Oriented liquid crystal networks (LCNs) can undergo reversible shape change at the macroscopic scale upon an order–disorder phase transition of the mesogens. This property is explored for developing ...soft robots that can move under external stimuli, such as light in most studies. Herein, electrically driven soft robots capable of executing various types of biomimetic locomotion are reported. The soft robots are composed of a uniaxially oriented LCN strip, a laminated Kapton layer, and thin resistive wires embedded in between. Taking advantage of the combined attributes of the actuator, namely, easy processing, reprogrammability, and reversible shape shift between two 3D shapes at electric power on and off state, the concept of a “Janus” soft robot is demonstrated, which is built from a single piece of the material and has two parts undergoing opposite deformations simultaneously under a uniform stimulation. In addition to complex shape morphing such as the movement of oarfish and sophisticated devices like self‐locking grippers, electrically powered “Janus” soft robots can accomplish versatile locomotion modes, including crawling on flat surfaces through body arching up and straightening down, crawling inside tubes through body stretching and contraction, walking like four‐leg animals, and human‐like two‐leg walking while pushing a load forward.
Soft robots based on liquid crystal polymers are built to possess two parts capable of simultaneous and opposite deformations upon an order–disorder phase transition. This design enables various electrically powered locomotion modes, including moving on flat surface through body arching up–straightening down, crawling in a tunnel‐like tube through body stretching–contraction, four‐leg walking, and two‐leg walking while pushing a load.
The ability to optically reconfigure an existing actuator of a liquid crystal polymer network (LCN) so that it can display a new actuation behavior or function is highly desired in developing ...materials for soft robotics applications. Demonstrated here is a powerful approach relying on selective polymer chain decrosslinking in a LCN actuator with uniaxial LC alignment. Using an anthracene‐containing LCN, spatially controlled optical decrosslinking can be realized through photocleavage of anthracene dimers under 254 nm UV light, which alters the distribution of actuation (crosslinked) and non‐actuation (decrosslinked) domains and thus determines the actuation behavior upon order‐disorder phase transitions. Based on this mechanism, a single actuator having a flat shape can be reconfigured in an on‐demand manner to exhibit reversible shape transformation such as self‐folding into origami three‐dimensional structures. Moreover, using a dye‐doped LCN actuator, a light‐fueled microwalker can be optically reconfigured to adopt different locomotion behaviors, changing from moving in the laser scanning direction to moving in the opposite direction.
Walk along: Selective polymer chain decrosslinking in a liquid crystal polymer network (LCN) actuator is demonstrated. Spatially controlled optical decrosslinking alters the distribution of the actuation (crosslinked) and non‐actuation (decrosslinked) domains, and determines the actuation behavior upon the order–disorder phase transition. By using a dye‐doped LCN actuator, a light‐fueled microwalker can be optically reconfigured.
Novel main‐chain liquid crystalline Diels—Alder dynamic networks (LCDANs) were prepared that exhibit unprecedented ease for actuator programming and reprocessing compared to existing liquid ...crystalline network (LCN) systems. Following cooling from 125 °C, LCDANs are deformed with aligned mesogens self‐locked at room temperature by slowly formed Diels–Alder (DA) bonds, which allows for the formation of solid 3D actuators capable of reversible shape change, and strip walker and wheel‐capable light‐driven locomotion upon either thermally or optically induced order–disorder phase transition. Any actuator can readily be erased at 125 °C and reprogrammed into a new one under ambient conditions. Moreover, LCDANs can be processed directly from melt (for example, fiber drawing) and from solution (for example, casting tubular actuators), which cannot be achieved with LCNs using exchangeable covalent bonds. The combined attributes of LCDANs offer significant progress toward developing easily programmable/processable LCN actuators.
Liquid crystalline dynamic networks can be shaped into 3D objects at room temperature while being stabilized by slowly formed Diels–Alder‐bonded (DA) cross‐links. The actuators demonstrate thermally or optically induced reversible shape change for the purpose of performing mechanical work or locomotion.
With the rapid development and extensive applications of phase-sensitive optical time-domain reflectometry to long distance pipeline safety monitoring, it is still challenging to find a very ...efficient way to achieve highly correct recognition and really deep understanding of physical events sensed in a wide dynamic environment, as the vibration signals usually exhibit non-linear and non-stationary characteristics caused by the complicated environments. In this paper, a dynamic time sequence recognition and knowledge mining method based on the hidden Markov models (HMMs) is proposed to solve this problem. First, local structure feature of the signal is extracted in multiple analysis domains in the time sequence order; and then the HMMs are trained, built, and used to mine the temporal evolution information and identify the sequential state process of typical events. The experimental results with real field test data show that the average recognition accuracy of this paper is as high as 98.2% for frequently encountered five typical events along buried pipelines. All the related performance metrics such as precision, recall, and F-score are better than those traditional machine learning methods such, RF, XGB, DT, and BN.
To better understand the behavior of attackers and describe the network state, we construct an LSTM-DT model for network security situation awareness, which provides risk assessment indicators and ...quantitative methods. This paper introduces the concept of attack probability, making prediction results more consistent with the actual network situation. The model is focused on the problem of the time sequence of network security situation assessment by using the decision tree algorithm (DT) and long short-term memory(LSTM) network. The biggest innovation of this paper is to change the description of the network situation in the original dataset. The original label only has attack and normal. We put forward a new idea which regards attack as a possibility, obtaining the probability of each attack, and describing the network situation by combining the occurrence probability and attack impact. Firstly, we determine the network risk assessment indicators through the dataset feature distribution, and we give the network risk assessment index a corresponding weight based on the analytic hierarchy process (AHP). Then, the stack sparse auto-encoder (SSAE) is used to learn the characteristics of the original dataset. The attack probability can be predicted by the processed dataset by using the LSTM network. At the same time, the DT algorithm is applied to identify attack types. Finally, we draw the corresponding curve according to the network security situation value at each time. Experiments show that the accuracy of the network situation awareness method proposed in this paper can reach 95%, and the accuracy of attack recognition can reach 87%. Compared with the former research results, the effect is better in describing complex network environment problems.
Two-dimensional (2D) materials have attracted tremendous research interest since the breakthrough of graphene. Their unique optical, electronic, and mechanical properties hold great potential for ...harnessing them as key components in novel applications for electronics and optoelectronics. Their atomic thickness and exposed huge surface even make them highly designable and manipulable, leading to the extensive application potentials. What’s more, after acquiring the qualification for being the candidate for next-generation devices, the assembly of 2D materials monomers into mass or ordered structure is also of great importance, which will determine their ultimate industrialization. By designing the monomers and regulating their assembling behavior, the exploration of 2D materials toward the next-generation circuits can be spectacularly achieved. In this review, we will first overview the emerging 2D materials and then offer a clear guideline of varied physical and chemical strategies for tuning their properties. Furthermore, assembly strategies of 2D materials will also be included. Finally, challenges and outlooks in this promising field are featured on the basis of its current progress.
Aims
Sonic hedgehog subtype medulloblastoma is featured with overactivation of hedgehog pathway and can be targeted by SMO‐specific inhibitors. However, the resistance is frequently developed leading ...to treatment failure of SMO inhibitors. W535L mutation of SMO (SMOW535L) is thought to be an oncogenic driver for Sonic hedgehog subtype MB and confer resistance to SMO inhibitors. The regulation network of SMOW535L remains to be explored in comparison with wild‐type SMO (SMOWT).
Methods
In this study, we profiled transcriptomes, methylomes, and interactomes of MB cells expression SMOWT or SMOW535L in the treatment of DMSO or SMO inhibitor, respectively.
Results
Analysis of transcriptomic data indicated that SMO inhibitor disrupted processes of endocytosis and cilium organization in MB cells with SMOWT, which are necessary for SMO activation. In MB cells with SMOW535L, however, SMO inhibitor did not affect the two processes‐related genes, implying resistance of SMOW535L toward SMO inhibitor. Moreover, we noticed that SMO inhibitor significantly inhibited metabolism‐related pathways. Our metabolic analysis indicated that nicotinate and nicotinamide metabolism, glycerolipid metabolism, beta‐alanine metabolism, and synthesis and degradation of ketone bodies might be involved in SMOW535L function maintenance. Interactomic analysis revealed casein kinase II (CK2) as an important SMO‐associated protein. Finally, we linked CK2 and AKT together and found combination of inhibitors targeting CK2 and AKT showed synergetic effects to inhibit the growth of MB cells with SMO constitutive activation mutation.
Conclusions
Taken together, our work described SMO‐related transcriptomes, metabolomes, and interactomes under different SMO status and treatment conditions, identifying CK2 and AKT as therapeutic targets for SHH‐subtype MB cells with SMO inhibitor resistance.
This work profiled transcriptomes, metabolomes, and interactomes of wild‐type and constitutively activated SMO and revealed CK2 and AKT as therapeutic targets for SHH‐subtype MB cells with SMO inhibitor resistance.
Transition‐metal alloys are currently drawing increasing attention as promising electrocatalysts for the alkaline hydrogen evolution reaction (HER). However, traditional ...density‐functional‐theory‐derived d‐band theory fails to describe the hydrogen adsorption energy (ΔGH) on hollow sites. Herein, by studying the ΔGH for a series of Ni−M (M=Ti, V, Cr, Mn, Fe, Co, Cu, Zn, Mo, W) bimetallic alloys, an improved d‐band center was provided and a potential NiCu electrocatalyst with a near‐optimal ΔGH was discovered. Moreover, oxygen atoms were introduced into Ni−M (O−NiM) to balance the adsorption/desorption of hydroxyl species. The tailored electrocatalytic sites for water dissociation can synergistically accelerate the multi‐step alkaline HER. The prepared O−NiCu shows the optimum HER activity with a low overpotential of 23 mV at 10 mA cm−2. This work not only broadens the applicability of d‐band theory, but also provides crucial understanding for designing efficient HER electrocatalysts.
An improved d‐band model is presented as a valid descriptor to reflect hollow‐site adsorption and hydrogen evolution reaction (HER) activity for multi‐elemental alloy catalysts. NiCu was found to have a near‐optimal ΔGH value and the introduction of O atoms into the Ni−Cu surface could accelerate water dissociation. Tailoring electrocatalytic sites for water dissociation can synergistically complete the multi‐step reactions of alkaline HER.
The aim of this paper is to provide a methodology to hesitant fuzzy multiple attribute decision making using technique for order preference by similarity to ideal solution (TOPSIS) and distance ...measures. Firstly, the inadequacies of the existing hesitant fuzzy TOPSIS method are analyzed in detail. Then, based on the developed hesitant fuzzy ordered weighted averaging weighted aver-aging distance (HFOWAWAD) measure, a modified hesitant fuzzy TOPSIS, called HFOWAWAD-TOPSIS is introduced for hesitant fuzzy multiple attribute decision making problems. Moreover, the advantages and some special cases of the HFOWAWAD-TOPSIS are presented. Finally, a numerical example about energy policy selection is provided to illustrate the practicality and feasibility of the developed approach.
The central issue in finance is how to select a portfolio in the financial market. The traditional artificial fish swarm algorithm (AFSA) is optimized in this paper, and the improved AFSA is used to ...solve the portfolio model. This model generates a uniform distribution operator using uniform distribution and combines it with the basic fish swarm algorithm. Uniform variation occurs when the variance of the optimal value of continuous convergence is within the allowable error. In this manner, the fish can escape the trap of the local extremum, obtaining the global optimal state. To validate the feasibility of improving AFSA, this paper conducts simulation experiments on portfolio problems using MATLAB tools. Experiments show that this model has an accuracy of 93.56 percent, which is 8.43 percent higher than that of the NSGA-II model and 3.76 percent higher than that of the multiobjective optimization model. The experiment shows that the algorithm in this paper can solve these types of problems well and that, using this model, the optimal portfolio investment decision scheme satisfying investors can be obtained. The optimized AFSA presented in this paper can serve as an important reference for investment portfolios and has a wide range of application possibilities in the investment market.