In the stereokinetic effect, two non-concentric circles rotating in the image plane are perceived as a tilted, deforming 3D cylinder extending in depth. The cylinder's finite length poses a theoretical puzzle: minimal deformation favors infinite depth by eliminating relative motion, whereas a slow 3D motion constraint would limit depth by reducing the velocity of the back circle. If both constraints operate, they should yield a finite length. We tested their contributions by measuring perceived cylinder length with four methods: linear perspective adjustment, wireframe sphere fitting, binocular disparity matching, and motor reaching. Across participants, all methods yielded precise within-subject measurements but revealed systematic quantitative differences between methods, with only linear perspective and reaching strongly correlated. Variations in rotational speed had no effect, providing no support for a slow 3D motion constraint. Instead, perceived length increased with inter-circle distance, supporting minimal deformation as a key constraint. We propose that the finite length may additionally reflect a generic viewpoint constraint disfavoring cylinders aligned with the line of sight, a hypothesis for future investigation. Quantitative estimates depend critically on measurement method, suggesting distinct computational mechanisms for stereo versus monocular cues and for pictorial versus action-oriented depth tasks.

Scientific confidence relies on the integrity and verifiability of primary observations, yet increasing experimental scales and computational complexities challenge traditional mechanisms of trust. In proteomics, community standards emphasize the public deposition of raw mass spectrometry (MS) data to enable reanalysis and reproducibility. However, recent advances in software capable of simulating MS data raise the possibility that datasets may be altered or generated entirely in silico and presented as experimentally acquired data. Here, we argue that current standards rarely distinguish between raw data formats in terms of their provenance guarantees or susceptibility to modification. To address this emerging vulnerability, we propose a hierarchy of evidence for MS-based reporting, analogous to established evidence hierarchies in medicine, in which confidence in reported findings increases with the traceability and verifiable provenance of raw data files. With this framework, we aim to support reviewers and readers in assessing the robustness of published claims and to stimulate discussion on strengthening data integrity safeguards in proteomics.

A central question in cognitive neuroscience is how the brain implements abstract computations that must generalize across superficially different inputs. Language provides a strong test case: the same grammatical operation, such as pluralization, can be realized through distinct rules and forms across languages. Whether such transformations rely on language-specific neural systems or on abstract mechanisms that generalize across linguistic contexts remains unresolved. Crucially, these transformations must be computed online and integrated into speech planning within a tightly constrained time window. Using magnetoencephalography (MEG), we tracked the millisecond dynamics of grammatical word-form transformations during semi-naturalistic phrase completion in humans of both sexes. Highly proficient Spanish-English bilinguals produced singular and plural noun forms in both languages in a design that fully orthogonalized semantic number, phonological changes, grammatical inflection and produced language. Adjusting words to fit their grammatical context engaged a left-lateralized fronto-temporal network beginning ∼100 ms after cue onset. Multivariate decoding revealed that the neural patterns supporting this computation generalized across languages, across different surface plural forms, and to pseudowords, demonstrating that abstractly equivalent operations are instantiated in the same neural substrates despite differences in linguistic form. Together, these findings provide time-resolved neural evidence for a language-general computational mechanism, showing that the brain implements grammatical transformations as abstract, generative operations. More broadly, they show how bilingualism can be used to probe general principles of neural organization, revealing how abstract computations may be shared and reused across representational systems.Significance Statement Human language relies on the ability to modify words to convey information like number and tense, but languages vary widely in how these transformations are implemented. This variation raises a fundamental question in cognitive neuroscience: do such transformations depend on language-specific neural systems, or are they processed by abstract neural mechanisms that generalize across languages? We demonstrate that Spanish-English bilinguals engage a shared left frontal-temporal network when producing grammatically appropriate forms in both languages. This common neural signature emerges early during speech planning and even generalizes to novel words. These findings indicate that the brain builds abstract, reusable neural mechanisms, consistent with models where language is organized by computational principles rather than by language-specific systems.

The Raspberry Pi is a widely used platform deployed across numerous domains, including Industry 4.0 environments. This places requirements on hardware and software in terms of IT and OT security. The whole platform has to be secured in a way that integrity and trustworthiness can be guaranteed – even when it is deployed in uncontrolled environments and operating outside the manufacturer’s physical control. Currently, that often is not the case and basic Trusted Computing concepts are not implemented in Raspberry Pi-based platforms. The presented approach enables entities such as SIEM systems and backend services to make use of the established Trusted Computing mechanism Remote Attestation to verify the integrity of a Raspberry Pi 4 and its software. Remote Attestation, in turn, depends on Measured Boot to record integrity measurements (hash values) of each boot stage into a TPM 2.0. However, the Raspberry Pi 4 official bootloader supports Secure Boot only. It neither provides Measured Boot functionality nor integrates with a TPM 2.0, preventing the establishment of a hardware-anchored measurement chain required for Remote Attestation. To address these limitations, this paper proposes a method that combines Secure Boot with Measured Boot to enable Remote Attestation on the Raspberry Pi 4. This combination establishes a complete Chain of Trust. While it comes with some shortfalls and cannot defend against sophisticated hardware-based attacks, it still results in a much higher security level for the Raspberry Pi. The proposed concept makes use of the official bootloader’s Secure Boot to load a signed second stage custom bootloader image based on U-Boot. Following that, U-Boot acts as the Measurement Root of Trust and measures the subsequent boot stages into the PCRs of a TPM 2.0. Although the proposal builds upon existing technologies, their integration into the Raspberry Pi boot chain has not been available in this form. The security enhancements have a significant impact on many use cases involving Raspberry Pi-based hardware, including industrial devices. It is particularly worth considering if the hardware platform already integrates a TPM 2.0 chip.

The catecholamine precursor Tyrosine has been linked to improved cognitive performance, but investigations into decision-making and reinforcement learning processes known to be under catecholamine control are sparse. We examined the impact of a single dose of Tyrosine (2g) on reinforcement learning and exploration in a large (n = 63) gender-balanced sample in a within-subjects preregistered study. Reinforcement learning performance was significantly improved under Tyrosine. Based on previous work, we preregistered the hypotheses that Tyrosine would reduce directed exploration, response times, and physiological arousal. However, neither response times nor physiological arousal revealed the predicted reductions. Computational modelling using an established pre-registered reinforcement learning model revealed that the performance improvement under Tyrosine was due to an increase value-driven exploitation, without affecting directed exploration. Non-preregistered modelling analyses then revealed that accounting for higher-order perseveration substantially improved model fit, and substantiated the observation of increased value-driven exploitation under Tyrosine. Furthermore, it revealed reliable reductions in directed exploration and value-independent perseveration under Tyrosine. Tyrosine thus improved reinforcement learning performance by stabilizing choice patterns in the service of optimizing reward accumulation, modulating several computational mechanisms thought to be under catecholamine control.

The rapid expansion of Internet of Things (IoT)-based systems has introduced unprecedented opportunities for automation, real-time monitoring, and data-driven decision-making across diverse sectors. This study presents a comprehensive analysis of advanced cybersecurity techniques for protecting IoT-based systems, focusing on a multi-layered security architecture integrating device-level protection, network security, edge intelligence, and cloud-based resilience mechanisms. The proposed framework emphasizes a holistic approach where lightweight encryption and authentication protocols secure resource-constrained IoT devices, while intrusion detection systems (IDS) and secure communication protocols enhance network-level protection. This integration of decentralized security with intelligent analytics enhances both short-term threat response and long-term data reliability. Unlike traditional single-layer security approaches, the proposed architecture leverages cross-layer integration to address the interconnected nature of IoT environments. By combining AI-driven intrusion detection, edge computing, and blockchain-based security mechanisms, the framework provides a scalable and adaptive solution to emerging cyber threats. The study also highlights key challenges, including scalability, interoperability, and energy efficiency, while proposing future directions such as federated learning and zero-trust architectures. Overall, this research contributes to the development of resilient and intelligent cybersecurity frameworks for next-generation IoT systems

The self-simulational theory of temporal extension (SST) describes an information-theoretically formalized mechanism by which the “width” of subjective temporality emerges from the architecture of self-modeling. In this paper, the perspective of the free energy principle will be assumed to cast the emergence of subjective temporal extension from first principles of the physics of self-organization and to formalize subjective temporal extension using information geometry. Using active inference, a deep parametric generative model of temporal inference is simulated, which realizes the described dynamics on a computational level. Two “biases” (i.e. variations) of time-perception emerge naturally from the simulated computational model. This concerns the intentional binding effect (i.e. the compression of the temporal interval between voluntarily initiated actions and subsequent sensory consequences) and empirically documented alterations of subjective time experience in deep states of meditative absorption (i.e. in minimal phenomenal experience). Generally, numerous systematic and domain-specific alterations of subjective temporal experience are computationally explained in a unified manner, as enabled by integration with current active inference accounts mapping onto the respective domains. This concerns, next to more general attentional and central tendency effects, the temporality-modulating role of valence, impulsivity, boredom, flow-states, near death-experiences, and various psychopathologies, among others. SST, from the perspective of the free energy principle, explains how the “width” of the subjective temporal moment emerges from first principles, accounting for why sometimes, subjective time seems to fly, and sometimes, moments feel like eternities; with the computational mechanism being readily deployable synthetically.

Meditative experience has long been associated with conceptual attenuation, reduced reactivity to phenomena, increased present moment perception, and more pleasant experience. However, the computational mechanisms underlying such meditative deconstruction are not well understood, with no formal computational models available to explicate how deconstruction alters perception and action during meditation. Using the active inference framework, I demonstrate that the phenomenology of deconstruction-in terms of conceptual attenuation, reduced reactivity, and shorter temporal scale perception-naturally emerges from the dynamics of hierarchical inference when the deconstructive notion of letting go is cast as a reduction in precision of beliefs about hidden states at a specific level of the generative model. I present a formal hierarchical three-level generative model and simulate deconstruction as an intervention in a facial recognition task, where the agent selects a letting-go policy when perceived affective valence becomes excessively negative. The results demonstrate that the capacity to deconstruct permits agents to self-regulate experienced affect via letting go. The model offers a novel perspective within the paradigm of computational phenomenology on conceptual attenuation, equanimity, stillness, and affect during meditative deconstruction.

Formation of stable exoplanetary systems around pulsars by capture: an exercise in computational classical mechanics

Bayesian integration is posited as a fundamental computational mechanism underlying multisensory integration, and feedforward neural networks have been proposed to instantiate optimal Bayesian integration (OBI). However, empirical and theoretical research highlights the prevalence of neural feedback projections, raising questions about how recurrent neural networks might contribute to multisensory OBI. We simulated a two-layer neural circuit computational model with reciprocal projections performing a perceptual discrimination task, in which sensory inputs comprise single or dual modalities. The model with reciprocal projections between sensory and decision-making modules can match, underperform, or outperform OBI, depending on feedforward-feedback interplay. This model performance variability accords with prior experimental data. In addition, our theoretical analysis reveals the importance of non-linear interactions within neuronal assemblies in mediating such multisensory integration behaviors. Our work suggests that sensory modalities can be entangled through top-down feedback, challenging the traditional view of their independence, while explaining deviations from OBI.

Balancing global exploration and local exploitation remains a central challenge in metaheuristic optimization, particularly for high-dimensional, nonlinear, and constrained problems encountered in engineering design and medical data analysis. This paper proposes the Booby Optimization Algorithm (BOA), a state-adaptive population-based metaheuristic inspired by avian dive-foraging behavior but formulated entirely through mathematical and computational mechanisms. BOA employs an adaptive state variable to regulate step magnitude and dynamically control transitions between global exploratory search and local exploitative refinement, augmented by nonlinear motion dynamics, stochastic perturbations, and a recovery strategy for diversity preservation. The algorithm is extensively evaluated on CEC benchmark functions with dimensionalities up to 100, four classical constrained engineering design problems, and feature selection and classification tasks on 14 real-world medical datasets using BOA-based hybrid models. Experimental results demonstrate that BOA consistently outperforms several state-of-the-art metaheuristics in terms of convergence speed, solution accuracy, and robustness, achieving near-optimal or best-known solutions with significantly reduced mean error and variance. In medical classification tasks, BOA-based feature selection attains a mean accuracy of 96.20% ± 1.05, alongside high sensitivity and specificity while effectively reducing feature dimensionality. These improvements are supported by rigorous statistical validation using Friedman, Nemenyi, and Wilcoxon tests (p < 0.001). Overall, the results establish BOA as an efficient and robust adaptive optimization framework suitable for complex engineering optimization and medical decision-support applications.

Rewards and punishments in reinforcement learning are encoded in both absolute and relative manners. Reference-point dependence, a valuation bias shared by adaptation-level and prospect theories, is often proposed as the computational mechanism underlying relative value encoding. However, the extent to which these behavioural and computational mechanisms are conserved across species remains to be fully understood. We therefore designed parallel reinforcement learning tasks in humans and rats to examine reference-point dependence across species. Behavioural analyses showed robust relative value encoding in both species, and computational modelling confirmed that reference-point dependence reliably accounts for behaviour in humans and rats. Despite these major similarities between species, some differences in behavioural and modelling parameters were observed. Overall, our study demonstrates that relative value encoding is a robust feature of reinforcement learning that is conserved across humans and rats.

The convergence of Sixth-Generation (6G) wireless networks and neuromorphic computing presents significant opportunities for intelligent, energy-efficient resource management in distributed architectures. This paper introduces Neuro6G-Agent, a hierarchical neuromorphic agentic intelligence framework that integrates Energy-Aware Spiking Neural Networks (EA-SNNs) with multi-agent reinforcement learning to enable energy-conscious cognitive collaboration across cloud-edge-end 6G deployments. The framework addresses three principal challenges in distributed 6G resource management: energy sustainability, end-to-end latency under ultra-dense connectivity, and security resilience against adversarial threats. A three-tier architecture is employed, comprising cloud orchestrators, edge coordinators, and end devices, each operating dedicated neuromorphic agents with autonomous decision-making and trust-aware collaborative learning capabilities. The framework incorporates adaptive threshold EA-SNNs for event-driven processing, a distributed trust computation mechanism for secure multi-agent cooperation, and a hierarchical resource optimization algorithm responsive to dynamic workload conditions. Experimental evaluation across three public benchmark datasets—DeepMIMO (6G channel modeling), DVS128 Gesture (neuromorphic sensing), and CICIDS-2017 (network intrusion detection) demonstrates a 34.7% reduction in energy consumption, a 28.3% decrease in end-to-end latency, and a 95.6% security threat detection accuracy compared to state-of-the-art baseline methods, validated across ten independent experimental runs (p < 0.01). These results confirm the viability of neuromorphic intelligence for addressing complex optimization challenges in next-generation wireless architectures.

The conformational behavior of mannitol, an important sweetener, was studied using Fourier transform microwave spectroscopy combined with a laser-ablation source in a supersonic expansion. Three conformers were identified based on their rotational constants obtained from spectral analysis. The intramolecular hydrogen-bonding networks observed in the two most abundant conformers suggest a potential pre-organization mechanism that could favor a bioactive topology compatible with the T1R2/T1R3 sweet taste receptor. Such structural arrangements are consistent with the geometric criteria of Shallenberger's AH-B glucophore model, potentially reducing the entropic penalty of binding and providing a molecular-level hypothesis for the origin of its sweetness. A detailed spectral analysis, along with autonomous reaction-discovery calculations using AutoMekin, also revealed the simultaneous formation of additional species in the jet. This demonstrates that combining computational autonomous mechanism generators with laser ablation experiments offers a powerful new approach for exploring complex reaction dynamics and identifying chemical species inaccessible to traditional methods.

The ability to rapidlydetect and evaluate potential threats is essential for survival and requires the integration of sensory information with internal state and previous experience. The lateral septum (LS)-an inhibitory structure in the limbic forebrain-is thought to integrate these higher-order cognitive signals to regulate defensive responses1,2. However, the cellular, circuit and computational mechanisms fundamental to this process remain unknown. Here we focus on the population of LS neurons that express the type 2 CRH receptor (LSCrhr2), a neuronal subset shown to be critical for state-dependent behavioural changes and threat responsivity3-7 in mice. We use a combination of single-cell calcium imaging, molecular sequencing and circuit dissection to reveal the spatial and functional organization of the cell types involved, the computations they perform and the information relayed by their upstream activators. We determine that LSCrhr2 population activity is required for cue-driven defensive actions by rapidly and dynamically encoding threat representations that predict behavioural outcomes. We find that these threat representations are formed through the convergence of various signals differentially represented by distinct LSCrhr2 subclasses, which are defined by their molecular features, spatial locations and input architectures. Notably, these responses reflect specific afferents from the hippocampus and hypothalamus that preferentially impart cue- and action-related signals, respectively. These findings establish a multifeatured organizational principle that underlies how the LS mediates motivated behaviours in response to environmental challenges.

Effectiveness of a Kalman filter model constrained by efficient coding in explaining the coexistence of repulsive and attractive perceptual biases.

Topology optimization (TO) is a powerful structural design method, but its application remains challenging due to the deep expertise and extensive development effort required. Traditional TO methods, tightly coupled with computational mechanics like finite element method (FEM), result in intrusive algorithms demanding a comprehensive system understanding. This paper presents SOPTX, a TO package based on FEALPy, which implements a modular architecture that decouples analysis from optimization, supports multiple computational backends (NumPy, PyTorch, JAX), and achieves a non-intrusive design. Core innovations include: (1) a cross-platform design supporting multiple backends for efficient CPU/GPU execution, while leveraging automatic differentiation (AD) for sensitivity computation; (2) fast matrix assembly techniques that overcome performance bottlenecks of traditional methods, significantly accelerating finite element computations; (3) a modular framework supporting TO problems for arbitrary dimensions and meshes, allowing flexible configuration of optimization workflows. Using the density-based method for a classic compliance minimization problem, numerical experiments demonstrate SOPTX’s significant improvements in computational speed and memory usage, showcasing its strong potential for research and engineering applications.

Humans are inherently social species. Our behavior is heavily influenced by the social environment and context. While the traditional social neuroscience approach has made significant progress in mapping isolated social cognitive processes, it often fails to capture the complexity of real-world social environment-where perception, decision-making, and interaction unfold simultaneously and contextually. In this review, we introduce naturalistic social neuroscience as a paradigm shift that bridges this gap through incorporating multidisciplinary naturalistic measurements, which integrates lab-based simulation, such as movie watching and virtual reality, real-world embedded measures, such as digital phenotyping and wearables devices, as well as the strategic integration of multiple approaches. The framework also includes an artificial intelligence (AI)-powered multilevel data analysis to synthesize behavioral, computational, and neurobiological data to reveal the mechanism underlying real-world behaviors. Finally, we propose opportunities, critical considerations, and future directions for pushing forward naturalistic social neuroscience. This review broadens the view and equips researchers with an extended toolkit for understanding the richness of social behavior.

We report the development of chemo- and stereoselectivephenolicβ-mannosylations catalyzed by precisely tuned bis-thiourea H-bonddonors. β-Mannosylation of phenols is demonstrated to occurselectively in the presence of primary and secondary alcohols as wellas thiols and thiophenols. Kinetic and computational studies supporta mechanism of nucleophile activation promoted by general base-catalysisand enhanced by aromatic interactions between the catalyst and thephenolic substrate. The reactions obey Michaelis–Menten kinetics,and selectivity for glycosylation of phenols over aliphatic alcoholscan be ascribed to a combination of stronger binding of the nucleophileto the catalyst and higher reaction rate of the bound complex. Incontrast, selectivity of phenols over more acidic thiophenols is achievedexclusively through preferential binding to the catalyst.

This paper introduces a novel IoT-Enhanced Virtual Power Plant (VPP) framework that integrates edge-fog computing, blockchain-secured communication, and AI-driven market mechanisms to optimize energy management in smart grids. The proposed system addresses critical challenges in traditional VPPs including high latency (> 500ms), cybersecurity vulnerabilities (68% of grids report IoT risks), and low user engagement (< 30% adoption). Our framework achieves sub-50ms response times through hybrid edge-fog computing, resolves 94% of security vulnerabilities via blockchain consensus, and increases user participation by 116% through personalized demand response. Extensive simulations using real-world datasets from Pecan Street and prototype deployment with 120 IoT-connected distributed energy resources (DERs) demonstrate 23.1% improvement in energy efficiency, 17.3% cost reduction for end-users, and 142.3% return on investment over five years. The system integrates vehicle-to-grid (V2G) technology, achieving 25% better renewable energy utilization while maintaining grid stability. Implementation using open-source platforms (OpenEMS, GridLAB-D) ensures scalability up to 10,000 + DERs with modular architecture supporting community-driven innovation.