeter PDSs in the solid phase remains a challenge. Herein, porous PDSs were facilely fabricated via a spheronization process utilizing pseudoboehmite powders and wet gelatin spheres as templates, which broke through the difficulty of preparing PDSs by one-step solid-state synthesis. Treating pseudoboehmite powder with nitric acid can improve the compressive strength of the PDSs. By controlling the rolling time and gelatin concentration of gelatin microspheres, the integrity, shell thickness, and double-shelled spacing of the gelatin microspheres were tuned. When the rolling time was 8-12 min, and the gelatin concentration in gelatin spheres was 250 g/L, and PDSs with a complete double-shelled structure, good mechanical property, and high specific surface area (327.5-509.6 m2/g) were obtained at 600 °C. The adsorption capacities of the PDSs for 100 mg/L Congo red solution (70.7 mg/g) were larger than those of single-shelled hollow spheres (49 mg/g), and larger diameters (608-862 µm) of the PDSs allow them to be rapidly separated from solution by a commercial sieve. This paper provides a facile and scalable method for the preparation of sub-millimeter PDSs and demonstrates their excellent adsorption capacity for Congo red solution.

Security Evaluation under Different Exchange Strategies Based on Heterogeneous CPS Model in Interdependent Sensor Networks.

In the real Internet of Everything scenario, many large-scale information systems can be converted into interdependent sensor networks, such as smart grids, smart medical systems, and industrial Internet systems. These complex systems usually have multiple interdependent sensor networks. Small faults or failure behaviors between networks may cause serious cascading failure effects of the entire system. Therefore, in this paper, we will focus on the security of interdependent sensor networks. Firstly, by calculating the size of the largest functional component in the entire network, the impact of random attacks on the security of interdependent sensor networks is analyzed. Secondly, it compares and analyzes the impact of cascading failures between interdependent sensor networks under different switching edge strategies. Finally, the simulation results verify the effect of the security of the system under different strategies, and give a better exchange strategy to enhance the security of the system. In addition, the research work in this article can help design how to further optimize the topology of interdependent sensor networks by reducing the impact of cascading failures.

Ameliorative effects of snake (Deinagkistrodon acutus) oil and its main fatty acids against UVB-induced skin photodamage in mice.

The effects of topically administered snake (Deinagkistrodon acutus) oil and its main fatty acid components on skin photodamage were explored. Epidermal thickness, mice body weight, antioxidant enzymes (superoxide dismutase, glutathione peroxidase, catalase), inflammatory cytokines (tumor necrosis factor alpha and interleukin-6), skin histology, collagen content, and metalloproteinase-1 indicators were analyzed. The results show that topical application of both snake oil and its main fatty acids recovered ultraviolet B (UVB) irradiation induced antioxidant enzymes depletion, prevented metalloproteinase-1. Snake oil and its main fatty acids suppressed dermal infiltration of inflammatory cells and reduced inflammatory cytokines levels. Notably, there was no significant difference in the antioxidant activity but a significant difference in the anti-inflammatory activity between fatty acids and snake oil under the same dose. Finally, snake oil and its main fatty acids inhibited UVB-induced histological damage such as epidermal thickening, collagen fiber and elastic fiber destruction. Our study demonstrated for the first time in KM mice that snake oil exhibited prominent photoprotection activity by protecting the activity of antioxidant enzymes and inhibiting inflammatory factors, as well as reducing the generation of MMP-1. What's more, the main fatty acids in snake oil play an important role in preventing photo-damage especially in protecting antioxidant enzyme activity.

Locating multiple diffusion sources in time varying networks from sparse observations.

Data based source localization in complex networks has a broad range of applications. Despite recent progress, locating multiple diffusion sources in time varying networks remains to be an outstanding problem. Bridging structural observability and sparse signal reconstruction theories, we develop a general framework to locate diffusion sources in time varying networks based solely on sparse data from a small set of messenger nodes. A general finding is that large degree nodes produce more valuable information than small degree nodes, a result that contrasts that for static networks. Choosing large degree nodes as the messengers, we find that sparse observations from a few such nodes are often sufficient for any number of diffusion sources to be located for a variety of model and empirical networks. Counterintuitively, sources in more rapidly varying networks can be identified more readily with fewer required messenger nodes.

Optimal localization of diffusion sources in complex networks.

Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications.

Synthetic (polymer) biology (membrane): functionalization of polymer scaffolds for membrane proteins.

A plethora of polymer-based scaffolds have been designed to facilitate biochemical and biophysical investigation of membrane proteins, with a common goal to stabilize and present them in a functional format. In this review, an up-to-date account of such polymer-based supports and incorporation methodologies are presented. Furthermore, conceptual and imminent technological advances, with associated technical challenges are proposed.

Platinum and palladium nanotubes based on genetically engineered elastin-mimetic fusion protein-fiber templates: synthesis and application in lithium-O2 batteries.

The coupling of proteins with self-assembly properties and proteins that are capable of recognizing and mineralizing specific inorganic species is a promising strategy for the synthesis of nanoscale materials with controllable morphology and functionality. Herein, GPG-AG3 protein fibers with both of these properties were constructed and served as templates for the synthesis of Pt and Pd nanotubes. The protein fibers of assembled GPG-AG3 were more than 10 µm long and had diameters of 20-50 nm. The as-synthesized Pt and Pd nanotubes were composed of dense layers of ~3-5 nm Pt and Pd nanoparticles. When tested as cathodes in lithium-O2 batteries, the porous Pt nanotubes showed low charge potentials of 3.8 V, with round-trip efficiencies of about 65% at a current density of 100 mA g(-1).

A knowledge generation model via the hypernetwork.

The influence of the statistical properties of the network on the knowledge diffusion has been extensively studied. However, the structure evolution and the knowledge generation processes are always integrated simultaneously. By introducing the Cobb-Douglas production function and treating the knowledge growth as a cooperative production of knowledge, in this paper, we present two knowledge-generation dynamic evolving models based on different evolving mechanisms. The first model, named "HDPH model," adopts the hyperedge growth and the hyperdegree preferential attachment mechanisms. The second model, named "KSPH model," adopts the hyperedge growth and the knowledge stock preferential attachment mechanisms. We investigate the effect of the parameters (α,ß) on the total knowledge stock of the two models. The hyperdegree distribution of the HDPH model can be theoretically analyzed by the mean-field theory. The analytic result indicates that the hyperdegree distribution of the HDPH model obeys the power-law distribution and the exponent is γ = 2 + 1/m. Furthermore, we present the distributions of the knowledge stock for different parameters (α,ß). The findings indicate that our proposed models could be helpful for deeply understanding the scientific research cooperation.