Networks with time structure from time series

We propose a method of constructing a network, in which its time structure is directly incorporated, based on a deterministic model from a time series. To construct such a network, we transform a linear model containing terms with different time delays into network topology. The terms in the model are translated into temporal nodes of the network. On each link connecting these nodes, we assign a positive real number representing the strength of relationship, or the “distance,” between nodes specified by the parameters of the model. The method is demonstrated by a known system and applied to two actual time series.     

Transition from the self-organized to the driven dynamical clusters

We study the mechanism of formation of synchronized clusters in coupled maps on networks with various connection architectures. The nodes in a cluster are self-synchronized or driven-synchronized, based on the coupling strength and underlying network structures. A smaller coupling strength region shows driven clusters independent of the network rewiring strategies, whereas a larger coupling strength region shows the transition from the self-organized cluster to the driven cluster as network connections are rewired to the bi-partite type. Lyapunov function analysis is performed to understand the dynamical origin of cluster formation. The results provide insights into the relationship between the topological clusters which are based on the direct connections between the nodes, and the dynamical clusters which are based on the functional behavior of these nodes.     

模糊测度在山区开挖岩体移动分析中的应用

将山区地下开挖所引起的上覆岩土体的移动视为一模糊事件,应用模糊数学中的模糊测度理论,推导了相应的地表下沉及水平移动的理论计算公式,对煤矿开采所引起的地表移动进行了计算分析。通过工程实例计算分析表明,理论计算结果与工程实测资料吻合的很好。     

Mechano-Activated Self-Immolation of Hydrogels via Signal Amplification

Cellular organisms possess intricate mechano-adaptive systems that enable them to sense forces and process them with (bio)chemical circuits for functional adaptation. Inspired by such processes, this study introduces a hydrogel system capable of mechanically activated and chemically transduced self-destruction. Our judiciously designed hydrogels can mechanically generate radicals that are processed and amplified in a self-propagating radical de-crosslinking reaction, ultimately leading to mechanically triggered self-immolation. We put such systems to work in mechano-induced debonding, and in a bilayer actuator, where swelling-induced bending generates sufficient force for selective degradation of one layer, leading to autonomous self-regulation associated with unbending. Our work helps define design criteria for molecularly controlled adaptive and self-regulating materials with embodied mechano-chemical information processing, and showcases their potential for adhesives and soft robotics.     

Self-Growing Organic Materials

The growth of living systems is ubiquitous. Living organisms can continually update their sizes, shapes, and properties to meet various environmental challenges. Such a capability is also demonstrated by emerging self-growing materials that can incorporate externally provided compounds to grow as living organisms. In this Minireview, we summarize these materials in terms of six aspects. First, we discuss their essential characteristics, then describe the strategies for enabling crosslinked organic materials to self-grow from nutrient solutions containing polymerizable compounds. The developed examples are grouped into five categories based on their molecular mechanisms. We then explain the mechanism of mass transport within polymer networks during growth, which is critical for controlling the shape and morphology of the grown products. Afterwards, simulation models built to explain the interesting phenomena observed in self-growing materials are discussed. The development of self-growing materials is accompanied by various applications, including tuning bulk properties, creating textured surfaces, growth-induced self-healing, 4D printing, self-growing implants, actuation, self-growing structural coloration, and others. These examples are then summed up. Finally, we discuss the opportunities brought by self-growing materials and their facing challenges.     

Evaluation of the effects of the presence of ZnO -TiO2 (50 %–50 %) on the thermal conductivity of Ethylene Glycol base fluid and its estimation using Artificial Neural Network for industrial and commercial applications

In this study, the thermal conductivity (k_nf) of ZnO -TiO₂ (50 %–50 %)/ Ethylene Glycol hybrid nanofluid using Artificial Neural Networks (ANNs) was predicted. The nanofluid was prepared at different volume fractions (φ) of nanoparticles (φ = 0.001 to 0.035) and temperatures (T = 25 to 50 °C). In this study, an algorithm is presented to find the best neuron number in the hidden layer. Also, a surface fitting method has been applied to predict the k_nf of nanofluid. Finally, the correlation coefficients, performances, and Maximum Absolute Error (MAE) for both methods have been presented and compared. It could be understood that the ANN method had a better ability in predicting the k_nf of nanofluid compared to the fitting method. This method not only showed better performance but also reached a better MAE and correlation coefficient.     

Controlled Diels–Alder “Click” Strategy to Access Mechanically Aligned Main-Chain Liquid Crystal Networks

Aligned liquid crystal polymers are materials of interest for electronic, optic, biological and soft robotic applications. The manufacturing and processing of these materials have been widely explored with mechanical alignment establishing itself as a preferred method due to its ease of use and widespread applicability. However, the fundamental chemistry behind the required two-step polymerization for mechanical alignment has limitations in both fabrication and substrate compatibility. In this work we introduce a new protection-deprotection approach utilizing a two-stage Diels–Alder cyclopentadiene-maleimide step-growth polymerization to enable mild yet efficient, fast, controlled, reproducible and user-friendly polymerizations, broadening the scope of liquid crystal systems. Thorough characterization of the films by DSC, DMA, POM and WAXD show the successful synthesis of a uniaxially aligned liquid crystal network with thermomechanical actuation abilities.     

Data Rate Aware Reliable Transmission Mechanism in Wireless Sensor Networks using Bayesian Regularized Neural Network approach

Wireless Sensor Networks consists of interconnected nodes that exchange information wirelessly enabling its deployment in innovative application areas. Network reliability streamline this exchange of information and communication technology to improve the overall performance of the network. The network reliability is categorized as: node reliability and link reliability. In this article, the node reliability is quantified with the help of packet reliability. The packet reliability is enhanced by optimizing the data rate using Bayesian Regularized Neural Network approach, which thus makes the network more reliable and sustainable. The optimization is carried out in three phases: network designing, data rate prediction and reliability evaluation. The network design includes the deployment of sensor nodes for gathering the communication data using NS-2.35. In the next phase, data rate prediction is carried out to enhance the reliability of the network. The reliability of a network is directly influenced by the packet loss ratio. According to research and the network experts, the acceptable threshold limit for the packet loss ratio is 5 percent. The data rate prediction is carried out to minimize the packet loss using the Bayesian Regularized Neural Network algorithm. The packet reliability is measured in terms of packet loss across the wireless network. Finally, a novel framework is presented for evaluating the packet reliability of the wireless network.     

Computing Arithmetic Functions Using Immobilised Enzymatic Reaction Networks

Living systems use enzymatic reaction networks to process biochemical information and make decisions in response to external or internal stimuli. Herein, we present a modular and reusable platform for molecular information processing using enzymes immobilised in hydrogel beads and compartmentalised in a continuous stirred tank reactor. We demonstrate how this setup allows us to perform simple arithmetic operations, such as addition, subtraction and multiplication, using various concentrations of substrates or inhibitors as inputs and the production of a fluorescent molecule as the readout.