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This work introduces Hybrid Membrane–Neural P systems (HMN P systems), a computational model that integrates principles from membrane computing and spiking neural P systems. The resulting framework offers a versatile foundation for the development of bio-inspired arithmetic architectures. Within this setting, we propose a compact family of arithmetic kernels capable of executing signed addition, subtraction, multiplication, and division in both modular and non-modular arithmetic domains. By leveraging intrinsic spike aggregation, spike–anti-spike annihilation, and exhaustive rule application, the proposed designs achieve efficient and reliable arithmetic computation in a constant number of simulation steps under exhaustive semantics and assuming synchronized input, independent of operand values. Addition and subtraction are executed intrinsically upon spike arrival, requiring no internal computation steps, while multiplication and division are completed in a single simulation step by one neuron. Furthermore, we introduce a modular-reduction kernel that operates in two simulation steps with a single neuron, and leverage its modular structure to construct modular multiplication and division through composition with non-modular arithmetic modules. Comparative evaluations against representative SNP and SNQ arithmetic designs demonstrate that HMN kernels achieve operand-independent execution time while requiring fewer neurons. Distinct from most existing approaches, the HMN framework natively supports signed operands through a dual-spike representation, thereby eliminating the need for auxiliary sign-handling mechanisms. Asynchronous spike arrivals can be managed by an optional synchronization membrane; since this mechanism is decoupled from the arithmetic kernels, its overhead is excluded from kernel performance and reported separately. Collectively, these results establish HMN systems as an efficient and modular platform for constant-time arithmetic computation, offering reusable arithmetic kernels that serve as a foundation for higher-level constructions, including those arising in elliptic-curve and modular arithmetic.

Bonellia nervosa is an understory tree with reverse phenology in tropical dry forests (TDFs), where seasonal water and temperature stress typically shape plant phenology and trait expression. This species is heliophytic and phreatophytic, relying on high light availability and deep-water access during the dry season. However, the role of dry-season light variation in influencing leaf traits of species with inverted phenology remains poorly understood. We examined how plant size, reproductive stage, and canopy structure influence trait variation in B. nervosa during the dry season. We measured plant height and diameter, reproductive status, and canopy structure using hemispherical photographs to estimate canopy openness, leaf area index, and transmitted light. Leaf structural traits included specific leaf area (SLA), thickness, water content, and stomatal density, while photochemical performance was assessed via chlorophyll fluorescence and rapid light curves. Principal component analysis and linear regression were used to examine trait–environment relationships. Photosynthetic efficiency was not affected by plant size or reproductive status. No strong trait correlations were observed for leaf water content and stomatal density. A negative relationship between canopy openness, transmitted light, and SLA indicates structural leaf adaptation to light conditions, with lower SLA values occurring under reduced light. In B. nervosa, leaf traits are driven more by light than by water availability during the dry season. This suggests that reverse phenology in phreatophytic species is functionally decoupled from seasonal water stress.

During the last five years, spiking neural P (SN P) systems have attracted a lot of attention in the field of cryptography since these systems can more efficiently support advanced and complex cryptographic algorithms due to their high computational capabilities. Specifically, these systems can be seen as a potential solution to efficiently performing asymmetric algorithms, which are more demanding than symmetric systems. This factor becomes critical, especially in resource-constrained single-board computer systems, since many of these systems are currently used to ensure the security of IoT applications in portable systems. In this work, we present for the first time the implementation of an asymmetric encryption algorithm called ElGamal based on spiking neural P systems and their cutting-edge variants. The proposed design involves the encryption and decryption processes. Specifically, we propose the design of a neural network to efficiently perform the extended Euclidean algorithm used in the decryption task. Here, we exert major efforts to create a compact and high-performance circuit to perform the extended Euclidean algorithm since the calculation of this algorithm is the most demanding when the decryption process is required. Finally, we perform several tests to show the computational capabilities of our proposal in comparison to conventional implementations on single-board computer systems. Our results show that the proposed encryption/decryption scheme potentially allows its use to ensure confidentiality, data integrity, and secure authentication, among other applications for resource-constrained embedded systems.

Key messageSpecific leaf area (SLA, cm2g− 1) is a fundamental leaf economics spectrum trait, which predicted the total carbon sequestered at the individual level in understory palms of Socratea exorrhiza.Evaluating intraspecific and ontogenetic variation in SLA is critical to understand how functional traits influence plant fitness and regeneration strategies. SLA is usually expressed as an average value per species. Its variation across ontogenetic stages and environmental gradients is poorly known, particularly in palms. I measured SLA in 112 palms of Socratea exorrhiza in the understory of a tropical rainforest in Costa Rica. Total carbon content sequestered per palm (kg) was estimated from an allometric equation. I determined the regression between Ln SLA and Ln carbon content, and then used principal components to summarize the regeneration strategy of S. exorrhiza by examining the allometry of stem length and diameter, number of fronds, number of stilt roots, stilt root cone height, slenderness ratio, SLA, and carbon content. SLA predicted total sequestered carbon (slope = − 4.33, r2 = 0. 52). Smaller values of SLA were associated with increased carbon content and larger palms. Two components explained 77% of the variation in functional traits. The first (76%) was dominated by stem diameter, height, stilt root cone, and carbon content (negatively associated with SLA) and reflected palm size; the second (15%) was dominated by slenderness ratio and number of leaves and reflected allocation to growth in height. The inverse relationship between SLA and sequestered carbon is consistent with the initial shade tolerant, conservative resource use strategy of S. exorrhiza.

We present allometric models for estimating total carbon content and above ground carbon (AGC) for the Arecaceae family, and for seven abundant neotropical palm species: the canopy species Socratea exorrhiza ( n = 10) and Iriartea deltoidea ( n = 10), the sub-canopy palm Euterpe precatoria ( n = 10), and the understory species Asterogyne martiana ( n = 15), Prestoea decurrens ( n = 10), Geonoma interrupta ( n = 10), and Chamaedorea tepejilote ( n = 22). Understanding the allometry of functional groups such as palms is critical for improving carbon stocks estimates in tropical forests and determining how allometric differences affect species functional diversity. The research was carried out in the tropical rainforests of the Caribbean slope of Costa Rica. We harvested 87 palms of a wide range of sizes, and separated them into roots, stems, and leaves, measured their fresh and dry biomass, and calculated their carbon content, tissue density, and dry mass fraction (dmf). Our general palm model estimating total carbon content based on these seven species and 87 samples accounted for 92% of the variation across species. We generated a similar model to estimate AGC and explained 91% of the variation. We compared our AGC model with two models used to estimate palm carbon content: Goodman et al. (2013) ’s and Chave et al. (2014) ’s models and found that all three converged on the estimation of AGC although our model was the most parsimonious because it achieved the same efficiency with only two variables, stem diameter and stem height. To improve the accuracy of allometric models we need to incorporate more species, a greater diversity of growth forms, a wider range of sizes, a larger sample size, and more diversity of habitats dominated by palms. Estimating carbon content using allometric approaches could benefit from more consistency in data collection across plant groups.

With the growing popularity of cryptocurrencies, which are an important part of day-to-day transactions over the Internet, the interest in being part of the so-called cryptomining service has attracted the attention of investors who wish to quickly earn profits by computing powerful transactional records towards the blockchain network. Since most users cannot afford the cost of specialized or standardized hardware for mining purposes, new techniques have been developed to make the latter easier, minimizing the computational cost required. Developers of large cryptocurrency houses have made available executable binaries and mainly browser-side scripts in order to authoritatively tap into users’ collective resources and effectively complete the calculation of puzzles to complete a proof of work. However, malicious actors have taken advantage of this capability to insert malicious scripts and illegally mine data without the user’s knowledge. This cyber-attack, also known as cryptojacking, is stealthy and difficult to analyze, whereby, solutions based on anti-malware extensions, blocklists, JavaScript disabling, among others, are not sufficient for accurate detection, creating a gap in multi-layer security mechanisms. Although in the state-of-the-art there are alternative solutions, mainly using machine learning techniques, one of the important issues to be solved is still the correct characterization of network and host samples, in the face of the increasing escalation of new tampering or obfuscation techniques. This paper develops a method that performs a fingerprinting technique to detect possible malicious sites, which are then characterized by an autoencoding algorithm that preserves the best information of the infection traces, thus, maximizing the classification power by means of a deep dense neural network.

In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years.

We consider the thermoelastic model following the type III theory for the Euler Bernoulli beam equation with tip. We prove that the corresponding semigroup is analytic. In particular, this implies: the smoothing effect over the initial data, the exponential stability of the semigroup and that the rate of decay of the semigroup is equal to the spectral bound of its generator (linear stability property).

During the last years, the demand for internet-of-things (IoT) resource-constrained devices has grown exponentially. To address this need, several digital methods have been proposed to improve these devices in terms of area and power consumption. Despite achieving significant results, improvement in these factors is still a challenging task. Recently, an emerging computational area has been seen as a potential solution to improving the performance of conventional binary circuits. In particular, this area uses a method based on spiking neural P systems (SN P) to create arithmetic circuits, such as adders, subtractors, multipliers, and divisors, since these components are vital in many IoT applications. To date, several efforts have been dedicated to decreasing the number of neurons and synapses to create compact circuits. However, processing speed is a persistent issue. In this work, we propose four compact arithmetic circuits with high processing speeds. To evaluate their performance, we designed a neuromorphic processor that is capable of performing four operations using dynamic connectivity. As a consequence, the proposed neuromorphic processor achieves higher processing speeds by maintaining low area consumption in comparison with the existing approaches.

Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.