@article {2527,
	title = {Generating, computing and recognizing with virus machines},
	journal = {Theoretical Computer Science},
	volume = {972},
	year = {2023},
	month = {07/2023},
	pages = {114077},
	publisher = {Springer},
	doi = {https://doi.org/10.1016/j.tcs.2023.114077},
	author = {Ram{\'\i}rez-de-Arellano, Antonio and David Orellana-Mart{\'\i}n and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {math11132797,
	title = {A Protocol for Solutions to DP-Complete Problems through Tissue Membrane Systems},
	journal = {Mathematics},
	volume = {11},
	number = {13},
	year = {2023},
	month = {06/2023},
	pages = {2797},
	publisher = {MDPI},
	edition = {13},
	abstract = {Considering a class\&nbsp;R\&nbsp;comprising recognizer membrane systems with the capability of providing polynomial-time and uniform solutions for NP-complete problems (referred to as a \&ldquo;presumably efficient\&rdquo; class), the corresponding polynomial-time complexity class PMCR\&nbsp;encompasses both the NP and\&nbsp;co-NP\&nbsp;classes. Specifically, when\&nbsp;R\&nbsp;represents the class of recognizer presumably efficient cell-like P systems that incorporate object evolution rules, communication rules, and dissolution rules, PMCR\&nbsp;includes both the DP and\&nbsp;co-DP\&nbsp;classes. Here, DP signifies the class of languages that can be expressed as the difference between any two languages in NP (it is worth noting that NP \&sube; DP and\&nbsp;co-NP\&sube;co-DP). As DP-complete problems are believed to be more complex than NP-complete problems, they serve as promising candidates for studying the P vs. NP problem. This outcome has previously been established within the realm of recognizer P systems with active membranes. In this paper, we extend this result to encompass any class\&nbsp;R\&nbsp;of presumably efficient recognizer tissue-like membrane systems by presenting a detailed protocol for transforming solutions of NP-complete problems into solutions of DP-complete problems.},
	issn = {2227-7390},
	doi = {10.3390/math11132797},
	url = {https://www.mdpi.com/2227-7390/11/13/2797},
	author = {David Orellana-Mart{\'\i}n and Ram{\'\i}rez-de-Arellano, Antonio and Andreu-Guzm{\'a}n, Jos{\'e} Antonio and {\'A}lvaro Romero-Jim{\'e}nez and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {doi:10.1142/S0129065723500235,
	title = {Using Virus Machines to Compute Pairing Functions},
	journal = {International Journal of Neural Systems},
	volume = {33},
	number = {05},
	year = {2023},
	note = {PMID: 36967221},
	month = {03/2024},
	pages = {2350023},
	publisher = {World Scientific},
	abstract = {Virus machines are computational devices inspired by the movement of viruses between hosts and their capacity to replicate using the resources of the hosts. This behavior is controlled by an external graph of instructions that opens different channels of the system to make viruses capable of moving. This model of computation has been demonstrated to be as powerful as turing machines by different methods: by generating Diophantine sets, by computing partial recursive functions and by simulating register machines. It is interesting to investigate the practical use cases of this model in terms of possibilities and efficiency. In this work, we give the basic modules to create an arithmetic calculator. As a practical application, two pairing functions are calculated by means of two different virus machines. Pairing functions are important resources in the field of cryptography. The functions calculated are the Cantor pairing function and the G{\"o}del pairing function.},
	doi = {10.1142/S0129065723500235},
	url = {https://doi.org/10.1142/S0129065723500235},
	author = {Ram{\'\i}rez-de-Arellano, Antonio and David Orellana-Mart{\'\i}n and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {BAI2023119916,
	title = {Sequence recommendation using multi-level self-attention network with gated spiking neural P systems},
	journal = {Information Sciences},
	year = {2023},
	pages = {119916},
	publisher = {Elsevier},
	address = {Amsterdam (The Netherlands)},
	abstract = {Sequence recommendation is used to predict the user{\textquoteright}s next potentially interesting items and behaviors. It not only focuses on the user{\textquoteright}s independent interaction behavior, but also considers the user{\textquoteright}s historical behavior sequence. However, sequence recommendation still faces some challenges: the existing models still have shortcomings in addressing long-term dependencies and fully utilizing contextual information in sequence recommendation. To address these challenges, we propose a four-channel model based on a multi-level self-attention network with gated spiking neural P (GSNP) systems, termed SR-MAG model. The four channels are divided into two groups, and each group is composed of an attention channel and an GSNP attention channel. Moreover, they process long-term sequences and short-term sequences respectively to obtain long-term or short-term attention channel features. These features are then passed through a self-attention network to effectively extract user context information. The proposed SR-MAG model is tested on three real datasets and compared with 10 baseline methods. Experimental results demonstrate the effectiveness of the proposed SR-MAG model in sequence recommendation tasks.},
	keywords = {Gated spiking neural P systems, Nonlinear spiking neural P systems, Self-attention mechanism, Sequence recommendation},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2023.119916},
	url = {https://www.sciencedirect.com/science/article/pii/S0020025523015013},
	author = {Xinzhu Bai and Yanping Huang and Hong Peng and Jun Wang and Qian Yang and David Orellana-Mart{\'\i}n and Ram{\'\i}rez-de-Arellano, Antonio and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@proceedings {10.1007/978-3-031-06527-9_40,
	title = {Basic Arithmetic Calculations Through Virus-Based Machines},
	journal = {Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence},
	year = {2022},
	pages = {403{\textendash}412},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {In Natural Computing, several models of computation based on processes occurring in nature exist. While some of them are well-established computing framework, there are some types of devices that are underdeveloped. This is the case of Virus Machines, framework inspired by the movement of viruses between hosts, and how can they be replicated while certain events happen. The relevance of this work lies in the formal definition of the framework and both the insights presented about the formal verification of the different designs and the possible new research lines.},
	isbn = {978-3-031-06527-9},
	doi = {https://doi.org/10.1007/978-3-031-06527-9_40},
	author = {Ram{\'\i}rez-de-Arellano, Antonio and David Orellana-Mart{\'\i}n and Mario J. P{\'e}rez-Jim{\'e}nez},
	editor = {Ferr{\'a}ndez Vicente, Jos{\'e} Manuel and {\'A}lvarez-S{\'a}nchez, Jos{\'e} Ram{\'o}n and de la Paz L{\'o}pez, F{\'e}lix and Adeli, Hojjat}
}
