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The hydrogenation of carbon dioxide (CO2) to produce light olefins is one of the most promising ways to utilize CO2 in power plant flue gas. However, the low concentration of CO2 (~10%) and the existence of water steam in the flue gas pose great challenges for the catalyst design. To address these problems, we introduced a Mg promoter and hydrophobic component into the Fe-based catalyst to improve the CO2 adsorption capacity and weaken the negative effects of water. The yield of light olefins on an optimized multifunctional Fe-based catalyst increased by 37% in low-concentration CO2 hydrogenation with water steam. A variety of characterizations proved that the Mg promoter played critical roles in regulating the adsorption capacity of CO2, increasing the surface electron density of Fe species, and promoting the formation of iron carbide active sites. The hydrophobic component mainly contributed to constraining the oxidation of iron carbides via water steam. It benefited from the rational design of the catalyst, showing how our multifunctional Fe-based catalyst has great potential for practical application in CO2 utilization. [ABSTRACT FROM AUTHOR]

In this article, a robust index named first-order network coherence (FONC) for the multi-agent systems (MASs) with layered lattice-like structure is studied via the angle of the graph spectra theory. The union operation of graphs is utilized to construct two pairs of non-isomorphic layered lattice-like structures, and the expression of the index is acquired by the approach of Laplacian spectra, then the corresponding asymptotic results are obtained. It is found that when the cardinality of the node sets of coronary substructures with better connectedness tends to infinity, the FONC of the whole network will have the same asymptotic behavior with the central lattice-like structure in the considered classic graph frameworks. The indices of the networks were simulated to illustrate the the asymptotic results, as described in the last section. [ABSTRACT FROM AUTHOR]

The torsional stiffness of the reducer is commonly assumed to be static and tested using the hysteresis curve method. However, practical engineering experience shows that the torsional stiffness is dynamic rather than static, and the hysteresis curve method cannot accurately represent the reducer's torsional stiffness during actual operation. To address these issues, this study conducts theoretical research on the dynamic characteristics of torsional stiffness, introduces a new concept called dynamic torsional stiffness, and provides its analytical formula. A dynamic torsional stiffness testing method based on the transmission error method is proposed, and its feasibility and consistency with the hysteresis curve method are theoretically demonstrated. This method allows the torsional stiffness information of the reducer to be obtained under all operating conditions. Through experimental research, the limitations of the hysteresis curve method are explained, and the dynamic characteristics of the reducer's torsional stiffness, along with the effectiveness of the transmission error method, are verified. Consequently, the torsional stiffness information of the reducer under all operating conditions is obtained. In conclusion, this method is valuable for both theoretical and engineering applications, as it offers a more accurate representation of the dynamic torsional stiffness of the reducer in real-world scenarios. [ABSTRACT FROM AUTHOR]

This work studies the first-order coherence of noisy multi-agent networks with multi-layered structures. The coherence, which is a sort of performance index of networks, can be seen as a sort of measurement for a system's robustness. Graph operations are applied to design the novel multi-layered networks, and a graph spectrum approach, along with analysis methods, is applied to derive the mathematical expression of the coherence, and the corresponding asymptotic results on the performance index have been obtained. In addition, the coherence of these non-isomorphic multi-layered networks with three different graph parameters are compared and analyzed. We find that, when the cardinalities of the vertex sets of corresponding counterpart layers are the same, the multi-layered topology class with a balanced, complete, multi-partite structure has the best robustness of all the considered networks, if the sufficient conditions for the node-related parameters hold. Finally, simulations are given to verify the asymptotic results. [ABSTRACT FROM AUTHOR]

This study concentrates on a fixed-time distributed optimization problem for multi-agent systems (MASs) with input delay and external disturbances. First, by adopting the Artstein model reduction technique, the time-delay system is first transformed into a delay-free one, and external disturbances are then effectively eliminated by using an integral sliding mode control strategy. Second, a new centralized optimization mechanism is developed that allows all agents to reach the same state in a fixed time and then converge to the optimal value of the global objective function. Meanwhile, the optimization problem is extended to switching topologies. Moreover, as the gradient information of the global objective function is difficult to obtain in advance, we construct a decentralized optimization protocol that enables all agents to acquire the same state in a certain amount of time while also optimizing the global optimization problem. Finally, two numerical simulations are presented to validate the effectiveness and reliability of the developed control strategy. [ABSTRACT FROM AUTHOR]

The propagation of rumors on online social networks (OSNs) brings an awful lot of trouble to people's life and society. Aiming at combating rumors spreading on OSNs, two novel rumor-propagation models without and with time delays are proposed, which combine with the influence of the immune mechanism, isolation mechanism and network structure. Firstly, we analyze the existence of rumor equilibria and obtain some existence conditions of backward bifurcation. Secondly, the local stabilities of rumor-free and rumor equilibria are proved by using the Jacobian matrix method, and some critical conditions for the existence of Hopf bifurcation are acquired by selecting critical parameters and delays as bifurcation parameters. Furthermore, an optimal control method is proposed, which can prevent the spread of rumors within an expected time period and minimize the cost of control. Finally, some numerical simulations are provided to verify the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies a generalized distributed optimization problem for second-order multi-agent systems (MASs) over directed networks. Firstly, an improved distributed continuous-time algorithm is proposed. By using the linear transformation method and Lyapunov stability theory, some conditions are obtained to guarantee all agents' states asymptotically reach the optimal solution. Secondly, to reduce unnecessary communication transmission and control cost, an event-triggered algorithm is designed. Moreover, the convergence of the algorithm is proved, and the Zeno behavior can be avoided based on strict theoretic analysis. Finally, one example is given to verify the good performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This article mainly studies first-order coherence related to the robustness of the triplex MASs consensus models with partial complete graph structures; the performance index is studied through algebraic graph theory. The topologies of the novel triplex networks are generated by graph operations and the approach of graph spectra is applied to calculate the first-order network coherence. The coherence asymptotic behaviours of the three cases of the partial complete structures are analysed and compared. We find that under the condition that the number of nodes in partial complete substructures n tends to infinity, the coherence asymptotic behaviour of the two sorts of non-isomorphic three-layered networks will be increased by r − 1 2 (r + 1) , which is irrelevant to the peripheral vertices number p; when p tends to infinity, adding star copies to the original triplex topologies will reverse the original size relationship of the coherence under consideration of the triplex networks. Finally, the coherence of the three-layered networks with the same sorts of parameters, but non-isomorphic graphs, are simulated to verify the results. [ABSTRACT FROM AUTHOR]

The diversification of Tiliacoreae and the speciation of Eleutharrhena are closely linked to Southeast Asia's most northerly rainforests which originate from the Himalayan uplift. Migration routes across biogeographical zones within the Asian clade, including those of Eleutharrhena, Pycnarrhena, and Macrococculus, and their population structures are still unexplored. We combine endocarp morphology, phylogenetic analyses, divergence time estimation, ancestral area reconstruction, as well as SCoT method to reconstruct the past diversification of Eleutharrhena macrocarpa and to understand their current distribution, rarity, and evolutionary distinctiveness. The disjunct, monospecific, and geographically restricted genera Eleutharrhena and Macrococculus both have a dry aril, a unique feature in Menispermaceae endocarps that further confirms their close relationship. Pycnarrhena and Eleutharrhena appeared during the end of the Oligocene c. 23.10 million years ago (Mya) in Indochina. Eleutharrhena speciation may be linked to climate change during this time, when humid forests became restricted to the northern range due to the Himalayan uplift. Differentiation across the Thai–Burmese range could have contributed to the isolation of the Dehong populations during the Miocene c. 15.88 Mya, when exchange between India and continental Asia ceased. Dispersal to the Lanping–Simao block and further differentiation in southeastern and southern Yunnan occurred during the Miocene, c. 6.82 Mya. The specific habitat requirements that led to the biogeographic patterns observed in E. macrocarpa contributed to a low genetic diversity overall. Population 1 from Dehong, 16 from Pu'er, and 20 from Honghe on the East of the Hua line have a higher genetic diversity and differentiation; therefore, we suggest that their conservation be prioritized. [ABSTRACT FROM AUTHOR]

In this paper, the leader-following consensus problem of first-order nonlinear multi-agent systems (FONMASs) with external disturbances is studied. Firstly, a novel distributed fixed-time sliding mode manifold is designed and a new static event-triggered protocol over general directed graph is proposed which can well suppress the external disturbances and make the FONMASs achieve leader-following consensus in fixed-time. Based on fixed-time stability theory and inequality technique, the conditions to be satisfied by the control parameters are obtained and the Zeno behavior can be avoided. In addition, we improve the proposed protocol and propose a new event-triggering strategy for the FONMASs with multiple leaders. The systems can reach the sliding mode surface and achieve containment control in fixed-time if the control parameters are designed carefully. Finally, several numerical simulations are given to show the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

The system model on synchronizability problem of complex networks with multi-layer structure is closer to the real network than the usual single-layer case. Based on the master stability equation (MSF), this paper studies the eigenvalue spectrum of two k-layer variable coupling windmill-type networks. In the case of bounded and unbounded synchronization domain, the relationships between the synchronizability of the layered windmill-type networks and network parameters, such as the numbers of nodes and layers, inter-layers coupling strength, are studied. The simulation of the synchronizability of the layered windmill-type networks are given, and they verify the theoretical results well. Finally, the optimization schemes of the synchronizability are given from the perspective of single-layer and multi-layer networks, and it was found that the synchronizability of the layered windmill-type networks can be improved by changing the parameters appropriately. [ABSTRACT FROM AUTHOR]

In this article, the consensus-related performances of the triplex multi-agent systems with star-related structures, which can be measured by the algebraic connectivity and network coherence, have been studied by the characterization of Laplacian spectra. Some notions of graph operations are introduced to construct several triplex networks with star substructures. The methods of graph spectra are applied to derive the network coherence, and some asymptotic behaviors of the indices have been derived. It is found that the operations of adhering star topologies will make the first-order coherence increase a constant value under the triplex structures as parameters tend to infinity, and the second-order coherence have some equality relations as the node related parameters tend to infinity. Finally, the consensus related indices of the triplex systems with the same number of nodes but non-isomorphic graph structures have been compared and simulated to verify the results. [ABSTRACT FROM AUTHOR]

Semantic segmentation of room maps is an essential issue in mobile robots' execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943. [ABSTRACT FROM AUTHOR]

This study aimed at the shortcomings of existing fixation algorithms that are image-based only, and an effective tea fixation state monitoring algorithm was proposed. An adaptive filtering algorithm was used to automatically filter the ineffective information. Using the energy extractor, the complete energy information of each fixation image was extracted. The image energy attention mechanism was used to identify the prominent features, and based on these, the energy data was mapped to generate the data points as the training data. The cluster idea was adopted, and the training data feed the features trainer. The trend center data of the tea processing energy clustering was generated from different color channels. The corresponding decision function was designed which is based on the distance of the cluster center. The fixation degree of each monitoring image set was measured by the decision function. The Euclidean distance of the energy clustering center of the three channels with the same fixation time progressively approached. The triangle formed by these three points had a trend of gradually shrinking, which was first discovered by us. The detection results showed high accuracy compared with the common classification algorithms. It indicates that the algorithm proposed has positive guiding and reference significance. [ABSTRACT FROM AUTHOR]

The foot arch index is an important index to evaluate the health of human feet and the biomechanics line, aiming at addressing the shortcomings of the low efficiency and slow speed of manual foot arch index measurement; in this work, an automatic foot arch index measurement method based on a flexible membrane pressure sensor was proposed. The distribution of plantar pressure data was obtained from the flexible membrane pressure sensor and converted into a digital image. The 8-neighborhood correlation pixel method was proposed to remove the interference of isolated noise points. In order to remove the toes' data without affecting the foot sole data, the row element association algorithm was proposed. The front and back endpoints of the foot were automatically located to obtain the foot length, and the foot arch index was also automatically obtained based on the foot arch pressure area. Whether it was a high arch foot, flat foot or normal foot, the method proposed in this paper could accurately and quickly distinguish them. The prototype was developed, and its feasibility and validity were verified by a series of tests. [ABSTRACT FROM AUTHOR]

Virtual-force algorithms (VFAs) have been widely studied for accurate node deployment in wireless-sensor-network (WSN) applications. Their main purpose is to achieve the maximum coverage area with the minimum number of sensor nodes in the target area. Recently, we reported a new VFA based on virtual spring force (VFA-SF) and discussed in detail the corresponding efficiency via statistical analysis. The optimized strategy by adding an external central force (VFA-SF-OPT) was presented, which effectively eliminates the coverage hole or twisted structure in the final network distribution. In this paper, the parameter effects on VFA-SF and the VFA-SF-OPT were further investigated: (1) Node velocity dramatically affects the convergence rate of the node-deployment process. (2) A suitable external central force improves equilibrium distance and reduces energy consumption. (3) The effects of VFA-SF and VFA-SF-OPT for different types of obstacles are discussed. Generally, by choosing suitable parameters, both VFA-SF and VFA-SF-OPT can effectively improve node deployment and energy consumption for the whole sensor network. The results give important insight in parameter selection and information fusion in the application of a large-scale WSN. [ABSTRACT FROM AUTHOR]

How to effectively deploy all wireless sensors and save a system's energy consumption is a key issue in current wireless sensor network (WSN) applications. Theoretical analysis has proven that a hexagonal structure is the best topology in the two-dimensional network, which can provide the maximum coverage area with the minimum number of sensor nodes and minimum energy consumption. Recently, many scientists presented their self-deployment strategies based on different virtual forces and discussed the corresponding efficiency via several case studies. However, according to our statistical analysis, some virtual force algorithms, e.g., virtual spring force, can still cause holes or twisted structure in a small region of the final network distribution, which cannot achieve the ideal network topology and will waste the system energy in real applications. In this paper, we first statistically analyzed the convergence and deployment effect of the virtual spring force algorithm to derive our question. Then we presented an optimized strategy that sensor deployment begins from the center of the target region by adding an external central force. At the early stage, the external force will be added to the most peripheral nodes to promote the formation of hexagonal topology and avoid covering holes or unusual structure. Finally, a series of independent simulation experiments and corresponding statistical results proved that our optimized deployment solution is very stable and effective, which can improve the energy consumption of the whole sensor network and be used in the application of a large scale WSN. [ABSTRACT FROM AUTHOR]

Co-doped Li2MoO3 was successfully synthesized via a solid phase method. The impacts of Co-doping on Li2MoO3 have been analyzed by X-ray photoelectron spectroscopy (XPS), X-ray powder diffraction (XRD), scanning electron microscope (SEM), and Fourier transform infrared spectroscopy (FTIR) measurements. The results show that an appropriate amount of Co ions can be introduced into the Li2MoO3 lattices, and they can reduce the particle sizes of the cathode materials. Electrochemical tests reveal that Co-doping can significantly improve the electrochemical performances of the Li2MoO3 materials. Li2Mo0.90Co0.10O3 presents a first-discharge capacity of 220 mAh·g−1, with a capacity retention of 63.6% after 50 cycles at 5 mA·g−1, which is much better than the pristine samples (181 mAh·g−1, 47.5%). The enhanced electrochemical performances could be due to the enhancement of the structural stability, and the reduction in impedance, due to the Co-doping. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments. [ABSTRACT FROM AUTHOR]

This work studies the first-order coherence of noisy multi-agent networks with multi-layered structures. The coherence, which is a sort of performance index of networks, can be seen as a sort of measurement for a system's robustness. Graph operations are applied to design the novel multi-layered networks, and a graph spectrum approach, along with analysis methods, is applied to derive the mathematical expression of the coherence, and the corresponding asymptotic results on the performance index have been obtained. In addition, the coherence of these non-isomorphic multi-layered networks with three different graph parameters are compared and analyzed. We find that, when the cardinalities of the vertex sets of corresponding counterpart layers are the same, the multi-layered topology class with a balanced, complete, multi-partite structure has the best robustness of all the considered networks, if the sufficient conditions for the node-related parameters hold. Finally, simulations are given to verify the asymptotic results.

The most potent catechin in green tea is (-)-epigallocatechin-3-gallate [(-)-EGCG], which, however, is unstable under physiological conditions. To discover more stable and more potent polyphenol proteasome inhibitors, we synthesized several novel fluoro-substituted (-)-EGCG analogs, named F-EGCG analogs, as well as their prodrug forms with all of -OH groups protected by acetate. We report that the prodrug form of one F-EGCG analog exhibited greater potency than the previously reported peracetate of (-)-EGCG to inhibit proteasomal activity, suppress cell proliferation, and induce apoptosis in human leukemia Jurkat T cells, demonstrating the potential of these compounds to be developed into novel anti-cancer and cancer-preventive agents.