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Spiking neural P systems (SN P systems) refer to a type of distributed parallel neural computation model in the membrane computing framework. In this work, a new type of SN P system is examined, i.e., spiking neural P systems with polarizations (PSN P systems), by taking inspiration from the polarized cell membrane of a neuron. For the PSN P system, the firing condition of rules is the neuron-associated polarization. This work focuses on examining the computation power of the PSN P systems adopting the sequential mode induced by the minimum spike number, where at a computation step, one (resp., all) of neurons that hold the minimum spike number within the neurons which can fire will fire, i.e., the min-sequentiality strategy (resp., min-pseudo-sequentiality strategy). We prove that PSN P systems adopting the min-sequentiality strategy or min-pseudo-sequentiality strategy are Turing universal as the number generating devices. These results indicate the computation power of PSN P systems is robust regarding their strategies of sequentiality.

Toehold-mediated DNA strand displacement endows DNA nanostructures with dynamic response capability. However, the complexity of sequence design dramatically increases as the size of the DNA network increases. We attribute this problem to the mechanism of toehold-mediated strand displacement, termed exact strand displacement (ESD), in which one input strand corresponds to one specific substrate. In this work, we propose an alternative to toehold-mediated DNA strand displacement, termed fuzzy strand displacement (FSD), in which one-to-many and many-to-one relationships are established between the input strand and the substrate, to reduce the complexity. We have constructed four modules, termed converter, reporter, fuzzy detector, and fuzzy trigger, and demonstrated that a sequence pattern recognition network composed of these modules requires less complex sequence design than an equivalent network based on toehold-mediated DNA strand displacement.

Cell-like P systems where communication between the regions are carried out by rules of type symport/antiport are considered. These systems compute by changing the places of objects with respect to the membranes, and not by changing the objects themselves. The environment plays an active role in the sense that it not only can receive objects from the system, but also send objects into it. There is an alphabet associated with the environment whose elements appear in an arbitrary large number of copies at the initial configuration. This property seems too strong from a complexity view, but it has been widely exploited in the design of efficient solutions to computationally hard problems when some mechanisms (inspired by mitosis and membrane fission) allowing to construct an exponential workspace in linear time, are considered. In this paper, complexity aspects of P systems with symport/antiport rules and membrane division are considered when the set associated with the environment is the emptyset. It is shown that the role of the environment is irrelevant for such kind of P systems, in contrast with the well known results concerning to its relevance when membrane separation is used instead of membrane division.

It is widely observed that life activities are regulated through conformational transitions of biological macromolecules, which inspires the construction of environmental responsive nanomachines in recent years. Here we present a thermal responsive DNA origami dimers system, whose conformations can be cyclically switched by thermal cycling. In our strategy, origami dimers are assembled at high temperatures and disassembled at low temperatures, which is different from the conventional strategy of breaking nanostructures using high temperatures. The advantage of this strategy is that the dimers system can be repeatedly operated without significant performance degradation, compared to traditional strategies such as conformational transitions via i-motif and G-quadruplexes, whose performance degrades with sample dilution due to repeated addition of trigger solutions. The cyclic conformational transitions of the dimers system are verified by fluorescence curves and AFM images. This research offered a new way to construct cyclic transformational nanodevices, such as reusable nanomedicine delivery systems or nanorobots with long service lifetimes.

Large-scale multiobjective optimization problems (LSMOPs) are challenging for existing approaches due to the complexity of objective functions and the massive volume of decision space. Some large-scale multiobjective evolutionary algorithms (LSMOEAs) have recently been proposed, which have shown their effectiveness in solving some benchmarks and real-world applications. They merely focus on handling the massive volume of decision space and ignore the complexity of LSMOPs in terms of objective functions. The complexity issue is also important since the complexity grows along with the increment in the number of decision variables. Our previous study proposed a framework to accelerate evolutionary large-scale multiobjective optimization via problem reformulation for handling large-scale decision variables. Here, we investigate the effectiveness of LSMOF combined with decomposition-based MOEA (MOEA/D), aiming to handle the complexity of LSMOPs in both the decision and objective spaces. Specifically, MOEA/D is embedded in LSMOF via two different strategies, and the proposed algorithm is tested on various benchmark LSMOPs. Experimental results indicate the encouraging performance improvement benefited from the solution of the complexity issue in large-scale multiobjective optimization.

This paper proposes a novel connected components labeling (CCL) approach that introduces a gamma signal to record certain mask pixels’ values to eliminate duplicated pixel checking and regulate the labeling process for higher efficiency. A new block-based two-scan CCL algorithm, Eight-Connected Gamma-Signal-regulated (ECGS) algorithm, is designed and developed by applying this approach to evaluate a block of 2 × 2 pixels (with just 6 mask pixels) in each iteration such that the total number of operations is considerably reduced and the labeling efficiency is significantly improved. The experiments conducted on a public benchmark, YACCLAB (Yet Another Connected Components Labeling Benchmark), have demonstrated that the proposed ECGS algorithm can outperform current state-of-the-art CCL algorithms for a number of digital images.

Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning functions, is introduced. Such systems can dynamically strengthen and weaken connections among neurons during the computation. A class of specific SN P systems with simple Hebbian learning function is constructed to recognize English letters. The experimental results show that the SN P systems achieve average accuracy rate 98.76% in the test case without noise. In the test cases with low, medium, and high noises, the SN P systems outperform back propagation neural networks and probabilistic neural networks. Moreover, comparing with spiking neural networks, SN P systems perform a little better in recognizing letters with noise. The result of this paper is promising in terms of the fact that it is the first attempt to use SN P systems in pattern recognition after many theoretical advancements of SN P systems, and SN P systems exhibit the feasibility for tackling pattern recognition problems.

Conventional switching ripple suppressor design methods require the domain expert to tune the design parameters, which is attributed to the multiple and complicated performance requirements and real-world restrictions in switching ripple suppressor design. However, the expert tuning method is time-consuming and inefficient, and the designed switching ripple suppressors under the given topology can only achieve a barely satisfactory performance which can be far from the optima. In this study, we propose a systematic design method from the perspective of many-objective optimization with constraints handling for high-performance switching ripple suppressor design. Meanwhile, an inductor-trap-inductor-capacitor-inductor (LTLCL) switching ripple suppressor is proposed for attenuating the harmonic currents around the multiple switching frequencies and guaranteeing − 60 dB/decades attenuation in the high-frequency bands. In the proposed design method, the LTLCL switching ripple suppressor design problem is treated as a many-objective optimization problem with constraints, and an evolutionary algorithm is designed to solve this problem, furthermore, a general decision making method is used to select the final design scheme. Experimental results have verified the admirable performance of the proposed switching ripple suppressor and the effectiveness of our design method.

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization.

In nature, allostery is the principal approach for regulating cellular processes and pathways. Inspired by nature, structure-switching aptamer-based nanodevices are widely used in artificial biotechnologies. However, the canonical aptamer structures in the nanodevices usually adopt a duplex form, which limits the flexibility and controllability. Here, a new regulating strategy based on a clamp-like triplex aptamer structure (CLTAS) was proposed for switching DNA polymerase activity via conformational changes. It was demonstrated that the polymerase activity could be regulated by either adjusting structure parameters or dynamic reactions including strand displacement or enzymatic digestion. Compared with the duplex aptamer structure, the CLTAS possesses programmability, excellent affinity and high discrimination efficiency. The CLTAS was successfully applied to distinguish single-base mismatches. The strategy expands the application scope of triplex structures and shows potential in biosensing and programmable nanomachines.

Asynchronous tissue P systems with symport/antiport rules are a class of parallel computing models inspired by cell tissue working in a non-synchronized way, where the use of rules is not obligatory, that is, at a computation step, an enabled rule may or may not be applied. In this work, the notion of local synchronization is introduced at three levels: rules, channels, and cells. If a rule in a locally synchronous set of rules (resp., cells or channels) is used, then all enabled rules in the same locally synchronous set of rules (resp., whose involved channels or cells) should be applied in a maximally parallel manner and the implementation of these rules is finished in one computation step. The computational power of local synchronization on asynchronous tissue P systems with symport/antiport rules at the three levels is investigated. It is shown that asynchronous tissue P systems with symport/antiport rules and with locally synchronous sets of rules, channels, or cells are all Turing universal. By comparing the computational power of asynchronous tissue P systems with or without local synchronization, it can be found that the local synchronization is a useful tool to achieve a desired computational power.

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this paper, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insight into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness and applicability to practical tasks such as acoustic and visual pattern recognition. The remarkable results highlight the effectiveness and potential merits of our methods. Importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them including neuromorphic computing., Comment: 13 pages